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I. Choi1, H. La Monica1, S.L. Naismith2, A. Rahmanovic2, L. Mowszowski2, N. Glozier1


1. Central Clinical School, Brain and Mind Centre, Faculty of Medicine and Health, University of Sydney, Australia; 2. Charles Perkins Centre, Faculty of Science, School of Psychology and the Brain and Mind Centre, University of Sydney, Australia

Corresponding Author: Dr Isabella Choi, 94 Mallett Street, Camperdown, NSW 2050, Australia,, +612 8627 7240.

J Prev Alz Dis 2021;
Published online June 23, 2021,



Communicating personal Alzheimer’s disease risk profiles based on validated risk algorithms may improve public knowledge about risk reduction, and initiate action. This proof of concept pilot trial aimed to test whether this is feasible and potentially effective and/or harmful. Older at-risk adults (N=24) were provided with their personal Alzheimer’s disease risk profile online, which contained information on their personal risk level, scores and tailored recommendations to manage modifiable risk factors. After receiving the risk profile, participants were significantly more accurate in identifying risk and protective factors, and revised their perceived risk to be lower than their initial estimate. There was no apparent harm seen in psychological distress or dementia-related worry. This shows preliminary support for the feasibility of delivering personal dementia risk profiles to low risk, help-seeking older adults in an online format. A definitive trial examining behavioural outcomes and testing in groups with higher risk profiles is now warranted.

Key words: Risk communication, health literacy, psychological distress, prevention, Alzheimer’s disease.



Up to a third of Alzheimer’s disease cases can be prevented through improved education and reduction of modifiable risk factors (1). Growing evidence from multi-domain interventions shows that targeting modifiable risk factors can reduce risk of Alzheimer’s disease (AD) and improve cognition (2, 3). However, lack of knowledge about dementia and its risk factors among the public is a major barrier to individuals implementing behavioural and lifestyle change and, in turn, to dementia risk reduction (4).
Having an accurate understanding of one’s personal risk of future disease is considered essential for engaging in behaviours for risk reduction. Most health behaviour change models, including the Health Belief Model, identify four major constructs that surround health behaviour: health literacy, perceived susceptibility, motivation to change, and perceived barriers to change (5). However, there is poor health literacy of dementia risk, risk factors, and prevention strategies among the public. A systematic review found that almost half of the general public agreed that dementia was a normal and non-preventable part of ageing (6). Mental activity, healthy diet, physical exercise and social engagement were the most commonly nominated ways to reduce risk, but other well established risk factors such as vascular risk (including smoking, high blood pressure, high cholesterol, obesity), low education, poor mental health, brain trauma, and environmental toxins were rarely mentioned (6-8). Only around 25% of Australians were confident they could reduce risk (8). However, there is support that improving dementia health literacy has a positive impact on risk reduction. People who had a strong belief that dementia risk could be reduced, had moderate to high knowledge of risk-reduction behaviours, or had high confidence that risk reduction can be achieved were almost twice as likely to take action to reduce risk (9).
Communicating personal dementia risk level and risk factors to at-risk adults, based on validated AD risk algorithms, may be one way to improve dementia risk knowledge and engage people in risk reduction behaviours. A number of dementia risk algorithms have been validated for the general population and can identify those with high risk with acceptable predictive ability (10). For instance, the Australian National University Alzheimer’s Disease Risk Index (ANU-ADRI) includes various modifiable risk factors that have been validated for middle-age and older adults that are easily assessed via self-report (11, 12). These evidence-based algorithms, along with personalised risk factor feedback and recommendations to reduce risk, can be delivered online for wide access, allowing people to screen for their risk without having had to first consult with a physician. Older adults are able to use technology proficiently and over 90% of help-seeking older adults with some degree of cognitive impairment use the Internet at home, most commonly for emails (13). Such online dementia risk algorithms already exist and are made freely available to the public, for instance, the CAIDE Risk Score App allows users to detect their dementia risk, obtain information on modifiable risk factors, and receive suggestions on how to modify their risk (14).
Surveys have found that there is high interest among older adults in knowing their risk of AD (15, 16) While there may be potential benefits to disclosing risk, there are also concerns that this could cause negative psychological outcomes. For instance, about 30% of older adults who were interested in knowing their risk also actively worried about developing AD (17). Evidence from a systematic review suggests that disclosure of increased AD risk was not associated with anxiety or depression, but did lead to heightened test-related distress, long-term care insurance uptake and health-related behaviour changes (18).
This pilot trial explores the feasibility and acceptability of communicating online personal dementia risk profiles to at-risk older adults and the impact on dementia health literacy, motivation to engage in behaviour change, and potential harmful psychological effects.




This is an uncontrolled proof of concept pilot trial. The study was approved by the University of Sydney Human Research Ethics Committee (protocol number: 2019/669) and registered with the Australian New Zealand Clinical Trials Registry (ANZCTRN12619001242112p).


People attending the Healthy Brain Ageing Clinic, a specialised memory research clinic for people aged 50 years or older in Sydney, Australia, who were clinically diagnosed with mild cognitive impairment (MCI) or subjective cognitive decline (SCD) between October 2019 to May 2020 were recruited. Those who had dementia or pre-existing severe cognitive impairment due to neurological conditions, psychosis, intellectual disability, substance misuse, stroke, or acquired brain injury were excluded.
As part of standard clinic procedure, all clinic attendees completed self-report measures and were assessed by a geriatrician or neurologist, a psychologist, and a neuropsychologist for a review of medical and psychiatric history, mood, and cognitive functioning. Diagnoses were rated according to consensus, including at least two neuropsychologists and one specialist, and were used to exclude those with a dementia diagnosis and other exclusion criteria. Within two weeks of the clinic assessment, attendees received neuropsychological test result feedback over the phone from a neuropsychologist. The clinic does not provide treatment but refers attendees to suitable clinical investigations, e.g. sleep studies, if warranted, as well as clinical trials for which they are deemed eligible.
After receiving neuropsychological test feedback from a neuropsychologist, eligible attendees were invited via a telephone call to participate in the current study. Interested people had to have an email account and were emailed a survey link via REDCap (Research Electronic Data Capture), a web-based research management platform, with the Participant Information Statement and Consent Form. Participants gave consent to extract relevant data collected from their recent clinic assessment to populate their personal dementia risk profile.


Participants completed self-report baseline measures online via REDCap. Participants’ demographic and risk information were extracted from their standard clinic assessment to compile their personal dementia risk profile using the ANU-ADRI (11). Risk factors in the model included: age, gender, highest level and total number of years of education completed, body mass index, diabetes, depression, high cholesterol, traumatic brain injury, smoking status, alcohol intake, social engagement, physical activity, cognitive activity, and diet.
Within two weeks of completing the baseline self-report measures, participants were emailed a pdf document with their personal dementia risk profile. The risk profile contained standard information about dementia, an explanation of their personal dementia risk profile, and information about the ANU-ADRI risk model. Participants were presented with a visual representation of their risk level in the form of a thermometer showing their risk from 0 to 100, along with an explainer “Your risk of developing dementia is low/ moderate/ high. It is estimated that XX out of 100 people with your risk factors will develop dementia in their lifetime” (Figure 1). They were reminded that this is an estimate based on their risk factors rather than a definitive guarantee, and that there are some risk factors they cannot change but some they could potentially work on to reduce their risk. Participants also received a summary of the dementia risk factors included in the risk model and their scores on each risk factor. They were told why the risk factor was important for brain functioning and were given tailored recommendations to manage each risk factor based on their risk factor score, as well as links for more information.
One week after receiving their risk profile, participants received an email asking them to complete the online post-intervention measures in REDCap. After completing all study measures, participants were reimbursed with a $20 gift card in return for their time.

Figure 1. Example of the risk level and risk factor feedback provided in the personal risk profile




Primary outcome: Dementia health literacy

Participants were asked “How likely do you think that you will get Alzheimer’s disease in your lifetime?” to assess perceived risk on a scale, where 0%=certain not to happen and 100%=certain to happen. To examine accuracy of perceived risk, the participant’s perceived risk was subtracted from their ANU-ADRI risk estimate. We recoded the difference (D) into a categorical variable, with <−10% indicating overestimation, >10% indicating underestimation, and accurate if −10% ≤ D ≤ 10%, in accordance with previous studies (19). Similarly, to examine change in perceived risk, participants’ perceived risk at post-intervention was subtracted from their perceived risk at baseline (d), and <-10% indicates increased perceived risk, >10% indicates reduced perceived risk, and −10% ≤ d ≤ 10% indicates no change.
We adapted the dementia risk and protective factors questionnaire in the MijnBreincoach survey (20) to assess for knowledge of dementia risk factors. We included five additional modifiable risk and protective factors that were identified in the ANU-ADRI (11) and the Lancet Commission for dementia prevention (21) (i.e. traumatic brain injury, social activity, sleep, education, and age), totalling 19 risk factors. Additional questions asked about barriers to improving brain health, confidence in risk reduction (8), and worry about getting dementia (7).

Secondary outcome: Motivation to Change Lifestyle and Health Behaviours for Dementia

The Motivation to Change Lifestyle and Health Behaviours for Dementia Risk Reduction (MCLHB-DRR) Scale is designed to assess beliefs and attitudes about lifestyle and health behavioural changes for dementia risk reduction among middle-aged and older adults (22). The scale includes (27) items matched onto seven subscales that reflect the seven concepts of the Health Behaviour Model. All items are rated on a 5-point Likert scale from 1 (strongly disagree) to 5 (strongly agree). The scale has moderate to high internal consistencies for the seven subscales, and moderate test-retest reliability (18). Cronbach’s alpha for each of the subscales in this study were: perceived susceptibility (.916), perceived severity (.331), perceived benefits (.715), perceived barriers (.878), cues to action (.656), general health motivation (.638), and self-efficacy (.615).

Secondary outcome: Psychological distress

The K10 is a commonly used screening scale for non-specific psychological distress validated for use in Australia (23). The K10 has also been demonstrated as having moderate sensitivity to symptom change in an Australian sample (24). Scores on the K10 range from 10 to 50, and a score of 30 or more indicates a severe level of distress. Cronbach’s alpha in this study was 0.841.

Secondary outcome: Dementia-related worry

The Dementia Worry Scale was used to assess dementia-related worry (25). It has strong internal consistency and test-retest reliability. It consists of 12 items with scores ranging from 15 to 60. Cronbach’s alpha in this study was 0.908.

User evaluation

We adapted a five-point scale (from 1= not at all to 5= completely) previously used to assess user experience of a dementia information website (26). Participants were asked whether the information provided was engaging and easy to understand as well as how helpful they found the risk profile and how much they felt they had learned (from 1=nothing at all to 5=a great deal). Additionally, participants were asked if they required more information about their dementia risk profile and were given the option to discuss their experience of using the risk profile with a researcher in a telephone interview.

Data analysis

Data was analysed using SPSS version 23.0. Descriptive statistics regarding participant and baseline characteristics were analysed. Fischer’s exact tests and paired samples t-tests were used to test for differences between outcome measures pre- and post- receiving the dementia risk profile. All p-values were two-sided with an alpha of 0.05 to test for significance.



Demographics and baseline characteristics

Overall, 24 eligible participants participated in the trial (Figure 2). Participants’ ages ranged from 53 to 87, with a mean age of 69.54 years (SD 7.69). Over half of the participants (54%) were female and majority spoke English as a first language (83%). Majority were tertiary educated (75%), 21% had completed a trade certificate, and 4% had completed high school. Majority were retired (58%), 29% were employed, and 13% were unemployed. Over half were married or in a de facto relationship (58%), 25% were widowed or divorced, and 17% had never married. The majority (75%) of participants had MCI and 25% had SCD.
Almost all participants (96%; 23/24) were considered to have Low Risk of developing AD (ANU-ADRI score of less than 17), and one participant (4%) was considered as having High Risk (ANU-ADRI score of greater than 27). Participants’ perceived risk of developing AD ranged from 10-99 (M=51.63, SD=23.85), with the majority of participants overestimating their personal risk (87.5%; 21/24).

Figure 2. Participant flow

Pre-post change on dementia health literacy

All participants completed the post-intervention questionnaires. After receiving their personal dementia risk profile, participants’ perceived risk of developing AD ranged from 3-81 (M=38.83, SD=25.02). There was a significant decrease in perceived risk among the group (p = .010). A total of 18 participants (75%) still overestimated their level of risk whereas the remaining 6 correctly identified their level of risk (25%). Eleven participants (45.8%) reported a reduction in their perceived risk, eleven (45.8%) reported no change, and two participants’ (8.3%) perceived risk had increased.
The average number of correctly identified risk and protective factors increased from a mean of 11.42 items (SD= 4.50) at baseline to 13.96 items (SD= 3.98) (t1,23= -3.839, p=.001) at follow-up.

Pre-post change on motivation to engage in behaviour change and psychological effects

There was a significant reduction on the perceived susceptibility subscale of the MCLHB (t1,23=4.416, p<.001) from baseline (M=12.86; SD=3.30) to 1-week follow up (M=10.29; SD=4.21), but no change on the other subscales. There was no change on the K10 or Dementia Worry Scale.
Participants’ self-reported worry about getting dementia was significantly reduced at follow-up, from 2.75 (SD=.85) to 2.29 (SD=.91) (t1,23= 3.412, p= .002). The most common barriers to reducing risk factors at baseline were lack of knowledge (45.8%; 11/24), followed by health problems (25%; 6/24). At 1-week follow up, the most common barriers were lack of motivation (29.2%; 7/24), health problems (29.2%; 7/24), and difficulty with organisation (25%; 6/24). There was no change in confidence to take action to change risk.

User evaluation

Overall, 70.9% (17/24) of participants agreed that the information in the personal risk profile was engaging, 79.2% (19/24) agreed the information was easy to understand, 79.2% (19/24) agreed it was helpful, and 79.2% (19/24) reported they learned a good deal from their personal risk profile.
Two participants participated in the optional telephone interview with a researcher. Both expressed surprise at their lower than expected AD risk feedback, and identified difficulty addressing some of their risk factors (e.g. getting motivated to exercise). One participant agreed that seeking guidance from a health professional may support them to work on their risk factors.



This pilot study aimed to explore the feasibility, acceptability and potential impact of providing an online personal dementia risk profile to help-seeking older adults at risk of developing AD on risk knowledge, motivation to change health-related behaviours, and psychological effects. To our knowledge, this is the first study focusing on the effects of communicating personal risk profile using risk algorithms based on epidemiological risk factors. Communication of the personal dementia risk profile led to more accurate knowledge of AD risk factors and improved understanding of perceived susceptibility among patients with MCI and SCD. Importantly, there was no negative effect of communicating the personal risk profile online on psychological distress or dementia-related worry among our participants. Participants mostly had a low risk of developing AD, but still reported reduced worry about getting dementia after receiving their risk profile. These findings support the feasibility and acceptability of using dementia risk algorithms to deliver personal risk profiles to low risk older adults in an online format, and indicate that providing this information can improve AD health literacy without a negative impact on psychological wellbeing.
However, there was little evidence in this study that providing personal risk profiles as a standalone intervention was sufficient to motivate change in behaviours to address AD risk factors. Although providing the dementia risk profile addressed one main barrier for risk reduction at baseline (i.e. lack of knowledge of dementia risk factors), participants reported that lack of motivation, health problems, and difficulty with organisation became the main barriers after receiving their risk profile. This suggests that older adults need extra support to effect behavioural change. The personal dementia risk profile could potentially be used as part of a collaborative, shared decision-making approach to address these barriers by guiding and engaging users, carers and clinicians to choose several high impact or easy-to-change risk factors to focus on, and by providing feedback on the change in risk level if risk factors are modified. Trials are underway to test the impact of a tailor-made online lifestyle programme targeting modifiable risk factors on risk score and health behaviours and compliance to health advice (27). There may also be a role for clinicians to follow up with specific guidance on addressing health problems and to assist the older adult to develop a personalised risk reduction plan. A recent rapid review on approaches to healthy ageing interventions for older adults demonstrated that optimal interventions are those that incorporate collaborative approaches with shared decision-making and behavioural change techniques (28). In this regard, the personal dementia risk profile represents a useful tool that clinicians can draw on to present evidence-based, tailored, health and risk information, which in turn can stimulate a collaborative decision-making process around which health/lifestyle factors to target and how best to achieve long-lasting behaviour change.
An interesting finding was that most participants overestimated their risk of developing AD, even after receiving their personal risk profile. This is possibly reflective of our cohort which was composed of help-seeking older adults seeking an evaluation at a memory clinic and were concerned with developing dementia. The continuing high levels of perceived risk at follow up is unsurprising given that previous research has found that even among individuals who accurately recalled their communicated AD risk, over 50% did not fully adjust their perceived risk to match the communicated risk, and that high baseline AD risk perception was the strongest predictor of overestimation of risk (29). It is also possible that participants may not have readily accepted the communicated risk after receiving lower than expected risk feedback, as seen by interview participants expressing surprise at their communicated risk.
Nonetheless, this has implications for supporting clinicians to communicate AD risk information to people with MCI. A survey found that 90% of neurologists said they counselled MCI patients on their risk of AD in general terms but only 60% communicated AD risk in numeric terms (30). Our findings provide preliminary support that patients with MCI or SCD understood numeric AD risk information and risk factors even when it is communicated online without support, and that the multifaceted approach with a clear visual representation and accompanying explanatory text may have facilitated understanding. Clinicians are encouraged to discuss numeric risk estimates with patients with visual aids, explain how these are estimated from risk models, and explore reasons for discord to improve risk appraisal.
There are several important considerations in interpreting the findings. The study sample was a well-educated inner-city cohort who have expressed concern about their memory and were highly motivated to seek help. Participants already knew that they did not have dementia. Their relatively positive reactions to their personal risk profile may reflect their personal interest in managing their brain health or because they were reassured of having low risk. It is unclear whether a general population or primary care sample, who had subjective memory concerns and not assessed for AD, would have similar reactions. It should be noted that a number of older adults approached to take part in the trial declined due to not wanting to know their risk or because they felt overwhelmed. Further research is needed to explore these concerns about knowing one’s personal risk.
The majority of our sample had low risk of developing AD, and it is unknown how moderate or high risk adults would respond to their personal dementia risk profile. There is some indication that high risk individuals, such as those who screen positive to genetic biomarkers, have heightened test-related distress (18). In order for dementia risk profiles to be widely and safely distributed to older adults in public health programs, particularly if they are to be delivered online in the absence of immediate clinical support, it is important to understand how moderate or high risk older adults react to their personal dementia risk profile and to monitor any potential adverse reactions. Finally, this was a pilot trial with a small sample size and short-term follow up. Longer-term follow up and randomised controlled trials to examine effects of communication of personal risk of developing AD are required.
The application of dementia risk algorithms to identify those at risk and to promote and encourage risk reduction behaviour is still in its early stages. This study provides preliminary support for the utility of using risk models that incorporate accessible and potentially modifiable risk factors to communicate personal dementia risk profiles to at-risk older adults.


Acknowledgements: The authors would like to acknowledge Professor Kaarin J. Anstey and Dr Sarang Kim for permission to use the ANU Alzheimer’s Disease Risk Index and their advice on adapting it to the Healthy Brain Ageing clinic measures. We thank the participants who have helped make this research possible.

Funding: This study was supported by a Dementia Australia Research Foundation project grant award. The sponsors had no role in the design and conduct of the study; in the collection, analysis, and interpretation of data; in the preparation of the manuscript; or in the review or approval of the manuscript.

Ethical standards: The study was approved by the University of Sydney Human Research Ethics Committee (protocol number: 2019/669) and registered with the Australian New Zealand Clinical Trials Registry (ANZCTRN12619001242112p).

Conflict of interest: The authors confirm that there are no known conflicts of interest associated with this publication.



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A. Rostamzadeh1, J. Stapels1, A. Genske2, T. Haidl1, S. Jünger2, M. Seves1, C. Woopen2,3, F. Jessen1,4


1. Department of Psychiatry and Psychotherapy, University of Cologne, Medical Faculty, Cologne, Germany; 2. Cologne Center for Ethics, Rights, Economics, and Social Sciences of Health (CERES), University of Cologne, Köln, Germany; 3. Institute for the History of Medicine and Medical Ethics, Research Unit Ethics, University of Cologne,   Faculty of Medicine and University Hospital Cologne, Cologne, Germany; 4. German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany

Corresponding Author: Ayda Rostamzadeh, MD, Department of Psychiatry and Psychotherapy, University of Cologne, Kerpener Straße 62, 50937 Cologne, Germany, Phone: +49 (0)221 – 478 3870, Fax: +49 (0)221 – 478 6030, E-Mail:

J Prev Alz Dis 2019;
Published online September 16, 2019,



Background: Health literacy (HL) refers to the capacity to access, understand, appraise and apply information for decision-making and acting in health-related matters. In the field of Alzheimer’s disease (AD), expanding technologies of early disease detection, disease course prediction and eventually personalized prevention confront individuals at-risk with increasingly complex information, which demand substantial HL skills. Here we report current findings of HL research in at-risk groups.
Methods: Search strings, referring to HL, AD, amyloid and risk, were developed. A systematic review was conducted in PUBMED, Cochrane Library, PsycINFO, and Web of Science to summarize the state of evidence on HL in at-risk individuals for Alzheimer’s dementia. Eligible articles needed to employ a validated tool for HL, mention the concept or one dimension (access, understand, appraise and apply information for decision-making and acting).
Results: 26 quantitative and 9 qualitative studies addressing at least one dimension of HL were included. Overall, there is evidence for a wish to gain knowledge about the own brain status and risk of dementia. Psychological distress may occur and the subjective benefit-risk estimation may be modified after risk disclosure. Effects on lifestyle and planning may occur. Overall understanding and appraisal of information related to AD risk seem variable with several impacting factors. In mild cognitive impairment (MCI) basic HL skill seem to be affected by cognitive dysfunction.
Conclusions: Systematic assessment of HL in at-risk population for AD is sparse. Findings indicate the paramount importance of adequate communication with persons at risk, being sensitive to individual needs and preferences. Substantial research needs were identified.

Key words: Health literacy, individuals at risk, access, understanding and evaluating health information, decision making in health, mild cognitive impairment, Alzheimer’s disease.



Current state of research in early detection of Alzheimer’s disease

Alzheimer’s disease (AD), as the underlying cause of Alzheimer’s dementia, has become a major public health challenge. The pathophysiological processes of AD start decades before its symptom onset and can be identified early in the course of the disease by biomarkers of amyloid and tau deposition as well as of neurodegeneration (1). Current research criteria allow biomarker-based diagnosis in the prodromal and even in the asymptomatic stage, long before functional disability of dementia becomes apparent (1–3). This has stimulated extensive research on early disease identification, dementia risk-prediction and prevention strategies in AD with the ultimate aim of slowing the disease course by impacting on modifiable risk factors in lifestyle-based interventions (4) and targeting molecular pathways of AD by pharmacological approaches (5–7). In all such cases, interventions start at a pre-dementia stage and focus on selected groups of at-risk individuals.

Health Literacy in at-risk groups for Alzheimer’s dementia

Health literacy (HL) is a new concept which can be described as the specific knowledge, competency and skills with respect to health-related matters (8). Sørensen et al. (9) integrated existing concepts and describe HL as a person’s ability to (1) access, (2) understand and (3) appraise medical or clinical issues and (4) apply health information. As HL reflects these multidimensional abilities and skills, it is believed to play an essential role in the design and eventually success of selective and targeted prevention programs. In the case of AD, selective prevention addresses healthy individuals with an increased risk for AD, such as healthy carriers of the risk-enhancing Apolipoprotein E4 (APOE4) genotype, while targeted prevention in AD aims at symptomatic individuals with subjective cognitive decline (SCD) or mild cognitive impairment (MCI) (2, 3). To date, knowledge about HL in individuals with very early AD and increased risk of dementia is very limited, which significantly limits the development of adequate communication approaches for research and clinical practice. Taking into account that the longitudinal deterioration of cognitive functioning in these patients will affect HL skills progressively, there are unique challenges concerning counselling and informed consent procedures, but also public health campaigns. In addition, there is little knowledge on the overall engagement of such individuals in the health-care system.
This is, to the best of our knowledge, the first review systematically investigating the current state of knowledge about HL in at-risk individuals for Alzheimer’s dementia. The goal of this review is to provide a summary of the evidence on how at-risk groups for Alzheimer’s dementia gain access to, understand, appraise and apply risk-related health information in decision-making and acting. We focus specifically on studies which employed biomarkers of amyloid pathology or genetic risk factors.



Conceptual framework

This review is based on the integrative model of HL developed by the HLS-EU Consortium in 2012, and on the Australian HL concept outlined in the Health Literacy National Statement (AHLNS) (10).
Using the definition by Sørensen et al. (9) we developed a search strategy that covers HL as an umbrella term as well as its four subdomains (access, understand, appraise, apply). For the purpose of this review, we decided to split the step “apply” into two sub categories: decision-making and action; this allows us to better understand gaps between the phases of intention (decision-making) and actual health behavior change (action).

Search strategy

Search strings consisted of three sections that were combined using the Boolean Operator “AND”. One section was referring to HL, one was the term “Alzheimer’s disease” and “amyloid” and one was “risk” and “risk factors” (see appendix 1 for the detailed search strings). The final search was carried out in PUBMED, Cochrane Library, PsycINFO and Web of Science. The period of literature search was between March 2017 and March 2018 (last read out 21.03.2018). Furthermore, the reference lists of all publications included in this review were hand searched for additional studies. Search strategy, screening and data selection were carried out in accordance with the PRISMA criteria (11). This review is registered in the international prospective register of systematic reviews (PROSPERO) with the  registration number: CRD42016052345 ( The date of registration was 12.12.2016.

Paper selection/Inclusion criteria

We included studies that investigated individuals at risk of developing Alzheimer’s dementia. These were individuals with SCD or MCI and those with a family history of AD or with biological risk, such as a genetic predisposition (i.e. healthy siblings of individuals with monogenic early-onset AD (EOAD) or carriers of the APOE4 allele) or individuals with evidence for amyloid-pathology measured by biomarkers. To be included in this review, articles had to be published in a peer-reviewed journal and written in English or German. No starting time point regarding the publication date was applied. This review considered all types of study designs including quantitative (such as observational, prospective and retrospective cohort studies, clinical trials), qualitative and mixed methods designs. To be included, studies had to use a validated tool to measure HL or examine any of its dimensions (access, understanding, appraisal, decision-making or action) as a primary or secondary outcome. Alternatively, studies had to mention the concept of HL plus at least one of the four dimensions in the title or abstract.

Screening and Assessment

Articles were screened for eligibility based on title and abstract by two independent reviewers and then checked independently according to the inclusion criteria. Any discrepancies in rating were resolved through discussion, and when necessary, a third reviewer judged the respective publication. If eligibility was unclear, but studies deemed potentially relevant by title or abstract, a full text review was performed as in all articles selected for full text review.

Data extraction

Due to the heterogeneity of study designs and outcomes,  we decided to conduct a narrative review. The complex nature of the research question made it necessary to include a variety of study types including different quantitative formats as well as qualitative interview studies. Therefore, we applied the methodology of a mixed-methods review. For data extraction and synthesis, we followed recommendations for appraising evidence from different study types within one review (12, 13). In order to ensure adequate data extraction, an evaluation matrix for data analyses was designed based on the inclusion criteria and our research question (see appendix 2). Along this form, study characteristics (author, title, journal, year of publication and country of origin) were extracted. The next steps included extraction of additional information on study design, characteristics and population and regarding the main outcome measures. We applied a segregated methodology (14), where we first analyzed and synthesized data from quantitative studies, before we examined qualitative findings in a separate step. In the final step, all findings were combined in a narrative synthesis. The narrative includes the target population characteristics, the HL process, the methodology, the study setting, and the type of outcome. Thematic categories were predefined based on the research question and were further refined during the data analysis process. Data analyses was performed by two independent reviewers and in case of any discrepancies, a third reviewer judged re-evaluated.

Quality assessment (Risk of bias)

The quality of included studies was evaluated by two independent reviewers using a standardized set of criteria proposed by Hawker et al. in 2002 for mixed-methods reviews (see appendix 3) (15). Discrepancies between raters were resolved by discussion and where necessary re-assessed by a third reviewer.



Included Studies

The initial search yielded 7804 papers. 112 articles were identified through reference check. 43 articles were selected for full text review. After full text review, 35 studies fulfilled the inclusion criteria for analysis. The detailed selection process is depicted in figure 1.

igure 1. PRISMA Flow-Chart of paper selection

igure 1. PRISMA Flow-Chart of paper selection


The 35 papers meeting the inclusion criteria are summarized in appendix 4. A total of 27 studies were carried out in the USA (16, 17, 26–35, 18, 36–42, 19–25), two in Austria (43, 44) and one in each of the following countries: Cuba (45), the Netherlands (46), Germany (47), United Kingdom (48), Belgium (49) and Sweden (50). We neither found studies that considered HL as a basic concept for the particular investigation, nor studies using established assessment tools for HL, such as HLS-EU, HLQ, REALM or TOFLHA. However, we identified studies where at least one aspect of HL was actively applied. The majority of identified studies used customized tools for their individual study purposes to measure single domains of HL. Four studies used established measures for single domains of HL, such as the health numeracy scale (24, 43) and the tool for Capacity to Consent to Treatment, an instrument to evaluate medical-decision making capacity (30, 31). Regarding the target populations, we identified reports with symptomatic individuals such as MCI patients (9 studies) and patients with SCD (1 study). Furthermore, we identified studies on healthy adults with first-degree relatives suffering from Alzheimer’s dementia (21 studies) and healthy first-degree relatives of individuals with monogenetic early onset AD EOAD (4 studies). Three studies investigated disclosure on brain amyloid-status in individuals with MCI and SCD (40, 41, 49). We included one qualitative study addressing mild Alzheimer’s dementia patients and their relatives, because we considered the results of the interviews important regarding the patients’ decision-making capacities, self-determination and autonomy (47). A total of 20 studies were carried out as part of the REVEAL-program (Risk Evaluation and Education for AD), which is a series of multi-site randomized controlled clinical trials with the objective to evaluate the impact of genetic risk assessment and disclosure in healthy adults with first-degree relatives with Alzheimer’s dementia (35, 38).
According to the quality assessment a total of 11 publications were rated as “good,” 21 as “fair” and 3 papers as “poor” (see appendix 3). The articles rated as “poor” were not excluded from the study, because the findings were considered as relevant for our research question, but need to be evaluated with consideration due to the quality rating.

Findings for the different HL domains

As highlighted above, individuals at risk for Alzheimer’s dementia can be grouped into asymptomatic individuals, such as healthy adults with first-degree relatives with Alzheimer’s dementia, and into symptomatic individuals, including MCI and SCD patients. Therefore, we decided to present the results of the two target populations separately. The thematic fields of the findings within the different HL domains are summarized in table 1.

Table 1. Content analysis within the different HL domains, clustered in cognitively impaired and unimpaired individuals

Table 1. Content analysis within the different HL domains, clustered in cognitively impaired and unimpaired individuals


Access to health-related information on AD

Access in cognitively impaired individuals

Access to health-related information in cognitively impaired individuals has not been investigated in the included studies.

Access in cognitively unimpaired individuals

Two studies reported that first-degree relatives of AD patients obtained and accessed health-related information about AD mainly informally through spouses, friends, literature, patient organizations and mass media and less likely obtained disease-related information through health care providers (20, 50).


Understanding in cognitively impaired individuals

Three studies investigated understanding, in terms of medical decision-making capacity in individuals with MCI and very mild dementia, using the validated Capacity to Consent to Treatment Instrument (CCTI) (30, 31). In addition, health numeracy, i.e. the ability to understand and use health-related numerical information, was studied in healthy and cognitively impaired individuals between the age of 50 – 95 years (43). The studies showed that even MCI is associated with reduced competencies such as “understanding”, “appreciation”, and “reasoning”. This is most likely related to impairment in basic cognitive domains including executive function and calculation abilities.
In contrast, two other studies that analyzed the impact of AD risk communication on MCI patients and their caregivers (26), as well as the effects of standardized counselling and risk disclosure of biomarker-based AD diagnosis (41), revealed that study participants generally comprehended the medical information.

Understanding in cognitively unimpaired individuals

Six studies investigated the ability to understand health and dementia-risk-related issues in healthy individuals with AD-affected relatives (16, 22, 27, 33, 34, 50). At one-year post-disclosure only 48% of the participants were able to recall their lifetime risk estimate correctly, whereas 76% were able to report correctly the number of copies of the (“risk-enhancing”) APOE4 allele they carried. Overall the studies indicated, that participants more likely remembered general information provided during the counselling and disclosure sessions, such as dichotomous information as being a risk gene carrier or not, than more complex information as specific genotypes or lifetime risk estimates. Furthermore, the studies indicated knowledge differences between ethnic groups, which were related to cultural factors and to inequalities in healthcare system access.


Appraisal in cognitively impaired individuals

Studies on appraisal of risk and AD-related health information in cognitively impaired individuals covered a wide range of aspects such as framing effects (44) and experiences as well as coping mechanisms when receiving diagnostic information (40, 46, 48, 49).
Framing effects refer to a bias that can be generated by the setting and the specific wording when communicating information. One study indicated that MCI and mild AD patients showed stronger proneness to framing effects as compared to controls. Such framing effects correlated inversely with performance in the domains of verbal and figural memory, attention span, executive functions and mental complex calculation (44).
One research group reported that MCI patients were highly interested in their brain amyloid status (49) and were aware of possible emotional effects after disclosure. Two studies in individuals with SCD and MCI reported effects of disclosure of amyloid status in real (40) and in hypothetical scenarios (48). Both concluded that psychological well-being was not negatively affected, when counselling and disclosure were performed at a specialized center and psychoeducational materials were offered.
Contrary, one study that used a qualitative approach to explore the experiences and coping mechanisms of MCI patients, indicated that the discussion of cognitive decline as a consequence of the amyloid status  provoked negative emotions and reactions as well as problematic interaction with the family and the social environment (46). It was not investigated how these negative effects influence appraisal of health-related information, decision-making or health behavior, especially the decision to receive testing for AD.

Appraisal in cognitively unimpaired individuals

The REVEAL-study group analyzed participants’ perceptions of benefits and advantages (pros) and risks and limitations (cons) of genetic susceptibility testing (21). Initially, participants appraised the benefits as being more important than the limitations and risks. After performing genetic testing and disclosure of the test results, however, participants tended to have a less positive attitude and concerns increased. The result that benefits are initially prevailing in the decision-making process was confirmed by a Swedish study (50), where relatives of AD-patients stated, that, if available, they would go through a pre-symptomatic AD test. These results were confirmed in a Cuban study (45) that investigated the attitudes and knowledge of healthy relatives from a large Cuban family with monogenic EOAD patients.
The authors of the REVEAL-study found that risk disclosure of the APOE genotype had an impact on the individual participants’ perceived risk of developing AD (22, 25, 29), however, it did not have a clinically relevant psychological impact on the participants (28).
Two qualitative interview studies investigated experiences and coping mechanisms of cognitively healthy at-risk individuals for Alzheimer’s dementia (27, 39). During the interviews some of the participants expressed feelings of helplessness and appraised their life and health as uncontrollable when reflecting about their risk of developing Alzheimer’s dementia. Reported concerns referred to personal health and life perspective as well as to effects on other family members (27).
The REVEAL Qualitative Research Initiative (REVEAL-QRI) explored appraisal and coping preferences (39). The appraisal of potential emotional reactions to the APOE-genotype disclosure played a crucial role in the decision whether to proceed with genetic susceptibility testing or not. After APOE-genotype disclosure individuals developed problem-focused coping mechanisms such as financial planning and creating advance directives, but also search for more information and usage of biomedical tests to further clarify the individual risk. These findings were confirmed by quantitative research within the REVEAL-study group (16, 33).

Apply: Decision-Making

Decision-Making in cognitively impaired individuals

Semi-structured interviews with MCI patients to capture the motivation to participate in real (49) and hypothetical (48) AD biomarker disclosure revealed that the most common reasons for choosing biomarker result disclosure were better understanding of their own brain condition and being able to make better decisions about future life planning. The need for a definite diagnosis preoccupied most of the participants. Possible negative social or legal consequences of the knowledge of one’s biomarker status were not put forward. The authors found that MCI was associated with poorer financial and healthcare decision-making capacity, which in turn was related to worse global cognitive functioning (24).

Decision-Making in cognitively unimpaired individuals

One study examined the intentions for APOE genotyping within six hypothetical scenarios (32) and revealed that the overall interest in genetic testing was high and that greater interest in testing was associated with male gender and with scenarios describing high test accuracy, detailed information on risk and most importantly available treatment options. Seemingly, the appraisal of possible emotional reactions to the APOE-genotype disclosure are decisive whether to proceed with genetic susceptibility testing or not (39).
Intentions of changing health-related behavior were examined in a secondary analysis from data of the REVEAL-study (20, 37). The authors reported that APOE4-positive participants were more likely to think about insurance changes than APOE4-negative participants, especially regarding long-term care coverage.

Apply: Effects on health behavior

Behavior change in cognitively impaired individuals

One study in patients with mild AD reported that access to specialized health services, such as medical specialists or memory clinics, is mainly initiated by spouses or primary care physicians, but not by the patients themselves (47).
Within a clinical trial, individuals with SCD and a positive first-degree family history for AD completed amyloid positron-emission tomography (PET) scans and were engaged in a psychoeducational intervention with regard to AD (40) Participants who learned about their positive PET result reported that they had changed their lifestyle in terms of more physical exercise, healthier diet and planning ahead.

Behavior change in cognitively unimpaired individuals

The REVEAL-study group examined motivational aspects for participation in the APOE-disclosure study. The participants rated the following reasons as most important: contribution to research (94%), arrangement of personal affairs (87%), hope that effective treatment will be developed (87%), arrangement of long-term care (81%), preparation of family for the possibility of illness (78%) and doing things sooner than planned (75%) (35, 36). Regarding participation in clinical trials with APOE-genotyping the study group identified higher household income, age below 60 years, Caucasian ethnicity and college graduate education status as predictors for seeking of genetic testing (20, 35, 38).
Health behavior changes were examined in secondary analyses from data of the REVEAL-study. Five studies found an association between health behavior changes and APOE genotype (19, 20, 23, 35, 42). APOE4-positive participants were more likely to use additional medication or dietary supplements, mostly vitamins and/or botanical supplements and more likely to show changes in diet and exercise than APOE4-negative participants. The rates of health behavior change were similar in the APOE4-negative participants and in the control group.



This systematic review shows that research on HL in at-risk individuals for Alzheimer’s dementia is currently very limited. Existing results are fragmented and based on mostly small and often non-representative samples. The current literature shows that single aspects of HL have been addressed in research projects, but the concept of HL as a whole has not been embedded in study designs or outcome evaluations.
Nutbeam (8) has conceptualized three levels of HL, the functional level, the interactive level and the critical level. Current research in subjects at risk of Alzheimer’s dementia focuses on individual domains of HL, including knowledge, attitudes, risk perception, lifestyle changes etc., which corresponds to the functional and the interactive level of HL (8). A core competence of functional literacy is sufficient cognitive capacity, which constitutes the basis for any further HL skill. This is of significant importance in HL related to AD, since a fraction of the at-risk population (MCI) is cognitively impaired and others will become impaired in the course of the disease. At the same time, these individuals are faced with increasingly complex medical information due to rapid technical advances in the field early AD diagnosis, risk prediction and future treatments. This review highlights that reduced mental flexibility and amnestic and executive deficits severely impact on basic functional HL skills, such as comprehension (24, 30, 31, 43, 48). The literature indicates that complex risk information is challenging for lay persons, and that information overload and the degree of information complexity may lead to misunderstanding and misperception of the presented information (16, 22, 34). This could be indicated by either deficits in understanding risk-related information correctly, or an overestimation of benefits of genetic testing for AD, which may be due to several potential causes such as framing of the risk information, the need for certainty or the belief that there might be treatment for AD in the near future.
Individuals with higher levels of HL, such as the interactive level, are more likely to communicate their diagnosis and test results with family, friends, and their social environment. Discussing health-related topics such as positive and negative effects of early AD detection within the social environment can have a positive impact on psychological well-being and may result in a more reasonable and informed opinion in the individual but also in the social environment. Findings suggest that the role of the caregivers is of major importance, since their active presence in the disclosure session can positively contribute to the cognitive processing of information by the MCI patient and hence facilitate the comprehension of risk information (26). Interactive HL was only addressed by one study, where individuals discussed health-related topics with their social environment (17). The authors concluded that beliefs about AD risks and causes, genetic testing and development of treatment impact on the interaction pattern of the individuals with their social environment. Conversely, this process may influence the way medical information is appraised and may lead to a more reasonable and informed decision-making.
According to Nutbeam’s HL concept, critical HL may deserve stronger attention in the context of AD. Critical HL is defined as a combination of advanced literacy skills and social skills, enabling an individual to critically analyze information and to use it to exert greater control over life events and situations. Findings from our review, in line with other research, show the strong wish for clarity concerning one’s health risks and the need for clarification and a definite diagnosis, respectively (28, 40, 41, 49). Participants are willing to accept invasive diagnostics in order to attain certainty and control. In some cases, however, test outcomes may lead to the necessity of further assessments, and uncertainty may even increase. In addition, research on risk perception consistently suggests that people translate the statistical probability of a risk into a dichotomous information. When individuals are informed to be at risk, this may subjectively be equaled as being ill, including related negative psychosocial consequences. This indicates a potential misconception of risk states as opposed to disease diagnosis. Critical HL may therefore play an important role in the context AD, since it may enable individuals to engage in decision-making in better accordance with their personal values and preferences.
Overall our findings highlight the paramount importance of adequate communication with persons at risk of AD, which is sensitive to individual needs, skills, and preferences. Health care professionals are faced with unique complexities in the communication process when consulting MCI patients, as these patients have difficulties in handling and remembering abstract and complex risk information. Finally, HL skills are affected progressively in an individual with ongoing cognitive decline. In order to meet the individual requirements and needs of persons at risk for AD, there is a clear need to be flexible and responsive to patients’ preferences for more basic or more detailed information. This is in line with the abovementioned conceptual framework underlying this review, i.e. that in order to promote individual HL, it needs to be embedded in a health literate environment (9).
A limitation of our review is, that most identified reports are based on data from the REVEAL-study group which provides a more comprehensive understanding of HL among offsprings of individuals with AD, who are considering genetic susceptibility testing for AD. However, this is a rare case since genetic susceptibility testing is not an established method for risk prediction in clinical practice. In fact, at present, APOE genotyping is not recommended in most guidelines, not even in the diagnostic process of patients with dementia (35). The current approaches of early AD detection and risk prediction of Alzheimer’s dementia encompass biomarker-based diagnostics with cerebrospinal fluid (CSF) analysis or positron-emission tomography (PET). Regarding these technologies, studies on HL in at-risk groups are very limited. Furthermore, the findings of our review are limited in terms of evidence on HL in individuals at-risk for Alzheimer’s dementia as an outcome and particularly regarding the assessment of validated tools for HL or its sub-dimensions.



This systematic review highlights the current research on HL of at-risk individuals for Alzheimer’s dementia and reflects the need for more systematic research in this field. The results show that studies concentrate on single domains of HL, but do not comprehensively investigate all steps required for reliable decision-making. A particular challenge in the field of AD is that cognitively impaired individuals are faced with a number of disease-immanent cognitive barriers which hinder them to apply HL skills the way cognitively “normal” individuals would do. Therefore, it will be of utmost importance to analyze their needs, and to provide essential information to facilitate their informed decision-making in the context of specific medical situations (e.g. biomarker-based predictive diagnosis of AD). Herein, it will be critical to adjust to patients’ individual skills, needs, and preferences with respect to the level of detail of the information provided. One future direction for research is to identify the necessary requirements for an informed decision-making and for improved counselling of individuals at risk.


Contributors: Ayda Rostamzadeh and Julia Stapels conducted the literature review and were responsible for data analysis, data interpretation and writing the article. Mauro Seves and Theresa Haidl functioned as third reviewer to resolve discrepancies in the inclusion process. Anna Genske and Saskia Jünger developed the research methodology and assisted in writing the paper. All authors were engaged in the conceptualization, reviewed and commented on the manuscript. Frank Jessen and Christiane Woopen contributed to the conceptualization, writing, review of drafts of the article and obtained funding.

Funding: This project is funded by the Robert Bosch Foundation („Health literacy of persons at risk – from information to action (RisKomp)”; grant number 11.5.A402.0002.0) and by the Federal Ministry of Education and Research – BMBF as part of the Network of European Funding for Neuroscience Research – ERA-NET NEURON („Ethical and Legal Framework for Predictive Diagnosis of Alzheimer’s Disease: Quality of Life of Individuals at Risk and their Close Others” (PreDADQoL); funding number: 01GP1624). Both joint projects are conducted under the leadership of the Cologne Center for Ethics, Rights, Economics, and Social Sciences of Health (ceres). The sponsors did not have any influence on study initiation, conducting and reporting. The sponsors had no role in the design and conduct of the study; in the collection, analysis, and interpretation of data; in the preparation of the manuscript; or in the review or approval of the manuscript.

Acknowledgement: We are grateful to thank Christian Albus, Kristina Enders, Marc Hellstern, Samia Peltzer, Kerstin Rhiem, Stephan Ruhrmann and Rita Schmutzler for their valuable input and fruitful discussions during project meetings and scientific workshops. Moreover, we acknowledge Sophie Heseler for her support doing test searches that helped us refine our search strategy. Finally, we wish to thank Nicole Skoetz for her excellent methodological advice.

Declaration of conflicting interests: The authors declared no potential conflicts of this article.

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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