Between-day reliability of common muscle fatigue measures during a repeated upper limb fatigue protocol
Bibliographic record
Abstract
Neuromuscular fatigue manifestation is of interest in both basic neurophysiological and applied (e.g. sporting, ergonomics) contexts. A unique challenge in fatigue research is that experimental sessions often need to be collected across many days to allow for adequate recovery. The purpose of this study was to examine the between-day reliability of several surface electromyography- and strength-based fatigue measures in response to a repeated fatigue protocol. Twenty participants (10 M, 10 F) performed an isometric elbow flexion fatigue protocol on three different days. The reliability of commonly used amplitude- and frequency-based myoelectric and performance-based indicators of fatigue were assessed using traditional reliability assessment methods. Baseline MVC strength (N) demonstrated excellent between-day reliability (ICCA, 1: 0.96, 95%CI[0.92, 0.98]) with good absolute reliability (SEM: 5.10%, MD95: 14.1%). The absolute reliability of all slope-based fatigue measures was low. However, %MVC Slope (ICC: 0.67, [49, .82]), %MnPF Slope (ICC: 0.75, [.60, .87]), and endurance time (ICC: 0.60, [0.39, 0.77]) had poor/moderate to good relative reliability. Baseline MVC strength was shown to be very repeatable between days. Caution is recommended when using slope-based fatigue measures for cyclic repetitive upper limb tasks, as slope-based measures of muscle fatigue were shown to have low between-session reliability.
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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".