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

Autor: Salim A; Baker Heart & Diabetes Institute, Melbourne, Australia. salim.a@unimelb.edu.au.; Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia. salim.a@unimelb.edu.au.; School of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia. salim.a@unimelb.edu.au., Brakenridge CJ; Active Life Lab, South-Eastern Finland University of Applied Sciences, Mikkeli, Finland.; Physical Activity Laboratory, Baker Heart & Diabetes Institute, Melbourne, Australia.; Centre for Urban Transitions, Swinburne University of Technology, Melbourne, Australia., Lekamlage DH; Baker Heart & Diabetes Institute, Melbourne, Australia.; Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia., Howden E; Baker Heart & Diabetes Institute, Melbourne, Australia., Grigg R; Physical Activity Laboratory, Baker Heart & Diabetes Institute, Melbourne, Australia., Dillon HT; Baker Heart & Diabetes Institute, Melbourne, Australia.; Institute for Physical Activity and Nutrition, Deakin University, Melbourne, VIC, Australia., Bondell HD; School of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia., Simpson JA; Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia., Healy GN; School of Human Movement and Nutrition Sciences, The University of Queensland, Brisbane, Australia., Owen N; Physical Activity Laboratory, Baker Heart & Diabetes Institute, Melbourne, Australia.; Centre for Urban Transitions, Swinburne University of Technology, Melbourne, Australia., Dunstan DW; Physical Activity Laboratory, Baker Heart & Diabetes Institute, Melbourne, Australia.; Institute for Physical Activity and Nutrition, Deakin University, Melbourne, VIC, Australia., Winkler EAH; School of Human Movement and Nutrition Sciences, The University of Queensland, Brisbane, Australia.
Jazyk: angličtina
Zdroj: BMC medical research methodology [BMC Med Res Methodol] 2024 Sep 30; Vol. 24 (1), pp. 222. Date of Electronic Publication: 2024 Sep 30.
DOI: 10.1186/s12874-024-02311-5
Abstrakt: 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 < 0.001) better accuracy (MDAPE [IQR] = 0.15 [0.06-0.25] vs. 0.23 [0.13-0.53)]) and agreement (Bias Mean [SD]=-0.03[0.11] vs. 0.14 [0.11]) than the proprietary software, estimated the usual sedentary bout duration more accurately (MDAPE[IQR] = 0.11[0.06-0.26] vs. 0.42[0.32-0.48]), and had better agreement (Bias Mean [SD] = 3.91[5.67] minutes vs. -11.93[5.07] minutes). With the ALLO-Active dataset, STEPHEN and STEPCODE did not improve the estimation of proportion of time spent sedentary, but STEPHEN estimated usual sedentary bout duration more accurately than the proprietary software (MDAPE[IQR] = 0.19[0.03-0.25] vs. 0.36[0.15-0.48]) and had smaller bias (Bias Mean[SD] = 0.70[8.89] minutes vs. -11.35[9.17] minutes).
Conclusions: STEPHEN can characterize the proportion of time spent being sedentary and usual sedentary bout length. The methodology is available as an open access R package available from https://github.com/limfuxing/stephen/ . The package includes trained models, but users have the flexibility to train their own models.
(© 2024. The Author(s).)
Databáze: MEDLINE
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