Pan Dong-Ni, Wei Dong-Guo, Zhao Yejing, Zhang Jie, Zhao Yanyan, Shen Ji, Cui Han, Wang Junyi, Zeng Yanjia, Zhou Yixiang, Fan Dingyao, Wang Wen, Shi Yuanyuan, Dong Zuofu, Wen Qi, Chen Feifan, Lin CuiZhu, Ma Xin, Li Jing
School of Psychology, Beijing Language and Culture University, Beijing, PR China.
Department of Geriatrics, Institute of Geriatric Medicine, Beijing Hospital, National Center of Gerontology, Chinese Academy of Medical Sciences, Beijing, PR China.
Innov Aging. 2025 Jun 19;9(8):igaf062. doi: 10.1093/geroni/igaf062. eCollection 2025 Aug.
Early detection of mild cognitive impairment (MCI) is vital for managing cognitive decline in older adults. Hand movements are closely linked to cognitive function, prompting this study to develop a virtual reality (VR)-based wearable system to capture detailed hand movements. The main goal was to assess the system's potential in predicting cognitive health and aiding MCI diagnosis.
The study involved 607 participants aged 60-84 (mean age 67.41 ± 4.71 years). Each completed four VR tasks while wearing the system, which recorded fine hand movement data. Cognitive function was assessed using the Beijing version of the Montreal Cognitive Assessment (MoCA-BJ). Statistical analyses were conducted to correlate hand movement metrics with cognitive performance.
Participants with cognitive impairments performed worse on VR-based fine motor tasks. Metrics from tests like the Pegboard, Block Placement-Flipping, and Tapping Tests were predictive of cognitive abilities. Indicators related to finer movements and non-dominant (left) hand use showed superior predictive power, achieving an AUC of 0.687 for predicting MCI, comparable to machine learning models such as Random Forest (0.762) and SVM (0.644).
Hand movement data can provide valuable insights into cognitive function in older adults, highlighting the importance of fine motor skills in early MCI detection. This VR-based system could serve as a useful clinical tool for assessing cognitive health and supporting MCI diagnosis, enabling timely intervention strategies for cognitive decline.
早期发现轻度认知障碍（MCI）对于管理老年人的认知衰退至关重要。手部动作与认知功能密切相关，促使本研究开发一种基于虚拟现实（VR）的可穿戴系统，以捕捉详细的手部动作。主要目标是评估该系统在预测认知健康和辅助MCI诊断方面的潜力。
该研究纳入了607名年龄在60 - 84岁（平均年龄67.41±4.71岁）的参与者。每位参与者在佩戴该系统时完成四项VR任务，该系统记录精细的手部动作数据。使用北京版蒙特利尔认知评估量表（MoCA - BJ）评估认知功能。进行统计分析以关联手部动作指标与认知表现。
认知障碍参与者在基于VR的精细运动任务上表现更差。诸如钉板测试、积木放置 - 翻转测试和敲击测试等测试的指标可预测认知能力。与更精细动作以及非优势（左手）使用相关的指标显示出更高的预测能力，预测MCI的曲线下面积（AUC）达到0.687，与随机森林（0.762）和支持向量机（0.644）等机器学习模型相当。
手部动作数据可为老年人的认知功能提供有价值的见解，凸显了精细运动技能在早期MCI检测中的重要性。这种基于VR的系统可作为评估认知健康和支持MCI诊断的有用临床工具，从而为认知衰退制定及时的干预策略。
