Effects of Strength Training on Neck Muscle Function and Tenderness in Patients with Chronic Headache: A Secondary Analysis of a Clinical Trial
Bibliographic record
Abstract
Background/Objectives: This study presents a secondary analysis from a previously published trial on strength training and postural correction in chronic headache patients. Here, we investigate changes in neck muscle function and tenderness, and their relationship with headache symptoms. Methods: A total of 22 headache patients from a single-arm open-label trial were included in this study to assess muscle function and tenderness. The maximum voluntary contraction of neck flexion and extension, shoulder elevation, and craniocervical flexion test were performed at baseline, week eight, and week 14. The extension/flexion ratio of the neck, the rate of force development, and the early rate of force development for shoulder elevation were calculated. Muscle tenderness was analyzed using the total tenderness score (TTS) and correlations between these outcomes and headache changes were explored. Results: After the intervention muscle tenderness significantly decreased (−5.6 ± 6.4; p < 0.001) and significant improvements in muscle function were observed. Correlations of muscle function showed a significant and moderate correlation between TTS and extension/flexion ratio (Spearman rho: 0.567, p = 0.014). Conclusions: The results indicate that strength training and postural correction improve muscle function and reduce pericranial tenderness in patients with chronic headaches. These findings suggest that muscle tenderness and extension/flexion ratio may be useful for monitoring exercise interventions focused on improving the strength and balance of the neck in patients with chronic headaches.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".