Nutritional Strategies to Prevent Hypoglycemia at Exercise in Diabetic Adolescents
Bibliographic record
Abstract
UNLABELLED: Studies on nutritional strategies to prevent exercise-induced hypoglycemia in adolescents with type 1 diabetes are scarce. OBJECTIVE: This study aimed to compare the effect of two food strategies on blood glucose (BG) during and after 60 min of moderate-intensity exercise. METHODS: Subjects performed exercise 120 min after breakfast in three conditions: 1) standardized breakfast + preexercise placebo beverage (PL), 2) standardized breakfast + preexercise CHO beverage (8 mg of CHO·kg of body weight·min of exercise; CHO), or 3) protein-supplemented breakfast (8 mg of protein·kg of body weight·min of exercise added to the standardized breakfast) + preexercise placebo beverage (PROT). As soon as BG falls <4 mmol·L or symptomatic hypoglycemia occurred during exercise, the session was stopped and CHO tablets were provided to correct hypoglycemia. RESULTS: Ten subjects (age = 14.0 ± 1.5 yr) participated in all conditions. BG decreased by 6.0 ± 1.9, 1.0 ± 3.1, and 4.6 ± 1.9 mmol·L in PL, CHO, and PROT conditions, respectively (P < 0.05). The proportion of subjects reaching hypoglycemic values or sensations of hypoglycemia was significantly different between conditions with 4/10, 1/10, and 0/10 in the PL, CHO, and PROT conditions (P < 0.05). CONCLUSIONS: The preexercise CHO beverage induced the least dramatic BG decrease during exercise. The PROT breakfast induced an overall similar BG drop compared to PL, a larger BG drop compared to CHO, but a similar rate of hypoglycemia compared to CHO. Our results suggest that taking CHO supplement before unplanned exercise is still the best strategy to prevent exercise-induced hypoglycemia in an adolescent population. However, a protein supplement strategy may also have some benefits in limiting the rate of hypoglycemia during and immediately after exercise.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".