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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.



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

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S. Rios-Romenets1, M. Giraldo-Chica1, H. López1, F. Piedrahita1, C. Ramos1, N. Acosta-Baena1, C. Muñoz1, P. Ospina1, C. Tobón1, W. Cho3, M. Ward3, J.B. Langbaum2, P.N. Tariot2, E.M. Reiman2, F. Lopera1


1. University of Antioquia, Medellin, Colombia; 2. Banner Alzheimer’s Institute, Phoenix, Arizona, United States; 3. Genentech Inc./Roche, South San Francisco, California, United States

Corresponding Author: Silvia Rios-Romenets, MD, Medical Director and Deputy API Colombia, Neuroscience Group of Antioquia, Calle 62 No. 52 – 59, Medellín, Antioquia, Colombia, Phone: 57-4-2196424, 2196425, Fax: 57-4-2196444,

J Prev Alz Dis 2018;5(1):55-64
Published online November 7, 2017,



The Alzheimer’s Prevention Initiative (API) Autosomal Dominant Alzheimer’s Disease (ADAD) trial evaluates the anti-amyloid-β antibody crenezumab in cognitively unimpaired persons who, based on genetic background and age, are at high imminent risk of clinical progression, and provides a powerful test of the amyloid hypothesis.  The Neurosciences Group of Antioquia implemented a pre-screening process with the goals of decreasing screen failures and identifying participants most likely to adhere to trial requirements of the API ADAD trial in cognitively unimpaired members of Presenilin1 E280A mutation kindreds.  The pre-screening failure rate was 48.2%: the primary reason was expected inability to comply with the protocol, chiefly due to work requirements.  More carriers compared to non-carriers, and more males compared to females, failed pre-screening.  Carriers with illiteracy or learning/comprehension difficulties failed pre-screening more than non-carriers.  With the Colombian API Registry and our prescreening efforts, we randomized 169 30-60 year-old cognitively unimpaired carriers and 83 non-carriers who agreed to participate in the trial for at least 60 months.  Our findings suggest multiple benefits of implementing a pre-screening process for enrolling prevention trials in ADAD.

Key words: Autosomal dominant Alzheimer’s disease, Alzheimer’s prevention initiative, registry, pre-screening.



Clinical trials for Alzheimer’s disease (AD) have been shifting toward preclinical and prodromal stages of the disease (1, 2).  The evolving understanding of the earliest stages in the AD continuum have generated preclinical trials in both genetic-at-risk and amyloid-at-risk cohorts, defined by an absence of clinically detectable impairment but the presence of either a genetic mutation that confers near certainty of developing symptomatic AD, or biomarker evidence of AD-related pathology (3, 4).  Finding individuals in these categories, who are in either the prodromal or unimpaired phases of disease, has thus become a major challenge for such trials (2, 5, 6).  Trial candidates are typically unaware of their risk and may not be seeking evaluation or treatment.  Given the hypothesis that disease-modifying treatments might be most effective when initiated early, prevention trials are likely to proliferate in the near future (7).  There is thus an urgent need for novel recruitment strategies in AD prevention trials (3, 7, 8) that efficiently identify participants most likely to meet clinical trial requirements.
The Colombian Alzheimer’s Prevention Initiative (API) Autosomal Dominant AD (ADAD) trial is a collaborative project involving the Neurosciences Group of Antioquia (GNA), the Banner Alzheimer’s Institute, Genentech/Roche, and the National Institute on Aging.  The trial evaluates the anti-Aβ antibody crenezumab in cognitively unimpaired 30-60 year-old members of the Colombian PSEN1 E280A kindred (NCT01998841 for trial design), the world’s largest ADAD kindred, including carriers randomized to active treatment or placebo and non-carriers assigned to placebo only (this eliminates the need for genetic disclosure and provides a nested cohort study of placebo-treated carriers and non-carriers).  The study capitalizes on the unusual size of this ADAD kindred, including 5806 cognitively and genetically assessed kindred members and 1117 living mutation carriers, and on the well-established ages of onset for cognitive impairment (44±5 years) and for dementia (49±5 years) in the carriers (10).
To recruit eligible candidates, the sponsor team and GNA created a pre-enrollment registry (9) and devised a pre-screening process with three goals: to decrease screen failures, to permit recruitment to the trial in a manner that maintained the correct ratio of mutation carriers to non-carriers, and to optimize compliance and adherence.  We describe here the Colombian API ADAD trial pre-screening process, and report the main reasons for pre-screen failures, information that may be useful for other preclinical trials.



Step 1: Generation of lists of eligible candidates from the Colombian API Registry

The pre-screening process started with the generation of lists of potentially eligible candidates from the Colombian API Registry (9).  Persons with early onset, potentially familial AD, as well as their healthy relatives, age 8 years and older, are eligible for the Colombian API Registry.  Registrants undergo at least one general medical and neurological evaluation, cognitive assessment, and genetic testing, after which they are identified as being part of a new or existing pedigree.  Registrants remain blind to their genetic status, except for those with symptomatic AD.  The only GNA staff unblinded to genotype were those responsible for balancing genotype of participants referred to the trial; these staff members had no role in trial operations.  GNA, through SISNE2 (a secure and closed enterprise database system), manages extensive information including demographics, medical, neurological, and neuropsychological evaluations, in order to support various research projects involving PSEN1 E280A families.  In addition, GNA created a database of pedigrees, using the Cyrillic pedigree drawing software (Cherwell Scientific, Acton, MA) and Progeny genetic pedigree software (Ambry Genetics, Aliso Viejo, CA), of all registered families including those affected by the PSEN1 E280A mutation.  Demographic and medical information was captured using a WEB application and Postgresql Data base while the pedigree information was collected using progeny genetics.  Lists of registrants potentially eligible for the trial were created from SISNE2.  The unblinded data analyst filtered all potentially eligible candidates according to their PSEN1 E280A carrier status (in a 2:1 carrier/non-carrier ratio to match the randomization ratio of carriers/non-carriers in the API Colombia clinical trial), some of the key inclusion/exclusion (I/E) criteria for the trial (30-60 years of age, not known to be cognitively unimpaired or to have significant medical conditions) and marked all eligible candidates as “active” for pre-screening.

Step 2:  Detailed review of Inclusion/Exclusion data available from the Colombian API Registry

The clinical history of each active candidate was reviewed by a trial investigator, who evaluated health status and whether I/E criteria were potentially met (blinded to genetic status).  Eligibility was classified as “probable” (appeared to meet all protocol I/E criteria), “possible” (appeared to meet nearly all I/E criteria but full eligibility remained to be confirmed during screening) and “ineligible” (definitely or probably excluded).  When the candidates were considered ineligible for further pre-screening, they were designated as “inactive.”

Step 3:  Informed Consent meetings

All probably and possibly eligible candidates were contacted by telephone and invited, with their intended study partners, to group meetings at which the principal investigator described the trial, possible risks and benefits of participation, trial procedures, and reimbursement for transportation, missed work, and meals as a result of attending study visits.  Participants and study partners had opportunities to ask questions during and after the meeting.  They were given the Informed Consent Form (ICF), a companion illustrated study brochure, and diagrams of the study visits to review at home with their families.  The companion study brochure was created by GNA, using clear language and pictures explaining in detail the main goals of the trial, its duration, the schedule of visits, all procedures including lumbar puncture, MRI and PET scans, potential risks and benefits, information about the investigational product and method of study drug administration, the requirement for double contraception, and the role of the study partner. In addition, it provided information about the availability of a health plan for participants to ensure timely evaluation, treatment and follow up of possible adverse events, conduct additional testing if needed, offer contraception, and provide gynecological and other specialist evaluations for participants in instances where their standard medical care could not address health concerns in a timely way or at all.  Participants were given the opportunity to have a letter sent to their place of work noting that they are participating in a research study and explaining why they would need to miss work periodically.  They were provided telephone numbers of key study personnel as well as the Ethics Committee (EC) and an independent attorney in the event they desired legal consultation.

Step 4: Pre-screening questionnaire and reconfirmation of eligibility

At the end of the IC meeting, each candidate filled out a pre-screening questionnaire assessing current health status, past medical history, past and concurrent medications, substance use, plans regarding conception, and availability of a reliable study partner.  A qualified study team member reviewed this information, clarified ambiguous information, and updated the information in SISNE2.  One of the investigators then rendered a clinical judgment regarding likelihood of eligibility of each candidate; those who remained “probably” eligible and remained interested were scheduled for a screening visit.  An investigator assessed the capacity to provide informed consent and the candidate’s/partner’s understanding of the potential risks and benefits of trial participation.  Before signing the ICF, each candidate had the opportunity to ask questions.  The entire pre-screening process was approved by the local Ethics Committee.


Data analysis

Using SISNE2, data from all eligible candidates were filtered using an algorithm based on PSEN1 E280A status (using a 2:1 carrier/non-carrier ratio), clinical records, and selected trial inclusion/exclusion criteria.  Weightings were assigned to each I/E criterion according, in part, to its variability over time.  For example, “planning to conceive” received a low weight because this could change from one evaluation to another; conversely, “having suffered severe head trauma” received a high weight because of its relative stability.  Failures in criteria with low weightings did not affect the candidate’s likelihood of being selected for a pre-screening list.  Descriptive anonymized statistics were calculated for the demographic data (age, gender, schooling, marital status and geographic location) and genetic status of the population considered during the pre-screening process in order to determine their impact on failures.  Fisher’s exact tests were used for inferential testing of categorical variables; percentages of pre-screening failures in carriers vs. non-carriers were used only if blinding to genotype could be protected.



Pre-screening for the trial occurred from November, 2013, to December, 2016, at the Sede de Investigaciones Universitaria, Medellin.  Overall, 1782 persons from the Colombian API Registry failed to qualify for pre-screening.  A total of 50 pre-screening lists (blinded to E280A status, and preserving a 2:1 carrier/non-carrier ratio) were reviewed for a total of 623 eligible candidates.  Of those, 201 (32.3%) did not attend an IC meeting because they either clearly did not meet I/E criteria or they decided not to come.  Among the candidates who attended an IC meeting: 99/422 (23.5 %) failed pre-screening after review of the pre-screening questionnaire.  A total of 8/422 (1.9%) did not come to a screening visit after passing all pre-screening requirements.  Overall, 54 (18%) probably eligible and 246 (82%) possibly eligible candidates failed pre-screening.
The total pre-screening failure rate was 48.2% (300 candidates); demographic characteristics are described in Table 1. There were no significant differences between pre-screening failures versus non-failures in marital status or geographic location; nevertheless, a higher pre-screening failure rate was seen in participants age 50-54 years (12.3% vs 5.8%, p=0.004) and in participants age 60 (6.7% vs 1.6%, p=0.0016).  Carriers failed more than non-carriers (77.7% vs. 66.3%, p=0.0018); men failed more than women (62.2 % vs. 54%, p=0.042), and candidates with no formal education failed more than those with any schooling (5.7% vs. 1.2%, p=0.003).

Table 1. Demographic characteristic and geographic locations of pre-screening failures vs. non-failures

Table 1. Demographic characteristic and geographic locations of pre-screening failures vs. non-failures

Bold values are considered significant (p-value <0.05)


The most frequent causes for pre-screening failures were: 118/300 (39.3%) expected inability to comply with the protocol, 39/300 (13.0%) mild cognitive impairment due to AD based on investigator judgment, 39/300 (13.0%) not in good health, 25/300 ( 8.3%) substance dependence, 22/300 (7.3%) learning/comprehension difficulties or illiteracy, 19/300 (6.3%) planning to conceive, 18/300 ( 6.0%) dementia due to AD based on investigator judgment, 8/300 (2.7%) contraindication to MRI, and 12/300 (4.1%) other reasons.
Looking further at barriers to protocol compliance, almost half 55/118 (46.6 %) were due to work; 31/118 (26.3 %) were averse to required testing and/or potential adverse effects of study drug; 16/118 (13.6%) lived outside the country (6/16) or far from trial sites (10/16); 4/118 (3.4%) could not participate because of time constraints related to child care or care of family members with AD dementia, and 12/118 (10.2 %) due to a combination of reasons.
Medical exclusions were due to: 10/39 (25.6%) cardiovascular diseases, 9/39 (23.1%) metabolic/endocrine disorders, 6/39 (15.4%) autoimmune disorders, 5/39 (12.8%) severe traumatic brain injury, 3/39 (7.7 %) psychiatric diseases, 2/39 (5.1%) cerebrovascular disease, 2/39 (5.1%) movement disorders, 1/39 (2.5%) tuberculosis, and 1/39 (2.5%) complications of meningitis.  The most common pre-screen failure for substance dependence included combined dependence on marijuana and cocaine 21/25 (84%), marijuana dependence 3/25 (12.0%); and alcohol dependence 1/25 (4.0%).  Twenty-two individuals who were fully functional and independent failed due to illiteracy or learning/comprehension difficulties: 7/22 (31.8%) were illiterate and had no formal education; another 12/22 (55%) had one to two years of education but could not read and write, and 3/22 (13.6%) could read and write but could not understand conversation.
Non-carriers failed significantly more than carriers due to inability to comply with protocol requirements (53.7 % vs 35.0%, p=0.007), and carriers failed significantly more than non-carriers due to illiteracy or learning/comprehension difficulties (9.0% vs. 1.5 %, p= 0.035).  Males were more likely than females to fail due to substance dependence (15.9% vs. 1.9%, p<0.0001) (Table 2).

Table 2. Pre-screening failures according to genetic status and gender

Table 2. Pre-screening failures according to genetic status and gender

Note.  n = number, I-inclusion, E-exclusion; Comparisons performed with Fisher’s exact test; Bold values denote p-value <0.05



With the Colombian API Registry and our pre-screening efforts, we enrolled 169 30-60 year-old cognitively unimpaired carriers and 83 non-carriers from the PSEN1 E280A kindred who met all eligibility criteria, agreed to comply with the time commitments and contraception requirements involved in a trial of at least 60 months’ duration.  The pre-screening failure rate was high 300/623 (48.2%); the primary reason being expected inability to comply with the protocol, chiefly due to work requirements.  More carriers compared to non-carriers, and more males compared to females, failed pre-screening.  Carriers were more likely to fail pre-screening because of illiteracy or learning/comprehension difficulties.  There were more pre-screen failures age 50-54 and 60 years, presumably because the median age of onset of dementia in PSEN1 E280A is about age 50 (10) and because 60 years was the maximum age to for trial eligibility.
Among those who failed pre-screening due to work issues, the main factors were anticipated absences from work due to drug administration schedule, time required for major procedures, and the duration of the trial (at least 60 months).  Most eligible subjects were young (30-39 years), where conception was a life priority.  A substantial minority (about 26.3%) of candidates expressed fears about the required testing and/or potential adverse effects of study drug (11).
Males failed pre-screening more than females: the majority of candidates who failed due to substance dependence were male (88%), similar to rates reported elsewhere (12).  Of note, most substance-dependent males (84%) were cocaine-dependent concurrent with marijuana-dependence, in contrast to other studies in the general population, which described up to 23% of concomitant dependence to both substances (13).
Carriers failed significantly more during pre-screening than non-carriers.  This may be due in part to the fact that they were more likely to meet criteria for dementia due AD, as expected in this population.  On the other hand, failure due to meeting criteria for MCI did not differ between carriers vs. non-carriers, possibly because MCI can occur for a variety of reasons (14).  Notably, we observed that almost all candidates (21/22) who failed due to illiteracy were carriers.  All of them were functional and independent in daily activities, and we could not determine if these difficulties were related to lower/borderline IQ, specific learning disabilities undiagnosed in childhood, or environmental factors (e.g., limited access to schooling, poverty).  Given evidence that children at genetic risk for ADAD have functional and structural brain changes and abnormal levels of plasma Aβ1-42 (15), it is possible that there are neurodevelopmental changes associated with preclinical AD.  Further studies in children from PSEN1 E280A and other ADAD families may help identify specific preclinical or neurodevelopmental differences in carriers.
Using the Colombian API Registry (9) as a source for the API ADAD clinical trial and using a structured pre-screening process helped identify eligible candidates efficiently before formal screening, and allowed trial recruitment ensuring a 2:1 carrier/non-carrier ratio.  Providing a detailed IC helped identify candidates at risk of poor compliance.  We believe our pre-screening process prevented high rates of screen failure, saving cost and participant burden by quickly and efficiently eliminating possible participants who were not likely to make it through the screening process, and identified candidates most likely to adhere to the demands of this trial.  By the end of the third year of the trial retention of participants was 96.6%.
Limitations of our pre-screening process should be noted.  The duration of pre-screening was almost three years: unimpaired carriers may have developed AD symptoms during this time.  However, the lengthy process meant that some candidates who initially failed were eventually able to be reconsidered: those with transient health issues or who were pregnant, or those who initially declined for personal reasons and changed their minds.  It is our impression that those who changed their minds about trial participation may have done so in part because of an extensive media campaign developed by GNA about the importance of the Colombian API Registry and the trial.  Second, pre-screening failure was categorized by one main criterion, but it is likely that some candidates failed more than one criterion.  Third, there was lack of detailed information about some recent Registrants.  Finally, although analytical approaches such as logistic regression to examine determinants of screen failure might afford more insights, we chose approaches that would not unblind genetic status.  The results are descriptive and exploratory, and conclusions based on genetic status and gender should be interpreted cautiously.
In conclusion, our findings suggest multiple benefits of creating a registry of people potentially interested in research of this nature and implementing a pre-screening process as an effective strategy for enrolling prevention clinical trials in ADAD.  They provide a detailed picture of the population at risk for ADAD in Colombia that may be relevant to other prevention trials.  In addition, future prevention clinical trials might benefit from less burdensome protocol designs, including reduced frequency of study drug administration, and more flexible I/E criteria.


Funding: The Colombian Alzheimer’s Prevention Initiative (API) Autosomal Dominant AD (ADAD) trial is funded by Genentech Inc., Roche, the National Institutes of Health, National Institute on Aging (1RFAG041705-01A1, 1R01AG055444-01) the Banner Alzheimer’s Foundation, an anonymous international foundation, and Comité para el Desarrollo de la Investigación (CODI) from University of Antioquia

Acknowledgements: The authors gratefully acknowledge families with PSEN1 E280A mutations from Colombia.

Author disclosures: Rios-Romenets, Giraldo-Chica, López, Piedrahita, Ramos, Acosta-Baena, Muñoz, Ospina, Tobón, Lopera, Langbaum, Reiman and Tariot report the funding disclosures above.  S. Rios-Romenets receives grant and contract support from the NIA, Genentech/Roche, and an anonymous foundation to develop the API ADAD Registry and help conduct the API ADAD Trial in Colombia.  M. Giraldo-Chica receives grant and contract support from the NIA, Genentech/Roche, and an anonymous foundation to develop the API ADAD Registry and help conduct the API ADAD Trial in Colombia.  H. Lopez receives grant and contract support from the NIH, Genentech/Roche, and an anonymous foundation to develop the API ADAD Registry and help conduct the API ADAD Trial in Colombia.  F. Piedrahita receives grant and contract support from the NIA, Genentech/Roche, and an anonymous foundation to develop the API ADAD Registry and help conduct the API ADAD Trial in Colombia.  C. Ramos receives grant and contract support from the NIA, Genentech/Roche, and an anonymous foundation to develop the API ADAD Registry and help conduct the API ADAD Trial in Colombia.  N. Acosta-Baena receives grant and contract support from the NIA, Genentech/Roche, and an anonymous foundation to develop the API ADAD Registry and help conduct the API ADAD Trial in Colombia.  C. Muñoz receives grant and contract support from the NIA, Genentech/Roche, and an anonymous foundation to develop the API ADAD Registry and help conduct the API ADAD Trial in Colombia.  P. Ospina receives grant and contract support from the NIA, Genentech/Roche, and an anonymous foundation to develop the API ADAD Registry and help conduct the API ADAD Trial in Colombia.  C. Tobon receives grant and contract support from the NIA, Genentech/Roche, and an anonymous foundation to develop the API ADAD Registry and help conduct the API ADAD Trial in Colombia.  F. Lopera receives grant and contract support from the NIA, Genentech/Roche, and an anonymous foundation to develop the API ADAD Registry and help conduct the API ADAD Trial in Colombia.  One co-author, a member of the GNA team, is a relative of PSEN1 E280A families.  P. Tariot receives grant and contract support from the NIA, Genentech/Roche, and an anonymous foundation.  J. Langbaum receives grant and contract support from the NIA, Genentech/Roche, and an anonymous foundation to develop.  E. Reiman receives grant and contract support from the NIA, Genentech/Roche, and an anonymous foundation.  P. Tariot also reports the following (pertinent for the last two years): consulting fees from Abbott Laboratories, AbbVie, AC Immune, Acadia, Auspex, Boehringer-Ingelheim, Brain Test, Inc., California Pacific Medical Center, Chase Pharmaceuticals, Clintara, CME Inc., Glia Cure, Insys Therapeutics, Pfizer, and T3D; Consulting fees and research support from AstraZeneca, Avanir, Lilly, Lundbeck, Merck and Company, and Takeda; Research support only from Amgen, Avid, Functional Neuromodulation (f(nm)), GE, and Novartis; he is a contributor to a patent owned by the University of Rochester, “Biomarkers of Alzheimer’s disease” and owns stock options in Adamas.  J. Langbaum also reports the following (pertinent for the last two years): consulting fees from Biogen and Lilly.  E. Reiman also reports: he is a paid research consultant to Alkahest, Alzheon, Axovant, Biogen, Denali, Pfizer, and United Neuroscience and Zinfandel. W. Cho and M. Ward are full time employee of Genentech and are Roche equity holders.

Ethical standards:  The entire pre-screening process was approved by the local Ethic Committee (Comité de Investigaciones y Ética en Investigaciones del Hospital Pablo Tobon Uribe).



1.    Peterson RC: Barriers for prevention and prodromal AD trials. J Prev Alz Dis 2016; 3:66-67.
2.    Reiman EM, Langbaum JB, Tariot PN, et al. CAP-advancing the evaluation of preclinical Alzheimer disease treatments. Nat Rev Neurol 2016; 12:56-61.
3.    Sperling R, Cummings M, Donohue M, Aisen P. Global Alzheimer’s Platform Trial Ready Cohorts for the Prevention of Alzheimer’s Dementia. J Prev Alz Dis  2016; 3:185-187.
4.    Reiman EM, Langbaum JB, Fleisher AS, et al. Alzheimer’s Prevention Initiative: a plan to accelerate the evaluation of presymptomatic treatments. J Alzheimers Dis 2011; 26 Suppl 3:321-329.
5.    Calamia M, Bernstein JP, Keller JN. I’d Do Anything for Research, But I Won’t Do That: Interest in Pharmacological Interventions in Older Adults Enrolled in a Longitudinal Aging Study. PLoS One 2016; 11:e0159664.
6.    Gauthier S, Albert M, Fox N, et al. Why has therapy development for dementia failed in the last two decades? Alzheimers Dement 2016; 12:60-64.
7.    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; 12:1113-1115.
8.    Watson JL, Ryan L, Silverberg N, Cahan V, Bernard MA. Obstacles and opportunities in Alzheimer’s clinical trial recruitment. Health Aff (Millwood) 2014; 33:574-579.
9.    Rios-Romenets S, Lopez H, Lopez L, et al. The Colombian Alzheimer’s Prevention Initiative (API) Registry. Alzheimers Dement 2017; 13:602-605
10.    Acosta-Baena N, Sepulveda-Falla D, Lopera-Gomez CM, et al. Pre-dementia clinical stages in presenilin 1 E280A familial early-onset Alzheimer’s disease: a retrospective cohort study. Lancet Neurol 2011; 10:213-220.
11.    Grill JD, Karlawish J, Elashoff D, Vickrey BG. Risk disclosure and preclinical Alzheimer’s disease clinical trial enrollment. Alzheimers Dement 2013; 9:356-359 e351.
12.    Bobzean SA, DeNobrega AK, Perrotti LI.Sex differences in the neurobiology of drug addiction. Exp Neurol 2014; 259:64-74.
13.    McRae AL, Hedden SL, Malcolm RJ, Carter RE, Brady KT. Characteristics of cocaine- and marijuana-dependent subjects presenting for medication treatment trials. Addict Behav 2007; 32:1433-1440.
14.    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; 7:270-279.
15.    Quiroz YT, Schultz AP, Chen K, et al. Brain Imaging and Blood Biomarker Abnormalities in Children With Autosomal Dominant Alzheimer Disease: A Cross-Sectional Study. JAMA Neurol 2015; 72:912-919.



K. Zhong, J. Cummings


Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, Nevada

Corresponding Author: Jeffrey Cummings, MD, ScD, Cleveland Clinic Lou Ruvo Center for Brain Health, 888 W. Bonneville Avenue, Las Vegas, NV 89106; Phone: (702) 483-6029; Fax: (702) 702-722-6584, Email:

J Prev Alz Dis 2016;3(3):123-126
Published online May 10, 2016,


Abstract is an interactive digital platform designed to establish an online community-based registry with a well characterized population who are willing and ready to participate in clinical trials.  It challenges the  community to get involved in a brain-healthy life style to reduce the risk of brain disorders.  Users can choose to browse the site anonymously, sign up for updates and news, or register to get a “brain checkup” by completing questionnaires on their health and family history, demographic data, and other relevant life style information.  In return, the registrants will receive their individualized Brain Health Index (BHI), and a personalized brain health report with customized recommendations. The site strives to empower registrants and enhance the user experiences through an interactive dashboard which acts as an engagement and motivation tool.
The information provided by the registrants allows the site to identify individuals that are interested in and may be appropriate for trials.  Of 7,142 users with data available for review,  71 % are women,  58 % are 55 years old or older, 31 % have a family history of dementia, and 42  % are concerned about their memory.  Among  4,150 who completed the BHI, 1,389 finished the Cognitive Function Instrument (CFI) (33%).  As of April 2016, 3,869 have opted in for clinical trials,  out of that 931 resided in the Las Vegas area, where  38  individuals were prescreened and 4 were randomized for A4 clinical trials to the Lou Ruvo Center for Brain Health in Las Vegas.  The site will contribute to the evolving understanding of using web-based approaches to information capture and clinical trial recruitment.       


Key words: Alzheimer’s disease, registry,, algorithm, brain health index, cognitive function instrument.



Although more than two-thirds of Americans polled in a recent survey said they would likely participate in a clinical trial if recommended by their physician, fewer than 10% actually do so (1). Low levels of participation contribute to the failure of many studies to reach their recruitment goals, delaying treatment advances, threatening the internal validity of the trial, and raising questions about the generalizability of results (2).  Among older individuals, participation in research studies has declined in the past twenty years, despite the urgent need to better understand the natural history and develop interventions for diseases of aging (3).        
Active engagement of the public in scientific research is critical to increasing efficiency of clinical trials. Reasons cited for the low levels of participation in clinical trials include lack of awareness among physicians and patients about the availability and benefits of clinical trials, lack of general practitioner engagement in the clinical trials process, and perceived burden or risk to participants (4). In the United States, the Patient Centered Outcomes Research Institute (PCORI) was established in 2010 to help close the gaps between patients, families, caregivers, and clinicians. In a systematic literature review commissioned by PCORI, a variety of patient engagement methods were explored, such as partnering with patients in study design or including them on advisory committees; however the authors of the study were unable to recommend best practices (5).  
A survey by the Pew Internet & American Life Project in 2011 found that a large majority of US residents have internet access, and of those, 80% go online for health information (6). Social networking sites such as PatientsLikeMe (7) provide opportunities for individuals to share personal health data with others who have the same condition (8). Over 2,500 conditions are currently represented on the site, with a total of 380,000 members. PatientsLikeMe also partners with researchers seeking participants in studies.
Web-based patient recruitment has had only minimal systematic exploration and a science of web-based participant engagement is needed.  They have had their greatest application thus far in very large trials, rare disease trials, and trials involving minority patients (9).They have also accelerated recruitment to some cancer trials (10).  Their use in trials for neurodegenerative disease is very preliminary but promising based on success in other trial settings.  
The success of Internet-based patient engagement tools for non-Alzheimer trials led our group at the Cleveland Clinic to create a new website called Healthy Brains ( using a push-pull strategy to 1) reduce the risk of late-life cognitive decline through education about a brain healthy lifestyle, 2) collect preliminary information about participants, and 3) engage them to join a registry aimed at recruiting subjects for clinical trials.  


Building a Research Registry on the Six Pillars of Brain Health is built upon the concept of Six Pillars of Brain Health: physical exercise, mental fitness, healthy diet and nutrition, social connectedness, sleep and stress control, and treatment of medical risks such as high cholesterol and hypertension. Numerous epidemiologic studies have suggested that addressing these modifiable risk and protective factors may delay the onset of dementia (11, 12), prompting a number of multi-domain intervention studies that target these modifiable lifestyle factors (3-5). One comprehensive analysis of risk factors suggested that up to one third of cases of Alzheimer’s disease (AD) are attributable to life-style-related risk factors (13).  A 10% reduction in the prevalence of risk factors through life style modification would reduce the prevalence of AD in 2050 by 8.3% worldwide. offers three ways of user interactions: 1) they can browse the website anonymously to gather information and acquaint themselves with the content; 2) they can choose to provide their email address to receive regular newsletters and other brain health updates; and 3) they can register with a user name and password and are encouraged to complete the brain health questionnaires. As part of the Brain Health community, they can access assessments, brain tools, clinical trials, and libraries of information.  Registering entitles the user to a free “brain check-up”. Individuals provide their consent to agree to participate in the Cleveland Clinic Healthy Brains registry and complete questionnaires that collect demographic data as well as information on medical and family history, life-style choices, and cognitive function. These data are compiled into a novel personalized Brain Health Index (BHI; score range 0-100). The users also receive a score for each of the six pillars in addition to the total BHI.  This information allows the individual to make life style choices to optimize each pillar and improve their total BHI.  Each registrant receives customized recommendations based on their individual scores about how to maintain and improve brain health. Users interact with the site through a personal dashboard, which tracks serial BHI measures as an individual  makes life style adjustments to improve brain health (Figure 1).  The dashboard also enables the capture of remote monitoring data, such as information from a wearable device.  A companion App was created and can be downloaded for free via iTunes store.  Users can follow BHI improvements with brain healthy changes in lifestyle.  The BHI is intended to function as an engagement and motivational tool to encourage frequent visits to the website.  This may increase the likelihood of capturing longitudinal information from participants.  


Figure 1. Dashboard example


Optional memory assessments are also available on to those who have completed the BHI assessments.  An on-line version of the Cognitive Function Instrument (CFI) (14) has been constructed and implemented to allow users to rate their own memory.  
The site also assesses participants’ interest in clinical trials and provides information about the importance of trials as well as their benefits and risks. Among the benefits are early access to investigational treatments and being able to receive medical care at leading healthcare institutions. Moreover, in some disease areas such as cancer, participation in clinical trials has been associated with improved outcomes (15, 16). Individuals who express interest in clinical trials and provide additional permission to be contacted about trials are linked to the Global Alzheimer Platform (GAP) Registry (Cummings 2016, this issue of JPAD) and connected either to the Cleveland Clinic Lou Ruvo Center for Brain Health if they live near Las Vegas, Nevada or Cleveland, Ohio; or to the Alzheimer’s Association’s TrialMatch (17). The site also serves patients with non-AD disorders and may refer individuals to the National Multiple Sclerosis Society(18) or the Parkinson’s Disease Fox Trial Finder(19) as appropriate.

Registry Data

As of April 7, 2016, 27,542 users had executed 189,807 page views and had implemented 38,586 sessions. A session is defined as when a user is actively engaged with the website. Of the users, 29% were returning to the site and 71% were new to the site.
Among all users, 7,142 have registered for BHI by creating a user name and password and by providing answers to some or all of the questions.  The data available for review revealed that 71% of the users are female and 58% are 55 years of age or older. Figure 2 shows the age distribution of the users who have registered.  The youngest user was 11 years old while the oldest was 96 years old. Of those reporting, 31% had a family history of dementia, 25% had a family history of AD, 42% had concerns about their memory, and 0.41% had a diagnosis of AD (Figure 2).


Figure 2. Age distribution of those who have registered


Registries have global reach.  Of the sessions recorded on, 91.2% were from  users residing in the US, but there were users from many more countries including India, Brazil, South Africa, and the Philippines.  
The users showed substantial interest in the information available on the website. Of the 7,142 registrants, 4,150 (58%) completed all questions of the six pillars and had calculated their BHI, 3,869 showed interest in participating in clinical trials (54%) and over 4,991 requested the bi-weekly newsletter.  
Of the 3,869 registrants who completed the BHI, 1,389 completed the CFI.  A total of 31.7% (441) of the CFI completers are over the age of 65.  The CFI results suggest that many of the older participants are concerned about their memory; 71% of respondents have scores of 10 or above (of a total possible score of 14) (Figure 3).  


Figure 3. Number of individuals over age 65 with specific scores on the Cognitive Function Instrument (CFI)(N=441)


A key purpose of the initiative is to enlist individuals in clinical trials.  To assess the flow of individuals from the website to local clinical trials, we examined the number of individuals identified on the website and eventually randomized to a specific trial.  The population began with 7,142 who registered on the site; of these, 3,869 indicated an interest in clinical trials.  Nine hundred thirty-one of these were in the area close to the Cleveland Clinic Lou Ruvo Center for Brain Health in Las Vegas, Nevada.  Of those deemed eligible by the site based on review of the data, site personnel conducted a telephone screen to determine initial eligibility in 38 individuals.  Eight of these were brought to the site for screening and 4 were randomized to the A4 study (Figure 4).


Figure 4. From registry to randomization


These preliminary observations allow tentative conclusions and raise important questions about the use of websites to gather AD-related and trial-relevant information.  Most of the users are women and strategies that engage female users are likely to be most successful.  Brain health information is of interest to people and a substantial number of visitors completed the BHI and their attention was drawn to lifestyle areas where improvements could be made.  Clinical trial interest is high suggesting that promoting brain health does not lead to a decreased interest in at least learning more about available experimental therapies.  The users are information-hungry and many requested linked newsletter with information updates.  
With additional information and more users, deep analyses can begin regarding the relationship of age to BHI and to CFI and the correlations of BHI to CFI.  The relationship of cognitive variables such as CFI to website variables such as questionnaire completion, interest in newsletters, and interest in trials can also be interrogated.
Website observations also raise questions relating to how best to use electronic media to optimize information collection and trial participation.  What motivates users to stay on the site; why do some complete the BHI and others do not; how many of those with an interest in clinical trials will qualify for a trial; and how many will visit the trial site and be randomized?  How valid are the data and do they accurately reflect the clinical state of the potential participant?  Answers to these questions will inform website construction and expectations for trial participant delivery to trial sites based on website interactions. The randomization of four persons to the A4 trial based on the web-based approach suggests that the process can be successful.


Conclusions engages individuals through an educational approach, providing them useful, understandable, personalized information that they can track over time. The idea behind this approach is that enabling people to see the progress they are making will empower them to take a more active role in maintaining a healthy brain and provide an entry point into the clinical trials process.   
The site and registry also provide researchers an opportunity to explore factors that lead to continued engagement and clinical trial participation, as well as best practices for engaging participants through web-based approaches. A potential risk of this approach is that participants may believe that lifestyle approaches to prevention represent a viable alternative to clinical trials. Preliminary data shows that users have a robust interest in both the BHI and possible participation in clinical trials.  The site led to successful enrollment of several registrants in a clinical trial.  As the site evolves, it will be important to build in components that emphasize both the benefits and limitations of primary prevention through lifestyle changes, and perhaps add as a seventh pillar of brain health, participation in clinical trials. Interrogation of the information most predictive of trial enrollment will be a critical aspect of advancing web-based recruitment.  With this information, websites can fulfill the goal of guiding the individual from registry to randomization.  


Acknowledgements: The authors acknowledge the editorial assistance of Lisa Bain and financial support for Healthybrains from Caesars Foundation and Keep Memory Alive (KMA).

Disclosures: KZ has no disclosures. Dr. Cummings has received research support from Avid Pharmaceuticals, Teva Pharmaceuticals, and CogState.  Dr. Cummings has provided consultation to Abbvie, Acadia, Actinogen, Adamas, Alzheon, Anavex, Astellas, Avanir, Boehinger-Ingleheim, Eisai, Forum, GE Healthcare, Genentech, Intracellular Therapies, ISIS, Lilly, Lundbeck, MedAvante, Merck, Neurim, Neurotrope, Novartis, Orion, Otsuka, Pfizer, Piramal, QR, Resverlogix, Roche, Roivant, Suven, Takeda, and Toyama companies.  Dr. Cummings owns the copyright of the Neuropsychiatric Inventory (NPI).  Dr. Cummings has stock options in Prana, Neurokos, ADAMAS, MedAvante, and QR pharma.  

Ethical standards: All participants provided consent using an IRB-approved e-consent methodology.

Conflict of interest: The study was funded by Caesars Foundation, a non-profit, and neither Dr. Cummings nor Dr. Zhong are employed by Caesars Foundation or any of its subsidiaries.



1.    Research America. Research! America’s Clinical Research Polling (2001-2013). 2013.
2.    Grill JD, Karalwish J. Addressing the challenges to successful recruitment and retention in Alzheimer’s disease clinical trials. Alzheimers Res Ther 2010;2:34.
3.    Gao L, Green E, Barnes LE, et al. Changing non-participation in epidemiological studies of older people: evidence from the Cognitive Function and Ageing Study I and II. Age Ageing 2015;44:867-873.
4.    Institute of Medicine. Public engagement and clinical trials: new models and disruptive technologies: workshop summary. Washington, DC: The National Acadamies Press, 2012.
5.    Domecq JP, Prutsky G, Elraiyah T, et al. Patient engagement in research: a systematic review. BMC Health Serv Res 2014;14:89.
6.    Kuehn BM. Patients go online seeking support, practical advice on health conditions. JAMA 2011;305:1644-1645.
7.    PatientsLikeMe. PatientsLikeMe. 2016.
8.    Brownstein CA, Brownstein JS, Williams DS, III, Wicks P, Heywood JA. The power of social networking in medicine. Nat Biotechnol 2009;27:888-890.
9.    Tan MH, Thomas M, MacEachern MP. Using registries to recruit subjects for clinical trials. Contemp Clin Trials 2015;41:31-38.
10.    Rimel BJ, Lester J, Sabacan L, et al. A novel clinical trial recruitment strategy for women’s cancer. Gynecol Oncol 2015;138:445-448.
11.    Mangialasche F, Kivipelto M, Solomon A, Fratiglioni L. Dementia prevention: current epidemiological evidence and future perspective. Alzheimers Res Ther 2012;4:6.
12.    Qiu C, Xu W, Fratiglioni L. Vascular and psychosocial factors in Alzheimer’s disease: epidemiological evidence toward intervention. J Alzheimers Dis 2010;20:689-697.
13.    Norton S, Matthews FE, Barnes DE, Yaffe K, Brayne C. Potential for primary prevention of Alzheimer’s disease: an analysis of population-based data. Lancet Neurol 2014;13:788-794.
14.    Amariglio RE, Donohue MC, Marshall GA, 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;72:446-454.
15.    Abu-Hejleh T, Chrischilles EA, Halfdanarson TR, et al. The Effect of Receiving Treatment Within a Clinical Trial Setting on Survival and Quality of Care Perception in Advanced Stage Non-Small Cell Lung Cancer. Am J Clin Oncol 2014;39:126-131.
16.    Robinson WR, Ritter J, Rogers AS, Tedjarati S, Lieberenz C. Clinical trial participation is associated with improved outcome in women with ovarian cancer. Int J Gynecol Cancer 2009;19:124-128.
17.    Alzheimer’s Association. TrialMatch. 2016.
18.      National Multiple Sclerosis Society.
19.      Fox Trial Finder.


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.

Arizona Alzheimer’s Registry: Strategy and Outcomes of a Statewide Research Recruitment Registry

K.T. Saunders1, J.B. Langbaum2,3, C.J. Holt4, W. Chen1, N. High2,3, C. Langlois2,3, M. Sabbagh3,5, P.N. Tariot2,3

1. University of Arizona College of Medicine Phoenix, AZ, USA; 2. Banner Alzheimer’s Institute Phoenix, AZ, USA; 3. Arizona Alzheimer’s Consortium Phoenix, AZ, USA; 4. Saint Joseph’s Hospital and Medical Center, Phoenix, AZ, USA; 5. Banner Sun Health Research Institute, Sun City, AZ, USA

Corresponding Author: Kelley T. Saunders, University of Arizona College of Medicine Phoenix, AZ, USA,

J Prev Alz Dis 2014;1(2):74-79

Published online September 22, 2014,


BACKGROUND: The Arizona Alzheimer’s Consortium (AAC) created the Arizona Alzheimer’s Registry, a screening and referral process for people interested in participating in Alzheimer’s disease related research. The goals of the Registry were to increase awareness of Alzheimer’s disease research and accelerate enrollment into AAC research studies.

METHODS : Participation was by open invitation to adults 18 and older. Those interested provided consent and completed a written questionnaire. A subset of Registrants underwent an initial telephone cognitive assessment. Referral to AAC sites was based on medical history, telephone cognitive assessment, and research interests.

RESULTS: A total of 1257 people consented and 1182 underwent an initial cognitive screening. Earned media (38.7%) was the most effective recruitment strategy. Participants had a mean age of 68.1 (SD 10.6), 97% were Caucasian, had 15.2 (SD 2.7) mean years of education, and 60% were female. 30% reported a family history of dementia and 70% normal cognition. Inter-rater agreement between self-reported memory status and the initial telephone cognitive assessment had a kappa of 0.31-0.43. 301 were referred to AAC sites.

CONCLUSION: IThe Registry created an infrastructure and process to screen and refer a high volume of eager Registrants. These methods were found to be effective at prescreening individuals for studies, which facilitated AAC research recruitment. The established infrastructure and experiences gained from the Registry have served as the prototype for the web-based Alzheimer’s Prevention Registry, a national registry focusing on Alzheimer’s disease prevention research.

Key words: Alzheimer’s disease, registry, prevention, recruitment.



Alzheimer’s disease (AD) poses a significant public health challenge, since age is a major risk factor and the population of those 65 and older is doubling over the next 20 years(1). Current estimates of the prevalence of AD suggest that 4.7 million Americans are living with this disease; by 2050 that number is expected to almost triple(2). The United States government has established a national plan to address this public health challenge with the first goal being to prevent and effectively treat AD by 2025(3). Enrollment into randomized controlled trials to assess the efficacy of both prevention and treatment strategies is a vital goal of the National Plan to Address Alzheimer’s Disease (3). There are numerous potential AD therapeutics under investigation(4) and thus large volumes of eligible volunteers are required.

As the number of AD-related research studies continues to increase, there is a need to rapidly communicate with and screen large numbers of potential participants to both inform them and gauge their interest in participation, thereby helping to overcome recruitment challenges (5, 6). Although disease-specific registries have typically been created either to collate medical record data from patients with rare diseases or as a public health instrument, they can also serve as a research pre- enrollment mechanism. For example, the Leon Thal Symposium of 2010 explored the use of registries to aid with recruitment into AD research (7). Community helplines and AD registries provide advantages such as enhancing pre-screening capabilities, assessing site feasibility, and laying the foundation for cohort studies (8, 9).

In, 2006 the Arizona Alzheimer’s Consortium (AAC) created the Arizona Alzheimer’s Registry (“Registry ») to facilitate enrollment into AD research studies being conducted at AAC sites. This paper describes the design, implementation experience, and outcomes of this Registry.



The AAC was the first statewide NIA-funded AD research consortium and includes Arizona State University, Banner Alzheimer’s Institute (BAI), Banner Sun Health Research Institute, Barrow Neurological Institute, Mayo Clinic Arizona, Translational Genomics Research Institute (TGen), and University of Arizona. The AAC charged BAI with creating a screening and referral process for people interested in participating in AD related research in their communities, as well as a relational database for all relevant information.  This process and database was called the Arizona Alzheimer’s Registry and was hosted at BAI.  The goals of the Registry were to increase awareness of research in the fields of dementia and AD, expedite enrollment into AD related research studies, and increase research activity within the AAC.  The aim of the Registry was to match motivated Registrants into AAC clinical research studies according to interest, location, and eligibility. An early and ongoing element of the process was creating an accurate catalogue of AAC research projects.


Anyone age 18 and older was eligible to participant in the Registry, although recruitment activities (disclosed below) targeted ages 50 and older. Interested individuals were required to be able to communicate in English and cooperate with symptom assessment and study procedures. No other criteria were required. Respondents provided written informed consent under guidelines approved by the human subjects committee at Western Institutional Review Board (WIRB).  Cognitively impaired individuals provided written assent in addition to written informed consent from a legally authorized representative.

Participants were recruited using a variety of methods including community events such as memory screenings, lectures, seminars, and conferences targeting either professionals or the general public.  Registry flyers, brochures, and letters were distributed at public events, displayed in AAC clinics, and through mass-mailings. Public service announcements and paid advertising through print, radio, and television media were utilized. The Registry created and maintained a website ( as both a source of information and a registration tool.

Interested individuals contacted the Registry directly by mail, telephone, email, website, or in-person at public events. Trained Registry staff collected the participant’s contact information (name, address, and telephone number), provided a brief description of the Registry and offered a welcome packet for those interested in the program.  The welcome packet included a letter, brochure, comprehensive questionnaire, and consent form. The questionnaire included a medical history, current medication use, family history of dementia, research interests and availability, and geographic preferences for research site location.  After receiving the completed consent and questionnaire, Registry staff contacted the Registrant or authorized representative via telephone for a verbal review of the consent form and telephone assessment.  During the telephone consent review, pertinent information provided in the questionnaire was reviewed for accuracy or clarification.

Telephone assessment

The telephone assessment included some or all of the following:  self reported memory status, functional memory assessment (10), Telephone Interview Cognitive Screen, modified (TICSm) (11, 12), Rey Auditory Verbal Learning Test (AVLT) (13), and Mild Cognitive Impairment Screen (MCIS) (11). Figure 1 depicts s the telephone assessment process.  Subjective memory status was characterized as “normal for age,” “mild memory loss,” or “significant memory loss.”  At the completion of the assessment a Registrant was assigned one of three possible outcomes:  no impairment, possible cognitive impairment, or possible dementia.  Subsequent follow-up telephone assessments were conducted on a subset of participants as funding permitted.  An algorithm was developed to select those whose follow-up assessment could result in a change in diagnostic category and thus potentially initiate a new site referral.

Figure 1. Sequence of telephone assessment components



An AAC clinician reviewed the assessment outcome in conjunction with pertinent medical history: this determined the Registrant’s referral trajectory.  Efforts were made to refer potentially eligible Registrants to existing AAC studies based on research interests and geographic preference.  If no such referral could be made, Registrants were retained in the Registry database for possible referral to future studies.  In some cases, additional clinical assessment with the Registrant’s primary care physician (PCP), referral to a specialist, or to the “Confirm” sub-study was recommended.  The Confirm sub-study was added to the Registry process when we learned that a significant minority of Registrants wished to have an evaluation of their cognitive concerns within the context of research; AAC medical providers offered this.  Registrants who were referred to AAC research sites were then screened for eligibility and contacted by the AAC site that received the referral.  AAC sites that received referrals were requested to provide feedback to the Registry regarding the referral outcome, and assumed responsibility for Registrants enrolled into studies.  All data were maintained in a secure electronic database housed at BAI (Filemaker Pro 11.0v3, San Francisco, CA).

Data analysis

Data from July 1, 2006 through June 22, 2011, the period during which funding was available, were included.  Analyses and descriptive statistics were calculated using STATA 11.0 (StataCorp, College Station, TX).  Self-reported memory status and telephone cognitive assessment measures were calculated for inter-rater agreement using Cohen’s Kappa.  Three kappa statistics were calculated each using different 3×3 tables for agreement.  The first calculation assigned perfect agreement as self reported status of no memory impairment matching the telephone cognitive assessment of no impairment, self-report of mild memory impairment matching the presence of possible cognitive impairment on assessment, and significant memory impairment matching the presence of possible dementia on assessment.  The second calculation used a weighted assignment with perfect agreement as in the first calculation and partial agreement of 0.5 for mild memory loss and possible dementia and significant memory loss and possible cognitive impairment.  The final calculation accepted perfect agreement as a self-report of no memory impairment matching the assessment of no impairment and any self-report of memory impairment matching any assessment of possible cognitive impairment or possible dementia.  Standard interpretation of kappa values was used (14).



Figure 2 depicts the progression of Registrants.  A total of 2263 contacted the Registry, and 2032 were provided a Welcome Packet.  Of those that received a packet, 1257 were consented into the Registry and 1182 underwent initial telephone assessment.


Figure 2. Registry consort diagram depicting the progress of volunteers through stages of participation



The demographic characteristics of people who contacted the Registry between 2006 and 2011 are displayed in Table 1. The mean and median age at the time of contact and intake into the registry was 68 years old.  Registrants were predominantly white (97.3%), non-Hispanic (94.0%), female (60.3%), and were highly educated with a median of 16 years of education.  Most were married and living at home with a spouse or other family member.  A total of 681 (30.1%) reported having a family history of dementia.  A total of 247 (19.9%) reported having a diagnosis of dementia or cognitive impairment, with AD being the most commonly reported dementia syndrome (60.3%).

Individuals primarily contacted the Registry to enroll themselves (78.0%) or their spouses (12.9%) and the method of contact used most frequently was telephone (62.8%) or email (22.3%).  Table 2 provides the results of multiple marketing methods used to recruit potential enrollees.  Earned media produced the largest percent of potential enrollees (38.7%), with newspaper articles being the most successful (31.8%).

Cognitive status and assessment

Baseline self-reported memory status was categorized as normal 1037 (61.2%), mild memory loss 483 (28.5%), significant memory loss 168 (9.9%), or unknown 7 (0.4%).

Telephone assessment outcomes as of the first assessment completed were no impairment 681 (57.5%), possible cognitive impairment 269 (22.7%), and possible dementia 234 (19.8%).  Table 3 shows the inter-rater agreement for the initial round of telephone assessment.

Table 1. Registry participant demographics



Non-treatment, brain imaging, lifestyle intervention and prevention were the most popular study type preference with 1095 (87.1%), 1045 (83.1%), 980 (78.0%), and 943 (75.0%) respondents respectively.  Multiple selections were permissible.  When asked if interested in genetic testing 48.3% responded “yes.”  Most people 1007 (83.9%) were available to begin participation in a study immediately and 853 (67.9%) were willing to attend study visits at a frequency of once a month.

There was a wide array of study types available, including non-intervention studies for people with and without memory problems and treatment trials for people with a diagnosis of AD or Mild Cognitive     Impairment (MCI), and the number of studies enrolling at AAC sites fluctuated over time.  A total of 301 Registrants were referred to AAC sites for possible participation in ongoing studies.



The feasibility of constructing and executing a statewide AD research registry was demonstrated in this endeavor.  An infrastructure and process to screen and refer a high volume of potential research participants was created, awareness about AD research opportunities was increased, a large number of people were enrolled, and hundreds of potential participants were referred to AAC sites.  The Registry model was well received by the general public and served as a mechanism for Registrants to assess their own cognitive status while making a contribution to the scientific community.  Registrants were generally highly motivated and many reported having a family history of dementia, suggesting that this personal experience may motivate individuals to join a registry of this nature.

Table 2. Referral source as reported by participants

Table 3. Inter-rater agreement of self reported memory status and telephone assessment

The highly individualized consent and screening components required one-on-one engagement.  As the Registry evolved and funding fluctuated, it was necessary to shift the emphasis away from recruitment of new enrollees and instead focus on retention. As a result, only a subset of Registrants received follow-up telephone cognitive assessments.  We acknowledge that, ideally, regular follow-up (one of the most expensive Registry activities(15-17)) would have been conducted with every Registrant, but resources did not permit this.

Inter-rater agreement between subjective memory status and the telephone cognitive assessment was found to be poor.  It is possible that some Registrants were worried well with concerns not discernable with our objective assessment tools; however, our measures may not have the sensitivity required to detect early cognitive changes.  It is important to note that a substantial number of Registrants anecdotally noted that they joined the Registry in order to investigate their concerns about their memory and thinking abilities, indicating that perhaps the Registry served as the initial stepping stone in an effort to seek medical advice.  A review of research on the relationship between memory complaints and cognitive testing suggests that subjective concerns may be predictive of dementia even when assessment does not reveal impairment(18).

Registrants with ambiguous telephone screening findings presented a challenge when it came to referral to studies.  When telephone screening revealed a possible, previously unrecognized, cognitive disorder, Registrants were encouraged to follow up with their physicians or, in some cases, a specialist to assess whether there was an identifiable problem and to clarify diagnosis, which would have helped for accuracy of research referral. However, Registrants rarely followed through on this recommendation.  The Confirm sub-study was successful at resolving these ambiguities, increasing referral to appropriate studies, and serving as a retention mechanism, although it significantly expanded the scale of Registry operations.

The quantity of study referrals was considered to be the primary measure of success of the Registry as opposed to the number of successful enrollments into studies.  At the time the Registry was developed, AD treatment trials were becoming increasingly more complicated with intricate inclusion and exclusion criteria and lengthy trial durations.  At the same time, there were very few studies available to cognitively normal individuals who made up the majority of Registry enrollees.  Moreover, many of the cognitively normal participants were primarily interested in prevention studies, which were not widely available.  Therefore, while the total number of research referrals was considered satisfactory, referrals were of course limited by the nature of the studies available, and would have been higher, for example, in the current era, when large prevention studies are or will be enrolling volunteers.

There are some limitations to this project.  For instance, interested volunteers for the Registry were self-selected and, as anticipated, were not representative of the general population of Arizona.  Registrants were mostly white, non-Hispanic, female, and highly educated older adults, similar to what is typically observed in other AD-related cohort studies and clinical trials (1).  The Registry model relied on Registrants completing a lengthy written questionnaire, returning it by mail, and undergoing a telephone screening assessment at enrollment.  This process may have been viewed as prohibitively burdensome to otherwise interested participants.

Results from the present study may help with future recruitment efforts. For instance, use of an abbreviated enrollment process focused only on pertinent demographic characteristics, rather than lengthy medical history questionnaire and telephone-based cognitive assessments, would be less burdensome to individuals and would likely result in more registry enrollees. Moreover, since inclusion criteria vary from study to study, and medical history can change frequently, this refined approach may optimize registry efficiency. Additionally, a robust mechanism for tracking referral outcomes may provide a greater appreciation of registry efficacy.

Use of the Internet for research registries has significant potential(6), although since there are still many individuals without regular access to the Internet, it is imperative that there still be phone and/or paper-based registry options available.  Given the need for increasing the participation of minority and other under-served communities in AD research, future efforts should consider focused recruitment of these individuals(19).  A substantial number of cognitively healthy adults joined the Registry expressing interest in prevention research studies. The established infrastructure and experiences gained from this Registry have served as the prototype for the web-based Alzheimer’s Prevention Registry (, a registry focusing on raising awareness of AD prevention research and informing its members about study opportunities in their communities.

Acknowledgments: Portions of this study were presented at the American Geriatrics Society 2012 Annual Scientific Meeting, Seattle, WA and the 2012 Arizona Alzheimer’s Consortium 14th Annual Conference, Phoenix. Arizona State University, Banner Alzheimer’s Institute, Barrow Neurological Institute at St. Joseph’s Hospital and Medical Center, Mayo Clinic Arizona, State of Arizona, Banner Sun Health Research Institute, Translational Genomics Research Institute, and University of Arizona. Funding: NIA ADCC P30 AG19610 and the State of Arizona supported the Registry. Other funding supported by the American Federation for Aging Research – Medical Student Training in Aging Research. The sponsors had no role in the design and conduct of the study; in the collection, analysis, and interpretation of data; in the preparation of the manuscript; or in the review or approval of the manuscript. Conflict of interest: Dr. Saunders received research support from the American Federation for Aging Research – Medical Student Training in Aging Research. None of the authors have any financial interests, relationships or affiliations relevant to the subject of this manuscript. Conflict of interest: Dr. Saunders received research support from the American Federation for Aging Research – Medical Student Training in Aging Research. None of the authors have any financial interests, relationships or affiliations relevant to the subject of this manuscript.



  1. National Institute on Aging. 2010 Alzheimer’s disease progress report. NIH Publication. National Institutes of Health; 2011. Report No.: 11-7829.
  2. Hebert LE, Weuve J, Scherr PA, Evans DA. Alzheimer disease in the United States (2010-2050) estimated using the 2010 census. Neurology. 2013 May 7;80(19):1778-83.
  3. U.S. Department of Health and Human Services. National plan to address Alzheimer’s disease: 2013 update. 2013.
  4. Pogacic Kramp V. List of drugs in development for neurodegenerative diseases: Update october 2011. Neurodegener Dis. 2012;9(4):210-83.
  5. Grill JD, Karlawish J. Addressing the challenges to successful recruitment and retention in Alzheimer’s disease clinical trials. Alzheimers Res Ther. 2010 Dec 21;2(6):34.
  6. Turning to the internet for Alzheimer’s trial volunteers [Internet].: Biomedical Research Forum, LLC; 2013 [updated November, 27; cited December 4, 2013]. Available from:
  7. Khachaturian ZS, Petersen RC, Snyder PJ, Khachaturian AS, Aisen P, de Leon M, et al. Developing a global strategy to prevent Alzheimer’s disease: Leon thal symposium 2010. Alzheimers Dement. 2011 Mar;7(2):127-32.
  8. Austrom MG, Bachman J, Altmeyer L, Gao S, Farlow M. A collaborative Alzheimer disease research exchange: Using a community-based helpline as a recruitment tool. Alzheimer Disease & Associated Disorders. 2010;24:S49- 53.
  9. Iliffe S, Curry L, Kharicha K, Rait G, Wilcock J, Lowery D, et al. Developing a dementia research registry: A descriptive case study from north thames DeNDRoN and the EVIDEM programme. BMC Med Res Methodol. 2011 Jan 27;11(1):9.
  10. Mezey M, Maslow K. Recognition of dementia in hospitalized older adults.

    Try This: Best Practices in Nursing Care for Hospitalized Older Adults with Dementia. 2007(D5).

  11. Shankle WR, Romney AK, Hara J, Fortier D, Dick MB, Chen JM, et al.

    Methods to improve the detection of mild cognitive impairment. Proc Natl Acad Sci U S A. 2005 Mar 29;102(13):4919-24.

  12. Welsh K, Breitner J, Magruder-Habib K. Detection of dementia in the elderly using telephone screening of cognitive status. Neuropsychiatry Neuropsychol Behav Neurol. 1993;6:103-10.
  13. Rey A. L’examen psychologique dans les cas d’encephalopathie tramatique.

    Archives de Psychologie. 1941;28:215-85.

  14. Altman DG. Inter-rator agreement. In: Practical Statistics for Medical Research. London: Chapman & Hall; 1991. p. 403-9.
  15. Goldberg J, Gelfand HM, Levy PS. Registry evaluation methods: A review and case study. Epidemiol Rev. 1980;2:210-20.
  16. Solomon DJ, Henry RC, Hogan JG, Van Amburg GH, Taylor J. Evaluation and implementation of public health registries. Public Health Rep. 1991 Mar- Apr;106(2):142-50.
  17. Richesson R, Vehik K. Patient registries: Utility, validity and inference. Adv Exp Med Biol. 2010;686:87-104.
  18. Jonker C, Geerlings MI, Schmand B. Are memory complaints predictive for dementia? A review of clinical and population-based studies. Int J Geriatr Psychiatry. 2000 Nov;15(11):983-91.
  19. A conference devoted to better engaging clinical trial volunteers [Internet].: Biomedical Research Forum LLC; 2013 [updated November 26; cited December 4, 2013]. Available from: