Detection of sedentary time and bouts using consumer-grade wrist-worn devices: a hidden semi-Markov model.

ABSTRACT:

Background

Wrist-worn data from commercially available devices has potential to characterize sedentary time for research and for clinical and public health applications. We propose a model that utilizes heart rate in addition to step count data to estimate the proportion of time spent being sedentary and the usual length of sedentary bouts.

Methods

We developed and trained two Hidden semi-Markov models, STEPHEN (STEP and Heart ENcoder) and STEPCODE (STEP enCODEr; a steps-only based model) using consumer-grade Fitbit device data from participants under free living conditions, and validated model performance using two external datasets. We used the median absolute percentage error (MDAPE) to measure the accuracy of the proposed models against research-grade activPAL device dat

SUBMITTER: Salim A 

PROVIDER: S-EPMC11440759 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature