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 2 weeks of ecological momentary assessment (EMA) questionnaires on stress and emotions (active EMA, aEMA) alongside t...
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Format: | Article |
Language: | English |
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Frontiers Media S.A.
2021-09-01
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Series: | Frontiers in Digital Health |
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Online Access: | https://www.frontiersin.org/articles/10.3389/fdgth.2021.694233/full |
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author | Tim Kaiser Björn Butter Samuel Arzt Björn Pannicke Björn Pannicke Julia Reichenberger Julia Reichenberger Simon Ginzinger Jens Blechert Jens Blechert |
author_facet | Tim Kaiser Björn Butter Samuel Arzt Björn Pannicke Björn Pannicke Julia Reichenberger Julia Reichenberger Simon Ginzinger Jens Blechert Jens Blechert |
author_sort | Tim Kaiser |
collection | DOAJ |
description | 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. |
first_indexed | 2024-12-20T01:58:33Z |
format | Article |
id | doaj.art-34a2529678bd43e5b6b24f78d13baeb5 |
institution | Directory Open Access Journal |
issn | 2673-253X |
language | English |
last_indexed | 2024-12-20T01:58:33Z |
publishDate | 2021-09-01 |
publisher | Frontiers Media S.A. |
record_format | Article |
series | Frontiers in Digital Health |
spelling | doaj.art-34a2529678bd43e5b6b24f78d13baeb52022-12-21T19:57:24ZengFrontiers Media S.A.Frontiers in Digital Health2673-253X2021-09-01310.3389/fdgth.2021.694233694233Time-Lagged Prediction of Food Craving With Qualitative Distinct Predictor Types: An Application of BISCWITTim Kaiser0Björn Butter1Samuel Arzt2Björn Pannicke3Björn Pannicke4Julia Reichenberger5Julia Reichenberger6Simon Ginzinger7Jens Blechert8Jens Blechert9Clinical Psychology and Psychotherapy, Department of Psychology, University of Greifswald, Greifswald, GermanyEating Behavior Laboratory, Department of Psychology, Paris-Lodron-University of Salzburg, Salzburg, AustriaMultiMediaTechnology, University of Applied Sciences Salzburg, Salzburg, AustriaEating Behavior Laboratory, Department of Psychology, Paris-Lodron-University of Salzburg, Salzburg, AustriaDepartment of Psychology, Centre for Cognitive Neuroscience, Paris-Lodron-University of Salzburg, Salzburg, AustriaEating Behavior Laboratory, Department of Psychology, Paris-Lodron-University of Salzburg, Salzburg, AustriaDepartment of Psychology, Centre for Cognitive Neuroscience, Paris-Lodron-University of Salzburg, Salzburg, AustriaMultiMediaTechnology, University of Applied Sciences Salzburg, Salzburg, AustriaEating Behavior Laboratory, Department of Psychology, Paris-Lodron-University of Salzburg, Salzburg, AustriaDepartment of Psychology, Centre for Cognitive Neuroscience, Paris-Lodron-University of Salzburg, Salzburg, AustriaFood 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.https://www.frontiersin.org/articles/10.3389/fdgth.2021.694233/fullfood cravingstime-laggedidiographic modelsBISCWITecological momentary assessmentpassive sensing |
spellingShingle | Tim Kaiser Björn Butter Samuel Arzt Björn Pannicke Björn Pannicke Julia Reichenberger Julia Reichenberger Simon Ginzinger Jens Blechert Jens Blechert Time-Lagged Prediction of Food Craving With Qualitative Distinct Predictor Types: An Application of BISCWIT Frontiers in Digital Health food cravings time-lagged idiographic models BISCWIT ecological momentary assessment passive sensing |
title | Time-Lagged Prediction of Food Craving With Qualitative Distinct Predictor Types: An Application of BISCWIT |
title_full | Time-Lagged Prediction of Food Craving With Qualitative Distinct Predictor Types: An Application of BISCWIT |
title_fullStr | Time-Lagged Prediction of Food Craving With Qualitative Distinct Predictor Types: An Application of BISCWIT |
title_full_unstemmed | Time-Lagged Prediction of Food Craving With Qualitative Distinct Predictor Types: An Application of BISCWIT |
title_short | Time-Lagged Prediction of Food Craving With Qualitative Distinct Predictor Types: An Application of BISCWIT |
title_sort | time lagged prediction of food craving with qualitative distinct predictor types an application of biscwit |
topic | food cravings time-lagged idiographic models BISCWIT ecological momentary assessment passive sensing |
url | https://www.frontiersin.org/articles/10.3389/fdgth.2021.694233/full |
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