Abstract
Abstract
Background
Ecological momentary assessment (EMA) is a tool that captures emotional states, experiences, and behaviors in real or near-real time. Concurrent EMA and continuous glucose monitoring (CGM) data can provide an in-depth understanding of the impacts of psychosocial factors on momentary glucose levels. However, study methodology varies widely for EMA and CGM data collection and analysis.
Objective
This scoping review aims to summarize the objectives, methodologies, and outcomes of studies analyzing concurrent biopsychosocial EMA and CGM data in diabetes.
Methods
This study was conducted following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. Original research studies evaluating participants with diabetes, with concurrent collection and analysis of EMA and CGM data, were included in this review. A total of 782 studies were identified from PubMed, Embase, and EBSCOhost from May 2009 to December 2025. From these, duplicates, non-original research articles, and articles not measuring concurrent EMA and CGM data were excluded, resulting in a total of 19 studies included in this review. Methodological data were abstracted and summarized, including study characteristics, EMA protocols and outcomes, CGM outcomes, and integrated EMA and CGM study objectives. Methodological gaps across the included studies were identified and summarized.
Results
Of the 19 studies, 16 primarily recruited adult populations, and the majority of these studies (n=14, 74%) included participants with type 1 diabetes. The median EMA delivery duration was 14 days (range 3‐18 days), with a median of 5.5 prompts delivered per day (range 1‐28). EMA outcomes included a variety of biopsychosocial factors (emotion, self-care behaviors, interpersonal interactions, symptoms, and cognition). Seventy-four percent analyzed blinded CGM data over a median of 14 (range 3‐18) days. Forty-two percent used nonstandardized CGM glucose outcomes (postprandial glucose, eating patterns, and hypoglycemic events). Integrated EMA and CGM data analysis answered a broad array of study objectives, including the impact of psychosocial factors on momentary glucose metrics; the influence of momentary glucose on emotional states, mood, personal behaviors, sleep, and cognition, or vice versa; and study protocol or mobile app optimization, among others. Methodological gaps included lack of standardization of EMA and CGM outcomes, duration of data capture, reporting of statistical analyses, and study population.
Conclusions
This review provides a detailed summary of the methodology and outcomes of original research articles using combined EMA and blood glucose data measured via CGM. These combined methods allow for an opportunity to elucidate relationships between psychosocial factors and momentary glucose, an understanding of which is imperative for the development of future psychological and behavioral interventions in diabetes. However, standardization of protocols and expansion of participant populations for future EMA and CGM data collection and analysis are needed to ensure data accuracy, reproducibility, and generalizability.