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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".