A Multi-Faceted Approach to Understanding the Effects of Fatigue on Muscle and Kinematic Variability
Bibliographic record
Abstract
An in vivo human model of muscular and kinematic indeterminacy, the shoulder joint offers potential insight on how the central nervous system optimizes several competing biophysical variables. Muscle fatigue is a condition that impacts several such biophysical variables and can instigate load-sharing; centrally mediated muscle control changes utilized to manage several optimality variables like effort, metabolic cost, and joint load. However, considerable variability in fatigue-mediated shoulder kinematic and muscle activity changes has challenged the ability to make clear inferences on fatigue-mediated shoulder control changes. The goal of this dissertation was to quantify fatigue-mediated shoulder control changes from a multifaceted perspective, in hopes that comprehensive analyses and novel methodologies would further our understanding of the mechanisms that drive centrally mediated shoulder control. Of specific interest was the intent to identify factors which may explain some of the fatigue-mediated shoulder variability that remains unclear. Chapter 3 of this dissertation used coactivation ratios of the scapular stabilizers to describe muscle control changes following a shoulder fatigue task. Shoulder kinematic and coactivation responses to fatigue were variable, yet results indicated that 20-40% of individuals may increase their risk of subacromial impingement syndrome due to fatigue. Chapter 4 of this dissertation investigated how individuals adapt their fatigue-mediated muscular and kinematic responses when completing the same fatigue task from chapter 3 a second time. Participants demonstrated more aggregate kinematics on day 2, while consolidating a more mechanically efficient posture that may minimize serratus anterior fatigue exposure. Chapter 5 harnessed an optimal control biomechanical shoulder model to predict shoulder kinematics changes associated with isolated scapular stabilizer muscle weakness. This study identified a key role of serratus anterior for stabilizing arm elevation, and identified shoulder muscle synergies which may compensate for serratus anterior weakness. Chapter 6 sought to identify how fatigue-mediated changes in muscle elastic modulus may affect muscle control strategies. However, muscle stiffness appeared to be sensitive to the type of exercise/fatigue stimulus which was unforeseen and may be an important consideration for fatigue-related variability. This dissertation concludes by summarizing the integrated findings of chapters 3-6 and proposing new considerations for fatigue-mediated shoulder muscle control.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".