Search

OBJECTIVE: To demonstrate measurement precision of cognitive domains in the Alzheimer's Disease Neuroimaging Initiative (ADNI) data set. METHOD: Participants with normal cognition (NC), mild cognitive impairment (MCI), and Alzheimer's disease (AD) were included from all ADNI waves. We used data from each person's last study visit to calibrate scores for memory, executive function, language, and visuospatial functioning. We extracted item information functions for each domain and used these to calculate standard errors of measurement. We derived scores for each domain for each diagnostic group and plotted standard errors of measurement for the observed range of scores. RESULTS: Across all waves, there were 961 people with NC, 825 people with MCI, and 694 people with AD at their most recent study visit (data pulled February 25, 2019). Across ADNI's battery there were 34 memory items, 18 executive function items, 20 language items, and seven visuospatial items. Scores for each domain were highest on average for people with NC, intermediate for people with MCI, and lowest for people with AD, with most scores across all groups in the range of -1 to +1. Standard error of measurement in the range from -1 to +1 was highest for memory, intermediate for language and executive functioning, and lowest for visuospatial. CONCLUSION: Modern psychometric approaches provide tools to help understand measurement precision of the scales used in studies. In ADNI, there are important differences in measurement precision across cognitive domains. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Introduction: Prior observational work in a heterogeneous cohort of participants with mild cognitive impairment suggests the Amsterdam Instrumental Activities of Daily Living Questionnaire (A-IADL-Q) may have greater sensitivity for functional decline than the more established Alzheimer's Disease Cooperative Study–Activities of Daily Living (ADCS-ADL) scale. However, the relative utility of the A-IADL-Q versus the ADCS-ADL for clinical trials in early Alzheimer's disease (AD) remains uncertain. Methods: We compared baseline and longitudinal performance of the A-IADL-Q and ADCS-ADL in participants with biomarker-confirmed prodromal (pAD; n = 158) or mild (mAD; n = 283) AD enrolled in the 18-month Tauriel study of semorinemab (NCT03289143). Results: The A-IADL-Q exhibited numerically stronger discrimination between pAD and mAD participants at baseline per Cohen's d analyses and similar sensitivity to longitudinal decline across cohorts over 18 months relative to the ADCS-ADL. Discussion: The comparable performance of the ADCS-ADL and A-IADL-Q supports the utility of the A-IADL-Q in early AD clinical trials. Highlights: The Amsterdam Instrumental Activities of Daily Living Questionnaire (A-IADL-Q) may be more sensitive than the Alzheimer's Disease Cooperative Study–Activities of Daily Living Scale (ADCS-ADL) for distinguishing prodromal and mild Alzheimer's disease (AD). A-IADL-Q and ADCS-ADL are similarly sensitive to decline in early AD over 18 months. Comparable performance of these indices supports A-IADL-Q use in future AD trials. Additional AD clinical trial data could extend findings across more diverse cohorts.

Self-perceived cognitive functioning, considered highly relevant in the context of aging and dementia, is assessed in numerous ways-hindering the comparison of findings across studies and settings. Therefore, the present study aimed to link item-level self-report questionnaire data from international aging studies.We harmonized secondary data from 24 studies and 40 different questionnaires with item response theory (IRT) techniques using a graded response model with a Bayesian estimator. We compared item information curves to identify items with high measurement precision at different levels of the self-perceived cognitive functioning latent trait. Data from 53,030 neuropsychologically intact older adults were included, from 13 English language and 11 non-English (or mixed) language studies.We successfully linked all questionnaires and demonstrated that a single-factor structure was reasonable for the latent trait. Items that made the greatest contribution to measurement precision (i.e., 'top items') assessed general and specific memory problems and aspects of executive functioning, attention, language, calculation, and visuospatial skills. These top items originated from distinct questionnaires and varied in format, range, time frames, response options, and whether they captured ability and/or change.This was the first study to calibrate self-perceived cognitive functioning data of geographically diverse older adults. The resulting item scores are on the same metric, facilitating joint or pooled analyses across international studies. Results may lead to the development of new self-perceived cognitive functioning questionnaires guided by psychometric properties, content, and other important features of items in our item bank. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

BackgroundThe COVID-19 pandemic has major influence on lifestyle and mental health, which might affect brain-health and increase the risk of cognitive decline, particularly in older adults. We aimed to describe changes in modifiable risk factors related to brain-health in older adults after one year of COVID-19 restrictions.MethodsAn online survey was disseminated between February and March 2021 to 17,773 registrants of the Dutch Brain Research Registry, aged ≥50, without a self-reported diagnosis of mild cognitive impairment or dementia. Participants were asked to report potential changes in behaviors during the COVID-19 pandemic, compared to pre-pandemic, in eight domains related to brain health: physical activity, sleep, feeling of memory decline, perceived stress, feeling of loneliness, diet, alcohol consumption, and smoking. We used negative binomial regression analyses to relate (socio)demographics, subjective memory complaints and COVID-19 related aspects (fear of, or current/past COVID-19 infection) to the number of reported detrimental and beneficial changes as dependent variable.Results3,943 participants (66 ± 8 years old; 76% female; 71% highly educated) completed the survey. After one year of COVID-19-restrictions, 74% reported at least one detrimental lifestyle change unfavorable for their brain health, most frequently reported were feelings of loneliness, sleep problems, and less physical activity. 60% of participants reported at least one beneficial change, which were most often more physical activity, healthier dietary habits, and less alcohol consumption. Individuals who are younger [incidence rate ratio (IRR) = 0.99, 95% CI = 0.98–0.99], female (1.20, 1.11–1.30), living alone (1.20, 1.11–1.28) and in urban environments (1.18, 1.08–1.29), who are less satisfied with their income (1.38, 1.17–1.62), experiencing subjective memory complaints (1.40, 1.28–1.52) and those with a past or current (1.19, 1.06–1.34) or fear of a COVID-19 infection (1.33, 1.25–1.42) reported higher numbers of detrimental changes.DiscussionThe COVID-19 pandemic has influenced lifestyle in both positive and negative ways. We identified (socio)demographic factors associated with more detrimental changes in modifiable risk factors related to brain health, suggesting that some individuals are more vulnerable for the impact of the COVID-19 pandemic. These findings provide an opportunity for targeted prevention and education to promote a healthy lifestyle during and after the pandemic.

Additional file 1: Additional details on vignettes. Provides additional information on how the vignettes were made, what activities they are composed of, and lists all vignettes shown to participants.

Introduction: In this multicenter study on subjective cognitive decline (SCD) in community-based and memory clinic settings, we assessed the (1) incidence of Alzheimer's disease (AD) and non-AD dementia and (2) determinants of progression to dementia.Methods: Eleven cohorts provided 2978 participants with SCD and 1391 controls. We estimated dementia incidence and identified risk factors using Cox proportional hazards models.Results: In SCD, incidence of dementia was 17.7 (95% Poisson confidence interval 15.2-20.3)/1000 person-years (AD: 11.5 [9.6-13.7], non-AD: 6.1 [4.7-7.7]), compared with 14.2 (11.3-17.6) in controls (AD: 10.1 [7.7-13.0], non-AD: 4.1 [2.6-6.0]). The risk of dementia was strongly increased in SCD in a memory clinic setting but less so in a community-based setting. In addition, higher age (hazard ratio 1.1 [95% confidence interval 1.1-1.1]), lower Mini-Mental State Examination (0.7 [0.66-0.8]), and apolipoprotein E epsilon 4 (1.8 [1.3-2.5]) increased the risk of dementia.Discussion: SCD can precede both AD and non-AD dementia. Despite their younger age, individuals with SCD in a memory clinic setting have a higher risk of dementia than those in community-based cohorts. (C) 2018 The Authors. Published by Elsevier Inc.

Introduction: The Dutch Brain Research Registry aims to facilitate online recruitment of participants for brain disease studies. Methods: Registrants were primarily recruited through an online social media campaign. The registration process included a short questionnaire, which was subsequently used in the prescreening process to match participants to studies. Results: In the first 18 months, 17,218 registrants signed up (58±11 years old, 78% female). Out of 34,696 study invitations that were sent, 36% were accepted by registrants, of which 50% to 84% were finally enrolled, resulting in 10,661 participants in 28 studies. Compared to non-participants, study participants were more often older, male, more highly educated, retired or unemployed, non-smoking, healthier, and more often had a family member with dementia. Discussion: The Dutch Brain Research Registry facilitates effective matching of participants to brain disease studies. Participant factors related to study enrollment may reflect facilitators or barriers for participation, which is useful for improving recruitment strategies.

Purpose The Quality of Life Alzheimer’s Disease Scale (QoL-AD) is commonly used to assess disease specific health-related quality of life (HRQoL) as rated by patients and their carers. For cost-effectiveness analyses, utilities based on the EQ-5D are often required. We report a new mapping algorithm to obtain EQ-5D indices when only QoL-AD data are available. Methods Different statistical models to estimate utility directly, or responses to individual EQ-5D questions (response mapping) from QoL-AD, were trialled for patient-rated and proxy-rated questionnaires. Model performance was assessed by root mean square error and mean absolute error. Results The response model using multinomial regression including age and sex, performed best in both the estimation dataset and an independent dataset. Conclusions The recommended mapping algorithm allows researchers for the first time to estimate EQ-5D values from QoL-AD data, enabling cost-utility analyses using datasets where the QoL-AD but no utility measures were collected.

Objective:Commonly used measures of instrumental activities of daily living (IADL) do not capture activities for a technologically advancing society. This study aimed to adapt the proxy/informant-based Amsterdam IADL Questionnaire (A-IADL-Q) for use in the UK and develop a self-report version.Design:An iterative mixed method cross-cultural adaptation of the A-IADL-Q and the development of a self-report version involving a three-step design: (1) interviews and focus groups with lay and professional stakeholders to assess face and content validity; (2) a questionnaire to measure item relevance to older adults in the U.K.; (3) a pilot of the adapted questionnaire in people with cognitive impairment.Setting:Community settings in the UK.Participants:One hundred and forty-eight participants took part across the three steps: (1) 14 dementia professionals; 8 people with subjective cognitive decline (SCD), mild cognitive impairment (MCI), or dementia due to Alzheimer's disease; and 6 relatives of people with MCI or dementia; (2) 92 older adults without cognitive impairment; and (3) 28 people with SCD or MCI.Measurements:The cultural relevance and applicability of the A-IADL-Q scale items were assessed using a 6-point Likert scale. Cognitive and functional performance was measured using a battery of cognitive and functional measures.Results:Iterative modifications to the scale resulted in a 55-item adapted version appropriate for UK use (A-IADL-Q-UK). Pilot data revealed that the new and revised items performed well. Four new items correlated with the weighted average score (Kendall's Tau-.388,-.445,-.497,-.569). An exploratory analysis of convergent validity found correlations in the expected direction with cognitive and functional measures.Conclusion:The A-IADL-Q-UK provides a measurement of functional decline for use in the UK that captures culturally relevant activities. A new self-report version has been developed and is ready for testing. Further evaluation of the A-IADL-Q-UK for construct validity is now needed.

Additional file 1.

Additional file 1: Table 1. GRM Item parameters and item information values. Item parameter and item information values estimated in the reference sample used for differential item functioning detection in the Swiss sample. Item parameters are shown as parameter ± standard error. Abbreviations: GRM, Graded Response Model; α, discrimination parameter; β’s, extremity parameters.

Additional file 1. First-round Questionnaire. The file contains the questionnaire from the first round; it was downloaded from EFS survey on 23. September 2019: https://ww2.unipark.de/www/print_survey.php?syid=515897&menu_node=print2 .

Additional file 4: Table 3. Differential Item Functioning from empirical data. Chi-square and McFadden’s ΔR2 values as obtained in differential item functioning (DIF) analyses from the empirical data. Items flagged for DIF are displayed italic in blue. Empirical cut-offs were set a priori at α < .01 for statistically significant DIF, and ΔR2 > .035 for clinically meaningful DIF.

Additional file 3: Figure 1. Item characteristic curves.

Additional file 2: Table 2.Investigation of local independence. Item pairs with large residuals (> 0.25) in the one-factor model fit

Additional file 5: Table 4. Differential Item Functioning Monte Carlo Simulations. The values displayed represent the 99th-percentile threshold values for chi-square p-values and McFadden’s ΔR2 values, obtained from Monte Carlo simulations under the assumption that there is no DIF. When the values found in the empirical data set are more extreme (i.e., smaller p-value and larger ΔR2 value) than those found in the Monte Carlo simulations, this suggests there is DIF. Items flagged for DIF are displayed italic in blue.