Predicting food craving in everyday life through smartphone-derived sensor and usage data
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 a...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-06-01
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| Series: | Frontiers in Digital Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fdgth.2023.1163386/full |
| _version_ | 1797795089056006144 |
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| author | Thomas Schneidergruber Jens Blechert Jens Blechert Samuel Arzt Björn Pannicke Björn Pannicke Julia Reichenberger Julia Reichenberger Ann-Kathrin Arend Ann-Kathrin Arend Simon Ginzinger |
| author_facet | Thomas Schneidergruber Jens Blechert Jens Blechert Samuel Arzt Björn Pannicke Björn Pannicke Julia Reichenberger Julia Reichenberger Ann-Kathrin Arend Ann-Kathrin Arend Simon Ginzinger |
| author_sort | Thomas Schneidergruber |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-13T03:12:36Z |
| format | Article |
| id | doaj.art-11e72912aa9143979ce237fe03215818 |
| institution | Directory Open Access Journal |
| issn | 2673-253X |
| language | English |
| last_indexed | 2024-03-13T03:12:36Z |
| publishDate | 2023-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Digital Health |
| spelling | doaj.art-11e72912aa9143979ce237fe032158182023-06-26T10:04:15ZengFrontiers Media S.A.Frontiers in Digital Health2673-253X2023-06-01510.3389/fdgth.2023.11633861163386Predicting food craving in everyday life through smartphone-derived sensor and usage dataThomas Schneidergruber0Jens Blechert1Jens Blechert2Samuel Arzt3Björn Pannicke4Björn Pannicke5Julia Reichenberger6Julia Reichenberger7Ann-Kathrin Arend8Ann-Kathrin Arend9Simon Ginzinger10Department Creative Technologies, University of Applied Sciences Salzburg, Salzburg, AustriaDepartment of Psychology, Paris-Lodron-University of Salzburg, Salzburg, AustriaCentre for Cognitive Neuroscience, Paris-Lodron-University of Salzburg, Salzburg, AustriaDepartment Creative Technologies, University of Applied Sciences Salzburg, Salzburg, AustriaDepartment of Psychology, Paris-Lodron-University of Salzburg, Salzburg, AustriaCentre for Cognitive Neuroscience, Paris-Lodron-University of Salzburg, Salzburg, AustriaDepartment of Psychology, Paris-Lodron-University of Salzburg, Salzburg, AustriaCentre for Cognitive Neuroscience, Paris-Lodron-University of Salzburg, Salzburg, AustriaDepartment of Psychology, Paris-Lodron-University of Salzburg, Salzburg, AustriaCentre for Cognitive Neuroscience, Paris-Lodron-University of Salzburg, Salzburg, AustriaDepartment Creative Technologies, University of Applied Sciences Salzburg, Salzburg, AustriaBackgroundFood 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.https://www.frontiersin.org/articles/10.3389/fdgth.2023.1163386/fullfood cravingtime-laggedpredictionecological momentary assessmentpassive sensingpersonalized modeling |
| spellingShingle | Thomas Schneidergruber Jens Blechert Jens Blechert Samuel Arzt Björn Pannicke Björn Pannicke Julia Reichenberger Julia Reichenberger Ann-Kathrin Arend Ann-Kathrin Arend Simon Ginzinger Predicting food craving in everyday life through smartphone-derived sensor and usage data Frontiers in Digital Health food craving time-lagged prediction ecological momentary assessment passive sensing personalized modeling |
| title | Predicting food craving in everyday life through smartphone-derived sensor and usage data |
| title_full | Predicting food craving in everyday life through smartphone-derived sensor and usage data |
| title_fullStr | Predicting food craving in everyday life through smartphone-derived sensor and usage data |
| title_full_unstemmed | Predicting food craving in everyday life through smartphone-derived sensor and usage data |
| title_short | Predicting food craving in everyday life through smartphone-derived sensor and usage data |
| title_sort | predicting food craving in everyday life through smartphone derived sensor and usage data |
| topic | food craving time-lagged prediction ecological momentary assessment passive sensing personalized modeling |
| url | https://www.frontiersin.org/articles/10.3389/fdgth.2023.1163386/full |
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