Non-local muscle fatigue effects on muscle strength, power, and endurance in healthy individuals: A systematic review and meta-analysis
Bibliographic record
Abstract
Objective: To examine whether non-local muscle fatigue occurs following performance of a fatiguing bout of exercise of a different muscle(s).Design: Systematic review and meta-analysis.Search and Inclusion: A systematic literature search using a Boolean search strategy was conducted with PubMed, SPORTDiscus, Web of Science, and Google Scholar in April 2020 and was supplemented with additional ‘snowballing’ searches up to September 2020. To be included in our analysis, studies had to include at least one intentional performance measure (i.e., strength, endurance, or power), which if reduced could be considered evidence of muscle fatigue, and also had to include the implementation of a fatiguing protocol to a location (i.e., limb or limbs) that differed to those for which performance was measured. We excluded studies that measured only mechanistic variables such as electromyographic, or spinal/supraspinal excitability. After search and screening, 52 studies were eligible for inclusion including 57 groups of participants (median sample = 11) and a total of 303 participants.Results: The main multilevel meta-analysis model including all effects sizes (278 across 50 clusters [median = 4, range = 1 to 18 effects per cluster) revealed a trivial point estimate with high precision for the interval estimate (-0.02 [95%CIs = -0.14 to 0.09]), yet with substantial heterogeneity (Q(277) = 642.3, p < 0.01), I2 = 67.4%). Subgroup and meta-regression analyses showed that NLMF effects were not moderated by study design (between vs. within-participant), homologous vs. heterologous effects, upper or lower body effects, participant training status, sex, age, the time of post-fatigue protocol measurement, or the severity of the fatigue protocol. However, there did appear to be an effect of type of outcome measure where both strength (0.11 [95%CIs = 0.01 to 0.21]) and power outcomes had trivial effects (-0.01 [95%CIs = -0.24 to 0.22]), whereas endurance outcomes showed moderate albeit imprecise effects (-0.54 [95%CIs = -0.95 to -0.14]).Conclusions: Overall, the findings do not support the existence of a general NLMF effect; however, when examining specific types of performance outcomes there may be an effect specifically upon endurance-based outcomes (i.e., time to task failure). However, there are relatively fewer studies that have examined endurance effects or mechanisms explaining this possible effect, in addition to fewer studies including women or younger and older participants, and considering causal effects of prior training history through the use of longitudinal intervention study designs. Thus, it seems pertinent that future research on NLMF effects should be redirected towards these still relatively unexplored areas.
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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 | 0.019 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.038 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".