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Record W4410846226 · doi:10.1101/2025.05.29.25328403

Glucose-lowering effects of physical activity in type 1 diabetes: A causal modelling and matched-pair analysis approach

2025· preprint· en· W4410846226 on OpenAlexfundno aff
Catherine L. Russon, John Pemberton, Brad Metcalf, Emma Cockcroft, Michael Allen, Robert Andrews

Bibliographic record

VenuemedRxiv · 2025
Typepreprint
Languageen
FieldMathematics
TopicAdvanced Causal Inference Techniques
Canadian institutionsnot available
FundersYork UniversityLeona M. and Harry B. Helmsley Charitable Trust
KeywordsType 2 diabetesPhysical activityInternal medicineDiabetes mellitusEndocrinologyPsychologyMedicineChemistryEconometricsMathematicsPhysical medicine and rehabilitation

Abstract

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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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.042
metaresearch head score (Gemma)0.081
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.042
Threshold uncertainty score0.221

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0420.081
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.007
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0030.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.077
GPT teacher head0.361
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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