Internet-Delivered Dialectical Behavioral Therapy Skills Training for Chronic Pain: Protocol for a Randomized Controlled Trial
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,030 | 0,026 |
| Méta-épidémiologie (sens strict) | 0,007 | 0,003 |
| Méta-épidémiologie (sens large) | 0,013 | 0,005 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,004 | 0,004 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,007 | 0,009 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,122 | 0,018 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».