Exploring the use of CGM-based biological feedback for improving health behaviors: A scoping review protocol
Bibliographic record
Abstract
Background: Biological feedback is increasingly being used as a behavior change technique within personalized health interventions. One form of biological feedback that has recently become more accessible is continuous glucose monitoring (CGM), which can be used to provide individuals with insights on the impact of various behaviors (e.g., diet, physical activity) on glucose levels. Nevertheless, there is little guidance on how CGM is implemented within interventions that address health behaviors. Objectives: The objectives of this scoping review are to (1) identify the populations where CGM has been used as a behavior change tool, and (2) characterize how CGM is implemented within interventions to address behavior. Methods: A search strategy was developed to capture studies incorporating the use of CGM-based biological feedback to promote health behavior change. The search was implemented within the following electronic databases: Elsevier Embase, Cochrane Central Register of Controlled Trials, EBSCOhost PsycINFO, and ProQuest Dissertations & Theses Global. Additional studies were captured by examining the bibliographies of relevant reviews. Two trained reviewers used DistillerSR® (Evidence Partners; Ottawa, Canada) to screen studies for eligibility and extract data. Eligible studies were primary analyses of randomized controlled trials conducted in humans ≥18 years that included CGM-based biological feedback to promote behavior change as part of the intervention. Data extracted from eligible articles were focused on the methodology used to implement the intervention and control group (e.g., duration of CGM wear, format and frequency of feedback, etc.). Results: Database searches were completed in January 2024. A total of 3,995 abstracts were screened. Data from 32 included articles was extracted and is currently being analyzed. The scoping review is planned for completion in 2024. Conclusions: To our knowledge, this will be the first scoping review to describe the characteristics of how CGM is being used within interventions promoting behavior change. Results can be used to guide the design and implementation of future interventions incorporating the use of CGM-based biological feedback aimed at addressing health behaviors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.103 | 0.090 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.014 | 0.014 |
| Bibliometrics | 0.024 | 0.018 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.055 | 0.012 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".