Outcome Expectations and Osteoarthritis: Association of Perceived Benefits of Exercise With Self‐Efficacy and Depression
Bibliographic record
Abstract
OBJECTIVE: Outcome expectancy is recognized as a determinant of exercise engagement and adherence. However, little is known about which factors influence outcome expectations for exercise among people with knee osteoarthritis (OA). This is the first study to examine the association of outcome expectations for exercise with demographic, physical, and psychosocial outcomes in individuals with knee OA. METHODS: We performed a cross-sectional analysis of the baseline data from a randomized trial of tai chi versus physical therapy in participants with symptomatic knee OA. Knee pain was evaluated using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). Outcome expectations for exercise, self-efficacy, depression, anxiety, stress, and social support were measured using standard instruments. Logistic regression models were utilized to determine associations with outcome expectations. RESULTS: ; 69.1% of the participants were female, 51.5% were white, the mean disease duration was 8.6 years, and the mean WOMAC knee pain and function scores were 260.8 and 906.8, respectively. Higher outcome expectations for exercise were associated with greater self-efficacy (odds ratio [OR] 1.25 [95% confidence interval (95% CI) 1.11-1.41]; P = 0.0004), as well as with fewer depressive symptoms (OR 0.84 for each 5-point increase [95% CI 0.73-0.97]; P = 0.01). Outcome expectancy was not significantly associated with sex, race, education, pain, function, radiographic severity, social support, anxiety, or stress. CONCLUSION: Our results suggest significant associations between outcome expectations for exercise and self-efficacy and depression. Future studies should examine how these relationships longitudinally affect long-term clinical outcomes of exercise-based treatment for knee OA.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".