Performance evaluation of algorithms to estimate daily sedentary time using wrist-worn sensors in free-living adults.

ABSTRACT: Given the limited real-world testing of algorithms for wrist-worn sensors to estimate sedentary time, we examined the performance of 21 algorithms in free-living adults. Seventy-one adults (35-65 years) wore a GENEActiv (wrist) and an activPAL (thigh) sensor for up to 10 days. activPAL was our reference measure. We estimated sedentary time (hours/day) using 21 classification algorithms, including cut point and machine-learning methods. Valid days from each monitor were matched by date and mean values were calculated. Equivalence testing (±10%) and linear regression were used to compare each algorithm's estimate to the reference, over all participants and by sex and age. activPAL recorded a mean of 9.4 hours/d sedentary. Five of 21 algorithms (24%) estimated sedentary time within 10% (±0.94

SUBMITTER: Matthews CE 

PROVIDER: S-EPMC12363551 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature