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Journal article
Refining an algorithm-powered just-in-time adaptive weight control intervention: A randomized controlled trial evaluating model performance and behavioral outcomes
Health informatics journal, v 26(4), pp 2315-2331
Dec 2020
PMID: 32026745
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
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 (
= 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.
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Details
- Title
- Refining an algorithm-powered just-in-time adaptive weight control intervention: A randomized controlled trial evaluating model performance and behavioral outcomes
- Creators
- Stephanie P Goldstein (Corresponding Author) - Brown UniversityJ Graham Thomas - Brown UniversityGary D Foster - University of PennsylvaniaGabrielle Turner-McGrievy - University of South Carolina SystemMeghan L Butryn - Drexel UniversityJames D Herbert - University of New EnglandGerald J MartinEvan M Forman - Drexel University
- Publication Details
- Health informatics journal, v 26(4), pp 2315-2331
- Publisher
- Sage
- Number of pages
- 17
- Grant note
- F32 HL143954 / NHLBI NIH HHS
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Center for Weight, Eating and Lifestyle Science (WELL) [Historical]
- Web of Science ID
- WOS:000512244000001
- Scopus ID
- 2-s2.0-85079175136
- Other Identifier
- 991019168255704721
UN Sustainable Development Goals (SDGs)
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Health Care Sciences & Services
- Medical Informatics