ArticleInformatics for health & social care2021
Geographically-explicit Ecological Momentary Assessment (GEMA) Architecture and Components: Lessons Learned from PMOMS.
Article in Informatics for health & social care, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis 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.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled 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
- Environmental influences on community participation among people with multiple sclerosis: A mixed methods study.PloS one · 2026Article
- Integrating health geography and behavioral economic principles to strengthen context-specific behavior change interventions.Translational behavioral medicine · 2024Article
- Trends in Stress Throughout Pregnancy and Postpartum Period During the COVID-19 Pandemic: Longitudinal Study Using Ecological Momentary Assessment and Data From the Postpartum Mothers Mobile Study.JMIR mental health · 2021Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Geographically explicit Ecological Momentary Assessment (GEMA), an extension of Ecological Momentary Assessment (EMA), allows to record time-stamped geographic location information for behavioral data in the every-day environments of study participants. Considering that GEMA studies are continually gaining the attention of researchers and currently there is no single approach in collecting GEMA data, in this paper, we propose and present a GEMA architecture that can be used to conduct any GEMA study based on our experience developing and maintaining the Postpartum Mothers Mobile Study (PMOMS). Our GEMA client-server architecture can be customized to meet the specific requirements of each GEMA study. Key features of our proposed GEMA architecture include: utilization of widely used smartphones to make GEMA studies practical; alleviation of the burden of activities on participants by designing clients (mobile applications) that are very lightweight and servers that are heavyweight in terms of functionality; utilization of at least one positioning sensor to determine EMA contexts marked with locations; and communication through the Internet. We believe that our proposed GEMA architecture, with the illustrated foundation for GEMA studies in our exemplar study (PMOMS), will help researchers from any field conduct GEMA studies efficiently and effectively.
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Registered trials
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