Time Lag and Accuracy of Continuous Glucose Monitoring During High Intensity Interval Training in Adults with Type 1 Diabetes
Why this work is in the frame
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Bibliographic record
Abstract
Background: This study investigated the accuracy of real-time continuous glucose monitoring (rtCGM) during high intensity interval training (HIIT) in patients with type 1 diabetes (T1D). Methods: Seventeen participants with T1D, using multiple daily injections (MDI) with basal insulin glargine 300 U/mL (Gla-300), completed four fasted HIIT sessions over 4 weeks while wearing a Dexcom rtCGM G4 Platinum system. Each exercise consisted of high intensity interval cycling and multimodal training over 25 min. Reference venous plasma glucose (PG) was measured at 60- and 10-min before exercise (Stage 1), every 10 min during exercise and then every 15 min until 180 min after the end of exercise (Stage 2: during exercise and 45-min early recovery; Stage 3: 45 min to 3 h after the end of exercise); and at 6-, 10-, and 13-h postexercise (Stage 4). Results: In the 64 HIIT sessions that resulted in hyperglycemia, PG increased 90.0 ± 32.4 mg/dL (mean ± standard deviation), peaking at 68.0 ± 18.4 min from the start of HIIT. Mean absolute relative difference was highest during exercise and early recovery (Stage 2) at 17.8%, versus Stage 1 (10.4%), Stage 3 (10.6%), and Stage 4 (11.5%) ( P < 0.001). During Stage 2, rtCGM showed a significant negative bias of 35.3 mg/dL ( P < 0.001) compared to reference glucose. Lag time to reach the half-maximal glucose rise was 35 min in rtCGM versus PG. The Surveillance Error Grid found that in Stage 2, only 65.5% of paired values were in the no-risk zone and the %15/15 was 50%, significantly lower than the other stages ( P < 0.001). Conclusions: During HIIT and early recovery, there is an increase in lag time and a related decline in accuracy of Dexcom rtCGM G4, compared to pre-exercise and later recovery, in patients with T1D using MDI.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 it