Muscle reoxygenation is slower after higher cycling intensity, and is faster and more reliable in locomotor than in accessory muscle sites
Bibliographic record
Abstract
Introduction: Wearable near-infrared spectroscopy (NIRS) can be used during dynamic exercise to reflect the balance of muscle oxygen delivery and uptake. This study describes the behaviour and reliability of postexercise reoxygenation with NIRS as a function of exercise intensity at four muscle sites during an incremental cycling test. We discuss physiological components of faster and slower reoxygenation kinetics in the context of sport science and clinical applications. We hypothesised that reoxygenation would be slower at higher intensity, and that locomotor muscles would be faster than accessory muscles. We quantified test-retest reliability and agreement for each site. Methods: Twenty-one trained cyclists performed two trials of an incremental cycling protocol with 5-min work stages and 1-min rest between stages. NIRS was recorded from the locomotor vastus lateralis and rectus femoris muscles, and accessory lumbar paraspinal and lateral deltoid muscles. Reoxygenation time course was analysed as the half-recovery time (HRT) from the end of work to half of the peak reoxygenation amplitude during rest. Coefficient of variability (CV) between participants, standard error of the measurement (SEM) within participants, and intraclass correlation coefficient (ICC) for test-retest reliability were evaluated at 50%, 75%, and 100% peak workloads. A linear mixed-effects model was used to compare differences between workloads and muscle sites. Results: HRT was slower with increasing workload in the VL, RF, and PS, but not DL. VL had the fastest reoxygenation (lowest HRT) across muscle sites at all workloads (HRT = 8, 12, 17 s at 50%, 75%, 100% workload, respectively). VL also had the greatest reliability and agreement. HRT was sequentially slower between muscle sites in the order of VL < RF < PS < DL, and reliability was lower than for the VL. Discussion: This study highlights the potential for using wearable NIRS on multiple muscle sites during exercise. Reoxygenation kinetics differ between local muscle sites with increasing intensity. Moderate-to-good reliability in the VL support its increasing use in sport science and clinical applications. Lower reliability in other muscle sites suggest they are not appropriate to be used alone, but may add information when combined to better reflect systemic intensity and fatigue during exercise at different intensities.
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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 | 0.001 | 0.003 |
| 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".