Please use this identifier to cite or link to this item: https://doi.org/10.1017/S1368980021000598
Title: The potential of artificial intelligence in enhancing adult weight loss: A scoping review
Authors: Chew, HSJ
Ang, WHD
Lau, Y 
Keywords: Artificial intelligence
behaviour change
diet
eating
exercise
obesity
physical activity
self-control
self-regulation
weight
Issue Date: 2020
Publisher: Cambridge University Press (CUP)
Citation: Chew, HSJ, Ang, WHD, Lau, Y (2020). The potential of artificial intelligence in enhancing adult weight loss: A scoping review. Public Health Nutrition : Jan-59. ScholarBank@NUS Repository. https://doi.org/10.1017/S1368980021000598
Abstract: © 2021 Cambridge University Press. All rights reserved. Objective: To present an overview of how artificial intelligence (AI) could be used to regulate eating and dietary behaviours, exercise behaviours and weight loss. Design: A scoping review of global literature published from inception to 15 December 2020 was conducted according to Arksey and O'Malley's five-step framework. Eight databases (CINAHL, Cochrane-Central, Embase, IEEE Xplore, PsycINFO, PubMed, Scopus and Web of Science) were searched. Included studies were independently screened for eligibility by two reviewers with good interrater reliability (k= 0.96). Results: 66 out of 5573 potential studies were included, representing more than 2,031 participants. Three tenets of self-regulation were identified - self-monitoring (n=66, 100%), optimization of goal-setting (n=10, 15.2%) and self-control (n= 10, 15.2%). Articles were also categorised into three AI applications namely machine perception (n=50), predictive analytics only (n=6), and real-time analytics with personalised micro-interventions (n=10). Machine perception focused on recognizing food items, eating behaviours, physical activities and estimating energy balance. Predictive analytics focused on predicting weight loss, intervention adherence, dietary lapses and emotional eating. Studies on the last theme focused on evaluating AI-assisted weight management interventions that instantaneously collected behavioural data, optimised prediction models for behavioural lapse events and enhance behavioural self-control through adaptive and personalized nudges/prompts. Only six studies reported average weight losses (2.4% to 4.7%) of which two were statistically significant. Conclusion: The use of AI for weight loss is still undeveloped. Based on this study findings, we proposed a framework on the applicability of AI for weight loss but cautioned its contingency upon engagement and contextualisation.
Source Title: Public Health Nutrition
URI: https://scholarbank.nus.edu.sg/handle/10635/187621
ISSN: 13689800
14752727
DOI: 10.1017/S1368980021000598
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