The Long-Term Effects of Physical Activity on Blood Glucose Regulation: A Model to Unravel Diabetes Progression
Bibliographic record
Abstract
Physical activity plays a key role in the prevention of type 2 diabetes. However, despite the numerous clinical evidences, there are still no mathematical models that satisfactorily describe the effects of physical activity on the progression of diabetes, preventing its onset or slowing down its course. Instead, there are models describing the influence of single training sessions of physical activity on blood glucose and insulin levels in the short term. In this article we propose a novel model for the long term effects of physical activity on diabetes progression, by exploiting and adapting an existing short-term model of physical activity. A pivotal role in the proposed model is played by interleukin-6 released during physical activity and known to be fundamental in maintaining pancreatic beta cells production and therefore satisfactory insulin secretion. The proposed simulation scenarios show how a modeling approach of physical activity that neglects the interleukin-6 action is not sufficient to capture the cumulative effects of physical exercise on disease progression. Indeed, preliminary results pave the way to natural extensions of the model to account for model-based control techniques for the long-term control of diabetes through personalized lifestyle interventions, properly accounting for the effects of physical activity on the long-term dynamics of blood glucose.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".