Normal aging often leads to cognitive decline, and oldest old people, over 80 years old, have a 15% risk of developing neurodegenerative diseases. Therefore, it is important to have appropriate tools to assess cognitive function in old age. The study aimed to provide new norms for neuropsychological tests used to evaluate the cognitive abilities in people aged 80 years and older in France, focusing on the impact of education and gender differences.
Method:107 healthy participants with an average age of 85.2 years, with no neurological history or major cognitive deficits were included. A comprehensive neuropsychological assessment was performed, covering several cognitive functions such as memory, visuospatial abilities, executive functions, attention, processing speed, and praxis.
Results:Individuals with lower levels of education performed poorly on some tests and took longer to complete. Gender differences were observed, with women outperforming men in verbal episodic memory, while men showed better performance in visuoconstructive tasks. The participants showed lower performance in verbal episodic memory compared to norms established in previous French studies. In relation to executive functions, participants were slower to perform complex tasks than participants in previous studies.
Conclusion:This study provides cognitive norms specifically adapted to the oldest old population, which differ from established norms for younger aging adults. It highlights the importance of including these norms in future clinical and scientific investigations. The findings underscore the importance of education on cognitive abilities and emphasize the need to consider gender differences when assessing cognitive functions in aging populations.
Objective: The ability to remotely monitor cognitive skills is increasing with the ubiquity of smartphones. The Mobile Toolbox (MTB) is a new measurement system that includes measures assessing Executive Functioning (EF) and Processing Speed (PS): Arrow Matching, Shape-Color Sorting, and Number-Symbol Match. The purpose of this study was to assess their psychometric properties.
Method: MTB measures were developed for smartphone administration based on constructs measured in the NIH Toolbox® (NIHTB). Psychometric properties of the resulting measures were evaluated in three studies with participants ages 18 to 90. In Study 1 (N = 92), participants completed MTB measures in the lab and were administered both equivalent NIH TB measures and other external measures of similar cognitive constructs. In Study 2 (N = 1,021), participants completed the equivalent NIHTB measures in the lab and then took the MTB measures on their own, remotely. In Study 3 (N = 168), participants completed MTB measures twice remotely, two weeks apart.
Results: All three measures exhibited very high internal consistency and strong test-retest reliability, as well as moderately high correlations with comparable NIHTB tests and moderate correlations with external measures of similar constructs. Phone operating system (iOS vs. Android) had a significant impact on performance for Arrow Matching and Shape-Color Sorting, but no impact on either validity or reliability.
Conclusions: Results support the reliability and convergent validity of MTB EF and PS measures for use across the adult lifespan in remote, self-administered designs.
Objective: The Amsterdam Instrumental Activities of Daily Living Questionnaire (A-IADL-Q) is well validated and commonly used to assess difficulties in everyday functioning regarding dementia. To facilitate interpretation and clinical implementation across different European countries, we aim to provide normative data and a diagnostic cutoff for dementia.
Methods: Cross-sectional data from Dutch Brain Research Registry (N = 1,064; mean (M) age = 62 ± 11 year; 69.5% female), European Medial Information Framework-Alzheimer's Disease 90 + (N = 63; Mage = 92 ± 2 year; 52.4% female), and European Prevention of Alzheimer's Dementia Longitudinal Cohort Study (N = 247; Mage = 63 ± 7 year; 72.1% female) were used. The generalized additive models for location, scale, and shape framework were used to obtain normative values (Z-scores). The beta distribution was applied, and combinations of age, sex, and educational attainment were modeled. The optimal cutoff for dementia was calculated using area under receiver operating curves (AUC-ROC) and Youden Index, using data from Amsterdam Dementia Cohort (N = 2,511, Mage = 64 ± 8 year, 44.4% female).
Results: The best normative model accounted for a cubic-like decrease of IADL performance with age that was more pronounced in low compared to medium/high educational attainment. The cutoff for dementia was 1.85 standard deviation below the population mean (AUC = 0.97; 95% CI [0.97-0.98]).
Conclusion: We provide regression-based norms for A-IADL-Q and a diagnostic cutoff for dementia, which help improve clinical assessment of IADL performance across European countries.
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