Changes in Accuracy of Continuous Glucose Monitoring Using Dexcom G4 Platinum Over the Course of Moderate Intensity Aerobic Exercise in Type 1 Diabetes
Bibliographic record
Abstract
Continuous glucose monitoring (CGM) systems help diabetes management in patients with type 1 diabetes (T1D) but could have lower accuracy during exercise. We aim to evaluate the dynamics of CGM accuracy during exercise in patients with T1D. Secondary analysis of data was carried out on 22 patients with T1D (glycated hemoglobin [HbA 1c ]: 7.3% ± 1.0%, diabetes duration: 23 ± 13 years), who did three exercise sessions (45 min at 60% VO 2max on an ergocycle, 3 h postmeal) with paired Dexcom G4 Platinum, and capillary glucose values that were collected every 5 min. Dexcom accuracy was evaluated using sensor bias (SB) and absolute relative difference (ARD). For dynamics of SB analysis, data pairs following hypoglycemia correction were excluded. The analyzed data included 792 pairs (594 during 66 exercise sessions, 198 at rest before exercise). Median ARD was 8.44 (5.35–12.13)% at rest and increased to 16.77 (10.75–26.72)% during exercise ( P < 0.001). During exercise, mean SB values evolved from T0 minutes = 5.95 ± 16.04 mg/dL (exercise start); T5 = 9.55 ± 16.40; T10 = 13.51 ± 18.02; T15 = 15.32 ± 20.36; T20 = 17.30 ± 18.92; T25 = 19.46 ± 17.48; T30 = 21.08 ± 19.64; T35 = 19.10 ± 20.36; T40 = 19.82 ± 20.18; and T45 = 18.02 ± 20.90 (exercise end). CGM overestimated capillary at a mean SB of 14.23 ± 16.76 mg/dL over the whole exercise session. CGM accuracy decreased during moderate aerobic exercise as previously described. However, the trend to overestimate capillary glucose was maintained at relatively stable values within 15 min of exercise initiation, which could help patients in their clinical decisions. Similar analyses would be needed for other types of exercise.
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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 | 0.001 | 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 | 0.001 |
| 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 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".