Internet-Delivered Dialectical Behavioral Therapy Skills Training for Chronic Pain: Protocol for a Randomized Controlled Trial.

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.

Disparate impacts on online information access during the Covid-19 pandemic.

The COVID-19 pandemic has stimulated important changes in online information access as digital engagement became necessary to meet the demand for health, economic, and educational resources. Our analysis of 55 billion everyday web search interactions during the pandemic across 25,150 US ZIP codes reveals that the extent to which different communities of internet users enlist digital resources varies based on socioeconomic and environmental factors. For example, we find that ZIP codes with lower income intensified their access to health information to a smaller extent than ZIP codes with higher income. We show that ZIP codes with higher proportions of Black or Hispanic residents intensified their access to unemployment resources to a greater extent, while revealing patterns of unemployment site visits unseen by the claims data. Such differences frame important questions on the relationship between differential information search behaviors and the downstream real-world implications on more and less advantaged populations.

The No Worries Trial: Efficacy of Online Dialectical Behaviour Therapy Skills Training for Chronic Pain (iDBT-Pain) Using a Single Case Experimental Design.

Emotion dysregulation frequently co-occurs with chronic pain, which in turn leads to heightened emotional and physical suffering. This cycle of association has prompted a recommendation for psychological treatment of chronic pain to target mechanisms for emotion regulation. The current trial addressed this need by investigating a new internet-delivered treatment incorporating emotional skills training from dialectical behavioral therapy (DBT). Using a single-case experimental design that is suited to heterogeneous populations and can demonstrate efficacy with a small sample, three participants with chronic pain were recruited. Participants received four weeks of online DBT skills training (iDBT-Pain intervention) which incorporated one-on-one sessions over Zoom and a web app. Results revealed compelling evidence for the intervention on the primary outcome of emotion dysregulation and were promising for the secondary outcome of pain intensity. Improvement was also identified on pre-and post-measures of depression, coping behaviors, sleep problems, wellbeing, and harm avoidance, indicating that the intervention may positively influence other factors related to chronic pain. Overall, the trial provides preliminary efficacy for the intervention to improve chronic pain. However, we recommend further investigation of the iDBT-Pain intervention, either in single case trials, which when conducted with scientific rigor may be aggregated to derive nomothetic conclusions, or in a group-comparison trial to compare with usual modes of treatment. PERSPECTIVE: This trial advances understanding of emotion-focused treatment for chronic pain and provides evidence for a viable new technological treatment. Importantly, as an internet-delivered approach, the iDBT-Pain intervention is accessible to those with restricted mobility and remote communities where there are often limited psychological services for people with chronic pain.

Efficacy of the iDBT-Pain skills training intervention to reduce emotional dysregulation and pain intensity in people with chronic pain: protocol for a single-case experimental design with multiple baselines.

INTRODUCTION: Difficulties in emotional regulation are key to the development and maintenance of chronic pain. Recent evidence shows internet-delivered dialectic behaviour therapy (iDBT) skills training can reduce emotional dysregulation and pain intensity. However, further studies are needed to provide more definitive evidence regarding the efficacy of iDBT skills training in the chronic pain population. METHODS AND ANALYSIS: A single-case experimental design (SCED) with multiple baselines will be used to examine the efficacy of a 4-week iDBT-Pain skills training intervention (iDBT-Pain intervention) to reduce emotional dysregulation and pain intensity in individuals with chronic pain. The iDBT-Pain intervention encompasses two components: (1) iDBT-Pain skills training sessions (iDBT-Pain sessions) and (2) the iDBT-Pain skills training web application (iDBT-Pain app). Three individuals with chronic pain will be recruited and randomly allocated to different baseline phases (5, 9 or 12 days). Following the baseline phase, participants will receive six 60-90 min iDBT-Pain sessions approximately 4 or 5 days apart, delivered by a psychologist via Zoom. To reinforce learnings from the iDBT-Pain sessions, participants will have unlimited use of the iDBT-Pain app. A 7-day follow-up phase (maintenance) will follow the intervention, whereby the iDBT-Pain sessions cease but the iDBT-Pain app is accessible. Emotional regulation, as the primary outcome measure, will be assessed using the Difficulties in Emotion Regulation Scale. Pain intensity, as the secondary outcome measure, will be assessed using a visual analogue scale. Generalisation measures will assess psychological state factors (depression, anxiety and coping behaviour), alongside sleep quality, well-being and harm avoidance. SCEDs are increasingly considered effective designs for internet-delivered psychological interventions because SCED enables the investigation of interindividual variability in a heterogeneous population such as chronic pain. ETHICS AND DISSEMINATION: This trial was approved by the University of New South Wales (HC200199). Results will be published in peer-reviewed journals. TRIAL REGISTRATION NUMBER: ACTRN12620000604909.

Parallel Journeys of Patients with Cancer and Depression: Challenges and Opportunities for Technology-Enabled Collaborative Care.

Depression is common but under-treated in patients with cancer, despite being a major modifiable contributor to morbidity and early mortality. Integrating psychosocial care into cancer services through the team-based Collaborative Care Management (CoCM) model has been proven to be effective in improving patient outcomes in cancer centers. However, there is currently a gap in understanding the challenges that patients and their care team encounter in managing co-morbid cancer and depression in integrated psycho-oncology care settings. Our formative study examines the challenges and needs of CoCM in cancer settings with perspectives from patients, care managers, oncologists, psychiatrists, and administrators, with a focus on technology opportunities to support CoCM. We find that: (1) patients with co-morbid cancer and depression struggle to navigate between their cancer and psychosocial care journeys, and (2) conceptualizing co-morbidities as separate and independent care journeys is insufficient for characterizing this complex care context. We then propose the parallel journeys framework as a conceptual design framework for characterizing challenges that patients and their care team encounter when cancer and psychosocial care journeys interact. We use the challenges discovered through the lens of this framework to highlight and prioritize technology design opportunities for supporting whole-person care for patients with co-morbid cancer and depression.

Data-Driven Implications for Translating Evidence-Based Psychotherapies into Technology-Delivered Interventions.

Mobile mental health interventions have the potential to reduce barriers and increase engagement in psychotherapy. However, most current tools fail to meet evidence-based principles. In this paper, we describe data-driven design implications for translating evidence-based interventions into mobile apps. To develop these design implications, we analyzed data from a month-long field study of an app designed to support dialectical behavioral therapy, a psychotherapy that aims to teach concrete coping skills to help people better manage their mental health. We investigated whether particular skills are more or less effective in reducing distress or emotional intensity. We also characterized how an individual's disorders, characteristics, and preferences may correlate with skill effectiveness, as well as how skill-level improvements correlate with study-wide changes in depressive symptoms. We then developed a model to predict skill effectiveness. Based on our findings, we present design implications that emphasize the importance of considering different environmental, emotional, and personal contexts. Finally, we discuss promising future opportunities for mobile apps to better support evidence-based psychotherapies, including using machine learning algorithms to develop personalized and context-aware skill recommendations.

Squares: Supporting Interactive Performance Analysis for Multiclass Classifiers.

Performance analysis is critical in applied machine learning because it influences the models practitioners produce. Current performance analysis tools suffer from issues including obscuring important characteristics of model behavior and dissociating performance from data. In this work, we present Squares, a performance visualization for multiclass classification problems. Squares supports estimating common performance metrics while displaying instance-level distribution information necessary for helping practitioners prioritize efforts and access data. Our controlled study shows that practitioners can assess performance significantly faster and more accurately with Squares than a confusion matrix, a common performance analysis tool in machine learning.