The Relationship Between Neck Pain and Physical Activity
Bibliographic record
Abstract
Neck pain is a significant societal burden due to its high prevalence and healthcare costs. While physical activity can help to manage other forms of chronic musculoskeletal pain, little data exists on the relationship between physical activity and neck pain. The purpose of this study was to compare physical activity levels between individuals with neck pain and healthy controls, and then to relate disability, fear of movement, and pain sensitivity measures to physical activity levels in each of the two participant groups. 21 participants were recruited for each of the two participant groups (n = 42). Data collection included the use of the Neck Disability Index, the Tampa Scale for Kinesiophobia, electrocutaneous (Neurometer® CPT) and pressure stimulation (JTech algometer) for quantitative sensory testing, and 5 days of subjective (Rapid Assessment of Physical Activity) and objective (BioTrainer II) measurements of physical activity. Analysis of Variance and Pearson's Correlation were used to determine if differences and relationships exist between dependent variables both within and between groups. The results show that individuals with mild neck pain and healthy controls do not differ in subjectively and objectively measured physical activity. While participants with neck pain reported higher neck disability and fear of movement, these factors did not significantly relate to physical activity levels. Perceived activity level was related to pain threshold and tolerance at local neck muscles sites (C2 paraspinal muscle and upper trapezius muscle), whereas measured activity was related to generalized pain sensitivity, as measured at the tibialis anterior muscle site.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".