Time-varying moderation of treatment outcomes by illness duration and comorbid depression in generalized anxiety disorder

Time-varying effect modeling with intensive longitudinal data: Examining dynamic links among craving, affect, self-efficacy and substance use during addiction recovery

Time-varying effect modeling (TVEM), a statistical technique for modeling dynamic patterns of change, presents new opportunities to study biobehavioral health processes. TVEM is particularly useful when applied to intensive longitudinal data (ILD) because it permits highly flexible modeling of outcomes over continuous time, as well as of associations between variables and moderation effects. TVEM coupled with ILD is ideal for the study of addiction. This article provides a general overview of using TVEM, particularly when applied to ILD, to better enable addiction scientists to conduct novel analyses that are important to realizing the dynamics of addiction-related processes. It presents an empirical example using ecological momentary assessment data from participants throughout their first 90 days of addiction recovery to estimate the (1) associations between morning craving and same-day recovery outcomes, (2) association between morning positive and negative affect and same-day recovery outcomes and (3) time-varying moderation effects of affect on the association between morning craving and recovery outcomes. We provide a didactic overview in implementing and interpreting the aims and results, including equations, computer syntax and reference resources. Our results highlight how affect operates as both a time-varying risk and protective factor on recovery outcomes, particularly when considered in combination with experiences of craving (i.e. dynamic moderation). We conclude by discussing our results, recent innovations and future directions of TVEM for advancing addiction science, including how 'time' can be operationalized to probe new research questions.

A Precision Treatment Model for Internet-Delivered Cognitive Behavioral Therapy for Anxiety and Depression Among University Students: A Secondary Analysis of a Randomized Clinical Trial

Importance

Guided internet-delivered cognitive behavioral therapy (i-CBT) is a low-cost way to address high unmet need for anxiety and depression treatment. Scalability could be increased if some patients were helped as much by self-guided i-CBT as guided i-CBT.

Objective

To develop an individualized treatment rule using machine learning methods for guided i-CBT vs self-guided i-CBT based on a rich set of baseline predictors.

Design, Setting, and Participants

This prespecified secondary analysis of an assessor-blinded, multisite randomized clinical trial of guided i-CBT, self-guided i-CBT, and treatment as usual included students in Colombia and Mexico who were seeking treatment for anxiety (defined as a 7-item Generalized Anxiety Disorder [GAD-7] score of ≥10) and/or depression (defined as a 9-item Patient Health Questionnaire [PHQ-9] score of ≥10). Study recruitment was from March 1 to October 26, 2021. Initial data analysis was conducted from May 23 to October 26, 2022.

Interventions

Participants were randomized to a culturally adapted transdiagnostic i-CBT that was guided (n = 445), self-guided (n = 439), or treatment as usual (n = 435).

Main Outcomes and Measures

Remission of anxiety (GAD-7 scores of ≤4) and depression (PHQ-9 scores of ≤4) 3 months after baseline.

Results

The study included 1319 participants (mean [SD] age, 21.4 [3.2] years; 1038 women [78.7%]; 725 participants [55.0%] came from Mexico). A total of 1210 participants (91.7%) had significantly higher mean (SE) probabilities of joint remission of anxiety and depression with guided i-CBT (51.8% [3.0%]) than with self-guided i-CBT (37.8% [3.0%]; P = .003) or treatment as usual (40.0% [2.7%]; P = .001). The remaining 109 participants (8.3%) had low mean (SE) probabilities of joint remission of anxiety and depression across all groups (guided i-CBT: 24.5% [9.1%]; P = .007; self-guided i-CBT: 25.4% [8.8%]; P = .004; treatment as usual: 31.0% [9.4%]; P = .001). All participants with baseline anxiety had nonsignificantly higher mean (SE) probabilities of anxiety remission with guided i-CBT (62.7% [5.9%]) than the other 2 groups (self-guided i-CBT: 50.2% [6.2%]; P = .14; treatment as usual: 53.0% [6.0%]; P = .25). A total of 841 of 1177 participants (71.5%) with baseline depression had significantly higher mean (SE) probabilities of depression remission with guided i-CBT (61.5% [3.6%]) than the other 2 groups (self-guided i-CBT: 44.3% [3.7%]; P = .001; treatment as usual: 41.8% [3.2%]; P < .001). The other 336 participants (28.5%) with baseline depression had nonsignificantly higher mean (SE) probabilities of depression remission with self-guided i-CBT (54.4% [6.0%]) than guided i-CBT (39.8% [5.4%]; P = .07).

Conclusions and Relevance

Guided i-CBT yielded the highest probabilities of remission of anxiety and depression for most participants; however, these differences were nonsignificant for anxiety. Some participants had the highest probabilities of remission of depression with self-guided i-CBT. Information about this variation could be used to optimize allocation of guided and self-guided i-CBT in resource-constrained settings.

Trial Registration

ClinicalTrials.gov Identifier: NCT04780542.

Psychotherapeutic treatments for generalized anxiety disorder: cognitive and behavioral therapies, enhancement strategies, and emerging efforts

INTRODUCTION

Generalized anxiety disorder (GAD) is common and disabling. Different versions of cognitive behavioral therapy (CBT) have been tested, but no treatment works for everyone. Therefore, researchers have attempted approaches to enhance CBT.

AREAS COVERED

The current narrative review examines meta-analyses and individual trials of CBT-based treatments for GAD. We focus on CBT and its cognitive and behavioral components as well as efforts to enhance CBT and its dissemination and generalizability. Enhancement efforts included interpersonal and emotional processing therapy, mindfulness-based CBT, emotion regulation therapy, intolerance of uncertainty therapy, the unified protocol, metacognitive therapy, motivational interviewing, and contrast avoidance targeted treatment. Emerging strategies to enhance dissemination have focused on technologically based treatments. Attempts at generalizability have included examination of efficacy within diverse racial and ethnic groups.

EXPERT OPINION

We conclude that CBT is efficacious, and a number of enhancement efforts have shown some promise in improving upon CBT in single trials. However, more research is needed, particularly efforts to determine which enhancements work best for which individuals and what are the mechanisms of change. Furthermore, few technological interventions have been compared to active treatments. Finally, much more attention needs to be paid to ethnic and racial diversity in randomized controlled trials.

Barriers to Building More Effective Treatments: Negative Interactions Amongst Smoking Intervention Components

Meaningfully improved mental and behavioral health treatment is an unrealized dream. Across three factorial experiments, inferential tests in prior studies showed a pattern of negative interactions suggesting that better clinical outcomes are obtained when participants receive fewer rather than more intervention components. Further, relatively few significant main effects were found in these experiments. Modeling suggested that negative interactions amongst components may account for these patterns. This paper evaluates factors that may contribute to such declining benefit: increased attentional or effort burden; components that produce their effects via the same capacity limited mechanisms, making their effects subadditive; and a tipping point phenomenon in which those near a hypothesized "tipping point" for change will benefit markedly from weak intervention while those far from the tipping point will benefit little from even strong intervention. New research should explore factors that cause negative interactions amongst components and constrain the development of more effective treatments.