Glucose‐lowering effects of physical activity in type 1 diabetes: A causal modelling and matched‐pair analysis approach
Bibliographic record
Abstract
AIMS: To evaluate the acute glucose-lowering effect of bouts of physical activity (PA) for hyperglycaemia in individuals with type 1 diabetes, using a within-subject matched-pairs causal design to approximate the control condition of no activity. METHODS: Data comprised 1546 PA bouts of 10-30 min from 482 participants in the T1DEXI and T1DEXIP cohorts where glucose was >10 mmol/L. Each PA bout was matched [starting glucose, glucose rate of change, insulin on board (IOB) and glucose variability (CV)] to a matched non-PA period within the same individual using a weighted k-nearest neighbours algorithm (SMD <0.01). PRIMARY OUTCOME: Change in glucose from PA onset to 20 min post-activity. SECONDARY OUTCOMES: Predictors of glucose response and rate of hypoglycaemia incidence. RESULTS: PA [median 23 min: IQR (20, 30)] led to a mean glucose change of -2.2 mmol/L (p < 0.001), compared with 0.3 mmol/L (p < 0.001) during matched non-PA periods (mean difference: -1.9 mmol/L (p < 0.0001)). No significant differences by age, activity type or intensity were observed. The strongest predictors of PA-induced glucose change were (in order) glucose rate of change, starting glucose, CV, duration and IOB. A heatmap using starting glucose and glucose rate of change was developed to guide real-time decision-making. PA-induced hypoglycaemia risk was very low (<2%). CONCLUSION/INTERPRETATION: Using PA to lower high glucose levels is an effective and safe strategy, and when guided by CGM, it can become a personalised tool for type 1 diabetes education.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".