Time-Lagged Prediction of Food Craving With Qualitative Distinct Predictor Types: An Application of BISCWIT

Food craving (FC) peaks are highly context-dependent and variable. Accurate prediction of FC might help preventing disadvantageous eating behavior. Here, we examine whether data from two weeks of Ecological Momentary Assessment (EMA) questionnaires on stress and emotions (active EMA, aEMA) alongside temporal features and smartphone sensor data (passive EMA, pEMA) are able to predict FCs approximately 2.5 hours into the future in N = 46 individuals. A logistic prediction approach with feature dimension reduction via Best Item Scale that is Cross-Validated, Weighted, Informative and Transparent (BISCWIT) was performed. While overall prediction accuracy was acceptable, passive sensing data alone was equally predictive to psychometric data. The frequency of which single predictors were considered for a model, was rather balanced, indicating that aEMA as well as pEMA models were fully idiosyncratic.