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 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 | 0.044 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".