Use of digital self-care solutions for diabetes long-term management: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Diabetes mellitus is a significant global health challenge, requiring innovative strategies to improve management and mitigate complications. Digital health technologies offer promising solutions to enhance diabetes self-care by providing real-time feedback, improving communication and supporting data-driven decision-making. Despite the increasing adoption of digital self-care interventions, there is a lack of comprehensive synthesis of evidence on their impact, accessibility and integration into healthcare systems. This scoping review aims to map existing research on digital self-care solutions for diabetes management, identify knowledge gaps and highlight best practices and key factors influencing adoption. METHODS AND ANALYSIS: The review will follow Arksey and O'Malley's framework and adhere to Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guidelines. A systematic search will be conducted in Medline, Scopus, Embase, CINAHL and Google Scholar, focusing on studies published from January 2004 to December 2024 in English, French, Arabic, Portuguese, Spanish, Italian, Czech, Slovak and Chinese. Studies reporting on digital self-care solutions for diabetes management will be included, covering experimental and quasi-experimental study designs. Data extraction will cover study and participant characteristics, digital solution features, and barriers and facilitators to adoption. Ethical and equity considerations will also be analysed using established frameworks. Two reviewers will independently screen studies, with discrepancies resolved by a third reviewer. ETHICS AND DISSEMINATION: This scoping review will provide a comprehensive understanding of digital self-care solutions for diabetes management, offering insights to inform future research and enhance self-care practices globally. Findings will be disseminated through peer-reviewed publications, conferences and interest holder engagements to inform clinical practice and policy development. As this study involves the review of existing literature, ethical approval is not required.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.081 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.022 | 0.016 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.076 | 0.018 |
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".