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Updated: Oct 3, 2025
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Concept Development and Use of an Automated Food Intake and Eating Behavior Assessment Method
Published on: February 19, 2021
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Validity and Feasibility of the Monitoring and Modeling Family Eating Dynamics System to Automatically Detect
Brooke Marie Bell1,2, Ridwan Alam3,4, Abu Sayeed Mondol5
1Department of Chronic Disease Epidemiology, School of Public Health, Yale University, New Haven, CT, United States.
JMIR Mhealth and Uhealth
|February 18, 2022
Summary
Smartwatches accurately detect eating events, showing high compliance with ecological momentary assessment (EMA) in families. This technology offers a feasible solution for objective dietary assessment in real-world settings.
Area of Science:
- Dietary assessment and nutritional epidemiology
- Human-computer interaction and wearable technology
- Behavioral science and health psychology
Background:
- Traditional dietary assessment methods have limitations.
- Emerging technologies offer potential solutions for accurate dietary intake measurement.
- The Monitoring and Modeling Family Eating Dynamics (M2FED) study utilizes smartwatches and ecological momentary assessment (EMA).
Purpose of the Study:
- To assess participant compliance with two distinct EMA protocols in the M2FED study.
- To evaluate the performance (validity) of a smartwatch algorithm for automatic eating event detection.
- To explore the feasibility of using wearable sensors combined with EMA for dietary assessment in families.
Main Methods:
- 20 families (58 participants) engaged in a 2-week observational study.
- Participants wore smartwatches and responded to time- and event-triggered EMA questionnaires.
- Compliance rates and smartwatch algorithm precision were calculated; logistic regression identified compliance predictors.
Main Results:
- Overall EMA compliance was high (89.26%), with specific rates for time-triggered (89.7%) and event-triggered (85.7%) assessments.
- Smartwatch algorithm achieved 76.5% true positive detection rate with 0.77 precision.
- Compliance varied by time of day, family member participation, weekend status, and deployment day.
Conclusions:
- Ecological momentary assessment (EMA) is a feasible method for collecting ground-truth eating data.
- Smartwatch-based automatic eating detection shows promise for objective dietary assessment.
- Combining wearable sensors with EMA enhances mobile health technology accessibility for researchers.
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