Retest Reliability of Force-Time Variables of Neck Muscles Under Isometric Conditions
Bibliographic record
Abstract
CONTEXT: Proper conditioning of the neck muscles may play a role in reducing the risk of neck injury and, possibly, concussions in contact sports. However, the ability to reliably measure the force-time-based variables that might be relevant for this purpose has not been addressed. OBJECTIVE: To assess the between-days reliability of discrete force-time-based variables of neck muscles during maximal voluntary isometric contractions in 5 directions. DESIGN: Cohort study. SETTING: University research center. PATIENTS OR OTHER PARTICIPANTS: Twenty-six highly physically active men (age = 21.6 ± 2.1 years, height = 1.85 ± 0.09 m, mass = 81.6 ± 9.9 kg, head circumference = 0.58 ± 0.01 m, neck circumference = 0.39 ± 0.02 m). INTERVENTION(S): We used a custom-built testing apparatus to measure maximal voluntary isometric contractions of the neck muscles in 5 directions (extension, flexion, protraction, left lateral bending, and right lateral bending) on 2 separate occasions separated by 7 to 8 days. MAIN OUTCOME MEASURE(S): Variables measured were peak force (PF), rate of force development (RFD), and time to 50% of PF (T(50)PF). Reliability indices calculated for each variable comprised the difference in scores between the testing sessions, with corresponding 95% confidence intervals, the coefficient of variation of the typical error of measurement (CV(TE)), and intraclass correlation coefficients (ICC [3,3]). RESULTS: No evidence of systematic bias was detected for the dependent measures across any movement direction; retest differences in measurements were between 1.8% and 2.7%, with corresponding 95% confidence interval ranges of less than 10% and overlapping zero. The CV(TE) was lowest for PF (range, 2.4%-6.3%) across all testing directions, followed by RFD (range, 4.8%-9.0%) and T(50)PF (range, 7.1%-9.3%). The ICC score range for all dependent measures was 0.90 to 0.99. CONCLUSIONS: Discrete variables representative of the force-generating capacity of neck muscles under isometric conditions can be measured with an acceptable degree of reliability. This finding has possible applications for investigating the role of neck muscle strength-training programs in reducing the risk of injuries in sport settings.
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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".