Does acute dehydration affect the neuromuscular function in healthy adults?—a systematic review
Bibliographic record
Abstract
The effects of acute dehydration on neuromuscular function have been studied. However, whether the mechanisms underpinning such function are central or peripheral is still being determined, and the results are inconsistent. This systematic review aims to elucidate the influence of acute dehydration on neuromuscular function, including a novel aspect of investigating the central and peripheral neuromuscular mechanisms. Three databases were used for the article search: PubMed, Web of Science, and Scopus. Studies were included if they had objective measurements of dehydration, muscle performance, and electromyography data or transcranial magnetic stimulation or peripheral nerve stimulation measurements with healthy individuals aged 18-65 years. Twenty-three articles met the eligibility criteria. The studies exhibited considerable heterogeneity in the methods used to induce and quantify dehydration. Despite being inconsistent, the literature shows some evidence that acute dehydration does not affect maximal strength during isometric or moderate-speed isokinetic contractions. Conversely, acute dehydration significantly reduces maximal strength during slow-speed isokinetic contractions and fatigue resistance in response to endurance tasks. The studies report that dehydration does not affect the motor cortical output or spinal circuity. The effects occur at the peripheral level within the muscle.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".