Machine learning techniques in eating behavior e-coaching

Abstract

The rise of internet and mobile technologies (such as smartphones) provide a harness of data and an opportunity to learn about peoples' states, behavior, and context in regard to several application areas such as health. Eating behavior is an area that can benefit from the development of effective e-coaching applications which utilize psychological theories and data science techniques. In this paper, we propose a framework of how machine learning techniques can effectively be used in order to fully exploit data collected from a mobile application ("Think Slim") which is designed to assess eating behavior using experience sampling methods. The overall goal is to analyze individual states of a person status (emotions, location, activity, etc.) and assess their impact on unhealthy eating. Building on data collected from different participants, a classification algorithm (decision tree tailored to longitudinal data) is used to warn people prior to a possible unhealthy eating event and a clustering algorithm (hierarchical agglomerative clustering) is used for profiling the participants and generalize for new users of the application. Finally, a framework to offer feedback via adaptive messages (intervention) and recommendations prior to possible unhealthy eating events is presented. Results from applying our methods reveal that participants can be clustered to six robust groups based on their eating behavior and that there are specific rules that discriminate which conditions lead to healthy versus unhealthy eating. Consequently, these rules can be utilized to provide adaptive semi-tailored feedback to users who, through this method, are assisted in learning under which conditions are more prone to unhealthy eating. Effectiveness of the approach is confirmed by observing a decreasing trend in rule activation towards the end of intervention period.

Comparison of Geographic Information System and Subjective Assessments of Momentary Food Environments as Predictors of Food Intake: An Ecological Momentary Assessment Study

“…The energy content from meals consumed at restaurants has been found to contribute to most daily energy requirements [ 40 ]; thus, the presence of restaurants may be an appropriate target to reduce daily energy intake. Furthermore, some individuals may be particularly susceptible to eating unhealthy foods only when out [ 41 ]. Although this study did not examine the within-person differences in the healthiness or energy intake derived from food intake when out, it was able to examine how eating can be prompted by cues in the immediate environment.…”

The potential of artificial intelligence in enhancing adult weight loss: a scoping review

“…Ten studies evaluated the use of AI-assisted weight management interventions that instantaneously optimise prediction models for behavioural risk profiling (e.g., low, medium and high risk) and enhance behavioural self-control through adaptive and personalised messages/feedback/prompts (Table 4). The interventions were all delivered through smartphone apps, namely OnTrack (used in three of the included studies) (48)(49)(50) , Sweetech app (26) , Calfit app (54) , Lark's AI health coach app (53) , Think Slim app (106) , SmartCare app (51) , MyBehaviour (52) and one without a name. In general, the mobile app interventions used either wrist-worn activity trackers, smartphone in-built accelerometers or EMA to track one's physical activity.…”

Deep learning–based prediction model of occurrences of major adverse cardiac events during 1-year follow-up after hospital discharge in patients with AMI using knowledge mining

“…Machine learning techniques are being effectively used in order to collect medical data from divers' sources [14]. There were also various machine learning-based classification methods for the prediction model of CVD occurrences, such as random forests (RF) [15], neural networks (NN) [16][17][18], and support vector machines (SVM) [19,20].…”