The effects of diabetes self-management programs on clinical and patient reported outcomes in older adults: a systematic review and meta-analysis
Bibliographic record
Abstract
Objectives: With diabetes self-management continuing to become more complex for older adults, self-management programs have been shown to support this population in meeting their multifaceted medical needs. Building on our previous systematic review and meta-analysis, we aimed to update the literature on the effectiveness of diabetes self-management programs and investigate the impact of specific self-management interventions on clinical and patient-reported outcomes. Methods: We updated our literature search in the following databases: Medline, EMBASE, PsychINFO, CINAHL and Cochrane Database of Randomized Controlled Trials from November 2013 to July 2023 for studies that may fit our inclusion criteria. Two independent reviewers screened and extracted data from the included group of studies. Results: A total of 17 studies with 21 comparison arms met the inclusion criteria, totalling 5976 older adults (3510 individuals randomized to self-management programming and 2466 to usual care). The pooled effectiveness of diabetes self-management programs in older adults on glycemic control (hemoglobin A1C) was a reduction of -0.32 (95% CI -0.44, -0.19). Specifically, the most effective approach on glycemic control (A1C) was the use of feedback (-0.52%; 95% CI -0.68, -0.36). Overall, self-management programs improved behaviour change outcomes, with feedback interventions being most effective (standardized mean difference [SMD] 0.91; 95% CI 0.39, 1.43). The effect of self-management programs on body mass index, weight and lipids were statistically and clinically significant. Conclusions: The evidence for diabetes self-management programs for older adults demonstrates a small but clinically meaningful reduction in A1C, improvement in patient-reported outcomes (behaviour, self-efficacy, knowledge), and other clinical outcomes (BMI, weight and lipids). The specific strategy used in diabetes self-management programs for older adults should be considered to achieve optimal results on outcomes.
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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 | 0.017 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.038 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".