Financial incentives in the management of diabetes: a systematic review
Bibliographic record
Abstract
METHODS: Web of Science, Cochrane library and PubMed were systematically searched up to January 2024 to identify studies examining the impact of financial incentives on diabetes management in patients. Studies were evaluated based on the robustness of their methodology, participant numbers, and quality scores. The Cochrane risk-of-bias tool was applied for randomized controlled trials, while the Newcastle-Ottawa Scale was used for non-randomized controlled trials to assess study quality. Due to the heterogeneity of the included studies, a narrative synthesis approach was utilized. RESULTS: In the study, we included 12 published research studies. Five studies investigated the influence of financial incentives on patient behavior, all demonstrating a significant positive impact on behaviors such as blood glucose monitoring, medication adherence, and physical activity. 10 studies analyzed the impact of financial incentives on HbA1c levels in diabetes patients. Among them, 5 studies reported that financial incentives could improve HbA1c levels through longitudinal historical comparisons. The other 5 studies did not find significant improvements compared to the control group. Three studies explored long-term effects, two studies targeting the adolescent population had no impact, and one study targeting adults had a positive impact. CONCLUSIONS: In summary, this review found that financial incentives can positively influence patient behavior and enhance compliance, but their impact on HbA1c levels is inconsistent. Financial incentives may help adult patients maintain behavior even after the withdrawal of incentives.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".