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The purpose of this study it to build a machine learning model to predict dietary lapses with comparable accuracy, sensitivity, and specificity to previous literature while recovering predictor interactions. The sample for the current study consisted of merged data from two separate studies of individuals with obesity/overweight (total N = 87). Participants completed six ecological momentary assessment surveys per day where they were asked about 16 risk factors of lapse and if they had lapsed from their dietary prescriptions since the previous survey. Alcohol consumption and self-efficacy were the most prevalent in the top 10 stable interactions. Alcohol consumption decreased the protective effect of self-efficacy, motivation, and planning. Higher planning predicted higher risk for lapse only when consuming alcohol. Low motivation, hunger, cravings, and lack of healthy food availability increased the protective effect of self-efficacy. Higher self-efficacy increased risk effect of positive mood and having recently eaten a meal on lapse. For individuals with lower levels of self-efficacy, planning increased the risk of lapse. Alcohol intake and self-efficacy interact with several variables to predict dietary lapses, and these interactions should be targeted in just-in-time adaptive interventions that deliver interventions for lapses.

Titel:	Application of machine learning to discover interactions predictive of dietary lapses.
Autor/in / Beteiligte Person:	Sala, M ; Taylor, A ; Crochiere, RJ ; Zhang, F ; Forman, EM
Link:	Full Text Finder (Volltext)
Zeitschrift:	Applied psychology. Health and well-being, Jg. 15 (2023-08-01), Heft 3, S. 1166-1181
Veröffentlichung:	Oxford : Blackwell, 2023
Medientyp:	academicJournal
ISSN:	1758-0854 (electronic)
DOI:	10.1111/aphw.12432
Schlagwort:	Humans Overweight Risk Factors Machine Learning Diet Obesity
Sonstiges:	Nachgewiesen in: MEDLINE Sprachen: English Publication Type: Journal Article; Research Support, Non-U.S. Gov't Language: English [Appl Psychol Health Well Being] 2023 Aug; Vol. 15 (3), pp. 1166-1181. <i>Date of Electronic Publication: </i>2022 Dec 26. MeSH Terms: Diet* ; Obesity* ; Humans ; Overweight ; Risk Factors ; Machine Learning References: Adams, M. A., Sallis, J. F., Norman, G. J., Hovell, M. F., Hekler, E. B., & Perata, E. (2013). An adaptive physical activity intervention for overweight adults: A randomized controlled trial. PLoS ONE, 8(12), e82901. https://doi.org/10.1371/journal.pone.0082901. ; Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T. ; Ajzen, I. (2002). Perceived behavioral control, self-efficacy, locus of control, and the theory of planned behavior1. 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The confounded self-efficacy construct: Conceptual analysis and recommendations for future research. Health Psychology Review, 10(2), 113-128. https://doi.org/10.1080/17437199.2014.941998. Contributed Indexing: Keywords: alcohol use; dietary lapse; machine learning; obesity; overweight; self-efficacy Entry Date(s): Date Created: 20221227 Date Completed: 20230809 Latest Revision: 20230810 Update Code: 20240513

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Application of machine learning to discover interactions predictive of dietary lapses.