AUTHOR=Schneidergruber Thomas , Blechert Jens , Arzt Samuel , Pannicke Björn , Reichenberger Julia , Arend Ann-Kathrin , Ginzinger Simon 

TITLE=Predicting food craving in everyday life through smartphone-derived sensor and usage data

JOURNAL=Frontiers in Digital Health

VOLUME=Volume 5 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2023.1163386

DOI=10.3389/fdgth.2023.1163386

ISSN=2673-253X

ABSTRACT=Background:  Food craving precedes unhealthy eating behaviors such as overeating or binge eating and is thus a promising intervention target. Yet, craving varies rapidly across the day and responds to external and internal context changes. This makes it a candidate for just in time adaptive interventions (JITAI), which, however, requires that it can be predicted ahead of time.
Objective: To investigate whether upcoming food cravings could be detected from passive smartphone sensor data (excluding geolocation information) without the need for EMA-questionnaires.
Methods: A sample of 56 participants recorded their food craving 6 times a day for 14 days as dependent variable. Predictor variables were environmental noise, light, device movement, screen activity, notifications and time of the day. For each participant we determined the best fitting food craving split and prediction algorithm in 10-fold cross-classification with a 75/25 train/test split.
Results: Individual high vs. low craving ratings could be predicted with a mean Area Under the Curve (AUC) of 0.78 which outperformed a baseline model trained on past craving values in 85% of participants.  
Conclusions: Passive sensing of craving-preceding states seems viable based on person specific baseline data and individualized machine learning approaches. Craving prediction allows implementation of craving-preventive JITAIs.Within subject modeling increases the feasibility of the notoriously challenging task of craving prediction.