Neck Pain and Disability: Are They Related to Dysfunctional Breathing and Stress?
Bibliographic record
Abstract
Purpose: People with neck pain are likely to have negative respiratory findings. The purpose of this study was to investigate the relationship between neck pain and dysfunctional breathing and to examine their relationship to stress. Method: This cross-sectional study included 49 participants with neck pain and 49 age- and sex-matched controls. We measured neck pain using the numeric rating scale (NRS); neck disability using the Neck Disability Index (NDI); dysfunctional breathing using the Nijmegen Questionnaire (NQ), Self-Evaluation of Breathing Questionnaire (SEBQ), breath hold time, and respiratory rate (RR); and stress using the Perceived Stress Scale (PSS). Results: Participants with neck pain scored higher on the NQ ( p < 0.01) and the SEBQ ( p < 0.01) than controls. NQ and SEBQ scores correlated moderately with NDI scores ( r > 0.50; 95% CI: 0.25, 0.68 and 0.33, 0.73, respectively) and PSS scores ( r > 0.50; 95% CI: 0.29, 0.78 and 0.31, 0.73, respectively). SEBQ scores showed a fair correlation with NRS scores and RR a fair correlation with NDI scores. Conclusions: Participants with neck pain had more dysfunctional breathing symptoms than participants without neck pain, and dysfunctional breathing was correlated with increased neck disability and increased stress. The NQ and SEBQ can be useful in assessing dysfunctional breathing in patients with neck pain.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".