Agile development of a digital exposure treatment for youth with chronic musculoskeletal pain: protocol of a user-centred design approach and examination of feasibility and preliminary efficacy
Bibliographic record
Abstract
INTRODUCTION: Chronic pain affects a significant number of children and impacts multiple domains including social, emotional and behavioural functioning, and negatively impacts family functioning. Roughly 5% of youth with chronic pain experience moderate to severe pain-related disability, with pain-related fear and avoidance of activities being identified as substantial barriers to treatment engagement. Evidence supports targeted psychological and physical interventions to address these barriers (eg, graded-exposure treatment), but accessibility to intervention is undermined by a shortage of services outside of urban areas, high treatment-related costs, and long provider waitlists; highlighting the need to develop digitally delivered behavioural intervention, using agile and iterative study designs that support rapid development and timely dissemination. METHODS AND ANALYSIS: This study seeks to develop an effective and scalable intervention for youth with chronic pain and their caregivers. This paper presents a user-centred protocol for the development and refinement of a digital exposure treatment for youth and caregivers, as well as the study design to examine feasibility and preliminary efficacy of the treatment using single-case experimental design (SCED). Assessments include daily diaries, completed from baseline and daily throughout the intervention (~6 weeks), and at 3-month follow-up, as well as self-report measures completed at baseline, end of intervention and 3-month follow-up. Primary outcomes include treatment satisfaction, treatment expectancy, adherence to daily dairies and functional disability. Secondary outcomes are pain-related fear and avoidance of activities, pain catastrophising and pain acceptance. We will present descriptive and model-based inference analyses, based on SCED reporting guidelines. We will calculate effect sizes for each individual on each outcome. We will examine mean treatment expectancy, credibility and satisfaction scores, and patient drop-out percentage. ETHICS AND DISSEMINATION: This study is approved by the Institutional Review Board at Stanford University (protocol #53323). Findings will be actively disseminated through peer-reviewed journals, conference presentations and social media. TRIAL REGISTRATION NUMBER: NCT05079984.
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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 | 0.037 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".