Diabetes self‐management programmes in older adults: a systematic review and meta‐analysis
Bibliographic record
Abstract
AIM: The evidence for self-management programmes in older adults varies in methodological approaches, and disease criteria. Using predetermined methodological criteria, we evaluated the effect of diabetes-specific self-management programme interventions in older adults. METHODS: The EMBASE, MEDLINE and Cochrane Central Register of Controlled Trials databases were searched from January 1980 to November 2013, as were reference lists from systematic reviews, meta-analyses and clinical practice guidelines. A total of 13 trials met the selection criteria, which included 4517 older adult participants; 2361 participants randomized to a diabetes self-management programme and 2156 to usual care. RESULTS: The pooled effect on HbA(1c) was a reduction of -2 mmol/mol (-0.2%; 95% CI -0.3 to -0.1); tailored interventions [-3 mmol/mol (-0.2%; 95% CI -0.4 to -0.1)] or programmes with a psychological emphasis [-3 mmol/mol (-0.2; 95% CI -0.4 to -0.1)] were most effective. A pooled treatment effect on total cholesterol was a 5.81 mg/dl reduction (95% CI -10.33 to -1.29) and non-significant reductions in systolic and diastolic blood pressure. CONCLUSIONS: Diabetes self-management programmes for older adults demonstrate a small reduction in HbA(1c), lipids and blood pressure. These findings may be of greater clinical relevance when offered in conjunction with other therapies.
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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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.030 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".