Task failure reveals range-dependent neuromuscular fatigue in shoulder muscles during arm elevation
Bibliographic record
Abstract
Repeated upper limb performance fatigues periscapular and/or rotator cuff muscles, which may alter humeral and scapular kinematics leading to increased subacromial impingement syndrome risk. However, the specific changes in neuromuscular patterns across different phases of a functional movement performed to failure remain unclear. This study investigated neuromuscular fatigue during a repeated arm elevation task to failure in asymptomatic individuals. Participants elevated their dominant arm while holding ∼ 30 % of their maximal isometric shoulder flexion load until task failure. High-density surface electromyography (HD-sEMG) was recorded from the pectoralis major (PM), biceps brachii (BB), and trapezius (UT, MT, LT) to accurately identify innervation zones and to enhance signal validity, while bipolar sEMG collected from the deltoid (AD, LD, PD), serratus anterior (SA) and infraspinatus (IN). Root mean square (RMS, %MVIC) and mean power frequency (MPF) were analyzed between three ranges of arm elevation (bottom, middle, top) at baseline and failure. During the middle and top ranges of arm elevation at task failure increased sEMG amplitude and/or decreased MPF of the PM, AD, BB, SA, LD, IN, UT, and MT were present. These results reveal that neuromuscular fatigue was highly dependent on the range of motion, with significant interactions showing that fatigue-related increases in RMS and decreases in MPF were most prominent in the middle and top ranges. These findings suggest that muscle function is altered precisely within the ranges where impingement risk is greatest, providing a potential neuromuscular basis for fatigue-related injury. Building off of these results, future work should use advanced techniques like high-density EMG and shear wave elastography to define how these fatigue-related changes in neuromuscular responses influence motor unit recruitment strategies, muscle force production, and subsequent alterations to SAS and injury risk.
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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".