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Updated: Aug 6, 2026
06:21
Concept Development and Use of an Automated Food Intake and Eating Behavior Assessment Method
Published on: February 19, 2021
Snacking Behaviors in Relation to Stress and Physical Activity Using Automated Dietary Assessment: Observational
Femke J de Gooijer1,2, Alex van Kraaij2, Marlou Lasschuijt1
1Department of Agrotechnology and Food Sciences, Division of Human Nutrition and Health, Wageningen University & Research, Wageningen, The Netherlands.
JMIR Formative Research
|July 23, 2026
Summary
The SnackBox tool accurately measured snacking behaviors. Snacking was not linked to stress but was more likely before vigorous physical activity, aiding personalized dietary interventions.
Area of Science:
- Dietary assessment
- Behavioral science
- Human-computer interaction
Background:
- Snacking constitutes a significant portion of daily energy intake (19-34%).
- Traditional self-report methods for studying snacking behaviors are prone to bias.
- The SnackBox offers an automated solution for objective snack consumption measurement in real-world settings.
Purpose of the Study:
- To investigate the relationship between snacking and perceived stress using SnackBox technology.
- To explore the association between snacking and physical activity levels via SnackBox data.
- To validate the utility of the SnackBox as a dietary assessment tool.
Main Methods:
- Forty-seven office workers participated over two weeks, using the SnackBox at work and home.
- Snack consumption (grams, time) was automatically recorded by the SnackBox.
- Perceived stress was monitored via ecological momentary assessments; physical activity was tracked using wearables.
Main Results:
- A total of 2199 snacking events were recorded, with average daily snack intake of 769 kcal.
- No significant association was found between perceived stress and snacking likelihood (P=.69).
- Snacking was significantly more probable before vigorous physical activity (OR=2.00, P<.001).
Conclusions:
- The SnackBox is a valuable technology probe for studying snacking in relation to various factors.
- This automated approach improves understanding of snacking behaviors.
- Findings support the development of personalized interventions and predictive models for dietary habits.
