Optimal Insulin Correction Factor in Post–High-Intensity Exercise Hyperglycemia in Adults With Type 1 Diabetes: The FIT Study
Bibliographic record
Abstract
OBJECTIVE Postexercise hyperglycemia, following high-intensity interval training (HIIT) in patients with type 1 diabetes (T1D), is largely underrecognized by the clinical community and generally undertreated. The aim of this study was to compare four multipliers of an individual’s insulin correction factor (ICF) to treat post-HIIT hyperglycemia. RESEARCH DESIGN AND METHODS The FIT study had a randomized, crossover design in physically active subjects with T1D (mean ± SD age 34.9 ± 10.1 years, BMI 25.5 ± 2.5 kg/m2, and HbA1c 7.2 ± 0.9%) using multiple daily injections. Following an 8-week optimization period, with 300 units/mL insulin glargine used as the basal insulin, subjects performed four weekly sessions of 25 min of HIIT. If hyperglycemia (>8.0 mmol/L) resulted, subjects received a bolus insulin correction 15 min post-HIIT, based on their own ICF, adjusted by one of four multipliers: 0, 50, 100, or 150%. RESULTS Seventeen subjects completed 71 exercise trials, of which 64 (90%) resulted in hyperglycemia. At 40 min postexercise, plasma glucose (PG) increased from mean ± SD 8.8 ± 1.0 mmol/L at baseline to 12.7 ± 2.4 mmol/L (increase of 3.8 ± 1.5 mmol/L). After correction, adjusted mean ± SE PG was significantly reduced for the 50% (−2.3 ± 0.8 mmol/L, P < 0.01), 100% (−4.7 ± 0.8 mmol/L, P < 0.001), and 150% (−5.3 ± 0.8 mmol/L, P < 0.001) arms but had increased further in the 0% correction arm. Both the 100 and 150% corrections were more effective than the 50% correction (P < 0.01 and P < 0.001, respectively) but were not different from each other. Hypoglycemia was rare. CONCLUSIONS In post-HIIT hyperglycemia, correction based on a patient’s usual ICF is safe and effective. Optimal PG reduction, with minimal hypoglycemia, occurred in the 100 and 150% correction arms.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".