COST-EFFECTIVENESS OF EXERCISE PROGRAMS IN TYPE 2 DIABETES
Bibliographic record
Abstract
BACKGROUND: A randomized controlled trial has shown that supervised, facility-based exercise training is effective in improving glycemic control in type 2 diabetes. However, these programs are associated with additional costs. This analysis assessed the cost-effectiveness of such programs. METHODS: Analysis used data from the Diabetes Aerobic and Resistance Exercise (DARE) clinical trial which compared three different exercise programs (resistance, aerobic or a combination of both) of 6 months duration with a control group (no exercise program). Clinical outcomes at 6 months were entered for individual patients into the UKPDS economic model for type 2 diabetes adapted for the Canadian context. From this, expected life-years, quality-adjusted life-years (QALYs) and costs were estimated for all patients within the trial. RESULTS: The combined exercise program was the most expensive ($40,050) followed by the aerobic program ($39,250), the resistance program ($38,300) and no program ($31,075). QALYs were highest for combined (8.94), followed by aerobic (8.77), resistance (8.73) and no program (8.70). The incremental cost per QALY gained for the combined exercise program was $4,792 compared with aerobic alone, $8,570 compared with resistance alone, and $37,872 compared with no program. The combined exercise program remained cost-effective for all scenarios considered within sensitivity analysis. CONCLUSIONS: A program providing training in both resistance and aerobic exercise was the most cost-effective of the alternatives compared. Based on previous funding decisions, exercise training for individuals with diabetes can be considered an efficient use of resources.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".