Demographic and disease characteristics associated with pain intensity, kinesiophobia, balance, and fall self-efficacy among people with osteoarthritis: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Osteoarthritis (OA) is a common degenerative joint disease leading to significant pain, mobility limitation, economic burden, reduced quality of life, and disability among adults globally. Psychological factors related to pain intensity (PI), kinesiophobia, fall self-efficacy (FSE), and balance may lead to a poor OA prognosis. This study was designed to explore the association between PI, kinesiophobia, FSE, balance, and age, gender, marital status, site of OA, duration, symmetry, comorbidity, and adaptive behaviours among patients with knee or hip OA. METHODS: This cross-sectional study involved 70 purposively selected participants aged 59.91 ± 11.12 years. Numeric pain rating scale, Tampa scale for kinesiophobia, fall-efficacy scale, and timed up and go test were used to measure PI, kinesiophobia, FSE, and balance, respectively. Statistical analyses were completed with the Pearson correlation test, independent samples t-test, and multiple linear regression. RESULTS: The participants were mainly women (n = 59, 84.3%). However, there was no gender difference in the reported PI, kinesiophobia, FSE, and balance. There was a significant correlation between FSE and balance (r = 0.422, p<0.001). Kinesiophobia was significantly associated with the presence of comorbidity (β = 0.240, p = 0.001) and knee OA (β = 0.208, p<0.042). There was an association between FSE and the use of a walking aid (β = -0.442, p<0.042), stop-for-rest during walking (β = -0.292, p = 0.002), presence of comorbidity (β = 0.209, p = 0.014), and bilateral lower limb OA (β = 0.167, p = 0.057). Balance was associated with the use of a walking aid (β = -0.421, p<0.001) and stop-for-rest during walking (β = - 0.294, p = 0.006). CONCLUSION: Osteoarthritis-related psychological distress affects both men and women. This study support integration of psychological outcomes in the assessment, management, and follow-up of people with lower limb osteoarthritis. Moreover, comorbidity worsened psychological distress among people with osteoarthritis. Therefore, the traditional biomedical management of osteoarthritis can be optimised by timely diagnosis and treatment of comorbidities, and the inclusion of psychotherapy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".