Are Strength Indicators and Skin Temperature Affected by the Type of Warm-Up in Paralympic Powerlifting Athletes?
Bibliographic record
Abstract
(1) Background: the present study aimed to evaluate the effect of different types of warm-ups on the strength and skin temperature of Paralympic powerlifting athletes. (2) Methods: the participants were 15 male Paralympic powerlifting athletes. The effects of three different types of warm-up (without warm-up (WW), traditional warm-up (TW), or stretching warm-up (SW)) were analyzed on static and dynamic strength tests as well as in the skin temperature, which was monitored by thermal imaging. (3) Results: no differences in the dynamic and static indicators of the force were shown in relation to the different types of warm-ups. No significant differences were found in relation to peak torque (p = 0.055, F = 4.560, η2p = 0.246 medium effect), and one-repetition maximum (p = 0.139, F = 3.191, η2p = 0.186, medium effect) between the different types of warm-ups. In the thermographic analysis, there was a significant difference only in the pectoral muscle clavicular portion between the TW (33.04 ± 0.71 °C) and the WW (32.51 ± 0.74 °C) (p = 0.038). The TW method also presented slightly higher values than the SW and WW in the pectoral muscles sternal portion and the deltoid anterior portion, but with p-value > 0.05. (4) Conclusions: the types of warm-ups studied do not seem to interfere with the performance of Paralympic Powerlifting athletes. However, the thermal images showed that traditional warm-up best meets the objectives expected for this preparation phase.
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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) | 0.000 | 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".