jpad journal

AND option

OR option



J.D. Grill1,2,3,4, A. Kind5,6,7, D. Hoang1, D.L. Gillen1,8


1. Institute for Memory Impairments and Neurological Disorders, University of California Irvine, Irvine CA, USA; 2. Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, California, USA; 3. Department of Neurobiology and Behavior, University of California Irvine, Irvine, California, USA; 4. Institute for Clinical and Translational Science, University of California Irvine, Irvine, California, USA; 5. Center for Health Disparities Research, Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA; 6. Department of Medicine, Division of Geriatrics, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA; 7. Madison VA Geriatrics Research Education and Clinical Center, Middleton VA Hospital, Madison, Wisconsin, USA; 8. Department of Statistics, University of California Irvine, Irvine, California, USA

Corresponding Author: Joshua Grill, PhD, 3204 Biological Sciences III, University of California Irvine, Irvine, CA 92697,USA,, t: (949) 824-5905,
f: (949) 824-0885

J Prev Alz Dis 2021;
Published online August 26, 2021,



BACKGROUND: Disparities in clinical research participation perpetuate broader health disparities. Recruitment registries are novel tools to address known challenges in accrual to clinical research. Registries may accelerate accrual, but the utility of these tools to improve generalizability is unclear.
Objective: To examine the diversity of a local on-line recruitment registry using the Area Deprivation Index (ADI), a publicly available metric of neighborhood disadvantage.
Design: Retrospective analysis.
Setting: Data were collected in the University of California Irvine Consent-to-Contact Registry.
Participants: We categorized N=2,837 registry participants based on the ADI decile (collapsed into quintiles) using a state-based rankings.
Measurements: We examined the proportion of enrollees per ADI quintile and quantified the demographics of these groups. We assessed willingness to participate in studies involving unique research procedures among the ADI groups.
Results: Although registry enrollees represented the full spectrum of the ADI, they disproportionately represented less disadvantaged neighborhoods (lowest to highest quintiles: 42%, 30%, 15%, 6%, 7%). Compared to participants from less disadvantaged neighborhoods, participants from more disadvantaged neighborhoods were more often female, of non-white race, and Hispanic ethnicity. Despite demographic differences, ADI groups were observed to have similar willingness to participate in research studies.
Conclusions: People from more disadvantaged neighborhoods may be underrepresented in recruitment registries, increasing the risk that they will be underrepresented when using these tools to facilitate prospective recruitment to clinical research. Once enrolled in registries, participants from more disadvantaged neighborhoods may be equally willing to participate in research. Efforts to increase representation of participants from disadvantaged neighborhoods in registries could be an important first step toward increasing the generalizability of clinical research.

Key words: Registry, recruitment, neighborhood, diversity, disparities.




Clinical research is rarely inclusive of populations that reflect the full US population (1, 2). To address disparities in participation, researchers should consider applying recruitment approaches that are responsive to the mechanistic lens provided by the breadth of the National Institute on Aging Health Disparities Framework ( One of these considerations is socioeconomic contextual disadvantage, or “neighborhood disadvantage.” This construct can be measured using the census block-group level Area Deprivation Index (ADI), a marker for social determinants of health within these discrete geo-areas that may promote or impair human health such as employment, income, education and housing quality factors (3, 4). The most disadvantaged neighborhoods in the US as measured by ADI tend to have higher proportions of African American/Black, Hispanic and Native American residents; are often located within inner city urban or highly rural areas; and tend to have higher rates of complex chronic medical conditions like heart disease, diabetes and chronic pulmonary disease. Higher ADI is associated with poorer late life health outcomes (5-8), including risk for Alzheimer’s disease and related dementias (ADRD) (9-11). ADI is available freely to the public through the University of Wisconsin Neighborhood Atlas, a customizable mapping and data platform that makes this information easily accessible to investigators recruiting to prospective clinical research studies (3, 12).
Recruitment registries are relatively new tools meant to address a crisis in ADRD clinical research recruitment (13). Registries enroll large populations of potentially eligible and willing participants for research studies, in an effort to accelerate accrual to new studies once they begin. Several questions remain about the effectiveness of these tools (14), especially as it relates to diversifying research populations (15-17). We developed the UC Irvine Consent-to-Contact (C2C) Registry, a local on-line recruitment registry in Orange County, California.(18) Multiple strategies have been applied to enroll participants in the C2C Registry, including community, direct mail, and electronic outreach (18, 19). As yet, these strategies have not included specific geo-targeted approaches. In this exploratory study, we examined the representativeness of C2C Registry, based on state ADI deciles. To our knowledge, this is the first assessment of ADI within a recruitment registry. We also assessed whether specific recruitment techniques were more frequently sources of high ADI participants and whether ADI was associated with stated research preferences when enrolling in the C2C Registry.



Data source and participants

We performed an exploratory descriptive analysis of the C2C Registry for outcomes related to ADI using data from participants enrolled on or before 09/29/2020. This local on-line recruitment registry was developed and launched in 2016 to accelerate accrual to clinical research studies at the University of California Irvine (UCI), with a particular emphasis on preclinical AD trials.(18) To be eligible for the C2C, participants must be 18 years of age or older. All participants provided informed consent electronically. Registry enrollment requires completion of demographic and clinical questionnaires on-line, estimated to take approximately 20 minutes to complete. Demographic and clinical information is self-reported and has been described previously (18). Participants self-described race and ethnicity, queried as separate categories, each offering a “prefer not to answer” option. Recruitment sources were captured at enrollment and included earned media, community outreach activities, postcard mailings, e-mail, Internet, social media, and referrals from physicians or others. Nine questions determined enrollee’s willingness to be contacted for studies that involve: (1) modification of diet or physical activity, (2) cognitive testing, (3) blood draws, (4) magnetic resonance imaging (MRI), (5) positron emission tomography (PET) imaging, (6) FDA approved medications, (7) investigational medications, (8) lumbar puncture (LP), and (9) autopsy. Research attitudes were assessed by the validated Research Attitude Questionnaire (RAQ(20)), which uses Likert scales to examine participants’ agreement with 7-items, scored 1-5 (Range: 7-35), with higher scores indicating more positive research attitudes. Enrollees also complete the Cognitive Function Instrument (CFI), a 14-item measure of subjective cognitive performance (Range: 0-14), with higher scores indicating more complaints (21, 22).

ADI assessment

We used C2C enrollees’ permanent addresses to determine their ADI. The ADI incorporates 17 measures originally drawn from the long-form Census related to education, employment, housing-quality, and poverty (7), to rank the deprivation of US census block groups (~1500 people). From these, an index ranking is created to compare a specific census block to state or national norms, typically presented as deciles (3).
The ADI can be used for research purposes. For example, using the ADI based on 2000 Census data, Kind et al (7) found that the risk of living in a disadvantaged neighborhood is similar to that of having a chronic lung disease, like emphysema, and worse than that of health conditions such as diabetes when it comes to readmission risk. Using the ADI, Joynt Maddox and colleagues (23) added social risk factors including neighborhood disadvantage to models used to calculate penalties under the CMS’s Hospital Readmission Reduction Program. They found that accounting for these factors had a major impact on safety-net hospitals that serve patients from the most disadvantaged neighborhoods; over half would have seen a decline in their readmission penalty if such an adjustment had been applied. Most recently, the ADI has also been employed for COVID vaccine allocation in a number of US states as a means by which to most efficiently and effectively allocate resources to areas of greatest need (24).
To use the ADI, we downloaded the data through the Neighborhood Atlas ( and linked to C2C enrollee addresses using 12-digit Federal Information Processing Standards (FIPS) code via the US Census Bureau Geocoder ( C2C records with a 12-digit FIPS code were then matched to a locally download California 2015 ADI v2 dataset where ADI scores were obtained.
All ADI were calculated at the block group level. We examined C2C enrollees’ ADI using state-based norms. Adequate information to determine ADI was missing for N=1284 records (e.g. providing a PO Box, rather than a street address at enrollment). We also compared the C2C ADI distributions to the larger Orange County population, using data from the 2019: American Communities Survey 5-Year Estimates Detailed Tables (


The Institutional Review Board at UCI approved this study.


We assessed the relative representation of ADI deciles among enrolled C2C participants. We hypothesized that high ADI participants are underrepresented in this recruitment registry. We used geocoding maps to illustrate the distribution of ADI decile representation among C2C enrollees. We used descriptive statistics (mean and standard deviation for continuous responses, and frequency and percentage for discrete responses) to summarize the demographic characteristics of C2C enrollees by ADI, discretized into California state-specific quintiles. We further quantified willingness to participate across ADI quintiles. To do so, we characterized the frequency with which individuals from the differing ADI categories agreed to be contacted about studies that required the nine research procedures noted above. Given the descriptive nature of the research presented, inferential statements are not presented to avoid over-interpretation of exploratory results.



Among 4315 participants enrolled in the C2C Registry as of 09/29/2020, sufficient data were available for 2759 to link to the ADI. The supplementary table compares those with ADI information to those lacking it. Though no major differences were apparent between these groups, the group lacking ADI information was less often of white race (78% vs 83%), less often had two or more comorbidities (37% vs. 42%), and less often took three or more concomitant medications (40% vs. 50%). Among those with available ADI information, each of the ADI deciles was represented, though the distribution of enrollees was skewed toward lower deprivation. Forty-two percent of enrollees resided in the lowest ADI quintile (i.e., least neighborhood disadvantage), compared to only 7% and 6% in the highest and second-highest ADI quintiles (Figure 1A). In contrast, the distribution of ADI strata among all Orange County residents was skewed toward more disadvantaged neighborhoods, in particular for the tenth ADI decile. Figure 1B illustrates the geographic spread of C2C enrollees, coded by their ADI decile.

Figure 1. (A) Histogram plots of the relative proportions of ADI categories for C2C Registry enrollees (in orange, right-hand y-axis) and for the overall population in Orange County (blue, left hand y-axis). (B) Geocoded map of enrollees in the C2C Registry based on their state ADI index. Illustrated dots represent individual enrollees with added noise, using the R Jitter function, to protect participant confidentiality. ADI, Area Deprivation Index; C2C, Consent-to-Contact


Individuals from the highest ADI quintiles were observed to be more often female and to more often self-report being from a non-white race or Hispanic ethnicity (Table 1). Participants from the lowest ADI quintile had the highest average level of education. The lowest ADI quintile had the lowest proportion of participants self-reporting three or more comorbid medical conditions. Recruitment sources were similar across the ADI groups, although email produced less than half of the registrants in the highest ADI quintile, compared to 51-61% of the lower quintiles. CFI scores were observed to be lowest in the lowest ADI quintile and highest in the highest ADI quintile. RAQ scores were similar across the ADI groups.
The proportions willing to be contacted about studies among the ADI quintiles were highly consistent for each research procedure (Table 2). Across ADI categories, the proportions willing to participate were highest for research requiring cognitive testing and lowest for research requiring lumbar puncture.

Table 1. Demographic and clinical characteristics of C2C enrollees across ADI categories

*includes American Indian or Alaska Native, Native Hawaiian or Pacific Islander, those who refused, and missing.


Table 2. Willingness to participate across ADI categories in the C2C Registry



ADRD research faces critical challenges in recruiting samples that ensure generalizable results. Participants are consistently young, well educated, and from high socioeconomic status, compared to the general population (25). In this study, we examined socioeconomic diversity using the ADI in our local on-line recruitment registry, an example of an increasingly utilized tool to accelerate clinical research accrual. We found that participants in our registry were representative of all strata of the ADI but, as we hypothesized, were disproportionately from the lowest ADI strata (least disadvantaged neighborhoods). As has been noted in previous studies (12), participants from high ADI strata were more frequently of non-white races and Hispanic ethnicity. Education levels were notably high among all ADI strata and we observed no differences in the overall willingness to participate in research. This finding may suggest that registries, in particular those registries that are effective in recruiting high ADI participants, may offer a valuable opportunity to diversify ADRD studies.
There are numerous important implications to these findings. Ensuring the diversity of clinical research studies, especially clinical trials of new therapies, is a critical area of need. Relatively few research participants are non-white race or Hispanic ethnicity (26, 27), despite African Americans and Hispanics being at greatest risk for dementia (28). Biased homogeneous samples limit generalizability and risk misunderstanding of effect modification of treatment safety or efficacy (29). Barriers to registry recruitment may be lower than barriers to participation in clinical studies (30), since the risks and requirements are generally modest. This may create an opportunity to enroll diverse populations in registries for the purpose of engaging them and increasing participation in clinical research studies (15). The current results may suggest that geotargeted recruitment efforts will be essential to increasing the diversity of registry participants and that successfully doing so may permit careful selection for recruitment to prospective studies to ensure representation of different socioeconomic groups, since ADI strata were equally willing to be contacted about types of research studies. Further research is needed, however, to understand whether barriers to recruitment to registries may differ among ADI strata. While we observed associations between ADI and race and ethnicity, other social determinants of health, such as direct measures of socioeconomic status, acculturation, and racism all may be critical to understand and address (25, 31).
The inclusion of more disadvantaged neighborhoods is an important consideration to ADRD recruitment efforts. The digital divide is narrowing, with most US adults having smartphones (32). This may create opportunities to use social media and electronic campaigns to better reach people from disadvantaged communities (33). To date, we have engaged in minimal effort to recruit to the C2C Registry through digital tools, but other work points to the potential utility of social media and other on-line recruitment strategies (34-37). Alternatively, more traditional recruitment approaches such as direct mail (38) and grassroots education (39, 40) present clear opportunities to target specific neighborhoods. Though our previous direct mail campaigns produced lower yield than expected, this method has been successful in other registries (13) and we did not previously test for potential effect modification by ADI (38). We also note new opportunities to enhance the use of direct mail, such as “quick response (QR)” codes that enable recipients to open a link or install an application using the camera on their cell phone or tablet device as a barcode-reader.
Although our registry does not perform objective cognitive testing, as do some others (41, 42), it has other important strengths. Participants in our registry provide self-reported data on cognitive performance using the CFI, which has been shown to differ among preclinical AD participants and biomarker negative controls (21, 43). Intriguingly, CFI scores were elevated among the high ADI group in the C2C Registry. This observation is similar to previous cross sectional (12) as well as longitudinal studies of cognitive performance (11). Although we have no data to consider potential mediators of these subjective complaints, it is conceivable that complaints could be driven by differences among the groups in brain volume (10, 44) or even AD neuropathology (9), reaffirming the potential importance of recruiting these groups to prospective research studies, such as preclinical AD trials.
Lack of differences among ADI strata were also important, including the lack of differences in willingness to be contacted about studies and for the RAQ. Work at other academic researcher centers engaged in community outreach has found that RAQ scores were lower among diverse communities, compared to more traditional research populations (45). Previous analyses of the diverse racial and ethnic groups that make up C2C Registry observed similar differences (17), but we found no such differences based on neighborhood disadvantage here. Future work should aim to elucidate relationships among other social determinants of health and research attitudes.
We note some important limitations of the current study. We did not have sufficient data to assess ADI on every registrant due to data missingness and some participants including only a PO Box address at enrollment. We are unable to assess whether missingness due to this factor is at random or more disproportionately affects specific ADI strata. If missingness were more prominent among high ADI strata, it might suggest that these data overestimate the underrepresentation of high ADI participants, but create more uncertainty about the examination of these participants in particular (e.g., their willingness to participate). We also acknowledge that self-reported willingness to be contacted about studies is not equivalent to the behavior of participating in a study. From our registry, we have referred participants to a large variety of studies and consistently achieve >30% enrollment of referred individuals. We cannot rule out that, despite similarities in indicated willingness, differences among ADI strata in actual study enrollment could still exist. Similarities in RAQ scores across ADI strata may argue against this possibility, however, and future research will examine this question.
In conclusion, people from more disadvantaged neighborhoods may be underrepresented in recruitment registries, increasing the risk that they will be similarly underrepresented when using these tools to facilitate prospective recruitment to clinical research. Once enrolled in a registry, these data suggest that participants from more disadvantaged neighborhoods may be equally willing to participate in research. Efforts to increase representation of participants from disadvantaged neighborhoods in registries could therefore be an important intervention to increase generalizability in clinical research studies.


Acknowledgements: The authors would like to acknowledge all participants in the C2C Registry. This registry was made possible by a donation from HCP, Inc. and is supported by NIA AG066519 and NCATS TR001414. Dr. Kind’s time is supported by R01AG070883, RF1AG057784 and P30AG062715.

Funding: NIA, Grant/Award Number: AG066519, AG070883, AG057784 and AG062715; NCATS, Grant/Award Number: UL1 TR001414

Conflicts of interest: Dr. Grill reports research support from Biogen, Eli Lilly, Genentech, and the NIH. He reports personal fees from SiteRx, outside the submitted work. Dr. Kind reports grants from NIH during the conduct of the study; grants from NIH, grants from VA, outside the submitted work; Mr. Hoang has nothing to disclose. Dr. Gillen reports service on Data Safety Monitoring Boards for Pfizer, Biomarin, Novo Nordisk, Novartis, Amgen, Celgene, CRISPR, AstraZeneca, Merck Serano, Array, Seattle Genetics, Genentech/Roche, UCB, Acerta, Juno Therapeutics, Medivation outside the submitted work. He has provided consulting services for Eli Lilly, ChemoCyntrix, FibroGen, GlaxoSmithKline, ProventionBio, Biom’Up outside the submitted work.





1. Watson JL, Ryan L, Silverberg N, Cahan V, Bernard MA. Obstacles and opportunities in Alzheimer’s clinical trial recruitment. Health affairs (Project Hope). 2014;33(4):574-9.
2. Fargo KN, Carrillo MC, Weiner MW, Potter WZ, Khachaturian Z. The crisis in recruitment for clinical trials in Alzheimer’s and dementia: An action plan for solutions. Alzheimers Dement. 2016;12(11):1113-5.
3. Kind AJH, Buckingham WR. Making Neighborhood-Disadvantage Metrics Accessible – The Neighborhood Atlas. The New England journal of medicine. 2018;378(26):2456-8.
4. Singh GK. Area deprivation and widening inequalities in US mortality, 1969-1998. American journal of public health. 2003;93(7):1137-43.
5. Arias F, Chen F, Fong TG, Shiff H, Alegria M, Marcantonio ER, et al. Neighborhood-Level Social Disadvantage and Risk of Delirium Following Major Surgery. Journal of the American Geriatrics Society. 2020 Dec;68(12):2863-2871. doi: 10.1111/jgs.16782. Epub 2020 Aug 31.
6. Durfey SNM, Kind AJH, Buckingham WR, DuGoff EH, Trivedi AN. Neighborhood disadvantage and chronic disease management. Health services research. 2019;54 Suppl 1:206-16.
7. Kind AJ, Jencks S, Brock J, Yu M, Bartels C, Ehlenbach W, et al. Neighborhood socioeconomic disadvantage and 30-day rehospitalization: a retrospective cohort study. Annals of internal medicine. 2014;161(11):765-74.
8. Sheets L, Petroski GF, Jaddoo J, Barnett Y, Barnett C, Kelley LEH, et al. The Effect of Neighborhood Disadvantage on Diabetes Prevalence. AMIA Annual Symposium proceedings / AMIA Symposium AMIA Symposium. 2017;2017:1547-53.
9. Powell WR, Buckingham WR, Larson JL, Vilen L, Yu M, Salamat MS, et al. Association of Neighborhood-Level Disadvantage With Alzheimer Disease Neuropathology. JAMA Netw Open. 2020;3(6):e207559.
10. Hunt JFV, Buckingham W, Kim AJ, Oh J, Vogt NM, Jonaitis EM, et al. Association of Neighborhood-Level Disadvantage With Cerebral and Hippocampal Volume. JAMA neurology. 2020;77(4):451-60.
11. Hunt JFV, Vogt NM, Jonaitis EM, Buckingham WR, Koscik RL, Zuelsdorff M, et al. Association of Neighborhood Context, Cognitive Decline, and Cortical Change in an Unimpaired Cohort. Neurology. 2021 May 18;96(20):e2500-e2512. doi: 10.1212/WNL.0000000000011918.Epub 2021 Apr 14.
12. Zuelsdorff M, Larson JL, Hunt JFV, Kim AJ, Koscik RL, Buckingham WR, et al. The Area Deprivation Index: A novel tool for harmonizable risk assessment in Alzheimer’s disease research. Alzheimers Dement (N Y). 2020;6(1):e12039.
13. Aisen P, Touchon J, Andrieu S, Boada M, Doody RS, Nosheny RL, et al. Registries and Cohorts to Accelerate Early Phase Alzheimer’s Trials. A Report from the E.U./U.S. Clinical Trials in Alzheimer’s Disease Task Force. The journal of prevention of Alzheimer’s disease. 2016;3(2):7.
14. Grill JD. Recruiting to preclinical Alzheimer’s disease clinical trials through registries. Alzheimers Dement (N Y). 2017;3(2):205-12.
15. Cocroft S, Welsh-Bohmer KA, Plassman BL, Chanti-Ketterl M, Edmonds H, Gwyther L, et al. Racially diverse participant registries to facilitate the recruitment of African Americans into presymptomatic Alzheimer’s disease studies. Alzheimers Dement. 2020 Aug;16(8):1107-1114. doi: 10.1002/alz.12048. Epub 2020 Jun 16.
16. Ashford MT, Eichenbaum J, Williams T, Camacho MR, Fockler J, Ulbricht A, et al. Effects of sex, race, ethnicity, and education on online aging research participation. Alzheimers Dement (N Y). 2020;6(1):e12028.
17. Salazar CR, Hoang D, Gillen DL, Grill JD. Racial and ethnic differences in older adults’ willingness to be contacted about Alzheimer’s disease research participation. Alzheimers Dement (N Y). 2020;6(1):e12023.
18. Grill JD, Hoang D, Gillen DL, Cox CG, Gombosev A, Klein K, et al. Constructing a Local Potential Participant Registry to Improve Alzheimer’s Disease Clinical Research Recruitment. J Alzheimers Dis. 2018;63(3):1055-63.
19. Gombosev A, Salazar CR, Hoang D, Cox CG, Gillen DL, Grill JD. Direct Mail Recruitment to a Potential Participant Registry. Alzheimer Dis Assoc Disord. 2021 Jan-Mar 01;35(1):80-83.doi: 10.1097/WAD.0000000000000368.
20. Rubright JD, Cary MS, Karlawish JH, Kim SY. Measuring how people view biomedical research: Reliability and validity analysis of the Research Attitudes Questionnaire. J Empir Res Hum Res Ethics. 2011;6(1):63-8.
21. Amariglio RE, Donohue MC, Marshall GA, Rentz DM, Salmon DP, Ferris SH, et al. Tracking Early Decline in Cognitive Function in Older Individuals at Risk for Alzheimer Disease Dementia: The Alzheimer’s Disease Cooperative Study Cognitive Function Instrument.JAMA Neurol. 2015 Apr;72(4):446-54. doi: 10.1001/jamaneurol.2014.3375.
22. Walsh SP, Raman R, Jones KB, Aisen PS. ADCS Prevention Instrument Project: the Mail-In Cognitive Function Screening Instrument (MCFSI). Alzheimer Dis Assoc Disord. 2006;20(4 Suppl 3):S170-8.
23. Joynt Maddox KE, Reidhead M, Hu J, Kind AJH, Zaslavsky AM, Nagasako EM, et al. Adjusting for social risk factors impacts performance and penalties in the hospital readmissions reduction program. Health services research. 2019;54(2):327-36.
24. Schmidt H, Gostin LO, Williams MA. Is It Lawful and Ethical to Prioritize Racial Minorities for COVID-19 Vaccines? JAMA. 2020 Nov 24;324(20):2023-2024. doi: 10.1001/jama.2020.20571.
25. Brewster P, Barnes L, Haan M, Johnson JK, Manly JJ, Napoles AM, et al. Progress and future challenges in aging and diversity research in the United States. Alzheimers Dement. 2019;15(7):9.
26. Shin J, Doraiswamy PM. Underrepresentation of African-Americans in Alzheimer’s Trials: A Call for Affirmative Action. Front Aging Neurosci. 2016;8:123.
27. Faison WE, Schultz SK, Aerssens J, Alvidrez J, Anand R, Farrer LA, et al. Potential ethnic modifiers in the assessment and treatment of Alzheimer’s disease: challenges for the future. International psychogeriatrics / IPA. 2007;19(3):539-58.
28. Mehta KM, Yeo GW. Systematic review of dementia prevalence and incidence in United States race/ethnic populations. Alzheimers Dement. 2017;13(1):72-83.
29. Oh SS, Galanter J, Thakur N, Pino-Yanes M, Barcelo NE, White MJ, et al. Diversity in Clinical and Biomedical Research: A Promise Yet to Be Fulfilled. PLoS Med. 2015;12(12):e1001918.
30. Rogers JL, Johnson TR, Brown MB, Lantz PM, Greene A, Smith YR. Recruitment of women research participants: the Women’s Health Registry at the University of Michigan. Journal of women’s health (2002). 2007;16(5):721-8.
31. Williams DR. Race and health: basic questions, emerging directions. Annals of epidemiology. 1997;7(5):322-33.
32. Perrin A, Anderson M. Share of U.S. adults using social media, including Facebook, is mostly unchanged since 2018
33. Pew Research Center. 2019 Pew Research Center;
34. Whitaker C, Stevelink S, Fear N. The Use of Facebook in Recruiting Participants for Health Research Purposes: A Systematic Review. J Med Internet Res. 2017;19(8):e290.
35. Hough D. Social media and participant recruitment: What we’ve learned so far
36. Dobkin RD, Amondikar N, Kopil C, Caspell-Garcia C, Brown E, Chahine LM, et al. Innovative Recruitment Strategies to Increase Diversity of Participation in Parkinson’s Disease Research: The Fox Insight Cohort Experience. J Parkinsons Dis. 2020;10(2):665-75.
37. Shaver LG, Khawer A, Yi Y, Aubrey-Bassler K, Etchegary H, Roebothan B, et al. Using Facebook Advertising to Recruit Representative Samples: Feasibility Assessment of a Cross-Sectional Survey. J Med Internet Res. 2019;21(8):e14021.
38. Waltman NL, Smith KM, Kupzyk KA, Lappe JM, Mack LR, Bilek LD. Approaches to Recruitment of Postmenopausal Women for a Community-Based Study. Nurs Res. 2019;68(4):307-16.
39. Ballard EL, Nash F, Raiford K, Harrell LE. Recruitment of black elderly for clinical research studies of dementia: the CERAD experience. The Gerontologist. 1993;33(4):561-5.
40. Carr SA, Davis R, Spencer D, Smart M, Hudson J, Freeman S, et al. Comparison of recruitment efforts targeted at primary care physicians versus the community at large for participation in Alzheimer disease clinical trials. Alzheimer Dis Assoc Disord. 2010;24(2):165-70.
41. Walter S, Clanton TB, Langford OG, Rafii MS, Shaffer EJ, Grill JD, et al. Recruitment into the Alzheimer Prevention Trials (APT) Webstudy for a Trial-Ready Cohort for Preclinical and Prodromal Alzheimer’s Disease (TRC-PAD). The journal of prevention of Alzheimer’s disease. 2020;7(4):219-25.
42. Weiner MW, Nosheny R, Camacho M, Truran-Sacrey D, Mackin RS, Flenniken D, et al. The Brain Health Registry: An internet-based platform for recruitment, assessment, and longitudinal monitoring of participants for neuroscience studies. Alzheimers Dement. 2018;14(8):1063-76.
43. Sperling RA, Donohue MC, Raman R, Sun CK, Yaari R, Holdridge K, et al. Association of Factors With Elevated Amyloid Burden in Clinically Normal Older Individuals. JAMA neurology. 2020;77(6):11.
44. Meeker KL, Wisch JK, Hudson D, Coble D, Xiong C, Babulal GM, et al. Socioeconomic Status Mediates Racial Differences Seen Using the AT(N) Framework. Ann Neurol. 2021;89(2):254-65.
45. Neugroschl J, Sewell M, De La Fuente A, Umpierre M, Luo X, Sano M. Attitudes and Perceptions of Research in Aging and Dementia in an Urban Minority Population. J Alzheimers Dis. 2016;53(1):69-72.


G.A. Jimenez-Maggiora1, S. Bruschi1, R. Raman1, O. Langford1, M. Donohue1, M.S. Rafii1, R.A. Sperling2, J.L. Cummings3, P.S. Aisen1 and the TRC-PAD Investigators*

1. Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, CA, USA; 2. Center for Alzheimer Research and Treatment, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA; 3. Department of Brain Health, School of Integrated Health Sciences, University of Las Vegas, Nevada; Cleveland Clinic Lou Ruvo Center for Brain Health, USA; * TRC-PAD investigators are listed at

Corresponding Author: GA Jimenez-Maggiora, Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, CA, USA,

J Prev Alz Dis 2020;4(7):226-233
Published online August 16, 2020,



BACKGROUND: The Trial-Ready Cohort for Preclinical/Prodromal Alzheimer’s Disease (TRC-PAD) Informatics Platform (TRC-PAD IP) was developed to facilitate the efficient selection, recruitment, and assessment of study participants in support of the TRC-PAD program.
Objectives: Describe the innovative architecture, workflows, and components of the TRC-PAD IP.
Design: The TRC-PAD IP was conceived as a secure, scalable, multi-tiered information management platform designed to facilitate high-throughput, cost-effective selection, recruitment, and assessment of TRC-PAD study participants and to develop a learning algorithm to select amyloid-bearing participants to participate in trials of early-stage Alzheimer’s disease.
Setting: TRC-PAD participants were evaluated using both web-based and in-person assessments to predict their risk of amyloid biomarker abnormalities and eligibility for preclinical and prodromal clinical trials. Participant data were integrated across multiple stages to inform the prediction of amyloid biomarker elevation.
Participants: TRC-PAD participants were age 50 and above, with an interest in participating in Alzheimer’s research.
Measurements: TRC-PAD participants’ cognitive performance and subjective memory concerns were remotely assessed on a longitudinal basis to predict participant risk of biomarker abnormalities. Those participants determined to be at the highest risk were invited to an in-clinic screening visit for a full battery of clinical and cognitive assessments and amyloid biomarker confirmation using positron emission tomography (PET) or lumbar puncture (LP).
Results: The TRC-PAD IP supported growth in recruitment, screening, and enrollment of TRC-PAD participants by leveraging a secure, scalable, cost-effective cloud-based information technology architecture.
Conclusions: The TRC-PAD program and its underlying information management infrastructure, TRC-PAD IP, have demonstrated feasibility concerning the program aims. The flexible and modular design of the TRC-PAD IP will accommodate the introduction of emerging diagnostic technologies.

Key words: Alzheimer’s, clinical trials, informatics, recruitment.



Alzheimer’s Disease (AD) has emerged as one of the most significant public health issues of the 21st century. In 2020, it was estimated that 5.8 million people in the United States (U.S.) were living with Alzheimer’s Disease; this number was expected to rise to 13.8 million by 2050 (1). The development of effective disease-modifying interventions for Alzheimer’s disease (AD) remains an enormously important world health need. As therapeutic research has expanded to early-stage interventions, the recruitment of minimally affected optimally selected study participants has been challenging (2). The challenges include: identifying potential asymptomatic participants with elevated amyloid levels who meet study criteria, expanding recruitment and participation of groups that have been traditionally underrepresented in AD clinical trials, and reducing the time and cost of screening failures (3). Novel approaches to improve participant selection and recruitment using sophisticated informatics platforms have been developed (4, 5). The Trial-Ready Cohort for Preclinical/Prodromal Alzheimer’s Disease (TRC-PAD) program was initiated with the overarching goal of accelerating therapeutic development for AD through the establishment of an infrastructure to ensure timely recruitment of targeted individuals into optimally designed trials (6). More specifically, TRC-PAD aimed to establish a biomarker-confirmed trial-ready cohort to facilitate recruitment into preclinical and prodromal AD trials by using an efficient, low-cost multi-stage selection process driven by an adaptive risk algorithm which combined web-based and in-person longitudinal assessment of participants. The TRC-PAD Informatics Platform (TRC-PAD IP) was developed to facilitate the selection, recruitment, and assessment of study participants in support of the TRC-PAD program. The purpose of this article is to describe the innovative architecture, workflows, and components of the TRC-PAD IP, demonstrate its utility, and discuss broader implications for the field of AD clinical trials.



TRC-PAD Informatics Platform

The TRC-PAD Informatics Platform (TRC-PAD IP) (Figure 1) was conceived as a secure, scalable, multi-tiered information management platform designed to facilitate high-throughput, cost-effective selection, recruitment, and assessment of TRC-PAD study participants. The program aims called for the construction of a multi-stage process that encompassed 1) a public-facing web-based registry, the Alzheimer Prevention Trials (APT) Webstudy registry, 2) an analytics platform capable of supporting the development and implementation of risk-based referral and screening algorithms, 3) a web-based referral management system, the Site Referral System (SRS), and 4) a regulatory-compliant clinical data management system to collect data for the standing Trial Ready Cohort (TRC). Given the complex structure and pathways of this process, tracking participants as they entered the program and transitioned from one stage to the next was also a critical requirement. The component systems underpinning this process are described in the following sections.

Figure 1. TRC-PAD Informatics Platform


APT Webstudy

The APT Webstudy ( is a public-facing online longitudinal study that invited members of the general population who were over 50 years of age and had sufficient interest in participating in AD research to register for a user account. Once registered, users were presented with a guided walk-through during which they provided their contact information and preferences, current AD-related diagnosis (if known), and interest in participating in AD-related research. Subsequently, users were offered additional study information, asked to consider the benefits and risks of study participation, and allowed to consent and enroll in the APT Webstudy using an online consent process. Participants were subsequently asked to provide additional information regarding their demographics, family and medical history, lifestyle, current AD biomarker status (if know), and complete assessments of their subjective memory perceptions using the Cognitive Function Instrument (CFI), and cognitive performance using the Cogstate Brief Battery (7-9). To minimize participant burden, the registration, consent, and assessment process was designed to take approximately 15 to 20 minutes to complete. Follow-up assessments, scheduled every 3 months, were designed to be completed in 10 to 15 minutes to promote participant retention.

Website Design

The APT Webstudy was designed with several features to support usability and engagement. Users had the option to create their accounts using a «1-click» social login instead of a traditional approach which requires a username, password, and email address. Participants were provided with a dashboard to view their previous assessment scores. They could also download a report with all their information and assessment scores. Participants received a quarterly newsletter via electronic mail developed by leading AD researchers which highlighted new developments in the field as well as news related to the TRC-PAD program. The website was designed to support multiple screen sizes (phone, tablet, desktop) using responsive web design principles (10).

Longitudinal Assessments

To improve the prediction of participant risk, the APT Webstudy was designed to incorporate unsupervised cognitive assessments as part of a longitudinal observational study. Thus, participants are invited to return to the website every 3 months to complete a new round of assessments and update their information as needed. These follow-up assessments are designed to take 10 to 15 minutes to complete. To encourage compliance, participants received quarterly electronic mail reminders to log-in to the APT Webstudy website to complete their web-based assessments.

Website Architecture

An important consideration in a public-facing website is the variability of website traffic patterns. Websites with highly volatile traffic patterns can exhibit occasional periods of poor performance or unavailability. This is especially true when an unexpected media event, such as a viral social media mention or a celebrity endorsement, draws a burst of traffic toward the website. To mitigate the effects of these unexpected traffic spikes, the APT Webstudy has an architecture that dynamically adjusts its capacity to respond to incoming requests as changes in traffic patterns emerge. This architecture combines a cloud-based fleet of webservers, a multi-region database cluster, and a dynamic networking configuration to allow the website to automatically activate and deactivate computational resources on-demand to maintain performance during high-traffic periods in a cost-effective manner.

Recruitment and Retention

Recruitment into the APT Webstudy relied on a combination of traditional and digital media strategies. These efforts included community-based events, local and national paid media campaigns, social media campaigns, and earned media. Additionally, a group of preexisting registries which included the Brain Health Registry (, the Alzheimer’s Prevention Registry (, Trial Match (, and Healthy Brains (, partnered with the TRC-PAD program to refer participants from their cohorts. These registries, collectively known as the “feeder” registries, provided the APT Webstudy with access to a large, engaged group of potential participants. The performance of these recruitment efforts was tracked by using Urchin Tracking Module (UTM) codes which associated participants with the campaign that referred them to the APT Webstudy (11).

Participant Technical Support

APT Webstudy participants were provided with both telephone- and email-based support channels. Telephone-based support requests were automatically transcribed and forwarded to the email-based support management system for follow-up. This architecture allowed the APT Webstudy support team to manage all incoming support requests via a single interface. Support cycle times and participant satisfaction were among the Key Performance Indicators (KPIs) used to assess and manage the performance of the support team.

Site Referral System

APT Webstudy participant data were regularly evaluated using an adaptive algorithm that assessed each participant’s risk of AD biomarker positivity and ranked participants based on predicted risk (12). Participants determined to have the highest risk were referred to the nearest TRC-PAD performance site based on their self-reported 5-digit Zip Code. Participant referrals were provided to performance sites via the Site Referral System (SRS), a secure website that allowed authorized site personnel to manage the site’s referral queue and ensure the timely disposition of each referral. Using the SRS, site personnel contacted participants and invited them to schedule an initial in-person screening visit to determine their eligibility for enrollment into the TRC-PAD standing cohort.

Website Design

A key concept guiding the design of the SRS was the idea that site referral queues would be managed collaboratively among multiple site personnel. To support collaboration, the SRS website incorporated several features that optimized team-based management and disposition of participant referrals. For example, as new referrals became available in the referral queue, site personnel received notifications via electronic mail. The status of every referral in the queue was summarized in a dashboard view facilitating management and reporting. Each referral provided site personnel with access to a summary of participant-reported demographic, medical history, lifestyle, and contact information, as well as status changes. As a final step, referrals were assigned an outcome code and marked complete.

Trial Ready Cohort

The TRC was conceived as the final stage in the TRC-PAD process. The initial target for the TRC was to enroll a standing longitudinal cohort of 2,000 biomarker-confirmed participants (50% preclinical and 50% prodromal) (6). An Electronic Data Capture (TRC EDC) system was developed to manage TRC participant data, based on the Alzheimer’s Therapeutic Research Institute (ATRI) EDC system. The TRC EDC was used to operationalize a multi-stage screening process and collect a rich set of clinical, neuropsychological, neuroimaging, and biospecimen data collected in a multi-site setting. The TRC EDC was validated to comply with CRF Title 21 Part 11 (13). Doing so allowed the TRC data to be eligible for use as run-in data in downstream clinical trials.

TRC EDC Design

The TRC EDC was built to provide centralized management of all critical cohort study dataflows and workflows. This approach provided study teams with broad transparency and management capabilities over the study’s complex dataflows and processes. Implementing this approach, however, has proven challenging for traditional systems, which struggle to scale up as larger and more complex data types are introduced. Historically, a solution to this problem has been the implementation of interconnected purpose-specific systems. While feasible, this solution suffers from several shortcomings: 1) supporting evolving study requirements requires complex multi-system impact analysis, 2) training on multiple systems increases the burden on study teams, and 3) data integrations require ongoing maintenance as systems are updated. The TRC EDC was designed to avoid these issues by implementing a cloud-native architecture, which allowed it to harness the full range of capabilities of the underlying cloud platform on which it was hosted. In this architecture, the TRC EDC served as an orchestration engine that coordinated and delegated computational workloads, such as uploading and processing large binary objects (e.g. medical images, sensor data, multimedia data, genetic data, graph data), to the underlying cloud-based service best-suited for the required function, without impacting the study team’s interaction with the system.

Site Network

An initial step toward the establishment of the TRC was the creation of the TRC-PAD site network, a set of 35 academic clinical sites distributed across the large population centers in the contiguous United States. Sites were selected based on multiple criteria including study team research experience and expertise, amyloid positron emission tomography (PET) imaging and radiotracer availability, and track record in AD/AD and related disorders (ADRD) clinical trials. Site selection and activation were conducted in two stages, the “vanguard” or initial phase, which included 8 pilot sites, and the broad activation phase. The vanguard phase was used as a learning exercise to test and refine TRC-PAD data management tools and processes. These learnings were applied during the broad activation phase to ensure the full site network was optimized.

Multi-stage Selection and Screening Process

The TRC-PAD selection and recruitment processes were managed by a series of learning algorithms that assessed participant data at multiple points. Newly acquired data were used to update participant risk predictions and rankings (12). These results informed the decision-making process used to graduate participants from one stage of TRC-PAD to the next, culminating with the final determination on enrollment eligibility into the standing TRC.

Referral to Downstream Clinical Trials

TRC participants who meet eligibility criteria will be referred for screening and potential enrollment to downstream clinical trials, temporarily suspending additional longitudinal assessments and data collection activities in the TRC EDC. In these cases, a link between a participant’s TRC data record and their downstream clinical trial data record will be established via the use of the National Institute on Aging’s (NIA) Global Unique Identifier (GUID) (14, 15). This link will allow for participant data to be integrated across cohorts and used to further inform the TRC-PAD learning algorithms.

Analytical Platform

TRC-PAD IP supported a single Analytical Platform (AP) that aggregated data from multiple sources in a single semi-structured repository, also known as a «data lake» (16). This approach combined the use of serverless computing methods with fast, low-cost object storage to facilitate the development and operationalization of multiple analysis workloads such as machine learning algorithms, statistical analyses, and reports.

Information Architecture

The TRC-PAD IP was constructed using open source web development and scientific computing tools hosted on Amazon Web Services (AWS), a public cloud computing platform (Table 1). The TRC-PAD program partnered with AWS’s consulting group, AWS Professional Services (AWS ProServ), to construct a secure, scalable, cost-effective computational infrastructure. The component systems of the TRC-PAD IP were built using a phased approach. Unique system-generated identification numbers (IDs) were assigned to each participant as they moved from one stage of TRC-PAD to the next. These IDs were linked across component systems to maintain the integrity of each participant’s data record while protecting confidentiality.

Table 1. Technology Components of the TRC-PAD Informatics Platform


Scalability and Cost

The TRC-PAD IP was built on cloud-based computational infrastructure that was optimized to support each component system. The infrastructure supporting the APT Webstudy, which was subject to temporary bursts of traffic due to recruitment campaigns or unexpected media events, was designed to dynamically adapt to changing web traffic patterns by increasing or decreasing its fleet of webservers, within set cost parameters. Likewise, the TRC EDC, which managed a large, regulatory-compliant cohort database, used a cost-effective, highly durable storage strategy. In this architecture, computational resources were used on-demand, reducing idle capacity and providing flexibility as the TRC-PAD program’s requirements evolved.

Security and Compliance

Ensuring participant confidentiality and data security were the primary requirements for the TRC-PAD IP. Thus, the TRC-PAD Informatics team worked with AWS ProServ to build a Health Insurance Portability and Accountability Act (HIPAA)-eligible architecture and implement AWS’s best practices (17). This architecture was designed to take advantage of multiple strategies to ensure data security and durability such as a multi-account structure, multi-region, encrypted data storage, and automated policy management. To further ensure data security, the TRC-PAD IP underwent annual security audits by an independent security firm.

Regulatory Oversight

Regulatory oversight of the APT Webstudy was provided by the University of Southern California Institutional Review Board (USC IRB). Regulatory oversight for the TRC was provided by Advarra, Inc., under a single IRB (sIRB) model. The sIRB model was established as a National Institute of Health (NIH) requirement for multi-site studies starting in 2018 to streamline the review of research that involves human subjects (18).



The APT Webstudy was launched on December 22, 2017, after a 6-month development period. The SRS and TRC EDC launched in May 2019. The TRC-PAD development team worked closely with the study team employing agile software development methods to design, build, test, and deploy these systems (19, 20).
As of July 6, 2020, 36,955 users had registered for an APT account. Of these registered users, 33,259 (90.0%) enrolled in the study via online consent and had completed more than 280,000 remote assessments (Figure 2). Recruitment into the APT Webstudy was driven by earned, owned, and shared media as well as feeder-based referrals. The APT cohort was geographically distributed across all 50 U.S. states, with participants concentrated in the coastal, midwestern, and southwestern states. Participant mobile device usage (43.0%) on the APT Webstudy was higher than initially expected. The demographic characteristics of the cohort are female (73.0%), non-Hispanic White (92.4%), with a mean age of 64.6 years (SD = 8.3). 87.7% agreed to have their contact information shared with the TRC sites. After one year of quarterly follow-up, 44.7% of participants were retained. The retention rate after two years of quarterly follow-up was 29.7%.

Figure 2. APT Webstudy Enrollment (December 22, 2017 to July 6, 2020)


TRC sites began in-person screening of participant referrals in August 2019. As of July 6, 2020, 27 of 35 TRC sites were activated and had received 1,675 risk-ranked participant referrals via the SRS. Of these, 246 (14.7%) participants were referred to the TRC for initial screening, 123 (50.0%) participants completed the initial screening visit, 99 (80.5%) participants were authorized for amyloid testing, 55 (55.6%) participants were biomarker-confirmed using amyloid PET or CSF assessment, 26 (47.3%) participants were found to be amyloid elevated, and 23 (88.5%) participants were enrolled into the TRC (Figure 3). The demographic characteristics of the standing TRC are female (51.2%), non-Hispanic White (94.2%), with a mean age of 72 years (SD = 7.8), and a mean SUVr of 1.14 (SD = 0.22). The median (interquartile range) cycle time from initial site referral via SRS to enrollment decision in the TRC was 28 (15 to 84) days. During this period, the TRC-PAD IP proved to be a scalable, cost-effective solution to support all stages of the TRC-PAD selection and recruitment.

Figure 3. TRC-PAD Participant Flows by Stage (as of July 6, 2020)



Our early experiences with the TRC-PAD program and its underlying information management infrastructure, TRC-PAD IP, have demonstrated feasibility concerning the program aims. The TRC-PAD IP supported recruitment, screening, and enrollment of TRC-PAD participants by leveraging a secure, scalable, cost-effective cloud-based information technology (IT) architecture. The APT Webstudy has demonstrated effectiveness in terms of selecting and recruiting individuals from the general population who have an elevated risk of amyloid positivity. The APT Webstudy has proven effective in remotely assessing participants on a longitudinal basis. The SRS has demonstrated effectiveness in terms of allowing TRC sites to manage site referrals promptly. The TRC EDC has supported the coordination of the multi-stage risk-based screening and enrollment process into the standing TRC. Much work remains to demonstrate the effectiveness of the TRC-PAD program in accelerating recruitment into downstream AD clinical trials.
Several limitations should be considered. First, the TRC-PAD program has struggled to select and recruit a representative sample of participants across multiple sociodemographic dimensions (21). Efforts to address this challenge have included updating the APT Webstudy to support Spanish-speaking participants, however, more work is needed. Second, the architecture has proven to be resilient during a few web traffic spikes but has yet to be subjected to the type of surge (>10-100x daily web traffic) associated with an unexpected mention in a national media platform. Third, the integrations with third-party platforms, such as the Cogstate Brief Battery, have proven to be fragile, requiring frequent maintenance and technical support for participants. Addressing these technical issues may serve to improve both participant retention and risk algorithm predictive performance.
In May 2019, the TRC-PAD principal investigators selected the first set of downstream clinical trials slated to utilize the standing TRC to recruit participants. These clinical trials are scheduled to begin recruiting participants in North America (U.S. and Canada) in May 2020. The efficiencies that will accrue to these clinical trials are two-fold: 1) by drawing participants from the TRC, the expectation is that recruitment into these clinical trials will be accelerated by reducing the number of screen failures; and 2) the TRC clinical, neuropsychological, biofluid, and imaging data will be available to use as high-quality run-in data for downstream clinical trials (22). These combined efficiencies should yield significant savings both in terms of resources and time.
As the TRC-PAD program has been established over the past few years, promising new AD biomarkers have been developed. Plasma-based AD biomarkers, for example, have been shown to effectively predict amyloid positivity (23). When fully validated, the introduction of these diagnostic tools into the TRC screening process may be used to further increase effectiveness. The flexible and modular design of the TRC-PAD IP will accommodate the introduction of these new technologies.
Finally, as news of the TRC-PAD program’s progress has spread, a global network of programs modeled on its approach has begun to take shape. Investigators in several countries have been collaborating with the TRC-PAD program leadership to establish similar programs in their home regions. By using global cloud computing infrastructure and the TRC-PAD IP as a model architecture, these programs have been able to rapidly establish similar platforms in their local jurisdictions. Once fully operational, the global TRC-PAD network aims to provide AD clinical trials with a steady stream of well-characterized, biomarker-confirmed participants yielding savings in time, effort, and expense.


Acknowledgments: The authors would like to thank the TRC-PAD participants and their families, sponsors and partners, investigators, site and coordinating center personnel for their contributions in support of the program. Dr. Cummings is supported by Keep Memory Alive (KMA); NIGMS grant P20GM109025; NINDS grant U01NS093334; and NIA grant R01AG053798.

Funding: This work was funded by the U.S. National Institute on Aging (NIA) (grant number 1R010AG053798).

Ethical standard: Institutional Review Boards (IRBs) approved these studies, and all participants gave informed consent before participating.

Conflict of interest: The authors report grants from National Institute on Aging, during the conduct of the study. None of the authors have additional financial interests, relationships or affiliations relevant to the subject of this manuscript.

Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.



1. 2020 Alzheimer’s disease facts and figures. Alzheimers Dement. 2020.
2. Watson JL, Ryan L, Silverberg N, Cahan V, Bernard MA. Obstacles And Opportunities In Alzheimer’s Clinical Trial Recruitment. Health Affairs. 2014;33(4):574-579.
3. Grill JD, Galvin JE. Facilitating Alzheimer Disease Research Recruitment. Alzheimer Disease & Associated Disorders. 2014;28(1):1-8.
4. Ritchie CW, Molinuevo JL, Truyen L, et al. Development of interventions for the secondary prevention of Alzheimer’s dementia: the European Prevention of Alzheimer’s Dementia (EPAD) project. Lancet Psychiatry. 2016;3(2):179-186.
5. Lovestone S, Consortium E. The European medical information framework: A novel ecosystem for sharing healthcare data across Europe. Learn Health Syst. 2020;4(2):e10214.
6. Aisen PS, Sperling RA, Cummings J, et al. The Trial-Ready Cohort for Preclinical/prodromal Alzheimer’s Disease (TRC-PAD) Project: An Overview. J Prev Alz Dis 2020; DOI: 10.14283/jpad.2020.45
7. Walsh SP, Raman R, Jones KB, Aisen PS, Group AsDCS. ADCS Prevention Instrument Project: the Mail-In Cognitive Function Screening Instrument (MCFSI). Alzheimer Dis Assoc Disord. 2006;20(4 Suppl 3):S170-178.
8. Li C, Neugroschl J, Luo X, et al. The Utility of the Cognitive Function Instrument (CFI) to Detect Cognitive Decline in Non-Demented Older Adults. J Alzheimers Dis. 2017;60(2):427-437.
9. Lim YY, Ellis KA, Harrington K, et al. Use of the CogState Brief Battery in the assessment of Alzheimer’s disease related cognitive impairment in the Australian Imaging, Biomarkers and Lifestyle (AIBL) study. J Clin Exp Neuropsychol. 2012;34(4):345-358.
10. Lane J, Barker T, Lewis J, Moscovitz M. Responsive Design. In: Foundation Website Creation with HTML5, CSS3, and JavaScript. Berkeley, CA: Apress; 2012.
11. Walter S, Clanton T, Langford O, et al. Recruitment into the Alzheimer Prevention Trials (APT) Webstudy for a Trial-Ready Cohort for Preclinical and Prodromal Alzheimer’s Disease (TRC-PAD). J Prev Alz Dis 2020; DOI: 10.14283/jpad.2020.46
12. Langford O, Raman R, Sperling RA, et al. Predicting amyloid burden to accelerate recruitment of secondary prevention clinical trials. J Prev Alz Dis 2020; DOI: 10.14283/jpad.2020.44
13. Title 21 Part 11 — Electronic Records; Electronic Signatures. In: Electronic Code of Federal Regulations; 1997.
14. Babcock D. The Role of GUIDs in Data Sharing. The NIA Alzheimer’s Disease Centers Program – National Alzheimer’s Coordinating Center. Published 2019. Accessed May 5, 2020.
15. Johnson SB, Whitney G, McAuliffe M, et al. Using global unique identifiers to link autism collections. J Am Med Inform Assoc. 2010;17(6):689-695.
16. O’Leary D. Embedding AI and Crowdsourcing in the Big Data Lake. IEEE Intelligent Systems. 2014;29(5):70-73.
17. Amazon Web Services: AWS Well-Architected Framework. Published 2019. Accessed April 27, 2020.
18. National Institutes of Health: NIH Policy on the Use of a Single Institutional Review Board for Multi-Site Research. Accessed April 28, 2020.
19. Beck K, Beedle M, van Bennekum A, et al. Manifesto for Agile Software Development. Accessed May 13, 2020.
20. Hohl P, Klünder J, van Bennekum A, et al. Back to the future: origins and directions of the “Agile Manifesto” – views of the originators. Journal of Software Engineering Research and Development. 2018;6(1):1-27.
21. Walter S, Langford O, Clanton T, et al. The Trial-Ready Cohort for Preclinical/Prodromal Alzheimer’s disease (TRC-PAD): Experience from the first 3 years. J Prev Alz Dis 2020; DOI: 10.14283/jpad.2020.47
22. Laursen DRT, Paludan-Müller AS, Hróbjartsson A. Randomized clinical trials with run-in periods: frequency, characteristics and reporting. Clin Epidemiol. 2019;11:169-184.
23. Ovod V, Ramsey KN, Mawuenyega KG, et al. Amyloid β concentrations and stable isotope labeling kinetics of human plasma specific to central nervous system amyloidosis. Alzheimers Dement. 2017;13(8):841-849.

What do you want to do ?

New mail

What do you want to do ?

New mail

What do you want to do ?

New mail

What do you want to do ?

New mail

What do you want to do ?

New mail

What do you want to do ?

New mail

What do you want to do ?

New mail

What do you want to do ?

New mail

What do you want to do ?

New mail

What do you want to do ?

New mail

What do you want to do ?

New mail

What do you want to do ?

New mail

What do you want to do ?

New mail

What do you want to do ?

New mail

What do you want to do ?

New mail

What do you want to do ?

New mail

What do you want to do ?

New mail

What do you want to do ?

New mail

What do you want to do ?

New mail



J.B. Langbaum1,5,8, N. High1, J. Nichols1,2, C. Kettenhoven1, E.M. Reiman*,1,3,4,6,7,8, P.N. Tariot*,1,4,8


1. Banner Alzheimer’s Institute, Phoenix, AZ, USA; 2. Midwestern University, Glendale, AZ, USA; 3. Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ, USA; 4. Department of Psychiatry, University of Arizona College of Medicine, Phoenix, AZ, USA; 5. Department of Neurology, University of Arizona College of Medicine, Phoenix, AZ, USA; 6. Neurogenomics Division, Translational Genomics Research Institute, Phoenix, AZ, USA; 7. Biodesign Institute, Arizona State University, Tempe, AZ, USA;
8. Arizona Alzheimer’s Consortium, Phoenix, AZ, USA; * co-senior authors.

Corresponding Author: Jessica B. Langbaum, Ph.D. Banner Alzheimer’s Institute, 901 E. Willetta Street, Phoenix, AZ, 85006, USA, Tel.: 602-839-2548, Fax: 602-839-6936, Email:

J Prev Alz Dis 2020;4(7):242-250
Published online May 25, 2020,



Background: Recruitment for Alzheimer’s disease (AD)-focused studies, particularly prevention studies, is challenging due to the public’s lack of awareness about study opportunities coupled with studies’ inclusion and exclusion criteria, resulting in a high screen fail rate.
Objectives: To develop an internet-based participant recruitment registry for efficiently and effectively raising awareness about AD-focused study opportunities and connecting potentially eligible volunteers to studies in their communities.
Methods: Individuals age 18 and older are eligible to join the Alzheimer’s Prevention Registry (APR). Individuals provide first and last name, year of birth, country, and zip/postal code to join the APR; for questions regarding race, ethnicity, sex, family history of AD or other dementia, and diagnosis of cognitive impairment, individuals have the option to select “prefer not to answer.” The APR website maintains a list of recruiting studies and contacts members who have opted in by email when new studies are available for enrollment.
Results: As of December 1, 2019, 346,661 individuals had joined the APR. Members had a mean age of 63.3 (SD 11.7) years and were predominately women (75%). 94% were cognitively unimpaired, 50% reported a family history of AD or other dementia, and of those who provided race, 76% were white. 39% joined the APR as a result of a paid social media advertisement. To date, the APR helped recruit for 82 studies.
Conclusions: The APR is a large, internet-based participant recruitment registry designed to raise awareness about AD prevention research and connect members with enrolling studies in their communities. It has demonstrated the ability to recruit and engage a large number of highly motivated members and assist researchers in meeting their recruitment goals. Future publications will report on the effectiveness of APR for accelerating recruitment and enrollment into AD-focused studies.

Key words: Registry, recruitment, Alzheimer’s disease.



Alzheimer’s disease (AD) remains one of the greatest medical, economic, and societal burdens in the United States (US) and globally (1). In the US, an estimated 5.7 million people are living with dementia due to AD, a number projected to reach 13.8 million by 2050 barring a medical breakthrough (2). Interventions that delay the symptomatic onset of AD by even by 1 or 2 years would have a major public health impact (3). With a heightened sense of urgency, numerous AD prevention studies are underway, with many more planned.
The sharp growth in AD prevention trials requires an unprecedented screening and enrollment funnel. Specifically, researchers will need to screen tens of thousands of cognitively healthy older adults to identify the thousands of individuals eligible to enroll in prevention trials (4). Additionally, the number of trials in affected individuals and their care partners continues to rise. In 2019, there were 156 trials for the treatment of AD, an increase from 2018 (5). Notably, this number does not include observational studies, which are also important to understanding AD and developing new interventions to treat or prevent it. We use the term “AD-focused studies” to capture these different study types (e.g., clinical trials, observational studies, etc.) and participant populations (e.g., preclinical, mild cognitive impairment (MCI), AD dementia, etc.). The recruitment goals for these AD-focused studies confront the AD field with a daunting challenge. In the US, regardless of disease area, the vast majority (85-90%) of studies experience significant delays in recruitment and enrollment (6). Nearly one-third of clinical trials under-enroll, and only 7% meet their target enrollment number on time (7). Numerous factors contribute to these difficulties. Recruitment is time-consuming, sometimes taking years to meet target sample sizes. This is in large part because screen failure rates for trials can reach as high as 85%, chiefly due to inclusion criteria. For example, in the case of AD-related studies, requiring presence of an AD biomarker or participants to have a specific genetic risk factor (8). Improving recruitment methods has become a critical priority for the AD field (8-12).
As the number of AD-focused studies increase there is a growing need for (1) increased awareness of research participation opportunities and (2) quick and efficient mechanisms to contact, characterize, and refer potentially eligible participants to studies (13). Initiatives such as the National Plan to Address AD call for greater attention to “increasing enrollment into clinical trials and other clinical research.” An outgrowth of the National Plan is the National Strategy for Recruitment and Participation in Alzheimer’s Disease Clinical Research which enumerated four goals, one of which focused specifically on the need to “build and improve research infrastructure of registries in order to recruit and retain more and more diverse qualified study participants” (13).
Participant recruitment registries are tools designed to reach out to, identify, characterize, and refer potentially eligible participants to studies, often with the goal of minimizing the percentage of people who screen fail or are otherwise found to be ineligible. In the US and globally, several AD-focused participant recruitment registries are being used at the local, regional, and national level (14-24). Each of these registries approach participant recruitment and engagement differently and the field is still gathering data on best practices to design and conduct recruitment registries in order to help accelerate enrollment into AD prevention-focused studies and trials (12, 13).
Here we describe the rationale, design and execution of, as well as enrollment metrics, member demographics, and key lessons learned from the Alzheimer’s Prevention Registry (APR). The APR is a large internet-based participant recruitment registry developed by the Banner Alzheimer’s Institute (BAI) researchers, leaders of the Alzheimer’s Prevention Initiative (API) program. Between 2009-2011, API leaders vetted with academic advisors and stakeholders initial designs for AD prevention trials in autosomal dominant AD (ADAD) and APOE4 homozygote populations; the APR was developed as a result of these discussions and a small group of academic advisors formed the APR Executive Committee who provided input and guidance during the early years of its development and launch. The initial design of the APR leveraged our experience leading the state-wide, paper-based Arizona Alzheimer’s Registry (AAR) (25) and was inspired by other large, internet-based participant recruitment registries (e.g., Army of Women, ResearchMatch, Fox Trial Finder) (26). The initial objectives were to create an efficient participant recruitment registry that would go beyond the needs of the API trials and help recruit for a range of AD prevention-focused studies that were, at the time, in initial stages of planning and not yet ready to enroll participants. Since the launch of the APR, and due to the still limited number of AD prevention trials, the program has expanded its reach to help with recruitment for a range of AD-focused studies and occasionally, studies focused on other related dementias or aging and cognition.



APR Overview

Individuals age 18 and older are eligible to join the APR via the website (NCT02022943). The age range was selected to allow younger adults the option to join the APR and share information and study opportunities with family and friends. The APR was determined to not be research by the Institutional Review Board (IRB) based on federal regulation 45 CFR 46 and associated guidance. Although individuals do not provide consent when joining the APR, they do agree to the APR’s privacy policy and are informed about BAI’s Notice of Privacy Practices (Health Insurance Portability and Accountability Act [HIPAA]).
In 2006, prior to the development of the APR, the Arizona Alzheimer’s Consortium (AAC) created the AAR which was then led by BAI. The AAR has been described previously (25). In brief, participation in the AAR was by open invitation to adults in Arizona aged 18 and older. Those interested provided consent and completed a written questionnaire. A subset of Registrants underwent telephone cognitive assessment. Referral to AAC sites for study opportunities was based on Registrants’ medical history, telephone cognitive assessment, and research interests. Between 2006 and 2011, 1257 people consented to the AAR, most of whom were cognitively unimpaired at a time when most AD-focused studies were enrolling individuals with cognitive impairment. The AAR proved to be operationally burdensome, requiring data entry from written questionnaires and staff to contact participants by telephone to administer cognitive assessments. These assessments were partially intended as engagement and retention tools for participants, however, due to budget constraints, not all underwent testing, and for those who did, the administration was intermittent. The infrastructure and experiences gained from the AAR served as the prototype for the APR.

APR Website and Member Experience

The APR website launched in May 2012. Within six months it became apparent that the initial website design, including requiring members to create an account using a username and password, made the process to join the APR too cumbersome. In addition, the initial website was optimized for viewing on traditional desktop computer rather than being “mobile friendly” for viewing on a smartphone or tablet. Lastly, the “call to action” messaging on the APR website was confusing, leading website visitors to believe that it was a request for monetary donations. To better understand APR members’ use of the website, in 2013 we invited APR members to complete a seven-question survey. The survey consisted of 5 Likert scale questions and 2 open response questions. Results from this survey, along with best practices and lessons learned from other registries and online recruitment/enrollment programs, were used to guide the first website redesign, which went live in July 2013 and was the first step in making the website mobile friendly.
After this first redesign, we used A/B tests to determine which changes to make to the APR website to maximize an outcome of interest, such as increasing enrollment into the APR, by randomly showing website visitors a “control” or “variation” message and then measuring which version was more effective for the intended outcome (27). A/B tests were conducted in October 2013 (testing the APR description/call to action on the website landing page), May 2014 (again testing the APR description/call to action on the website landing page), and September 2014 (testing what member contact and demographic information to collect on the main landing page versus a secondary page).

Recruitment and Enrollment into the APR

We used several recruitment strategies and tactics to raise awareness about the APR and enroll individuals into the program, including community talks, brochures, paid social media advertisements, and earned media coverage. The frequency in which these have occurred has varied since the APR’s launch. Source of enrollment is tracked via Urchin Tracking Module (UTM) codes and stored in the APR database; individuals are not asked where they heard about the APR. To check for accuracy, select UTM codes are checked for accuracy against reports from advertising partner. In 2015, we conducted a paid awareness campaign in partnership with an online advocacy community over a three-month period, asking their community members to electronically sign a petition stating they support AD prevention research (the petition was conceptual and not sent to anyone); those who signed were enrolled automatically in the APR. In 2015, we began a paid social media advertising campaign on platforms including Facebook to help raise awareness about the APR and its GeneMatch program (20); this campaign has run intermittently since 2015. Unlike the online petition campaign, the paid social media campaign directed individuals to the APR website where they could enroll. Funding amounts for these paid campaigns varied from year to year.

APR Member Engagement and Retention

Multiple strategies were used to help members stay engaged and connected with the APR. Members received email newsletters (titled The Alzheimer’s Prevention Bulletin [APB]) highlighting information about AD prevention research. Initially the APB was emailed to members on a quarterly basis but based on the results from the 2013 survey it was moved to monthly in 2014. Also, in 2014 we began sending a caregiver-focused newsletter to APR members who indicated that they are caring for someone with AD or other dementia. Since 2015, APR members have been able to manage their email subscription preferences directly via the APR website, giving members the ability to select the types of newsletters and study opportunity emails (e.g., prevention studies, studies for people with memory impairment, etc.) they would like to receive and unsubscribe at any point in time. Prior to 2015, APR members managed their email subscriptions via the email footer (e.g., selecting “unsubscribe” at the bottom of the email). In 2017, we added the caregiver newsletter option to the newsletter subscription list, making it available to any APR member who wished to receive it (i.e., they did not need to indicate that they are caring for someone with AD or other dementia). In 2018, we began including a brief, typically one- to three-question, survey in the APB every other month to provide readers an opportunity to express opinions about various AD-related topics. The survey results are shared with readers on the months without a survey.
In 2014, we implemented what is commonly referred to as a “drip email campaign” after a person joined the APR. These emails, sent at prespecified times after enrollment, acknowledged the person’s signup, described the APR, and provided information about study opportunities. The drip campaign emails have evolved over the years in terms of their format, content, number, and duration. In 2017 we expanded the drip campaign to include an anniversary email, thanking the person for being an APR member for another year. The anniversary email provided the member with the opportunity to update their APR profile and a reminder to update their email newsletter subscription preferences.
In 2016, we began a re-engagement campaign as a mechanism to reach out to APR members who had not opened one of our emails in the past six-months (“unengaged members”). As part of this campaign, and following email marketing best practices for email list “hygiene” (e.g., to help ensure APR emails are delivered to members’ inboxes and are not marked as “spam” or “junk”), we sent up to four emails to “unengaged members” reminding them about the APR and providing instructions if they wanted to stay enrolled in the APR or wish to be removed. If no action was taken after the fourth email, their enrollment was deactivated and they no longer received emails from the APR. As with the newsletters and drip campaign emails, the re-engagement campaign emails evolved over time, incorporating email list hygiene best practices from email marketing advisors (see Acknowledgements). In 2017, we changed email platform providers. Only members who opened an APR email within the past 6 months were transferred to the new platform, though their contact information remained in the APR database.

Identifying Studies to Promote to APR Members

APR staff searched publicly available websites (e.g., on a regular basis to identify newly enrolling AD-focused studies and attempted to contact the sponsor or investigator team to discuss notifying APR members about the study. In addition, the APR team staffed an information booth at AD-focused scientific conferences, such as the Alzheimer’s Association International Conference (AAIC), to raise awareness about the APR as a recruitment resource to researchers and study sponsors. Study investigators and sponsor teams also contacted the APR team by email or by completing a form on the APR website to inquire about how to list their study on the APR. The APR team collected relevant information such as study design, enrollment criteria, and IRB-approved recruitment materials that were reviewed by the APR Study Review Committee for goodness-of-fit for APR members.

Connecting APR Members to AD-focused Studies

The APR used two main methods for notifying members about study opportunities, a dedicated “Study Opportunities” section of the website and emails to APR members. In April 2014, we launched the first version of the “Study Opportunities” section of the website. The goal was to create an actionable part of the website so that when visitors come to the website, they can connect with a study opportunity immediately and the APR, in theory, could begin collecting metrics for study referrals. This section contained original, lay-friendly descriptions of the study opportunity (rather than pull information directly from other websites such as and the contact information for the study coordinator (or other relevant person/website) was shown after the website visitor (APR member or website visitor who found the APR through a search engine or other means of organic traffic) clicked “Learn More”, allowing the visitor to contact the enrolling study directly. The study description was approved by the enrolling study’s IRB. Importantly, the APR did not exchange Personally Identifiable Information (PII) or other sensitive information with the enrolling study, since website visitors contacted study staff directly.
In July 2017, we redesigned this section of the website, renaming it “Find a Study.” The redesigned section allowed website visitors to search for studies by study type (e.g., online study, observational study, clinical trial, etc.), by enrollment criterion (e.g., their age), keyword search, and/or by location (e.g., zip code or country). Over time, the design was refined, allowing website visitors to search for studies enrolling people with or without memory impairment. Between July 2018 and June 2019, the “Find a Study” section was updated to include a “contact form” for studies rather than listing the study coordinator’s contact information. An individual interested in a study was asked to complete the form with their name, email address and phone number, review and acknowledge the study’s eligibility criteria and authorize the APR to share their contact information with the enrolling study team. The APR team provided the enrolling study with a dashboard for tracking referrals. Under this new model, studies and/or sponsors were required to execute a data sharing agreement with BAI due to the transfer of PII.
As noted previously, the APR also used email communication with members to connect them with study opportunities. Beginning in 2014, in conjunction with the launch of the “Study Opportunities” section of the website, we began sending specific email campaigns to APR members notifying them when new study opportunities are available. We worked directly with the study/researcher/sponsor to design an email campaign that met their recruitment objectives. The campaigns ranged from small, targeted emails to APR members based on demographic information provided at signup (e.g., age, location) to large, “spread the word” campaigns that requested APR members’ assistance to tell their friends and family about a study opportunity. Regardless of the size of the campaign, the emails included a Uniform Resource Locator [URL] hyperlink to the specific study listing on the APR website where they are provided with more information about the study and the study coordinator’s contact information. The hyperlink contains a tracking mechanism (via UTM codes), providing limited enrollment metrics to the APR and the enrolling study.

Data Analyses

Recruitment and enrollment into the APR are ongoing. The current report includes data collected as of December 1, 2019. A/B tests were conducted and analyzed using Optimizely testing software and Optimizely’s Stats Engine using a two-tailed sequential likelihood ratio test with false discovery rate controls to calculate statistical significance while minimizing false declarations.



APR Member Demographics

As of December 1, 2019, 346,661 people had joined the APR. Member demographic and recruitment sources are shown in Table 1. Since the website designed evolved over time, and members have not always been required to answer all questions, the sample sizes for each question are provided. Members have a mean age of 63.3 (SD 11.7) years, 75% are female, 94% self-report being cognitively unimpaired, 50% have a family history of AD or other dementia, and of those who provide race and ethnicity, 76% identify as non-Hispanic white. Of the four recruitment / enrollment tactics, paid social media advertising campaigns resulted in the most people joining the APR (39%), followed closely by people visiting the APR website directly (e.g., learning about the APR in news article, being referred by a friend, attending a community lecture, etc.) (32%).

Table 1. Demographic Characteristics of APR members

* participants are able select multiple options, only those reported by 0.3% or more of participants are listed


APR Website and Member Experience

934 (9%) of APR members responded to the 2013 survey (Section 2.2). Topline results from the open response questions indicated that APR members wanted more frequent email communication with the latest news (communication had been quarterly email newsletters) and the APR signup processes needed to be simplified with fewer “clicks.”
The October 2013 A/B test found that the variation landing page would increase annual enrollment by 8%. The May 2014 A/B test found that the variation landing page would increase annual enrollment by 11%. The September 2014 A/B test did not find a difference between the control and variation, leading us to conclude that we could collect additional contact and demographic information at the first step of enrollment without negatively impacting signups while helping with data cleanliness.

APR Member Engagement and Retention

In 2019, the average APB email open rate was 45% (compared to nonprofit healthcare industry average of 16%); average email click rate was 24% (compared to the industry average of 1.6%) (28). In 2019, response rates to the brief surveys in the APB ranged from 4.1%-10% among those members who opened the email.
As of December 1, 2019, 86,175 people were considered “actively engaged” members of the APR. In 2017, just prior to when we changed email platforms, the APR had 268,194 members, of whom 85,790 had opened an APR email within the past 6 months. As a result, we only transferred 85,790 to the new email platform. The email addresses from the remaining 182,404 members were not transferred to the new platform but their information remained in the APR database. Since this time, approximately 54,000 members (a mixture of members transferred to the new platform and members who joined after the email platform transition) have been added to the re-engagement campaign on the new email platform, and nearly 10,000 (18.5%) have been re-engaged and remained enrolled in the APR. Examining re-engagement “failure” rates by source of enrollment, 46% of those who joined via online advocacy community petition were not able to re-engaged successfully, followed by 37% of those who joined after seeing a paid social media advertisement, followed by 17% of those who joined by visiting the APR website directly or were referred by another source. Other members are considered unengaged because they either unsubscribed from receiving APR emails, marked APR emails as spam (and therefore no longer receive APR emails), or provided an invalid email address during enrollment. Since the launch of the APR, approximately 15% of members unsubscribed from APR emails. Examining unsubscribing by source of enrollment, 17% of those who joined by visiting the APR website directly or were referred by another source unsubscribed, followed by 16% of those who joined by online community / petition, followed by 12% who joined after seeing a paid social media advertisement.

Connecting APR Members to AD-focused Studies

As of December 1, 2019, the APR helped recruit for 82 AD-focused studies. New studies are being added on an ongoing basis to the “Find a Study” page of the website. As mentioned previously, only anecdotal data about APR member enrollment into in-person studies is available. Based on UTM data, APR has helped to enroll 10,005 participants into the Alzheimer’s Prevention Trials (APT) Webstudy, 6,559 into the Brain Health Registry [BHR], and 950 into Seven studies are currently utilizing the “contact form” recruitment model and 250 referrals have been sent to study staff thus far. Future publications will use data from the “contact form” to report on the effectiveness of APR for accelerating recruitment and enrollment into AD-focused studies.

APR as a Foundation for other Enrollment Initiatives

The APR served as a foundation for GeneMatch, a novel, trial-independent research enrollment program led by the API team at BAI, designed to recruit and refer cognitively healthy adults to AD prevention studies based in part on their APOE test results (NCT02564692) (20). GeneMatch was launched as a program of the APR in 2015 and as of April 2019, had enrolled just over 90,000 participants. In addition, we have shared our experience developing and leading large-scale recruitment registries to help others accomplish shared and complementary goals. This includes participating in the Dementia Research Recruitment Platform Global Collaborative (APR/GeneMatch [USA], BHR [USA], Hersenonderzoek [Netherlands], Join Dementia Research [UK], TrialMatch [USA], and StepUp for Dementia Research [Australia]), sharing data and lessons learned with the Global Alzheimer’s Platform (GAP), and participating in the development of the National Institute on Aging (NIA) National Strategy for Recruitment and Participation in Alzheimer’s and Related Dementias Clinical Research (13). In addition, we have helped with technological advances, including providing the Frontotemporal Dementia Disorders Registry (FTDDR) with the ability to use all software, code, and learnings of the APR for their program.



The APR, launched in 2012, is a large, internet-based, participant recruitment registry for AD-focused studies, having enrolled over 346,000 members and helped 82 studies try to meet their enrollment goals. The APR was created at a time when several AD prevention trials were on the horizon, but not yet ready to begin recruiting participants. The initial objectives were to create an efficient participant recruitment registry that would go beyond the needs of the API trials and help recruit for a range of AD prevention-focused studies that were, at the time, in initial stages of planning and not yet ready to enroll participants. As a result, the initial design of the APR focused on providing members with news and information about AD and prevention research to keep them engaged and retained until studies began recruitment. Since its launch, and due to the still limited number of AD prevention trials, the APR has expanded its reach to help with recruitment for a range of AD-focused studies and occasionally, studies focused on other related dementias or aging and cognition. Along the way, the APR has provided a foundation for other efforts (e.g., GeneMatch), partnered with other national and international efforts to share learning and develop strategies to accelerate enrollment into AD-focused studies, and provided technological assistance to other registries.
The APR website and enrollment process have evolved since 2012. For example, based on learnings from the AAR, the APR was designed intentionally to collect minimal contact and demographic information at enrollment. The manner in which members are presented with request(s) to provide this information changed over time. The initial website design presented all the requested contact and demographic information on one page, requiring people to scroll down the webpage. This layout was conducive to completing enrollment process on desktop or laptop computers, but not mobile devices such as a smartphone or tablet. The first redesign made the process simpler, requiring just an email address to enroll in the APR, and then asked for the remaining information (e.g., name, year of birth, zip/postal code, family history, etc.) on a subsequent page. While this made it quite easy for people to join the APR, we lacked key pieces of information to help connect members to studies (e.g., age, zip/postal code), not all members answered all questions, and the approach affected data cleanliness (i.e., if members shared an email address). Over a series of A/B tests, we were able to land on a middle ground in which we collected a few key pieces of information initially (email address, first and last name, zip/postal code, country, and year of birth), and then presented members with a secondary webpage requesting additional information (e.g., diagnosis of cognitive impairment, family history of dementia, etc.), allowing members to select “prefer not to answer” (as opposed to letting them skip answering the question), and the opportunity to manage directly their email newsletter subscriptions. That said, the APR has incomplete demographic profiles for some members, particularly those who enrolled in the early years.
We created the APR with the initial goal of connecting members to AD prevention study opportunities. However, when the APR was launched in 2012, few prevention studies were recruiting participants. As a result, we needed to identify other mechanisms to keep members engaged and connected to the APR so that when such study opportunities became available, there was a large community of individuals ready to be notified. Based on the 2013 survey, we focused our efforts on two main areas: email newsletters and website content. Over the years, we have refined our approach to the email newsletters, transitioning from quarterly to monthly distribution. We continue to strive to make the content appropriate for the general public, not scientists or researchers. The primary APR newsletter, the APB, continues to perform well compared to the healthcare industry standard (28).
As more study opportunities became available, it became apparent that we needed to modify the APR website to make it easier for members to search for study opportunities We developed a “Find a Study” page on the APR website which allows anyone (not just those enrolled in APR) to see all studies for which APR is helping to recruit as well as filter by key criteria such as location, study type, and age eligibility. Rather than pulling the study information from another website, such as, we developed a Study Opportunity description template which contains high level information about the study design and eligibility criteria. These lay-friendly Study Opportunity descriptions are written jointly by the APR team and recruiting study (or sponsor) and then submitted to the recruiting study’s IRB for approval. Until recently, the Study Opportunity description provided the contact information for the study (e.g., contact information for a study coordinator or link to study website) if a person was interested in learning more about the study and/or participating. However, this model did not allow APR to track referrals and or obtain accurate metrics of success for accelerating enrollment into study. As a result, in late 2019 we instituted the “Contact Form” model for new studies listing on APR. This allows interested individuals to give authorization to the APR to transfer their contact information to the enrolling study team via a secure dashboard (the dashboard also allows the enrolling study to track prospective participant referrals). The “Contact Form” model will be offered to studies already listed on the APR (i.e., existing studies) beginning in 2020. Moving forward, the APR will be able to provide more accurate referral metrics.
In addition to sharing information about recruiting studies on the APR website “Find a Study” page, we send announcements about study opportunities to members by email. The APR team works closely with the recruiting study/researcher/sponsor to develop an email campaign to meet their recruitment needs, ranging from a single email to APR members residing in a small radius from the study site and who might be eligible for a study based on their profile, to larger “spread the word” email campaigns to all APR members. The email contains a hyperlink which takes the person to the APR website for a full description of the study and information about next steps if they want to learn more about the study. In addition to sending emails about study opportunities new to APR members, beginning in 2019, the APR began sending members email notifications about studies for which they may be newly eligible (e.g., they now meet the study’s age eligibility).
APR has used a variety of recruitment strategies and tactics to enroll members, such as community talks, earned media (i.e., news articles), and paid social media advertisements. Paid online advocacy community petitions and social media advertisements have resulted in the largest numbers of enrollees, although a sizeable percentage unsubscribe from email communications or are unable to be re-engaged successfully. Once APR can accurately track members’ interest in studies then we will also be able to examine whether the source of enrollment into the APR is a factor in members’ willingness to consider study opportunities as well as the return on investment for the different recruitment strategies and tactics.
Despite using a variety of recruitment strategies and tactics, APR members are predominantly female and self-report being non-Hispanic, white, similar to reports from other internet-based recruitment registries (16). This may be the result of a combination of many factors including the design of and language on the APR website as well as the recruitment strategies and tactics used (13). In addition, women are more likely than men to search for health information online compared, even though men and women are equally likely to have internet access and go online (29). More needs to be done to better understand the barriers and facilitators to enrollment for men and underrepresented racial and ethnic groups as well as understand whether women, in their “health information gathering role” are sharing information from the APR with male family members and friends. In addition, a concerted effort is needed to understand why a sizeable percentage of members prefer not to provide their race/ethnicity during initial enrollment, perhaps adapting strategies found to be effective at a local level to internet-based registries (30-32). Identification and removal of these potential barriers, as well as implementation of new recruitment solutions is critically important to meet the goal of enrolling diverse populations into AD prevention trials (33).
We acknowledge several limitations of the APR. By design, the APR collects minimal information from members and does not assess their cognitive functioning, relying instead and on self-reported information provided at enrollment with the option to update at enrollment anniversaries. As a result, some members’ profiles may be inaccurate and there may be cases in which a person joins the APR more than once using different email addresses. For these and other reasons, the APR encourages members to review study inclusion criteria and emphasizes to study sites and sponsors the importance of prescreening referrals from the APR. The APR no longer requires members to create an account by establishing an APR username and password. This feature was removed in 2013 because it posed difficulty to members, although with usernames and passwords becoming increasingly common, we are considering reintroducing it as an optional feature in the future. Another limitation is that APR members are not representative of the general population. All participants must have an email address to join the APR. This requirement is a potential barrier for individuals who do not have access to or use email on a routine basis. Moreover, APR members are not representative of the general population with regard to sex, race or ethnicity. Separate efforts are underway to better understand how to communicate the importance of participating in AD-focused studies to men and underrepresented racial and ethnic populations, as well studies to understand the impact the APR website design may have on enrollment of people from diverse backgrounds. The APR is also only available in English due to the staffing requirements needed if the program were to be made available in other languages. For example, in addition to needing to translate all content on the website content and in the newsletters, we would need bilingual staff available to answer members’ phone calls and emails. Moreover, there is concern that offering the APR in languages other than English would create false expectations for the availability of study opportunities for non-English speakers in the US. The APR will continue to monitor this and will adapt as needed.
Despite these and other limitations, APR has demonstrated its ability to enroll hundreds of thousands of adults into an internet-based, participant recruitment registry for AD-focused studies, keep members engaged, and help a large number of studies try to meet their enrollment goals. Member engagement and retention continue to be key areas of focus as well as implementing mechanisms that allow the APR to track its effectiveness at helping investigators effectively and efficiently meet their enrollment goals. The efforts of the APR and its GeneMatch program, along with complementary efforts from other local (e.g., Butler Alzheimer’s Prevention Registry, North Carolina Brain Health Registry, University of California Irvine [UCI] Consent-to-Contact [C2C], Wisconsin Registry for Alzheimer’s Prevention [WRAP]), national (e.g., APT Webstudy, BHR, GAP, MindCrowd, TrialMatch), and international (Hersenonderzoek [Netherlands], Join Dementia Research [UK], StepUp for Dementia Research [Australia]) recruitment registry programs have the potential to accelerate enrollment into the growing number of AD-focused studies, thereby helping to advance AD research in ways that would not otherwise be possible.


Acknowledgements: We are grateful for the support of our past and current partners and colleagues at Banner Alzheimer’s Institute, The Reis Group, Innolyst and Provoc. We appreciate the support and guidance of the APR Executive Committee: Paul Aisen, Marilyn Albert, Maria Carrillo (ex officio), Meryl Comer, Jeffrey Cummings, Jennifer Manly, Ronald Petersen, Nina Silverberg (ex officio), Reisa Sperling, Gabriel Strobel, and Michael Weiner.

Funding: This work is supported by grants from the National Institute on Aging (R01 AG063954 [JBL], P30 AG19610 [EMR]). The Alzheimer’s Prevention Registry has been supported by the Alzheimer’s Association, Banner Alzheimer’s Foundation, Flinn Foundation, Geoffrey Beene Gives Back Alzheimer’s Initiative, GHR Foundation, and the state of Arizona (Arizona Alzheimer’s Consortium).

Conflicts of interest: Jessica Langbaum, Nellie High, Cassandra Kettenhoven, Eric Reiman and Pierre Tariot: full employees of Banner Health. Jodie Nichols: no conflicts of interest.

Ethical Standards: The APR was determined to not be research by the IRB. Although individuals do not provide consent when joining the APR, they do agree to the APR’s privacy policy and are informed about BAI’s Notice of Privacy Practices, including HIPAA.

Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.



1. Wimo A, Guerchet M, Ali GC, et al. The worldwide costs of dementia 2015 and comparisons with 2010. Alzheimers Dement. 2017;13(1):1-7
2. Alzheimer’s Association. 2018 Alzheimer’s Disease Facts and Figures. Alzheimers Dement 2018;14(3):367-429
3. Brookmeyer R, Gray S, Kawas C. Projections of Alzheimer’s disease in the United States and the public health impact of delaying disease onset. Am.J.Public Health 1998;88(9):1337-42
4. Alber J, Lee AKW, Menard W, Monast D, Salloway SP. Recruitment of At-Risk Participants for Clinical Trials: A Major Paradigm Shift for Alzheimer’s Disease Prevention. J Prev Alzheimers Dis 2017;4(4):213-4
5. Cummings J, Lee G, Ritter A, Sabbagh M, Zhong K. Alzheimer’s disease drug development pipeline: 2019. Alzheimers Dement (N.Y.) 2019;5:272-93
6. Dowling NM, Olson N, Mish T, Kaprakattu P, Gleason C. A model for the design and implementation of a participant recruitment registry for clinical studies of older adults. Clin.Trials 2012;9(2):204-14
7. Strasser JE, Cola PA, Rosenblum D. Evaluating various areas of process improvement in an effort to improve clinical research: discussions from the 2012 Clinical Translational Science Award (CTSA) Clinical Research Management workshop. Clin.Transl.Sci. 2013;6(4):317-20
8. Grill JD, Galvin JE. Facilitating Alzheimer disease research recruitment. Alzheimer Dis.Assoc.Disord. 2014;28(1):1-8
9. Schneider LS. Recruitment methods for United States Alzheimer disease prevention trials. J Nutr.Health Aging 2012;16(4):331-5
10. Grill JD, Karlawish J. Addressing the challenges to successful recruitment and retention in Alzheimer’s disease clinical trials. Alzheimers.Res.Ther. 2010;2(6):34
11. Vellas B, Hampel H, Rouge-Bugat ME, et al. Alzheimer’s disease therapeutic trials: EU/US Task Force report on recruitment, retention, and methodology. J Nutr.Health Aging 2012;16(4):339-45
12. Aisen P, Touchon J, Andrieu S, et al. Registries and cohorts to accelerate early phase Alzheimer’s trials. A report from the E.U./U.S. Clinical Trials in Alzheimer’s Disease Task Force. J Prev Alz Dis 2016;3(2):68-74
13. Together we make the difference: National strategy for recruitment and participation in Alzheimer’s and related dementias clinical research National Institutes of Health NIoA. 2018 Oct.
14. Krysinska K, Sachdev PS, Breitner J, et al. Dementia registries around the globe and their applications: A systematic review. Alzheimers Dement. 2017;13(9):1031-47
15. Grill JD, Hoang D, Gillen DL, et al. Constructing a Local Potential Participant Registry to Improve Alzheimer’s Disease Clinical Research Recruitment. J Alzheimers Dis. 2018;63(3):1055-63
16. Weiner MW, Nosheny R, Camacho M, et al. The Brain Health Registry: An internet-based platform for recruitment, assessment, and longitudinal monitoring of participants for neuroscience studies. Alzheimers Dement. 2018;14(8):1063-76
17. Chadiha LA, Washington OG, Lichtenberg PA, et al. Building a registry of research volunteers among older urban African Americans: recruitment processes and outcomes from a community-based partnership. Gerontologist. 2011;51 Suppl 1:S106-S115
18. Johnson SC, Koscik RL, Jonaitis EM, et al. The Wisconsin Registry for Alzheimer’s Prevention: A review of findings and current directions. Alzheimers Dement.(Amst.) 2018;10:130-42
19. Vermunt L, Veal CD, Ter ML, et al. European Prevention of Alzheimer’s Dementia Registry: Recruitment and prescreening approach for a longitudinal cohort and prevention trials. Alzheimers Dement. 2018;14(6):837-42
20. Langbaum JB, Karlawish J, Roberts JS, et al. GeneMatch: a novel recruitment registry using at-home APOE genotyping to enhance referrals to Alzheimer’s prevention studies. Alzheimer’s and Dementia 2019;15(4):515-24
21. Rios-Romenets S, Lopez H, Lopez L, et al. The Colombian Alzheimer’s Prevention Registry. Alzheimer’s & Dementia 2017;13(5):602-5
22. Larsen ME, Curry L, Mastellos N, et al. Development of the CHARIOT Research Register for the Prevention of Alzheimer’s Dementia and Other Late Onset Neurodegenerative Diseases. PLoS.One. 2015;10(11):e0141806
23. Lim YY, Yassi N, Bransby L, Properzi M, Buckley R. The Healthy Brain Project: An Online Platform for the Recruitment, Assessment, and Monitoring of Middle-Aged Adults at Risk of Developing Alzheimer’s Disease. J Alzheimers Dis 2019;68(3):1211-28
24. Juaristi GE, Dening KH. Promoting participation of people with dementia in research. Nurs.Stand. 2016;30(39):38-43
25. Saunders KT, Langbaum JB, Holt CJ, et al. Arizona Alzheimer’s Registry: strategy and outcomes of a statewide research recruitment registry. J Prev Alz Dis 2014;1(2):74-9
26. IOM (Institute of Medicine). Models for Public Engagement. In Public Engagement and Clinical Trials: New Models and Disruptive Technologies: Workshop Summary. Washington, DC: The National Academies Press; 2012.
27. Online Controlled Experiments and A/B Tests Kohavi R, Longbotham R. Springer; 2016.
28. Benchmarks M+R. 2016.
29. Profiles of Health Information Seekers Pew Research Center. Washington, DC: Pew Research Center’s Internet & American Life Project; 2011 Feb 1.
30. Williams MM, Scharff DP, Mathews KJ, et al. Barriers and facilitators of African American participation in Alzheimer disease biomarker research. Alzheimer Dis.Assoc.Disord. 2010;24 Suppl:S24-S29
31. Hinton L, Carter K, Reed BR, et al. Recruitment of a community-based cohort for research on diversity and risk of dementia. Alzheimer Dis.Assoc.Disord. 2010;24(3):234-41
32. Dilworth-Anderson P, Williams SW. Recruitment and retention strategies for longitudinal African American caregiving research: the Family Caregiving Project. J Aging.Health. 2004;16(5 Suppl):137S-56S
33. Watson JL, Ryan L, Silverberg N, Cahan V, Bernard MA. Obstacles and opportunities in Alzheimer’s clinical trial recruitment. Health Aff.(Millwood.) 2014;33(4):574-9

What do you want to do ?

New mail

What do you want to do ?

New mail



C. Barger3, J. Fockler1,2, W. Kwang1,2, S. Moore3, D. Flenniken1,2, A. Ulbricht1,2, P. Aisen3, M.W. Weiner1,2 on behalf of the Alzheimer’s Disease Neuroimaging Initiative


1. Center for Imaging of Neurodegenerative Diseases, San Francisco Veteran’s Administration Medical Center, San Francisco, CA, USA; 2. University of California San Francisco (UCSF) Department of Radiology and Biomedical Imaging, San Francisco, CA, USA; 3. Alzheimer’s Therapeutic Research Institute, University of Southern California (USC) San Diego, CA, USA

Corresponding Author: Charissa Barger, Alzheimer’s Therapeutic Research Institute, University of Southern California (USC) San Diego, CA, USA,, 213-253-8387

J Prev Alz Dis 2020;
Published online February 7, 2020,



Background: Effective and measurable participant recruitment methods are urgently needed for clinical studies in Alzheimer’s disease.
Objectives: To develop methods for measuring recruitment tactics and evaluating effectiveness.
Methods: Recruitment tactics for the Alzheimer’s Disease Neuroimaging Initiative (ADNI3) were measured using web and phone analytics, campaign metrics and survey responses.
Results: A total of 462 new participants were enrolled into ADNI3 through recruitment efforts. We collected metrics on recruitment activities including 82,003 unique visitors to the recruitment website and 3,335 calls to study phone numbers. The recruitment sources that produced the most screening and enrollment included online advertisements, local radio and newspaper coverage and emails and referrals from registries.
Conclusions: Analysis of recruitment activity obtained through tracking methods provided some insight for effective recruitment. ADNI3 can serve as an example of how a data-driven approach to centralized participant recruitment can be utilized to facilitate clinical research.

Key words: Clinical trials, recruitment, Alzheimer’s disease.



In the United States, an estimated 5.8 million people are currently living with Alzheimer’s disease (AD) (1) and this is projected to increase to 13.8 million by 2050 (2). AD and other dementias are estimated to cost the United States $290 billion in healthcare, long-term care and hospice and that could reach as much as $1.1 trillion by 2050 (1). There is an increasing need of effective treatments and preventative measures for AD as the prevalence and overall costs of AD care will continue to dramatically rise within the next 30 years. However, a major obstacle in clinical research progress and developing new AD treatments is the identification and enrollment of participants in clinical research [3]. Because clinical trials commonly experience high screen failure rates, it is crucial to reach as many potential participants as possible and then implement more innovative ways to pre-screen a large volume of interested individuals. Difficulty in participant recruitment commonly results in early termination of clinical trials (4). In contrast, this issue can also lead to extending the duration of clinical trials, which in turn increases costs and delays study progress (3). Although participant recruitment has been primarily conducted at the research site-level, as studies become more competitive for participants, there is a greater need to expand efforts and provide referrals to research sites through more centralized activities, including utilizing online advertising, media coverage and web-based registries (4-8), that can reach a larger number of potential participants across multiple research sites.
Launched in 2004, the Alzheimer’s Disease Neuroimaging Initiative (ADNI), funded by the National Institutes of Health, is a longitudinal observational study aimed at discovering, optimizing, standardizing and validating clinical trial measures and biomarkers used in ongoing AD research. Participation in ADNI includes clinical/cognitive, imaging, biomarker and genetic assessments. It is now in its third phase of trials termed ADNI3, which is a continuation of ADNI, ADNI-GO and ADNI2. ADNI3’s cohort includes both new participants and participants retained from previous iterations of ADNI. Recruitment began September 2016 and is currently ongoing with aims to enroll up to 1,200 new participants across 59 research sites in the United States and Canada. Participants are ages 55 to 90 and enrolled across three cohorts: cognitively normal, mild cognitive impairment and mild AD dementia.
To address the need to expand recruitment efforts to targeted populations, the ADNI3‘s Coordinating Center utilized a range of recruitment activities that can be linked to measurable outcomes. The Coordinating Center collected recruitment metrics, which provided some preliminary insights on which methods were more effective at reaching a greater number of potential participants. By using a multi-faceted recruitment plan, ADNI3 can serve as an example of how a data-driven approach to centralized participant recruitment can be utilized to facilitate clinical research.



Recruitment Activities

On behalf of ADNI3, the Coordinating Center managed recruitment activities to connect potential participants directly with local research sites. These activities included: national newspaper and radio coverage; local TV and newspaper coverage; and search engine, website, social media and newspaper advertisements.
Additional recruitment activities were provided by internet-based registries including: the National Institute on Aging’s Alzheimer’s and related Dementias Education and Referral Center (ADEAR); Banner Alzheimer’s Institute’s Alzheimer’s Prevention Registry (APR); and University of California, San Francisco’s (UCSF) Brain Health Registry (BHR). These registries aim to provide a pool of screened potential participants who have provided information including contact information and demographics and have already expressed a willingness to participate in research (6, 9). The recruitment activities included: online study listings (provided by APR, ADEAR,, emails to their existing list of registrants (provided by APR and BHR) and direct referrals to research sites (provided by BHR). Direct BHR referrals applied exclusion/inclusion criteria to their pool of over 65,000 participants nationally to pre-screen and refer them to local research sites (9). The selected BHR participants were provided research site contact information and a referral code to be brought with them to their screening visit.

Website and Data Collection

Most digital activities funneled visitors to the ADNI3 recruitment website,, which was developed as a collaboration between the Alzheimer’s Therapeutic Research Institute (ATRI) at the University of Southern California and the Brain Health Registry (BHR) at UCSF.
The website, powered by BHR, provided education about ADNI3 with multiple links to a pre-screener questionnaire to guide visitors to complete it as the preferred call to action. The questionnaire asked study-specific questions to determine eligibility. Those who were potentially eligible were prompted to enter their contact information in a sign up form, which connected referrals to a local research site to enroll. The website also included a “research site finder” map with the ability to cater to the needs of individual research sites by pausing or stopping referrals from the website.
The website captured data using the methods listed below to track visitors during the recruitment process:
1. Trackable Links: Custom URLs were used to identify how web visitors arrived at the recruitment website. These were placed in all digital communication, advertisements, emails, articles and social media posts.
2. Sign Up Form: This web-based form collected contact information, birth year and zip codes from visitors to determine if there was a research site enrolling in their area. They were then provided with a referral code and the research sites’ contact information immediately or once their local site began enrolling.
3. Phone Numbers: Unique phone numbers were used to track phone calls to research sites from recruitment campaigns and the recruitment website. Masked local phone numbers, that rang through to the research site’s phone number, enabled tracking of how many calls were placed from the recruitment website. Also, an interactive voice response (IVR) system automatically forwarded calls for interested participants to their local site after providing their zip code. The system then logged any activity and provided data on what calls were placed and where they were forwarded. Additionally, the phone number a participant called was used as a referral code for measuring conversion into the study.
4. Contact Us Form: This web-based form allowed visitors to provide their contact information for a specific research site. This was provided to the site along with a referral code to track them into enrollment.
Additional information was collected and recorded in an electronic case report form at the in-person screening visit to determine where the participant heard about the study. Once the participant was in clinic, they were asked questions about how they heard about the study and whether they had a referral code in the screening questionnaire. Questions include: “How did you hear about the study?” and “From whom did you hear about the study?”. If the participant had a referral code, the site staff was responsible for entering the specific referral code into the online form. Any additional comments or specifics were collected in qualitative responses.

Data Analysis

All data was reported visually through an active dashboard, allowing all recruitment metrics described above to be seen in real-time, in one place. The dashboard, managed by the BHR, included, but was not limited to: the number of sign up forms completed and what site the interested participant was referred to, how many calls were placed from the recruitment website and how many consented study participants provided referral codes. Additionally, recruitment campaigns that could not have specific data attached, like radio interviews, could be filtered by date to show website activity within a specified timeframe. Google Analytics Dashboards were also used to view website traffic data and duration of web sessions. Figure 1 shows the flow of recruitment activities through the website to study enrollment with data displayed by the dashboard.
The dashboards were reviewed regularly to assess current recruitment activities and make informed decisions on recruitment tactics. We assessed increases in website traffic, use of trackable links, website activities completed and phone call volume to gauge effectiveness of recruitment activities compared to daily averages.

Figure 1. ADNI3 referral flow by recruitment Activities

Figure 1. ADNI3 referral flow by recruitment Activities



Enrollment Summary

As of August 2019, 892 individuals enrolled into ADNI3, including 430 participants that continued from ADNI2 and 462 newly enrolled participants. The average age of new participants was 71; 13% were minority participants, defined as either Latino/Hispanic or non-White; 54% were female; and the average education was 16.6 years.
Data collected at the screening visits showed that approximately 52% of new participants were enrolled through site-driven activities, such as physician referrals, community outreach events and patient database outreach. The remaining 48% of new participants were enrolled through recruitment activities managed by the Coordinating Center, which will be focused on for this paper.

Recruitment Efforts

Figure 2 summarizes the recruitment activities utilized by the Coordinating Center and subsequent data collected. As a result of central recruitment efforts, there have been 82,003 unique visitors to, 2,189 phone calls to the IVR phone number and 1,146 phone calls through local masked phone numbers. Of the visitors to the website, 15.5% engaged in some form of website activity by completing the pre-screener, contact us form, sign up form, or calling a site directly using the masked phone number. In total 12,728 referrals were provided to research sites through the recruitment website.

Figure 2. Recruitment activities conducted by the coordinating center

Figure 2. Recruitment activities conducted by the coordinating center

NOTE: Figure 2 includes 35% of screening and enrollment data that can accurately be linked to recruitment activities conducted by the Coordinating Center. Numbers included in Website Visitors and Contacted Research Site are 100 times the actual number. Condensed numbers were provided for visibility.


Of the 82,003 website visitors, 56,170 (68.5%) can be linked directly to the source of recruitment through trackable links. The majority of the website visitors tracked by links came from Facebook advertisements with 40,833 tracked users (72.7%). The trackable links identified only two individuals who had referral codes which were reported to site staff at the screening visit. In contrast, APR had only 2,319 tracked users (4.13%) but ultimately led to 13 referral codes reported at the screening visit.
764 in-person questionnaires were completed at the screening visit to collect information on where participants heard about the study. These included 132 referral codes but no local phone number referral codes. Of the questionnaires completed, several included conflicting information about the source of recruitment. For example, of the 206 individuals that selected a registry as the source from which they heard about the study, 13% indicated a conflicting source in how they heard about the study (5 from advertising, 2 from media coverage and 20 from site outreach).

Most Effective Recruitment Sources

The recruitment sources that produced the most referrals through include: Facebook and Google advertisements targeted by age and location; radio interviews in local markets with the Principal Investigator; articles and press releases in local newspapers; and emails from and APR. These activities resulted in 65% of the total website traffic, 46% of the total completed website pre-screeners and 26% of the total calls. 27% of new participant enrollment can be linked to a registry using the self-reported data collected at the screening visit. The most effective registry activity for recruitment was direct referrals provided by BHR resulting in at least 73 individuals screened and 48 enrolled, as shown in the referral codes collected.

Using Data to Improve Recruitment Activities

Analyzing data throughout the study provided insight on how recruitment activities were performing and helped to determine recruitment plans. Following the April 2018 email from APR, a total of 1,159 trackable links were collected on the website and 496 of these visitors (42.7%) were referred to a research site. As a result of this information, additional emails were conducted with APR to provide referrals to sites, which resulted in 1,265 more trackable links collected and 448 of these visitors (35.4%) were referred to a research site.

Use of Website Pre-screener

The most popular action on was to complete the pre-screener form (12%). Of those that completed the pre-screener, 56% were deemed eligible and referred to a site. This minimized site burden by eliminating approximately 4,294 participants deemed ineligible, or 72 individuals per site.



The major conclusion from this study was that quantitative data concerning all steps of the recruitment process provided useful information concerning the efficiency of each step. The data provided some insight into which recruitment activities were producing the most referrals, which was helpful for recruitment planning. We were able to connect a large number of recruitment activities to website traffic but data on the source of enrollment was only available for a small percentage due to several limitations.
Use of trackable links provided some insight on which recruitment activities led a potential participant to the website and subsequent enrollment. However, some trackable link data is not captured due to private browsing, firewalls and website blocking cookies. Furthermore, some responses to the screening questionnaire collected at the clinic visit conflicted with the referral code provided. These discrepancies might be due to human error from participants and study staff or might suggest that multiple touchpoints of recruitment are needed for successful enrollment. Our efforts to track campaigns by using local phone numbers that could serve as referral codes during the screening visit was ineffective, and no phone numbers were provided as referral codes. In addition, the more manual methods of tracking recruitment activities, such as the metrics that rely on site data entry, were completed partially or inconsistently.
We would recommend collecting self-report screening questionnaires and referral codes at the initial contact with the participant (i.e., during the pre-screening telephone call) to improve accuracy. Questions asked should be simplified for clarity with limited choices to avoid confusion and provide cleaner data. More robust methods for collecting referral codes should also be employed, such as limiting data entry fields to ensure consistency in referral codes or automating referral codes through the use of a QR codes.
Analyzing recruitment metrics, we were able to obtain a preliminary assessment of central recruitment efforts in real-time to inform recruitment planning. We increased the most successful recruitment activities based on these assessments. Additionally, we realized that the quality of the source was sometimes more important than the quantity of users reached. For instance, Facebook reached a far larger number of users than APR yet APR had a larger number of users who were screened in clinic. We believe this was because APR’s registry members already had a greater interest in participating in research studies whereas Facebook reached a broader audience. With more accurate and complete data, we would be able to more effectively measure tactics to gauge the most successful recruitment activities and connect these to actual enrollment.
Although the use of an online pre-screener is not a recruitment tactic, we found that it was an effective entry method for referrals to reduce site burden by eliminating referrals that do not meet the basic study criteria. This conclusion was possible due to tracking website activity including the number of online pre-screeners completed and the user’s subsequent eligibility status.
We found that central recruitment efforts using a multi-faceted approach of media coverage, online advertising and registry outreach can be effective at providing sites with eligible referrals, and subsequently leading to increased screening and enrollment. Driving recruitment activities to a central website with tracking capabilities allows efforts to be measured and evaluated for efficacy. This data can then be used to inform recruitment planning. Using this data-driven approach to centralized recruitment, investigators can effectively and efficiently pursue enrollment goals.


Acknowledgements: Michael Weiner, MD (UC San Francisco, Principal Investigator, Executive Committee); Paul Aisen, MD (UC San Diego, ADCS PI and Director of the Coordinating Center Clinical Core, Executive Committee, Clinical Core Leaders); Ronald Petersen, MD, PhD (Mayo Clinic, Rochester, Executive Committee, Clinical Core Leader); Clifford R. Jack, Jr., MD (Mayo Clinic, Rochester, Executive Committee, MRI Core Leader); William Jagust, MD (UC Berkeley, Executive Committee; PET Core Leader); John Q. Trojanowki, MD, PhD (U Pennsylvania, Executive Committee, Biomarkers Core Leader); Arthur W. Toga, PhD (USC, Executive Committee, Informatics Core Leader); Laurel Beckett, PhD (UC Davis, Executive Committee, Biostatistics Core Leader); Robert C. Green, MD, MPH (Brigham and Women’s Hospital, Harvard Medical School, Executive Committee and Chair of Data and Publication Committee); Andrew J. Saykin, PsyD (Indiana University, Executive Committee, Genetics Core Leader); John Morris, MD (Washington University St. Louis, Executive Committee, Neuropathology Core Leader); Leslie M. Shaw (University of Pennsylvania, Executive Committee, Biomarkers Core Leader); Enchi Liu, PhD (Janssen Alzheimer Immunotherapy, ADNI 2 Private Partner Scientific Board Chair); Tom Montine, MD, PhD (University of Washington) ; Ronald G. Thomas, PhD (UC San Diego); Michael Donohue, PhD (UC San Diego); Sarah Walter, MSc (UC San Diego); Devon Gessert (UC San Diego); Tamie Sather, MS (UC San Diego,); Gus Jiminez, MBS (UC San Diego); Danielle Harvey, PhD (UC Davis;); Michael Donohue, PhD (UC San Diego); Matthew Bernstein, PhD (Mayo Clinic, Rochester); Nick Fox, MD (University of London); Paul Thompson, PhD (USC School of Medicine); Norbert Schuff, PhD (UCSF MRI); Charles DeCArli, MD (UC Davis); Bret Borowski, RT (Mayo Clinic); Jeff Gunter, PhD (Mayo Clinic); Matt Senjem, MS (Mayo Clinic); Prashanthi Vemuri, PhD (Mayo Clinic); David Jones, MD (Mayo Clinic); Kejal Kantarci (Mayo Clinic); Chad Ward (Mayo Clinic); Robert A. Koeppe, PhD (University of Michigan, PET Core Leader); Norm Foster, MD (University of Utah); Eric M. Reiman, MD (Banner Alzheimer’s Institute); Kewei Chen, PhD (Banner Alzheimer’s Institute); Chet Mathis, MD (University of Pittsburgh); Susan Landau, PhD (UC Berkeley); Nigel J. Cairns, PhD, MRCPath (Washington University St. Louis); Erin Householder (Washington University St. Louis); Lisa Taylor Reinwald, BA, HTL (Washington University St. Louis); Virginia Lee, PhD, MBA (UPenn School of Medicine); Magdalena Korecka, PhD (UPenn School of Medicine); Michal Figurski, PhD (UPenn School of Medicine); Karen Crawford (USC); Scott Neu, PhD (USC); Tatiana M. Foroud, PhD (Indiana University); Steven Potkin, MD UC (UC Irvine); Li Shen, PhD (Indiana University); Faber Kelley, MS, CCRC (Indiana University); Sungeun Kim, PhD (Indiana University); Kwangsik Nho, PhD (Indiana University); Zaven Kachaturian, PhD (Khachaturian, Radebaugh & Associates, Inc and Alzheimer’s Association’s Ronald and Nancy Reagan’s Research Institute); Richard Frank, MD, PhD (General Electric); Peter J. Snyder, PhD (Brown University); Susan Molchan, PhD (National Institute on Aging/ National Institutes of Health); Jeffrey Kaye, MD (Oregon Health and Science University); Joseph Quinn, MD (Oregon Health and Science University); Betty Lind, BS (Oregon Health and Science University); Raina Carter, BA (Oregon Health and Science University); Sara Dolen, BS (Oregon Health and Science University); Lon S. Schneider, MD (University of Southern CaliforGroups Acknowledgements Journal Format nia); Sonia Pawluczyk, MD (University of Southern California); Mauricio Beccera, BS (University of Southern California); Liberty Teodoro, RN (University of Southern California); Bryan M. Spann, DO, PhD (University of Southern California); James Brewer, MD, PhD (University of California San Diego); Helen Vanderswag, RN (University of California San Diego); Adam Fleisher, MD (University of California San Diego); Judith L. Heidebrink, MD, MS (University of Michigan); Joanne L. Lord, LPN, BA, CCRC (University of Michigan); Ronald Petersen, MD, PhD (Mayo Clinic, Rochester); Sara S. Mason, RN (Mayo Clinic, Rochester); Colleen S. Albers, RN (Mayo Clinic, Rochester); David Knopman, MD (Mayo Clinic, Rochester); Kris Johnson, RN (Mayo Clinic, Rochester); Rachelle S. Doody, MD, PhD (Baylor College of Medicine); Javier Villanueva Meyer, MD (Baylor College of Medicine); Munir Chowdhury, MBBS, MS (Baylor College of Medicine); Susan Rountree, MD (Baylor College of Medicine); Mimi Dang, MD (Baylor College of Medicine); Yaakov Stern, PhD (Columbia University Medical Center); Lawrence S. Honig, MD, PhD (Columbia University Medical Center); Karen L. Bell, MD (Columbia University Medical Center); Beau Ances, MD (Washington University, St. Louis); John C. Morris, MD (Washington University, St. Louis); Maria Carroll, RN, MSN (Washington University, St. Louis); Sue Leon, RN, MSN (Washington University, St. Louis); Erin Householder, MS, CCRP (Washington University, St. Louis); Mark A. Mintun, MD (Washington University, St. Louis); Stacy Schneider, APRN, BC, GNP (Washington University, St. Louis); Angela Oliver, RN, BSN, MSG ; Daniel Marson, JD, PhD (University of Alabama Birmingham); Randall Griffith, PhD, ABPP (University of Alabama Birmingham); David Clark, MD (University of Alabama Birmingham); David Geldmacher, MD (University of Alabama Birmingham); John Brockington, MD (University of Alabama Birmingham); Erik Roberson, MD (University of Alabama Birmingham); Hillel Grossman, MD (Mount Sinai School of Medicine); Effie Mitsis, PhD (Mount Sinai School of Medicine); Leyla deToledo-Morrell, PhD (Rush University Medical Center); Raj C. Shah, MD (Rush University Medical Center); Ranjan Duara, MD (Wien Center); Daniel Varon, MD (Wien Center); Maria T. Greig, HP (Wien Center); Peggy Roberts, CNA (Wien Center); Marilyn Albert, PhD (Johns Hopkins University); Chiadi Onyike, MD (Johns Hopkins University); Daniel D’Agostino II, BS (Johns Hopkins University); Stephanie Kielb, BS (Johns Hopkins University); James E. Galvin, MD, MPH (New York University); Dana M. Pogorelec (New York University); Brittany Cerbone (New York University); Christina A. Michel (New York University); Henry Rusinek, PhD (New York University); Mony J de Leon, EdD (New York University); Lidia Glodzik, MD, PhD (New York University); Susan De Santi, PhD (New York University); P. Murali Doraiswamy, MD (Duke University Medical Center); Jeffrey R. Petrella, MD (Duke University Medical Center); Terence Z. Wong, MD (Duke University Medical Center); Steven E. Arnold, MD (University of Pennsylvania); Jason H. Karlawish, MD (University of Pennsylvania); David Wolk, MD (University of Pennsylvania); Charles D. Smith, MD (University of Kentucky); Greg Jicha, MD (University of Kentucky); Peter Hardy, PhD (University of Kentucky); Partha Sinha, PhD (University of Kentucky); Elizabeth Oates, MD (University of Kentucky); Gary Conrad, MD (University of Kentucky); Oscar L. Lopez, MD (University of Pittsburgh); MaryAnn Oakley, MA (University of Pittsburgh); Donna M. Simpson, CRNP, MPH (University of Pittsburgh); Anton P. Porsteinsson, MD (University of Rochester Medical Center); Bonnie S. Goldstein, MS, NP (University of Rochester Medical Center); Kim Martin, RN (University of Rochester Medical Center); Kelly M. Makino, BS (University of Rochester Medical Center); M. Saleem Ismail, MD (University of Rochester Medical Center); Connie Brand, RN (University of Rochester Medical Center); Ruth A. Mulnard, DNSc, RN, FAAN (University of California, Irvine); Gaby Thai, MD (University of California, Irvine); Catherine Mc Adams Ortiz, MSN, RN, A/GNP (University of California, Irvine); Kyle Womack, MD (University of Texas Southwestern Medical School); Dana Mathews, MD, PhD (University of Texas Southwestern Medical School); Mary Quiceno, MD (University of Texas Southwestern Medical School); Ramon Diaz Arrastia, MD, PhD (University of Texas Southwestern Medical School); Richard King, MD (University of Texas Southwestern Medical School); Myron Weiner, MD (University of Texas Southwestern Medical School); Kristen Martin Cook, MA (University of Texas Southwestern Medical School); Michael DeVous, PhD (University of Texas Southwestern Medical School); Allan I. Levey, MD, PhD (Emory University); James J. Lah, MD, PhD (Emory University); Janet S. Cellar, DNP, PMHCNS BC (Emory University); Jeffrey M. Burns, MD (University of Kansas, Medical Center); Heather S. Anderson, MD (University of Kansas, Medical Center); Russell H. Swerdlow, MD (University of Kansas, Medical Center); Liana Apostolova, MD (University of California, Los Angeles); Kathleen Tingus, PhD (University of California, Los Angeles); Ellen Woo, PhD (University of California, Los Angeles); Daniel H.S. Silverman, MD, PhD (University of California, Los Angeles); Po H. Lu, PsyD (University of California, Los Angeles); George Bartzokis, MD (University of California, Los Angeles); Neill R Graff Radford, MBBCH, FRCP (London) (Mayo Clinic, JacksonGroups Acknowledgements Journal Format 2 ville); Francine Parfitt, MSH, CCRC (Mayo Clinic, Jacksonville); Tracy Kendall, BA, CCRP (Mayo Clinic, Jacksonville); Heather Johnson, MLS, CCRP (Mayo Clinic, Jacksonville); Martin R. Farlow, MD (Indiana University); Ann Marie Hake, MD (Indiana University); Brandy R. Matthews, MD (Indiana University); Scott Herring, RN, CCRC (Indiana University); Cynthia Hunt, BS, CCRP (Indiana University); Christopher H. van Dyck, MD (Yale University School of Medicine); Richard E. Carson, PhD (Yale University School of Medicine); Martha G. MacAvoy, PhD (Yale University School of Medicine); Howard Chertkow, MD (McGill Univ., Montreal Jewish General Hospital); Howard Bergman, MD (McGill Univ., Montreal Jewish General Hospital); Chris Hosein, Med (McGill Univ., Montreal Jewish General Hospital); Sandra Black, MD, FRCPC (Sunnybrook Health Sciences, Ontario); Dr Bojana Stefanovic (Sunnybrook Health Sciences, Ontario); Curtis Caldwell, PhD (Sunnybrook Health Sciences, Ontario); Ging Yuek Robin Hsiung, MD, MHSc, FRCPC (U.B.C. Clinic for AD & Related Disorders); Howard Feldman, MD, FRCPC (U.B.C. Clinic for AD & Related Disorders); Benita Mudge, BS (U.B.C. Clinic for AD & Related Disorders); Michele Assaly, MA Past (U.B.C. Clinic for AD & Related Disorders); Andrew Kertesz, MD (Cognitive Neurology St. Joseph’s, Ontario); John Rogers, MD (Cognitive Neurology St. Joseph’s, Ontario); Dick Trost, PhD (Cognitive Neurology St. Joseph’s, Ontario); Charles Bernick, MD (Cleveland Clinic Lou Ruvo Center for Brain Health); Donna Munic, PhD (Cleveland Clinic Lou Ruvo Center for Brain Health); Diana Kerwin, MD (Northwestern University); Marek Marsel Mesulam, MD (Northwestern University); Kristine Lipowski, BA (Northwestern University); Chuang Kuo Wu, MD, PhD (Northwestern University); Nancy Johnson, PhD (Northwestern University); Carl Sadowsky, MD (Premiere Research Inst (Palm Beach Neurology)); Walter Martinez, MD (Premiere Research Inst (Palm Beach Neurology)); Teresa Villena, MD (Premiere Research Inst (Palm Beach Neurology)); Raymond Scott Turner, MD, PhD (Georgetown University Medical Center); Kathleen Johnson, NP (Georgetown University Medical Center); Brigid Reynolds, NP (Georgetown University Medical Center); Reisa A. Sperling, MD (Brigham and Women’s Hospital); Keith A. Johnson, MD (Brigham and Women’s Hospital); Gad Marshall, MD (Brigham and Women’s Hospital); Meghan Frey (Brigham and Women’s Hospital); Jerome Yesavage, MD (Stanford University); Joy L. Taylor, PhD (Stanford University); Barton Lane, MD (Stanford University); Allyson Rosen, PhD (Stanford University); Jared Tinklenberg, MD (Stanford University); Marwan N. Sabbagh, MD (Banner Sun Health Research Institute); Christine M. Belden, PsyD (Banner Sun Health Research Institute); Sandra A. Jacobson, MD (Banner Sun Health Research Institute); Sherye A. Sirrel, MS (Banner Sun Health Research Institute); Neil Kowall, MD (Boston University); Ronald Killiany, PhD (Boston University); Andrew E. Budson, MD (Boston University); Alexander Norbash, MD (Boston University); Patricia Lynn Johnson, BA (Boston University); Thomas O. Obisesan, MD, MPH (Howard University); Saba Wolday, MSc (Howard University); Joanne Allard, PhD (Howard University); Alan Lerner, MD (Case Western Reserve University); Paula Ogrocki, PhD (Case Western Reserve University); Leon Hudson, MPH (Case Western Reserve University); Evan Fletcher, PhD (University of California, Davis Sacramento); Owen Carmichael, PhD (University of California, Davis Sacramento); John Olichney, MD (University of California, Davis Sacramento); Charles DeCarli, MD (University of California, Davis Sacramento); Smita Kittur, MD (Neurological Care of CNY); Michael Borrie, MB ChB (Parkwood Hospital); T Y Lee, PhD (Parkwood Hospital); Dr Rob Bartha, PhD (Parkwood Hospital); Sterling Johnson, PhD (University of Wisconsin); Sanjay Asthana, MD (University of Wisconsin); Cynthia M. Carlsson, MD (University of Wisconsin); Steven G. Potkin, MD (University of California, Irvine BIC); Adrian Preda, MD (University of California, Irvine BIC); Dana Nguyen, PhD (University of California, Irvine BIC); Pierre Tariot, MD (Banner Alzheimer’s Institute); Adam Fleisher, MD (Banner Alzheimer’s Institute); Stephanie Reeder, BA (Banner Alzheimer’s Institute); Vernice Bates, MD (Dent Neurologic Institute); Horacio Capote, MD (Dent Neurologic Institute); Michelle Rainka, PharmD, CCRP (Dent Neurologic Institute); Douglas W. Scharre, MD (Ohio State University); Maria Kataki, MD, PhD (Ohio State University); Anahita Adeli, MD (Ohio State University); Earl A. Zimmerman, MD (Albany Medical College); Dzintra Celmins, MD (Albany Medical College); Alice D. Brown, FNP (Albany Medical College); Godfrey D. Pearlson, MD (Hartford Hosp, Olin Neuropsychiatry Research Center); Karen Blank, MD (Hartford Hosp, Olin Neuropsychiatry Research Center); Karen Anderson, RN (Hartford Hosp, Olin Neuropsychiatry Research Center); Robert B. Santulli, MD (Dartmouth Hitchcock Medical Center); Tamar J. Kitzmiller (Dartmouth Hitchcock Medical Center); Eben S. Schwartz, PhD (Dartmouth Hitchcock Medical Center); Kaycee M. Sink, MD, MAS (Wake Forest University Health Sciences); Jeff D. Williamson, MD, MHS (Wake Forest University Health Sciences); Pradeep Garg, PhD (Wake Forest University Health Sciences); Franklin Watkins, MD (Wake Forest University Health Sciences); Brian R. Ott, MD (Rhode Island Hospital); Henry Querfurth, MD (Rhode Island Hospital); Geoffrey Tremont, PhD (Rhode Island Groups Acknowledgements Journal Format 3 Hospital); Stephen Salloway, MD, MS (Butler Hospital); Paul Malloy, PhD (Butler Hospital); Stephen Correia, PhD (Butler Hospital); Howard J. Rosen, MD (UC San Francisco); Bruce L. Miller, MD (UC San Francisco); Jacobo Mintzer, MD, MBA (Medical University of South Carolina); Kenneth Spicer, MD, PhD (Medical University of South Carolina); David Bachman, MD (Medical University of South Carolina); Elizabeth Finger, MD (St. Joseph’s Health Care); Stephen Pasternak, MD (St. Joseph’s Health Care); Irina Rachinsky, MD (St. Joseph’s Health Care); John Rogers, MD (St. Joseph’s Health Care); Andrew Kertesz, MD (St. Joseph’s Health Care); Dick Drost, MD (St. Joseph’s Health Care); Nunzio Pomara, MD (Nathan Kline Institute); Raymundo Hernando, MD (Nathan Kline Institute); Antero Sarrael, MD (Nathan Kline Institute); Susan K. Schultz, MD (University of Iowa College of Medicine, Iowa City); Laura L. Boles Ponto, PhD (University of Iowa College of Medicine, Iowa City); Hyungsub Shim, MD (University of Iowa College of Medicine, Iowa City); Karen Elizabeth Smith, RN (University of Iowa College of Medicine, Iowa City); Norman Relkin, MD, PhD (Cornell University); Gloria Chaing, MD (Cornell University); Lisa Raudin, PhD (Cornell University); Amanda Smith, MD (University of South Florida: USF Health Byrd Alzheimer’s Institute); Kristin Fargher, MD (University of South Florida: USF Health Byrd Alzheimer’s Institute); Balebail Ashok Raj, MD (University of South Florida: USF Health Byrd Alzheimer’s Institute). Additionally, the authors would like to thank the Brain Health Registry team at UCSF, notably Rachel Nosheny, PhD, Scott Mackin, PhD, Diana Truran-Sacrey, Monica Camacho, Taylor Howell, Josh Hwang, Alexander Happ, Joseph Eichenbaum and Tirzah Williams.

Funding: The Alzheimer’s Disease Neuroimaging Initiative (ADNI) is funded by the National Institutes of Health (NIH) (Grant U19 AG024904). ADNI is made possible with funding from the NIH and private sector support raised by the Foundation for the NIH from the following organizations: AbbVie, Accelerate Cure/Treatment for Alzheimer’s Disease, Alector, Alzheimer’s Association, Araclon Biotech, BioClinica Inc, Biogen, Cogstate, Denali Therapeutics, Diamir, Eisai Inc, Eli Lilly and Company, EuroImmun, Genentech Inc, GE Healthcare, Janssen Alzheimer Immunotherapy Research & Development LLC, Lundbeck, Magou, Merck & Co Inc, PeopleBio, Pfizer Inc, Piramal, Roche, Saladax Biomedical, Servier, and Takeda.

Conflict of interest disclosure: Ms. Barger reports grants from the National Institutes of Health during the conduct of the study.

Ethical standards: Participants in ADNI3 provided written consent to participate with IRB-approved informed consent forms (ICF).



1.     Alzheimer’s Association. 2019 Alzheimer’s Disease Facts and Figures. Alzheimers Dement. 2019;15(3):321-87.
2.     Hebert LE, Weuve J, Scherr PA, Evans DA. Alzheimer disease in the United States (2010-2050) estimated using the 2010 Census. Neurology. 2013;80(19):1778-83.
3.    Fargo KN, Carrillo MC, Weiner MW, Potter WZ, Khachaturian Z. The crisis in recruitment for clinical trials in Alzheimer’s and dementia: An action plan for solutions. Alzheimers Dement. 2016;12(11):1113-5.
4.    Gauthier S, Albert M, Fox N, et al. Why has therapy development for dementia failed in the last two decades? Alzheimers Dement. 2016;12(1):60–4.
5.    Grill JD, Karlawish J. Addressing the challenges to successful recruitment and retention in Alzheimer’s disease clinical trials. Alzheimers Res Ther. 2010;2(6):34.
6.    Grill JD, Galvin JE. Facilitating Alzheimer disease research recruitment. Alzheimer Dis Assoc Disord. 2014;28(1):1–8.
7.    Watson JL, Ryan L, Silverberg N, Cahan V, Bernard MA. Obstacles and opportunities in Alzheimer’s clinical trial recruitment. Health Aff (Millwood). 2014;33(4):574–579.
8.    Knebl JA, Patki D. Recruitment of subjects into clinical trials for Alzheimer disease. J Am Osteopath Assoc. 2010;110:S43–S49.
9.    Weiner MW, Nosheny R, Camacho M, et al. The Brain Health Registry: An internet-based platform for recruitment, assessment, and longitudinal monitoring of participants for neuroscience studies. Alzheimers Dement. 2018;14(8):1063–1076.



B. Vellas1, L.J. Bain2, J. Touchon3, P.S. Aisen4


1. Gerontopole, INSERM U1027, Alzheimer’s Disease Research and Clinical Center, Toulouse University Hospital, Toulouse, France; 2. Independent Science Writer, Elverson, PA, USA; 3.Montpellier University, and INSERM U1061, Montpellier, France; 4. Alzheimer’s Therapeutic Research Institute (ATRI), Keck School of Medicine, University of Southern California, San Diego, CA, USA

Corresponding Author: B. Vellas, INSERM U1027, Alzheimer’s Disease Research and Clinical Center, Toulouse University Hospital, Toulouse, France.

J Prev Alz Dis 2019;
Published online April 4, 2019,



The 2018 Clinical Trials on Alzheimer’s Disease (CTAD) conference showcased recent successes and failures in trials of Alzheimer’s disease treatments. More importantly, the conference provided opportunities for investigators to share what they have learned from those studies with the goal of designing future trials with a greater likelihood of success. Data from studies of novel and non-amyloid treatment approaches were also shared, including neuroprotective and regenerative strategies and those that target neuroinflammation and synaptic function. New tools to improve the efficiency and productivity of clinical trials were described, including biomarkers and machine learning algorithms for predictive modeling.

Key words: Alzheimer’s disease, clinical trials, therapeutics, recruitment, predictive modeling



The Clinical Trials on Alzheimer’s Disease (CTAD) conference, held each year since 2008, has become a major venue for clinical trialists and other researchers from academia and industry to share learnings from recent clinical trials and learn about new therapeutics and diagnostics in development. Convened in Barcelona, Spain in October, 2018, CTAD welcomed approximately 1200 delegates from around the world to share data from studies conducted across the spectrum of development, ranging from early stage studies of novel therapeutics targeting a range of mechanisms, to results from later stage trials including those that failed to show efficacy. Investigators shared information on the pathophysiological underpinnings of these studies, clinical and biomarker results, as well as experiences with participant recruitment, outcome measures, trial design, and data management and analysis.


Gaining insight from negative trials

A central goal of the conference is to advance the development of effective treatments for Alzheimer’s disease (AD) by applying lessons learned in ongoing clinical trials as well as completed trials, including those that failed to demonstrate efficacy or that were terminated for other reasons.  For example, beta-secretase inhibitors (BACEi) emerged in recent years as one of the most promising classes of drugs for treatment of AD. However, disappointing results from multiple Phase 3 clinical trials have tempered some of the optimism surrounding BACE inhibition, prompting investigators from multiple companies developing drugs in this class to come together and jointly examine the potential benefits and risks of BACE inhibition.
BACE1 is an enzyme that cleaves the amyloid precursor protein as the first step in the production of Aβ peptide, the primary constituent of amyloid plaques in the AD brain. In animal models of AD, BACE1 deletion reduced production of Aβ and  plaque load, and improved cognition (1). Last year, Merck terminated a study of the BACE1 inhibitor verubecestat in mild to moderate AD when an interim analysis showed that the drug was ineffective at slowing the rate of cognitive decline (2). Reasoning that the drug might be more effective if given earlier in the disease process, before substantial buildup of plaque, the company continued their trial in prodromal AD. But in February 2018, the company stopped the trial after an independent data monitoring committee determined that there was no evidence of efficacy. Even more troubling, data presented at CTAD2018 showed that verubecestat treatment in prodromal AD was associated with worsening on all clinical measures and an increase in adverse events (3).
These results raise many unanswered questions. For example, the rapid onset of cognitive worsening might be related to a dramatic lowering of Aβ or to an off-target activity of the drug. Notable, the rapid loss of Aβ may have deleterious effects on neurotransmission or synaptic function (4). Key questions include whether the negative effect was related to dose, duration of treatment, or the specific population enrolled in the trial.
To promote open sharing of data from the many trials conducted by academic and industry scientists, the Alzheimer’s Association sponsored an emergency session at CTAD to explore emerging results from other BACE inhibitor trials, including Janssen’s EARLY trial of atabecestat in preclinical AD, which was terminated in May 2018 because of elevations in a liver enzyme seen in about a third of patients. Although the levels returned to baseline, Janssen determined that the benefit risk profile of the drug was not appropriate for a preventive treatment. A preliminary analysis of data from the study suggested that there was a rapid-onset, dose-related worsening on cognitive measures.  Data were also presented from a Phase 2 study of Lilly’s BACEi LY3202626, another study that was terminated early due to a low probability of success; these data also suggested adverse effects on cognition. Importantly, many of these trials have collected rich biomarker and imaging data, which will be analyzed in the coming months along with cognitive and clinical data to see what else can be learned. The newly formed Alzheimer’s Clinical Trials Consortium (ACTC) could serve as a neutral party to analyze data across multiple studies.
In a separate symposium, a panel explored whether BACE1 is a suitable drug target for the prevention and treatment of AD. Possible reasons for the disappointing results seen in many of the BACEi trials could be related to treatment starting too late or using doses that are too high. Indeed, it was suggested that BACE1 inhibition may be ideal for primary prevention and that dominantly inherited AD is a predictable model in which to study such interventions. The Dominantly Inherited Alzheimer’s Network (DIAN) and the Alzheimer’s Prevention Initiative (API) have shown that amyloid accumulation becomes significant as much as 15 years before symptoms appear in people with autosomal dominant forms of AD (5, 6), which gives investigators the opportunity to decide when and how to intervene using adaptive designs.


New data from ongoing trials

CTAD also provided sponsors the opportunity to share updated information on ongoing trials, especially of Phase 3 trials of amyloid targeting monoclonal antibodies. Highly anticipated data were presented to support claims presented earlier in 2018 suggesting disease-modifying effects for the anti-amyloid monoclonal antibody BAN2401, currently being developed jointly by Eisai and Biogen. The BAN2401 antibody is specifically aimed at species of the Aβ protein called protofibrils that are thought to be most toxic to brain cells. Top line results from the phase 2 study 201 indicated that the drug may slow cognitive decline and reduce plaque buildup in the brains of people with early AD. However, the data initially presented left unanswered questions about the degree to which participants’ APOE4 status may have skewed results of the study.
The reason for concern was that in the middle of study 201, health authorities decided that APOE4 carriers should no longer be randomized to the highest dose of BAN2401 because of the risk of developing amyloid-related imaging abnormalities (ARIA). This change led to an imbalance in the number of APOE4 carriers in the high-dose group, which was also the group showing the greatest treatment effect. At CTAD, investigators presented data from additional subgroup analyses as well as cerebrospinal fluid (CSF) biomarker results that were not previously available, which lent some support to Eisai’s theory that apparent treatment effects are driven by the drug itself and not to an imbalance of APOE4 carriers. Moreover, positron emission tomography (PET) imaging studies indicate that treatment with BAN2401 results in significant clearance of brain amyloid accompanied by favorable clinical effects. Nonetheless, some critics of the research remained uneasy about the complex trial design and unconvinced that concerns about the imbalance of APOE4 carriers had been laid to rest by the subgroup analyses. There was general enthusiasm for launching Phase 3 studies to provide definitive answers.


Revisiting older treatment strategies

At CTAD2018, vaccination re-emerged as a promising treatment strategy with data from a Phase 2a study of UB-311, an active anti-amyloid immunotherapy vaccine with the potential to prevent AD (7). Vaccination against amyloid is not a new idea. In 1999, Schenk and colleagues demonstrated in mice that active vaccination against neurotoxic forms of Aβ reduced levels of Aβ in the brain and improved performance on memory tasks (8). But a subsequent clinical trial of AN-1792, a vaccine against Aβ42, was terminated in 2002 because of an increased incidence of encephalitis, elicited by the production of cytotoxic T cells (9). The sponsor of the UB-311 study, United Neuroscience, Inc, has developed a platform that uses synthetic peptides to generate endogenous target-specific antibodies against a variety of antigens. UB-311 is a fully synthetic peptide vaccine that comprises two Aβ1-14 peptides fused to T-helper cell peptides in order to elicit a strong antibody response against Aβ while avoiding a cytotoxic T cell response (10). Data presented at CTAD showed no serious adverse events among 42 participants randomized to receive 7 doses of drug or placebo over 60 weeks, followed by 18 weeks of observations. Additional data from extensive biomarker, safety, and immunogenicity studies are expected by the end of 2019.
Another treatment approach, plasma exchange, has a long history in the treatment of other conditions and has recently emerged as a promising strategy against AD. Barcelona-based Grifols, S.A. reported encouraging results from a phase 2b/3 study of plasma exchange with albumin replacement in patients with moderate AD (AMBAR study) (11).
Grifols has been investigating the clinical use of plasma exchange with albumin replacement for the past 15 years. In an earlier pilot study, they showed that the technique resulted in a decrease in plasma Aβ, an increase in cerebrospinal fluid levels of Aβ, and improvements on clinical and functional neuroimaging measures. For the AMBAR study, they randomized nearly 350 participants with mild to moderate dementia to either placebo or one of three treatment groups. A sham procedure delivered to the placebo group simulated plasmapheresis so that patients, caregivers, and raters were all blind to whether the participant was receiving active treatment or placebo. The study was conducted at 41 sites in Spain and the United States.
All three treatment groups started the trial with conventional total plasma exchange with albumin replacement (TPE) once per week for two months followed by low-volume plasma exchange (LVPE) once per month for 12 months, with either a low (20 mg) or high (40 mg) dose of albumin. Two of the treatment groups also received an intravenous infusion of immunoglobulin (IVIG) to replace endogenous immunoglobulins. One group did not receive IVIG as an extra check on the safety of this product. Cognition, function and AD biomarkers were assessed at baseline, after the 2 months of TPE, and at 6, 9, 12, and 14 months. CSF biomarkers, magnetic resonance imaging (MRI), and fluorodeoxyglucose PET (FDG-PET) studies were conducted less frequently.
Participants in all three treatment arms showed a slowing of decline compared to placebo on both cognitive and functional measures, although the results were not statistically significant. However, when the results were reanalyzed with participants divided according to their baseline mini-mental status exam (MMSE) scores, patients with mild dementia (MMSE 22-26) showed no benefits from the treatment, while those with moderate dementia (MMSE 18-21) showed 61% slowing of decline on both the Alzheimer’s Disease Assessment Scale-cognitive subscale (ADAS-cog) and the Alzheimer’s Disease Cooperative Study-Activities of Daily Living (ADCS-ADL) scale. Safety was also assessed, and while the treated groups had a higher incidence of adverse events compared to placebo, the treatment was judged to be safe and well-tolerated. Nearly three quarters of participants (72%) completed the study despite the substantial burden of the trial.
The apparent strength of the effect in moderate but not mild AD patients was surprising and will be under further investigation. Future analyses of biomarkers and imaging studies and at least one additional clinical trial are planned to follow-up on this observation.


Continued interest in non-amyloid treatment strategies

The observed association of type 2 diabetes mellitus (T2D) with increased AD risk (12), along with preclinical studies that demonstrated improvement of brain function and synaptic health in mice treated with insulin has sparked interest in insulin as a treatment (13). Intranasal delivery of insulin results in rapid uptake in the central nervous system (14) and has been shown to improve memory and other cognitive measures in individuals with amnestic mild cognitive impairment (aMCI) or mild-to-moderate AD (15-17). Craft and colleagues at the Wake Forest School of Medicine in Winston-Salem, North Carolina, presented data from a multi-site Phase 2/3 study of intranasal insulin in patients with mild cognitive impairment (MCI) or mild AD (18).
For this trial, 289 participants at 26 US sites were randomized to receive either insulin or placebo daily for 12 months. The first 49 participants used Kurve Technology’s ViaNase ™ device to deliver the drug; however frequent malfunctions led the investigators to discontinue use of that device in favor of the newly-developed Precision Olfactory Delivery™ device (POD) from Impel NeuroPharma. Assessments were made at baseline and at three-month intervals until the end of the study, when participants were offered open-label insulin treatment for another 6 months. A change in cognitive function from baseline to 12 months served as the primary endpoint; secondary endpoints included assessments of daily function, imaging, and biomarker studies.
Although the study failed to show effectiveness of intranasal insulin delivered with the POD on any of the outcome measures, a pattern suggestive of benefit in the participants who used the ViaNase device was noted. Further analyses of the data, including analyses of the open-label extension and other pre-specified responder analyses are forthcoming, said Craft.
Interesting results were presented from a Phase 3 trial of another novel treatment strategy, this one using GV-971, a marine-derived oligosaccharide that has been shown to inhibit the formation of beta amyloid fibrils, reduce neuroinflammation, and affect the gut microbiota (19). The 36-week randomized controlled multicenter trial conducted by Shanghai’s Green Valley Pharmaceutical Company in 788 people diagnosed with mild-to-moderate AD demonstrated statistically significant improvements in cognition (assessed with the ADAS-cog) as early as week four, and continued improvement for the duration of the study. A non-significant trend towards improvement on the Clinicians Interview-Based Impression of Change (CIBIC+) was also observed. No improvements were seen on other secondary outcomes including measures of activities of daily living and neuropsychiatric symptoms. FDG-PET, an imaging study of brain metabolism, also showed no significant effects. The treatment was safe and well-tolerated.
A third innovative treatment strategy was presented by investigators from the Rocky Mountain Alzheimer’s Disease Center in Colorado (20). Based on the observation that people with rheumatoid arthritis have a reduced risk of AD, they hypothesized that cytokines produced by the innate immune system may be neuroprotective. To test this hypothesis, they injected transgenic AD mice with granulocyte-macrophage colony-stimulating factor (GM-CSF) and showed that it reduced brain amyloid by 40% after only seven days and reversed cognitive impairment in 14 days. They also reviewed data from bone-marrow transplant patients who had been given Leukine (also known as sargramostim), a recombinant form of human GM-CSF, and found that these patients showed significantly improved cognitive function. These data provided the rationale to test sargramostim in a placebo-controlled pilot study in patients with mild-to-moderate AD. Study participants who received a subcutaneous injection 5 days a week for three weeks showed a reduction in amyloid that correlated with an improvement in the mini-mental state examination (MMSE) score and no sign of ARIA. They are now planning a six-month trial of GM-CSF/sargramostim in mild-to-moderate AD.
A novel regenerative treatment that promotes neurogenesis in the brain is also in development as a first-in-class therapeutic for MCI and mild AD. Allopregnanolone (allo), a naturally occurring substance and metabolite of progesterone, has both regenerative and neuroprotective properties (21). Moreover, investigators at the Center for Innovation in Brain Science at the University of Arizona have demonstrated that allo induced neurogenesis and a change in synaptic connectivity and restored cognitive function in mice (22). At CTAD, they reported results from a multiple-dose Phase 1b/2a study in participants with MCI or mild AD (23).  While there was no statistically significant improvement in cognition, there was a trend in the right direction for improvements in executive function. No sex differences were seen and the treatment was well tolerated. They are now planning a Phase 2 randomized controlled trial that will enroll 200 mild AD patients to receive 4 mg of allo for 72 weeks.


Developing new tools to accelerate AD drug development

Developing new AD therapeutics requires not only a full pipeline of diverse agents, but novel and efficient analytic tools and trial designs as well. At CTAD, investigators described tools ranging from low to high tech that are being applied to studies testing a diverse set of therapeutic agents. At the low-tech end, for example, investigators at the Wake Forest School of Medicine are using mass mailings and the internet to identify potential participants for the COSMOS-Mind study, which is examining the potential of cocoa-flavanol extract to improve cognition in older adults (24). Interested participants were sent recruitment materials, contacted to confirm interest, screened via telephone, and if eligible, were then enrolled and randomized. Participants are assessed annually using a telephone-based cognitive composite outcome measure. Although results of the study will not be available for several years, the study has already demonstrated the feasibility of conducting a large, simple, cost-effective, and geographically diverse trial over the telephone, and has shown that older adults are willing to volunteer for telephone-based assessments.
At the other end of the technology spectrum, a machine learning algorithm has been developed by IQVIA to help identify prodromal AD patients in the general population (25). In the United States alone, there are currently 150 clinical trials seeking about 70,000 participants, making recruitment an enormous challenge. Moreover, the landscape has become more complicated as the number of procedures involved in trials has increased by about 70% and the number of countries conducting trials has more than doubled. IQVIA’s approach is to leverage huge healthcare datasets to build a predictive algorithm. To build the model, they used data from an initial cohort of more than 405 million subjects divided into a positive cohort (those with AD or who had been prescribed AD symptomatic drugs) and a control cohort (a matched sample of patients without an AD diagnosis or AD treatment). Using 24 months of medical history data from no more than 3 years prior to the AD diagnosis, they identified features such as diagnostic procedures, medical interventions, concomitant pathologies, and other characteristics that differentiated those with AD from those without AD across. They then compared the performance of different algorithms across different age groups and ranked risk factors for each age group based on the predictive value of that risk factor in that age group.
Predictive modeling can also enable precision medicine, according to data presented by Ariana Pharma in Paris, France. As part of the development of ANAVEX®2-73, an orally available selective sigma-1 receptor agonist, Ariana used an unbiased, data-driven machine learning platform that integrated clinical and genomic data to identify four key drivers of the response to the drug, including polymorphisms of two genes. This allowed them to extend their small open label study in a selected population, strengthening their hypothesis for this particular drug and supporting the notion that such precision pharmacology approaches can be used to identify patients who will benefit from particular drugs across a wide range of neurodegenerative diseases (26).
New biomarkers are also needed to increase the efficiency and productivity of trials. Speakers at CTAD described recent advances in the development of plasma biomarkers, which offer advantages for screening and diagnosis in terms of reduced cost and much greater availability to large populations. The Trial-Ready Cohort for Preclinical/Prodromal AD (TRC-PAD) project, funded by the National Institute on Aging will incorporate three different plasma biomarker assay platforms, enabling multi-center comparative field testing of the assessment tools (27). Ultrasensitive immunoassays for amyloid beta oligomers in CSF were also described, which offer the ability to demonstrate target engagement in clinical trials with a small number of participants. The Yale PET center has also recently developed a ligand for synaptic PET imaging, which could offer the first in vivo measure of synaptic density as an outcome measure in clinical trials of disease-modifying therapies, particularly those targeting synapses.


Take home lessons

CTAD 2018 offered a vision of new and better drugs, improved methods for identifying individuals in the earliest stages of disease, and improved diagnostic and staging tools, all aimed at providing better and more personalized treatment approaches for the millions of people living with or at risk of developing AD. The conference also highlighted lessons learned from completed and ongoing clinical trials that can be applied to future trials to improve the efficiency of those trials and accelerate the discovery and development of effective treatments for AD. While there is continued optimism regarding the potential efficacy of amyloid-targeting agents, disappointing results from many trials as well as increased understanding of the pathogenetic mechanisms that contribute to AD strongly supports pursuit of treatments targeting alternative mechanisms. Combination therapies that target multiple mechanisms may be needed, as well as treatments that address comorbid conditions that contribute to dementia. Other lessons learned at CTAD 2018 include:
•    Blood-based methods of detecting early markers of AD pathology have matured to the point of being ready for use in clinical trials but may still need to be refined for use in clinical care.
•    Incorporating a wide range of biomarkers into all clinical trials is essential to ensure that the maximum amount of information is provided by those trials.
•    Repurposing treatments that have been used for other diseases offers advantages in drug development since human safety data may be available.
•    To reduce the cost and expand access to clinical trials, remote assessments via telephone provide a feasible approach for testing cognition and detecting cognitive decline over time.
•    Extensive data in electronic medical records and genetics studies can be leveraged to predict risk of developing dementia, enable recruitment of presymptomatic individuals into clinical trials, and guide personalized approaches to treatment.

The next CTAD meeting, which will be held in San Diego December 4th through 7th, 2019 will build on each of these ideas. In addition to early reports on novel therapeutic strategies, we anticipate updates on the major industry and academic trials, validation data on leading biomarker candidates (including plasma markers of brain amyloid and neurodegeneration), and reports on new approaches to recruitment, retention, and improving diversity in AD clinical trials.  We also expect to see Phase 2 results from highly anticipated tau-targeting therapeutics.


Conflict of Interest: Dr. Vellas reports grants from Lilly, Merck, Roche, Lundbeck, Biogen, grants from Alzheimer’s Association, European Commission, personal fees from Lilly, Merck, Roche, Biogen, outside the submitted work. Ms. Bain reports compensation from CTAD CONGRESS for preparation of the manuscript. Dr. Touchon has nothing to disclose. Dr. Aisen reports grants from Lilly, personal fees from Proclara, other from Lilly, other from Janssen, other from Eisai, grants from Janssen, grants from NIA, grants from FNIH, grants from Alzheimer’s Association, personal fees from Merck, personal fees from Roche, personal fees from Lundbeck, personal fees from Biogen, personal fees from ImmunoBrain Checkpoint,  outside the submitted work.



1.    Hu X, Das B, Hou H, He W, Yan R. BACE1 deletion in the adult mouse reverses preformed amyloid deposition and improves cognitive functions. J Exp Med 2018;215:927-940.
2.    Egan MF, Kost J, Tariot PN, et al. Randomized Trial of Verubecestat for Mild-to-Moderate Alzheimer’s Disease. N Engl J Med 2018;378:1691-1703.
3.    Egan MF, Voss T, Mukai Y, et al. Results from the APECS trial. J Prev Alzheimers Dis 2018;5:S1.
4.    Vassar R. Editorial: Implications for BACE1 Inhibitor Clinical Trials: Adult Conditional BACE1 Knockout Mice Exhibit Axonal Organization Defects in the Hippocampus. J Prev Alzheimers Dis 2019;6:78-84.
5.    Bateman RJ, Xiong C, Benzinger TL, et al. Clinical and biomarker changes in dominantly inherited Alzheimer’s disease. N Engl J Med 2012;367:795-804.
6.    Fagan AM, Xiong C, Jasielec MS, et al. Longitudinal change in CSF biomarkers in autosomal-dominant Alzheimer’s disease. Sci Transl Med 2014;6:226ra230.
7.    Verma A, Yu HJ, Chen H-C, Wang CY. Active anti-amyloid immunotherapy with UB-311 vaccine: design, baseline data and study update of a Phase IIA, randomized, double-blind, placebo-controlled, 3-arm parallel-group, multicenter study. J Prev Alzheimers Dis 2018;5:S10.
8.    Schenk D, Barbour R, Dunn W, et al. Immunization with amyloid-beta attenuates Alzheimer-disease-like pathology in the PDAPP mouse. Nature 1999;400:173-177.
9.    Check E. Nerve inflammation halts trial for Alzheimer’s drug. Nature 2002;415:462.
10.    Wang CY, Wang PN, Chiu MJ, et al. UB-311, a novel UBITh((R)) amyloid beta peptide vaccine for mild Alzheimer’s disease. Alzheimers Dement (N Y) 2017;3:262-272.
11.    Paez A. AMBAR (Alzheimer’s Management by Albumin Replacement) Phase IIB/III Results. . J Prev Alzheimers Dis 2018;5:S8.
12.    Neth BJ, Craft S. Insulin Resistance and Alzheimer’s Disease: Bioenergetic Linkages. Front Aging Neurosci 2017;9:345.
13.    De Felice FG, Vieira MN, Bomfim TR, et al. Protection of synapses against Alzheimer’s-linked toxins: insulin signaling prevents the pathogenic binding of Abeta oligomers. Proc Natl Acad Sci U S A 2009;106:1971-1976.
14.    Salameh TS, Bullock KM, Hujoel IA, et al. Central Nervous System Delivery of Intranasal Insulin: Mechanisms of Uptake and Effects on Cognition. J Alzheimers Dis 2015;47:715-728.
15.    Craft S, Baker LD, Montine TJ, et al. Intranasal insulin therapy for Alzheimer disease and amnestic mild cognitive impairment: a pilot clinical trial. Arch Neurol 2012;69:29-38.
16.    Reger MA, Watson GS, Green PS, et al. Intranasal insulin administration dose-dependently modulates verbal memory and plasma amyloid-beta in memory-impaired older adults. J Alzheimers Dis 2008;13:323-331.
17.    Reger MA, Watson GS, Green PS, et al. Intranasal insulin improves cognition and modulates beta-amyloid in early AD. Neurology 2008;70:440-448.
18.    Craft S, Raman R, Chow T, et al. Primary results from a Phase II/III trial of intranasal insulin: A novel multi-target molecule and delivery mode for AD therapeutics. J Prev Alzheimers Dis 2018;5:S9.
19.    Xiao S, Zhang Z, Geng M. Phase 3 clinical trial for a novel oligosaccharide targeting multiple A-beta fragments in patients with mild-moderate AD in China J Prev Alzheimers Dis 2018;5:S10.
20.    Potter H, Woodcock JH, Boyd T, et al. Interim safety and efficacy results of pilot trial of GM-CSF/Sargramostim in mild to moderate AD. J Prev Alzheimers Dis 2018;5:S15.
21.    Irwin RW, Brinton RD. Allopregnanolone as regenerative therapeutic for Alzheimer’s disease: translational development and clinical promise. Prog Neurobiol 2014;113:40-55.
22.    Irwin RW, Wang JM, Chen S, Brinton RD. Neuroregenerative mechanisms of allopregnanolone in Alzheimer’s disease. Front Endocrinol (Lausanne) 2011;2:117.
23.    Brinton RD, Hernandez GD, Kono N, et al. Allopregnanolone regenerative therapeutic for mild cognitive impairment and mild Alzheimer’s disease: Phase 1B/2A outcomes update. J Prev Alzheimers Dis 2018;5:S12.
24.    Baker LD, Espeland MA, Rapp SR, et al. Cocoa supplement and multivitamin outcomes study of cognitive functin (COSMOS-MIND): Design of a large randomized clinical trial. J Prev Alzheimers Dis 2018;5:S20.
25.    Uspenskaya-Cadoz O, Alamuri C, Khinda S, et al. Machine learning algorithm helps identify non-diagnosed prodromal Alzheimer’s disease patients in general population. J Prev Alzheimers Dis 2018;5:S21.
26.    Hampel H, Afshar M, Parmentier F, et al. Longitudinal 148-week extension study for Anavex(R)2-73 Phase 2A Alzheimer’s disease demonstrates maintained activities of daily living score (ADCS-ADL) and reduced cognitive decline (MMSE) for patient cohort on high drug concentration and confirms role of patient selection biomarkers. J Prev Alzheimers Dis 2018;5:S43.
27.    Jimenez-Maggiora GA, Raman R, Rafii MS, Sperling RA, Cummings JL, Aisen PS. TRC-PAD: Accelerating participant recruitment in AD clinical trials through innovation. J Prev Alzheimers Dis 2018;5:S31.



L. Park1, C. Kouhanim1, S. Lee1, Z. Mendoza2, K. Patrick2, L. Gertsik2, C. Aguilar1, D. Gullaba1, S. Semenova1, S. Jhee1


1. PAREXEL International, Glendale, CA, USA; 2. California Clinical Trials Medical Group, Glendale, CA, USA

Corresponding Author: Dr. Lovingly Park, PAREXEL International, 1560 E. Chevy Chase Dr, Suite 140, Glendale, CA 91206, USA, Phone: +1 818 254 4389, Fax: +1 818 936 6844, Email:

J Prev Alz Dis 2019;2(6):135-138
Published online February 11, 2019,



Background: The recruitment challenges for MCI and AD subjects into clinical trials are well known, and this is particularly true for early phase studies.  Currently, only 10-20% of all patients who are referred for research from the community are trial eligible (Grill and Karlawish, 2011).  Due to the limited and specific study objectives in early phase study designs, these rates drop to approximately one patient every two months.  Barriers to research recruitment are multi-factorial, involving patient centered factors, issues related to caregiver/study partner participation, and aspects related to the involvement of their treating physicians.  To address this challenge, we implemented a Memory Clinic within PAREXEL’s Early Phase Clinical Pharmacology Unit.  Our objective was to significantly facilitate recruitment into AD clinical trials by providing resources and education to patients, their treating physicians, and caregivers in the community.
Method:  The Clinic’s primary goals were to increase research visibility and partnerships with local organizations and referring physicians.  Members of the research team co-sponsored community outreach events with local organizations, thereby increasing awareness about the services of this memory clinic.  Secondly, physician outreach was expanded to include those who were not previously amenable to clinical trial referrals.  Finally, Memory Clinic patients were given clinical evaluations, free of charge and the results were discussed with the patients and their caregivers.  If the patients were interested in hearing more about possible research opportunities, they were referred to the early phase unit for a screening visit.
Results: We found that new referrals for research participation significantly increased as a result of this new paradigm. In 2016, 12 patients diagnosed with MCI or AD per protocol, were referred to a research study and 3 were randomized.  In 2017, 98 patients were referred and 16 were enrolled   In addition, our referral network increased with 30 physicians over a 20 mile radius.  Collaborations with national non-profit organizations also increased, thereby increasing public awareness about the importance of research participation in the development of new treatments for Alzheimer’s Disease.
Conclusions:  In summary, community engagement and providing referring physicians with a clinical service improved recruitment significantly for our phase 1 unit.  Resource education, staff training, and dedicated medical professionals can significantly improve awareness about clinical research participation and provide additional participants over and above traditional recruitment methods and trial registry enrollment in a large urban area.

Key words: Enrollment, recruitment, early phase, clinical trials, Alzheimer’s disease



The prevalence of Alzheimer’s Disease (AD) is expected to reach 1.1 trillion by 2050, in the absence of effective pharmacological interventions (1). The last novel treatment approved for AD was in 2003, despite the fact that there are currently 105 candidates in development across all phases of clinical trials (2).  To address the challenge, a call to action by politicians, industry and academic leaders is ongoing, but even with the recent scientific advances in biomarker development, translational research, and clinical trial methodology, issues with AD recruitment hinder drug development (3, 4).  Only 10-20% meet criteria for participation when they are referred for research, resulting in a national enrollment rate of one patient per research site every two months (4).  Issues associated with eligibility are multi-factorial and involve patient-centered factors (e.g., comorbidities, concomitant medications, randomization to placebo), caregiver/study partner participation (e.g., availability), and limited involvement of their treating physicians (e.g., awareness about available trials).
A number of recruitment approaches exist to attract participants (e.g. appealing to previous research participants) (5).  There are also significant initiatives to create patient database registries to facilitate recruitment among community-dwelling elders (6).  However, there is limited evidence to suggest that these efforts successfully translate to enrollment into clinical trials. In response to the ongoing crisis facing patient recruitment, the National Institute of Aging and the Alzheimer’s Association funded programs to address this unmet need.  One such example is the Outreach, Recruitment, and Education Core (ORE) through the Alzheimer’s Disease Research Center (ADRC) across the United States.  Each ORE has a specific goal to educate the public about aging, Alzheimer’s Disease, cognitive health, and research participation through different outreach initiatives, expert lectures, and avenues for expert patient care and evaluation.  This resulted in the successful enrollment in some areas, proving that such models are effective and efficient ways of increasing patients’ access to research and clinical care in their communities (7).
Although ORE’s success is proven in academic settings and large teaching hospitals, the model had not been tested in commercial settings to evaluate its success in early phase (Phase I and IIa) clinical trials, where there is hesitation about research participation.  Some reasons relate to the focus on safety, pharmacokinetic and pharmacodynamics, with minimal therapeutic benefits to the patient.  Early phase studies also tend to be more burdensome due to more frequent visits (or longer inpatient stays) and complex procedures (e.g. lumbar puncture, neuroimaging, cognitive testing) (8).  These studies are also associated with higher risks, thereby excluding many older adults with common co-morbid medical conditions from participating.  As such, traditional recruitment methods of advertising and database mining, fail to make these studies attractive to patients and their families.  A more targeted and personalized approach to recruitment is required to overcome these hurdles and a model such as ORE can bridge the gap between recruitment targets and actual enrollment rates in early phase studies.
To test the model set forth by ORE, the PAREXEL Los Angeles Early Phase unit established a Memory Clinic, the goals of which were to facilitate recruitment by providing comprehensive neuropsychological evaluations to interested patients, perform consultation services to physicians, pre-identify appropriate patients for studies, and conducting educational activities about research participation. The Memory Clinic also provides information about resources available to the patient and their caregivers as it relates to healthy aging, dementia, and Alzheimer’s disease.



The Memory Clinic is a pilot program modeled after ORE which focuses on community events, self-referrals and increased physician outreach.  Patients are eligible for an evaluation free of charge, in exchange for hearing about clinical research and the overall goals of the clinic.  There is no obligation to participate in research studies.  Community outreach is done in a two-stage approach. First, a clinician or other member of the research team provides a lecture or a seminar on topics of interest such as healthy aging and dementia. At the completion of each lecture, psychometrists are on-site to administer questions regarding cognition and everyday life. This information is then reviewed by a clinician and an invitation for a comprehensive Memory Clinic evaluation is extended, if the complaints represented a marked decline from their previous level of functioning. Patients are also seen at the clinic through self, caregiver, or treating physician referral.  An important aspect of the clinic is to establish relationships with physicians in the community, educate them on the Memory Clinic goals and provide information on the utility of neuropsychological testing for clinical care.  Once they became familiar with the concept, they refer patients with the hope of obtaining cognitive test results to assist with  differential diagnosis and treatment planning.  Physicians are also provided with information about potential research opportunities for their patients.
During the evaluation the patient, or their surrogate, is provided with an informed consent and educated about the overall goals of the memory clinic. The patient is given a comprehensive clinical evaluation including a review of their medical history, current medications, and a semi-structured interview to obtain collateral information.  Patients are also administered tailored neuropsychological batteries, depending on the nature of the evaluation or referral question by the physician or family member.  The neuropsychological battery included tests assessing attention/processing speed, working memory, learning and memory, visuospatial ability, language, executive functioning and mood, using the Wechsler Test of Adult Reading (WTAR), Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), Trail Making Test (TMT), Controlled Oral Word Association Test (COWAT), and Geriatric Depression Scale (GDS). The WTAR is a norm-referenced standardized measure of premorbid intelligence in English-speaking individuals 16-89 years of age (9, 10).  The RBANS is a brief neuropsychological battery that consists of ten subtests which yield five index scores related to different cognitive domains (10).  The TMT is a neuropsychological test of visual attention and task switching which provides information about speed of processing, cognitive flexibility, and executive functioning and found to be significantly related to functional decline in dementia (11). The COWAT is a verbal fluency test found to have significant age related effects (12). The GDS is a 30-item self-report assessment administered to screen for depressive symptoms in the elderly (13). Functional impairment is established using semi-structured clinical interviews.  If additional tests are needed to further elucidate cognitive impairments, they are incorporated into the battery as appropriate. The tests are scored, interpreted, and written into a report that is shared with the patient and his or her medical provider upon request.  Patient performances were discussed during interdisciplinary team conferences where they were diagnosed by consensus.  If the patient meets criteria for Mild Cognitive Impairment or Alzheimer’s disease, they were subsequently invited for a feedback session to review their results with a clinician and discuss possible recommendations (14, 15). At the conclusion of the visit, participants were offered opportunities to screen for clinical research studies and if they are interested, a member of the project specific research team would meet with them individually.  If patients did not meet criteria, they were still presented with opportunities for research participation as a member of another population (i.e. healthy older adults, healthy normal volunteers, Parkinson’s disease, etc.)



Approximately 169 individuals were referred through community outreach services targeting those with memory loss or their treating physicians. Of all those who were evaluated through the Memory Clinic, 52 were found to have MCI, 40 had AD, and 30 were determined to be cognitively normal.  47 patients were found to have cognitive impairments as a result of other etiologies (e.g. non-AD dementia, psychiatric impairments, traumatic brain injury, epilepsy, etc.).  110 patients were referred for research and the overall pool was ethnically and linguistically diverse.  Approximately 60 patients spoke English and the neuropsychological characteristics of those who completed the neuropsychological battery in English (n=40) are provided in Table 1.  Those who were non-English speaking, (e.g. Korean, Spanish, and Arabic) were administered a flexible neuropsychological assessment and a clinical interview in their native language.  A total of 19 patients were eventually enrolled in a clinical trial.  Other goals of the clinic were achieved by increasing the referral network to over 30 physicians within a 20 mile radius from the Memory Clinic location.  Collaborations with national non-profit organizations also increased, thereby increasing public awareness about the importance of research participation in the development of new treatments for Alzheimer’s disease and other related disorders.

Table 1. Patient Characteristics for Age, Education, and RBANS Standard Scores

Table 1. Patient Characteristics for Age, Education, and RBANS Standard Scores

AD=Alzheimer’s Disease; MCI= Mild Cognitive Impairment, HNE=Healthy normal elderly or cognitively normal.



The findings from the Memory Clinic suggest that a model adopted in academia can be translated into a commercial clinical research setting with the proper clinicians and resources, resulting in increased awareness about clinical research and enrollment.  Comprehensive neuropsychological assessments are costly, have limited insurance coverage, and the scarcity of clinical resources can lead to waiting lists delaying a diagnosis and access to care.  Physician engagement in the research process increased as they became more aware of the importance of participation in clinical trials and the availability of research opportunities for their patients.  Connecting caregivers and their families with resources in the community was also an added incentive and the cross-referrals between professional organizations and the Memory Clinic proved to be a mutually rewarding collaboration.  As such, the Memory Clinic addressed common methodological challenges needed to improve recruitment and retention in AD clinical trials (3).
Our community outreach activities increased exponentially through the Memory Clinic.  Perceptions about clinical trials shifted as organizations were more inviting, as a result of the service to the community, as opposed to using their event as a platform solely for recruitment.  Educational seminars included information about the importance of research to the development of new treatments, as well as the medical and regulatory protections that are made on the patient’s behalf.  When patients and their families observed the relationship between members of the Memory Clinic and trusted organizations, they were more likely to request an appointment for an evaluation.
Caregiver resources were also addressed during Memory Clinic visits.  Families were taught about lifestyle changes and modifiable risk factors to improve cognition and prevent the progression of dementia.  Education related to the influences of caregiver distress on health and psychological functioning was discussed and caregivers were given information about how to seek respite to reduce their burden.  Recommendations to help improve daily function and communication with the patient were also provided.  Consistent with reported findings in the literature, caregiver access to this information through the Memory Clinic addressed an unmet need in the AD community and also increased overall interest in research participation (16).
An unintended benefit of the Memory Clinic, was the opportunity to evaluate patients from other neurologic populations and add them to the research database for future studies.  Due to the fact that the Memory Clinic was available as a community resource, patients with a wide range of cognitive impairments across different etiologies were invited for an assessment.  This was done primarily to increase our contacts with other patient populations for other future research opportunities.
While the Memory Clinic was useful in increasing our patient interactions and identifying people for research participation, many were still reluctant to participate in early phase clinical trials.  Of those who were, the selection criteria within the individual research protocols were prohibitive (e.g. many did not qualify based on the fact that they were recently diagnosed and subsequently not stabilized on their medications, or they had several comorbid conditions or medications that were prohibitive, or they were simply above the age limit).  Future research of our Memory Clinic data will include evaluating how the clinical diagnosis given as part of the clinical neuropsychological assessment differs from the criteria set forth by common AD protocols.  Moreover, strategies for recruitment will center on the importance of evolving patient engagement as outlined by other initiatives in the AD community (17).
Another common barrier to research participation was the requirement of invasive procedures as part of the early phase research studies.  Even if the patient was willing to engage in these procedures, their caregivers were reluctant to let them continue.  While innovative biomarkers serve as important pharmacokinetic and pharmacodynamic measures of signal detection in early phase trials, they also pose a challenge to recruitment. As the knowledge within the scientific community evolves, consideration may be given towards creating adaptive protocol designs to accommodate a patient’s level of comfort with the procedures and facilitate recruitment.
Existing literature shows that attitudes towards research are the strongest predictors of willingness to participate (18). A significant number of non-English speaking patients, were referred for research studies.  Utilizing multi-lingual staff with training in culturally competent neuropsychological testing increased the patient’s and their caregiver’s willingness to engage in the evaluation and hear about opportunities for research (19, 20). This has previously been proposed as a necessary component of improving recruitment rates in AD and future endeavors for the Memory Clinic will include expanding the ethnic subgroups to increase the heterogeneity of our overall research samples.
In summary, the Memory Clinic proved to be a useful model of recruitment within the setting of a commercial early phase research unit.  Addressing significant barriers to patient enrollment such as individual patient factors, caregiver influences, and involvement of their treating physicians was instrumental in the assessment of individuals interested in participation and subsequent research participation.  The Memory Clinic is a relatively recent concept in the unit and some of the original patients are returning for their annual follow-up visit.  Further work will be done to assess the patients’ longitudinal cognitive status and determine if attitudes towards research participation changes as a function of dementia severity.


Acknoledgement: The authors would like to thank Dr. Anita Mardian for her advocacy on behalf of the Memory Clinic, her efforts provided the PAREXEL Early Phase unit with the resources to launch this initiative.  The authors thank all patients and their caregivers for their participation in the Memory Clinic.  Their altruistic contribution to the field will hopefully lead to the development of effective treatments for AD.  Finally, we would also like to express our appreciation for our collaborators in the community.

Funding: Funding for the Memory Clinic initiative was provided by PAREXEL International.  These resources were used towards the assessment procedures, community outreach activities, testing materials, and promotional advertising.

Conflict of interest: Authors are employed by California Clinical Trials Medical Group or PAREXEL International at the time of this publication.

Ethical standards: Patients and/or legal proxies signed informed consent for the evaluation.



1.     Alzheimer’s A. 2016 Alzheimer’s disease facts and figures. Alzheimers Dement. 2016;12(4):459-509.
2.    Cummings J, Lee G, Mortsdorf T, Ritter A, Zhong K. Alzheimer’s disease drug development pipeline: 2017. Alzheimers Dement (N Y). 2017;3(3):367-384.
3.    Vellas B, Hampel H, Rouge-Bugat ME, et al. Alzheimer’s disease therapeutic trials: EU/US Task Force report on recruitment, retention, and methodology. J Nutr Health Aging. 2012;16(4):339-345.
4.    Grill JD, Galvin JE. Facilitating Alzheimer disease research recruitment. Alzheimer Dis Assoc Disord. 2014;28(1):1-8.
5.    Grill JD, Karlawish J. Addressing the challenges to successful recruitment and retention in Alzheimer’s disease clinical trials. Alzheimers Res Ther. 2010;2(6):34.
6.    Williams DE, Vitiello MV, Ries RK, Bokan J, Prinz PN. Successful recruitment of elderly community-dwelling subjects for Alzheimer’s disease research. J Gerontol. 1988;43(3):M69-74.
7.    Bachman DL, Stuckey M, Ebeling M, et al. Establishment of a predominantly African-American cohort for the study of Alzheimer’s disease: the South Carolina Alzheimer’s disease clinical core. Dement Geriatr Cogn Disord. 2009;27(4):329-336.
8.    Watson JL, Ryan L, Silverberg N, Cahan V, Bernard MA. Obstacles and opportunities in Alzheimer’s clinical trial recruitment. Health Aff (Millwood). 2014;33(4):574-579.
9.    Duff K, Patton D, Schoenberg MR, Mold J, Scott JG, Adams RL. Age- and education-corrected independent normative data for the RBANS in a community dwelling elderly sample. Clin Neuropsychol. 2003;17(3):351-366.
10.    Randolph C, Tierney MC, Mohr E, Chase TN. The Repeatable Battery for the Assessment of Neuropsychological Status (RBANS): preliminary clinical validity. J Clin Exp Neuropsychol. 1998;20(3):310-319.
11.    Marshall GA, Rentz DM, Frey MT, et al. Executive function and instrumental activities of daily living in mild cognitive impairment and Alzheimer’s disease. Alzheimers Dement. 2011;7(3):300-308.
12.    Rodriguez-Aranda C, Martinussen M. Age-related differences in performance of phonemic verbal fluency measured by Controlled Oral Word Association Task (COWAT): a meta-analytic study. Dev Neuropsychol. 2006;30(2):697-717.
13.    Yesavage JA. Geriatric Depression Scale. Psychopharmacol Bull. 1988;24(4):709-711.
14.    McKhann GM, Knopman DS, Chertkow H, et al. The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 2011;7(3):263-269.
15.    Albert MS, DeKosky ST, Dickson D, et al. The diagnosis of mild cognitive impairment due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 2011;7(3):270-279.
16.    Jennings LA, Reuben DB, Evertson LC, et al. Unmet needs of caregivers of individuals referred to a dementia care program. J Am Geriatr Soc. 2015;63(2):282-289.
17.    Vellas B. Recruitment, retention and other methodological issues related to clinical trials for Alzheimer’s disease. J Nutr Health Aging. 2012;16(4):330.
18.    Grill JD, Raman R, Ernstrom K, Aisen P, Karlawish J. Effect of study partner on the conduct of Alzheimer disease clinical trials. Neurology. 2013;80(3):282-288.
19.    Chow TW, Ross L, Fox P, Cummings JL, Lin KM. Utilization of Alzheimer’s disease community resources by Asian-Americans in California. Int J Geriatr Psychiatry. 2000;15(9):838-847.
20.    Perales J, Moore WT, Fernandez C, et al. Feasibility of an Alzheimer’s disease knowledge intervention in the Latino community. Ethn Health. 2018:1-12.



R. Milne1, E. Bunnik2, K. Tromp2, S. Bemelmans3, S. Badger1, D.Gove4, M. Maman5, M. Schermer2, L. Truyen6, C. Brayne1, E. Richard3


1. Institute of Public Health, University of Cambridge, United Kingdom; 2. Erasmus University Medical Centre, Rotterdam, the Netherlands; 3. Radboud University Medical Center, the Netherlands; 4. Alzheimer Europe; 5. Novartis Pharma AG, Basel, Switzerland; 6. Janssen Research & Development LLC.

Corresponding Author: Richard Milne, PhD, Institute of Public Health, University of Cambridge, 2 Wort’s Causeway Cambridge, UK, CB1 8RN,, 0044 (0)1223 761912

J Prev Alz Dis 2016;in press
Published online February 22, 2017,


There is growing interest in the development of novel approaches to secondary prevention trials in Alzheimer’s disease to facilitate screening and recruitment of research participants and to reduce the time and costs associated with clinical trials. Several international research collaborations are setting up research infrastructures that link existing research cohorts, studies or patient registries to establish ‘trial-ready’ or ‘readiness’ cohorts. From these cohorts, individuals are recruited into clinical trial platforms. In setting up such research infrastructures, researchers must make ethically challenging design decisions in at least three areas: re-contacting participants in existing research studies, obtaining informed consent for participation in a readiness cohort, and disclosure of Alzheimer’s disease-related biomarkers. These ethical considerations have been examined by a dedicated workgroup within the European Prevention of Alzheimer’s Dementia (EPAD) project, a trans-European longitudinal cohort and adaptive proof-of-concept clinical trial platform. This paper offers recommendations for the ethical management of re-contact, informed consent and risk disclosure which may be of value to other research collaborations in the process of developing readiness cohorts for prevention trials in Alzheimer’s disease and other disease areas.

Key words: Ethics,recruitment,readiness cohort,consent, disclosure, Alzheimer’s disease.




No new drugs for Alzheimer’s disease have become available in over a decade, despite significant research and development efforts and a high number of late phase clinical trials (1). This failure has been attributed in part to the choice of study population (1, 2): while the majority of past research has focused on patients with mild-to-moderate Alzheimer’s dementia, recent clinical trials have begun to concentrate on the earlier stages of the disease identified by revised research diagnostic criteria (3, 4). Such studies aim to prevent the development or progression of cognitive impairment among people who do not show any signs of dementia but show biomarker changes thought to be associated with increased risk of developing Alzheimer’s dementia (5–7).
Moreover, methodological issues associated with the definition of Alzheimer’s Disease itself, study recruitment, retention and inter-site variability have been identified as barriers to innovation (8–11). Several public-private research collaborations are currently involved in developing new models for conducting clinical trials in Alzheimer’s disease. These involve the establishment of ‘readiness’ (12) or ‘trial-ready’ cohorts. The primary goal of these longitudinal cohorts is to provide a well-characterised population of potential research participants for recruitment into prevention trials, to limit screening failure rates and to reduce recruitment time. The readiness cohorts themselves may be drawn from existing research registers and cohorts and feed into clinical trial ‘platforms’ (13).
The development of readiness cohorts for clinical trial platforms aimed at altering the natural history of underlying brain changes associated with Alzheimer’s dementia has been described as the ‘pivotal’ (12) element in the approach adopted by a growing number of large public-private initiatives in Alzheimer’s disease research. These include the European Prevention of Alzheimer’s Dementia project (EPAD) (12) and the Global Alzheimer’s Platform (GAP) (13). A simplified model of the approach developed by EPAD and GAP is shown in figure 1. Both projects recruit participants from existing registries and observational cohorts into a new longitudinal cohort study. Participants complete an extensive battery of cognitive tests and biological examinations and are followed over time. This cohort can then function both as a readiness cohort for secondary prevention trials – to be run on a clinical trial platform – and as a resource for disease modelling. Similar models are being adopted within the Dementias Platform UK, and within industry-sponsored studies (14).

Figure 1. Simplified model of a readiness cohort drawn from existing studies and leading to one or several clinical trials

Figure 1. Simplified model of a readiness cohort drawn from existing studies and leading to one or several clinical trials


The development of linked research infrastructures for secondary prevention studies represents an innovative approach to drug development for Alzheimer’s disease. It also raises distinct ethical questions which recombine those traditionally associated with the conduct of cohort studies and clinical trials. These questions include the recruitment of participants from existing studies, ensuring informed consent as participants move through the research process, and the disclosure of Alzheimer’s dementia risk biomarker status.

Ethical issues associated with the design and development of readiness cohorts

All medical research requires careful attention to ethical values and principles. General guidelines, declarations and frameworks exist to guide researchers accordingly (15–17). However, the specific concerns presented by different types of research conducted in different clinical domains may vary significantly. The ethical questions associated with research in Alzheimer’s dementia have long been primarily concerned with the potential risks and burdens of research and the capacity of people with dementia to provide informed consent (18–23). Since the identification of the ApoE susceptibility gene, these concerns has been accompanied by debate around the potential psychological impact on asymptomatic individuals of learning disease risk (24, 25). Most recently, specific attention has been paid to the issues associated with prevention and biomarker-led research in ‘preclinical’ populations (26, 27), in which biomarker levels presumed to reflect Alzheimer’s disease pathology and an assumed increased risk of later dementia are present while cognitive functioning is normal. These issues revolve around the impact on healthy individuals of receiving Alzheimer’s dementia risk information associated with a high degree of uncertainty and with no clear course of action to reduce risk.
In the following sections we identify three areas that require particular attention in the early stages of setting up long-term linked research projects involving readiness cohorts in Alzheimer’s disease research.  First, we cover the recruitment of participants from existing research cohorts and registers and the responsibilities of research collaborations in this area. Second, we outline ethical issues related to informed consent and recruitment into multi-stage, linked projects. The third and final area of discussion examines particular challenges associated with the disclosure of risk status within readiness cohorts linked to secondary prevention studies.

Recruiting from existing studies

Existing registries and longitudinal cohort studies can act as resources for the recruitment of well-characterised participants for Alzheimer’s disease research (10). The identification of eligible individuals or groups of participants within existing studies inevitably involves some level of screening of existing studies’ data. There are two pertinent issues in relation to this process. The first relates to how and by whom data is accessed in order to re-contact participants. The second relates to the ability of studies to re-contact and the procedure for doing so.
The screening of data from existing studies may be undertaken by researchers in the new readiness cohort, based on data shared by these existing studies; or it may be carried out by the local cohort investigators based on selection criteria provided by the readiness cohort. If the former approach is adopted, the access, use, processing, and sharing of research participants’ coded (or identifiable) personal health information and research data should adhere to international ethical guidelines, including the draft WMA declaration on ethical considerations regarding health databases and biobanks (28-30). In particular, in any screening process researchers must ensure that no personal data is shared without participants’ consent.
In the second case, existing studies can share only ‘metadata’ which describe the scope (and types) of data available and the number of people on whom they are held. These metadata can be used to establish whether or not the original study contains a substantial population of eligible participants. In such cases, no individual-level data are accessed by the new readiness cohort and consequently no consent from research participants is required. Local cohort investigators can then identify participants who meet the inclusion criteria for the new study and establish whether they are eligible to be re-contacted.
Approaches to re-contact for research by third parties have received comparatively little attention in discussions of biomedical research ethics (31), or within the governance structures of studies themselves. In re-contacting participants, researchers must ensure that participants’ autonomy is respected, that their privacy is protected and that they are not exposed to either unnecessary risks or unacceptable burdens through further participation. These principles apply to research more generally (15, 32), but may be particularly important in a context where individuals are asked to extend their existing participation in a new direction. The specific conditions for re-contact for readiness cohort should be established through discussions between existing studies and their institutional review boards or ethics committees. When consent for re-contact is not in place, investigators of the original study should first ask their participants to consent to being re-contacted about further studies. Only those who have consented can then be contacted about particular further studies. A decision flow for this process is shown in figure 2 below.

Figure 2. Recontact flow chart

Figure 2. Recontact flow chart


If participants are eligible to be re-contacted, the investigators of the original study can provide information about the new study and ask selected participants to contact the investigators of the new study if they are interested in taking part. In this approach, participants are asked to opt-in to taking part in the new study.
Finally, it is important to recognise that new readiness cohorts and other initiatives which draw on existing studies have a responsibility to minimise any wider negative impact. There is a risk that participation in a readiness cohort may increase drop-out rates from existing studies, particularly if taking part is a comparatively intensive experience. The existence of any such effect should be assessed as readiness cohorts develop, and researchers should involve working closely with investigators and participants in existing studies to ensure that the prior commitments and choices of the latter are not counteracted or frustrated. This also implies that individuals who decide to take part in the new readiness cohort should not be excluded from the original study through which they are contacted.

Ensuring informed consent

Readiness cohorts represent one stage on a research journey that may start in a clinical registry or observational cohort and end with participation in a clinical trial. In this respect they differ from traditional longitudinal cohorts or traditional clinical trials, as they extend over time and encompass various stages in which consent may be asked from participants by different parties and for different purposes.
The transitional nature of readiness cohorts presents a distinct challenge when it comes to ensuring that potential participants are fully informed about the scope of the project before consenting to take part. The requirement of informed consent aims to ensure that research participation is the result of autonomous choice (32, 33). Informed consent is given to a specific party or person for a specific task or activity, and research participants must be made aware of what is involved in this task, including the risks, potential benefits and burden of participation, and must choose voluntarily to take part. However, while full, relevant, and accurate information about a research project is indispensable (34) in order to give informed consent, this information may not be available in readiness cohorts, where the various stages of a project are distinct but interconnected, and where it is not clear what the final journey of any individual participant through the project will be. After all, participants in a readiness cohort do not know at the outset whether or not they will eventually be asked to participate in a clinical trial, and what that trial will entail.
The ethical concern in this context is that by only providing information about the specific activities for which informed consent is sought at a particular point in time, readiness cohorts would risk becoming ‘fish traps’ (35, 36) for their participants (see figure 3). When a participant initially consented to take part in the original registry or cohort, they did not consent to the tests and examinations of the new readiness cohort. Or when a participant initially consented to being re-contacted about further research studies, they did not consent to undergoing a lumbar puncture as part of the new readiness cohort, to learning about their Alzheimer’s disease risk, or taking drugs in the context of a clinical trial. With each step, the burdens and risks of research participation will accumulate incrementally. At the same time, once started on the path of participation, it may become progressively more difficult to withdraw. This ‘luring’ or ‘easing’ of participants into clinical trial participation would be a form of misleading or deceiving participants, which would run counter to the ethical requirement of informed consent.  It may also increase the risk of drop-out, jeopardising the scientific integrity of the data for the purpose of natural disease course monitoring by introducing additional bias.

Figure 3. The fish trap

Figure 3. The fish trap

The concept of the fish trap has been described in the context of prenatal screening (35, 36) where healthy pregnant women are not confronted with difficult reproductive decisions in a single stroke, but progressively, step-by-step. When they first consent to participate in screening, they do not (yet) consent to the steps that may follow – amniocentesis or chorionic villus sampling, and eventually termination of the pregnancy. With each step, however, it becomes more difficult to turn back.


In order to prevent a fish trap, global information about the whole study trajectory must be available to potential participants from the outset. A staged model for informed consent (37) should present information about the entire study journey to research participants at every step. Informed consent requires information specific to the stage for which consent is sought. At each step of the way, however, participants should also be informed about further stages of the research project and the project in its entirety.  A staged consent approach allows for study information to be repeated, rehearsed and built upon and for participants to be reminded that they are able to withdraw at any time without having to give any reasons. Without this general information, consent would not be informed consent. A simplified version of the staged model for informed consent that was adopted in EPAD is shown in figure 4 below.

Figure 4. The staged consent model adopted in EPAD

Figure 4. The staged consent model adopted in EPAD


Education and risk disclosure

A core ethical consideration associated with secondary prevention research in Alzheimer’s disease relates to the potential impact on research participants of learning that one is at elevated risk of developing Alzheimer’s dementia, based on either ApoE genotype or other biomarkers, such as beta-amyloid status (26, 38).  Concerns about disclosure of ApoE (39) and amyloid status (40, 41) revolve around the potential the psychological, behavioural and social harms of disclosure, particularly of amyloid status (42).
In the case of readiness cohort, decisions about whether or not to disclose risk information are encountered at three stages: during contact to take part in a readiness cohort; following cohort examinations and assessments; and as part of the recruitment from readiness cohorts into clinical trials.  At each stage, the pros and cons of disclosure differ.
At the first stage, that of contact to take part in the readiness cohort, the decision over whether or not to disclose risk status is shaped by the proposed constituency of the readiness cohort. If a readiness cohort aims to recruit only a population thought to be at increased risk of developing Alzheimer’s dementia, disclosure is essential in order to make individuals aware why they are considered eligible for the research – a well-established research-ethical requirement (32).  If, in contrast, the readiness cohort includes a wide range of risk states and researchers are blinded to why any individual is contacted, it is no longer clear why any individual participant has been contacted. However, the inclusion of ‘lower risk’ participants should not occur solely as a means of avoiding disclosure, not least because these participants would be subjected to potentially unnecessary risks and burdens as a consequence.
At the second stage, guidance frameworks for population-based research suggest that individual research results should be returned if consent is in place, if the findings are analytically valid, if they reveal a significant risk of a serious health condition, and finally if they are actionable (43).  Neither ApoE nor amyloid information has confirmed clinical utility and robust information about likely disease progression in individuals does not yet exist (44).  However, this may be changing (45) and indeed risk information may be of value to research participants regardless of the lack of effective treatments (46).
Finally, ApoE and amyloid status should be disclosed as part of a transparent enrolment process to secondary prevention clinical trials (47), as is the case in a number of ongoing studies (26, 48, 49). This reflects the requirement to make individuals aware of why they are considered eligible for research. As such, participants in readiness cohorts should be informed on what basis they are contacted for any clinical trial. However, there are important features of this process which differ from conventional clinical trial structures.
Within recruitment to conventional trial structures, participants can be provided with information at the point of screening for the study to enable them to decide whether or not risk information is something they would like to know (48, 50). In the context of readiness cohorts, participants are enrolled at a stage when it is not yet known whether or not they will participate in a trial or for what reasons (i.e. based on what biomarkers) they may be recruited. However, because participants are aware that the cohort exists to facilitate trials in preclinical/at-risk and prodromal Alzheimer’s disease populations, being invited to take part in a trial necessarily constitutes disclosure of preclinical/at-risk or prodromal status. To choose not to know this information would be to choose not to be contacted for a trial, which is incompatible with the primary goal of a readiness cohort.
The complexities associated with disclosing risk status within linked research structures prompt three core recommendations for establishing readiness cohorts.
Firstly, all participants entering readiness cohorts should be willing to learn biomarker results related to their risk of developing Alzheimer’s dementia and understand the uncertainty of that evidence, as there is no effective way of protecting an individual’s right not to know (this information) while preserving transparent enrolment procedures.
Secondly, even if no return of individual research results will occur during the cohort stage of research, the link to secondary prevention studies means that all potential participants should receive educational materials and/or briefings about risk information during the cohort consent process. This ensures that potential participants are informed about what biomarkers can and cannot tell them about their risk of developing Alzheimer’s dementia, and allows them to make an informed decision about whether this is information they would like to learn in the future.
Finally, readiness cohort researchers should work with trial sponsors to ensure that the disclosure of biomarker information from cohort examinations is carried out by appropriately qualified professionals.



The development of linked projects for Alzheimer’s disease research, which draw on existing research studies to establish readiness cohorts for secondary prevention clinical trials, raises distinct ethical challenges. These derive from the novelty of the approach and how it combines and reframes existing issues of concern. Particular ethical considerations and challenges revolve around the process of identifying and contacting potential participants in the readiness cohort; the staged provision of specific and general information to research participants in order to prevent them entering a ‘fish trap’, and the establishment of robust procedures for informing participants about the meaning of risk information and for disclosing risk. Recommendations in these areas are summarised in Box 1.
The concerns and recommendations detailed here suggest that it is critically important that researchers consider the journey of a research participant through the various stages of the research project from start to finish, from re-contact to clinical trial participation. This includes considering how challenges associated with later stages of research, such as recruitment from a readiness cohort into a clinical trial which may involve risk disclosure, affect the information that should be provided to participants during initial recruitment into the cohort. It is critical to the success of readiness cohorts, however, to carefully align re-contact, informed consent and risk disclosure processes throughout the research project.

Box 1. Recommendations

Box 1. Recommendations


Funding: This work was funded through the Ethical Legal and Social Implications work package of the European Prevention of Alzheimer’s Dementia (EPAD) study EPAD receives support from the Innovative Medicines Initiative Joint Undertaking under grant agreement n° 115736, resources of which are composed of financial contribution from the European Union’s Seventh Framework Programme (FP7/2007-2013) and EFPIA companies’ in kind contribution. RM was also funded through the UK National Institute of Health Research grant to the Cambridge Biomedical Research Centre.

Acknowledgements: We are grateful to members of the EPAD Ethics Advisory board, Jason Karlawish, Marianne Boenink, Xavier Carne and David Good for their comments on the discussions summarised in this paper.  We would also like to thank members of the EPAD consortium for discussion and comments related to the development of these recommendations. All authors contributed to the conception and development of the manuscript, have either drafted or critically revised the content, and have approved the final version of the manuscript.

Conflict of interest: LT is an employee of Janssen R&D LLC, EFPIA co-lead of the IMI-EPAD project and member of the Johnson&Johnson BioResearch Ethics Committee.  MM is an employee of Novartis Pharma AG. The opinions expressed in this article are those of the authors and do not necessarily reflect the views  of their employers or organizations. RM, EB, KT, SBe, SBa, DG, MS, CB and ER have no conflicts of interest with this paper.

Ethical stantards: N/A



1.    Schneider LS, Mangialasche F, Andreasen N, Feldman H, Giacobini E, Jones R, et al. Clinical trials and late-stage drug development for Alzheimer’s disease: an appraisal from 1984 to 2014. J Intern Med. 2014;275: 251–83. doi:10.1111/joim.12191
2.     Cummings JL, Morstorf T, Zhong K. Alzheimer’s disease drug-development pipeline: few candidates, frequent failures. Alzheimers Res Ther. 2014;6: 37. doi:10.1186/alzrt269
3.     Sperling RA, Aisen PS, Beckett LA, Bennett DA, Craft S, Fagan AM, et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 2011;7: 280–92. doi:10.1016/j.jalz.2011.03.003
4.     Dubois B, Feldman HH, Jacova C, Hampel H, Molinuevo JL, Blennow K, et al. Advancing research diagnostic criteria for Alzheimer’s disease: the IWG-2 criteria. Lancet Neurol. 2014;13: 614–29. doi:10.1016/S1474-4422(14)70090-0
5.     Carrillo MC, Brashear HR, Logovinsky V, Ryan JM, Feldman HH, Siemers ER, et al. Can we prevent Alzheimer’s disease? Secondary “prevention” trials in Alzheimer’s disease. Alzheimer’s Dement. 2013;9: 123–131.e1.
6.     Vellas B, Aisen PS, Sampaio C, Carrillo M, Scheltens P, Scherrer B, et al. Prevention trials in Alzheimer’s disease: An EU-US task force report. Prog Neurobiol. 2011;95: 594–600.
7.     Sperling R, Mormino E, Johnson K. The evolution of preclinical Alzheimer’s disease: implications for prevention trials. Neuron. Elsevier; 2014;84: 608–22. doi:10.1016/j.neuron.2014.10.038
8.     Vellas B, Hampel H, Rougé-Bugat ME, Grundman M, Andrieu S, Abu-Shakra S, et al. Alzheimer’s disease therapeutic trials: EU/US Task Force report on recruitment, retention, and methodology. J Nutr Health Aging. 2012;16: 339–45.
9.     Feldman HH, Haas M, Gandy S, Schoepp DD, Cross AJ, Mayeux R, et al. Alzheimer’s disease research and development: a call for a new research roadmap. Ann N Y Acad Sci. 2014;1313: 1–16. doi:10.1111/nyas.12424
10.     Aisen P, Touchon J, Andrieu S, Boada M, Doody R, Nosheny RL, et al. Registries and cohorts to accelerate early phase alzheimer’s trials. A report from the E.U/U.S. clinical trials in Alzheimer’s disease task force. J Prev Alzheimer’s Dis. 2016;3: 68–74.
11.     Ritchie CW, Terrera GM, Quinn TJ. Dementia trials and dementia tribulations: methodological and analytical challenges in dementia research. Alzheimers Res Ther. 2015;7: 31. doi:10.1186/s13195-015-0113-6
12.     Ritchie CW, Molinuevo JL, Truyen L, Satlin A, Van der Geyten S, Lovestone S. Development of interventions for the secondary prevention of Alzheimer’s dementia: the European Prevention of Alzheimer’s Dementia (EPAD) project. The Lancet Psychiatry. Elsevier; 2015;3: 179–186. doi:10.1016/S2215-0366(15)00454-X
13.     Cummings J, Aisen P, Barton R, Bork J, Doody R, Dwyer J, et al. Re-Engineering Alzheimer Clinical Trials: Global Alzheimer’s Platform Network. J Prev Alzheimer’s Dis. 2016;3: 114–120.
14.     Janssen. NCT02114372: Cognitive Health in Ageing Register: Investigational, Observational and Trial Studies in Dementia Research: Prospective Readiness Cohort Study (CHARIOT:PRO). 2016. Available:
15.     US Department of Health and Human Services. The Belmont report: Ethical principles and guidelines for the protection of human subjects of research. 1978;
16.     Beauchamp T, Childress J. Principles of biomedical ethics. Oxford: Oxford University Press; 2001.
17.     World Medical Association. Declaration of Helsinki. Ethical principles for medical research involving human subjects. 2013;
18.     Melnick VL, Dubler NN, Weisbard A, Butler RN. Clinical Research in Senile Dementia of the Alzheimer Type. J Am Geriatr Soc.; 1984;32: 531–536. doi:10.1111/j.1532-5415.1984.tb02240.x
19.     Whitehouse PJ. Future prospects for Alzheimer’s disease therapy: ethical and policy issues for the international community. Acta Neurol Scand.; 1996;94: 145–149. doi:10.1111/j.1600-0404.1996.tb05885.x
20.     Karlawish JH, Casarett D. Addressing the ethical challenges of clinical trials that involve patients with dementia. J Geriatr Psychiatry Neurol. 2001;14: 222–8.
21.     Kim SYH. The ethics of informed consent in Alzheimer disease research. Nat Rev Neurol. 2011;7: 410–4. doi:10.1038/nrneurol.2011.76
22.     Nuffield Council on Bioethics. Dementia: Ethical Issues. London; 2009.
23.     National Bioethics Advisory Commission. Research Involving Persons With Mental Disorders. Report and Recommendations of the National Bioethics Advisory Commission. Washington; 1998.
24.     High DM, Whitehouse PJ, Post SG, Berg L. Guidelines for addressing ethical and legal issues in Alzheimer disease research: a position paper. Alzheimer Dis Assoc Disord. 1994;8: 66–74.
25.     Post SG, Whitehouse PJ, Binstock RH, Bird TD, Eckert SK, Farrer LA, et al. The clinical introduction of genetic testing for Alzheimer disease. An ethical perspective. JAMA. 1997;277: 832–6.
26.     Molinuevo JL, Cami J, Carné X, Carrillo MC, Georges J, Isaac MB, et al. Ethical challenges in preclinical Alzheimer’s disease observational studies and trials: Results of the Barcelona summit. Alzheimers Dement. 2016; doi:10.1016/j.jalz.2016.01.009
27.     Karlawish J. Addressing the ethical, policy, and social challenges of preclinical Alzheimer disease. Neurology. 2011;77: 1487–93. doi:10.1212/WNL.0b013e318232ac1a
28.     World Medical Association. Declaration On Ethical Considerations Regarding Health Databases And Biobanks (draft). 2015.
29.     Nuffield Council on Bioethics. The collection, linking and use of data in biomedical research and health care: ethical issues. London; 2015.
30.     Knoppers B. Framework for responsible sharing of genomic and health-related data. Hugo J. Springer; 2014;8: 3. doi:10.1186/s11568-014-0003-1
31.     Beskow LM, Fullerton SM, Namey EE, Nelson DK, Davis AM, Wilfond BS. Recommendations for ethical approaches to genotype-driven research recruitment. Hum Genet. NIH Public Access; 2012;131: 1423–31. doi:10.1007/s00439-012-1177-z
32.     Council for International Organizations of Medical Sciences. International Ethical Guidelines for Biomedical Research Involving Human Subjects.. Bulletin of Medical Ethics. 2002.
33.     Manson NC, O’Neill O. Rethinking Informed Consent in Bioethics. Cambridge: Cambridge University Press; 2007.
34.     Faden R, Beauchamp T. A history and theory of informed consent. Oxford: Oxford University Press; 1986.
35.     de Wert G, Dondorp W. Ethiek van voortplantingsgeneeskunde. In: Heineman MJ, Evers JLH, Massuger LFAG, Steegers EAP, editors. Obstetrie en gynaecologie. Houten: Bohn Stafleu van Loghum; 2012. pp. 19–64. doi:10.1007/978-90-368-1191-0
36.     Jong A de. Prenatal screening à la carte? 2013; Unpublished PhD thesis. Available:
37.     Bunnik EM, Janssens ACJW, Schermer MHN. A tiered-layered-staged model for informed consent in personal genome testing. Eur J Hum Genet. Nature Publishing Group; 2013;21: 596–601. doi:10.1038/ejhg.2012.237
38.     Sperling RA, Karlawish J, Johnson KA. Preclinical Alzheimer disease-the challenges ahead. Nat Rev Neurol. 2013;9: 54–8. doi:10.1038/nrneurol.2012.241
39.     Goldman JS, Hahn SE, Catania JW, LaRusse-Eckert S, Butson MB, Rumbaugh M, et al. Genetic counseling and testing for Alzheimer disease: joint practice guidelines of the American College of Medical Genetics and the National Society of Genetic Counselors. Genet Med. 2011;13: 597–605. doi:10.1097/GIM.0b013e31821d69b8
40.     Johnson KA, Minoshima S, Bohnen NI, Donohoe KJ, Foster NL, Herscovitch P, et al. Appropriate use criteria for amyloid PET: a report of the Amyloid Imaging Task Force, the Society of Nuclear Medicine and Molecular Imaging, and the Alzheimer’s Association. Alzheimers Dement. 2013;9: e–1–16. doi:10.1016/j.jalz.2013.01.002
41.     Leuzy A, Zimmer ER, Heurling K, Rosa-Neto P, Gauthier S. Use of amyloid PET across the spectrum of Alzheimer’s disease: clinical utility and associated ethical issues. Amyloid. Informa UK Ltd. London; 2014;21: 143–8. doi:10.3109/13506129.2014.926267
42.    Bemelmans, S., K. Tromp, E. Bunnik, R. Milne, S. Badger, C. Brayne, M. Schermer, and E. Richard. 2016. “Psychological, Behavioral and Social Effects of Disclosing Alzheimer’s Disease Biomarkers to Research Participants – a Systematic Review.” Alzheimer’s Research & Therapy. 8:46 DOI: 10.1186/s13195-016-0212-z
43.     Knoppers BM, Deschênes M, Zawati MH, Tassé AM. Population studies: return of research results and incidental findings Policy Statement. Eur J Hum Genet. 2013;21: 245–7. doi:10.1038/ejhg.2012.152
44.     Noel-Storr AH, Flicker L, Ritchie CW, Nguyen GH, Gupta T, Wood P, et al. Systematic review of the body of evidence for the use of biomarkers in the diagnosis of dementia. Alzheimers Dement. 2013;9: e96–e105. doi:10.1016/j.jalz.2012.01.014
45.     Lingler JH, Klunk WE. Disclosure of amyloid imaging results to research participants: Has the time come? Alzheimer’s Dement. NIH Public Access; 2013;9: 741–744.e2. doi:10.1016/j.jalz.2012.09.014
46.     Gooblar J, Roe CM, Selsor NJ, Gabel MJ, Morris JC. Attitudes of Research Participants and the General Public Regarding Disclosure of Alzheimer Disease Research Results. JAMA Neurol. 2015; 1. doi:10.1001/jamaneurol.2015.2875
47.     Kim SYH, Karlawish J, Berkman BE. Ethics of genetic and biomarker test disclosures in neurodegenerative disease prevention trials. Neurology. 2015; WNL.0000000000001451–. doi:10.1212/WNL.0000000000001451
48.     Harkins K, Sankar P, Sperling R, Grill JD, Green RC, Johnson KA, et al. Development of a process to disclose amyloid imaging results to cognitively normal older adult research participants. Alzheimers Res Ther. 2015;7: 26. doi:10.1186/s13195-015-0112-7
49.     Tariot PN, Ho C, Langlois C, Reiman EM, Lopera F, Langbaum JB, et al. The Alzheimer’s Prevention Initiative. Alzheimer’s Dement. 2014;10: P247. doi:10.1016/j.jalz.2014.04.379
50.     Lingler JH, Butters MA, Gentry AL, Hu L, Hunsaker AE, Klunk WE, et al. Development of a Standardized Approach to Disclosing Amyloid Imaging Research Results in Mild Cognitive Impairment. J Alzheimers Dis. 2016;52: 17–24. doi:10.3233/JAD-150985


J. Cummings1, P. Aisen2, R. Barton3, J. Bork4, R. Doody5, J. Dwyer6, J. C. Egan3, H. Feldman7, D. Lappin8, L. Truyen9, S. Salloway10, R. Sperling11, G. Vradenburg4 for the GAP-NET Working Groups*

* GAP-NET Working Group: Paul Aisen, Russell Barton, Randy Bateman, Jason Bork, Adam Boxer, Mark Brody, William Burke, Jeffrey Cummings, Rachelle Doody, John Dwyer, Johanna Carmel, Howard Feldman, Debra Lappin, Allan Levey, Gad Marshall, Marshall Nash, Dorene Rentz, Craig Ritchie, Stephen Salloway, Lon Schneider, Joy Snider, Reisa Sperling, Pierre Tariot, Luc Truyen, George Vradenburg, Michael Weiner

1. Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, USA; 2. University of Southern California, Los Angeles, CA, USA; 3. Eli Lilly, Indianapolis, IN, USA; 4. Pintail Solutions, Indianapolis, IN, USA; 5. Baylor College of Medicine, Alzheimer’s Disease and Memory Disorder Center, Baylor, TX, USA; 6. Global Alzheimer’s Platform Foundation, USA; 7. University of British Columbia, Vancouver, BC, USA; 8. FaegreBD Consulting, Washington, DC, USA; 9. Johnson & Johnson, New Brunswick, NJ, USA; 10. Brown University, Providence, RI, USA; 11. Harvard Medical School, Boston, MA, USA 

Corresponding Author: Jeffrey Cummings, MD, ScD, Cleveland Clinic Lou Ruvo Center for Brain Health, 888 W Bonneville Ave, Las Vegas, NV  89106, T:  702.483.6029, F: 702.722.6584, E:

J Prev Alz Dis 2016;3(2):114-120
Published online March 4, 2016,


Alzheimer’s disease (AD) drug development is costly, time-consuming, and inefficient.  Trial site functions, trial design, and patient recruitment for trials all require improvement.  The Global Alzheimer Platform (GAP) was initiated in response to these challenges.  Four GAP work streams evolved in the US to address different trial challenges:  1) registry-to-cohort web-based recruitment; 2) clinical trial site activation and site network construction (GAP-NET); 3) adaptive proof-of-concept clinical trial design; and 4) finance and fund raising.  GAP-NET proposes to establish a standardized network of continuously funded trial sites that are highly qualified to perform trials (with established clinical, biomarker, imaging capability; certified raters; sophisticated management system. GAP-NET will conduct trials for academic and biopharma industry partners using standardized instrument versions and administration.  Collaboration with the Innovative Medicines Initiative (IMI) European Prevention of Alzheimer’s Disease (EPAD) program, the Canadian Consortium on Neurodegeneration in Aging (CCNA) and other similar international initiatives will allow conduct of global trials. GAP-NET aims to increase trial efficiency and quality, decrease trial redundancy, accelerate cohort development and trial recruitment, and decrease trial costs.  The value proposition for sites includes stable funding and uniform training and trial execution; the value to trial sponsors is decreased trial costs, reduced time to execute trials, and enhanced data quality. The value for patients and society is the more rapid availability of new treatments for AD.  

Key words: Global Alzheimer Platform, Alzheimer’s disease, clinical trials, recruitment, certification, registry, drug development, drug discovery.


Alzheimer’s disease (AD) is an increasing threat to public health, becoming more common as the world’s population ages.  There are currently 35 million individuals affected worldwide and this will grow to 120 million people or more by 2050 (1).  Despite the growing need, no new novel agent has been approved for the treatment of AD in over a decade (2).

There is a growing sense of urgency to re-engineer the approach to developing new therapies for AD.  The emerging catastrophe of dementia was a major theme of the G8 Summit led by United Kingdom (UK) Prime Minister, David Cameron in December 2012. In Europe the momentum for change led to the Innovative Medicines Initiative’s (IMI) European Prevention of AD (EPAD) program and in the US to an AD summit organized by the Global Chief Executive Officer (CEO) Initiative on Alzheimer’s (CEOi) and the New York Academy of Sciences in November 2013 and subsequent Global Alzheimer’s Platform (GAP) design and planning meetings in 2014 (3). Multiple stakeholders including industry leaders, academicians, government officials, non-governmental organizations, advocacy group leaders, and philanthropists participated in the GAP design and planning process. GAP conceptualized a transformative program for AD drug development and clinical trials addressing many of the issues contributing to the inefficiency, slowness and suboptimal quality of current AD clinical trials. Four work groups were established: registry-to-cohort (now called Trial-Ready Cohort for Preclinical and Prodromal AD [TRC-PAD]), site activation (now called GAP network [GAP-NET], innovative trial design, and GAP financing (3).  GAP is intended to deliver consistently high quality performance, enable novel trial designs (ie., adaptive trial designs), and incorporate mechanisms for information and data sharing designed to accelerate scientific learning and clinical translation (3).

AD drug development takes an average of 13 years and costs $5.6 billion (including the cost of failures and capitalization costs) (4). Phase 3 studies are the longest and most expensive element of the development cycle.  The high cost of Phases 2 and 3 reflect in part the non-integrated nature of the drug development ecosystem and requirement to reconstruct multiple resources to achieve each step of site preparation, trial conduct, and regulatory submission.  The absence of an organized AD clinical trial enterprise increases the time required to recruit for trials and to advance new therapies, increases the costs and duration of trials for sponsors, and delays the availability of treatment for persons in need of therapeutic intervention.  The financial costs and risks of AD drug development decreases the number of agents that can be advanced, discourages some potential sponsors from attempting to develop drugs for AD, and ultimately decreases the availability of new treatments for persons with or at risk for AD.

The purpose of the GAP-NET Working Group is to consider challenges to the conduct of clinical trials, identify solutions, and construct a standing clinical trial network that will conduct clinical trials with efficiency, cost-effectiveness, timeliness and high quality.  Here we describe the background, structure, and purpose of the network of clinical trial sites designed to advance this endeavor.

The Alzheimer’s Disease Clinical Trial Process is Broken

The system for implementation, conduct, and monitoring of AD clinical trials is broken (Table 1) (5, 6).  The compromised state of AD clinical trials contributes to the low success rate of AD drug development both directly by making it difficult to demonstrate a drug-placebo difference and indirectly by discouraging investment in AD drug development by pharmaceutical and biotechnology companies (2, 7).

Recruitment to trials is slow, with some trials recruiting at a rate as low as 0.2 patients per site per month and requiring 1-2 years to recruit patients for 6 month trials.  To compensate for slow recruitment, the number of trial sties is increased and inclusion of sites in multiple world regions is common (8, 9)  Increasing the number of trial sites invites greater inter-rater variability The effects of globalization on trial efficiency have not been thoroughly studied; recent analyses suggest that including many trial sites from multiple regions increases variability in baseline characteristics and in measures of disease course (10, 11).

Site rater training is redundant with repetitious training on the same instrument for every trial at every trial site even if training was recently completed by another sponsor.  Different sponsors may use slightly different versions of the same instruments requiring the raters at each site to remember these differences, increasing opportunities for errors and protocol deviations (12, 13). Rater drift is common, requiring on-going rater monitoring and remedial training (8).

Table 1. Challenges to the optimal conduct of AD clinical trials

AD – Alzheimer’s disease; IRB – institutional review board; LP – lumbar puncture; MRI – magnetic resonance imaging; PET –positron emission tomography


There is no standing network for industry trials, and a trials site network must be re-identified and re-constructed for each trial.  Contract research organizations (CROs) keep databases of trial site performance but these are not comprehensive, up-to-date, or publically available. Trial site availability and trial-related revenue fluctuate.  When few trials are available, sites may dismiss staff; when a new trial becomes available, sites must hire and retrain individuals.  Experienced staff with valuable expertise may be lost in this cycle.  Trial budgeting is pro-rated so start-up costs often must be covered from other funds.  Trial budgeting is usually based on visit costs with training, data entry, and administrative costs often uncompensated.  Inexperienced sites may be under-budget and fail for economic reasons, making it difficult to expand the total number of trial sites.

Administrative procedures such as contract and grant negotiation and budget review are repeated for each trial at each site.  Institutional Review Boards (IRBs) at each site review the protocol and make adjustments to the informed consent, creating variability in informed consent at sites across the trial as well as delay in trial site activation and trial initiation.

Because of the boom and bust nature of current AD trial processes, data collected on trial participants is lost to future trials, participants are not advised of the impact of their personal contributions, and the reasons for trial failure as well as relevant participant data are lost to the field.

The quality and uniformity of patient populations recruited for trials is suboptimal.  Recent studies have shown that approximately 20% of patients diagnosed with AD dementia and included in trials have no amyloid burden in the brain as determined by amyloid positron emission tomography (PET) (14, 15). The absence of amyloidopathy indicates that the diagnosis may be incorrect; the absence of the target pathology will compromise potential efficacy of any anti-amyloid therapies being investigated.

There is currently limited ability to identify amyloid-bearing individuals particularly in the preclinical and prodromal stages of AD.  Amyloid imaging or cerebrospinal fluid (CSF) amyloid studies are required to identify these study candidates, and there is a high percentage of screen failures. In a recent trial where a rigorous approach was taken to document the presence of pathological CSF AD biomarkers, more than 50% of subjects with possible prodromal AD had non-pathologic CSF testing (16).  Amyloid imaging is expensive and adds substantially to the total cost of trials.  Cerebrospinal fluid measures of amyloid beta-protein (Aβ), total tau and phospo-tau are more accessible and less costly, offering an alternative for biomarker diagnostic support.  However there are issues to resolve around the quality and reliability of the current assays (17) as well as the readiness of investigators to enroll patients in this procedure.


The lack of a well-funded, well-trained, fast-start, standing network of AD clinical trial sites is slowing the development of new agents for AD and contributing to the high cost of AD drug development.  It is this problem that GAP-NET is designed to address.  Adequate funding can stabilize sites, enabling them to retain key staff, shorten start-up times, recruit more rapidly, and retain subjects more effectively.  Established roles and responsibilities of salaried staff will insure the continuity of site performance in clinical and administrative roles.  Pre-identification of sites for the network will eliminate site surveys and shorten the times of trial network construction. Expert financial management systems and standardized master site agreements with sponsors will be established at GAP-NET sites and will enhance administrative efficiency.  Appropriate medication storage and accountability will be expected at GAP-NET sites. Data collection, entry, transfer systems, and expert use capability will be required of GAP-NET sites.  Currently un-funded activities such as data entry and responding to vendor queries will be performed better and more rapidly with proper subsidies. Sites will be monitored for data quality, start-up times, trial conduct, protocol compliance, recruitment rates, and retention of patients in trials. Trial-site metrics will be established and sites failing to perform appropriately will be excused from the network.  Development of a comprehensive site performance database will allow continuous remodeling of the network to include the best performing sites, enhance performance of successful sites, and identify site best practices.

Certification of raters will depend on demonstration of skill in administration of all tests relevant to the population appropriate for the GAP-NET trials.  Pre-certification and site qualification will reduce redundancy and decrease the burden on sites.  Use of agreed-upon versions of instruments will reduce variability, decrease site demands, and allow greater comparability across trials.  Training and certification of new raters will facilitate growth of site teams and expansion of the site network.

Improved site and personnel quality along with greater use of biomarkers will result in improved diagnostic accuracy and more uniform subject characteristics.  Seamless access to technical resources such as PET, magnetic resonance imaging (MRI) and lumbar puncture (LP) will be site requirements.

Use of central IRBs can also reduce trial start-up times by decreasing redundancy of reviews at each institution and establishing templates to which trials and reviewers can adhere.  Central IRBs such as the reliance model championed by the National Center for Advancing Translational Science (NCATS) will be considered for GAP-NET (18).

Identification and construction of trial-ready cohorts of patients through registries (discussed below) and other outreach mechanisms will abbreviate recruitment times.  More aggressive use of traditional and social media can help identify appropriate trial candidates and accelerate trial recruitment.

Enhanced site performance and recruitment will mean that fewer sites will be needed for trials. Smaller sample sizes will be required and variability in data collection and trial conduct will be reduced.  Requiring fewer sites and shortened recruitment periods will decrease the cost of trials and allow more drugs to be tested.  These advantages represent a value proposition for sponsors, attracting them to work with the network to conduct trials and advance therapeutics.  The value for those with or at risk of the disease is acceleration of innovative medicines.  GAP-NET will be available for both early stage proof-of-concept trials and for pivotal trials and will conduct trials across the spectrum of cognitive normal elderly to prodromal AD and AD dementia.

Inclusion of patients in higher quality trials that are better run, better supervised, and lead to better quality data is more ethical and reflects the precious resource that patient participation in clinical trials represents.  Recruitment of patients to trials that have little chance of leading to new therapies is at best disrespectful and must be discouraged.

Pre-competitive cooperation by pharmaceutical companies working with academic and other commercial entities is imperative for GAP-NET to succeed.  Use of the network will require that the sponsor agree to use the specific form of each instrument that has been selected.  Similarly, sponsors must agree that the certification and qualification of the sites is acceptable and need not be repeated for their trial.  Common language acceptable to sponsors and institutions hosting GAP-NET sites will need to evolve for budgets, contracts, and IRBs.  Sponsors will benefit in terms of cost and time savings and quality enhancement by accepting GAP-NET standards.  Innovative thinking within the biopharmaceutical industry is resulting in significantly increased transparency of clinical research and safety information and willingness to consider collaboration on study design, measures of clinical efficacy, and biomarkers (19). This collaborative approach will facilitate achieving GAP-NET objectives.   A goal of GAP-NET is to enhance data sharing among stakeholders to facilitate treatment development.

Cooperation of regulatory authorities (Food and Drug Administration [FDA] and European Medicines Agency [EMA]) is critical to the success of GAP-NET.  Test procedures, instrument choice, use of run-in data, and data collection and standards require regulatory discussion to assure the acceptability of data collected by GAP-NET for regulatory purposes.

GAP will collaborate with and learn from existing models of national and international site collaboration such as the NCATS, Alzheimer’s Disease Cooperative Study (ADCS), TransCelerate, and the European and Developing Countries Clinical Trials Partnership (ADCPT).  The Patient Centered Outcomes Research Institute (PCORI) and the National Institutes of Health (NIH) including National Institute of Aging (NIA), NCATS, and NIH-sponsored programs such as the NIH Health Care Systems Research Collaboratory will also have key roles in the success of GAP-NET.  The processes adopted by GAP-NET include many initiatives recommended in the Re-Engineering Clinical Trials Initiative of NIH (20-22).

Planned site and network characteristics of GAP-NET are presented in Table 2.


Table 2. Site and network characteristics of GAP-NET

DSMB – data safety and management board; IRB – institutional review board; LP – lumbar puncture; MRI – magnetic resonance imaging; PET –positron emission tomography; TRC-PAD – trial-ready cohort for prevention of Alzheimer’s disease


Sites will be included in GAP-NET with the aim of having enough sites within the network to conduct all clinical trials presented by sponsors. Both academic sites affiliated with major medical centers and independent non-academic sites will be included in the network.  Eleven pilot sites have been identified by GAP in its pilot phase.  It is anticipated that there may be up to 100 US sites in the GAP-NET, and these will collaborate with EPAD and Canadian Consortium on Aging and Neurodegeneration (CCNA) sites, as well as other nations’ sites meeting the GAP-NET standards, in order to conduct global trials.

Registry-to-Cohort Work Group

Novel mechanisms are required to speed patient recruitment to trials.  Recruitment constitutes the greatest bottleneck for clinical trial conduct in AD and in many other disorders (10). TRC-PAD is an innovative approach using registries to identify potential participants for trials.  The Brain Health Registry (BHR) will be central to the process of feeding a central GAP Registry, as will other registries and non-registry based outreach mechanisms such as collaborations with large physician practices and Medicare/Medicaid enrollment lists as well as use of mobile computing.

For the GAP registry, interested individuals will enroll on the web-based BHR or collaborating registry, provide demographic information, complete questionnaires, take online cognitive assessments and contribute genetic or other clinical information available through remote collection methods.  Based on these data, adaptive reiterative algorithms will be developed to select participants most likely to meet trial entry criteria (Figure 1). These potential subjects will be referred to GAP-NET sites for biomarker assessment (e.g., amyloid imaging) and further testing.  Amyloid imaging will be performed at least in part in conjunction with the Imaging Dementia – Evidence for Amyloid Scanning (IDEAS) Study.  Individuals meeting all criteria will comprise the GAP Cohort and will be entered into GAP-NET clinical trials (Figure 2). The algorithm-based approach is hypothesized to increase the number of patients referred to trials, improve the appropriateness of the referred subjects, reduce the screen failure rate, and decrease of cost of screening.  GAP-NET sites will receive referrals for trials from the GAP Cohort. The creation of trial-ready-cohorts is proposed as a means of speeding recruitment and shortening trial cycle times.  Other registries (;; site-based registries) will also be included in the TRC-PAD initiative as channels for referring patients to the GAP registry.  Tracking the trajectory of registry and cohort patients after registration and before randomization will provide additional information on drug-induced change in trajectory after trial entry and could help select patients for trials.  On-line assessments may reduce the burden on care partners and clinical trial sites to collect participant data.

Figure 1. Features to be included in a risk algorithm for identification of GAP-NET trial candidates

Figure 2. TRC-PAD mechanism for identifying patients for GAP-NET trials (BHR – Brain Health Registry)


European Prevention of Alzheimer’s Disease and Canadian Centers for Neurodegeneration and Aging Initiative

The European IMI inaugurated the EPAD project to create a network of trial sites and conduct clinical trials using adaptive designs to test multiple agents (23-27).  The 12 Trial Delivery Centers (TDCs) included in the EPAD network will have features similar to those of GAP-NET, and the two networks will collaborate to allow conduct of trials using sites in both the US and Western Europe.  Similarly, the CCNA is collaborating with GAP-NET to allow inclusion of Canadian sites in the execution of multi-regional trials.

Chinese, Japanese and South American site leaders are engaged in GAP-NET discussions. EPAD is focused on prevention trials in patients with preclinical or prodromal AD; GAP-NET plans to conduct Phase 2 and 3 trials in all stages of AD.

GAP-NET and Drug Development

GAP-NET cannot lead to new treatments without a concomitant improvement in AD drug discovery and delivery of a pipeline of high-quality pharmaceutical agents capable of impacting AD pathology.  GAP-NET can test drugs more quickly and can provide better data that will allow sponsors to more rapidly decide whether or not to advance a compound for further testing.  GAP-NET can assist in seeing that effective agents have a “quick win” in proof-of-concept trials and that ineffective agents “fail fast”,  reducing the investment in drugs that cannot succeed (28). GAP-NET will not increase the AD drug development success rate without having highly efficacious agents to test in trials.  Discovery of better treatments depends on deepening our understanding of the basic biology of AD, comprehending the cellular mechanisms of neurodegeneration, and distinguishing normal and abnormal aging.  GAP-NET is an engine that needs to be fueled by optimized compounds and combinations of agents developed in academic, pharmaceutical and biotechnology laboratories that meaningfully impact the biology of AD.  Investment in AD drug discovery is a key element in resolving the crisis posed by AD and will complement the transformative trial solution presented by GAP-NET.


GAP-NET intends no less than the re-engineering of AD clinical trials as currently conducted to a standing, structured, integrated, quality system capable of recruiting patients and efficiently escorting them through trials up to two years faster than today’s standard.  GAP-NET represents a disruptive transformation that will have effects throughout the AD drug development ecosystem; lessons learned from GAP-NET may influence organization of trials in other neurodegenerative disorders and other disease states (22). The GAP-NET will be expanded from the 11 sites in the pilot phase to the number of sites needed to enter patients and conduct trials in a timely way.  The total number of necessary sites will be reduced compared to current standards by enhanced recruitment, concentration of registry activity around GAP-NET sites, decreased data “noise”, and reduced screen fails.  Sites meeting quality criteria will be GAP-NET partners regardless of their academic or private nature; poor performing sites will be excused from the network.  An evolving clinical trial database will allow the investigation and publication of trial site best practices.  Structured introduction of new instruments such as patient- and caregiver-reported outcomes can be facilitated and systematically planned in the network and included in trials after regulatory review by FDA and EMA.  GAP-NET will develop capacity for site qualification (clinical, biomarker, imaging), rater certification, site monitoring, and growth of the number of available sites.  GAP-NET will be prepared to collaborate with IMI-EPAD, CCNA and other quality networks meeting GAP-NET standards to conduct trials worldwide.  GAP-NET together with a robust AD pipeline can deliver new treatments to patients faster.

Disclosures: Paul Aisen has served as a consultant to the following companies:  NeuroPhage, Elan, Eisai, Bristol-Myers Squibb, Eli Lilly, Merck, Roche, Amgen, Genentech, Abbott, Pfizer, Novartis, AstraZeneca, Janssen, Medivation, Ichor, Lundbeck, Biogen, iPerian, Probiodrug, Anavex, Abbvie, Janssen, Cohbar.  Dr. Aisen receives research support from Eli Lilly, the Alzheimer’s Association and the NIH [NIA U01-AG10483 (PI), NIA U01-AG024904 (Coordinating Center Director), NIA R01-AG030048 (PI), and R01-AG16381 (Co-I)]. Russell Barton is an employee of Eli Lilly.

Jason Bork is a Principle at Pintail Solutions and serves as a project management consultant to Global Alzheimer’s Platform Foundation.

Jeffrey Cummings has received in kind research support from Avid Radiopharmaceuticals and Teva Pharmaceuticals. He has provided consultation to AbbVie, Acadia, ADAMAS, Alzheon, Anavex, AstraZeneca, Avanir, Biogen-Idec, Biotie, Boehinger-Ingelheim, Chase, Eisai, Forum, Genentech, Intracellular Therapies, Lilly, Lundbeck, Merck, Neurotrope, Novartis, Nutricia, Otsuka, Pfizer, Prana, QR Pharma, Resverlogix, Roche, Suven, Takeda, and Toyoma companies. He has provided consultation to GE Healthcare and MedAvante and owns stock in ADAMAS, Prana, Sonexa, MedAvante, Neurotrax, and Neurokos. Dr. Cummings owns the copyright of the Neuropsychiatric Inventory. 

Rachelle Doody  provides consultation to AC Immune, Axovant, AZ Therapies, Biogen, Cerespir, Forum, Genentech, Hoffman-LaRoche, Shanghai Green Valley, Riovant, Suven, Transition, Takeda, VTV companies. She has research support (clinical trials) with Accera, Avanir, Genentech, Lilly, Merck, NIH/Sanofi, Pfizer, and Takeda.  Other – Hoffman LaRoche (DSMB), Lilly/UCSD (ADCS-DAPC).   Lastly, Dr. Doody has stock options with AZ Therapies, QR Pharma, Sonexa, and Transition.

John Dwyer is President and Founding Board Member of Global Alzheimer’s Platform and is Chairman of Telcare, Inc.

Johanna Egan is an employee of Eli Lilly.

Howard Feldman has provided consultation through University of British Columbia service agreements with Eli Lilly, Merck, Arena, GE HealthCare, Kyowa Hakko Kirin, Biogen Idec, ISIS Pharmaceuticals from 2012 to present.  Dr. Feldman has served on safety monitoring or diagnostic monitoring boards for Eisai and Genentech and has received research support / performed clinical trials sponsored by Pfizer, Eisai, Novartis, Janssen, Lundbeck, Genentech, Roche, and Astra Zeneca.  Between 2009-2011, he served as full-time employee of Bristol-Myers Squibb while on leave from University of British Columbia.

Debra Lappin is a Principal with FaegreBD Consulting.   In this capacity, she provides consultation to the Global Alzheimer’s Platform Foundation and GAP-Net.   FaegreBD Consulting provides consulting services to a wide range of pharmaceutical companies. 

Steven Salloway has received grant support from the GAP-NET Foundation which is directly related to the content of this manuscript.  He reports grant support and consultation fees from Biogen, Merck, Roche, Genentech, and Lilly, and grant support from Avid and Functional Neuromodulation.

Reisa Sperling has served as a consultant for Merck, Eisai, Janssen, Boehringer-Ingelheim, Isis, Lundbeck, Roche, and Genentech.

Luc Truyen is an employee of Johnson and Johnson.

George Vradenburg is Founder and Chair, USAgainstAlzheimer’s; Convener, The Global CEO Initiative on Alzheimer’s.


1. Alzheimer’s Disease International, Prince M, Guerchet M, Prina M. Policy Brief for Heads of Government: The Global Impact of Dementia 2013-2050. London: Alzheimer’s Disease International, 2013.

2. Cummings JL, Morstorf T, Zhong K. Alzheimer’s disease drug-development pipeline: Few candidates, frequent failures. Alzheimers Res Ther 2014;6:37.

3. The New York Academy of Sciences, The Global CEO Initiative on Alzheimer’s Disease. Alzheimers Disease Summit: The Path to  2025 Summary and Strategy Report. New York, NY: The New York Academy of Sciences; 2013 Dec 9. 

4. Scott TJ, O’Connor AC, Link AN, Beaulieu TJ. Economic analysis of opportunities to accelerate Alzheimer’s disease research and development. Ann N Y Acad Sci 2014;1313:17-34.

5. Becker RE, Greig NH. Increasing the success rate for Alzheimer’s disease drug discovery and development. Expert Opin Drug Discov 2012;7:367-370.

6. Becker RE, Greig NH. Why so few drugs for Alzheimer’s disease? Are methods failing drugs? Curr Alzheimer Res 2010;7:642-651.

7. Hyman BT, Sorger P. Failure analysis of clinical trials to test the amyloid hypothesis. Ann Neurol 2014;76:159-161.

8. Doody RS, Cole PE, Miller DS, et al. Global issues in drug development for Alzheimer’s disease. Alzheimers Dement 2011;7:197-207.

9. Cummings J, Reynders R, Zhong K. Globalization of Alzheimer’s disease clinical trials. Alzheimers Res Ther 2011;3:24.

10. Grill JD, Galvin JE. Facilitating Alzheimer disease research recruitment. Alzheimer Dis Assoc Disord 2014;28:1-8.

11. Henley DB, Dowsett SA, Chen YF, et al. Alzheimer’s disease progression by geographical region in a clinical trial setting. Alzheimers Res Ther 2015;7:43.

12. Connor DJ, Sabbagh MN. Administration and scoring variance on the ADAS-Cog. J Alzheimers Dis 2008;15:461-464.

13. Connor DJ, Sabbagh MN, Cummings JL. Comment on administration and scoring of the Neuropsychiatric Inventory in clinical trials. Alzheimers Dement 2008;4:390-394.

14. Doody RS, Thomas RG, Farlow M, et al. Phase 3 trials of solanezumab for mild-to-moderate Alzheimer’s disease. N Engl J Med 2014;370:311-321.

15. Salloway S, Sperling R, Fox NC, et al. Two phase 3 trials of bapineuzumab in mild-to-moderate Alzheimer’s disease. N Engl J Med 2014;370:322-333.

16. Coric V, Salloway S, van Dyck CH, et al. Targeting Prodromal Alzheimer Disease With Avagacestat: A Randomized Clinical Trial. JAMA Neurol 2015;72:1324-1333.

17. Mattsson N, Andreasson U, Persson A, et al. The Alzheimer’s Association external quality control program for cerebrospinal fluid biomarkers. Alzheimers Dement 2011;7:386-395.

18. Kaufmann P, O’Rourke P. Central institutional review board for an academic trial network. Acad Med 2015;90:321-323.

19. The New York Academy of Sciences, The Global CEO Initiative on Alzheimer’s Disease. Global Alzheimer’s Platform. Aligning resources to drive quality, efficiency and innovation in Alzheimer’s clinical trials. Prospectus. New York, NY: The New York Academy of Sciences; 2015 Mar. 

20. Zerhouni E. Medicine. The NIH Roadmap. Science 2003;302:63-72.

21. Zerhouni EA. US biomedical research: basic, translational, and clinical sciences. JAMA 2005;294:1352-1358.

22. Institute of Medicine. Envisioning a Transformed Clinical Trials Enterprise in the United States: Establishing an Agenda for 2020: Workshop Summary. Washington, DC: The National Academies Press, 2012.

23. Alexander BM, Wen PY, Trippa L, et al. Biomarker-based adaptive trials for patients with glioblastoma–lessons from I-SPY 2. Neuro Oncol 2013;15:972-978.

24. Lenz RA, Pritchett YL, Berry SM, et al. Adaptive, dose-finding phase 2 trial evaluating the safety and efficacy of ABT-089 in mild to moderate Alzheimer disease. Alzheimer Dis Assoc Disord 2015;29:192-199.

25. Ritchie CW, Molinuevo JL, Truyen L, et al. Development of interventions for the secondary prevention of Alzheimer’s dementia: the European Prevention of Alzheimer’s Dementia (EPAD) project. Lancet Psychiatry 2015.

26. Vellas B, Carrillo MC, Sampaio C, et al. Designing drug trials for Alzheimer’s disease: what we have learned from the release of the phase III antibody trials: a report from the EU/US/CTAD Task Force. Alzheimers Dement 2013;9:438-444.

27. Wason JM, Trippa L. A comparison of Bayesian adaptive randomization and multi-stage designs for multi-arm clinical trials. Stat Med 2014;33:2206-2221.

28. Paul SM, Mytelka DS, Dunwiddie CT, et al. How to improve R&D productivity: the pharmaceutical industry’s grand challenge. Nat Rev Drug Discov 2010;9:203-214.


D.E.C. Locke1, M. Chandler Greenaway2,5, N. Duncan2, J.A. Fields3, A.V. Cuc1, C. Hoffman Snyder4, S. Hanna3, A. Lunde3, G.E. Smith3

1. Mayo Clinic Arizona, Division of Psychology; 2. Emory University, Department of Neurology; 3. Mayo Clinic Rochester, Division of Neurocognitive Disorders; 4. Mayo Clinic Arizona, Department of Neurology; 5. Mayo Clinic Florida, Division of Psychology

Corresponding Author: Dona E.C. Locke, Division of Psychology, 13400 E. Shea Blvd., Mayo Clinic, Scottsdale, AZ 85331,, Phone: 480-301-8297, Fax 480-301-6258

J Prev Alz Dis 2014;1(3):143-150

Published online November 21, 2014,



BACKGROUND: A major potential barrier for studying behavioral interventions for patients with Mild Cognitive Impairment (MCI) is the willingness and ability of people to enroll in and adhere to behavioral interventions, especially when the intervention involves dyads of patients with MCI and support partners. Details regarding recruitment strategies and processes (such as number of dyads screened) are often missing from reports of behavioral trials. In addition, reports do not detail the reasons a potentially eligible candidate opts out of participation in a research study.

OBJECTIVES: To describe the challenges and successes of enrollment and retention in a behavioral trial for persons with MCI and their care partners, and to better understand barriers to participation from the patient’s point of view.

DESIGN: Multi-site, randomized trial. SETTING: Major medical centers.

PARTICIPANTS: Our accrual target for the study was 60 participants. Potential candidates were patients presenting to memory evaluation clinics whose resulting clinical diagnosis was MCI. A total of 200 consecutive potential candidates were approached about participating in the study across the three sites.

INTERVENTION: Detailed recruitment and retention data of a randomized trial comparing two behavioral interventions (memory notebook training versus computer training) provided in two separate training time frames (10 days versus 6 weeks). MEASUREMENTS: Structured interview with those declining to participate in the trial.

RESULTS: Overall recruitment 37% with a range of 13%-72% across sites. Overall retention 86% with a range of 74%-94% across sites.

CONCLUSIONS: The primary barriers to enrollment from the patient’s perspective were distance to the treatment center and competing comprehensive behavioral programming. However, retention data suggest that those dyads who enroll in behavioral programs are highly committed.

Key words: AMCI, behavioral intervention, recruitment, retention



Mild Cognitive Impairment (MCI), especially the memory or amnestic subtype, is considered a risk state for later development of dementia due to Alzheimer’s disease (1, 2). Prevalence of MCI is high and increases with age. In the Cardiovascular Health Cognition Study, a multicenter population study, the overall prevalence of MCI was 19% with increasing prevalence with age (19% in those younger than 75 years, 29% in those older than 85 years) (3). In a community-based sample followed longitudinally for 10 years, persons with MCI showed an increased risk of dementia over each 2 year evaluation interval (odds ratio = 3.9). Over time, Alzheimer’s disease has become more recognized as the etiology for a syndromic continuum from a pre-clinical stage to Mild Cognitive Impairment due to AD (4-6). With recognition of this disease continuum, there is increasing interest in secondary prevention strategies with the hope that treatment at the pre-clinical or MCI stage will delay or prevent progression to dementia.

Overall, medication trials for MCI have been disappointing. For example, in a randomized, double- blind, placebo-controlled, multi-center trial, there was no significant difference in the probability of progression from MCI to dementia between those treated with a placebo, Vitamin E or donepezil after three years of treatment (7). In a recent meta-analysis of eight clinical trials examining the effects of medications classified as “cognitive enhancers” (donepezil, rivastigmine, galantamine, or memantine) on MCI, the authors concluded that cognitive enhancers did not improve cognition or function in patients with MCI, and further, were associated with greater risk of gastrointestinal symptoms (8). Thus, there are no current FDA-approved medications for treatment of MCI.

To date, much of the behavioral treatment research in Alzheimer’s disease and other neurodegenerative conditions has focused on patients who had already progressed to dementia (9, 10). Given the lack of substantial medication options for people with MCI and increasing understanding of the risk state of MCI, there is increasing interest in behavioral approaches that may provide benefit in patients with MCI. This broadly includes cognitive rehabilitation, cognitive exercise, physical exercise, and nutritional wellness. The two behavioral approaches included in our randomized trial are cognitive rehabilitation approaches, involving use of a memory notebook for compensation for memory loss, as well as computerized cognitive exercise.

Memory notebooks are a form of compensation with validated efficacy in the treatment of memory impairment in traumatic brain injury patients (11). Our early work has shown that patients diagnosed with MCI can learn to use a memory notebook with structured training (12), and use of the memory notebook improves functioning and memory self-efficacy in individuals with MCI, improves partners’ mood, and decreases partners’ sense of burden compared to an untreated control group (13).

On the other hand, cognitive training, especially via computer-based exercise programs, has also increased in popularity and interest given epidemiological evidence that those most cognitively active are less likely to develop cognitive impairment with aging (14). Interventional trials in older subjects with normal cognitive functioning suggest improvement in cognitive abilities as well as a protective effect of cognitive training (15-18).

Whether studies involve cognitive training or memory compensation, a major potential barrier for studying behavioral interventions is the willingness and ability of people to enroll in and adhere to behavioral interventions, especially when the intervention involves dyads of MCI patients and support partners (19). For example, in a study involving comparison of cognitive training and physical activity or a combination of both, the researchers screened 343 potential subjects in order to enroll just 73 participants, a 21% enrollment rate (20). Most were excluded as they were ineligible after further screening related to study exclusion criteria, but it appears a portion were also merely unwilling to participate in the research program. However, the authors reported good to excellent retention rates, with 76% of patients completing the physical activity arm, 96% completing the cognitive training arm, and 90% of patients completing the combination arm.

Describing screen failures based on investigator defined exclusion criteria in descriptions of study enrollment provides good information about the participants that researchers feel would be unlikely to benefit from a study or perhaps would be at increased risk for adverse effect. However, information on the potential participant’s point of view and specifics about why they decline to participate in research is generally missing. Yet some of the reasons otherwise eligible patients decline to participate in behavioral research trials may present surmountable barriers that could be overcome by researchers if better understood. In addition, some of the reasons eligible participants decline to enroll may be related to research specifically and would not be present if the intervention were available as a part of a clinical program (e.g., concern about randomization results). Trevedi, et al. (19) noted that details regarding recruitment strategies and processes (such as number of dyads screened) are often missing from reports of behavioral trials. In addition, to our knowledge, even if the number of screened dyads is included, reports do not detail the reasons a participant opts out of participation in a research study. Our report is meant as a step in that direction by reporting such enrollment and retention details for our behavioral trial, both from the researcher perspective (i.e., screen failures) as well as the participant’s perspective.

With funding from the National Institute of Nursing Research (NINR), we undertook a pilot project to better understand whether persons with MCI would enroll and remain in an intensive trial comparing two behavioral interventions (memory notebook training versus computer training) provided in two separate training time frames (10 days versus 6 weeks). We intend to report the efficacy outcomes from that randomized comparison trial in a separate report. The goal of this report is to describe the challenges and successes of enrollment and retention in a behavioral trial for persons with MCI and their care partners, and to better understand barriers to participation from the patient’s point of view.




Memory Support System (MSS)

The MSS is a two-page-per-day calendar and note- taking system small enough to fit in a breast pocket or purse. The system and our training curriculum are described in detail in prior reports (12, 13). Briefly, the MSS includes three sections: 1) events that happen at a particular time, i.e., appointments, 2) events that can happen anytime, i.e., daily “to do” items, and 3) a journaling section, i.e., important thoughts or events that happened that day.

The MSS training curriculum utilizes three training stages from learning theory outlined by Sohlberg and Mateer (11): 1) an acquisition phase in which participants learn the sections of the MSS and their intended uses, 2) an application phase in which a participant is taught to apply MSS use to his/her daily life, and 3) an adaptation phase in which a participant practices incorporating the MSS into daily activities so as to make its use habitual.

Each training session provided orientation, modeling, practice use, and homework assignments. A typical agenda for a MSS training session included: 1) review and discussion of Intervention Plan/Questions related to the training phase (acquisition, application, or adaptation), 2) review of homework, 3) learning phase- appropriate instruction of MSS, and 4) assignment of next session’s homework.

Computer Training (Posit)

Those randomized to the computer training arm received copies of the MSS but without training. Each dyad completed computer activities on the same schedule as those receiving MSS training. Posit Science has developed a computer-based training program built on the principles of positive brain plasticity and designed for use by mature individuals. The training program (“Brain Fitness”) is focused on speech reception to strengthen an individual’s memory for speech. It has 6 modules name: Hi-Lo, Tell Us Apart, Match It, Listen and Do, Sound Replay and Story Teller.

Research to date has found: 1) participants with limited or no computer experience were capable of learning to perform the training exercises, 2) the training was safe and well tolerated by participants, 3) participants with MCI and cognitively normal older adults who trained on Brain Fitness also showed on average a 1/3 standard deviation improvement on memory and cognitive function (17, 21).

Training Schedules

In addition to comparing these two cognitive rehabilitation interventions, we were also interested in evaluating different training schedules. Each schedule provided 10 hours of intervention conducted either over 6 weeks or in 10 days.


All participants (whether receiving MSS or Posit) in each scheduled program (6-week or 10-day) were convened for educational group at each session. The education component is an adaption and synthesis of the Savvy Caregiver psychoeducational program (22) and the “Memory Club” educational program (23, 24). The education program in this study offered ten 45-minute group sessions with topics including Introduction to the Program, Living with MCI, Changes in Roles and Relationships, Sleep Hygiene, Steps to Healthy Brain Aging, Preventing Dementia, MCI and Depression, Nutrition and Exercise, Assistive Technologies, Participating in Research, Safety Planning, and Community Resources. As the 6-week program has 12 meeting dates but only 10 education sessions, dyads in the 6-week program did not have an educational session for the last two sessions of the program.

Booster Sessions

After completion of their 6-week or 10-day training, each participant was also seen at 3 months and 6 months for a follow-up visit and booster session. Upon arrival for each follow-up time point, the participant completed an MSS Adherence measure to determine their ongoing use of the memory support tool. For the MSS training group, if they scored 100% (10/10 points) on the Adherence measure they were merely encouraged to continue their use. If they scored less than 100%, they received a formal booster session involving training on the section of the calendar that they had trouble with on the adherence measure. If they received a booster, they returned again 1 week later for repeat assessment of their MSS use. The POSIT group automatically completed one booster session with the POSIT software at each follow-up point. Participants returned for their final follow-up visit at one year post program with no booster intervention.

Recruitment methods

Our accrual target for the study was 60 participants. Potential candidates were identified from consecutive patients presenting to memory evaluations clinics at the evaluating institution (Emory University, Atlanta, GA; Mayo Clinic Rochester, MN; Mayo Clinic Scottsdale, AZ). Those whose resulting clinical diagnosis was amnestic MCI (single domain or multi-domain) were approached about participation in the study. Potential participants were asked to take part in a study to determine if individuals with MCI benefit from memory support training or computerized brain fitness exercises to compensate for their memory loss. If the candidate was not interested, they were asked if they would be willing to answer questions about the reasons they did not wish to participate (See Appendix A). It is from this brief, structured interview that the information for this report was gathered. If the participant was interested, they were seen for an in-person eligibility visit to confirm eligibility.

Eligibility Criteria

  1. Dementia Rating Scale-2nd Edition (DRS-2, (25)) score of 115 or greater
  2. Functional Activities Questionnaire (FAQ, (26); [27]) total score below 6
  3. Program partner with a Folstein Mini Mental Status Exam (28) of 24 or greater
  4. Participant and partner free of severe depression suggesting more pressing need for psychiatric care [defined as Center for Epidemiological Studies-Depression (CES-D; (29)) total score less than 21].
  5. Either not taking or stable on nootropic(s) for at least 3 months
  6. English as primary language



A total of 200 consecutive potential candidates were approached about participating in the study across the three sites. Of those, 74 were agreeable to the study and completed an eligibility visit. Of those, 64 participants passed eligibility screening, consented to participation, and were randomized to one of the four arms of the treatment protocol (6-week MSS = 16; 6-week Posit = 14; 10-day MSS = 18; 10-day Posit = 16). An additional 10 subjects were found to be ineligible for the protocol after the formal eligibility visit. The majority of subjects were ineligible for multiple reasons (n=6). Four (4) were ineligible due to DRS-2 total score and FAQ total score. One (1) was ineligible due to DRS-2 total score as well as partner MMSE total score. One (1) was ineligible due to DRS-2 total score, FAQ total score, and partner CES-D total score. The remaining were ineligible due to DRS-2 total score (n=1), FAQ total score (n=1), partner MMSE total score (n=1), and partner CES-D total score (n-1).

The details and outcomes of those participants who completed the study will be presented in a separate outcomes report as the focus of this report is details of enrollment and retention. The remaining 126 (63%) were approached but declined to participate in the study. It is those 126 participants on whom this detailed report is based (See Figure 1).

Figure 1. Enrollment details




The mean age of those who declined to participate was similar to those enrolled in the study [75.7(8.4) vs 76.7(7.1); t=.827(184), p=.41; d=.13], but those who declined were less likely to have completed college, (enrolled=67% with college degree, declined=48% with a college degree, p=.01). There was no difference in gender (enrolled = 61% male, declined = 56% male; p=.48) or minority ethnicity status (enrolled = 90% white, declined= 98% white, p =.08). Excluding those decliners for whom lack of a program partner was the issue, there was also a higher tendency for decliners to have a non- spouse partner (e.g., adult child, friend), as a program partner (23%) than those who enrolled (9%; p=.03). Similar to the participant comparisons, there was no difference in identified program partner age from the partners of those who enrolled [70.2(12.5) vs 71.6(10.8); t=.487(141), p=.49; d=.12], but decliner program partners had fewer years of education than partners of those who enrolled [14.6(2.2) vs. 15.7(2.6); t=2.53(131), p=.01; d=0.44].

Of the 126 who declined to participate in the study, 7 (5.6%) declined to provide any further information about their reasons and therefore did not complete the standardized interview on that topic. Of the remaining 119, the most common reason for declining to participate was the distance required to travel to the center (n=39, 31%). The remaining reasons are listed in Table 1.

There was a site effect for tendency to decline to participate in the study. Emory University had the lowest rate of declining to participate (28%) while Mayo Clinic Rochester had the highest rate (87%; p>.001). Mayo Clinic Scottsdale had a 59% decline rate (p=.002 with Emory; p>.001 with Mayo Clinic Rochester). The reasons for declining are detailed by site in Table 1.

Table 1. Primary reasons for declining study participation overall and by study site (n=126)

Note. HABIT = Healthy Action to Benefit Independence and Thinking, a clinical intervention program for individual with MCI offered only at Mayo Clinic Rochester

At Mayo Clinic Rochester it is noted that there was a unique, competing, multi-component behavioral intervention clinical program being offered: Mayo Clinic’s Healthy Action to Benefit Independence and Thinking program (HABIT). HABIT is a 5-hour-per-day, 5-day-per-week, 2-week multi-component behavioral program for individuals diagnosed with MCI and a partner. The 5 components are 1 hour each of 1) daily physical exercise, 2) computer-based cognitive exercise (brain fitness), 3) patient and family education, 4) separate support groups for MCI patients and partners, and 5) memory support system compensation training (cognitive rehabilitation). That program was placed on hiatus while this research trial was being conducted. However, 30% of those approached for the study at Mayo Clinic Rochester expressed a desire to wait until the HABIT program was restarted rather than enroll in the research trial. These patients expressed a desire for the more comprehensive program despite the fact that the clinical program would come with significant out-of- pocket financial cost borne by the patient.

When this reason for declining to participate is removed from the equation, the remaining reasons for declining are very similar across sites: Distance to travel and lack of a program partner were the most prominent concerns. Of note, very few reported concerns about participating in research or in the randomization process for this trial. It is noted, however, that this is not a placebo-controlled trial, but rather an active-controlled trial of comparison of two different cognitive rehabilitation strategies.

Despite this variable rate of declining to participate in the trial across sites, retention of those who were eligible was consistently high. Eighty-six percent (55/64) of those who enrolled in the study completed the study intervention. At Mayo Clinic Rochester one participant was enrolled and remained interested in the study, but had to be withdrawn as the site was unable to recruit additional participants to fill that intervention session. The remaining enrolled participants at Mayo Clinic Rochester completed the intervention (6/6, 100%). Retention was slightly lower at Emory University (17/23, 74%) compared to Mayo Clinic Scottsdale (32/34, 94%) and Mayo Clinic Rochester (6/6, p=.03). Reasons for withdrawal at Emory University included: Feeling the intervention was not helpful (n=1), disappointment with the time format randomization (6 weeks vs. 10 day, n=1), withdrawal prior to intervention due to personal/family emergency (n=1), withdrawal during the intervention due to unrelated medical issue requiring immediate treatment (n=1), and unexpected scheduling conflict before the intervention even began (n=2). At Mayo Clinic Scottsdale both participants withdrew prior to the start of the intervention: one due to an unexpected scheduling conflict and one due to feeling the distance would be too far to travel after thinking about it more. There were no significant differences between those who withdrew from the study and those who completed the intervention on age (p=.32), patient gender (p=.13), FAQ total score (p=.73), DRS-2 total score (p=.65), MCI participant CES-D total score (p=.46) and partner CES-D total score (p=.15). There was a trend toward those who withdrew having fewer years of education than those who completed the intervention [14.6 (2.7) vs. 16.3(2.4); t=1.794(61), p=.08; d=0.68 (CI=.04 to 2.60)].




In a study involving people with Mild Cognitive Impairment and their support partners where participants were randomized to intervention (memory compensation versus computerized cognitive training) and schedule (10 sessions over 10 days versus 6 weeks) our overall recruitment rate of 37% was slightly lower than the mean recruitment rate of 51% seen in the review by Trivedi et al (19). However, the range of recruitment rates across this multi-site study is broad (13%-72%) and reflects the wide range also seen by those authors. On the other hand, our overall retention rate of 86% was higher than that found in that review. Our results support Trivedi et al.’s assertion that the primary challenge to behavioral trials involving dyads is in the recruitment phase of the study, rather than in the retention of those who enroll. Our high retention suggests that those who commit to intensive behavioral programs are highly committed. In our study, primary barriers to enrollment included distance to travel (31%), lack of a program partner (17%) and availability of a more comprehensive behavioral intervention for MCI at one of our institutions (30% at that site).

Regarding distance, it is noted that we cast a broad net when considering who to approach for the study. Each of our institutions attracts significant regional and national patient populations. However, we attempted to approach all persons in our practices diagnosed with MCI who lived regionally near our centers (e.g., Southeast Minnesota for Mayo Clinic Rochester, the greater Atlanta metropolitan areas and northern/central Georgia for Emory University, the greater Phoenix metropolitan area and surrounding suburbs for Mayo Clinic Scottsdale). We were desirous of over-inclusion of potential candidates given the goal of understanding recruitment barriers. Thus, it was not uncommon for us to approach patients who live an hour or more from our various institutions. This detailed analysis supports the fact that many evaluated at a tertiary care medical center from a distance will not find it feasible to return for such an intervention. However, we also note that we had some individuals in the study who did enroll in the program from such distances. It is also noted that the Mayo Clinic Rochester clinical HABIT program routinely has patients enroll who return to the area and pay for a 2-week hotel stay to participate in the program. Thus, although distance may continue to be a barrier, there are some patients who will bear the cost of a lengthy local stay. A way to boost this participation may be to offer financial support to those motivated patients who do not have the resources for a hotel stay, but would be willing to travel from a significant distance to participate in a behavioral intervention program. Developing mechanisms to allow temporary local housing for eligible candidates may entice that group of otherwise eligible and motivated participants. Alternatively, telemedicine continues to grow in popularity, and future studies may investigate the feasibility and efficacy of such a mode of intervention.

The second most common barrier was lack of a program partner. We find that the cognitive rehabilitation requires significant repetition given that the hallmark of MCI is short-term memory loss, with specific challenge retaining new information. In addition, a program partner with frequent contact with the patient also serves to encourage continued practice of the behavior post intervention. Finally, in research trials, specifically, many outcome measures involve the report of a collateral informant who observes how the patient is functioning pre and post intervention. Thus, strategies to overcome or reduce the barrier of lacking a study partner will involve both (1) strategies to help with the learning component of a behavioral intervention that are not dependent on a study partner and (2) strategies to measure outcomes that do not require an informant. For the former, programs could consider telephone follow-ups from the intervention provider or perhaps developing a pool of volunteer or professional partners who are trained to be paired with research participants to help complete learning trials (e.g. homework tasks, practice repetitions, real world trials).

Finally, it is noted that time commitment was a relatively low concern for this group. However, it was a concern for some. Paradoxically, the most common reason for declining to participate in our behavioral trial at Mayo Clinic Rochester was a desire to participate in a competing clinical program (HABIT) that actually required MORE time commitment and MORE out-of- pocket financial commitment than the research trial. We believe that the group of individuals who declined to participate in the study in favor of the HABIT program generally understood that the point of the research was to accumulate further evidence validating the effectiveness of the behavioral intervention under study. This group seems to have been willing to forego additional certainty regarding the value of the intervention in order to assure that they were receiving all available interventions. This speaks to the motivation of some MCI patients and their partners to do all they can to reduce the risk of progression to MCI no matter the strength of the evidence base. The prevalence of declining in order to receive the full HABIT program at Mayo Clinic Rochester also raises the dilemma in clinical research regarding how much evidence is sufficient before one begins to offer clinical services to people who are so strongly motivated. This is especially true when these behavioral interventions are associated with relatively low risk of adverse effects and have been shown to have benefit in other populations or with other outcomes (e.g., memory rehabilitation in TBI and stroke, group therapy and emotional wellness, physical exercise and physical health). However, there may be patient-centered study designs that can address patients’ desire to maximize treatment (or minimize under-treatment) including assignment to placebo (30) while specifically developing data on the behavioral intervention’s impact on MCI-relevant outcomes. We have recently been funded by the Patient Centered Outcomes Research Institute to conduct the Comparative Effectiveness of Behavioral Interventions to Prevent or Delay Dementia trial. In this study we will assign MCI patient/partner dyads to randomly receive 4 of the 5 components of the clinical HABIT program, thereby assuring them they will receive ‘80% of the treatment’.

The site specific differences in recruitment and retention rates may suggest further variables at play that were not measured specifically in this study. For example, Emory University had a very high recruitment rate compared to the other centers, but also had the lowest retention rate. Variables such as the skill/personality of the recruiter on the telephone, confidence/obedience of a patient to a well-liked referring physician in signing up for a research study may affect recruitment. On the other hand higher expectations after recruitment, the suitability/comfort of the facilities, or personalities of those delivering intervention may impact retention.

The only small demographic difference from those who enrolled in the trial and those who declined to participate was a slightly lower level of education in those who declined to participate the study. Overall, however, both groups were highly educated (some college) such that it is not clear entirely if this is a barrier. Nevertheless, it is worthy of consideration to determine the feasibility of and interest in such cognitive rehabilitation behavioral interventions for those with lower levels of educational exposure given this mild finding.

Future research should explore the impact of mitigating barriers to participation (e.g. subsidizing travel, alternatives for those without partners) on enrollment rates. In addition, these findings are informative for planning behavioral clinical trials. The 37% enrollment rate and 86% retention rate found in this study, suggest that any trial that requires x completers will need to have available a population of roughly x*3.1 candidates and enroll roughly x*1.14 participants. For example, a trial requiring 100 study completers will need to a pool of approximately 310 potential candidates to screen for the study and enroll 114 participants. These data may also have relevance to clinical practice in providing rough estimates for how many eligible clinical patients are likely to enroll in behavioral programs directed at MCI patients. These data also suggest that the vast majority of patients who enroll will complete the program.


Acknowledgement: This study was supported by NINR (R01 NR012419) for all sites. Additional Arizona support: NIA P30AG19610, NIA R01AG031581, and the Arizona Alzheimer’s Research Consortium; Additional Mayo Clinic Rochester support: Mayo Alzheimer’s Disease Research Center P50 AG16574; Additional Emory University support: AG025688.

Conflicts of Interest: The authors have no conflicts of interest to disclose.

Ethical standards: This study was approved by the IRB at each institution.


Appendix A. Declined Enrollment Telephone Script




  1. Petersen, R., et al., Mild cognitive impairment: Clinical characterization and outcome. Archives of Neurology, 1999. 56(3): p. 303-308.
  2. Ganguli, M., et al., Mild cognitive impairment, amnestic type an epidemiologic study. Neurology, 2004. 63: p. 115-121.
  3. Lopez, O., et al., Prevalence and classification of mild cognitive impairment in the Cardiovascular Health Study Cognition Study: part 1. Arch Neurol, 2003. 60(10): p. 1385-1389.
  4. Albert, M., et al., The diagnosis of mild cognitive impairment due to Alzheimer’s disease: recommendations from the National Institute on Aging- Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s & Dementia : The Journal of the Alzheimer’s Association, 2011. 7(3): p. 270-279.
  5. McKhann, G., et al., The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s & dementia : the journal of the Alzheimer’s Association, 2011. 7(3): p. 263-269.
  6. Sperling, R., et al., Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s & dementia : the journal of the Alzheimer’s Association, 2011. 7(3): p. 280-292.
  7. Petersen, R., et al., Vitamin E and donepezil for the treatment of mild cognitive impairment. The New England Journal of Medicine, 2005. 352(23): p. 2379-2388.
  8. Tricco, A.C., et al., Efficacy and safety of cognitive enhancers for patients with mild cognitive impairment: a systematic review and meta-analysis. CMAJ, 2013. 185(16): p. 1393-401.
  9. Clare, L., Woods, R.T., Cognitive rehabilitation and cognitive training for early-stage Alzheimer’s disease and vascular dementia. Cochrane Database Syst Rev 2008(4).
  10. Sitzer, D.I., E.W. Twamley, and D.V. Jeste, Cognitive training in Alzheimer’s disease: a meta-analysis of the literature. Acta Psychiatr Scand, 2006. 114(2): p. 75-90.
  11. Sohlberg, M.M. and C.A. Mateer, Training use of compensatory memory books: a three stage behavioral approach. Journal of clinical and experimental neuropsychology, 1989. 11(6): p. 871-91.
  12. Greenaway, M.C., et al., A Behavioral Rehabilitation Intervention for Amnestic Mild Cognitive Impairment. American Journal of Alzheimers Disease and Other Dementias, 2008. 23(5): p. 451-461.
  13. Greenaway, M.C., N.L. Duncan, and G.E. Smith, The memory support system for mild cognitive impairment: randomized trial of a cognitive rehabilitation intervention. Int J Geriatr Psychiatry, 2013. 28(4): p. 402-9.
  14. Geda, Y.E., et al., Computer activities, physical exercise, aging, and mild cognitive impairment: a population-based study. Mayo Clin Proc, 2012. 87(5): p. 437-42.
  15. Kueider, A.M., et al., Computerized cognitive training with older adults: a systematic review. PLoS One, 2012. 7(7): p. e40588.
  16. Rebok, G.W., et al., Ten-Year Effects of the Advanced Cognitive Training for Independent and Vital Elderly Cognitive Training Trial on Cognition and Everyday Functioning in Older Adults. J Am Geriatr Soc, 2014.
  17. Smith, G.E., et al., A cognitive training program based on principles of brain plasticity: results from the Improvement in Memory with Plasticity-based Adaptive Cognitive Training (IMPACT) study. Journal of the American Geriatrics Society, 2009. 57(4): p. 594-603.
  18. Zelinski, E.M., et al., Improvement in memory with plasticity-based adaptive cognitive training: results of the 3-month follow-up. Journal of the American Geriatrics Society, 2011. 59(2): p. 258-265.
  19. Trivedi, R.B., et al., Recruitment and retention rates in behavioral trials involving patients and a support person: a systematic review. Contemp Clin Trials, 2013. 36(1): p. 307-18.
  20. Legault, C., et al., Designing clinical trials for assessing the effects of cognitive training and physical activity interventions on cognitive outcomes: the Seniors Health and Activity Research Program Pilot (SHARP-P) study, a randomized controlled trial. BMC Geriatr, 2011. 11: p. 27.
  21. Barnes, D., et al., Computer-based cognitive training for mild cognitive impairment: results from a pilot randomized, controlled trial. Alzheimer Disease and Associated Disorders, 2009. 23(3): p. 205-210.
  22. Hepburn, K., et al., The Savvy Caregiver program: the demonstrated effectiveness of a transportable dementia caregiver psychoeducation program. J Gerontol Nurs, 2007. 33(3): p. 30-6.
  23. Gaugler, J.E., et al., The Memory Club: Providing support to persons with early-stage dementia and their care partners. Am J Alzheimers Dis Other Demen, 2011. 26(3): p. 218-26.
  24. Zarit, S.H., et al., Memory Club: a group intervention for people with early- stage dementia and their care partners. Gerontologist, 2004. 44(2): p. 262-9.
  25. Jurica, P.J., C.L. Leitten, and S. Mattis, DRS-2 : Dementia rating scale-2 : professional manual. 2001, Lutz, FL: Psychological Assessment Resources. 47 p.
  26. Pfeffer, R.I., et al., Measurement of functional activities in older adults in the community. J Gerontol, 1982. 37(3): p. 323-9.
  27. Teng, E., et al., Utility of the functional activities questionnaire for distinguishing mild cognitive impairment from very mild Alzheimer disease. Alzheimer Dis Assoc Disord, 2010. 24(4): p. 348-53.
  28. Folstein, M., S. Folstein, and P. McHugh, « Mini-mental state. » A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatry Research, 1975. 12(3): p. 189-198.
  29. Radloff, L.S., The CES-D Scale: A self-report depression scale for research in the general population. Applied Psychological Measurement, 1977. 1(3): p. 385-401.
  30. Welton, A.J., et al., Is recruitment more difficult with a placebo arm in randomised controlled trials? A quasirandomised, interview based study. BMJ, 1999. 318(7191): p. 1114-7.


P.-J. Ousset1,2,3, J. Cummings4, J. Delrieu1, V. Legrand5, N. Prins6, B. Winblad7, J. Touchon8, M.W. Weiner9, B. Vellas1,2

1. Gérontopôle, Department of Geriatrics, CHU Toulouse, Purpan University Hospital, Toulouse, France; 2. INSERM UMR 1027, Toulouse, France; 3. University of Toulouse III, Toulouse, France; 4. Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, USA; 5. ICON Clinical Research, Nanterre, France; 6. Alzheimer Center and the Neuroscience Campus Amsterdam, VU University Medical Center & ARC, Amsterdam, The Netherlands; 7. Department of Neurobiology, Care Sciences and Society, Karolinska Institutet-Alzheimer’s Disease Research Center, Karolinska Institutet, Stockholm, Sweden; 8. Faculty of Medicine, University of Montpellier, Montpellier, France; 9. School of Medicine, University of California-San Francisco, San Francisco, CA, USA; Department of Veterans Affairs Medical Center, San Francisco, CA, USA.

Corresponding Author: Pierre-Jean Ousset. Gérontopôle, Department of Geriatrics, CHU Toulouse, Purpan University Hospital, Toulouse, France. Email:

J Prev Alz Dis 2014;1(1):40-45

Published online November 19, 2014,



During the decade from 2002 to 2012, 99.6% of the 244 agents tested for efficacy in slowing the progression of Alzheimer’s’ disease (AD) failed to achieve their primary endpoints. At a CTAD symposium on November 14, 2013, in San Diego, USA, an international group of AD researchers met to discuss the evolution of trials over the past 10 years and proposed a number of changes intended to streamline and enhance the efficiency of clinical trials. Approximately 1,031 AD trials were conducted between 2000 and 2012. The number of patients per trial site tended to decrease over time necessitating a larger number of sites. The use of biomarkers for enrichment purposes, or as measures of target engagement or surrogate outcomes, results in higher screen failure and drop-out rates, adding to trial duration and/or costs. Present disease modifying AD trials ask for increasing logistical and technical requirements, necessitating the creation of highly specialized trial facilities and limiting the participation of smaller sites. Due to heavy administrative and regulatory task, only about 13% of the team’s time is used for the essential recruitment. Proposals and perspectives: Strategies suggested to improve the efficiency of recruitment include establishing “ready to go cohorts” in advance of trials using biomarkers and clinical measures. Simplification and harmonization of administrative procedures, including harmonization of certification procedures, are urgently needed. Alternative approaches, such as using the Internet to screen volunteers for possible inclusion needs to be evaluated. The AD drug development enterprise from discovery through clinical trials requires re-examination and re- organization if new drugs are to be delivered to patients in a timely way.

Key words: Alzheimer’s disease, clinical trials, biomarkers, recruitment.



At the 2013 Clinical Trials in Alzheimer’s Disease (CTAD) meeting in San Diego, one of the authors of the present manuscript (JC) stated that Alzheimer’s disease drug development is broken. He cited the fact that during the decade from 2002 to 2012, 99.6% of the 244 agents tested failed to achieve their primary endpoints. Only one drug tested during that period, the NMDA-receptor antagonist memantine (Namenda), was approved for the treatment of moderate to severe dementia.

These failures in drug development have been attributed to a combination of factors: tested compounds that were truly ineffective or unsafe, inappropriate trial designs, enrollment of subjects not likely to benefit from the treatment during the trial period, preclinical models that were not predictive of human outcomes, limited information from phase II studies that were unable to predict success in phase III, the absence of an expected decline in the placebo arm of the trials, and/or high variability across multiple study sites interfering with signal detection (1). Nonetheless, the increasing burden of the disease demands continued prioritization of Alzheimer’s disease (AD) drug development in industry, academia, clinical centers, and regulatory agencies. AD currently affects more than 35 million people worldwide, and the prevalence is expected to triple by 2050 (2). Alzheimer’s Disease International (ADI) has estimated the global cost of caring for people with dementia at $604 billion (USD). As the world’s population ages, these costs are expected to soar. The ADI predicts an 85% increase in costs by 2030 (3). Moreover, AD exacts a tremendous toll on families and caregivers (4)

Of the four drugs commonly used to treat AD, all alleviate symptoms but are not thought to slow the disease’s relentless progression. Newer drugs in development, however, increasingly focus on modifying the underlying disease by interfering with processes that lead to degeneration of the brain. Delaying the development of dementia by only a few years could dramatically decrease the worldwide prevalence.

The CTAD symposium on November 14, 2013 brought together an international group of AD researchers to discuss the evolution of clinical trials over the past 10 years and propose a number of changes intended to streamline and enhance the efficiency of clinical trials in order to ultimately reduce the increasing burden for patients, families, and society.


Evolution of AD clinical trials between 2000 and 2012

A historical analysis of trials conducted between 2000 and 2012 was performed by identifying publications listed in English on PubMed up to 2012. During that time, 1,031 AD trials were conducted worldwide; yet after peaking in 2009, the number of AD trials has tended to decline in recent years (Figure 1).

Figure 1. AD Clinical trials between 2000 and 2012

The number of clinical trials peaked in 2009 but has been declining since then

Among placebo controlled studies, most (53%) were phase II trials in mild to moderate AD conducted in Europe and North America. While about 85% of the trials met their recruitment targets, the number of patients per trial site tended to decrease over time. In phase III studies, the observed decline in recruitment per site necessitated more sites per study. One consequence of an increased number of sites is the possible involvement of less experienced investigators, leading potentially to decreased reliability of efficacy assessments. An increased number of trial centers also may increase variability, resulting in less statistical power. In addition to an increased number of trial sites, a 30% increase in recruitment duration was observed in phase III studies. Phase III is the most expensive phase of drug development, and slow recruitment means both higher costs and shorter patent life and market exclusivity during which costs can be recouped by trial sponsors.

Over the past five years, the methods used in AD clinical trials have also changed. We analyzed 182 clinical trials conducted between 2008 and 2013. About two- thirds of these were Phase II trials (n=122); the other (n=60) were Phase III trials. Numerous mechanisms of action were tested, with anti-amyloid agents comprising 25% of these trials.

Disease modification was the goal of 44% of the trials. Most (84%) of the trials were conducted in the mild-to- moderate stages of the disease, and only 5% were conducted in MCI due to AD, i.e. prodromal AD. As shown in Figure 2, patients enrolled in trials of symptomatic therapies had more severe disease (lower MMSE scores) compared to those enrolled in disease- modifying trials (MMSE score range [15.6-26.2] in disease modifying trials and [11.9-23.6] in symptomatic trials). However, during the period analyzed, there was a trend towards enrolling patients with less severe disease.

Figure 2. AD severity at inclusion in clinical trials between 2008 and 2013 (n=157)

Left panel shows that symptomatic trials enroll patients with lower MMSE scores than those enrolled in disease-modifying trials. Right panel shows a trend towards enrolling patients with less severe disease (greater MMSE score) between 2008 and 2013.

The most significant change over the time period tested was an increased use of biomarkers in clinical trials, both as inclusion criteria and outcome measures (figure 3). Biomarkers are likely to play an increasingly important role in early stage AD trials. The European Medicines Agency has qualified the use of volumetric MRI measurement of hippocampal volume as an inclusion criterion for early stage trials (5), and CSF measures of Aβ1-42 and total-tau and/or PET-amyloid imaging for mild to moderate AD trials (6), as they may enable identification of affected individuals even when symptoms are subtle. For interventional trials testing anti- amyloid therapies, identifying trial subjects with amyloid pathology using either amyloid PET imaging or CSF analysis should greatly enhance the ability of the trial to correctly assess treatment efficacy by screening out participants who do not have AD. This enrichment approach might thus avoid a problem observed in phase III trials of both solanezumab and bapineuzumab, where amyloid PET imaging revealed that about a quarter of patients enrolled lacked fibrillar amyloid pathology at baseline, suggesting that they did not have AD.

Figure 3. Change in use of biomarkers in drug trials (n=152) between 2008 and 2013

Left panel: use of biomarkers as inclusion criterion and as outcome. Right panel shows progression in use of biomarkers in drug trials between 2008 and 2013.

When biomarkers are used for enrichment of trial subjects, however, there is the added complication of a large number of screen failures (i.e., volunteers excluded from the trial because they do not meet inclusion criteria). Nonetheless, selecting the appropriate participants for trials of targeted therapies offers efficiencies in terms of number of subjects required per treatment arm.

However, biomarkers also add to patient burden, especially when they involve invasive procedures like lumbar puncture. This can increase the difficulty of recruitment, and may add to the number of candidates that must be screened; therefore, trials involving biomarkers often require larger number of centers and may take longer. Between 2011 and 2013, for example, the number of recruiting centers per study more than doubled from an average of 15 to an average of 33.

Table 1 compares disease-modifying trials that incorporate biomarkers with those that did not use biomarkers. These data show that the use of biomarkers is associated with an increase in study duration (p=0.06) and number of recruiting centers (p=0.07). Nevertheless, a third Phase III clinical trial for solanezumab, called EXPEDITION 3, has been started in patients with mild AD and will require evidence of amyloid burden (7). Thus, use of biomarkers as inclusion criteria, such as using PET or CSF studies to identify amyloid positivity may complicate recruitment procedures, but is considered necessary to assess efficacy of targeted therapies.

Table 1. Impact of the use of biomarkers in disease modifying trials


Consequences of trial modifications on research centers

AD clinical research faces a number of challenges relating to both recruitment and retention of subjects in trials. Indeed, the time it takes to recruit a sample is one of the most important factors influencing how long it takes to complete a trial. In four recent prevention trials, recruitment periods ranged from 1.75 to over 4.5 years (8). Patient characteristics that influence recruitment include the fact that older patients are often frail, with comorbidities and concomitant medications that seriously restrict their inclusion in trials. They also tend to be less proactive than patients with conditions such as cancer or HIV, in part because of the commonly held belief that dementia is a normal part of aging, and also because the cognitive deficit itself may prevent a patient from volunteering for a therapeutic trial. These beliefs and attitudes may also result in reluctance of the patient’s family members and caregivers to participate in the trial, and in the general practitioner’s reduced likelihood to recommend that a patient participates in a trial. Patients and caregivers may also decline to participate in a trial because of the burden the trial has on their daily life.

AD clinical trials also place increased personnel demands on the clinical study team; for example, requiring the involvement of neuropsychologists in addition to at least two physicians, study nurses, a study coordinator, a clinical research associate, and pharmacist. When biomarkers are included in the study protocol, sites may also need specialized instrumentation as well as technicians skilled in their use. Taken together, these requirements are time consuming and require additional administrative attention, necessitating the creation of highly specialized teams, which can limit the participation of smaller and less well equipped clinical trial sites.

Figure 4 illustrates the complex network of interactions that are required by a center conducting an AD clinical trial. This complexity affects the recruitment capacity of each center, thus requiring many centers to operate concurrently. Multiple centers may increase the variance in data collected, and thus require increased training and close monitoring to ensure that all trial personnel are using standardized procedures. This, in turn, increases the administrative and regulatory procedures that are required, which further complicates recruitment.

Figure 4. The complex network of interactions required to conduct an AD clinical trial

A survey was conducted in specialized therapeutic research centers within the French Research Network and the European Alzheimer Disease Consortium (EADC), between August and October 2013 to determine the time required to conduct a clinical trial. Respondents represented 14 centers of the French Network and 18 EADC Centers across 14 European countries. All of the centers included have dedicated research teams including physicians, clinical research assistants (CRAs), neuropsychologists, and nurses. The survey captured both total time and, within this total time, the time dedicated to various phases of a clinical trial: 1) recruitment (during the clinical activity or through specific information or search actions); 2) assessment (time spent directly with patients and caregivers); 3) pre- trial procedures (assessing feasibility, meetings, certifications, regulatory contacts, etc.); and 4) administrative/technical issues (case report form completion, monitoring, etc.).

Overall, specialized personnel at these centers devoted 45.9% of their time to clinical trials. Within this time spent in clinical trials, only about 13% was for recruitment, 37% on assessment, 22% on pre-trial procedures, and 28% on administrative/technical issues. Thus, half of the time is occupied by administrative and regulatory tasks, either in the preparatory procedures or during the trial itself. Investigators and CRAs shoulder most of the responsibility for recruitment.


Proposals and perspectives

Faced with the challenge of recruiting adequate numbers of subjects for clinical trials, clinical investigators have been evaluating current recruitment strategies and testing new strategies (8-11). Current recruitment strategies for AD drug trials typically begin by initiating contact with potential participants through doctors’ offices, traditional media (newspapers and radio), and advocacy and support groups. Approaches designed to increase referrals from doctor’s offices include educational seminars and outreach to primary care providers through local and national journals. It is also important for a medical team to orient itself to therapeutic research. All physicians working in a clinical unit should be informed of the research advances and available trials, and encouraged to participate actively in recruitment.

Strategies suggested to improve the efficiency of recruitment include establishing a “ready to go” cohort in advance of trials using biomarkers, subjective memory complaint, or other clinical measures. Advantages to having a pre-qualified cohort include reduced heterogeneity and inter-individual variability as well as reduced time to enroll patients and the ability to enroll a larger number of patients. In addition, acquiring run-in data may enable the identification of a slope change in an outcome measure before the intervention begins, which can also be used to enrich a study with subjects in the early stages of decline. For example, in the Multidomain Alzheimer disease Prevention Trial (MAPT), subjects were enrolled based on memory complaint without dementia (12). Repeated clinical assessments over the course of the trial, and biomarker studies in a subset of participants, should provide data that will inform future trial designs. Linkages and coordination between different existing cohorts, could provide an opportunity to create large databases useful in setting up of national or international, multicenter trial. It is, however, noteworthy that setting up this type of cohort would be costly and runs the risk of not having appropriate trials to offer participants.

For trials requiring a positive biomarker result for inclusion, enrolling a cohort with previous biomarker studies may increase the efficiency of a trial by enabling a more targeted trial design; however, requiring a biomarker study can further complicate recruitment [8] as well as the logistics and cost of conducting the trial. The Asymptomatic Anti-Amyloid for AD (A4 trial), which will screen potential subjects using amyloid PET imaging, should provide information about the impact of this approach on trial outcome and cost.

Participants in the symposium also identified other changes that could enhance recruitment, including simplification and harmonization of administrative procedures, and harmonization of certification procedures, in order to avoid duplication of certification for common assessment tools.

At a center level, one should encourage the use of easily administrable tools for screening potential participants. For example, the Toulouse Memory Clinic tested a new screening tool for potential registry participants. The tool asks three simple questions: 1) Is a caregiver/informant present and available? 2) Does the subject have sufficient autonomy for attending trial visits? 3) Are there no major contraindications or exclusion criteria present (e.g., uncontrolled somatic condition, active cancer within the past 6 months, severe renal or hepatic failure, or diagnosis of a major psychiatric illness)? If a subject responds affirmatively to all three questions, and agrees to participate in a placebo- controlled trial, currently available trials are reviewed for specific inclusion and exclusion criteria. If a match is identified, the subject is referred to the trial site; if no match is identified, the subject enters a follow-up cohort with reevaluation every six months. Over a three month period from July to September 2012, 1284 patients with cognitive concerns were evaluated at the clinic; of these 1000 (78%) completed the questionnaire, and 214 (17% were selected for research studies. However, only 10 of these patients (less than 1%) have been included in clinical trial in the same period.

Another approach is using the Internet to screen volunteers for possible inclusion in a registry. The Brain Health Registry (13), was developed by Michael Weiner and colleagues in collaboration with Cogstate and Lumos Labs. Lumos Labs is the creator of Lumosity, a web-based cognitive training platform that has already registered more than 50 million participants and has collected at least one year of cognitive performance data from 600,000 older adults. CogState has created computerized systems for assessing, monitoring, and improving cognition. The Brain Health Registry uses the tools provided by Lumos Labs and CogState, along with other measures to identify individuals who may be at risk of developing brain diseases including AD. Their goal is to build an on-line registry for recruitment, screening, and monitoring progression of cognitive change longitudinally. The registry will be available to investigators for all types of neuroscience studies, and can be used to assess the effect of treatments and dietary interventions, and to validate the use of devices to track changes in function.

A third innovation was presented by Dr. Cummings: Cleveland Clinic has formed an AD trial consortium across the geography of the United States in which Cleveland Clinic has facilities – Las Vegas, Nevada; Cleveland, Ohio; and Weston, Florida. This integrated system has centralized leadership, one institutional review board (IRB), and one set of standard operating procedures for clinical trials. This distributed site network facilitates patient recruitment into clinical trials and provides quality control oversight. Another ongoing project is the Alzheimer’s Prevention Initiative (API), which has proposed to evaluate investigational amyloid- modifying treatments in healthy people who, based on their age and genetic background, are at the highest imminent risk of developing symptomatic AD. API will investigate treatments using brain imaging, cerebrospinal fluid, and cognitive endpoints (14).



An emerging consensus in the field that treatments for AD should begin early in the disease process, combined with the increasing use of biomarkers, has changed the way clinical trials are conducted. Trials will of necessity be longer and more complex, which will increase the challenge of recruiting and retaining increasingly large numbers of participants. There is no easy nor single response to these challenges.

Accelerating and increasing recruitment could be achieved through a number of approaches, including establishing large networks of specialized clinical centers well connected with secondary care hospitals/structures, and capable of accessing large numbers of patients (15- 18). Through pre-screening activities or surveys, these sites could pre-identify “ready to go” cohorts of AD patients interested in participating in clinical trials and well supported by care-givers willing and able to dedicate time to accompany their relative during the conduct of protocols. This would accelerate the access to potential patients. Sites will still need to address patient concerns about participation, burden, and adherence to study protocols. These centers will need to have the capability of conducting both simple assessments for screening as well as more complex biomarker studies and other evaluations that will be needed for assessing treatment efficacy. Maximizing the efficiency of these centers will also require novel ways to reduce the burden of administrative procedures, with more streamlined training and certification processes as an example. Training, standardization, more rigorous quality oversight, and sharing of information can also help accelerate drug development for AD. Finally, increasing public awareness about the major health care challenge that AD represents, including the understanding of the crucial need for clinical research could be part of a multi- factorial solution to help advance drug development research in AD.


Conflicts of interest: PJO, JC, JD, BW, JT, MWW, BV have received consulting fees and research grant from severals companies involved in Alzheimer drug research. List available upon request. No specific funding was obtained for the preparation of this manuscript. VL is a member of ICON Clinical Research.



  1. Prins ND, Scheltens P. Treating Alzheimer’s disease with monoclonal antibodies: current status and outlook for the future. Alzheimers Res Ther. 2013 Nov 11;5(6):56

  2. Prince M, Bryce R, Albanese E, Wimo A, Ribeiro W, et al. The global prevalence of dementia: a systematic review and metaanalysis. Alzheimers Dement 2013, 9: 63-75 e62.

  3. Alzheimer’s Disease International (2010) World Alzheimer Report.

  4. Alzheimer’s Disease International (2013) Wolrd Alzheimer’s Report 2013. Journey of Caring: An analysis of long-term care for dementia.

  5. European Medicines Agency (2011) Qualification opinion of low hippocampal volume (atrophy) by MRI for use in regulatory clinical trials – in pre-dementia stage of Alzheimer’s disease. Accessed online 21 November, 2012 at:

  6. European Medicines Agency (2012) Qualification opinion of Alzheimer’s disease novel methodologies/biomarkers for the use of CSF AB 1-42 and t- tau and/or PET-amyloid imaging (positive/ negative) as biomarkers for enrichment, for use in regulatory clinical trials in mild and moderate Alzheimer’s disease. Accessed online 21 November, 2012 at

  7. Karran E, Hardy J. Antiamyloid therapy for Alzheimer’s disease–are we on the right road? N Engl J Med. 2014 Jan 23;370(4):377-8

  8. Schneider LS (2012) Recruitment methods for United States Alzheimer disease prevention trials. J Nutr Health Aging 2012 Apr;16(4):331-5..

  9. Grill JD, Galvin JE Facilitating Alzheimer Disease Research Recruitment.

    Alzheimer Dis Assoc Disord. 2014 Jan-Mar;28(1):1-8.

  10. Grill JD, Monsell SE Choosing Alzheimer’s disease prevention clinical trial populations. Neurobiol Aging 2014, 35: 466-471.

  11. Vellas B, Pesce A, Robert PH, Aisen PS, Ancoli-Israel S, et al. (2011) AMPA workshop on challenges faced by investigators conducting Alzheimer’s disease clinical trials. Alzheimer s Dement 2011, 7: e109-117.

  12. Carrie I, van Kan GA, Gillette-Guyonnet S, Andrieu S, Dartigues JF, et al.

    Recruitment strategies for preventive trials. The MAPT study (MultiDomain Alzheimer Preventive Trial. J Nutr Health Aging 2012, 16: 355-359.

  13. Brain Health Registry

  14. Reiman EM1, Langbaum JB, Fleisher AS, Caselli RJ, Chen K, Ayutyanont N, Quiroz YT, Kosik KS, Lopera F, Tariot PN. Alzheimer’s Prevention Initiative: a plan to accelerate the evaluation of presymptomatic treatments. J Alzheimers Dis. 2011;26 Suppl 3:321-9.

  15. Aisen PS, Vellas B. Passive immunotherapy for Alzheimer’s disease: what have we learned, and where are we headed? J Nutr Health Aging. 2013 Jan;17(1):49-50. doi: 10.1007/s12603-013-0001-3. PubMed PMID: 23299379.

  16. Andrieu S, Coley N, Gardette V, Subra J, Oustric S, Fournier T, Poulain JP, Coniasse-Brioude D, Igier V, Vellas B, Grand A; ACCEPT Study Group. Representations and practices of prevention in elderly populations: investigating acceptance to participate in and adhesion to an intervention study for the prevention of Alzheimer’s disease (ACCEPT study)–the need for a multidisciplinary approach. J Nutr Health Aging. 2012 Apr;16(4):352-4.

  17. Vellas B, Hampel H, Rougé-Bugat ME, Grundman M, Andrieu S, Abu-Shakra S, Bateman R, Berman R, Black R, Carrillo M, Donohue M, Mintun M, Morris J, Petersen R, Thomas RG, Suhy J, Schneider L, Seely L, Tariot P, Touchon J, Weiner M, Sampaio C, Aisen P; Task Force Participants. Alzheimer’s disease therapeutic trials: EU/US Task Force report on recruitment, retention, and methodology. J Nutr Health Aging. 2012 Apr;16(4):339-45.

  18. Vellas B. Recruitment, retention and other methodological issues related to clinical trials for Alzheimer’s disease. J Nutr Health Aging. 2012 Apr;16(4):330.