Characterizing and predicting person-specific, day-to-day, fluctuations in walking behavior.
Despite the positive health effect of physical activity, one third of the world's population is estimated to be insufficiently active. Prior research has mainly investigated physical activity on an aggregate level over short periods of time, e.g., during 3 to 7 days at baseline and a few months...
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Public Library of Science (PLoS)
2021
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oai:doaj.org-article:24eb912fff2745dfa1fa1c07cc830ddb2021-11-25T06:19:15ZCharacterizing and predicting person-specific, day-to-day, fluctuations in walking behavior.1932-620310.1371/journal.pone.0251659https://doaj.org/article/24eb912fff2745dfa1fa1c07cc830ddb2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0251659https://doaj.org/toc/1932-6203Despite the positive health effect of physical activity, one third of the world's population is estimated to be insufficiently active. Prior research has mainly investigated physical activity on an aggregate level over short periods of time, e.g., during 3 to 7 days at baseline and a few months later, post-intervention. To develop effective interventions, we need a better understanding of the temporal dynamics of physical activity. We proposed here an approach to studying walking behavior at "high-resolution" and by capturing the idiographic and day-to-day changes in walking behavior. We analyzed daily step count among 151 young adults with overweight or obesity who had worn an accelerometer for an average of 226 days (~25,000 observations). We then used a recursive partitioning algorithm to characterize patterns of change, here sudden behavioral gains and losses, over the course of the study. These behavioral gains or losses were defined as a 30% increase or reduction in steps relative to each participants' median level of steps lasting at least 7 days. After the identification of gains and losses, fluctuation intensity in steps from each participant's individual time series was computed with a dynamic complexity algorithm to identify potential early warning signals of sudden gains or losses. Results revealed that walking behavior change exhibits discontinuous changes that can be described as sudden gains and losses. On average, participants experienced six sudden gains or losses over the study. We also observed a significant and positive association between critical fluctuations in walking behavior, a form of early warning signals, and the subsequent occurrence of sudden behavioral losses in the next days. Altogether, this study suggests that walking behavior could be well understood under a dynamic paradigm. Results also provide support for the development of "just-in-time adaptive" behavioral interventions based on the detection of early warning signals for sudden behavioral losses.Guillaume ChevanceDario BarettaMatti HeinoOlga PerskiMerlijn OlthofPredrag KlasnjaEric HeklerJob GodinoPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 5, p e0251659 (2021) |
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EN |
| topic |
Medicine R Science Q |
| spellingShingle |
Medicine R Science Q Guillaume Chevance Dario Baretta Matti Heino Olga Perski Merlijn Olthof Predrag Klasnja Eric Hekler Job Godino Characterizing and predicting person-specific, day-to-day, fluctuations in walking behavior. |
| description |
Despite the positive health effect of physical activity, one third of the world's population is estimated to be insufficiently active. Prior research has mainly investigated physical activity on an aggregate level over short periods of time, e.g., during 3 to 7 days at baseline and a few months later, post-intervention. To develop effective interventions, we need a better understanding of the temporal dynamics of physical activity. We proposed here an approach to studying walking behavior at "high-resolution" and by capturing the idiographic and day-to-day changes in walking behavior. We analyzed daily step count among 151 young adults with overweight or obesity who had worn an accelerometer for an average of 226 days (~25,000 observations). We then used a recursive partitioning algorithm to characterize patterns of change, here sudden behavioral gains and losses, over the course of the study. These behavioral gains or losses were defined as a 30% increase or reduction in steps relative to each participants' median level of steps lasting at least 7 days. After the identification of gains and losses, fluctuation intensity in steps from each participant's individual time series was computed with a dynamic complexity algorithm to identify potential early warning signals of sudden gains or losses. Results revealed that walking behavior change exhibits discontinuous changes that can be described as sudden gains and losses. On average, participants experienced six sudden gains or losses over the study. We also observed a significant and positive association between critical fluctuations in walking behavior, a form of early warning signals, and the subsequent occurrence of sudden behavioral losses in the next days. Altogether, this study suggests that walking behavior could be well understood under a dynamic paradigm. Results also provide support for the development of "just-in-time adaptive" behavioral interventions based on the detection of early warning signals for sudden behavioral losses. |
| format |
article |
| author |
Guillaume Chevance Dario Baretta Matti Heino Olga Perski Merlijn Olthof Predrag Klasnja Eric Hekler Job Godino |
| author_facet |
Guillaume Chevance Dario Baretta Matti Heino Olga Perski Merlijn Olthof Predrag Klasnja Eric Hekler Job Godino |
| author_sort |
Guillaume Chevance |
| title |
Characterizing and predicting person-specific, day-to-day, fluctuations in walking behavior. |
| title_short |
Characterizing and predicting person-specific, day-to-day, fluctuations in walking behavior. |
| title_full |
Characterizing and predicting person-specific, day-to-day, fluctuations in walking behavior. |
| title_fullStr |
Characterizing and predicting person-specific, day-to-day, fluctuations in walking behavior. |
| title_full_unstemmed |
Characterizing and predicting person-specific, day-to-day, fluctuations in walking behavior. |
| title_sort |
characterizing and predicting person-specific, day-to-day, fluctuations in walking behavior. |
| publisher |
Public Library of Science (PLoS) |
| publishDate |
2021 |
| url |
https://doaj.org/article/24eb912fff2745dfa1fa1c07cc830ddb |
| work_keys_str_mv |
AT guillaumechevance characterizingandpredictingpersonspecificdaytodayfluctuationsinwalkingbehavior AT dariobaretta characterizingandpredictingpersonspecificdaytodayfluctuationsinwalkingbehavior AT mattiheino characterizingandpredictingpersonspecificdaytodayfluctuationsinwalkingbehavior AT olgaperski characterizingandpredictingpersonspecificdaytodayfluctuationsinwalkingbehavior AT merlijnolthof characterizingandpredictingpersonspecificdaytodayfluctuationsinwalkingbehavior AT predragklasnja characterizingandpredictingpersonspecificdaytodayfluctuationsinwalkingbehavior AT erichekler characterizingandpredictingpersonspecificdaytodayfluctuationsinwalkingbehavior AT jobgodino characterizingandpredictingpersonspecificdaytodayfluctuationsinwalkingbehavior |
| _version_ |
1718413912296128512 |