Factors Associated with Balance Confidence in Middle-aged People with Osteoarthritis Knee: An Observational Study
Bibliographic record
Abstract
Background: Balance confidence plays a vital role in determining functional independence and fall risk in individuals with knee osteoarthritis (KOA). Understanding the factors associated with balance confidence in middle-aged individuals with KOA is essential for developing targeted rehabilitation strategies. Objective: The objective of the study was to assess the association between balance confidence and clinical variables such as pain-related disability, functional reach, lower limb strength, and physical activity in middle-aged individuals with KOA. Methods: This observational cross-sectional study included 65 participants (aged 40–65 years) with clinically diagnosed KOA, recruited from a tertiary care physiotherapy outpatient department. Balance confidence was measured using the activities-specific Balance Confidence Scale–Gujarati (ABC-G). Clinical variables included pain and functional disability (Western Ontario and McMaster Universities Osteoarthritis Index [WOMAC]), dynamic balance (functional reach test, [FRT]), lower limb strength (five times sit-to-stand test, [5TSTS]), and physical activity (IPAQ-SF-SF). Multiple linear regression analysis was used to determine the predictors of balance confidence. Results: The multiple linear regression showed that WOMAC and FRT scores predicted the balance confidence in patients with KOA. Specifically, higher WOMAC scores were associated with lower ABC-G scores ( B = −0.457, P = 0.021), indicating that greater disability correlates with decreased balance confidence. Conversely, FRT scores were strongly linked to ABC-G ( B = 2.180, P < 0.001), highlighting the importance of dynamic balance. IPAQ-SF and 5TSTS scores were not significantly related to ABC-G ( P = 0.087 and P = 0.083), respectively. Body mass index (BMI) also demonstrated a negative but nonsignificant association with ABC-G ( B = −0.482, P = 0.160), suggesting that higher BMI may reduce confidence in balance but does not independently predict it. Conclusion: Pain-related disability and impaired dynamic balance significantly predict reduced balance confidence in middle-aged individuals with KOA. Targeted rehabilitation focusing on pain control and postural stability may enhance confidence and reduce the risk of falls in this population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".