Kinetic analysis of push-up exercises: a systematic review with practical recommendations
Bibliographic record
Abstract
Push-ups represent one of the simplest and most popular strengthening exercise. The aim of this study was to systematically review and critically appraise the literature on the kinetics-related characteristics of different types of push-ups, with the objective of optimising training prescription and exercise-related load. A systematic search was conducted in the electronic databases PubMed, Google Scholar and Science Direct up to April 2018. Studies that reported kinetic data (e.g. initial and peak-force supported by the upper-limbs, impact-force, peak-flexion-moment of the elbow-joint, rate of propulsive- and impact-, and vertebral-joint compressive-forces) related to push-ups and included trained, recreational and untrained participants, were considered. The risk of bias in the included studies was assessed using the Critical Appraisal Skills Programme scale. From 5290 articles retrieved in the initial search, only 26 studies were included in this review. Kinetic data for 46 push-up variants were assessed. A limitation of the current review is that the relationship between our findings and actual clinical or practical consequences is not statistically proven but can only be inferred from our critical descriptive approach. Overall, this review provides detailed data on specific characteristics and intensities of push-up variations, in order to optimise exercise prescription for training and rehabilitation purposes.
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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 | 0.014 | 0.058 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.018 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".