Chronic pain self-management support with pain science education and exercise (COMMENCE): study protocol for a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Previous research suggests that self-management programs for people with chronic pain improve knowledge and self-efficacy but result in negligible effects on function. This study will investigate the effectiveness self-management support with pain science education and exercise on improving function for people with chronic pain in comparison to a wait-list control. A secondary objective is to determine which variables help to predict response to the intervention. METHODS/DESIGN: This study will be an unblinded, randomized controlled trial with 110 participants comparing a 6-week program that includes self-management support, pain science education and exercise to a wait-list control. The primary outcome will be function measured by the Short Musculoskeletal Function Assessment - Dysfunction Index. Secondary outcomes will include pain intensity measured by a numeric pain rating scale, pain interference measured by the eight-item PROMIS pain interference item-bank, how much patients are bothered by functional problems measured by the Short Musculoskeletal Function Assessment - Bother Index, catastrophic thinking measured by the Pain Catastrophizing Scale, fear of movement/re-injury measured by the 11-item Tampa Scale of Kinesiophobia, sense of perceived injustice measured by the Injustice Experience Questionnaire, self-efficacy measured by the Pain Self-Efficacy Questionnaire, pain sensitivity measured by pressure pain threshold and cold sensitivity testing, fatigue measured by a numeric fatigue rating scale, pain neurophysiology knowledge measured by the Neurophysiology of Pain Questionnaire, healthcare utilization measured by number of visits to a healthcare provider, and work status. Assessments will be completed at baseline, 7 and 18 weeks. After the 18-week assessment, the groups will crossover; however, we anticipate carry-over effects with the treatment. Therefore, data from after the crossover will be used to estimate within-group changes and to determine predictors of response that are not for direct between-group comparisons. Mixed effects modelling will be used to determine between-group differences for all primary and secondary outcomes. A series of multiple regression models will be used to determine predictors of treatment response. DISCUSSION: This study has the potential to inform future self-management programming through evaluation of a self-management program that aims to improve function as the primary outcome. TRIAL REGISTRATION: ClinicalTrials.gov NCT02422459 , registered on 13 April 2015.
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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 | 0.033 | 0.030 |
| Meta-epidemiology (narrow) | 0.008 | 0.004 |
| Meta-epidemiology (broad) | 0.017 | 0.007 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.109 | 0.020 |
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".