Glucose or Intermittent High-Intensity Exercise in Glargine/Glulisine Users with T1DM
Bibliographic record
Abstract
INTRODUCTION: The effects of glargine/glulisine insulin regimen on exercise blood glucose (BG) and strategies to limit exercise-induced hypoglycemia are not well documented. Intermittent high-intensity exercise has been proposed to prevent hypoglycemia, but its effect in participants with type 1 diabetes using glargine/glulisine is unknown. METHODS: The study used a repeated-measures design with three randomly ordered exercise conditions. Eleven participants completed 60 min of moderate-intensity exercise at 50% VO(2peak) for all conditions. These conditions varied as follows: participants ingested 0 g of glucose preexercise (0G + MOD), 30 g of glucose preexercise (30G + MOD), or 0 g of glucose preexercise but performed brief high-intensity intervals interspersed every 2 min (0G + MOD/INT) during exercise. If BG fell <4 mmol·L(-1), a 20% dextrose solution was started to maintain BG between 4 and 5 mmol·L(-1). RESULTS: Consuming 30 g of glucose before exercise (30G + MOD) resulted in a higher preexercise BG (11.7 ± 2.7 mmol·L(-1)) compared with 0 g of glucose before exercise (0G + MOD, 7.8 ± 4.0, and 0G + MOD/INT, 9.2 ± 3.5mmol·L(-1)), P < 0.05. A dextrose infusion was required in 7/11, 4/11, and 1/11 participants for 0G + MOD, 0G + MOD/INT, 30G + MOD conditions, respectively, P < 0.02. The duration and the quantity of dextrose infused were greatest in the 0G + MOD condition, moderate in the to 0G + MOD/INT condition, and minimal in the 30G + MOD condition, P < 0.01. CONCLUSION: Our results suggest that both moderate-intensity exercise with a 30-g preexercise glucose beverage or interspersed with intermittent high-intensity sprints may be safe strategies to prevent hypoglycemia in glargine/glulisine users.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".