A 20-s Video-Based Assessment of Cognitive Frailty: Results from a Cohort Study within the Precision Aging Network

Cognitive frailty, the concurrent presence of mild cognitive impairment and physical frailty, poses a significant risk for adverse outcomes in older adults. Traditional assessments that rely on extensive walking tests or specialized equipment are impractical for routine or remote evaluations. This study evaluated a 20-s video-based Upper Frailty Meter (vFM) test, incorporating dual-task conditions, as a feasible tool for identifying cognitive frailty.

Data from 413 participants aged 50-79 years in the Healthy Minds for Life cohort were analyzed across four sites: the University of Arizona, Johns Hopkins University, Emory University, and the University of Miami. Cognitive function was measured using the Montreal Cognitive Assessment (MoCA), whereas frailty indices were derived from the vFM test. Participants performed repetitive elbow flexion extension under single-task (physical task only) and dual-task (physical task with concurrent cognitive exercise) conditions. Frailty phenotypes, including slowness, weakness, and exhaustion, were quantified using AI-based video kinematic analysis. Logistic regression and receiver operating characteristic (ROC) analyses evaluated the model's predictive accuracy for cognitive frailty.

Participants classified as cognitive frailty group (n = 53, 12.8%) demonstrated significantly higher frailty index scores compared to robust individuals (p < 0.001). Among all vFM-derived parameters, the dual-task slowness phenotype demonstrated the strongest correlation with MoCA scores (r = -0.282, p < 0.001) and emerged as the most predictive single marker for distinguishing the cognitive frailty group, demonstrating high clinical applicability (area under the curve [AUC] = 0.87). Combining single-task and dual-task metrics further enhanced predictive accuracy (AUC = 0.91), achieving sensitivity and specificity rates exceeding 85%. This combined approach significantly differentiated cognitive frailty from robust status, outperforming models based on age alone or single-task metrics.

The 20-s vFM test offers a practical, noninvasive, easy-to-implement, and accessible solution for objectively evaluating cognitive frailty, demonstrating high predictive accuracy in distinguishing at-risk individuals. Its integration into telehealth platforms could enhance early detection and enable timely interventions, promoting healthier aging trajectories. Further longitudinal studies are recommended to validate its utility in tracking cognitive and physical decline over time.