Does induced fatigue alter dynamic balance in athletes? A systematic review.
Bibliographic record
Abstract
OBJECTIVES: To determine the influence of induced fatigue on dynamic balance in healthy athletes. DESIGN: Systematic review. DATA SOURCES: PUBMED, MEDLINE, CINAHL, Sports Discus, and the Cochrane library from onset to May 28, 2019. ELIGIBILITY CRITERIA: Eligible studies included any study examining the effects of induced-fatigue on dynamic balance, as measured by the SEBT/YBT, in healthy athletic populations. Studies with a low risk of bias were considered scientifically admissible for a best evidence synthesis. RESULTS: Fifteen studies with low risk of bias were included - seven investigated recreational athletes while eight focused on competitive athletes. In the recreational population, five of the studies found significant decrease in dynamic balance following the fatiguing intervention. However, the remaining two concluded with insignificant changes. As for the competitive population, three studies showed significant effects of induced fatigue on dynamic balance, while five showed no effects. CONCLUSION: There are conflicting results regarding the effects of induced fatigue on dynamic balance. The majority of studies focused on competitive athletes found that fatigue did not alter their dynamic balance. Per contra, the majority of studies focused on recreational athletes concluded the opposite - fatigue did indeed affect dynamic balance.
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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 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".