One night of sleep restriction following heavy exercise impairs 3-km cycling time-trial performance in the morning
Bibliographic record
Abstract
The goal of this project was to examine the influence of a single night of sleep restriction following heavy exercise on cycling time-trial (TT) performance and skeletal muscle function in the morning. Seven recreational cyclists (age, 24 ± 7 years; peak oxygen consumption, 62 ± 4 mL·kg−1·min−1) completed 2 phases, each comprising evening (EX1) and next-morning (EX2) exercise sessions. EX1 and EX2 were separated by an assigned sleep condition: a full night of rest (CON; 7.1 ± 0.3 h of sleep) or sleep restriction through early waking (SR; 2.4 ± 0.2 h). EX1 comprised baseline testing (muscle soreness, isokinetic torque, and 3-km TT performance) followed by heavy exercise that included 60 min of high-intensity cycling intervals and resistance exercise. EX2 was performed to assess recovery from EX1 and included all baseline measures. Magnitude-based inferences were used to evaluate all variables. SR had a negative effect (very likely) on the change in 3-km TT performance compared with CON. Specifically, 3-km TT performance was ‘very likely’ slower during EX2 compared with EX1 following SR (−4.0% ± 3.0%), whereas 3-km TT performance was ‘possibly’ slower during EX2 (vs. EX1) following CON (−0.5% ± 3.0%). Sleep condition did not influence changes in peak torque or muscle soreness from EX1 to EX2. A single night of sleep restriction following heavy exercise had marked consequences on 3-km TT performance the next morning. Because occasional sleep loss is likely, strategies to ameliorate the consequences of sleep loss on performance should be investigated.
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 | 0.000 |
| 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) | 0.001 | 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".