ArticleJMIR mHealth and uHealth2020
Comparison of Geographic Information System and Subjective Assessments of Momentary Food Environments as Predictors of Food Intake: An Ecological Momentary Assessment 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 9 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The integration of geographic methods and ecological momentary assessment in public health research: A systematic review of methods and applications.Social science & medicine (1982) · 2024Pooled it
- STROBE-GEMA: a STROBE extension for reporting of geographically explicit ecological momentary assessment studies.Archives of public health = Archives belges de sante publique · 2024Pooled it
- Detecting Binge Eating Risk With Naturalistic Data and Machine Learning: A Comparative Observational Study.The International journal of eating disorders · 2026Observational
- [Understanding and changing social disparities in health behaviour: nutrition as an interface between social inequality and health].Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz · 2025Review
- Associations of takeaway outlets with takeaway food consumption and adiposity: longitudinal analysis of the Fenland cohort.Obesity (Silver Spring, Md.) · 2024Article
- University Food Environment Assessment Methods and Their Implications: Protocol for a Systematic Review.JMIR research protocols · 2024Article
- Socio-Spatial Segregation of Unhealthy Food Environments across Public Schools in Santiago, Chile.Nutrients · 2023Article
- Unpacking Habit With Bayesian Mixed Models: Dynamic Approach to Health Behaviors With Interchangeable Elements, Illustrated Through Multiple Sun Protection Behaviors.The quantitative methods for psychology · 2023Article
- Ecological Momentary Assessment: A Systematic Review of Validity Research.Perspectives on behavior science · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIt has been observed that eating is influenced by the presence and availability of food. Being aware of the presence of food in the environment may enable mobile health (mHealth) apps to use geofencing techniques to determine the most appropriate time to proactively deliver interventions. To date, however, studies on eating typically rely on self-reports of environmental contexts, which may not be accurate or feasible for issuing mHealth interventions.
objectiveThis study aimed to compare the subjective and geographic information system (GIS) assessments of the momentary food environment to explore the feasibility of using GIS data to predict eating behavior and inform geofenced interventions.
methodsIn total, 72 participants recorded their food intake in real-time for 14 days using an ecological momentary assessment approach. Participants logged their food intake and responded to approximately 5 randomly timed assessments each day. During each assessment, the participants reported the number and type of food outlets nearby. Their electronic diaries simultaneously recorded their GPS coordinates. The GPS data were later overlaid with a GIS map of food outlets to produce an objective count of the number of food outlets within 50 m of the participant.
resultsCorrelations between self-reported and GIS counts of food outlets within 50 m were only of a small size (r=0.17; P<.001). Logistic regression analyses revealed that the GIS count significantly predicted eating similar to the self-reported counts (area under the curve for the receiver operating characteristic curve [AUC-ROC] self-report=0.53, SE 0.00 versus AUC-ROC 50 m GIS=0.53, SE 0.00; P=.41). However, there was a significant difference between the GIS-derived and self-reported counts of food outlets and the self-reported type of food outlets (AUC-ROC self-reported outlet type=0.56, SE 0.01; P<.001).
conclusionsThe subjective food environment appears to predict eating better than objectively measured food environments via GIS. mHealth apps may need to consider the type of food outlets rather than the raw number of outlets in an individual's environment.
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