1. Detection of sedentary time and bouts using consumer-grade wrist-worn devices: a hidden semi-Markov model
- Author
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Agus Salim, Christian J. Brakenridge, Dulari Hakamuwa Lekamlage, Erin Howden, Ruth Grigg, Hayley T. Dillon, Howard D. Bondell, Julie A. Simpson, Genevieve N. Healy, Neville Owen, David W. Dunstan, and Elisabeth A. H. Winkler
- Subjects
Machine learning ,Step counts ,Heart rate ,Bouts ,Wearables data ,Medicine (General) ,R5-920 - Abstract
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 data as the referent. Bland-Altman plots summarized the individual-level agreement with activPAL. Results In OPTIMISE cohort, STEPHEN’s estimates of the proportion of time spent sedentary had significantly (p
- Published
- 2024
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