Neuromuscular Fatigue Following Cycling To Task Failure In Different Exercise Intensity Domains
Bibliographic record
Abstract
Neuromuscular (NM) fatigue can vary according to exercise intensity and duration; however, fatigue responses across the full spectrum of exercise intensities to task failure remain unclear. PURPOSE: To characterize the central and peripheral NM fatigue responses to cycling to task failure in different exercise intensity domains. METHODS: After a ramp incremental test, 12 healthy, active males (29 ± 5 years) underwent 4 cycling sessions to task failure at 5% below gas exchange threshold (moderate domain; MOD), 5% below the maximal metabolic steady state; HVY), 5% below peak power output (severe domain; SVR), and 120% of peak power output (extreme domain; EXT). At baseline, upon task failure (post), and following 1, 4, and 8 min of recovery, participants performed an NM assessment consisting of a maximal voluntary isometric contraction (MVC) with superimposed and potentiated high frequency doublets (Db100), followed by a low frequency doublet (Db10) and single twitch (QTw). Two-way repeated measures ANOVAs with Bonferroni post hoc examined 4 domains 5 timepoints. RESULTS: MVC declined to the same extent in all domains at post (all p > 0.05) and although not fully recovered at 8 min, the fastest recovery from post was displayed after EXT (+59.5 ± 79.0%) compared to MOD (+9.4 ± 32.0%, p = 0.035), HVY (+17.7 ± 21.8%, p = 0.021), and SVR (+29.8% ± 29.9%, p = 0.016). Voluntary activation at post was lowest after MOD (-11.1 ± 11.6%) compared to EXT (-0.5 ± 4.6%, p = 0.030), and all conditions fully recovered to baseline without differences between domains at 8 min (all p > 0.05). While QTwat post displayed greatest decline in EXT (-50.5 ± 14.7%) and least in MOD (-7.5 ± 28.3%, p = 0.011), EXT (+86.1 ± 69.2%) partially recovered by 8 min to match MOD values (+2.3 ± 17.7%, p = 0.865). Low frequency fatigue (Db10:100) at post was lowest in MOD (-8.6 ± 6.1%) compared to HVY (-18.4 ± 7.9%, p = 0.007), SVR (-22.8 ± 9.3%, p = 0.020), and EXT (-24.8 ± 10.7%, p < 0.001), but all conditions partially recovered from post to match MOD values at 8 min (+2.3 ± 7.2%, all p > 0.05). CONCLUSIONS: Although MVC declined to the same extent following exercise to task failure in all domains, our data confirm that the contribution of central and peripheral factors to this decline differed between conditions. Different NM fatigue recovery patterns were also demonstrated during recovery.
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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 | 0.000 | 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".