Bibliographic record
Abstract
Labile surfaces in the form of suspension straps are increasingly being used as a tool in resistance training programs. Pushing is a common functional activity of daily living and inherently part of a well-rounded training program. This study examined pushing exercises performed on stable surfaces and unstable suspension straps, specifically muscle activation levels and spine loads were quantified together with the influence of employing technique coaching. There were several main questions that this study sought to answer: Which exercises challenged particular muscles? What was the magnitude of the resulting spine load? How did stable and unstable surfaces differ? Did coaching influence the results? Fourteen men were recruited as part of a convenience sample (mean age, 21.1 ± 2.0 years; height, 1.77 ± 0.06 m; mean weight, 74.6 ± 7.8 kg). Data were processed and input to a sophisticated and anatomically detailed 3D model that used muscle activity and body segment kinematics to estimate muscle force-in this way, the model was sensitive to the individuals choice of motor control for each task; muscle forces and linked segment joint loads were used to calculate spine loads. Exercises were performed using stable surfaces for hand/feet contact and repeated where possible with labile suspension straps. Speed of movement was standardized across participants with the use of a metronome for each exercise. There were gradations of muscle activity and spine load characteristics to every task. In general, the instability associated with the labile exercises required greater torso muscle activity than when performed on stable surfaces. Throughout the duration of an exercise, there was a range of compression; the TRX push-up ranged from 1,653 to 2,128.14 N, whereas the standard push-up had a range from 1,233.75 to 1,530.06 N. There was no significant effect of exercise on spine compression (F(4,60) = 0.86, p = 0.495). Interestingly, a standard push-up showed significantly greater shear than TRX angle 1 (p = 0.02), angle 2 (p = 0.01), and angle 3 (p = 0.02). As with any training program for the elite or recreational athlete alike, specific exercises and programs should reflect one's injury history, capabilities, limitations, and training goals. Although none of the exercises examined here breached the NIOSH action limit for compression, those exercises that produced higher loads should be used relative to the individual. Thus, the atlas of muscle activation, compression, and shear forces provided can be used to create an appropriate program. Those individuals not able to tolerate certain loads may refer to the atlas and choose exercises that minimize load and still provide sufficient muscle activation. Conversely, an individual with a resilient back that requires an increased muscular challenge may choose exercises with higher muscle activation and spine load. This helps the individual, trainer, or coach in program design respecting individual differences and training goals.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".