An evaluation of how exercise position statement guidelines are being used in the real world in type 1 diabetes: Findings from the type 1 diabetes exercise initiative (T1DEXI)
Bibliographic record
Abstract
AIMS: Position statement guidelines should help people with type 1 diabetes (T1D) improve glucose outcomes during exercise. METHODS: In a 4-week observational study, continuous glucose, insulin, and nutrient data were collected from 561 adults with T1D. Glucose outcomes were calculated during exercise, post-exercise, and overnight, and were compared for sessions when participants used versus did not use exercise guidelines for open-loop (OL) and automated insulin delivery (AID) therapy. RESULTS: Guidelines requiring behaviour modification were rarely used while guidelines not requiring modification were often used. The guideline recommending reduced meal insulin before exercise was associated with lower time <3.9 mmol/L during exercise (-2.2 %, P=0.02) for OL but not significant for AID (-1.4 %, P=0.27). Compared to exercise with low glucose (<3.9 mmol/L) in prior 24-hours, sessions without recent low glucose had lower time <3.9 mmol/L during exercise (-1.2 %, P<0.001). The AID guideline for no carbohydrates before exercise when CGM is flat, or increasing, was not associated with improved glycaemia. CONCLUSIONS: Free-living datasets may be used to evaluate usage and benefit of position statement guidelines. Evidence suggests OL participants who reduced meal insulin prior to exercise and did not have low glucose in the prior 24 h had less time below range.
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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 | 0.027 | 0.097 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".