Examination of the Relationship Between Pain Intensity, Pain Perceptions, and Kinesiophobia in Patients with Non-Specific Chronic Musculoskeletal Pain
Bibliographic record
Abstract
Chronic musculoskeletal pain negatively affects patients’ quality of life, and pain perceptions may significantly influence rehabilitation outcomes. This study investigated the relationships among pain intensity, pain perceptions, and kinesiophobia in individuals with chronic musculoskeletal pain. No previous studies have examined these variables in combination. A cross-sectional observational study was conducted with 37 participants with non-specific chronic musculoskeletal pain for at least 6 months, affecting the neck (n = 8), lower back (n = 18), upper limbs (n = 5), lower limbs (n = 5), or shoulder (n = 1). The following validated tools were used: (a) Pain Beliefs and Perceptions Inventory (PBPI), (b) the Tampa Scale for Kinesiophobia (TSK), and (c) the Short-Form McGill Pain Questionnaire (SF-MPQ). Spearman r correlation analyses were performed. Total kinesiophobia scores were positively correlated with (a) total pain intensity (McGill score) (r = 0.37, p = 0.022), (b) present pain intensity (PPI) (r = 0.52, p = 0.001), (c) pain duration (r = 0.51, p = 0.001), (d) the “mystery” factor of pain perception (r = 0.41, p = 0.013), and (e) the Visual Analogue Scale (VAS) (r = 0.42, p = 0.009). The total pain perception scores were positively associated with the “fear of injury” factor of kinesiophobia (r = 0.36, p = 0.028). The McGill pain scores were strongly correlated with both PPI (r = 0.63, p = 0.001) and VAS (r = 0.51, p = 0.001). There is a significant relationship between pain perception and kinesiophobia levels in patients with chronic musculoskeletal pain. Limitations of the study include a small and heterogeneous sample regarding pain localization. Further research is required using larger, more homogeneous populations to confirm the present findings.
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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".