Influence of averaging method on muscle deoxygenation interpretation during repeated‐sprint exercise
Bibliographic record
Abstract
Near‐infrared spectroscopy (NIRS) is a common tool used to study oxygen availability and utilization during repeated‐sprint exercise. However, there are inconsistent methods of smoothing and determining peaks and nadirs from the NIRS signal, which make interpretation and comparisons between studies difficult. To examine the effects of averaging method on deoxyhaemoglobin concentration ([HHb]) trends, nine males performed ten 10‐s sprints, with 30 seconds of recovery, and six analysis methods were used for determining peaks and nadirs in the [HHb] signal. First, means were calculated over predetermined windows in the last 5 and 2 seconds of each sprint and recovery period. Second, moving 5‐seconds and 2‐seconds averages were also applied, and peaks/nadirs were determined for each 40‐seconds sprint/recovery cycle. Third, a Butterworth filter was used to smooth the signal, and the resulting signal output was used to determine peaks and nadirs from predetermined time points and a rolling approach. Correlation and residual analysis showed that the Butterworth filter attenuated the “noise” in the signal, while maintaining the integrity of the raw data (r = .9892; mean standardized residual −9.71 × 103 ± 3.80). Means derived from predetermined windows, irrespective of length and data smoothing, underestimated the magnitude of peak and nadir [HHb] compared to a rolling mean approach. Consequently, sprint‐induced metabolic changes (inferred from Δ[HHb]) were underestimated. Based on these results, we suggest using a digital filter to smooth NIRS data, rather than an arithmetic mean, and a rolling approach to determine peaks and nadirs for accurate interpretation of muscle oxygenation trends.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".