Daily Patterns of Accelerometer Activity Predict Changes in Sleep, Cognition, and Mortality in Older Men.

ABSTRACT

Background: There is growing interest in the area of "wearable tech" and its relationship to health. A common element of many of these devices is a triaxial accelerometer that can yield continuous information on gross motor activity levels; how such data might predict changes in health is less clear. Methods: We examined accelerometry data from 2,976 older men who were part of the Osteoporotic Fractures in Men (MrOS) study. Using a shape-naive technique, functional principal component analysis, we examined the patterns of motor activity over the course of 4-7 days and determined whether these patterns were associated with changes in polysomnographic-determined sleep and cognitive function (Trail Making Test-Part B [Trails B], Modified Mini-Mental State Examination [3MS]), as well as mortality over 6.5-8 years of follow-up. Results: In comparing baseline to 6.5 years later, multivariate modeling indicated that low daytime activity at baseline was associated with worsening of sleep efficiency (p < .05), more wake after sleep onset (p < .05), and a decrease in cognition (Trails B; p < .001), as well as a 1.6-fold higher rate of all-cause mortality (hazard ratio = 1.64 [1.34-2.00]). Earlier wake and bed times were associated with a decrease in cognition (3MS; p < .05). Having a late afternoon peak in activity was associated with a 1.4-fold higher rate of all-cause mortality (hazard ratio = 1.46 [1.21-1.77]). Those having a longer duration of their daytime activity with a bimodal activity pattern also had over a 1.4-fold higher rate of cardiovascular-related mortality (hazard ratio = 1.42 [1.02-1.98]). Conclusions: Patterns of daily activity may be useful as predictive biomarkers for changes in clinically relevant outcomes, including mortality and changes in sleep and cognition in older men.