دورية أكاديمية
Application of machine learning to discover interactions predictive of dietary lapses
| العنوان: | Application of machine learning to discover interactions predictive of dietary lapses |
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| المؤلفون: | Sala, Margaret, Taylor, A., Crochiere, R. J., Zhang, F., Forman, E. M. |
| المساهمون: | orcid:0000-0002-1560-649x |
| بيانات النشر: | Blackwell |
| سنة النشر: | 2023 |
| المجموعة: | Yeshiva Academic Institutional Repository (YAIR - Yeshiva University) |
| مصطلحات موضوعية: | alcohol use, dietary lapse, machine learning, obesity, overweight, self-efficacy |
| الوصف: | Journal Article; Research Support, Non-U.S. Gov't ; 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. (© 2022 International Association of Applied Psychology.) |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| تدمد: | 17580854 |
| العلاقة: | Applied Psychology. Health and Well-Being;15(3); Sala, M., Taylor, A., Crochiere, R. J., Zhang, F., & Forman, E. M. (2023). Application of machine learning to discover interactions predictive of dietary lapses. Applied Psychology. Health and Well-Being, 15(3), 1166–1181.; https://ezproxy.yu.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&AuthType=ip,sso&db=cmedm&AN=36573066&site=eds-live&scope=siteTest; https://hdl.handle.net/20.500.12202/9747Test; https://doi.org/10.1111/aphw.12432Test |
| DOI: | 10.1111/aphw.12432 |
| الإتاحة: | https://doi.org/20.500.12202/9747Test https://doi.org/10.1111/aphw.12432Test https://hdl.handle.net/20.500.12202/9747Test https://ezproxy.yu.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&AuthType=ip,sso&db=cmedm&AN=36573066&site=eds-live&scope=siteTest |
| حقوق: | Attribution-NonCommercial-NoDerivs 3.0 United States ; http://creativecommons.org/licenses/by-nc-nd/3.0/usTest/ |
| رقم الانضمام: | edsbas.319D1150 |
| قاعدة البيانات: | BASE |
| تدمد: | 17580854 |
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| DOI: | 10.1111/aphw.12432 |