Building motivation and sustainability into the prescription and recommendations for physical activity and exercise therapy: The evidence
Bibliographic record
Abstract
Client adherence to exercises prescribed by a physical therapist is very important to successful treatment outcomes. Unfortunately, many clients struggle with adherence and thus efficient and effective motivational interventions are desirable. The purpose of this article was to review the available evidence for: 1) the modifiable factors associated with adherence to physical therapy recommended exercise and 2) the efficacy of exercise intervention efforts to make conclusions and suggestions toward practice. Articles were limited to English peer-reviewed journals and published from 1993 to 2008. Major findings from 13 studies were summarized based on common subtopics of: outcome expectations, self-efficacy expectations, cognitive-behavioural and educational interventions, and intervention medium. The review provided evidence for the importance of self-efficacy in exercise adherence to physical therapy, but it showed that current cognitive-behavioural interventions have had limited effectiveness. It was recommended that future research broaden the scope of predictor variables with social ecological designs, increase the length of prospective follow-up in assessments, include larger and more diverse samples, and focus on innovative aesthetic and affective-based intervention strategies.
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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".