Efficacy of Continuous Real-Time Blood Glucose Monitoring During and After Prolonged High-Intensity Cycling Exercise: Spinning with a Continuous Glucose Monitoring System
Bibliographic record
Abstract
BACKGROUND: Hypoglycemia is the most common and serious side effect of insulin therapy in type 1 diabetes (T1DM), frequently occurring both during and after vigorous exercise. Late-onset hypoglycemia (LOH) is of great concern, occurring 1-36 h after exercise, often going unnoticed during sleep. Repeated exposure to LOH causes autonomic glucose counterregulatory failure and sometimes coma and death. Continuous glucose monitoring systems have recently emerged as a potentially important tool in diabetes management, allowing individuals to track glucose levels continuously and learn how various behaviors influence glucose control. METHODS: In this pilot study, we determined the efficacy of using a real-time continuous glucose monitoring system (Guardian RT, Minimed, Northridge, CA) to detect blood glucose excursions associated with exercise and LOH (i.e., blood glucose concentration <4 mM) after exercise in individuals with T1DM. Five subjects with T1DM were monitored before, during, and after a 60 min vigorous spin class using Guardian RT (48 h in total). RESULTS: Following the exercise, three of the five subjects had LOH, while the other two experienced decreases in blood glucose concentrations to 4 mM. The Guardian RT monitor was effective in notifying all of the subjects of such glycemic excursions over the 48 h surveillance period. A strong correlation (r = 0.89, P < 0.001) was found between conventional self-monitoring of blood glucose and Guardian RT data pairs. CONCLUSION: These limited data suggest that nocturnal LOH occurs commonly following vigorous exercise and that a Guardian RT is a useful and important diagnostic tool. Further study into clinical strategies for preventing hypoglycemia associated with this common form of mixed aerobic and anaerobic exercise is urgently needed through insulin modification and carbohydrate supplementation.
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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 | 0.004 |
| 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) | 0.000 | 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".