Using Intervention Mapping in the Systematic Development of a Behaviour Change Intervention to Enhance Exercise Adherence among People with Persistent Musculoskeletal Pain
Bibliographic record
Abstract
Purpose: This article describes the first four steps of the intervention mapping framework used to design a programme aimed at increasing adherence to prescribed exercise by people with persistent musculoskeletal pain. Method: In Step 1, a systematic review and qualitative study was completed to inform Step 2 and the identification of the Health Action Process Approach as an appropriate theoretical framework for establishing two programme objectives: enhancing self-management and providing tailored and accessible exercise instructions. Step 3 encompassed the selection of the programme methods, and the programme is described in Step 4. The resulting programme provides virtually delivered motivational interviewing and an app-based exercise programme to support individuals’ adherence to exercise. Results: The resulting intervention was assessed in a proof-of-concept feasibility and acceptability study and was shown to be feasible and acceptable. Refinements to the programme included additional tailoring of the exercise app and modifying the motivational interviewing schedule. Conclusions: Using the intervention mapping approach enabled us to successfully develop an intervention aimed at supporting the development of self-management behaviours and addressing maladaptive beliefs as a means of enhancing individuals’ adherence to exercise. Evaluation and implementation of the intervention should now be carried out.
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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 | 0.123 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".