Behavioral Periodicity Detection from 24 h Wrist Accelerometry and Associations with Cardiometabolic Risk and Health-Related Quality of Life

Abstract EN

Periodicities (repeating patterns) are observed in many human behaviors.

Their strength may capture untapped patterns that incorporate sleep, sedentary, and active behaviors into a single metric indicative of better health.

We present a framework to detect periodicities from longitudinal wrist-worn accelerometry data.

GENEActiv accelerometer data were collected from 20 participants (17 men, 3 women, aged 35–65) continuously for 64.4±26.2 (range: 13.9 to 102.0) consecutive days.

Cardiometabolic risk biomarkers and health-related quality of life metrics were assessed at baseline.

Periodograms were constructed to determine patterns emergent from the accelerometer data.

Periodicity strength was calculated using circular autocorrelations for time-lagged windows.

The most notable periodicity was at 24 h, indicating a circadian rest-activity cycle; however, its strength varied significantly across participants.

Periodicity strength was most consistently associated with LDL-cholesterol (r’s = 0.40–0.79, P’s < 0.05) and triglycerides (r’s = 0.68–0.86, P’s < 0.05) but also associated with hs-CRP and health-related quality of life, even after adjusting for demographics and self-rated physical activity and insomnia symptoms.

Our framework demonstrates a new method for characterizing behavior patterns longitudinally which captures relationships between 24 h accelerometry data and health outcomes.