Improving Adherence to Exercise: Do People With Knee Osteoarthritis and Physical Therapists Agree on the Behavioral Approaches Likely to Succeed?
Bibliographic record
Abstract
OBJECTIVE: To describe which behavior change techniques (BCTs) to promote adherence to exercise have been experienced by people with knee osteoarthritis (OA) or used by physical therapists, and to describe patient- and physical therapist-perceived effectiveness of a range of BCTs derived from behavioral theory. METHODS: Two versions of a custom-designed survey were administered in Australia and New Zealand, one completed by adults with symptomatic knee OA and the second by physical therapists who had treated people with knee OA in the past 6 months. Survey questions ascertained the frequency of receiving/prescribing exercise for knee OA, BCTs received/used targeting adherence to exercise, and perceived effectiveness of 36 BCTs to improve adherence to prescribed exercise. RESULTS: A total of 230 people with knee OA and 143 physical therapists completed the survey. Education about the benefits of exercise was the most commonly received/used technique by both groups. People with knee OA rated the perceived effectiveness of all BCTs significantly lower than the physical therapists (mean difference 1.9 [95% confidence interval 1.8-2.0]). When ranked by group mean agreement score, 2 BCTs were among the top 5 for both groups: development of specific goals related to knee pain and function; and review, supervision, and correction of exercise technique at subsequent treatment sessions. CONCLUSION: Goal-setting techniques related to outcomes were considered to be effective by both respondent groups, and testing of interventions incorporating these strategies should be a research priority.
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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 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".