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A Real-Time Eating Detection System for Capturing Eating Moments and Triggering Ecological Momentary Assessments to Obtain Further Context: System Development and Validation Study

BACKGROUND: Eating behavior has a high impact on the well-being of an individual. Such behavior involves not only when an individual is eating, but also various contextual factors such as with whom and where an individual is eating and what kind of food the individual is eating. Despite the relevanc...

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Autores principales: Bin Morshed, Mehrab, Kulkarni, Samruddhi Shreeram, Li, Richard, Saha, Koustuv, Roper, Leah Galante, Nachman, Lama, Lu, Hong, Mirabella, Lucia, Srivastava, Sanjeev, De Choudhury, Munmun, de Barbaro, Kaya, Ploetz, Thomas, Abowd, Gregory D
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7775824/
https://www.ncbi.nlm.nih.gov/pubmed/33337336
http://dx.doi.org/10.2196/20625
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author Bin Morshed, Mehrab
Kulkarni, Samruddhi Shreeram
Li, Richard
Saha, Koustuv
Roper, Leah Galante
Nachman, Lama
Lu, Hong
Mirabella, Lucia
Srivastava, Sanjeev
De Choudhury, Munmun
de Barbaro, Kaya
Ploetz, Thomas
Abowd, Gregory D
author_facet Bin Morshed, Mehrab
Kulkarni, Samruddhi Shreeram
Li, Richard
Saha, Koustuv
Roper, Leah Galante
Nachman, Lama
Lu, Hong
Mirabella, Lucia
Srivastava, Sanjeev
De Choudhury, Munmun
de Barbaro, Kaya
Ploetz, Thomas
Abowd, Gregory D
author_sort Bin Morshed, Mehrab
collection PubMed
description BACKGROUND: Eating behavior has a high impact on the well-being of an individual. Such behavior involves not only when an individual is eating, but also various contextual factors such as with whom and where an individual is eating and what kind of food the individual is eating. Despite the relevance of such factors, most automated eating detection systems are not designed to capture contextual factors. OBJECTIVE: The aims of this study were to (1) design and build a smartwatch-based eating detection system that can detect meal episodes based on dominant hand movements, (2) design ecological momentary assessment (EMA) questions to capture meal contexts upon detection of a meal by the eating detection system, and (3) validate the meal detection system that triggers EMA questions upon passive detection of meal episodes. METHODS: The meal detection system was deployed among 28 college students at a US institution over a period of 3 weeks. The participants reported various contextual data through EMAs triggered when the eating detection system correctly detected a meal episode. The EMA questions were designed after conducting a survey study with 162 students from the same campus. Responses from EMAs were used to define exclusion criteria. RESULTS: Among the total consumed meals, 89.8% (264/294) of breakfast, 99.0% (406/410) of lunch, and 98.0% (589/601) of dinner episodes were detected by our novel meal detection system. The eating detection system showed a high accuracy by capturing 96.48% (1259/1305) of the meals consumed by the participants. The meal detection classifier showed a precision of 80%, recall of 96%, and F1 of 87.3%. We found that over 99% (1248/1259) of the detected meals were consumed with distractions. Such eating behavior is considered “unhealthy” and can lead to overeating and uncontrolled weight gain. A high proportion of meals was consumed alone (680/1259, 54.01%). Our participants self-reported 62.98% (793/1259) of their meals as healthy. Together, these results have implications for designing technologies to encourage healthy eating behavior. CONCLUSIONS: The presented eating detection system is the first of its kind to leverage EMAs to capture the eating context, which has strong implications for well-being research. We reflected on the contextual data gathered by our system and discussed how these insights can be used to design individual-specific interventions.
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spelling pubmed-77758242021-01-07 A Real-Time Eating Detection System for Capturing Eating Moments and Triggering Ecological Momentary Assessments to Obtain Further Context: System Development and Validation Study Bin Morshed, Mehrab Kulkarni, Samruddhi Shreeram Li, Richard Saha, Koustuv Roper, Leah Galante Nachman, Lama Lu, Hong Mirabella, Lucia Srivastava, Sanjeev De Choudhury, Munmun de Barbaro, Kaya Ploetz, Thomas Abowd, Gregory D JMIR Mhealth Uhealth Original Paper BACKGROUND: Eating behavior has a high impact on the well-being of an individual. Such behavior involves not only when an individual is eating, but also various contextual factors such as with whom and where an individual is eating and what kind of food the individual is eating. Despite the relevance of such factors, most automated eating detection systems are not designed to capture contextual factors. OBJECTIVE: The aims of this study were to (1) design and build a smartwatch-based eating detection system that can detect meal episodes based on dominant hand movements, (2) design ecological momentary assessment (EMA) questions to capture meal contexts upon detection of a meal by the eating detection system, and (3) validate the meal detection system that triggers EMA questions upon passive detection of meal episodes. METHODS: The meal detection system was deployed among 28 college students at a US institution over a period of 3 weeks. The participants reported various contextual data through EMAs triggered when the eating detection system correctly detected a meal episode. The EMA questions were designed after conducting a survey study with 162 students from the same campus. Responses from EMAs were used to define exclusion criteria. RESULTS: Among the total consumed meals, 89.8% (264/294) of breakfast, 99.0% (406/410) of lunch, and 98.0% (589/601) of dinner episodes were detected by our novel meal detection system. The eating detection system showed a high accuracy by capturing 96.48% (1259/1305) of the meals consumed by the participants. The meal detection classifier showed a precision of 80%, recall of 96%, and F1 of 87.3%. We found that over 99% (1248/1259) of the detected meals were consumed with distractions. Such eating behavior is considered “unhealthy” and can lead to overeating and uncontrolled weight gain. A high proportion of meals was consumed alone (680/1259, 54.01%). Our participants self-reported 62.98% (793/1259) of their meals as healthy. Together, these results have implications for designing technologies to encourage healthy eating behavior. CONCLUSIONS: The presented eating detection system is the first of its kind to leverage EMAs to capture the eating context, which has strong implications for well-being research. We reflected on the contextual data gathered by our system and discussed how these insights can be used to design individual-specific interventions. JMIR Publications 2020-12-18 /pmc/articles/PMC7775824/ /pubmed/33337336 http://dx.doi.org/10.2196/20625 Text en ©Mehrab Bin Morshed, Samruddhi Shreeram Kulkarni, Richard Li, Koustuv Saha, Leah Galante Roper, Lama Nachman, Hong Lu, Lucia Mirabella, Sanjeev Srivastava, Munmun De Choudhury, Kaya de Barbaro, Thomas Ploetz, Gregory D Abowd. Originally published in JMIR mHealth and uHealth (http://mhealth.jmir.org), 18.12.2020. https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mHealth and uHealth, is properly cited. The complete bibliographic information, a link to the original publication on http://mhealth.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Bin Morshed, Mehrab
Kulkarni, Samruddhi Shreeram
Li, Richard
Saha, Koustuv
Roper, Leah Galante
Nachman, Lama
Lu, Hong
Mirabella, Lucia
Srivastava, Sanjeev
De Choudhury, Munmun
de Barbaro, Kaya
Ploetz, Thomas
Abowd, Gregory D
A Real-Time Eating Detection System for Capturing Eating Moments and Triggering Ecological Momentary Assessments to Obtain Further Context: System Development and Validation Study
title A Real-Time Eating Detection System for Capturing Eating Moments and Triggering Ecological Momentary Assessments to Obtain Further Context: System Development and Validation Study
title_full A Real-Time Eating Detection System for Capturing Eating Moments and Triggering Ecological Momentary Assessments to Obtain Further Context: System Development and Validation Study
title_fullStr A Real-Time Eating Detection System for Capturing Eating Moments and Triggering Ecological Momentary Assessments to Obtain Further Context: System Development and Validation Study
title_full_unstemmed A Real-Time Eating Detection System for Capturing Eating Moments and Triggering Ecological Momentary Assessments to Obtain Further Context: System Development and Validation Study
title_short A Real-Time Eating Detection System for Capturing Eating Moments and Triggering Ecological Momentary Assessments to Obtain Further Context: System Development and Validation Study
title_sort real-time eating detection system for capturing eating moments and triggering ecological momentary assessments to obtain further context: system development and validation study
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7775824/
https://www.ncbi.nlm.nih.gov/pubmed/33337336
http://dx.doi.org/10.2196/20625
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