Effectiveness of digital health interventions for diabetes: systematic review of systematic reviews
Bibliographic record
Abstract
Abstract Background The use of digital health technologies to tackle diabetes has been particularly flourishing in recent years. Previous studies have shown to varying degrees that these technologies can have an impact on diabetes prevention and management. Objective The aim of this review is to summarize the best evidence regarding the effectiveness of digital health interventions to improve one or more diabetes indicators. Methods We included all types of interventions aimed at evaluating the effect of digital health on diabetes. We considered at all types of digital interventions (mobile health, teleconsultations, tele-expertise, electronic health records, decision support systems, e-learning, etc.). We included systematic reviews published in English or French over the last 29 years, from January 1991 to December 2019, that met the inclusion criteria. Two reviewers independently reviewed the titles and abstracts of the studies to assess their eligibility, and extracted relevant information according to a predetermined grid. Any disagreement was resolved by discussion and consensus between the two reviewers, or involved a third author as referee. Results In total in our review of journals, we included 10 reviews. The outcomes of interest were clinical indicators of diabetes that could be influenced by digital interventions. These outcomes had to be objectively measurable indicators related to diabetes surveillance and management that are generally accepted by diabetes experts. Six of the ten reviews showed moderate to large significant reductions in glycated hemoglobin (HBA1c) levels compared to controls. Most reviews reported overall positive results and found that digital health interventions improved health care utilization, behaviours, attitudes, knowledge and skills. Conclusion Based on a large corpus of scientific evidence on digital health interventions, this overview could help identify the most effective interventions to improve diabetes indicators.
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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 | 0.025 | 0.124 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.012 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".