  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.
