Effect of Segmental, Localized Lower Limb Cooling on Dynamic Balance
Bibliographic record
Abstract
PURPOSE: This study aimed to determine the effect of cooling progressively greater portions of the lower extremities on dynamic balance and neuromuscular activation. METHODS: Ten healthy males (22.8 ± 3.4 yr, 76.5 ± 9.1 kg) performed one room air temperature control (22.4°C ± 0.8°C) and three trials of cold water immersion at 12°C (lateral malleolus, ankle; lateral femoral epicondyle, knee; anterior superior iliac spine, hip) for 10 min before performing a unipedal balance test (Star Excursion Balance Test (SEBT)) with their dominant limb. Muscle activation of the vastus lateralis, biceps femoris, tibialis anterior, and lateral gastrocnemius was measured with surface EMG during the SEBT. RESULTS: Core temperature remained euthermic throughout all trials. Gastrocnemius temperature decreased from control (30.4°C ± 0.5°C) with knee (23.7°C ± 1.7°C) and hip immersion (22.4°C ± 1.0°C), whereas vastus lateralis temperature decreased from control (33.7°C ± 1.7°C) with hip immersion (27.3°C ± 2.0°C) (P < 0.01 for all comparisons). Cold water immersion influenced mean anterior and posterior reach distance on the SEBT in a dose-dependent fashion. Compared with those in control, mean anterior and posterior SEBT reach distances were not decreased with ankle (-1.38% and -0.74%, respectively) and knee immersion (-2.48% and -2.74%), whereas hip immersion significantly reduced SEBT by 4.73% and 4.05% (P < 0.05, d = 0.52-0.58). Muscle activation was largely unaffected as the lower extremities were cooled, with only the lateral gastrocnemius during the anterior SEBT approaching a decrease (P = 0.059). CONCLUSIONS: Cooling larger portions of the lower extremities progressively affect dynamic balance, and thermal protection strategies should focus on maintaining temperature in the large muscle mass of the thigh.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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".