Background: Remote, scalable cognitive assessment could improve detection and longitudinal monitoring of age-related cognitive change, but few studies have validated comprehensive digital neuropsychological batteries administered entirely at home. Objective: This study aims to evaluate the #feasibility, acceptability, and short-term test-retest reliability of a remotely delivered digital neuropsychological battery (portable automated rapid testing [PART]) in cognitively healthy older adults. Methods: We screened 82 English-speaking, cognitively healthy older adults aged 50 to 85 years and mailed configured tablets to participants. Researchers remotely administered a battery of neuropsychological assessments spanning language fluency (Boston Naming Test, verbal fluency: letter “F,” supermarket items, and animals), memory (verbal paired associates, word list recall, and logical memory recall), praxis memory (clock drawing and constructional praxis [CP]), and executive functioning (trail making test [TMT]) using the PART app twice, approximately 1 month apart. Task comfortability was summarized with a comfort index based on participants’ self-reports after completing each task. Reliability analyses included paired t tests to detect group-level shifts in performance, Pearson correlations for short-term stability, and Bland-Altman limits of agreement (LoA) to quantify bias and within-person variability. Performance across all tasks was also compared to comparative samples from large normative studies using equivalent paper-and-pencil versions of these tasks. Results: A total of 72 participants completed the first time point (T1), 64 completed the second time point (T2), and the analytic sample comprised a total of 63 participants. Overall comfort was high across sessions (mean comfort index was 88% at T1 and 84% at T2). Memory and executive function measures showed moderate test-retest correlations (r=0.39-0.73), while clock drawing and CP showed low or nonsignificant associations, likely due to ceiling effects. Paired tests indicated small but significant practice effects for verbal paired associates (t62=−3.10; P=.003) and word list (t57=−2.50; P=.02), with substantial practice effects for logical memory (story A: t56=−2.95; P=.005) and TMT part A (t50=−3.97; P

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Failing The Test #marytrump #marytrumpmedia #cognitiveassessment #fail

Failing The Test #marytrump #marytrumpmedia #cognitiveassessment #fail

Failing The Test #marytrump #marytrumpmedia #cognitiveassessment #fail

Background: Digital short cognitive tests administered by medical assistants (MAs) in general practitioners’ (GPs) practices have great potential for the timely identification of patients with dementia, because they can lead to targeted specialist referrals or to immediate reassurance of patients regarding their perceived concerns. However, integration of this testing approach into clinical practice requires good #usability for the test itself, especially for cognitively impaired older adults. Objective: In this implementation study, the digital version of the Montreal Cognitive Assessment (MoCA) Duo was conducted by MAs in general practice. We tested if the interaction with the test is associated with #usability problems for the patients and aimed to find additional relevant constructs that should be considered for the potential implementation of such digital tests into clinical practice. We focused the study on subjective success, #usability, and workload as well as their association with the result of the cognitive test to assess whether the MoCA Duo can be implemented into general practice. Methods: In total, 10 GPs took part in the study. Within their practices, 299 GP patients (aged 51-97 years) with cognitive concerns completed the MoCA Duo administered by MAs. Subsequently, patients and MAs completed digital questionnaires regarding the interaction with the app. #usability was measured using the adapted System #usability Scale, and perceived workload using the National Aeronautics and Space Administration Task Load Index. For the perceived workload, we included an assessment of the patient by the MA. Results of the MoCA Duo were supplied to the GPs for their consultation with the patient. Results: The results indicated good #usability for the MoCA Duo. Self-assessment by the patients indicated that 64% (191/299) could perform in the test to the best of their ability, affected by their MoCA score. We found significant higher #usability ratings by patients with better MoCA scores as well as by younger patients. Furthermore, the perceived workload showed overall medium workload. We found significant correlations between the subjective perceived workload of the patients and the assessment by MAs. Self-assessments as well as assessments by the MAs were significantly influenced by the MoCA scores and the age of the participants. Conclusions: The results indicate good #usability of the digital MoCA within the sample, supporting the idea that the resulting scores are adequate to assess cognitive status without dependence on technological affinity. Furthermore, the results highlight the relevance of heterogenous samples for comparable evaluation studies, based on the significant effect of cognitive status and age on #usability and workload.

Background: Recent advances in cognitive digital assessment methodology, including high-frequency, ambulatory assessments, promise to improve the detection of subtle cognitive changes. Computational modeling approaches may further improve the sensitivity of digital cognitive assessments to detect subtle cognitive changes by capturing features that map onto core cognitive processes. Objective: We explored the validity of a brief smartphone-based adaptation of a visual working memory task that has shown sensitivity for detecting preclinical Alzheimer disease risk. We aimed to optimize properties of the task for computational cognitive feature extraction with drift diffusion modeling. Methods: We analyzed data from 68 participants (n=47, 69% women; n=55, 81% White; mean age 49, SD 14; range 24-80 years) who completed 60 trials for each of 16 variations of a visual working memory binding task (the Color Shapes task) on smartphones, over an 8-day period. A drift diffusion model was fit to the response time and accuracy data from the task. We experimentally manipulated 3 properties of the Color Shapes task (study time, probability of change, and choice urgency) to test how they yielded differences in key drift diffusion model parameters (drift rate, initial bias toward a response option, and caution in decision-making). We also evaluated how an additional task property, the test array size, impacted responses across all conditions. For array size, we tested a whole display of 3 shapes against a single probe of 1 shape only. Results: The 3 task property manipulations yielded the following results: (1) increasing the ratio of different responses was credibly associated with higher initial bias toward the different response (mean 0.06, SD 0.02 for the whole display; mean 0.15, SD 0.02, for the single probe condition); (2) increasing the choice urgency during the test phase was credibly associated with decreased caution in decision-making in the single probe condition (mean −0.04, SD 0.02) but not in the whole display (mean −0.01, SD 0.02); and (3) contrary to expectation, longer study times did not yield a credibly faster drift rate but produced credibly slower ones for the whole display condition (mean −0.28, SD 0.05) and a null effect for the single probe condition (mean 0.01, SD 0.05). In addition, as expected, we found that individual differences in drift rate were associated with age in both array sizes (r=−0.45 with Bayes factor=191), with older participants having a slower drift rate. Older participants also showed higher caution (r=0.42 with Bayes factor=80.76) in the single probe condition. Conclusions: We identified a version of the Color Shapes task optimized for smartphone-based cognitive assessments in real-world settings, with data designed for analysis through computational cognitive modeling. Our proposed approach can advance the development of tools for efficient and effective early detection and monitoring of risk for Alzheimer disease.

Background: Elective surgeries for older adults are increasing. Machine learning could enhance risk assessment, influencing surgical planning and postoperative care. Preoperative cognitive assessment may facilitate early detection and management of postoperative delirium (POD). Objective: This study aims to assess machine learning models' predictive ability for POD, focusing on added predictive value of the neuropsychological assessments before elective surgery. Methods: This retrospective cohort study analyzed data from the multicenter PAWEL and PAWEL-R studies, encompassing older patients (≥70 years) undergoing elective surgeries from July 2017 to April 2019. A total of 1624 patients (52.3% male, N = 850; mean age [SD], 77.9 [4.9] years) were included, with POD diagnosis made before discharge. Sociodemographic, clinical, surgical, and neuropsychological features were collected pre- and intraoperatively by care providers. Machine learning model performance was evaluated using the area under the receiver operating characteristic curve (AUC), with permutation testing for significance and SHapley Additive exPlanations to identify effective neuropsychological assessments. Results: Predicting POD before surgery with a random forest model achieved an AUC of 0.760. Incorporating all pre- and intraoperative features into the model yielded a slightly higher AUC of 0.783, with no statistically significant difference observed (P= .243). While cognitive factors alone were not strong predictors (AUC=0.617), specific tests within neuropsychological assessments, such as the Montreal Cognitive Assessment and Trail Making Tests, showed high feature attribution and play a crucial role in further enhancing prediction before surgery. Conclusions: Preoperative risk prediction for POD can increase risk awareness in presurgical assessment and improve perioperative management in older patients at a high risk for delirium. Clinical Trial: The study was registered with the German Clinical Trials Register under the identifiers DRKS12797 and DRKS13311.

Background: Cognitive tasks are essential tools in psychology and neuroscience research, offering insights into various mental processes such as attention, perception, and memory. These tasks often involve simple stimuli, such as alphanumeric characters and shapes, and a large number of trials, a deliberate design choice aimed at controlling for extraneous variables and ensuring the reliability of the results. While this methodological approach is crucial for maintaining experimental rigor and validity, it may inadvertently lead to issues regarding participant engagement. Objective: The main objective of the present empirical study was to investigate whether gamified versions of 3 established cognitive tasks, namely the Visual Search, the Whack-the-Mole, and the Corsi block tasks yield the same patterns of results as their traditional counterparts. These three tasks were chosen because they are commonly used to assess attention and visual short-term memory, which are crucial for many everyday tasks. Methods: Participants executed our gamified versions of the Visual Search, the Whack-the-Mole, and the Corsi Block in one of three conditions: in Immersive Virtual Reality (VR-Lab), in a desktop VR environment presented in the lab (Desktop-Lab), and in a desktop VR environment experienced at home (Desktop-Remote). Results: The pattern of results from these gamified tasks replicated those reported in the literature about their traditional counterparts that typically employ simple stimuli and are longer in duration. Notably, some differences in findings were observed across the three administration conditions (VR-Lab, Desktop-Lab, and Desktop-Remote). In the Visual Search and the Whack-the-Mole tasks participants were 250ms and 70ms faster to respond in the VR-Lab than in the Desktop-Lab condition. Moreover, in the Whack-the-Mole task, participants were 160ms faster when carrying out the task in the Desktop-Lab than in the Desktop-Remote condition. Conclusions: Despite these differences, overall, the findings of the study confirm the potential of VR technology as a tool for assessing cognitive performance with tasks that can potentially increase participant engagement.

Background: Age-related differences in motor skills have been extensively studied, with growing interest in using behavioral data for cognitive assessment. Compared to traditional tools like the Mini-Mental State Examination or Cognitive Impairment Screening Test, behavior-based methods offer the advantage of shorter testing durations, less learning effects, and continuous data tracking. Hand movements, in particular, provide a practical way to gather motor performance data with fewer spatial constraints. This study aims to explore whether hand rotation movement can effectively distinguish age-related motor skill differences, with future applications potentially extending to cognitive assessments, including early detection of mild cognitive impairment. Objective: This study investigates whether hand rotation movements can be used to distinguish 2 age groups, young adults (aged 20-29 years) and older adults (aged 65-80 years). We hypothesize that differences in hand motion control ability will exist between the 2 groups. In total, 7 hand motion measurement indicators related to single hand test indicators, time comparison indicators between rotations, and angle comparison indicators between rotations were defined to test this hypothesis, aiming to identify meaningful indicators for older adults experiencing normal aging before conducting experiments on patients with mild cognitive impairment or dementia. Methods: A total of 68 participants, 39 older adults (aged 65-80 years) and 29 young adults (aged 20-29 years), all capable of normal arm, hand, and finger movements, participated in the experiment. Participants sat facing a webcam and were asked to perform hand rotation movements as quickly and accurately as possible with both hands for 10 seconds. They performed 3 trials with a 30-second break in between. For statistical verification, we set the significance level at .05 and analyzed the data using the generalized estimation equations model to assess the effects of the between-subject factor (age group: younger vs older) and the within-subject factors (hand: left vs right, and trials 1, 2, and 3). Results: Among the 7 measured indicators, 3 (total rotation count, angle, and time) showed statistically significant differences between age groups. Younger participants performed more rotations (B=5.29, P=.002), demonstrated a greater range of motion (B=1334.37, P=.007), and completed the task in less time (B=0.99, P=.003), indicating age-related differences in upper limb motor function. Trial order also had a significant main effect on rotation count and angle. Trial 1 differed significantly from trials 2 and 3, while no difference was observed between trials 2 and 3, suggesting that trial 1 may reflect a practice effect. Conclusions: The findings revealed that the older adult group demonstrated statistically significant differences compared to the young adult group in their ability to control hand rotation movements. A learning effect was observed across the 3 trials, suggesting that the first trial should be discarded for use as a stable measurement. Trial Registration: