Strength Training and Posture Correction of the Neck and Shoulder for Patients with Chronic Primary Headache: A Prospective Single-Arm Pilot Study
Bibliographic record
Abstract
Background: Few studies have examined exercise-based treatments for migraine and tension-type headache (TTH), and even fewer have focused on strength training and chronic headache, as these present greater challenges. Objectives: This study aimed to evaluate the effectiveness of a group-based neck and shoulder strength training intervention combined with postural correction for patients with chronic headache. Methods: This prospective, single-arm, uncontrolled pilot study with a pre–post design included patients with chronic migraine (n = 10) and TTH (n = 12) who participated in an 8-week group-based program consisting of neck and shoulder strength training three times per week, along with instructions for postural correction. The primary outcome was change in headache frequency. Secondary outcomes included changes in the intensity and duration of headache, number of days of analgesic use, and functionality. Results: In total, 22 patients completed the intervention and were included in the analysis. Headache frequency decreased at follow-up for the overall group (r = 0.531; p = 0.014). In-depth analysis showed that 45% of participants experienced an average reduction of 38% in headache frequency. Additionally, large to moderate effect sizes were observed for the secondary outcomes. Conclusions: This is the first study to introduce a group-based exercise program targeting the neck and shoulder muscles, combined with postural correction and standard pharmacological treatment, for patients with chronic primary headache. It was found to be a safe, well-tolerated, useful, and promising intervention for improving headache frequency, duration, and functionality.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".