Upper extremity muscle activity and joint loading changes between the standard and powerlifting bench press techniques
Bibliographic record
Abstract
The bench press is a common activity found in many exercise regimens. Powerlifters often adopt non-standard techniques to potentially enhance maximal capability. The purpose of this research was to examine muscle activation and joint loading differences between the powerlifting (Arch) and standardised techniques. Twenty experienced male lifters completed lifts at an instructed cadence in the arch and the National Strength and Conditioning Association standard techniques at 25%, 50% and 75% of their self-reported one rep maximum. The arch technique increased latissimus dorsi mean and peak activation (p < 0.0001), generating activation of approximately 13% maximal voluntary contraction, regardless of percentage of the one rep maximum lifted. The standardised technique resulted in integrated shoulder moments that were 8% larger (p < 0.0001). This latissimus dorsi activation paired with decreased shoulder loading in the arch technique likely acts to minimise the amount of time spent in the "sticking region", where most lift efforts fail. It is possible to use this technique to increase latissimus dorsi activation, without increasing overall shoulder loading. The technique-specific differences can be used in performance or rehabilitation-based programmes to increase muscular output of some muscles without increasing overall loading.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".