Measuring intra-individual physical activity variability using consumer-grade activity devices

Many existing sedentary behavior and physical activity studies focus on primary outcomes that assess change by comparing participants' activity from baseline to post-intervention. With the widespread availability of consumer-grade devices that track activity daily, researchers do not need to re...

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Main Authors: Vered Lev (Author), Marily A. Oppezzo (Author)
Format: Book
Published: Frontiers Media S.A., 2023-09-01T00:00:00Z.
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100 1 0 |a Vered Lev  |e author 
700 1 0 |a Marily A. Oppezzo  |e author 
245 0 0 |a Measuring intra-individual physical activity variability using consumer-grade activity devices 
260 |b Frontiers Media S.A.,   |c 2023-09-01T00:00:00Z. 
500 |a 2673-253X 
500 |a 10.3389/fdgth.2023.1239759 
520 |a Many existing sedentary behavior and physical activity studies focus on primary outcomes that assess change by comparing participants' activity from baseline to post-intervention. With the widespread availability of consumer-grade devices that track activity daily, researchers do not need to rely on those endpoint measurements alone. Using activity trackers, researchers can collect remote data about the process of behavior change and future maintenance of the change by measuring participants' intra-individual physical activity variability. Measuring intra-individual physical activity variability can enable researchers to create tailored and dynamic interventions that account for different physical activity behavior change trajectories, and by that, improve participants' program adherence, enhance intervention design and management, and advance interventions measurements' reliability. We propose an application of intra-individual physical activity variability as a measurement and provide three use cases within interventions. Intra-individual physical activity variability can be used: prior to the intervention period, where relationships between participants' intra-individual physical activity variability and individual characteristics can be used to predict adherence and subsequently tailor interventions; during the intervention period, to assess progress and subsequently boost interventions; and after the intervention, to obtain a reliable representation of the change in primary outcome. 
546 |a EN 
690 |a behavior change 
690 |a sedentary behaviors 
690 |a physical activity intervention 
690 |a activity trackers 
690 |a intra-individual 
690 |a wearables 
690 |a Medicine 
690 |a R 
690 |a Public aspects of medicine 
690 |a RA1-1270 
690 |a Electronic computers. Computer science 
690 |a QA75.5-76.95 
655 7 |a article  |2 local 
786 0 |n Frontiers in Digital Health, Vol 5 (2023) 
787 0 |n https://www.frontiersin.org/articles/10.3389/fdgth.2023.1239759/full 
787 0 |n https://doaj.org/toc/2673-253X 
856 4 1 |u https://doaj.org/article/8bc2e7ca48914bce9752fe1cfd284b5d  |z Connect to this object online.