The Efficacy of Upper-Extremity Elastic Resistance Training on Shoulder Strength and Performance: A Systematic Review
Bibliographic record
Abstract
Elastic resistance exercise is a popular mode of strength training that has demonstrated positive effects on whole-body strength and performance. The purpose of this work was to identify the efficacy of elastic resistance training on improving upper limb strength and performance measures for the shoulder. Seven online databases were searched with a focus on longitudinal studies assessing shoulder elastic training strength interventions. In total, 1367 studies were initially screened for relevancy; 24 full-text articles were included for review. Exercise interventions ranged from 4-12 weeks, assessing pre-/post-strength and performance measures inclusive of isometric and isokinetic strength, 1RM strength, force-velocity tests, and throwing-velocity tests. Significant increases in various isometric strength measures (IR:11-13%, ER:11-42%, FL: 14-36%, EXT: 4-17%, ABD: 8-16%), 1RM strength (~24% in bench press), force-velocities, throwing- and serve-velocities (12%) were all observed. Elastic resistance training elicited positive effects for both strength and performance parameters regardless of intervention duration. Similar significant increases were observed in isometric strength and 1RM strength across durations. Isokinetic strength increases were variable and dependent on the joint velocity conditions. Quantifying the dosage of appropriate exercise prescription for optimal strength and performance gains is inconclusive with this study due to the heterogeneity of the intervention protocols.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".