Cardiovascular Health Benefits of Exercise Training in Persons Living with Type 1 Diabetes: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Exercise is advocated in the management of type 1 diabetes (T1D), but the effects of different forms of exercise training on cardiovascular risk factors in T1D still remain unclear. The aim of this study was to conduct a systematic review and meta-analysis on exercise training for cardiovascular risk factors in T1D. Six electronic databases were systematically searched for randomized or non-randomized controlled studies reporting associations between exercise training and cardiovascular risk factors in T1D. Weighted mean differences (WMD) of each cardiovascular risk factor between exercise groups and control groups were calculated using a random effects model. A total of 24 studies reported the effects of exercise training on cardiovascular risk factors. Exercise training increased maximal aerobic power (3.01 mL·kg−1·min−1, 95% confidence interval, CI, 0.94 to 6.38) and reduced glycated hemoglobin (HbA1c; −0.45%, 95% CI −0.73 to −0.17), daily insulin dosage (−0.88 U·kg−1, 95% CI −1.27 to −0.48), and total cholesterol (−0.38 mmol·L−1, 95% CI −0.71 to −0.04). Exercise training did not lead to consistent changes in body mass index (BMI), blood pressure, triglycerides, high-density lipoprotein cholesterol (HDL-C), or low-density lipoprotein cholesterol (LDL-C). In persons living with T1D, exercise training is associated with a beneficial cardiovascular profile, including improvements in lipid profile, glycemic control (decreased daily insulin dosage and HbA1c), and aerobic fitness.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.031 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".