Internet-Delivered Dialectical Behavioral Therapy Skills Training for Chronic Pain: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Emotion dysregulation is key to the development and maintenance of chronic pain, feeding into a cycle of worsening pain and disability. Dialectical behavioral therapy (DBT), an evidence-based treatment for complex transdiagnostic conditions presenting with high emotion dysregulation, may be beneficial to manage and mitigate the emotional and sensory aspects of chronic pain. Increasingly, DBT skills training as a key component of standard DBT is being delivered as a stand-alone intervention without concurrent therapy to help develop skills for effective emotion regulation. A previous repeated-measure single-case trial investigating a novel technologically driven DBT skills training, internet-delivered DBT skills training for chronic pain (iDBT-Pain), revealed promising findings to improve both emotion dysregulation and pain intensity. OBJECTIVE: This randomized controlled trial aims to examine the efficacy of iDBT-Pain in comparison with treatment as usual to reduce emotion dysregulation (primary outcome) for individuals with chronic pain after 9 weeks and at the 21-week follow-up. The secondary outcomes include pain intensity, pain interference, anxiety symptoms, depressive symptoms, perceived stress, posttraumatic stress, harm avoidance, social cognition, sleep quality, life satisfaction, and well-being. The trial also examines the acceptability of the iDBT-Pain intervention for future development and testing. METHODS: A total of 48 people with chronic pain will be randomly assigned to 1 of 2 conditions: treatment and treatment as usual. Participants in the treatment condition will receive iDBT-Pain, consisting of 6 live web-based group sessions led by a DBT skills trainer and supervised by a registered psychologist and the iDBT-Pain app. Participants in the treatment-as-usual condition will not receive iDBT-Pain but will still access their usual medication and health interventions. We predict that iDBT-Pain will improve the primary outcome of emotion dysregulation and the secondary outcomes of pain intensity, pain interference, anxiety symptoms, depressive symptoms, perceived stress, harm avoidance, social cognition, sleep quality, life satisfaction, and well-being. A linear mixed model with random effects of individuals will be conducted to investigate the differences between the baseline, 9-week (primary end point), and 21-week (follow-up) assessments as a function of experimental condition. RESULTS: Recruitment started in February 2023, and the clinical trial started in March 2023. Data collection for the final assessment is planned to be completed by July 2024. CONCLUSIONS: If our hypothesis is confirmed, our findings will contribute to the evidence for the efficacy and acceptability of a viable intervention that may be used by health care professionals for people with chronic pain. The results will add to the chronic pain literature to inform about the potential benefits of DBT skills training for chronic pain and will contribute evidence about technologically driven interventions. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12622000113752; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=383208&isReview=true. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/41890.
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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 | 0.030 | 0.026 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.005 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.122 | 0.018 |
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".