Timely implementation of interventions to slow cognitive decline among older adults requires accurate monitoring to detect changes in cognitive function. Data gathered using wearable devices that can continuously monitor factors known to be associated with cognition could be used to train machine learning models and develop wearable-based cognitive monitoring systems. Using data from over 2,400 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 cognition based on 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 processing speed, working memory, and attention compared to other cognitive subdomains. Our work provides proof of concept that wearable-based cognitive monitoring systems may be a viable alternative to traditional methods for monitoring processing speeds, working memory, and attention. We further identified novel metrics that could be targets in future causal studies seeking to better understand how sleep and activity parameters influence cognitive function among older adults.

翻译：及时实施干预以延缓老年认知功能衰退，需要精确的监测手段来检测认知功能的变化。利用可穿戴设备持续收集与认知相关因素的数据，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。基于美国国家健康与营养调查（NHANES）中超过2400名老年人的数据，我们构建了预测模型，根据三项测量不同认知功能领域的测试结果，区分认知正常与认知较差的老年人。在重复交叉验证中，CatBoost、XGBoost和随机森林模型在基于加工速度、工作记忆和注意力预测认知时表现最佳（中位AUC >0.82），优于即时与延迟回忆（中位AUC >0.72）及类别词语流畅性（中位AUC >0.68）。与其它认知子领域相比，活动与睡眠参数也与加工速度、工作记忆和注意力关联更密切。本研究证明基于可穿戴设备的认知监测系统可成为监测加工速度、工作记忆和注意力的传统方法的可行替代方案。此外，我们识别出新型指标，可为未来探究睡眠与活动参数如何影响老年认知功能的因果研究提供靶点。