Characterizing time-of-day physical activity patterns across cognitive trajectories in older adults: a longitudinal cohort study

Declining cognitive function is a growing health challenge, yet how cognitive decline influences time-of-day physical activity (PA) patterns remains underexplored. Among cognitively unimpaired participants from the Atherosclerosis Risk in Communities Study, we modeled general and domain-specific cognitive trajectories and related them to accelerometry-derived PA a median of 8.2 years later in 811 participants (mean baseline age 73.1 years) using function-on-scalar and multivariable regression. Slower cognitive decline was associated with more favorable midday PA (10 AM–3 PM), including higher movement volume, less sedentary time, fewer interruptions to active periods, and greater activity variability.

In adjusted linear regression models, associations with PA patterns derived from a window as short as 11 AM-1 PM were comparable to those with 24-hour measures. The executive function trajectory exhibited the strongest associations with PA patterns, and over the longest period of the day; the language trajectory showed non-linear associations, and the peak association for the memory trajectory with PA occurred in the late morning (10:30–11:30 AM). Associations between cognitive decline and activity variability were stronger than for other PA metrics.

Our findings highlight the potential of PA patterns, particularly activity variability, as a non-invasive way to help monitor cognitive decline before clinical impairment emerges. We thank the staff and participants of the ARIC study for their valuable contributions. This work was supported by the National Institutes of Health under grant R01AG075883 and grant R01NS060910.

A.A.W. was supported by grant number K01AG076967 from the National Institute on Aging, National Institutes of Health. R.J.D. was supported by grant K01AG080122 and R01AG103722 from the National Institute on Aging. The ARIC study has been funded in whole or in part with federal funds from the National Heart, Lung, and Blood Institute, National Institutes of Health, and U.S.

Department of Health and Human Services via contracts 75N92022D00001, 75N92022D00002, 75N92022D00003, 75N92022D00004, and 75N92022D00005. Department of Epidemiology, Johns Hopkins University, Baltimore, MD, USA Sunan Gao, Anis Davoudi, Lacey Etzkorn, Amal A. Wanigatunga & Jennifer A.

Schrack Department of Biostatistics, Johns Hopkins University, Baltimore, MD, USA Center on Aging and Health, Johns Hopkins University, Baltimore, MD, USA Anis Davoudi, Lacey Etzkorn, Amal A. Schrack Department of Kinesiology and Health, Rutgers University, New Brunswick, NJ, USA Department of Neurology, Robert Wood Johnson Medical School, Rutgers University, New Brunswick, NJ, USA Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Department of Mental Health, Johns Hopkins University, Baltimore, MD, USA C.M.C. consults for Bayer and Johnson & Johnson on methods development for wearable and implantable technologies. J.A.S is an advisor for Grasshopper Health.