Sakal Collin, Li Tingyou, Li Juan, Li Xinyue
Department of Data Science, College of Computing, City University of Hong Kong, Hong Kong SAR, China.
Center on Aging Psychology, Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.
NPJ Aging. 2024 Nov 25;10(1):56. doi: 10.1038/s41514-024-00177-x.
Timely implementation of interventions to slow cognitive decline among older adults requires accurate monitoring to detect changes in cognitive function. Factors known to be associated with cognition that can be gathered from accelerometers, user interfaces, and other sensors within wearable devices could be used to train machine learning models and develop wearable-based cognitive monitoring systems. Using data from over 2400 older adults in the National Health and Nutrition Examination Survey (NHANES) we developed prediction models to differentiate older adults with normal cognition from those with poor cognition based on outcomes from three cognitive tests measuring different domains of cognitive function. During repeated cross-validation CatBoost, XGBoost, and Random Forest models performed best when predicting poor cognition based on tests measuring processing speed, working memory, and attention (median AUCs ≥0.82) compared to immediate and delayed recall (median AUCs ≥0.72) and categorical verbal fluency (median AUC ≥ 0.68). Activity and sleep parameters were also more strongly associated with poor cognition based on tests assessing processing speed, working memory, and attention compared to other cognitive subdomains. Our work provides proof of concept that data collatable through wearable devices such as age, education, sleep parameters, activity summaries, and light exposure metrics could be used to differentiate between older adults with normal versus poor cognition. We further identified metrics that could be targets in future causal studies seeking to better understand how sleep and activity parameters influence cognitive function among older adults.
及时实施干预措施以减缓老年人的认知衰退需要进行准确监测，以检测认知功能的变化。可从可穿戴设备中的加速度计、用户界面和其他传感器收集的已知与认知相关的因素，可用于训练机器学习模型并开发基于可穿戴设备的认知监测系统。利用国家健康与营养检查调查（NHANES）中2400多名老年人的数据，我们开发了预测模型，根据三项测量不同认知功能领域的认知测试结果，区分认知正常的老年人和认知较差的老年人。在重复交叉验证过程中，与即时和延迟回忆（中位数AUC≥0.72）以及分类语言流畅性（中位数AUC≥0.68）相比，当基于测量处理速度、工作记忆和注意力的测试预测认知较差时，CatBoost、XGBoost和随机森林模型表现最佳（中位数AUC≥0.82）。与其他认知子领域相比，基于评估处理速度、工作记忆和注意力的测试，活动和睡眠参数与认知较差的关联也更强。我们的工作提供了概念验证，即通过可穿戴设备可整理的数据，如年龄、教育程度、睡眠参数、活动总结和光照指标，可用于区分认知正常与认知较差的老年人。我们进一步确定了一些指标，这些指标可能是未来因果研究的目标，旨在更好地了解睡眠和活动参数如何影响老年人的认知功能。
