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

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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 2 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 ~2.5 h 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 and pEMA models were fully idiosyncratic.

Metadaten
Author:	Tim Kaiser, Björn Butter, Samuel Arzt, Björn Pannicke, Julia Reichenberger, Simon Ginzinger, Jens Blechert
URN:	urn:nbn:de:gbv:9-opus-64400
DOI:	https://doi.org/10.3389/fdgth.2021.694233
ISSN:	2673-253X
Parent Title (English):	Frontiers in Digital Health
Publisher:	Frontiers Media S.A.
Place of publication:	Lausanne
Document Type:	Article
Language:	English
Date of first Publication:	2021/09/20
Release Date:	2022/11/25
Tag:	BISCWIT; eating behavior; ecological momentary assessment; food cravings; idiographic models; mobile health; passive sensing; time-lagged
GND Keyword:	-
Volume:	3
Article Number:	694233
Page Number:	8
Faculties:	Universitätsmedizin / Klinik für Psychiatrie und Psychotherapie
Licence (German):	Creative Commons - Namensnennung

Open Access