ArticleJMIR mHealth and uHealth2020
A Real-Time Eating Detection System for Capturing Eating Moments and Triggering Ecological Momentary Assessments to Obtain Further Context: System Development and Validation Study.
Article in JMIR mHealth and uHealth, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Psychological-dietary interactions in chronic disease management: mechanisms and integrated intervention strategies.Frontiers in nutrition · 2026Review
- Are affective states associated with disordered eating behaviors in the daily lives of sexual minority and heterosexual young women?: an ecological momentary assessment study.Annals of behavioral medicine : a publication of the Society of Behavioral Medicine · 2025Article
- Article
- HabitSense: A Privacy-Aware, AI-Enhanced Multimodal Wearable Platform for mHealth Applications.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2024Article
- Toward Concurrent Identification of Human Activities with a Single Unifying Neural Network Classification: First Step.Sensors (Basel, Switzerland) · 2024Article
- Measuring and shaping the nutritional environment via food sales logs: case studies of campus-wide food choice and a call to action.Frontiers in nutrition · 2024Article
- Validity and Feasibility of the Monitoring and Modeling Family Eating Dynamics System to Automatically Detect In-field Family Eating Behavior: Observational Study.JMIR mHealth and uHealth · 2022Observational
- Measuring Self-Esteem with Passive Sensing.International Conference on Pervasive Computing Technologies for Healthcare : [proceedings]. International Conference on Pervasive Computing Technologies for Healthcare · 2020Article
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Authors and funding
13 authors.
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Abstract
backgroundEating 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.
objectiveThe 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.
methodsThe 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.
resultsAmong 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.
conclusionsThe 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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