Continuous Glucose Monitoring Profiles in Elite-Level Professional European Football Players
Bibliographic record
Abstract
Background: This descriptive observational study reports on continuous glucose monitoring (CGM) data, using a novel glucose biosensor (Abbott Libre Sense Glucose Sport Biosensor), during professional game play and during daily life in elite European football players. Methods: Eighteen healthy male elite football players (age: 27.5 ± 5.1 years; height 180.1 ± 7.2 cm, weight 74.2 ± 9.1 kg, UEFA Champions League club) participated, with a subset examined for a single game for active (n = 10) and reserve (n = 4) players. Group comparisons used unpaired t -tests or Wilcoxon rank-sum tests; within-group differences used repeated measures one-way analysis of variance or Friedman test. Descriptive statistics were summarized for 24-hour data for daytime (06:00 am -10:59 pm ) and nighttime (11:00 pm -05:59 am ). Results: Higher mean CGM glucose was observed during-game in active compared with reserve players (159 ± 23 vs 133 ± 25 mg/dL, P = .09), with significantly higher time above range (TAR, 72.8 ± 32.02 vs 29.7 ± 37.9%, P = .04) and lower time in range (TIR, 26.7 ± 31.9 vs 70.3 ± 37.9%, P = .04). In the 90 minute pre- to 180 minute post-game period, TAR (57.3 ± 26.6% vs 16.1 ± 20.2%, P = .02) and mean iG (149 ± 19 vs 123 ± 14 mg/dL, P = .02) remained higher for active players. For all 18 players, TIR was 89.4 ± 11.7 and 91.6 ± 13.7%, TAR was 5.9 ± 6.7 and 2.9 ± 5.7%, and time below range was 4.5 ± 10.5 and 5.3 ± 13.2% for day and night, respectively. Conclusions: This observational study suggests that elite European footballers may have significant increases in glycemia, as measured by CGM, supporting the notion that mild hyperglycemia can occur during and after active competition in healthy and metabolically normal athletes, perhaps because of competition stress.
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 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".