Abstract
To present an overview of how artificial intelligence (AI) could be used to regulate eating and dietary behaviours, exercise behaviours and weight loss. 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). Sixty-six out of 5573 potential studies were included, representing more than 2031 participants. Three tenets of self-regulation were identified - self-monitoring (n 66, 100 %), optimisation 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 recognising 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 personalised nudges/prompts. Only six studies reported average weight losses (2·4-4·7 %) of which two were statistically significant. The use of AI for weight loss is still undeveloped. Based on the current study findings, we proposed a framework on the applicability of AI for weight loss but cautioned its contingency upon engagement and contextualisation.
Highlights
MethodsThis scoping review was structured according to the fivestep framework by Arksey and O’Malley, and results were presented according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR) guidelines (online supplementary material, Supplemental Table S1)(42,43)
The literature search was narrowed down to adults from 18–64 years old to enhance the focus and clarity of this inquiry. This scoping review was structured according to the fivestep framework by Arksey and O’Malley, and results were presented according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR) guidelines[42,43]
One study used reinforcement learning[29], five used liner/logistic regression[83,89,94,100,102] and other classifiers with more unique machine learning algorithms include multi-armed bandit[52], radial basis function network[95], behavioural analytics algorithm[54] and Sojourn[93]. Through this systematic scoping review, we found and included sixty-six studies that showed the potential uses of artificial intelligence (AI) in regulating eating and dietary behaviours, exercise behaviours and weight loss
Summary
This scoping review was structured according to the fivestep framework by Arksey and O’Malley, and results were presented according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR) guidelines (online supplementary material, Supplemental Table S1)(42,43). Functions of artificial intelligence in self-regulation of weight loss-related behaviours We categorised the included articles into three AI applications, namely machine perception (n 50), predictive analytics only (n 6)(47,55,82,105,107,111) and real-time analytics with personalised micro-interventions (n 10)(26,49–54,107) (Fig. 3). Portion size (often commonly seen food categories including burger, fried rice, Important results experiment) as compared with existing algorithms Recognition accuracies using decision trees in the crossvalidations ranged from 95·52 to 97·70 % The measured and predicted metabolic equivalents of task exhibited a strong positive correlation Correctly recognised the thirteen activity types 88·1 % of the time, which is 12·3 % higher than using a hip accelerometer alone. Location Images of food item/group/type Images of food size, shape, colour, portion and texture Chewing sound (bone-conducted food breakdown sounds) Verbal food description or nutrition label Wireless signals trackers, smartphone in-built accelerometers or EMA to track one’s physical activity. AI-optimised interventions include individually optimised (i.e., at each of the 24 intervention points, participants receive the intervention with the highest reward score for them so far, except when the system is ‘exploring’) or group-optimised
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