Preventing Exercise-Induced Hypoglycemia in Type 1 Diabetes Using Real-Time Continuous Glucose Monitoring and a New Carbohydrate Intake Algorithm: An Observational Field Study
Bibliographic record
Abstract
BACKGROUND: Real-time (RT) continuous glucose monitoring (CGM) offers the possibility to better manage glucose levels during exercise in active individuals with type 1 diabetes mellitus (T1DM). However, studies have yet to determine the appropriate actions to take when glucose levels are trending toward hypoglycemia. The purpose of this observational field study was to test the effectiveness of RT-GCM and a new carbohydrate intake algorithm designed for maintaining euglycemia during sports. METHODS: During a 2-week sports camp, 25 adolescents (8-17 years old) with T1DM were fitted with a RT-CGM device and instructed to ingest fast-acting carbohydrates (8-20 g, depending on the concentration of glucose at the time of RT-CGM alert and rates of change in glycemia) when glucose levels were trending toward hypoglycemia. Rates of change in glucose were measured before and after algorithm use, and the incidence of hypoglycemia was documented. RESULTS: With RT-CGM and algorithm use, euglycemia was largely maintained with modest amounts of carbohydrate intake, even when glucose levels were initially dropping at an elevated rate (>0.55 mmol/L per 5 min). Mild biochemical hypoglycemia (3.0-3.9 mmol/L) occurred just twice out of 22 uses of the algorithm (9%) when trend arrows alerted the subjects that glucose levels were dropping. When glucose levels were already below target (<5.0 mmol/L), mild hypoglycemia occurred five times out of 13 events (38%), despite 16 g of carbohydrate being ingested. Average glucose levels during sports in the 60 min following algorithm use were 5.8 ± 1.2 mmol/L, 5.3 ± 1.0 mmol/L, and 6.2 ± 0.8 mmol/L in the 20-, 16-, and 8-g carbohydrate intake protocols when glucose levels were initially on target but dropping toward hypoglycemia. CONCLUSION: When coupled with RT-CGM, a new carbohydrate intake algorithm prevents hypoglycemia and maintains euglycemia during exercise, particularly if patients ingest carbohydrate when trend arrows alert them of a drop in glycemia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".