Overreached endurance athletes demonstrate alterations in exercising carbohydrate utilization applicable to training monitoring
Bibliographic record
Abstract
Purpose: To investigate whether carbohydrate utilization is altered during exercise in overreached endurance athletes, and to examine the utility of continuous glucose monitors (CGM) to detect overreaching status.Methods: Eleven endurance athletes (M:8, F:3) completed a 5-week training block consisting of 1-week of reduced training (PRE), 3-weeks of high-intensity overload training (POST), and 1-week of recovery training (REC).Participants completed a Lamberts and Lambert Submaximal Cycling Test (LSCT) and 5km time-trial at PRE, POST, and REC timepoints, 15min following the ingestion of a 50g glucose beverage with glucose recorded each minute via CGM.Results: Performance in the 5km time-trial was reduced at POST (∆-7±10W, P=0.04, η p 2 =0.35) and improved at REC (∆12±9W from PRE, P=0.01, η p 2 =0.66), with reductions in peak lactate (∆-3.0±2.0mmol/L,P=0.001, η p 2 =0.71), peak HR (∆-6±3bpm, P<0.001, η p 2 =0.86), and Hooper-Mackinnon well-being scores (∆10±5a.u., P<0.001, η p 2 =0.79), indicating athletes were functionally-overreached.The respiratory exchange ratio was suppressed at POST relative to REC during the 60% (POST: 0.80±0.05,REC: 0.87±0.05,P<0.001, η p 2 =0.74), and 80% (POST:0.93±0.05,REC: 1.00±0.05,P=0.003, η p 2 =0.68) of HR-matched submaximal stages of the LSCT.CGM glucose was reduced during HR-matched submaximal exercise in the LSCT at POST (P=0.047,η p 2 =0.36), but not the 5km time-trial (P = 0.07, η p 2 =0.28) in overreached athletes.Conclusion: This preliminary investigation demonstrates a reduction in blood glucose and carbohydrate oxidation during submaximal exercise in overreached athletes.The use of CGM during submaximal exercise following standardised nutrition could be employed as a monitoring tool to detect overreaching in endurance athletes.
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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".