STROBE-GEMA: a STROBE extension for reporting of geographically explicit ecological momentary assessment studies | Semantic Scholar (2024)

Mobile communication devices are revolutionizing research on mental health and well-being by physically linking momentary experience sampling to objective measures of socio-ecological context in time and place.

This paper proposes and presents a GEMA architecture that can be used to conduct any GEMA study based on the experience developing and maintaining the Postpartum Mothers Mobile Study (PMOMS), and suggests utilization of widely used smartphones to make GEMA studies practical.

This proof of concept study assessed the acceptability and feasibility of acquiring and combining EMA and GPS data from adult smokers with attention deficit hyperactivity disorder (ADHD).

The subjective food environment appears to predict eating better than objectively measured food environments via GIS, and mHealth apps may need to consider the type of food outlets rather than the raw number of outlets in an individual’s environment.

The use of smartphone-delivered EMIs using self-reported EMAs to promote behavior change is an emerging area of research, with few studies evaluating efficacy.

Findings from this review suggest that in order to identify best practices for EMA methodology in nutrition and physical activity research among youth, more standardized EMA reporting is needed.

There remain several promising areas of ambulatory assessment research and clinical applications in EDs going forward, including integration of passive data collection, use of EMA in treatment evaluation and design, evaluation of dynamic system processes, inclusion of diverse samples, and development and evaluation of adaptive, tailored EMIs such as just-in-time adaptive interventions.

A high-level overview of the scientific topics covered by the NIEHS workshop and themes that emerged as areas for future work, including reducing measurement errors and uncertainty in exposure estimates, and improving data accessibility, data interoperability, and computational approaches for more effective multiscale and multi-source data integration, along with potential solutions.

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