Evaluating the Reliability of a Novel Neck-Strength Assessment Protocol for Healthy Adults Using Self-Generated Resistance with a Hand-Held Dynamometer
Bibliographic record
Abstract
PURPOSE: To evaluate the intra- and inter-session test-retest agreement of a novel neck-strength assessment protocol using a hand-held dynamometer. BACKGROUND: A literature review found a lack of neck-strength assessment protocols that are both portable and reliable. Hand-held dynamometry is a portable and inexpensive method of assessing muscle strength, but it is not commonly used for neck-strength assessment. METHODS: A hand-held dynamometer was used to evaluate neck strength in 30 healthy participants. The device measured maximum force in cervical flexion, extension, side flexion, side flexion with rotation, and pure rotation, using the ipsilateral hand to apply isometric resistance over 3 seconds. Three measurements were taken over 6-8 days. RESULTS: Test-retest intra-class correlation coefficients (ICCs) showed high reliability, ranging from 0.94 to 0.97 for all tested directions from Trial 1 to Trial 2 (intra-session reliability, ICC [2,1], absolute). ICC values demonstrated good to high inter-session reliability, ranging from 0.87 to 0.95 for all tested directions from Trial 1 to Trial 3 (ICC [2,1], absolute). CONCLUSION: The results suggest that the five test positions of the neck and upper-quadrant strength assessment protocol can be performed using hand-held dynamometry with good to high reliability.
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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 | 0.019 | 0.034 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".