Wearables and Smartphones for Tracking Modifiable Risk Factors in Metabolic Health: Protocol for a Scoping Review
Bibliographic record
Abstract
BACKGROUND: Metabolic diseases, such as cardiovascular diseases and diabetes, contribute significantly to global mortality and disability. Wearable devices and smartphones are increasingly used to track and manage modifiable risk factors associated with metabolic diseases. However, no established guidelines exist on how to derive meaningful signals from these devices, often hampering cross-study comparisons. OBJECTIVE: This study aims to systematically overview the current empirical literature on how wearables and smartphones are used to track modifiable (physiological and lifestyle) risk factors associated with metabolic diseases. METHODS: We will conduct a scoping review to overview how wearable and smartphone-based studies measure modifiable risk factors related to metabolic diseases. We will search 5 databases (Scopus, Web of Science, PubMed, Cochrane Central Register of Controlled Trials, and SPORTDiscus) from 2019 to 2024, with search terms related to wearables, smartphones, and modifiable risk factors associated with metabolic diseases. Eligible studies will use smartphones or wearables (worn on the wrist, finger, arm, hip, and chest) to track physiological or lifestyle factors related to metabolic diseases. We will follow the reporting guideline standards from PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) and the JBI (Joanna Briggs Institute) guidance on scoping review methodology. Two reviewers will independently screen articles for inclusion and extract data using a standardized form. The findings will be synthesized and reported qualitatively and quantitatively. RESULTS: Data collection is expected to begin in November 2024; data analysis in the first quarter of 2025; and submission to a peer-reviewed journal by the second quarter of 2025. We expect to identify the degree to which wearable and smartphone-based studies track modifiable risk factors collectively (versus in isolation), and the consistency and variation in how modifiable risk factors are measured across existing studies. CONCLUSIONS: Results are expected to inform more standardized guidelines on wearable and smartphone-based measurements, with the goal of aiding cross-study comparison. The final report is planned for submission to a peer-reviewed, indexed journal. This review is among the first to systematically overview the current landscape on how wearables and smartphones measure modifiable risk factors associated with metabolic diseases. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/59539.
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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 | 0.089 | 0.108 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.014 | 0.020 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.103 | 0.021 |
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".