Towards optimizing exercise prescription for type 2 diabetes: modulating exercise parameters to strategically improve glucose control
Bibliographic record
Abstract
Abstract Type 2 diabetes (T2D) is a complex and multifaceted condition clinically characterized by high blood glucose. The management of T2D requires a holistic approach, typically involving a combination of pharmacological interventions as well as lifestyle changes, such as incorporating regular exercise, within an overall patient-centred approach. However, several condition-specific and contextual factors can modulate the glucoregulatory response to acute or chronic exercise. In an era of precision medicine, optimizing exercise prescription in an effort to maximize glucose lowering effects holds promise for reducing the risk of T2D complications and improving the overall quality of life of individuals living with this condition. Reflecting on the main pathophysiological features of T2D, we review the evidence to highlight how factors related to exercise prescription can be modulated to target improved glucose control in T2D, including the frequency, intensity, total volume, and timing (e.g., pre- vs. post-prandial) of exercise, as well as exercise modality (e.g., aerobic vs. resistance training). We also propose a step-by-step, general framework for clinicians and practitioners on how to personalize exercise prescription to optimize glycemic control in individuals living with T2D.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".