Frontiers in Digital Health (Jun 2023)
Predicting food craving in everyday life through smartphone-derived sensor and usage data
Abstract
BackgroundFood craving relates to unhealthy eating behaviors such as overeating or binge eating and is thus a promising target for digital interventions. Yet, craving varies strongly across the day and is more likely in some contexts (external, internal) than in others. Prediction of food cravings ahead of time would enable preventive interventions.ObjectiveThe objective of this study was to investigate whether upcoming food cravings could be detected and predicted from passive smartphone sensor data (excluding geolocation information) without the need for repeated questionnaires.MethodsMomentary food craving ratings, given six times a day for 14 days by 56 participants, served as the dependent variable. Predictor variables were environmental noise, light, device movement, screen activity, notifications, and time of the day recorded from 150 to 30 min prior to these ratings.ResultsIndividual high vs. low craving ratings could be predicted on the test set with a mean area under the curve (AUC) of 0.78. This outperformed a baseline model trained on past craving values in 85% of participants by 14%. Yet, this AUC value is likely the upper bound and needs to be independently validated with longer data sets that allow a split into training, validation, and test sets.ConclusionsCraving states can be forecast from external and internal circumstances as these can be measured through smartphone sensors or usage patterns in most participants. This would allow for just-in-time adaptive interventions based on passive data collection and hence with minimal participant burden.
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