The Effects of Ankle Braces on Lower Extremity Electromyography and Performance During Vertical Jumping: A Pilot Study
Bibliographic record
Abstract
International Journal of Exercise Science 12(1): 15-23, 2019. Ankle braces have been hypothesized to prevent ankle injuries by restricting range of motion (ROM) and improving proprioception at the ankle. As such, ankle braces are commonly worn by physically active individuals to prevent ankle injuries. Despite their widespread use, the effects that ankle braces have on athletic performance measures, such as vertical jumping, remains unclear. Furthermore, although ankle braces are known to restrict normal ROM at the ankle, little is known about the effects that ankle braces have on the lower extremity proximal to the ankle, specifically muscular activation. Therefore, the purpose of this pilot study was to determine if lower extremity surface electromyographic activity (sEMG) and performance was affected in 5 males and 5 females by wearing softshell (AE) and semi-rigid (T1) ankle braces during a Vertical Jump Test, and to establish a basis for future investigation. Vertical jump height was not significantly affected (p > .05) in the AE (37.49 ± 11.61 cm) and T1 (36.3 ± 11.77 cm) ankle brace conditions, relative to the no brace (38.17 ± 12.01 cm) condition. No significant differences in sEMG of the lateral gastrocnemius and biceps femoris were present across conditions. There was a tendency for sEMG of the rectus femoris to decrease when wearing AE (195.71 ± 100.43 %MVC) and T1 (183.308 ± 92.73 %MVC) braces, compared to no braces (210.08 ± 127.46 %MVC), and warrants further investigation using a larger sample. Until more research is conducted, however, clinicians should not be concerned about ankle braces significantly affecting proximal muscle activation during vertical jumping.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".