Short Daily-Life Walking Bouts and Poor Self-Reported Health Predict the Onset of Depression in Community-Dwelling Older People: A 2-Year Longitudinal Cohort Study.

Abstract

This study aimed to assess whether the amount and quality of daily-life walking obtained using wearable technology can predict depression onset over a 2-year period, independently of self-reported health status. Longitudinal cohort study. Three-hundred twenty-two community-dwelling older people recruited in Sydney, Australia. Participants were assessed at baseline on (1) depressive symptoms using the Patient Health Questionnaire-9; (2) average weekly physical activity levels over the past month using the Incidental and Planned Activity Questionnaire, (3) clinical mobility tests (ie, short physical performance battery, timed up-and-go test, 6-m walk test); and (4) amount and quality of daily-life walking assessed with a trunk accelerometer (MoveMonitor, McRoberts) for 1week. Participants were followed up for onset of depressive symptoms for 2years at 6-monthly intervals. Daily-life walking (ie, gait intensity in the mediolateral axis, daily step counts, duration of longest walk) and self-rated health predicted the new onset of depressive symptoms at 2years in univariable logistic regression models. In multivariable models containing a self-rated health measure, clinical mobility tests were not predictive of the onset of depressive symptoms. In contrast, a measure of daily-life walking (duration of longest walking bout) was identified as a significant predictor of depressive symptom onset [standardized odds ratio (SOR) 2.44, 95% CI 1.62-3.76] independent of self-rated health (SOR 1.51, 95% CI 1.16-1.96), with these 2 measures achieving a satisfactory prediction accuracy (area under the curve= 0.67, sensitivity: 0.78, specificity: 0.52). A risk algorithm based on daily-life walking bouts and self-reported health demonstrated good accuracy for the prediction of depression onset in older people over 2years. Wearable sensor data compared favorably with clinical mobility screens and may add important independent information for screening for depression among older people.