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

Background: Cognitive frailty, the concurrent presence of mild cognitive impairment (MCI) 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-second video-based Upper Frailty Meter (vFM) test, incorporating dual-task conditions, as a feasible tool for identifying cognitive frailty.
Methods: 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.
Results: 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.
Conclusions: The 20-second vFM test offers a practical, non-invasive, 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.

背景：认知衰弱（Cognitive frailty）指同时存在轻度认知障碍（Mild Cognitive Impairment, MCI）与躯体衰弱，会显著增加老年人出现不良结局的风险。传统评估手段依赖长距离步行测试或专业设备，无法适用于常规或远程评估场景。本研究评估了一项基于视频的20秒上肢衰弱评估测试（Upper Frailty Meter, vFM），该测试纳入双任务范式，作为识别认知衰弱的可行工具。
方法：本研究对“健康终身心智”（Healthy Minds for Life）队列中的413名年龄介于50至79岁的参与者数据展开分析，研究覆盖四个站点：亚利桑那大学、约翰·霍普金斯大学、埃默里大学以及迈阿密大学。认知功能采用蒙特利尔认知评估量表（Montreal Cognitive Assessment, MoCA）进行测评，衰弱指数则通过vFM测试计算得到。参与者分别在单任务（仅完成躯体运动任务）与双任务（同时完成躯体运动任务与认知任务）条件下完成重复肘关节屈伸动作。包括运动迟缓、肌力减退与疲劳感在内的衰弱表型，通过基于人工智能（Artificial Intelligence, AI）的视频运动学分析进行量化。本研究采用逻辑回归与受试者工作特征（Receiver Operating Characteristic, ROC）分析，评估模型对认知衰弱的预测准确性。
结果：被归类为认知衰弱组的参与者（n=53，占比12.8%），其衰弱指数得分显著高于健康强健个体（p<0.001）。在所有vFM衍生参数中，双任务运动迟缓表型与MoCA得分的相关性最强（r=-0.282，p<0.001），且成为区分认知衰弱组的最优单一预测标志物，展现出较高的临床应用价值（曲线下面积[Area Under the Curve, AUC]=0.87）。联合单任务与双任务指标可进一步提升预测准确性（AUC=0.91），灵敏度与特异度均超过85%。该联合方案可显著区分认知衰弱与健康强健状态，性能优于仅基于年龄或单任务指标的模型。
结论：这项20秒vFM测试为客观评估认知衰弱提供了一种实用、无创、易于实施且便于获取的方案，在区分高风险个体时展现出较高的预测准确性。将其整合至远程医疗平台，可提升早期检测能力并实现及时干预，助力老年人实现更健康的衰老轨迹。建议开展后续纵向研究，以验证其在长期追踪认知与躯体功能衰退方面的应用价值。