The effects of endurance training on muscle oxygen desaturation during incremental exercise tests: a systematic review and meta-analysis
Bibliographic record
Abstract
Objective Minimum muscle oxygen saturation (SmO2min) measured via near-infrared spectroscopy (NIRS) is a common measure during incremental exercise testing (IET). Our objective was to determine the effects of pre-to-post endurance training on SmO2min (ΔSmO2min) during an IET, using a meta-analysis. Data sources MEDLINE, EMBASE, and SPORTDiscus. Study selection Studies including healthy individuals had to meet the following criteria: (1) endurance training intervention; (2) peripheral muscle NIRS; (3) incremental exercise test pre/post training; (4) SmO2 or analogous saturation parameter measured. Analysis A PEDro scale was used for risk of bias analysis. A random effect meta-analysis model was used to synthesize the effect of training on ΔSmO2min in individual studies. Statistical heterogeneity was quantified using I2 statistic. A meta-regression was used to estimate the effect of training on the relationship between peak cycling power output (Wpeak), peak pulmonary oxygen uptake (V˙O2peak), and ΔSmO2min. A mixed-effect model was used to estimate categorical variables. Results Five studies met the inclusion criteria. No difference in SmO2min was detected following training pre- and post-intervention IETs. A trend for an effect of training on the relationship between Wpeak and ΔSmO2min was observed (p = 0.06). Conclusion This meta-analysis showed no effects of endurance training on SmO2min during an IET. Our results showed a trend for an effect of training on the relationship between Wpeak and ΔSmO2min, with no effect for V˙O2peak and ΔSmO2min. It is possible that SmO2min is not affected by endurance training, and may be used as a physiological marker for improvements in submaximal performance rather than at peak.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.036 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".