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Refining an algorithm-powered just-in-time adaptive weight control intervention: A randomized controlled trial evaluating model performance and behavioral outcomes
Suboptimal weight losses are partially attributable to lapses from a prescribed diet. We developed an app (OnTrack) that uses ecological momentary assessment to measure dietary lapses and relevant lapse triggers and provides personalized intervention using machine learning. Initially, tension betwee...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8925642/ https://www.ncbi.nlm.nih.gov/pubmed/32026745 http://dx.doi.org/10.1177/1460458220902330 |
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author | Goldstein, Stephanie P Thomas, J Graham Foster, Gary D Turner-McGrievy, Gabrielle Butryn, Meghan L Herbert, James D Martin, Gerald J Forman, Evan M |
author_facet | Goldstein, Stephanie P Thomas, J Graham Foster, Gary D Turner-McGrievy, Gabrielle Butryn, Meghan L Herbert, James D Martin, Gerald J Forman, Evan M |
author_sort | Goldstein, Stephanie P |
collection | PubMed |
description | Suboptimal weight losses are partially attributable to lapses from a prescribed diet. We developed an app (OnTrack) that uses ecological momentary assessment to measure dietary lapses and relevant lapse triggers and provides personalized intervention using machine learning. Initially, tension between user burden and complete data was resolved by presenting a subset of lapse trigger questions per ecological momentary assessment survey. However, this produced substantial missing data, which could reduce algorithm performance. We examined the effect of more questions per ecological momentary assessment survey on algorithm performance, app utilization, and behavioral outcomes. Participants with overweight/obesity (n = 121) used a 10-week mobile weight loss program and were randomized to OnTrack-short (i.e. 8 questions/survey) or OnTrack-long (i.e. 17 questions/survey). Additional questions reduced ecological momentary assessment adherence; however, increased data completeness improved algorithm performance. There were no differences in perceived effectiveness, app utilization, or behavioral outcomes. Minimal differences in utilization and perceived effectiveness likely contributed to similar behavioral outcomes across various conditions. |
format | Online Article Text |
id | pubmed-8925642 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
record_format | MEDLINE/PubMed |
spelling | pubmed-89256422022-03-16 Refining an algorithm-powered just-in-time adaptive weight control intervention: A randomized controlled trial evaluating model performance and behavioral outcomes Goldstein, Stephanie P Thomas, J Graham Foster, Gary D Turner-McGrievy, Gabrielle Butryn, Meghan L Herbert, James D Martin, Gerald J Forman, Evan M Health Informatics J Article Suboptimal weight losses are partially attributable to lapses from a prescribed diet. We developed an app (OnTrack) that uses ecological momentary assessment to measure dietary lapses and relevant lapse triggers and provides personalized intervention using machine learning. Initially, tension between user burden and complete data was resolved by presenting a subset of lapse trigger questions per ecological momentary assessment survey. However, this produced substantial missing data, which could reduce algorithm performance. We examined the effect of more questions per ecological momentary assessment survey on algorithm performance, app utilization, and behavioral outcomes. Participants with overweight/obesity (n = 121) used a 10-week mobile weight loss program and were randomized to OnTrack-short (i.e. 8 questions/survey) or OnTrack-long (i.e. 17 questions/survey). Additional questions reduced ecological momentary assessment adherence; however, increased data completeness improved algorithm performance. There were no differences in perceived effectiveness, app utilization, or behavioral outcomes. Minimal differences in utilization and perceived effectiveness likely contributed to similar behavioral outcomes across various conditions. 2020-12 2020-02-06 /pmc/articles/PMC8925642/ /pubmed/32026745 http://dx.doi.org/10.1177/1460458220902330 Text en https://creativecommons.org/licenses/by-nc/4.0/Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). Article reuse guidelines: sagepub.com/journals-permissions (https://uk.sagepub.com/en-gb/journals-permissions) |
spellingShingle | Article Goldstein, Stephanie P Thomas, J Graham Foster, Gary D Turner-McGrievy, Gabrielle Butryn, Meghan L Herbert, James D Martin, Gerald J Forman, Evan M Refining an algorithm-powered just-in-time adaptive weight control intervention: A randomized controlled trial evaluating model performance and behavioral outcomes |
title | Refining an algorithm-powered just-in-time adaptive weight control intervention: A randomized controlled trial evaluating model performance and behavioral outcomes |
title_full | Refining an algorithm-powered just-in-time adaptive weight control intervention: A randomized controlled trial evaluating model performance and behavioral outcomes |
title_fullStr | Refining an algorithm-powered just-in-time adaptive weight control intervention: A randomized controlled trial evaluating model performance and behavioral outcomes |
title_full_unstemmed | Refining an algorithm-powered just-in-time adaptive weight control intervention: A randomized controlled trial evaluating model performance and behavioral outcomes |
title_short | Refining an algorithm-powered just-in-time adaptive weight control intervention: A randomized controlled trial evaluating model performance and behavioral outcomes |
title_sort | refining an algorithm-powered just-in-time adaptive weight control intervention: a randomized controlled trial evaluating model performance and behavioral outcomes |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8925642/ https://www.ncbi.nlm.nih.gov/pubmed/32026745 http://dx.doi.org/10.1177/1460458220902330 |
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