Glucose-lowering effects of physical activity in type 1 diabetes: A causal modelling and matched-pair analysis approach
Bibliographic record
Abstract
Abstract OBJECTIVE To evaluate the acute glucose-lowering effect of bouts of physical activity (PA) for hyperglycemia in individuals with type 1 diabetes (T1D), using a within-subject matched-pairs causal design to approximate the control condition of no activity. RESEARCH DESIGN AND METHODS Data comprised 1546 PA bouts of 10-30 min from 482 participants in the T1DEXI and T1DEXIP cohorts where glucose was >180 mg/dL (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 neighbors algorithm (SMD <0.01). Primary outcome: Change in glucose from PA onset to 20 minutes post-activity. Secondary outcomes: Predictors of glucose response and rate of hypoglycemia incidence. RESULTS PA [median 23 minutes: IQR (20, 30)] led to a mean glucose change of -40 mg/dL (-2.2 mmol/L, p<0.001), compared to -5 mg/dL (0.3 mmol/L, p<0.001) during matched non-PA periods (mean difference: -35 mg/dL[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 hypoglycemia risk was very low (<2%). CONCLUSIONS Using PA to lower high glucose levels is an effective and safe strategy, and when guided by CGM, it can become a personalized tool for T1D education. ARTICLE HIGHLIGHTS Why did we undertake this study? To test whether short bouts of physical activity can safely and effectively lower high glucose levels in people with type 1 diabetes. What specific question(s) did we want to answer? Does 10–30 minutes of physical activity, started when glucose is high, reduce glucose more than doing nothing, and what factors affect this response? What did we find? Across 1,546 activity bouts, 23 minutes of exercise lowered glucose by 40 mg/dL (2.2 mmol/L)—eight times more than rest—with <2% hypoglycemia risk. What are the implications of our findings? Short bouts of activity quickly and safely lower high glucose, with drop size best predicted by starting level and trend—making it easy to teach and implement using a simple heatmap. Twitter Summary Short bouts of physical activity (10–30 min) lower glucose by 40 mg/dL (2.2 mmol/L) during hyperglycemia in people with type 1 diabetes, compared to 5 mg/dL (0.3 mmol/L) with no activity. Top predictors: starting glucose, rate of change, variability, duration, insulin on board. Hypoglycemia risk was very low (<2%). CGM-guided activity is a safe, effective, and personalisable glucose-lowering tool. #T1D #CGM #Exercise Graphical Abstract
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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 | 0.042 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.002 | 0.001 |
| 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".