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J.T. McDaniel, R.J. McDermott, T. Schneider


Southern Illinois University Carbondale, USA

Corresponding Author: Justin T McDaniel, Southern Illinois University Carbondale, USA,



BACKGROUND: Although studies have examined the geographic distribution of dementia among the general population in order to develop geographically targeted interventions, no studies have examined the geographic distribution of subjective cognitive decline (SCD) among military veterans specifically.
Objectives: To map the geographic distribution of subjective cognitive decline from 2011-2019 in the United States among military veterans.
Design: Cross-sectional.
Setting: United States.
Participants: Individuals reporting previous service in the United States Armed Forces.
Measurements: Using 2011 Behavioral Risk Factor Surveillance System (BRFSS) data, which is last year for which geocoded SCD data is publicly available, we estimated the survey-weighted county-level prevalence of veteran SCD for counties with >30 veterans (43 counties in 7 states). We then developed a Fay-Herriot small area estimation linear model using auxiliary data from the Census, with county-level veteran-specific covariates including % >65 years old, % female, % college educated, and median income. Following model validation, we created beta-weighted predictions of veteran SCD for all USA counties for 2011-2019 using relevant time-specific Census auxiliary data. We provide choropleth maps of our predictions.
Results: Results of our model on 43 counties showed that county-level rates of SCD were significantly associated with all auxiliary variables except annual income (F = 1.49, df = 4, 38). Direct survey-weighted rates were correlated with model-predicted rates in 43 counties (Pearson r = 0.32). Regarding predicted rates for the entire USA, the average county-level prevalence rate of veteran SCD in 2011 was 13.83% (SD = 7.35), but 29.13% in 2019 (SD = 14.71) – although variation in these rates were evident across counties.
Conclusions: SCD has increased since 2011 among veterans. Veterans Affairs hospitals should implement plans that include cognitive assessments, referral to resources, and monitoring patient progress, especially in rural areas.

Key words: Alzheimer’s disease, subjective cognitive decline, veterans, geographic distribution, country, maps.



Subjective cognitive decline (SCD), which is a self-reported measure of increasing severity of memory loss or confusion, may be one of the earliest symptoms of Alzheimer’s disease (1). Undertaking an intervention as soon as possible following noticeable SCD may delay its progress, and thus, its subsequent deleterious health effects, including mortality (2). In the United States (U.S.) an estimated 6.2 million people have Alzheimer’s disease (3, 4). An estimated one in nine people in the U.S. general population aged 45 and older (11.1%) report SCD, a figure that rises to 11.6% for persons 65 and older (5). The U.S. Centers for Disease Control and Prevention (CDC) labels SCD as a public health issue of growing concern (6).
Taylor et al. (7) show that military veterans are more likely to report SCD than nonveterans (13.6% vs. 10.8%), possibly due to the increased risk of exposure to risk factors during military service (8). Although some studies have examined the distribution of cognitive decline in the general population to develop geographically targeted interventions (9, 10), no studies have examined the geographic distribution of SCD among military veterans specifically. Given the lack of a published choropleth map of military veteran SCD rates in the U.S. that would enable a focus on geographically targeted interventions, we sought identification of temporal and geographic trends in SCD rates among military veterans.



Data Source and Sample

We acquired data from the CDC’s 2011 Behavioral Risk Factor Surveillance System (BRFSS), the most recent year for which subjective cognitive decline data are publicly available with county-level geocodes (11). BRFSS is a telephone survey of non-institutionalized adults (e.g., persons who are not in nursing homes or on active duty in the military) in the U.S., aged > 17 years. Topics covered in the BRFSS include, but are not limited to, health behaviors, healthcare access, and chronic diseases. In 2011, the BRFSS had a response rate of 53% for landline telephone respondents and 28% for cell phone respondents.
During the 2011 BRFSS survey waves, seven states included the subjective cognitive decline survey questions: Hawaii, Illinois, New Hampshire, South Carolina, Tennessee, West Virginia, and Wisconsin. Consequently, the analytic sample in this study was limited to residents in those seven states. We delimited the dataset to individuals who answered in the affirmative to the following question about military service status (n = 6,108): “Have you ever served on active duty in the United States Armed Forces, either in the regular military or in a National Guard or military reserve unit?”


SCD was measured using the following question: “During the past 12 months, have you experienced confusion or memory loss that is happening more often or is getting worse?” Closed-ended response options to the aforementioned question included yes, no, don’t know, or refuse to answer. Of 6,108 military veteran respondents, 53 veterans indicated that they did not know whether they had experienced SCD and 93 refused to answer the question. Subsequently, we delimited the dataset only to military veterans who responded with an answer of yes or no to the SCD question (n = 5,962).
The BRFSS protocol also asked participants: “What county do you live in?” In response to this question, survey participants stated the name of the county in which they lived at the time of the survey. The CDC subsequently coded each person’s geographic location response with a federal information processing standard (FIPS) number.

Data Analysis

Phase 1

For the present study, we followed the area-level Fay-Herriot (12) approach to estimate the prevalence of SCD among military veterans in small areas (i.e., counties). As Li and Lahiri (13) note, “the Fay-Herriot model has been widely used in small area estimation and related problems for a variety of reasons, including its simplicity, its ability to protect confidentiality of microdata and its ability to produce design-consistent estimators” (p. 882). Given our analysis involves a subset of the general population (e.g., military veterans), we selected the Fay-Herriot approach especially for it’s ability to retain the confidentiality of microdata. In the first phase of our analysis, we calculated direct survey-weighted prevalence rates of SCD for each county in the BRFSS dataset. Only counties with ≥ 30 military veterans in the denominator of this rate were included in the analysis given that the National Center on Health Statistics has suggested that rates with denominators < 30 produce unstable estimates (14). In total, our analyses revealed that 43 counties met the criteria for inclusion in the study, with county samples ranging from 32 to 599 (M = 87, SD = 92).

Phase 2

Following procedures outlined in Fay and Herriot’s (12) paper, we obtained auxiliary data (i.e., county-level independent variables) for each of the 43 counties in our dataset. Specifically, we obtained 2011 county-level summary measures for each of these counties in the following areas from the U.S. Census Bureau’s American Community Survey (15): (a) the percent of military veterans aged ≥ 65 years, (b) percent of military veterans reporting female as their sex, (c) percent of military veterans with at least a bachelor’s degree, and (d) median annual income for military veterans. The previously listed auxiliary variables have been shown to influence cognitive decline (16). Our selection of 4 auxiliary variables also was determined, in part, by considerations of power and model stability. That is, with only 43 counties in our dataset we did not want to overextend our model with > 4 predictor variables (17). Subsequently, we regressed our county-level prevalence rates of SCD on these 4 county-level auxiliary variables in a multivariable regression model. Although it would have been useful to explore interaction effects in this model, we were unable to do so owing to the limited sample size. Model estimation was performed in Stata IC version 16.

Phase 3

After we fit a model to the data, we conducted an internal model validation procedure following the guidelines of Zhang et al. (18). Specifically, we calculated a Pearson correlation coefficient for the relationship between our modeled county-level prevalence rates and the county-level prevalence rates based on the raw survey data. We also created a scatterplot for the relationship between these two variables using the “ggplot2” package in R Studio version 3.6.1. As in Zhang et al.’s (18) study, a statistically significant and positive correlation coefficient was indicative of consistency between the modeled rates and the direct survey estimates.

Phase 4

Following model validation, as described in phase 3, we obtained comprehensive and nationally representative county-level summary data (i.e., data for 3,220 counties in the U.S.) on our 4 previously described military veteran-specific auxiliary variables from the 2011-2019 U.S. Census Bureau’s American Community Surveys. Subsequently, we applied the beta coefficients calculated in our linear regression model (i.e., from phase 2) to the U.S. Census Bureau county-level summary data to estimate predicted prevalence rates of SCD for all U.S. counties from 2011 to 2019. Using ESRI ArcGIS version 10.5.1., we developed county-level choropleth maps of our predictions by year, as well as by percentage change from 2011-2019.



Phase 1

Table 1 shows the characteristics of the military veterans included in our 43 counties. All 7 states with available data on SCD also included at least one county with a military veteran sample size ≥ 30. The aggregated prevalence rate of SCD in the sample, Table 1 shows, was 11.96% (SD = 32.50). County-level prevalence rates of SCD ranged from 2.38% (i.e., Shelby County, Tennessee) to 22.02% (i.e., Horry County, South Carolina), with an average of 12.24% (SD = 4.91).

Table 1. Demographic characteristics of the military veteran sample with available data on subjective cognitive decline, BRFSS 2011 (n = 5,962)


Phase 2

Table 2 shows the results of our multivariable linear regression model, where the BRFSS survey-weighted rates of SCD (n = 43) were regressed on 4 auxiliary variables obtained from the U.S. Census Bureau. Although we fit models with 1-3 auxiliary variables, the model with all 4 auxiliary variables explained the greatest amount of variance in SCD among veterans. Model 4 indicates that rates of SCD at the county level were positively associated with the percent of veterans aged ≥ 65 years, positively associated with the percent of veterans reporting female as their sex, and negatively associated with the percent of veterans with a college degree.

Table 2. Fay Herriot models for the prediction of subjective cognitive decline among military veterans

a. Unstandardized beta and robust standard error; b. e-5, scientific notation


Phase 3

We obtained predictions from the model described in phase 2 for all 43 counties for which military veteran data was available. We calculated a Pearson correlation coefficient between the county-level predictions and the direct survey estimates. As Figure 1 shows, our internal model validation procedure revealed that SCD rates modeled with our linear regression model were consistent with the direct survey estimates (r = 0.32, p = 0.03).

Figure 1. Scatterplot illustrating the relationship between the predicted prevalence rates of SCD based on our linear regression model and the direct survey-weighted prevalence estimates (n = 43)


Phase 4

Given the results of our internal model validation procedure, we used the model coefficients [y = –0.19 + (0.02*X1) + (0.04*X2) + (0.000016*X3) – (0.02*X4)] to estimate the prevalence of SCD among military veterans in 3,220 counties in the U.S. from 2011 to 2019. Figure 2 shows the results of our predictions across the 9-year study period. In 2011, the average county-level prevalence rate of veteran SCD, across all counties in the U.S. (i.e., not just the original 43 counties), was 13.83% (SD = 7.35), but in 2019, the estimated average county-level prevalence rate of veteran SCD was 29.13% (SD = 14.71) – although variation in these rates were evident across U.S. counties.
Using the formula for percent change (i.e., Y2 – Y1/Y1), we examined changes in SCD rates among military veterans by county for 2011-2019. Our analysis revealed that 2,948 counties (91.55%) had increases in predicted military veteran SCD for 2011-2019, with counties in Texas, Georgia, Kentucky, Kansas, West Virginia, Montana, and Puerto Rico having the highest increases (i.e., > 1000% increase).

Figure 2. Predicted county-level prevalence rates of SCD among military veterans from 2011-2019 in the United States



Using publicly available, de-identified data from a nationally representative health indicator survey and auxiliary data from the U.S. Census Bureau, we were able to estimate a predictive model of SCD among military veterans. We subsequently used our predictive model to estimate the prevalence of SCD among military veterans in 3,220 counties across the U.S. Results showed that the prevalence of military veteran SCD increased in 91.55% of counties, with counties in Texas, Georgia, Kentucky, Kansas, West Virginia, Montana, and Puerto Rico having the greatest increases.
Research demonstrates that early intervention in Alzheimer’s disease can have quality of life and economic benefits (19). The prevention of Alzheimer’s disease may be carried out with pharmacological (20) or behavioral approaches, especially those that target cardiovascular problems, which are risk factors for cognitive decline (21). For military veterans, specifically, greater enrollment in the U.S. Department of Veterans Affairs (VA) programs focused on physical activity and nutrition, such as the “MOVE!” weight management program (22), may be particularly advantageous in preventing cognitive decline. Complementary and integrative medicine approaches, such as the VA THRIVE program (23), also have been shown to improve mental functioning in military veterans and may be applicable to the prevention of Alzheimer’s disease.


Several limitations accompany the methods and results of the present study. First, although the measure of SCD used in this study has been used widely, it only includes one self-report item and, therefore, may be subject to recall or social desirability bias. Second, several counties in the present study were excluded due to unstable or unavailable data, which prevented a more comprehensive picture of the geographic distribution of SCD in military veterans. Third, although other county level predictors may have been beneficial for inclusion in our predictive model, we were limited due to the small sample size of counties obtained from the BRFSS dataset.
Those limitations notwithstanding, this is the first study to map the prevalence of SCD among military veterans in U.S. counties. Our analysis revealed geographic variation in the distribution of SCD prevalence in the U.S., which implies a need for geographically targeted interventions that may prevent or moderate the progression of cognitive decline in veterans.


Funding: No funding was received for this study.

Conflicts of interest/Competing interests: The authors have no conflicts of interest to disclose.

Availability of data and material:

Code availability: Not applicable.

Ethical standards: Because this study used de-identiied data freely available on the web, this study was considered exempt from IRB review.



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X.-T. Wang1, Z.-T. Wang2, H.-Y. Hu1, Y. Qu1, M. Wang1, X.-N. Shen3, W. Xu1, Q. Dong3, L. Tan1,2,*, J.-T. Yu3,*


1. Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China; 2. College of Medicine and Pharmaceutics, Ocean University of China, Qingdao, China; 3. Department of Neurology and Institute of Neurology, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China

Corresponding Author: Prof. Jin-Tai Yu, MD, PhD, Department of Neurology and Institute of Neurology, Huashan Hospital, Shanghai Medical College, Fudan University, 12th Wulumuqi Zhong Road, Shanghai 200040, China; Or Prof. Lan Tan, MD, PhD, Qingdao Municipal Hospital, Qingdao University, China. E-mail address: (J.T. Yu); (L. Tan).

J Prev Alz Dis 2021;3(8):277-285
Published online May 28, 2021,



Background: Subjective cognitive decline (SCD) as an early pathological manifestation of brain aging has become more prevalent among older adults.
Objectives: We aimed to investigate the associations of subjective cognitive decline (SCD) with the combined risk of cognitive impairment and dementia.
Design: We performed a systematic review and meta-analysis via searching Embase, PubMed and Cochrane electronic databases from January 1 st 1970 to June 4th, 2020.
Setting: Prospective cohort studies
Participants: Healthy individuals were recruited from community, clinics and population.
Measurements: Healthy individuals with SCD were classified into exposure groups, while those without were considered as the reference group. Adjusted relative risks (RR) were estimated in a random-effects model. Both primary and subgroup analyses were conducted.
Results: Of 28,895 identified studies, 21 studies containing 22 cohorts were eligible for inclusion in the meta-analysis. SCD increased the risk of subsequent cognitive disorders (RR=2.12, 95% confidence intervals [CI] =1.75-2.58, I2=87%, P<0.01). To be specific, SCD conferred a 2.29-fold excess risk for cognitive impairment (RR=2.29, 95% CI=1.66-3.17, I2=83%, P<0.01) and a 2.16-fold excess risk for dementia (RR=2.16, 95% CI=1.63-2.86, I2=81%, P<0.01). In subgroup analyses, participants with SCD in the subgroup of 65-75 years old, long-education (>15 years) subgroup and subgroup of clinics showed a higher risk of developing objective cognitive disorders.
Conclusions: SCD is associated with an increased combined risk of cognitive impairment and incident dementia and should be considered a risk factor for objective cognitive disorders.

Key words: Subjective cognitive decline, cognitive impairment, dementia, systematic review, meta-analysis.



Longer life expectancy has led to the growth of the older population, and older adults might account for nearly 16% of the world’s population by 2050 (1). Disorders of aging, especially neurodegenerative changes, which eventually result in dementia, has become an increasing concern, in recent years (2). With a lack of curative treatments for cognitive impairment and dementia, many studies have focused on identifying risk factors at the prodromal and preclinical stages of Alzheimer’s disease (AD) (3). As an early pathological manifestation of brain aging, subjective cognitive decline (SCD), has become a research hotspot (4).
An international working group called the Subjective Cognitive Decline Initiative (SCD-I) focusing on advances in related research has been established (5). SCD could be defined as a self-experienced persistent decline in cognitive capacity in comparison with a previously normal status, which is unrelated to an acute event. Moreover, normal age-, sex- and education-adjusted performance on standardized cognitive tests is used to classify mild cognitive impairment (MCI) (6, 7). SCD has several alternative names, including subjective cognitive complaints (SCC) (8, 9), subjective memory decline (SMD) (10) and subjective memory complaints (SMC) (11). A previous systematic analysis provided evidence for the prognostic validity of memory complaints to predict the risk for subsequent dementia and cognitive impairment (12), while it might ignore the baseline cognitive status of included individuals. Besides, healthy controls without memory complaints should be taken into the consideration as the reference group to ensure the preciseness of analysis. Therefore, we conducted this meta-analysis in healthy population with more strict inclusion criteria. Our aim was to explore the association of SCD with the combined risk of cognitive impairment and dementia in longitudinal studies.



Search Strategy

This meta-analysis was conducted following the guidelines of the Meta-analysis Of Observational Studies in Epidemiology (MOOSE) (13) and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) (14). PubMed, Embase and Cochrane databases were searched with the same strategy ‘(subjective memory decline OR concern* OR complaint* OR SCD OR SMC OR SMD OR SCC) AND (risk OR association) AND (dementia OR cogniti* OR alzheimer* OR MCI OR mild cognitive impairment)’ from January 1st, 1970 to June 4, 2020. Conference abstracts and unpublished studies were also reviewed. Additional studies were identified by screening related reviews and reference lists of studies. If full texts were unavailable, we contacted corresponding authors.

Study Selection

The study selection process was described in Fig.1. There were 28,895 studies from three databases, after deleting duplicates in the EndNote. Studies which met the following criteria were eligible: (1) studies investigating the association of subjective memory complaints with cognitive impairment or dementia (all-cause dementia [ACD] or vascular dementia [VaD] or AD); (2) prospective longitudinal studies with a follow-up of at least 6 months; (3) studies including cognitively normal participants at baseline who were divided into an exposure group with subjective cognitive concerns (assessed by various questionnaires) and a reference group without complaints; (4) studies using recognized diagnostic criteria for objective cognitive performance (including cognitive impairment or dementia) as an end point of the study, such as the criteria made by National Institute on Aging-Alzheimer’s Association (NIA-AA). We did not place language restrictions upon the eligibility criteria of included studies. Randomized clinical trials were excluded, as therapies, psychological suggestions and interventions provided may influence the associations of subjective memory complaints with cognitive impairment and incident dementia. Moreover, people with psychoactive medication use, neurological disease (e.g. Parkinson’s disease, epilepsy, and multiple sclerosis), history of brain lesion (e.g. infection and infarction), head trauma or other systematic diseases of sufficient severity to adversely affect cognition were also excluded. First, after screening the titles and abstracts, we excluded the articles unconcerned with our topic, and only included topic related ones (n=165) for further selection. We then read full texts of those potential eligible articles, searched bibliographies of relevant reviews or meta-analyses, and finally selected 21 articles based on the criteria mentioned above.

Data Extraction

We extracted authors, year of publication, study period, country, language, sample size, inclusion or exclusion criteria, source of participants, age, numbers of male and female individuals, education, follow-up time, methods of diagnosis, count data, unadjusted and adjusted estimates of odds ratio (OR), relative risk (RR), hazard risk (HR) and their 95% confidence intervals (CI) for cognitive impairment or incident dementia. As for we encountered some studies from same cohorts, we chose the study with the largest number of included participants at the baseline. Among effective values reported in the studies, we chose the maximally adjusted estimates. If effective values were not available directly, we used RR calculated by the ratio comparing the of incident rates of cognitive impairment or dementia between exposed and reference groups. Information was first extracted by one investigator, and then checked independently by another two authors. Discrepancies were resolved by discussion. When the data we required were not available in the article, we contacted the corresponding authors for original information.

Quality Assessment

The Newcastle-Ottawa Scale (NOS) has been used to assess the quality of published non-randomized studies in meta-analyses (15, 16). The NOS contains eight items which can be categorized into three dimensions (selection, comparability and outcome). A star system is employed to allow a semi-quantitative assessment of study quality, with a maximum of one star for each item except the comparability item which allows the assignment of two stars (17). The highest quality studies could be awarded a maximum of nine stars.

Statistical Analysis

We mainly analyzed the pooled RR, showing whether individuals with SCD at baseline were more likely than those without to develop cognitive impairment and dementia during follow-up in our study. Given that ORs tend to overestimate the effect sizes compared with RRs/HRs particularly when the incidence is not low, we transformed ORs into RRs using the following algorithm:(18)

RRadjusted = ORadjusted /[(1 − P0) + (P0 × ORadjusted]

P0 indicates the incidence of endpoint (dementia or cognitive decline) in the non-exposed group of the cohort. When P0 is not available, the incidence rate of total sample was used as a proxy.(18) HR, compared with RR, additionally considering the factor of time, might be approximately equal to RR at a point in time. Effective values across studies were combined to provide overall estimates and their 95% CIs using random-effects DerSimonian-Laird models (19). Participants with cognitive disorders were additionally stratified into cognitive impairment and dementia groups. Further subgroup analyses (stratified by different age, sex, year of education, follow-up time and source) were also conducted to investigate whether other factors would change the results using the same models. When calculating RR and 95%CI in subgroup analyses, participants without SCD in each subgroup were considered as the reference group. Each subgroup of basic characteristics might include at least three studies to ensure the reliability of subgroup analyses.
Heterogeneity between studies was assessed by I2 test statistics for each analysis. An I2 of less than 25% is considered as no statistical heterogeneity, 25% to 50% as low statistical heterogeneity, 50% to 75% as medium statistical heterogeneity, and more than 75% as high statistical heterogeneity (20). Meta-regression analyses (n≥10) were also conducted with robust variance estimation, assessing the potentially important covariates that might exert a substantial impact on between-study heterogeneity. Sensitivity analyses were additionally carried out to explore the source of heterogeneity by excluding one study at a time.
We also evaluated the potential publication bias with funnel plots for the outcomes, the symmetry of which was detected by Egger’s test. Egger’s test, also known as linear regression method, uses standard normal deviate and precision of included studies to establish regression equation (21). Moreover, if statistically significant publication bias was detected, the trim-and fill method was used to adjust for bias. A two-tailed P values <0.05 is considered as statistically significant. Statistical analyses were conducted in R (R programming).



Basic characteristics of included studies

A total of 21 studies (22-42) were selected for our meta-analyses (Fig.1). In a study conducted by Snitz (36), both the individuals from communities and clinics were divided into SCD and non-SCD groups. We consider this study as two independent cohorts to include in our meta-analysis. Therefore, 22 cohorts were ultimately included in our meta-analyses. The basic characteristics of included studies are presented in Table 1. A total of 47,805 individuals from the 22 studies were included at baseline. The median of the mean age was 72.70 years old (ranging from 62.8 to 83.31 years old), except one study (25) which did not report its mean age. The female proportion of included studies ranged from 46.46% to 100% and the average years of education was more than 9 years. The average length of follow-up across studies ranged from 2 to 18 years (mean: 5.4; standard deviation [SD]: 3.6), showing an obvious difference among studies, which might contribute to higher heterogeneity. Effective values are also presented in Table 1, ranging from 0.20 to 70.10.

Figure 1. The flowchart of study selection

*: A study contains two cohorts, &: two studies with specific data on both cognitive impairment and dementia; #: Only three of five studies with specific data of both Alzheimer’s disease and non-Alzheimer’s disease.


Table 1. Basic characteristics of included studies

SD: standard deviation; EDU: education; FU: follow-up; RR: relative ratio; NA: not accessible; AD: Alzheimer’s disease; 95%CI: 95% confidence interval; GP: general practitioner; AgeCoDe: German Study on Ageing, Cognition, and Dementia in Primary Care Patients; preDIVA: Prevention of Dementia by Intensive Vascular Care trial; ADRC: University of Pittsburgh Alzheimer Disease Research Center; MYHAT: the Monongahela-Youghiogheny Health Aging Team study; OSHPE: the Obu Study of Health Promotion for the Elderly; ISAAC: the Intelligent Systems for Assessing Aging Changes study; MADRC: Massachusetts Alzheimer’s Disease Research Center longitudinal cohort; NACC: the National Alzheimer’s Coordinating Center; LADIS: Leukoaraiosis and Disability; NA: not accessible; MAAS: the Maastricht Aging Study; ACT: the Adult Changes in Thought study; MSHA: the Manitoba Study of Health and Aging;


Methods for assessing SCD (eg. “Do you feel like your memory is becoming worse?”) and criteria of diagnosing cognitive impairment or dementia, like NIA-AA, are presented in eTable 1 and eTable 2, respectively (Supplementary materials). Bias assessment based on the Newcastle-Ottawa Scale is provided in Supplementary eTable 3. All the included studies were of high quality, as they all got 7 or more than 7 stars (a maximum of 9 stars) (43).

Results of primary analyses

In the primary analyses, SCD showed an increased risk of developing subsequent cognitive impairment or dementia in Fig.2 (RR=2.12, 95%CI=1.75-2.58, I2=87%, P<0.01). Among the 22 cohorts included in our study, 11 cohorts with cognitive impairment as the outcome showed that 1,481 out of the total 8,346 individuals progressed into cognitive impairment at the last follow-up visit, demonstrating a significant association between SCD and cognitive impairment (RR=2.29, 95%CI=1.66-3.17, I2=83%, P<0.01) (Fig.3). And among the participants with SCD, the risk of developing dementia (RR=2.16, 95%CI=1.63-2.86, I2=81%, P<0.01) was similar to that of developing cognitive impairment (Fig.3). Individuals in four studies (24, 26, 29, 34) progressed to either cognitive impairment or dementia. Moreover, two (24, 34) of the four studies showed separate incidence rates of cognitive impairment and dementia. The other two studies showed incidence rate ratios of mixed cognitive disorders, which made it difficult for us to get the numbers of individuals who progressed to different types of cognitive disorders.

Figure 2. SCD shows a significant association with the risk of developing objective cognitive disorders

RR: relative risk, CI: confidence interval, SCD: subjective cognitive decline.


Figure 3. SCD shows significant associations with cognitive impairment and dementia

RR: relative risk, CI: confidence interval, SCD: subjective cognitive decline


Results of subgroup analyses

For further analysis, all included studies were stratified into subgroups based on their demographic characteristics, including age, female proportion, years of education, follow-up time and source of participants (Supplementary eTable 4). We observed that SCD conferred an excess risk of subsequent cognitive impairment in the individuals aged 65-75 years old (RR=2.29, 95%CI=1.83-2.88, I2=87%, P<0.01) (Supplementary eFig 1). SCD showed similar risks for cognitive disorders in the two subgroups stratified by female proportion (Female>50%: RR=2.18, 95%CI=1.26-3.75, I2=75%, P<0.01; Female≤50%: RR=2.11, 95%CI=1.69-2.64, I2=89%, P<0.01) (Supplementary eFig.2). There was a trend for well-educated individuals (>15 years) to be more strongly influenced by SCD (RR=3.71, 95%CI=2.10-6.56, I2=79%, P<0.01) (Supplementary eFig.3). In the subgroup with longer follow-up, individuals with SCD had a nearly doubled risk of progression to cognitive disorders (cognitive impairment and dementia) compared to those without (RR=1.98, 95%CI=1.61-2.44, I2=89%, P<0.01) (Supplementary eFig.4). In the subgroup of different settings, individuals with SCD showed approximately twice higher risks for cognitive disorders in community (RR=2.08, 95%CI=1.58-2.75, I2=88%, P<0.01) and population (mixed settings) groups (RR=1.93, 95%CI=1.37-2.72, I2=89%, P<0.01), as well as a four times higher risk for cognitive disorders in clinics (RR=4.25, 95%CI=1.08-16.77, I2=85%, P<0.01), compared with those without SCD (Supplementary eFig.5). The influence of SCD on the risks of cognitive disorders in various subgroups were summarized in eFig.6 (Supplementary materials).

When we further divided cognitive disorders into cognitive impairment and dementia, subgroup analyses were also conducted and the results were shown in eTable 5 and eTable 6 (Supplementary materials). In the subgroup analyses of the 11 cohorts focused on cognitive impairment, individuals with SCD had a higher risk of subsequent cognitive impairment in the subgroup of female proportion>50% (RR=2.64, 95%CI=1.61-4.33, I2=87%, P<0.01) (eFig.7) and in the subgroup of > 15 years of education (RR=2.64, 95%CI=1.61-4.33, I2=87%, P<0.01) (eFig.8). Additionally, the influence of SCD on cognitive impairment showed nearly no marked difference between individuals with and without the APOE ε4 allelic gene (APOE ε4+, RR=1.67, 95%CI=1.07-2.61, I2=58%, P=0.07; APOE ε4-, RR=1.89, 95%CI=1.17-3.03, I2=85%, P<0.01) (eFig.9). Individuals with SCD also showed higher risks of cognitive impairment in subgroup of 65-75 years old (RR=2.69, 95%CI=1.79-4.04, I2=83%, P<0.01), subgroup of shorter follow-up (RR=3.49, 95%CI=2.14-5.69, I2=0%, P<0.01) and subgroup of individuals from clinics (RR=8.06, 95%CI=1.68-38.67, I2=66%, P=0.09). Results on the influence of SCD on the progression into cognitive impairment were summarized in eFig.10 (Supplementary materials).

In the subgroup analysis of the cohorts which progressed into dementia, SCD individuals in the subgroup of short follow-up time showed a higher incidence rate of dementia (RR=3.40, 95%CI=1.46-7.89, I2=34%, P=0.22) (eFig.11). Moreover, when we classified dementia into AD and Non-AD groups, SCD showed a significant association with AD (RR=2.39, 95%CI=1.00-5.74, I2=76%, P<0.01), while it had a non-significant association with non-AD dementia (RR=1.37, 95%CI=0.93-2.03, I2=0%, P=0.73) (eFig.12). Results in subgroups of 75-85 years old (RR=1.75, 95%CI=1.43-2.14, I2=0%, P=0.93), female proportion more than 50% (RR=2.10, 95%CI=1.44-3.05, I2=85%, P<0.01) and individuals from clinics (RR=1.77, 95%CI=1.41-2.22, I2=0%, P=0.62) might need further investigation, since they were limited by the numbers of included studies in the subgroups. Results on the influence of SCD on the progression into dementia were summarized in Supplementary eFig.13.

Meta-Regression Analysis, Sensitivity Analysis and Publication Bias

Based on the results of meta-regression analysis (Supplementary eTable 7), the influence of the covariates on heterogeneity, such as participant’s mean age (p=0.163; 95%CI, -0.100238 to 0.0181451; τ2=0.1979), female proportion (p=0.271; 95%CI, -3.221842 to 0.9562178; τ2=0.2041), years of education (p=0.329; 95%CI, -0.068722 to 0.1892374; τ2=0.3314) and length of follow-up (p=0.231; 95%CI, -0.0968754 to 0.0248457; τ2=0.1917) were not statistically significant, as the two-tailed P values were all greater than 0.05, ranging from 0.072 to 0.928.
The sensitivity analysis showed two studies (36, 38) significantly influenced the heterogeneity (Supplementary eFig.14). When each of the studies was excluded separately, the heterogeneities still remained at 84%. The funnel plot showed relatively bilateral symmetry and the p value was 0.4557 (Supplementary eFig.15), indicating no publication bias.



SCD was associated with a higher risk of subsequent cognitive disorders, which increased that SCD would possibly elevate the risk. Individuals with SCD showed higher risks of subsequent cognitive impairment and dementia both of which were more than two-fold compared with those without. When data were stratified by their basic characteristics, participants with SCD in subgroup of 65-75 years old, subgroup of female proportion more than 50%, long education subgroup, short follow-up subgroup and subgroup of individuals from clinics had higher risks of objective cognitive disorders. SCD participants showed significant higher risk of developing cognitive impairment compared to non-SCD participants, but there was nearly no marked difference in the rate of progression to cognitive impairment between individuals with/without the APOE ε4 allelic gene in SCD participants. Moreover, SCD participants also showed a significantly higher risk of developing AD dementia rather than non-AD dementia compared with those without SCD.
Biological alternations induced by SCD could occur before objective cognitive decline, such as gray matter volume reduction (44). Individuals with SCD have also been reported to have larger white matter hyperintensity (WMH) volumes, hippocampal atrophy (45) and increased β-amyloid (Aβ) deposition (46, 47), which are typical characteristics of AD. Furthermore, some studies illustrated that SCD was a subjective symptom reflecting anxiety or depression about senility and health rather than neurodegenerative causes (48, 49) and was just a risk factor rather than a mechanism underlying preclinical AD or other neurodegenerative dementias (6), as many participants with SCD might not develop subsequent cognitive impairment or even dementia (50). Hence, SCD was more likely to be a risk factor for cognitive disorders. Previous studies also suggested that cognitively unimpaired individuals with SCD were at a significantly increased risk of future objective cognitive disorders and clinical progression to symptomatic disease stages (12, 36, 51) which was in accordance with our results that SCD conferred excess risks of subsequent cognitive impairment and dementia. Furthermore, individuals with SCD were considered as high-risk individuals and they need necessary interventions during stages at which objective cognitive impairment remains clinically unapparent.(52)
What was more, Wang found that age modified the association between SCD and future cognitive disorders, with HR decreasing from 6.0 at age 70 to 1.6 at age 80 (42). Though previous studies have proven that older elderly are more likely to develop cognitive disorders than younger elderly (12, 35, 53, 54), older elderly may have a casual attitude towards their cognitive conditions. Older elderly are less likely to worry about themselves, so subjective complaints from younger elderly are likely to be more predictive than those from older elderly. Therefore, this might explain our result that the influence of SCD was more obvious in the subgroup of older elderly. In the subgroup analysis by female proportion, individuals with SCD showed a nearly 2.5 times risk of developing cognitive impairment than those without in the subgroup of female proportion more than 50%, which was consistent with the previous conclusion that women were prone to cognitive impairment (27). A previous study reported that education affected the process of memory decline (55). Well-educated people usually seem knowledgeable, and more concerned about their health, suggesting their self-reported of SCD is more accurate. For this possible reason, longer education may contribute to an increased risk of progression from SCD to cognitive disorders, which was in accordance with the results of our subgroup analysis stratified by education including the one of all 22 studies with the outcome of cognitive disorders and the one of the 11 studies with the outcome of cognitive impairment.
Individuals with SCD in the subgroup of follow up>3years showed lower risks of developing cognitive disorders, especially dementia, compared with the subgroup of not more than 3 years, which might be explained by the increased drop-out rate or increased mortality of participants during longer follow-up. Several studies (5, 36) clearly showed that settings might affect the influence of SCD. In our study, SCD showed the strongest association with cognitive disorders in individuals chosen from clinics, as people might be classified explicitly and diagnosed in clinical settings, using available and easily measurable criteria and standard definitions of cognitive impairment or/and dementia. Moreover, previous studies also illustrated that patients in clinics were more likely to experience the first sign or the preclinical stage of a neurodegenerative disease (47, 56). A study found a significant effect of APOEε4 on memory (57). And our result suggested that SCD was a risk factor for cognitive impairment independent of the APOEε4 gene, which was likely to be limited by insufficient samples. Additionally, some individuals with SCD showed gray matter volume reduction (44) and greater similarity to an AD gray matter pattern (58) compared with subjects without SCD, which was consistent with our subgroup analyses.
There was considerable heterogeneity, which might be due to the different characteristics of individuals. Therefore, we conducted specific analyses, such as subgroup analyses based on different characteristics of studies and sensitivity analysis to find out cohorts which were significantly different from others. Apart from basic characteristics, measurements of SCD have also been reported to influence the risk of developing cognitive impairment (51). Cohorts included in our study used different assessments of SCD, which might be one of the factors leading to a bit higher heterogeneity. Recruiting larger samples, comparing important characteristics of participants, unifying the assessment of SCD and searching for methods to lower drop-out rates are necessary in future well-designed longitudinal studies.
The primary strength of our meta-analysis lies in the unity in design of studies (prospective longitudinal studies). The prospective longitudinal study minimized the potential influence of recall and selection bias, which might be inevitable in retrospective design. Besides, our retrieval was comprehensive, since we screened the three databases involving almost all available assays. Also, our search term contained, as more as possible, expressions of the same meaning we focus on (including SCD and dementia), and used “OR” as conjunctions for expressions of the same meanings, which could expand our retrieval range. Furthermore, our studies had independent blind assessments or reliable diagnostic criteria of outcomes (cognitive impairment and dementia), which were reflected in the Newcastle-Ottawa Scale questionnaire (Supplementary eTable 3). Studies included are all of high quality (Newcastle-Ottawa Scale ≥ 6 stars) (43), without having publication bias. Overall, the result that SCD increases the risk of subsequent cognitive impairment and dementia is reliable.


There are some limitations in our meta-analysis. First, various questionnaires had different criteria for identifying SCD, which might contribute to a lack of uniformity in diagnosis of SCD. In addition, due to the association of patients and informants, the accuracy of SCD detection could be easily influenced by informants’ expectations of being normal. Second, during the follow-up, as time went on, more and more participants dropped out. Those who are lost to follow-up usually tend to be older, sicker, and have lower socioeconomic status, which might lead to attrition bias. Finally, the reliability of our subgroup analyses might be oppugned owing to our insufficient studies in certain subgroups and the possibility of type I error. Future studies are required to reduce these limitations and make more reliable inferences.



In conclusion, SCD is associated with an increased risk of objective cognitive disorders, including cognitive impairment and incident dementia. Individuals with SCD in subgroup of 65-75 years old, subgroup of female proportion more than 50%, longer education subgroup and subgroup of individuals from clinics showed higher risks of cognitive disorders. SCD deserve more attention, as it could serve as a potential target for early intervention trials in cognitive disorders.


Acknowledgements: None.

Funding: This study was supported by grants from the National Key R&D Program of China (2018YFC1314700), Shanghai Municipal Science and Technology Major Project (No.2018SHZDZX01) and ZHANGJIANG LAB, Tianqiao and Chrissy Chen Institute, and the State Key Laboratory of Neurobiology and Frontiers Center for Brain Science of Ministry of Education, Fudan University.

Conflicting interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Author’s Contributions: JTY, QD and LT conceptualized and designed the study. XTW, ZTW, HYH, YQ, MW, XNS and WX conducted the study. XTW, ZTW, HYH, YQ and MW analyzed and extracted data. XTW, ZTW and JTY wrote the first draft of the manuscript. All authors reviewed the manuscript.

Ethical Standards: None





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L.M.P. Wesselman1, A.K. Schild2, A.M. Hooghiemstra1,3, D. Meiberth2, A.J. Drijver4, M.v. Leeuwenstijn-Koopman1, N.D. Prins1, S. Brennan5, P. Scheltens1, F. Jessen2,6, W.M. van der Flier1,7, S.A.M. Sikkes1,8


1. Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands; 2. Department of Psychiatry, University Hospital Cologne, Medical Faculty, Cologne, Germany; 3. Department of Medical Humanities, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1089a, 1081 HV Amsterdam, The Netherlands; 4. Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Neurology, The Netherlands; 5. The Adapt Centre, & The Institute of Neuroscience, Trinity College Dublin; 6. German Center for Neurodegenerative Disorders (DZNE), Bonn-Cologne, Germany; 7. Department of Epidemiology and Biostatistics, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands; 8. Clinical Developmental Psychology & Clinical Neuropsychology, Faculty of Behavioural and Movement Sciences (FGB), Vrije University Amsterdam, Amsterdam, the Netherlands.

Corresponding Author: Linda M.P. Wesselman, Alzheimer Center Amsterdam and Department of Neurology, Amsterdam Neuroscience, Amsterdam UMC, P.O. Box 7057, 1007 MB Amsterdam, the Netherlands, Telephone: +31-204440816; Fax: +31-204448529; E-mail:

J Prev Alz Dis 2020;3(7):184-194
Published online March 2, 2020,



Background: Online programs targeting lifestyle have the potential to benefit brain health. We aimed to develop such a program for individuals with subjective cognitive decline (SCD). These individuals were reported to be at increased risk for dementia, and report both an intrinsic need for brain health information and motivation to participate in prevention strategies. Co-creation and user-evaluation benefits the adherence to and acceptance of online programs. Previously, we developed a prototype of the online program in co-creation with the users .
Objectives: We now aimed to evaluate the user-experiences of our online lifestyle program for brain health.
Design: 30-day user test; multi-method.
Setting: Participants were recruited in a memory clinic and (online) research registries in the Netherlands (Alzheimer Center Amsterdam) and Germany (Center for memory disorders, Cologne).
Participants: Individuals with SCD (N=137, 65±9y, 57% female).
Measurements: We assessed user-experiences quantitatively with rating daily advices and usefulness, satisfaction and ease of use questionnaires as well as qualitatively using telephone interviews.
Results: Quantitative data showed that daily advices were rated moderately useful (3.5 ±1.5, range 1-5 points). Participants (n=101, 78%) gave moderate ratings on the programs’ usability (3.7±1.3, max 7), ease of learning (3.6±1.9) and satisfaction (4.0±1.5), and marginal ratings on the overall usability (63.7±19.0, max 100). Qualitative data collected during telephone interviews showed that participants highly appreciated the content of the program. They elaborated that lower ratings of the program were mainly due to technical issues that hindered a smooth walk through. Participants reported that the program increased awareness of lifestyle factors related to brain health.
Conclusions: Overall user-experience of the online lifestyle program was moderate to positive. Qualitative data showed that content was appreciated and that flawless, easy access technique is essential. The heterogeneity in ratings of program content and in program use highlights the need for personalization. These findings support the use of online self-applied lifestyle programs when aiming to reach large groups of motivated at-risk individuals for brain health promotion.

Key words: Lifestyle, dementia, subjective cognitive decline, eHealth, prevention.

Abbreviations: SCD: subjective cognitive decline; MCI: mild cognitive impairment; SUS: System Usability Scale; USE: User Satisfaction and Ease of use.



The World Health Organization (WHO) Global Action Plan on Dementia emphasized the need for campaigns to increase public awareness and understanding of dementia (1). Recent studies found that knowledge about prevention and treatment of dementia remains poor and that there is a need for adequate dementia prevention education (2, 3).
The body of evidence on the association between a healthy lifestyle and brain health keeps growing (4). Risk factors for dementia due to Alzheimer’s disease (AD), such as lifestyle factors, are suggested to be partly modifiable (5). A healthy lifestyle may therefore decrease the risk for AD dementia. Since the etiology of AD is complex and multifactorial, recommendations are made to target several risk factors simultaneously (6, 7). Indeed, a multifactorial intervention has been found to improve or maintain cognitive functioning in people at risk for dementia (8). However, this intervention was offered face-to-face, which is beneficial for program use because of personal contact, but is relatively expensive and limits possibilities to reach a larger group of individuals. Offering intervention programs online has an important advantage because it offers the opportunity to reach many users, in particular in remote areas (9).
Our international EuroSCD-project aimed to develop an online lifestyle program for brain health. Individuals with subjective cognitive decline (SCD) experience cognitive decline in absence of objective cognitive impairments. SCD has previously been reported to be a risk factor for dementia and AD (10, 11). Therefore, individuals with SCD might be an ideal target group for online interventions. This at-risk group might present at memory clinics, their GP or research registries, and was found to be motivated to participate in prevention strategies (12). Individuals at-risk might benefit most from prevention strategies aimed at optimizing brain health or preventing cognitive decline (13, 14).
Our recent review and meta-analysis on online lifestyle programs for brain health suggested that these programs could indeed benefit brain health (15). However, the programs that we reviewed were heterogeneous in content and set-up. Further, characteristics and the methods and results of evaluations of the programs were often not described consistently. More specifically, it was often unclear how user-participation was operationalized and thus how users were involved during the development of the programs (15). This is an important aspect during the development of online programs, because it is essential to involve future users during development. With the users’ input, a program will better fit the users’ needs, which benefits acceptance and adherence, and thereby the implementation of sustainable innovations (16). Previously, we investigated barriers and facilitators for the use of an online lifestyle program in individuals with SCD (12). We found that both program characteristics and personal factors need to be considered, with trustworthiness, user-friendliness, and personalization being important facilitators. We implemented these results during the development of an online lifestyle program for brain health. In co-creation with the users, we developed and adapted the program in multiple iterations. We now aimed to evaluate user-experiences of our online lifestyle program in Dutch and German individuals with SCD, using both quantitative and qualitative methods.



Project and study design

This study is part of the European ‘Subjective cognitive decline in preclinical Alzheimer’s Disease: European initiative on harmonization and on a lifestyle-based prevention strategy’ project (Euro-SCD; JPND_PS_FP-689-019), which aims to develop an online lifestyle program for individuals with SCD. The Euro-SCD project is a collaboration between the Alzheimer Center Amsterdam, the Netherlands (17), Hospital Clinic Barcelona, Spain, and the Center for memory disorders, University Hospital Cologne, Germany. The study was conducted in accordance with Good Clinical Practice (GCP) Guidelines, applicable national guidelines, and to the Declaration of Helsinki. The local ethical committees approved this study and all participants provided informed consent.
The current study was conducted in the Netherlands and Germany (Figure 1: study overview). First, we conducted a feasibility study in the Netherlands to evaluate practicalities and study procedures. This allowed us to improve the online program and optimize the planned study procedures. Subsequently, we performed a 30-day online user test in both the Netherlands and Germany to evaluate user-experiences.

Figure 1. Study overview

Figure 1. Study overview

NOTE: This Figure illustrates the study overview. During the feasibility study, using an iterative process, the program was adapted and study procedures were optimized. The 30-day online user test was quantitatively evaluated with questionnaires, rating of daily advices and data log, and qualitatively by follow-up telephone interviews in a subsample of participants. USE: User Satisfaction and Ease of use questionnaire; SUS: System Usability Scale. a: conducted in the Netherlands, b: recruited via Dutch Brain Health Registry, c: recruited via Cologne Alzheimer dementia prevention registry.



Individuals with SCD were recruited through either a memory clinic or research registry:
1) memory clinic: we included individuals that visited the Alzheimer Center Amsterdam because of cognitive complaints. They underwent clinical work-up including clinical evaluation, neuropsychological assessment, and MRI scan. Although not mandatory, an informant was present in most cases during consults and assessments. When all clinical investigations were normal, and no cognitive disorder could be objectified, patients were labelled as having SCD ((17) i.e. clinical criteria for MCI, dementia or psychiatric disorder not fulfilled, no neurological diseases known to cause memory complaints (e.g. Parkinson’s disease, epilepsy), HIV, abuse of alcohol or other substances). Individuals were invited for study participation based on the following criteria: I) diagnosis of SCD II) age 50 years or older, and III) owning a smartphone, tablet or computer.
2) research registries: we included individuals that signed up for research registries, a) the Dutch Brain Health Registry (online register; which facilitates participant recruitment for neuroscience studies and is open for individuals of any age; b) the Cologne Alzheimer dementia prevention registry [Kölner Alzheimer Präventionsregister (KAP)], which is open for individuals of any age interested in the field of dementia. Through newsletters individuals receive information on research and are asked to participate in scientific studies. Individuals were invited for study participation based on the following criteria: I) self-reported experience of memory loss as assessed by either the question “Do you have memory complaints?” (Dutch registry) or the SCD interview (18) (German registry), II) age 50 years or older, III) no diagnosis of Alzheimer’s disease, another type of dementia or mild cognitive impairment, as assessed through self-report, and VI) owning a smartphone, tablet or computer to access the online lifestyle program. No informant information was available for the participants from the research registries.

Online lifestyle program

Hello Brain is a European Project (FP7 grant no 304867) led by Trinity College Dublin. Hello Brain comprises a website and app which are available in English French and German. The website shares information and videos about the brain, brain health and brain research. The App aims to support users to live a brain healthy life by giving daily suggestions called ‘brain buffs’. There are five brain buff categories: physical activity, social activity, mental activity, lifestyle (nutrition, smoking, alcohol) and attitude (referring to stress management and positive thinking; 30 brain buffs per category). Participants are instructed to read the brain buff and are encouraged to engage in the described activity. If the user cannot or does not want to conduct a specific brain buff, a new brain buff can be requested.
For the current project, a collaboration was started between the EuroSCD team and Trinity College Dublin. We first investigated the preferences and wishes for an online lifestyle program in an international group of users (12). Then, in collaboration with users and a technical party, we adapted the program HelloBrain (Dutch: HalloHersenen, German: HalloGehirn; Appendix 1: details and screenshots). The scientific content was translated and the modules were adapted in order for the interactive module to become the main module. Additional brain buffs were created by a team of brain researchers and added to the program (15 per category) in order to allow tailoring based on a personal profile. The overall lay-out of HelloBrain was changed to a calmer look-and-feel by applying the grey background, that was included in some of the original HelloBrain screens, to all screens while keeping the colorful details.

Feasibility study

After the above mentioned adaptations, we performed a feasibility study to evaluate accessibility and the study procedures, to collect qualitative feedback and optimize study procedures for the online user test. We used 4 iterations of user input and adaptations to create a version of the program that was ready to evaluate user-experiences in a 30-day user test.

Focus groups

In 4 focus groups (memory clinic + Dutch Brain Health Registry, total N=17: 67±6y, 65% female) the language and structure of the program was evaluated. Specific topics were hierarchy of screens (wireframe), language, lay-out, and the wording of reminders and instructions. Feedback was translated into technical and content-related adaptations, and passed on to the developers. We used an iterative process, meaning that after each focus group the program was adapted. In the next focus group the adapted version of the program was evaluated.

Technical pilot

We conducted a technical pilot to evaluate accessibility of the program. Accessibility was defined as the ability to log in to the website or the app independently, with devices at home. Participants (memory clinic, N=5: 61±8y, 80% female) received access to the program through the website or the app for 2 weeks. All technical issues raised by the participants were collected and adaptations were made.

Pilot test phase

To evaluate feasibility of the planned study procedures, we conducted a pilot test phase in which we included 43 SCD subjects (Dutch Brain Health Registry, 65±8y, 66% female). Participants received account information by email and were instructed to use the program for 30 days. Users were able to email the researchers and if necessary, we initiated contact by telephone. At the end of the test-period, participants received digital questionnaires by email to evaluate the procedure of sending online questionnaires, having participants filling out the questionnaires and adequate data collection.

30-day user test: user-experiences


Finally, we conducted a 30-day user test to evaluate user-experiences. Individuals from the Dutch Brain Health Registry and the Cologne Alzheimer dementia prevention registry were approached. These individuals were not involved in previous phases of the program development.


Participants received account information and could access the program for 30 days. Participants were instructed to use the program on a daily basis and complete one brain buff each day. Besides the daily brain buff, participants could access the information on brain health as they liked. After the 30-day user test the participants received self-report questionnaires to evaluate the program. In addition, participants were asked whether they were willing to share their experiences during a telephone interview. Study procedures slightly differed between centers, because of characteristics of the research registers (online in the Netherlands, on paper after an in-person information session in Germany) and requirements of the Cologne ethical committee to send information via post instead of email.


Data log

During the online user test, log data regarding the usage of the program were collected. Log data entailed number of log ins, log outs, brain buffs completed, brain buffs passed, and page visits during the test period.

Usefulness of daily advices

After indicating that a brain buff was completed, participants were asked to provide a rating of the usefulness of the brain buff. This rating was illustrated with 1 to 5 stars. Participants were invited to leave a comment.

Usability, ease of learning and satisfaction

We used the User Satisfaction and Ease of use (USE) (19) and the System Usability Scale (SUS) questionnaire (20) to assess perceived user-experiences of the online program. The USE questionnaire includes items on usefulness (e.g. is the program perceived as useful, does it have value to the user), ease of learning (e.g. is it easy to learn how the program works) and satisfaction (e.g. does it fulfill the wishes and expectations of the user), with scores ranging from 1 to 7. We used the domain scores for usefulness, ease of learning and satisfaction, by averaging the scores of items per domain. The SUS questionnaire includes 10 items on usability (scores ranging from 1-5; e.g. degree of convenience when using the program). The SUS questionnaire includes both positive and negative items. Total SUS score (range 0-100) was calculated by subtracting 1 from positive items and inversing negative items (5 – item score), summing these scores and multiplying with 2.5 (20). For both questionnaires higher scores indicate better ratings.

Qualitative exploration of user-experiences

We held semi-structured telephone interviews to gain more insight in the questionnaire results and to discuss additional topics. We chose a random sample (N=30) from participants that indicated to be willing to participate in the telephone interview. Aspects that were deemed most important to improve, good and useful aspects of the program and communication during the user test were discussed. In case the questionnaire results needed clarification, the interviewer posed specific questions.

Frequency of Internet use

In the Dutch subsample, a question regarding frequency of internet use was included in the usability questionnaire. In a German subsample frequency of internet use was discussed during the in-depth interview.

Data analysis

Analyses of quantitative data were conducted using SPSS version 22. Descriptive methods were used to describe demographics, average ratings of daily advices per category, use of the program (data log) and user-experience scores (questionnaires) in means and standard deviation, or percentages. Analysis of variance was used to compare questionnaire scores of Dutch and German participants, and to compare the ratings between brain buff categories. P-values of ≤0.05 were considered significant. Qualitative data was collected during the telephone interviews. Every interview was summarized in a short report. All comments were summarized independently by two researchers (LW, AKS). Data was then structured by these researchers upon consensus, in order to identify themes that were of importance to the participants when using the program of when participating in this study.



Feasibility study

Focus groups

We let the participants discuss terminology within the program. At first, we kept some English terms in the program. The participants proposed to use Dutch language only. We discussed which terms should be incorporated to replace the English terms. ‘Brain buff’ became ‘Oppepper’ (Dutch for ‘Boost’), and although the category name ‘Attitude’ also translates to the Dutch ‘Attitude’, participants preferred a different wording (‘Houding’; Dutch synonym for ‘Attitude’). Participants agreed with the order and hierarchy of the screens (the wireframe). Upon their input the button for instructions was enlarged and placed more prominently, and we added ‘Uitleg’ (Dutch for ‘Explanation’) underneath this circled question mark symbol. Participants mentioned that back-and-forth buttons needed to be more prominent, which we adapted accordingly, and the hierarchy of the current location should be visible. Therefore, so called ‘Breadcrumbs’ were added to the page. Breadcrumbs are a simple display of the current location in the program, and easy way to click to a location with higher hierarchy (e.g. Start page / Brain Health / Neuroplasticity). Participants mentioned that they would prefer more instructions when entering the main screen. Together with the technical party and participants we came up with the solution to add a highlighting instruction, which highlights and explains all parts of the screen one by one.

Technical pilot

Of the 5 participants that evaluated the accessibility of the program, nearly all (4/5) reported a smooth download and log in without any assistance. One participant was not able to log in, as a result of a problem with the internet browser. Together with the technical party, the issue was resolved. After log in, 2 participants reported several technical bugs, such as wrong linking between pages or not enough variation in the daily advices, which was caused by an algorithm error. These issues were solved by the technical party.

Pilot test phase

Sending and receiving the questionnaires digitally went well. Participants did not report problems filling out the digital questionnaires. Almost all communication was done via email and online questionnaires. Some participants liked the efficient communication and felt that they were skilled enough to work online, while others would have preferred personal contact throughout the test-phase and provide feedback by telephone. Some participants mentioned that they would have liked an ‘emergency hotline’ in order to have personal contact by telephone in case they would have needed help when using in the program. Based on participants’ suggestions, we made the instructions for the online user test more detailed.

30-day user test: user-experiences


In total, 137 SCD subjects (55 Netherlands, 82 Germany) were included in the online user test. Participants were on average 65.1±8.6 years of age, 57% female and participants completed 11.3±1.9 years of education. German participants had on average more years of education (12.6±1.4) compared to Dutch participants (10.2±1.9, p<.01). The majority of the participants reported to use the internet on a daily basis (>90% of a subsample; Dutch N=55, German N=15).

Data log

In total, 120 (88%) participants used the online lifestyle program during the 30-day test period, whereas 17 (12%) participants did not log in. On average, participants reported to have completed 31±31 daily advices and requested a different brain buff 23±40times during these 30 days. Participants switched between pages on average 117 times (from brain buff screen to informative module and back, or within the informative module).

Usefulness of brain buffs

In total, participants rated 3266 brain buffs with a mean score of 3.5 (±1.5, max 5). The mean ratings differed between categories (F(4,3261)=5,725, p=.000). In general, buffs in the Attitude (3.6±1.4) and Physical activity (3.6±1.4) categories were higher appreciated than Lifestyle advices (3.3±1.6, resp. p<.001 and p=.001). Brain buffs of all categories received scores ranging from 1 to 5 stars and rankings were accompanied by both positive and negative comments. While some participants really liked a brain buff (“It would be very easy and fun to do this every day”) others disliked the same brain buffs (“I have never liked this and I will not do this today”). This diversity in appreciation of the categories, is presented in Figure 2.

Figure 2. Variety in reported usefulness of brain buffs

Figure 2. Variety in reported usefulness of brain buffs

NOTE: This figure illustrates the percentages of brain buffs that received 1 to 5 stars ratings per category, and presents a negative (1 star, left) and a positive (5 stars, right) quotes for each category for illustrative purposes


Usability, ease of learning and satisfaction

The questionnaire was completed by 101 participants (response rate 74%; 37 Netherlands, 64 Germany). Participants gave on average moderate scores on items of the USE questionnaire (max 7): usefulness 3.7±1.3, ease of learning 3.6±1.9 and satisfaction 4.0±1.5 points. Dutch participants rated the program higher on these 3 domains compared to German participants (Dutch: 4.1±1.3, 4.8±1.5, 4.4±1.4 vs. German: 3.4±1.3, 2.4±1.5, 3.7±1.6; p<.05). The average score for usability on the SUS questionnaire was 63.7±19 out of 100, which translates to ‘OK to good’ (21) and did not differ between Dutch and German participants. Figure 3 presents the heterogeneity of user-experience scores within the total group.

Figure 3. Heterogeneity in user-experiences

Figure 3. Heterogeneity in user-experiences


Qualitative exploration of user-experiences

Table 1 gives a summary of the qualitative feedback illustrated by quotes. Participants mentioned that they would prefer a personalized program, meaning that it would fit their specific preferences. For example, with content based on their current lifestyle and preferred lifestyle category. Some of the participants used the program mainly for information, while others mainly liked the interactive part. When asked what was most important to improve, participants mostly mentioned to optimize technical aspects of the program to ensure a smooth walk-through.
Most participants mentioned to highly appreciate the content of the program. They liked to have a platform available to read about the brain and brain health, and to have access to a trustworthy source of information. When specifically asked what they liked most about the program, participants reported that the program induced awareness of lifestyle factors that are related to brain health. While most participants knew that physical exercise is related to brain health, they were often not aware of the relation between nutrition or social activities and brain health. Some participants mentioned that the program was positive and induced motivation to live healthier. Others were stimulated to look at their current lifestyle, felt confirmation that they have a healthy lifestyle or were motivated to continue with current lifestyle changes.

Table 1. Summary of in-depth exploration of user-experiences, collected during telephone interviews

Table 1. Summary of in-depth exploration of user-experiences, collected during telephone interviews

NOTE: This Table presents qualitative feedback, which was provided by 30 participants during the telephone interviews.



We developed an online lifestyle program for brain health and found that its’ overall user-experience was moderate to positive. Qualitatively, participants reported to appreciate the content of the program and having a trustworthy source of information on lifestyle and brain health. Quantitative scores on usefulness and ease of learning showed room for improvement. We observed high heterogeneity in the preference of specific lifestyle topics, which emphasizes the need for personalization.
Content on the brain and brain health of the online program, as offered in the brain buffs and the information pages, was highly appreciated by the participants. Both the brain buffs and the information pages were reported to be interesting and useful. Many participants reported to have learned new things. Often they were not aware that all the lifestyle factors that were included in the program were associated to brain health. Previous studies into the attitudes towards prevention of AD and related dementias highlighted the need to improve the beliefs and attitudes towards dementia prevention (1, 3, 22). Our study showed that a tool with information on lifestyle and brain health can contribute to the awareness on modifiable risk factors of dementia.
Involving the users throughout the process of development of an online program is expected to benefit usability and thereby adherence to the program. Our recent review, however, showed that for online lifestyle programs aimed at brain health it was often unclear whether and how users were involved during development and evaluation of the program (15). For example, a study on adherence to lifestyle interventions for dementia prevention found that adherence was lowest for the unsupervised computer-based cognitive training compared to other supervised trainings (23). However, user-involvement during development and evaluation was not described and therefore it remains unclear whether this could have benefitted adherence rates. In this study we aimed to evaluate and optimize user-experiences. When a program will be implemented internationally, it is important to explore cultural differences. Our multinational participatory research design increases the quality of output and sustainability, but also ensures culturally appropriate research, which is of importance when developing an international application (24). As a next step, additional options to increase the impact of the program should be explored. It might be worthwhile to evaluate integration of persuasive technologies that aim to influence behavior and attitudes. If such technologies are used the right way, it is more likely that users reach health-related goals (25).
The heterogeneity in the ratings of brain buffs, the frequent requests for different brain buffs and the qualitative feedback emphasize the need for personalization. Personalization has also been identified as one of the principles to increase appreciation and overall adherence to an online intervention (26, 27). In the current version of the program, part of the content was personalized, since users could request a different brain buff and could access information as they wished. Participants mentioned that they would prefer to receive brain buffs based on their current lifestyle behavior. Further evaluation and integration of personalization options, such as adapting lifestyle advices based on current lifestyle habits, could improve user-experience and thereby adherence to the program.
Lessons learned from the qualitative input of the users, mainly entail the preference for tailoring based on current lifestyle behavior. In addition, participants mentioned different possible effects of the program. Therefore, it might be interesting to rethink the most appropriate outcome measures of future lifestyle-based interventions in SCD. While changes in lifestyle or brain health might seem obvious, effects on psychological well-being or fear for dementia could also be worth consideration.
The quantitative ratings evaluating user-experience were moderate, which was lower than we expected. We identified room for improvement, particularly in ease of learning. Meaning that additional adaptations are necessary to improve instructions and clarity within the program. Differences in ratings could have several reasons, such as education, cultural differences, differences in reporting and differences in digital skills – which we did not assess systematically. Regarding the 30-day user test, German participants reported difficulties when learning to use the program. Some of the technical difficulties occurred only in the German back-end. Although we fixed these technical issues and thoroughly tested the program, we could not rule out remaining minor issues, possibly contributing to the differences in these scores. In our previous study (12), we found that German individuals with SCD used the Internet less often compared to the Dutch participants. However, information on the current Dutch sample and a subset of the German sample showed that over 90% of the participants uses the internet on a daily basis and therefore frequency of internet use is unlikely to influence perceived difficulties. Further, we did not match participants from the feasibility study and 30-day user test. Therefore, we cannot rule out the influence of demographical differences on perceived usability and satisfaction during development and the actual test phase.
In summary, qualitative feedback on the programs’ content was positive, while quantitative feedback on program characteristics showed room for improvement. This discrepancy between the positive qualitative feedback and the moderate quantitative ratings emphasizes the importance of combining methods when evaluation usability of eHealth applications, which was also emphasized in a recent scoping review on methods of usability testing in the development of eHealth applications (28).
This study had some limitations. First, the development and feasibility study took place in the Netherlands, and not in Germany. However, we believe it is promising that participants with different nationalities appreciated the same program, strengthening feasibility to offer one program in multiple countries. Second, a selection bias might have occurred, a study on an online program could have attracted individuals with better digital skills. However, the digital literacy of the participants varied from limited to very skilled, which was also reflected in the variety of feedback regarding ‘ease-of-learning’. Since an online program will only be used by those able and willing to access a program online, the current participants seem representative for the actual target group. Third, we did not have detailed information on drop-outs and therefore cannot describe their characteristics. We did however encourage all individuals to complete the questionnaires and we interviewed individuals independent of their attitude towards the program. Fourth, the participants were recruited based on different SCD criteria between the memory clinic and the research registries. However, we deem the population representative for the heterogeneous populations that can be recruited via memory clinics and brain health registries, and results are generalizable as such. Finally, based on the data log we cannot make a distinction between merely clicking through the program and attentively reading pages. Therefore, we could not take frequency or duration of active participation into account. In the future, this information could be considered when evaluating user-experiences and lifestyle effects of the program.
The strengths of the study include the study design. This was a multicenter study conducted in Germany and the Netherlands. This international character contributes to the generalizability of the findings to other European populations. Results also suggest that although some differences were found, one online tool for multiple European countries would be feasible. Second, we involved the target population throughout the process of development and evaluation. Co-creation is expected to increase the extent to which the tool fits the users’ preferences and digital skills and thereby acceptation and impact of the innovation in further stages (29, 30). Therefore, the users’ input was crucial and resulted in an online lifestyle program fitting the needs and preferences of individuals with SCD. Third, we combined methods to assess user-experiences of the online lifestyle program. The quantitative and qualitative methods were found to complement each other. Finally, we focused on individuals with SCD, who do not show cognitive deficits but at group level are at increased risk for cognitive decline. Therefore, this group is of clinical relevance in the context of dementia prevention. Individuals with SCD also report a need for brain health information, which has not yet been fulfilled since trustworthy sources are still lacking. This group is willing to participate in prevention strategies, which was also observed during recruitment and led to a higher inclusion number than planned.
In conclusion, in this study we developed and evaluated an online lifestyle program for brain health in individuals with SCD. We found that the overall user-experience of our program was moderate to positive. Participants appreciated content on lifestyle and brain health. The variety in preferences for different categories highlighted the need for personalization. It was feasible to offer this online lifestyle program in an at-risk population with SCD. Online self-applied lifestyle programs seem useful when aiming to reach large groups of motivated at-risk individuals for brain health promotion.


Conflicts of interest: The authors state no conflicts of interest. Ms. Wesselman reports grants from JPND/ZonMw, grants from Stichting Equilibrio, during the conduct of the study; Dr. Schild reports grants from Bundesministerium für Bildung und Forschung, during the conduct of the study; Dr. Hooghiemstra has nothing to disclose; Mr. Meiberth reports grants from Bundesministerium für Bildung und Forschung, during the conduct of the study; Ms. Drijver has nothing to disclose; Ms. v Leeuwenstijn-Koopman has nothing to disclose; Dr. Prins has nothing to disclose; Dr. Brennan has nothing to disclose; Dr. Scheltens has nothing to disclose; Dr. Jessen reports grants from Bundesministerium für Bildung und Forschung, during the conduct of the study; Dr. van der Flier reports grants from JPND/ZonMw, during the conduct of the study; Dr. Sikkes reports grants from JPND/ZonMw, grants from Stichting Equilibrio, during the conduct of the study.

Funding: The project is supported through the following funding organizations under the aegis of JPND (; JPND_PS_FP-689-019): Germany, Bundesministerium für Bildung und Forschung (BMBF grant number: 01ED1508), the Netherlands, ZonMw grant no. 733051043. It was additionally supported by a research grant from Stichting Equilibrio. W.M. van der Flier is recipient of a grant by Gieskes-Strijbis fonds. S. Sikkes is recipient of a ZonMw Off Road grant (grant no. 451001010).

Acknowledgements: We thank all participants for their contribution to this research project. We thank Roxelane BV. and specifically Rudolf Wolterbeek, Brian Fa Si Oen and Max Hasenaar for their contribution to this project representing the technical party within the collaboration. We thank Mark Dubbelman for data visualization. We thank the founders of (Trinity College Dublin, supported by European Union’s Seventh Framework Program for research, grant no. 304867) for the fruitful collaboration. The website shares easy-to-understand information and animations about the brain, brain health and brain research. The freely available interactive app, Hello Brain Health, aims to support users to live a brain healthy life by giving daily suggestions called ‘brain buffs’. The app is available on the project website, the App Store and Google Play. The Alzheimer Center Amsterdam is supported by Alzheimer Nederland and Stichting VUmc fonds. Research of the Alzheimer Center Amsterdam is part of the neurodegeneration research program of Neuroscience Amsterdam. Wiesje van der Flier holds the Pasman chair. is funded by ZonMw-Memorabel (project no 73305095003), a project in the context of the Dutch Deltaplan Dementie, the Alzheimer’s Society in the Netherlands and the Brain Foundation Netherlands.

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.





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O. Rodriguez-Gomez1, A. Sanabria1, A. Perez-Cordon1, D. Sanchez-Ruiz1, C. Abdelnour1, S. Valero1,2, I. Hernandez1, M. Rosende-Roca1, A. Mauleon1, L. Vargas1, M. Alegret1, A. Espinosa1, G. Ortega1, M. Guitart1, A. Gailhajanet1, O. Sotolongo-Grau1, S. Moreno-Grau1, S. Ruiz1, M. Tarragona1, J. Serra1, E. Martin1, E. Peleja1, F. Lomeña3, F. Campos3, A. Vivas4, M.Gomez-Chiari4, M.A. Tejero4, J. Giménez4, P. Pesini5, M. Sarasa5, G.Martinez1,6,7, A. Ruiz1, L. Tarraga1, M.Boada1

1. Fundació ACE. Alzheimer Treatment and Research Center. Barcelona, Spain; 2. Psychiatry Department, Hospital Universitari Vall d’Hebron, CIBERSAM, Universitat Autonoma de Barcelona,Barcelona, Spain; 3. Servei de Medicina Nuclear, Hospital Clínic i Provincial. Barcelona, Spain; 4. Departament de Diagnòstic per la Imatge. Clínica Corachan, Barcelona, Spain; 5. Araclon Biotech©. Zaragoza, Spain; 6. Iberoamerican Cochrane Centre, Barcelona, Spain; 7. Faculty of Medicine and Dentistry, Universidad de Antofagasta, Antofagasta, Chile

Corresponding Author: Octavio Rodriguez-Gomez, MD., Gran Via De Carles III, 85 BIS. CP: 08028. Barcelona. Spain, E-mail:, Fax: 0034 934193542, Telephone number: 0034 934304720

J Prev Alz Dis 2017;4(2):100-108
Published online November 15, 2016,


Background: Long-term longitudinal studies with multimodal biomarkers are needed to delve into the knowledge of preclinical AD. Subjective cognitive decline has been proposed as a risk factor for the development of cognitive impairment. Thus, including individuals with SCD in observational studies may be a cost-effective strategy to increase the prevalence of preclinical AD in the sample.
Objectives: To describe the rationale, research protocols and baseline characteristics of participants in the Fundació ACE Healthy Brain Initiative (FACEHBI).
Design: FACEHBI is a clinical trial (EudraCT: 2014-000798-38) embedded within a long-term observational study of individuals with SCD.
Setting: Participants have been recruited at the memory clinic of Fundació ACE (Barcelona) from two different sources: patients referred by a general practitioner and individuals from an Open House Initiative.
Participants: 200 individuals diagnosed with SCD with a strictly normal performance in a comprehensive neuropsychological battery.
Measurements: Individuals will undergo an extensive neuropsychological protocol, risk factor assessment and a set of multimodal biomarkers including florbetaben PET, structural and functional MRI, diffusion tensor imaging, determination of amyloid species in plasma and neurophthalmologic assessment with optical coherence tomography.
Results: Two hundred individuals have been recruited in 15 months. Mean age was 65.9 years; mean MMSE was 29.2 with a mean of 14.8 years of education.
Conclusions: FACEHBI is a long-term study of cognition, biomarkers and lifestyle that has been designed upon an innovative symptom-based approach using SCD as target population. It will shed light on the pathophysiology of preclinical AD and the role of SCD as a risk marker for the development of cognitive impairment.

Key words: Subjective cognitive decline, biomarkers, preclinical AD, longitudinal study.


The prevalence of dementia is increasing in developed societies due to social and demographic changes, and this trend is expected to worsen within the next decades. This epidemic progression could pose a threat to public health, to such an extent that the World Health Organization has declared dementia control a global health prority (1). The disappointing results of the clinical trials in patients with Alzheimer´s disease (AD) dementia (2) or even mild cognitive impairment (MCI) have highlighted the necessity to act earlier (3). In this context, the earliest stages of AD are becoming a topic of major scientific interest.  Nowadays, advancing research has provided a large amount of knowledge of the phenomena involved in the transition from mild cognitive impairment to dementia, but much less is known about the events that lead individuals that are strictly normal from a cognitive viewpoint to develop cognitive impairment (4). In this regard, strong evidence exists that the pathophysiological process of Alzheimer´s disease (AD) begins many years before the onset of the clinical symptoms, leading to the formulation of the biomarker-defined construct of preclinical AD (5). Deep knowledge of this process is essential to develop diagnostic and prognostic markers. Additionally, it will allow a  better selection of individuals at risk for preventive trials and monitorization of the efficacy of treatments intended to modify the course of the disease. However, our present understanding of preclinical AD is far from complete and the very definition of the concept is controversial to date (5-7). Currently, we do not have enough knowledge of the prognostic implications of biomarker positivity for cognitively unimpaired individuals (8). This can lead to problems regarding disclosure of biomarker results and may raise ethical concerns when implementing clinical trials with potentially harmful drugs in this population (9). Furthermore, we need to deepen our understanding of the dynamics of the different biological processes and their related biomarkers along the disease continuum. The more accepted model to date relies largely on studies focused on individuals with rare dominantly inherited variants of AD (10), and we cannot postulate that this model necessarily fits into the typical forms of the disease. In fact, cohorts of late onset AD (LOAD) have shown that not all  the  patients follow the same sequence of events (11).
Given all these gaps in the understanding of the preclinical stage of AD, long-term longitudinal studies are needed and, in fact, significant efforts have been made to clarify the more relevant etiological and pathophysiological aspects of the disease. Studies that include cohorts of healthy controls such as the Alzheimer´s Disease Neuroimaging Initiative (ADNI) (12), Australian Imaging, Biomarkers & Lifestyle Flagship Study of Aging (AIBL) (13) or Mayo Clinic Study of Aging (MCSA) (14) are crucially helping  broaden the knowledge of the field. Despite this, new studies with innovative designs and protocols are needed to address the multiple questions that still remain unanswered.
Metacognition is a research topic that is receiving increasing attention because evidence suggests that some individuals with preclinical AD are able to perceive and report a sensation of loss of cognitive abilities. Thus, many studies in individuals without cognitive impairment show a cross-sectional correlation between the presence of subjective cognitive decline (SCD) and AD biomarkers positivity (15). Longitudinal studies also found that individuals with SCD have a higher risk of developing MCI and dementia (16). Hence, including individuals with SCD in observational studies may be a cost-effective strategy to increase the prevalence of preclinical AD in the sample.
Here we present the Fundació ACE Healthy Brain Initiative (FACEHBI), a long-term observational study carried out with a sample of individuals with SCD. We use a multimodal biomarker approach intended to capture the more relevant molecular, structural and functional processes present in the earliest phases of AD combined with a highly comprehensive and sensitive neuropsychological protocol. Special attention is also paid to lifestyle, personality traits, psychological symptoms, and modifiable risk factors that have been linked to AD in several epidemiological studies (17). In the present work we will describe the rationale, design and baseline demographic characteristics of FACEHBI.



FACEHBI is a single-center prospective observational study. The FACEHBI protocol received the approval from the Spanish Drug Agency (AEMPS for its initials in Spanish) and has been registered as phase 1 clinical trial (CT) (EudraCT: 2014-000798-38, approval date 26th September 2014). Due to regulatory requirements we had to request approval from the AEMPS because at the moment of study design florbetaben (FBB) had not yet been approved for clinical use in Europe. The duration of the CT will be two years, although FACEHBI  was intended to be a long-term study.


The general aims of the FACEHBI long-term study are: a) to determine which clinical, neuropsychological, genetic, biochemical and neuroimaging variables better correlate with the amyloid burden measured with FBB PET in a cross-sectional manner; b) to determine in a longitudinal way which clinical, genetic, neuropsychological, biochemical  and neuroimaging variables better predict the development of cognitive and functional impairment in individuals with SCD; c) to explore the relationships between the different biomarkers at different points in time and the longitudinal evolution of each biomarker in the transition from cognitive normality to cognitive impairment; d) to explore cross-sectionally which clinical, biomarker and  psychological variables better correlate with subjective cognition measures; e) to explore if subjective cognition measures can be a useful tool to predict the development of cognitive impairment in individuals with normal objective cognition.
The primary objective of the CT is to determine if elevated baseline levels of brain ß amyloid measured with FBB PET are correlated with a greater decline in Face Name Associative Memory Exam (FNAME) (18) scores after two years of follow- up.


FACEHBI will use a convenience sample of 200 individuals diagnosed with SCD at Fundació ACE. SCD is defined by the coexistence of cognitive complaints and a strictly normal performance in a comprehensive neuropsychological battery. The sample has been obtained from two different sources: individuals referred by their physicians to our memory clinic for study of cognitive impairment and individuals who came to our institution through an Open House Initiative (OHI) (19). Since the inception of OHI in 2008, Fundació ACE has been holding open house days in which any citizen of Barcelona can sign up for free cognitive screening without the need of physician referral. OHI encompasses a community service and a recruitment strategy for research studies.
Inclusion criteria were: a) subjects older than 49 years; b) subjective cognitive complaints defined as a score of ≥ 8 on MFE-30, the Spanish version of the Memory Failures in Everyday Life Questionnaire (20); c) MMSE ≥ 27; d) CDR=0; e) performance in Fundació ACE Neuropsychological Battery (NBACE) (21) within the normal range for age and educational level; f) literate.
Exclusion criteria were: a) evidence of impairment in daily life activities; b) relevant symptoms of anxiety or depression defined as a score of ≥ 11 on Hospital Anxiety and Depression Scale (HADS) (22); c) presence of other psychiatric diagnosis; d) history of alcoholism and epilepsy; e) presence of auditory or visual impairment sufficient to interfere with neuropsychological assessment; f) known renal or liver failure (due to lack of data on FBB pharmacokinetics in this clinical scenario).

Visits and procedures scheduled

The first phase of FACEHBI in the framework of the CT will have a duration of  two years for each subject, comprising three visits with one-year interval between them (Figure 1).

Figure1. Shows a briefing of FACEHBI flowchart

The baseline visit and visit two (for the CT) are identical and include exhaustive anamnesis, physical and neurological examination, an extensive neuropsychological protocol and a set of self-administered questionnaires that explore issues related to personality and lifestyle. These visits also comprise a battery of multimodal biomarkers including: a) FBB PET; b) structural MRI, functional MRI, diffusion tensor imaging DTI; c) blood extraction for standard biochemical analysis, genetic analysis and determination of amyloid species; and d) neurophthalmologic assessment with retinal OCT.
The intermediate visit consists of a neurological visit, an abbreviated neuropsychological protocol including NBACE, HADS and MFE-30, blood extraction for amyloid determination and neurophthalmologic assessment with OCT.
All the procedures of each visit should be done within a time window of three months.

Neuropsychological assessment

The baseline visit and visit two include an extensive neuropsychological protocol that examines exhaustively all the domains of cognition (Table 1).

Table 1. Shows the different neuropsychological tests and the cognitive functions explored


In addition, the participants will be offered a set of self-administered questionnaires to be filled in at home and delivered later (Table 2).

Table 2. Shows the different self-administered questionnaires of FACEHBI

MRI acquisition

All MRI scans will be acquired prior to FBB PET. MRI will be performed on a 1,5 T Siemens Magneton Aera (Erlangen, Germany) using 32-channel head coil. Anatomical T1-weighted images for voxel-based morphometry (VBM) will be acquired using a rapid acquisition gradient-echo 3D MPRAGE sequence with the following parameters: TR 2.200ms, TE 2,66ms, TI 900ms,  slip angle 8º, FOV 250mm, slice thickness 1mm and isotropic voxel size1x1x1mm. DTI (Diffusion Tensor Images) scans will be acquired using EPI (Echo-planar images) diffusion-weighted sequences with 64 encoding directions, b=0 and 1000 s/mm2.  Resting State functional MRI study (rsfMRI) will be obtained using a BOLD signal sequence with prospective correction movement for posterior processing. In addition to the MRI Imaging protocol, the subjects will receive axial T2-Weighted, 3D isotropic FLAIR and axial T2*Weighted sequences to detect significant vascular pathology or microbleeds.

FBB PET acquisition

FBB-PET scans will be obtained with a Siemens© Biograph molecular-CT machine. PET images will be acquired in 20 minutes starting from 90 minutes after intravenous administration of 300 Mbq of florbetaben(18F) radio tracer (NeuraCeq©), administered as a single slow intravenous bolus (6 sec/mL) in a total volume of up to 10 mL.

Neuroimaging processing

MRI cortical and subcortical segmentation will be carried out with Freesurfer 5.3. The Freesurfer cortical thickness pipeline involves intensity normalization, registration to Talairach space, skull stripping, segmentation of white matter (WM), tessellation of the WM boundary, and automatic correction of topological defects. Hippocampus volume, cortex mean thickness and white matter hypointensities (WMH) will be determined from the segmentation.
DTI images will be also processed with FSL. The images will be eddy-corrected, skull-stripped, fitted to a diffusion tensor model for each voxel and co-registered to the standard space template FMRIB58. Fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AxD) and radial diffusivity (RD) will be calculated for the regions of the white matter John Hopkins University Atlas.
fMRI imaging will be processed with AFNI 16.0.19 and FSL 5.0. Scans will be converted to NIfTI-1 format and then a procedure for slice-timing correcting, motion correcting (realignment), despiking, realigment to anatomic space and noise correction will be applied. The resulting images will be registered to the MNI space and the default network will be calculated between the ROI defined by the Destrieux Atlas.
FBB-PET will be processed with FSL 5.0 suite. The FBB-PET images will be co-registered onto structural images. Amyloid cortical retention SUVR will be determined as the mean value of the cortical regions segmented on MRI and normalized by the cerebellum gray matter uptake. A cutoff of  SUVR = 1.45 has been selected as amyloid positivity criterion, based in previous studies (35). FBB-PET images will only be interpreted by readers trained in the interpretation of PET images with FBB-PET

Blood sampling, APOE genotyping, blood amyloid species determination

Blood samples after fasting will be obtained in all the visits for standard biochemical analysis, determination of blood amyloid species, APOE genotyping and DNA banking.
Genomic DNA will be extracted from 200µl of human whole blood using Maxwell® 16 Blood DNA purification kit (Promega) according to the manufacturer’s instructions. APOE rs7412 and rs42358 markers will be genotyped using real-time PCR. PCR reactions will be performed in a final volume of 5µl, using 11ng of genomic DNA, 0.3µM of each amplification primer, and  2.65µl of 2X SYBR Fast Master Mix (Kapa Biosystems). We will use an initial denaturation step at 95 °C for 2 min, followed by 33 cycles at 95 °C for 10 s, and at 69 °C for 30 s. Melting curves will be 95 °C for 15 s (ramping rate 5.5 °C s), 45 °C for 15 s (ramping rate of 5.5°C s−1) and 95 °C for 15 s (ramping rate of 5.5°C s−1). In the last step of each melting curve a continuous fluorimetric register will be performed by the system at one acquisition register per each degree Celsius. Melting peaks and genotype calls will be obtained using the Eco Real-Time PCR system (Illumina).
Blood samples for amyloid assays will be collected in polypropylene vials with EDTA; they will be immediately centrifuged and aliquoted. The aliquots of plasma and the remaining cell pellet will be immediately frozen at −80◦C and sent to the Araclon laboratory. Aβ1-40 and Aβ1-42 will be measured in plasma using two specific sandwich ELISA kits, ABtest 40 and ABtest 42 (Araclon Biotech, Zaragoza, Spain) according to the supplier’s instructions. Before analysis, each plasma sample will split in an undiluted aliquot and another aliquot pretreated by 1 : 3 dilution in a formulated sample buffer intended to break interactions of Aβ with other plasma components. Thus, levels of free and total Aβ1- 40 and Aβ1-42 will be separately determined in undiluted plasma and diluted plasma, respectively.

Neuro-ophthalmologic assessment

Subjects will undergo an ophthalmologic assessment in all the visits including a brief structured ophthalmologic anamnesis, visual acuity assessment with ETDRS and intraocular pressure determination with Goldmann applanation tonometer. OCT will be performed with a 3D OCT-1 Maestro system (Topcon Inc, Tokyo, Japan) using the Radial (Dia.6.0mm Overlap 4), 3D Macula(V) (7.0×7.0mm) and 3D Disc (6.0×6.0mm) protocols. An ophthalmologist will review all the clinical records in order to rule out ophthalmologic pathologies and ensure quality of the images.

Sample size and power calculation

Assuming that the prevalence of amyloid positivity will be about 10% in our sample (preliminary data) (36), FACEHBI has a statistical power of 80% to detect differences bigger than 9.5 points in total FNAME score between PET positive and PET negative subjects.

Statistical analysis

In this work we present the baseline socio-demographic characteristics of the sample. We calculated mean and standard deviation for continuous variables using SPSS 20 (SPSS Inc.Chicago, IL). Dichotomous variables are presented as percentages.

Ethical standards

The FACEHBI protocol received approval from the ethics committee of the Hospital Clinic i Provincial (Barcelona, Spain) (EudraCT: 2014-000798-38). All the participants signed written informed consent prior to any evaluation according to Spanish biomedical laws and to the principles of the Declaration of Helsinki. It is worth noting that, according to the decision of the local ethics committee,  amyloid status will not be disclosed to participants.


The recruitment of subjects began in December 2014 and by the end of March 2015 all the individuals had undergone all the procedures of the baseline visit. (See figure 2).

Figure 2. Shows the rhythm of recruitment and completion of procedures of the baseline visit (FBB PET is the last procedure for each subject in this visit).

215 individuals were recruited. There were four screening failures: two due to inability to undergo MRI and two more due to evidence of cognitive impairment in neuropsychological assessment. 11 individuals withdrew informed consent prior to undergoing all baseline procedures. Finally, 200 subjects completed the baseline visit and will be followed yearly according to schedule. There were no serious adverse events related to florbetaben. Baseline socio-demographic characteristics of the sample are shown in table 3.

Table 3. Shows the baseline demographic characteristics of the FACEHBI participants

The majority of the participants (62.5%) were women, with a mean of 14.76 years of education. It is worth noting that mean educational level of the patients with SCD referred to Fundació ACE by their physician was lower: 10.70 years. First degree family history of dementia was frequent among FACEHBI participants reaching the 60%. All the participants were fluent in Spanish and a high percentage (91%) was bilingual Spanish-Catalan. The most frequent vascular risk factor was dyslipidemia (41.5%) followed by HTA (31.5%), whereas only 4% of the subjects were diabetic. Comparing the two different recruitment methods, the majority of individuals (69%) came from OHI, the rest had been referred by other physicians to our memory clinic.


Long-term longitudinal studies with multimodal biomarkers are needed to delve into the knowledge of the first stages of AD pathophysiology. These studies will deepen understanding of the temporal and causal relationship of the different biological processes involved in neurodegeneration and ultimately determine which biomarker or biomarkers are more accurate and cost-effective to predict the development of cognitive impairment. Advancing research suggests that in the design of new studies we must take into account the high etiological and biological complexity involved in the clinical expression of cognitive impairment. For example, focusing the study exclusively on amyloid cascade-related events is a biased approach since clinic-pathological studies show that in the majority of individuals clinically diagnosed with AD there is actually a mix of different pathologies, vascular damage being especially frequent in the elderly (37). Furthermore, cognitive impairment should not be considered as the result of a specific pathology (or pathologies) in a direct and proportional way, because there is strong evidence that different individuals with the same degree of pathologic burden display different levels of cognitive impairment (38). Thus, cognitive performance can be conceptualized as a result of equilibrium between brain pathology and factors that promote general brain resilience and cognitive reserve (39). Therefore, factors related to cognitive reserve should be taken into account when designing studies to understand the dynamics of the transition from normality to cognitive impairment. Moreover, this research field should not be limited to tracking the measurable biological processes, but it must also consider the different modifiable risk factors (17) in order to understand their mechanism of action.
FACEHBI is an initial clinical trial embedded within a more ambitious long-term longitudinal study of risk factors, lifestyle, biomarkers and cognition carried out in a cohort of individuals without objective cognitive impairment.
The FACEHBI protocol includes a set of multimodal biomarkers that will allow measuring longitudinally different pathophysiological processes that coexist in preclinical AD. We will measure baseline brain amyloidosis and longitudinal amyloid deposit through FBB-PET. FBB is an amyloid ligand that showed to be highly sensitive and specific for the detection of amyloid plaques in the brain, taking brain pathology as the gold standard (40).
Structural magnetic resonance imaging (sMRI) will allow us to precisely determine the degree of atrophy in different brain regions and the pattern of cortical thickness. T2 weighted and magnetic susceptibility sequences will be helpful in detecting ischemic and hemorrhagic brain damage that exists to some degree in the majority of cases of late onset AD and that adds to neurodegeneration as pathological substrate of cognitive impairment (37). Additionally, thanks to diffusion-weighted sequences we will be able to quantify the degree of disruption of white matter microstructure, which is an important substrate of the structural connectivity. DTI measures have been shown to be altered early in preclinical AD, even when volumetric measures remain unchanged (41). FACEHBI will explore the value of this promising tool as an early prognostic and diagnostic marker. Thanks to resting-state functional MRI, we will be able to measure the activation of different brain regions and functional networks, thus assessing the functional connectivity that has been shown to be altered in preclinical AD (42). The correlations between fMRI, structural measures, cognitive performance and amyloid burden can improve our understanding of the functional brain mechanisms underlying cognitive reserve (43).
However, PET and MRI are relatively expensive and time-consuming techniques, thus not suitable for the screening of big populations. In the face of the epidemic progression expected for AD, the development of economic and innocuous biomarkers is of great interest from a public health point of view. In this regard, our protocol includes determination of amyloid species in blood that has shown promising results (44). Optical coherence tomography (OCT) of the retina is another inexpensive, quick and innocuous biomarker included in the FACEHBI protocol. Several studies showed that the inner retinal layers are thinner in AD and MCI compared to healthy controls (45). Nevertheless, the possible alteration of retinal structures in preclinical AD has not been studied to date.
We propose an extensive neuropsychological protocol intended to track the subtle cognitive changes that occur in preclinical AD. FACEHBI will assess the diagnostic value of FNAME in particular, which is a new sensitive neuropsychological tool specifically designed to detect individuals with preclinical AD. FNAME score has been shown to be correlated to amyloid burden in cognitively unimpaired individuals (18). The correlation between biomarkers and the different cognitive measures will allow us to construct neuropsychological composites to better predict amyloid positivity and longitudinal decline. Special attention will be paid  to language assessment in FACEHBI. Early and subtle language impairments have so far been explored less intensively than memory and executive function in the context of preclinical AD (46). Our sample is composed of a majority of bilingual individuals, including simultaneous, early and late bilinguals. We will explore the suggested relationship between bilingualism, cognitive reserve and risk of AD (47).
AD research in the last decades has been reductionist, focusing on neurodegenerative processes related to amyloid cascade hypothesis and excluding from the studies patients with other causes for brain damage. However, clinic-pathological studies show us that the majority of clinically diagnosed AD cases have actually mixed brain pathology (37). FACEHBI has broad inclusion criteria not excluding a priori individuals with vascular brain burden. We believe that this design allows for a more naturalistic approach to the processes involved in late life cognitive impairment. Another important decision was to exclude individuals with relevant symptoms of anxiety and depression. The relationship between psychiatric symptoms and cognitive impairment is complex. It is well known that psychiatric symptoms can affect cognitive performance in the absence of underlying neurodegenerative disease; conversely, late life depression has been robustly reported as a risk factor for developing AD (48). Specifically in the context of SCD the vast majority of studies show that self-perception of cognitive decline is more strongly correlated to anxiety and depression than to neurodegenerative diseases. The international SCD initiative (SCD-I) in his consensus conceptual framework for research on SCD in preclinical AD recommends excluding individuals with severe anxiety or depression symptoms (15). With this in mind, we decided to establish a cut-off point of 11 in HADS as an exclusion criterion.
Several methodological problems arise when designing observational studies on preclinical AD, because this condition is a biomarker-defined construct for which clinical diagnosis is elusive. Hence, the challenge is to find the way to increase the prevalence of preclinical AD in the sample without using expensive biomarkers as inclusion criteria. Enrichment strategies based on genetic factors such as APOE may pose ethical problems and can lead to biological bias, since a big proportion of individuals that will suffer AD despite being negative for this genetic factor are excluded from the study. The age can be effective as enrichment strategy (49); however, the establishment of an advanced age as inclusion criterion precludes a long observation window hampering the study of some slowly progressive processes that define AD pathophysiology.
FACEHBI is designed upon an innovative symptom-based approach for enrichment using SCD as target population. The results will clarify if such a strategy can be useful to increase the prevalence of preclinical AD in the sample. In addition, this project will broaden the knowledge of the nosology of SCD. Most studies agree that subjective cognition is strongly related to personality traits and psychiatric symptoms such as anxiety and depression (15). The FACEHBI protocol includes comprehensive scales to measure all these factors. In individuals with SCD, informant-based reporting of cognitive symptoms has been shown to be better correlated with objective cognition and biomarkers than self-reporting (28, 50). FACEHBI addresses this issue using SCD-Q that includes an informant-completed questionnaire (28). Ultimately, the usefulness of subjective cognition measures to predict cognitive impairment in apparently healthy individuals is a key research topic that this project will help clarify.
Our experience with FACEHBI shows that innovative strategies of patient engagement such as Open House Initiative (OHI) are successful at recruiting individuals with SCD. We have been able to recruit more than 200 individuals from a single site in 15 months, most of whom (almost 70%) came from OHI.
The socio-demographic characteristics of our sample confirm previous findings of other groups showing that individuals who are more likely to volunteer for research studies are women and tend to have a higher level of education and a particular interest in the issue being studied (51). In the case of FACEHBI, this interest can be easily explained by the high frequency (60.5%) of family history of dementia. Our sample is strikingly homogeneous regarding ethnicity, probably reflecting that immigration from other countries is a very recent phenomenon in the history of Spain. The prevalence of vascular risk factors in our sample is relatively low compared with other studies of cognitive aging like BMI (13, 14). Mean age of our sample is also relatively young; this means that a long follow-up period will be required to obtain relevant results. On the other hand, this young sample followed up for a long time with biomarkers will allow capturing very early phenomena in the continuum of preclinical AD. The prevalence of APOEε4 carriers in our sample (26%) is higher compared with our control population (18.5%) (52), reflecting that participants in FACEHBI as a whole have a higher risk of developing AD.
We acknowledge limitations in the design of FACEHBI. CSF analysis is not included in the protocol, which can lead to the loss of relevant information. Nevertheless, FACEHBI includes a comprehensive set of biomarkers of amyloidosis and neurodegeneration. Furthermore, lumbar puncture is an invasive process, not completely innocuous, that can cause pain or discomfort to the patient. Thus, the inclusion of mandatory lumbar puncture in the protocol may result in subject´s reluctance to participate. We have chosen to prioritize subject´s comfort to ensure long-term adherence to the protocol, which is an essential goal of a study of this nature. We are aware of the fact that the strict neuropsychological inclusion criteria of FACEHBI (scores lower than -1.5 SD in any cognitive measure of NBACE are not allowed) result in a lower prevalence of amyloid positive subjects at baseline compared to a more liberal definition of SCD. However, this approach ensures that the sample does not include any individual with MCI at baseline and allows capturing early phenomena present in the pre-MCI stage of SCD. Participants in FACEHBI make up a non-homogeneous convenience sample recruited through two different methods: referrals from other physicians and an Open House Initiative. We recognize that from a methodological point of view it can lead to several biases that would be avoidable using a probabilistic sampling. Nevertheless, some of these biases can probably lead to a higher prevalence of preclinical AD in convenience samples (19). Despite the fact that the impact of the recruitment methods has not been studied specifically in SCD, a study that compared the rate of hippocampal atrophy in two samples of healthy controls from two different settings (volunteers and random sampling from the whole population) found a higher rate of atrophy in volunteers (53). Hence, in studies of SCD addressing preclinical AD a population-based approach is probably not the better design when the sample is relatively small because a low prevalence of preclinical AD in the sample can result in a lack of statistical power. Moreover, the fact that FACEHBI uses two different recruitment strategies will be useful to evaluate the influence of these sampling methods in terms of preclinical AD enrichment. We hope these results will help other groups  design studies of cognitively healthy individuals.
In conclusion, FACEHBI is an innovative longitudinal study designed to delve into the knowledge of the pathophysiology of preclinical AD. We are confident that this project will shed light on the evolution of subjective and objective cognition along AD continuum and the role that SCD can play as a risk marker of AD.

Acknowledgements: We acknowledge all FACEHBI participants for their generosity and their trust in our institution. We also want to thank our sponsors for making this project possible and all of the investigators from the Fundació ACE Barcelona Alzheimer Treatment and Research Center, Hospital Clinic and Clínica Corachan for their close collaboration and continuous intellectual input. We are indebted to Trinitat Port-Carbó and her family for their support to Fundació ACE research programs.

Funding: Funds from Fundació ACE Insitut Catalàde Neurociències  Aplicades , Grifols ®, Piramal ® and Araclon Biotech® are supporting the FACEHBI study. The sponsors were not involved in the study design, data collection, analysis or interpretation. The only exception is Araclon Biotech©, who is in charge of the determination of amyloid species in blood. The sponsors have reviewed the manuscript and have given their approval.

The FACEHBI study group: Merce Boada1, Agustin Ruiz1, Lluis Tarraga1, Octavio Rodriguez-Gomez1, Isabel Hernandez1, Maitee Rosende-Roca1, Ana Mauleon1, Liliana Vargas1, Domingo Sanchez-Ruiz1, Carla Abdelnour1, Asunción Lafuente1, Montserrat Alegret1, Angela Sanabria1, Alba Perez-Cordon1, Ana Espinosa1, Gemma Ortega1, Susana Ruiz1, Marina Tarragona1, Oscar Sotolongo-Grau1, Sonia Moreno-Grau1, Sergi Valero1,2, Judit Serra1, Elvira Martin1, Esther Peleja1, Marina Guitart1, Anna Gailhajanet1, Susana Diego1, Marta Ibarria1, Pilar Cañabate1, Mariola Moreno1, Silvia Preckler1, Mar Buendia1, Ana Pancho1, Gabriel Martinez1, Miguel Castilla-Marti1,3, Assumpta Vivas4, Marta Gomez-Chiari4, Miguel Angel Tejero4, Joan Gimenez4, Francisco Lomeña5, Francisco Campos5, Javier Pavia5, Rosella Gismondi6, Santiago Bullich6, Manuel Sarasa7, Pedro Pesini7,Inmaculada Monleon7, Virginia Pérez-Grijalba7, Noelia Fandos7, Judith Romero7, Marcelo Berthier8 (1. Fundació ACE. Alzheimer Treatment and Research Center. Barcelona, Spain; 2. Psychiatry Department, Hospital Universitari Vall d’Hebron, CIBERSAM, Universitat Autonoma de Barcelona,Barcelona, Spain; 3. Valles Ophthalmology Research (VOR), Sant Cugat del Vallés, Barcelona, Spain; 4. Departament de Diagnòstic per la Imatge. Clínica Corachan, Barcelona, Spain; 5. Servei de Medicina Nuclear, Hospital Clínic i Provincial. Barcelona, Spain.6 Piramal Imaging GmbH, Berlin, Germany; 7. Araclon Biotech©. Zaragoza, Spain; 8. Cognitive Neurology and Aphasia Unit (UNCA). University of Malaga).

Conflict of Interest: M. Sarasa and P. Pesini are employees of Araclon Biotech.


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