Probing The Influence Of Exertion/Rest Pacing On Predicted Muscle Fatigue
Bibliographic record
Abstract
Fatigue from repetitive muscle contractions reduces strength capacity, which can negatively affect performance and indirectly increase the risk of acute and chronic musculoskeletal injury. As such, there is utility in being able to proactively predict muscle fatigue accumulation and recovery based on varied intensity (i.e. percentage of maximal muscle exertion) and volume (i.e. contraction vs. rest time). Despite utilizing intensity and volume in exercise prescription and ergonomics methods, the pacing at which these contraction profiles occur are likely not accounted for. For example, a lift with a 2 s contraction followed by a 2 s rest and a lift with a 30s exertion followed by a 30s rest can both have the same volume (50%) at a given intensity, but how may the fatigue development differ in these two conditions? PURPOSE: To determine if contractions with different repetition rates, but the same overall intensity and volume, result in different levels of predicted muscle fatigue. METHODS: This study uses a simulation approach to examine the effects of multiple pacing scenarios on fatigue accumulation. A computational motor-unit model (Potvin & Fuglevand, 2017) was modified to include a recovery component. Twelve different exertion frequencies (60[i.e. 0.5 s exertion, 0.5 s rest], 30, 20, 15, 12, 10, 6, 5, 4, 3, 2, and 1[i.e. 30s exertion, 30s rest]) were tested over a 1-minute cycle while maintaining one of three volumes (25%, 50%, 75%). To represent an ergonomics-relevant scenario, cycles were simulated continuously for 8 hours to represent a full day of fatiguing work. The remaining strength capacity of all units served as the dependent variable. RESULTS: Tasks with slower pacing (ie. low reps but longer contractions) resulted in greater muscle fatigue than those with faster pacing when volume was fixed. Across a range of intensities (25%, 50% and 75%), the low rep conditions resulted in 0.71%, 2.98%, and 4.90% more fatigue, respectively. However, there is a plateau, and only tasks below 10 reps/min induce substantially greater fatigue. The effect of pacing was more pronounced for lower volumes when normalized to how much fatigue was induced. CONCLUSION: An advanced muscle fatigue model predicted that sustained exertions induce more fatigue compared to shorter but more frequent exertions for the same amount of total work.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".