Wrist Splint Effects on Muscle Activity and Force during a Handgrip Task
Bibliographic record
Abstract
Wrist splints are commonly prescribed to limit wrist motion and provide support at night and during inactive periods but are often used in the workplace. In theory, splinting the wrist should reduce wrist extensor muscle activity by stabilizing the joint and reducing the need for co-contraction to maintain posture. Ten healthy volunteers underwent a series of 24 10-s gripping trials with surface electromyography on 6 forearm muscles. Trials were randomized between splinted and nonsplinted conditions with three wrist postures (30 degrees flexion, neutral, and 30 degrees extension) and four grip efforts. Custom-made Plexiglas splints were taped to the dorsum of the hand and wrist. It was found that when simply holding the dynamometer, use of a splint led to a small (<1% MVE) but significant reduction in activity for all flexor muscles and extensor carpi radialis (all activity <4% maximum). At maximal grip, extensor muscle activity was significantly increased with the splints by 7.9-23.9% MVE. These data indicate that splinting at low-to-moderate grip forces may act to support the wrist against external loading, but appears counterproductive when exerting maximal forces. Wrist bracing should be limited to periods of no to light activity and avoided during tasks that require heavy efforts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.002 | 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".