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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10516569/ https://www.ncbi.nlm.nih.gov/pubmed/37744687 http://dx.doi.org/10.3389/fdgth.2023.1239759 |
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author | Lev, Vered Oppezzo, Marily A. |
author_facet | Lev, Vered Oppezzo, Marily A. |
author_sort | Lev, Vered |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10516569 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-105165692023-09-23 Measuring intra-individual physical activity variability using consumer-grade activity devices Lev, Vered Oppezzo, Marily A. Front Digit Health Digital Health 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. Frontiers Media S.A. 2023-09-06 /pmc/articles/PMC10516569/ /pubmed/37744687 http://dx.doi.org/10.3389/fdgth.2023.1239759 Text en © 2023 Lev and Oppezzo. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (https://creativecommons.org/licenses/by/4.0/) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Digital Health Lev, Vered Oppezzo, Marily A. Measuring intra-individual physical activity variability using consumer-grade activity devices |
title | Measuring intra-individual physical activity variability using consumer-grade activity devices |
title_full | Measuring intra-individual physical activity variability using consumer-grade activity devices |
title_fullStr | Measuring intra-individual physical activity variability using consumer-grade activity devices |
title_full_unstemmed | Measuring intra-individual physical activity variability using consumer-grade activity devices |
title_short | Measuring intra-individual physical activity variability using consumer-grade activity devices |
title_sort | measuring intra-individual physical activity variability using consumer-grade activity devices |
topic | Digital Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10516569/ https://www.ncbi.nlm.nih.gov/pubmed/37744687 http://dx.doi.org/10.3389/fdgth.2023.1239759 |
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