Evaluation of Training with Elastic Bands on Strength and Fatigue Indicators in Paralympic Powerlifting
Bibliographic record
Abstract
Background: Variable resistance training has recently become a component of strength and conditioning programs. Objective: This randomized counterbalanced cross-over study aimed to investigate the use of elastic bands (EB) and the traditional method (TRAD) and force indicators in a training session. Methods: 12 Paralympic athletes (age: 28.60 ± 7.60 years) participated in this three-week study. In the first week, the participants were familiarized with EB and TRAD and were tested for maximal repetition (1-RM). The research occurred in weeks 2 and 3, which included the pre-post training, during which the following measures were extracted: maximum isometric force (MIF), the peak torque (PT), rate of force development (RFD), fatigue index (FI), and time to MIF (Time). The athletes performed two tests, EB and TRAD, separated by a one-week interval. Results: Significant differences were found between the pre- and post-test for 1RM (p = 0.018, η2p = 0.412), MIF (p = 0.011, η2p = 0.415), PT (p = 0.012, η2p = 0.413), and RFD (p = 0.0002, η2p = 0.761). With the use of EB, there was a difference in RFD between TRAD before and EB after (p = 0.016, η2p = 0.761). There were significant differences in the before and after for FI between TRAD and EB (p < 0.001) and for Time (p < 0.001), indicating that training with the use of elastic bands promotes overload, characterized by increased fatigue and decreased strength. Conclusions: Training with EB did not decrease 1RM, PT, MIF or RFD, however, there was an increase in fatigue and time to reach MIF when compared to the method with fixed resistance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".