Interpretation bias modification for hostility to facilitate smoking cessation in a sample with elevated trait anger: A randomized trial.

Problematic anger is linked with multiple adverse smoking outcomes, including cigarette dependence, heavy smoking, and cessation failure. A smoking cessation intervention that directly targets anger and its maintenance factors may increase rates of smoking cessation. We examined the efficacy of an interpretation bias modification for hostility (IBM-H) to facilitate smoking cessation in smokers with elevated trait anger. Participants were 100 daily smokers (mean age = 38, 62% female, 55% white) with elevated anger were randomly assigned to eight computerized sessions of either IBM-H or a health and relaxation video control condition (HRVC). Participants in both conditions attempted to quit at mid-treatment. Measures of hostility, anger, and smoking were administered at pre-, mid-, post-treatment, as well as at up to three-month follow-up. Compared to HRVC, IBM-H led to greater reductions in hostile interpretation bias, both at posttreatment and follow-up. IBM-H also led to statistically significant reductions in hostility only at posttreatment, and trait anger only at three-month follow-up. Both conditions experienced reductions in smoking, although they did not differ in quit success. We discuss these findings in the context of literature on anger and smoking cessation and provide directions for future research.

Natural language processing for mental health interventions: a systematic review and research framework.

Neuropsychiatric disorders pose a high societal cost, but their treatment is hindered by lack of objective outcomes and fidelity metrics. AI technologies and specifically Natural Language Processing (NLP) have emerged as tools to study mental health interventions (MHI) at the level of their constituent conversations. However, NLP's potential to address clinical and research challenges remains unclear. We therefore conducted a pre-registered systematic review of NLP-MHI studies using PRISMA guidelines (osf.io/s52jh) to evaluate their models, clinical applications, and to identify biases and gaps. Candidate studies (n = 19,756), including peer-reviewed AI conference manuscripts, were collected up to January 2023 through PubMed, PsycINFO, Scopus, Google Scholar, and ArXiv. A total of 102 articles were included to investigate their computational characteristics (NLP algorithms, audio features, machine learning pipelines, outcome metrics), clinical characteristics (clinical ground truths, study samples, clinical focus), and limitations. Results indicate a rapid growth of NLP MHI studies since 2019, characterized by increased sample sizes and use of large language models. Digital health platforms were the largest providers of MHI data. Ground truth for supervised learning models was based on clinician ratings (n = 31), patient self-report (n = 29) and annotations by raters (n = 26). Text-based features contributed more to model accuracy than audio markers. Patients' clinical presentation (n = 34), response to intervention (n = 11), intervention monitoring (n = 20), providers' characteristics (n = 12), relational dynamics (n = 14), and data preparation (n = 4) were commonly investigated clinical categories. Limitations of reviewed studies included lack of linguistic diversity, limited reproducibility, and population bias. A research framework is developed and validated (NLPxMHI) to assist computational and clinical researchers in addressing the remaining gaps in applying NLP to MHI, with the goal of improving clinical utility, data access, and fairness.

Prevalence and correlates of cannabis use among individuals with DSM-5 social anxiety disorder: Findings from a nationally representative sample.

Cannabis use disorder (CUD) and frequency of use are highly related to social anxiety disorder (SAD). With updates to diagnostic criteria of psychiatric disorders and recent changes in cannabis laws, the present study sought to explore the relationships between cannabis use, CUD, and social anxiety in a large nationally representative sample of individuals with lifetime (N = 1255) and past-year SAD (N = 980). Notably, we found that at the symptom level, at least weekly cannabis use was significantly related to fear or avoidance of social situations interfering with relationships in both samples. Weekly + cannabis use and CUD were significantly associated with lifetime SAD symptom severity, but only weekly + cannabis use was related to SAD severity in the past-year sample. We also found that weekly + cannabis use but not CUD was related to greater odds of seeking treatment for SAD and suicide attempt history. Overall, these data provide an updated examination of cannabis use and SAD using DSM-5 criteria and a large nationally representative sample and also highlight the importance of weekly + cannabis use as a marker of severity and suicide risk in individuals with SAD.

Effect of cognitive-behavioral techniques for problem gambling and gambling disorder: A systematic review and meta-analysis.

AIMS: To measure the effect of cognitive-behavioral techniques (CBTs) on gambling disorder severity and gambling behavior at post-treatment and follow-up. METHOD: Seven databases and two clinical trial registries were searched to identify peer-reviewed studies and unpublished studies of randomized controlled trials. The Cochrane Risk of Bias tool assessed risk of bias in the included studies. A random effect meta-analysis with robust variance estimation was conducted to measure the effect of CBTs relative to minimally treated or no treatment control groups. RESULTS: Twenty-nine studies representing 3991 participants were identified. CBTs significantly reduced gambling disorder severity (g = -1.14, 95% CI = -1.68, -0.60, 95% prediction interval [PI] = -2.97, 0.69), gambling frequency (g = -0.54, 95% CI = -0.80, -0.27, 95% PI = -1.48, 0.40) and gambling intensity (g = -0.32, 95% CI = -0.51, -0.13, 95% PI = -0.76, 0.12) at post-treatment relative to control. CBTs had no significant effect on follow-up outcomes. Analyses supported the presence of publication bias and high heterogeneity in effect size estimates. CONCLUSIONS: Cognitive-behavioral techniques are a promising treatment for reducing gambling disorder and gambling behavior; however, the effect of cognitive-behavioral techniques on gambling disorder severity and gambling frequency and intensity at post-treatment is overestimated, and cognitive-behavioral techniques may not be reliably efficacious for all individuals seeking treatment for problem gambling and gambling disorder.

An Integrative Engagement Model of Digital Psychotherapy: Exploratory Focus Group Findings.

BACKGROUND: Digital mental health interventions, such as 2-way and asynchronous messaging therapy, are a growing part of the mental health care treatment ecosystem, yet little is known about how users engage with these interventions over the course of their treatment journeys. User engagement, or client behaviors and therapeutic relationships that facilitate positive treatment outcomes, is a necessary condition for the effectiveness of any digital treatment. Developing a better understanding of the factors that impact user engagement can impact the overall effectiveness of digital psychotherapy. Mapping the user experience in digital therapy may be facilitated by integrating theories from several fields. Specifically, health science's Health Action Process Approach and human-computer interaction's Lived Informatics Model may be usefully synthesized with relational constructs from psychotherapy process-outcome research to identify the determinants of engagement in digital messaging therapy. OBJECTIVE: This study aims to capture insights into digital therapy users' engagement patterns through a qualitative analysis of focus group sessions. We aimed to synthesize emergent intrapersonal and relational determinants of engagement into an integrative framework of engagement in digital therapy. METHODS: A total of 24 focus group participants were recruited to participate in 1 of 5 synchronous focus group sessions held between October and November 2021. Participant responses were coded by 2 researchers using thematic analysis. RESULTS: Coders identified 10 relevant constructs and 24 subconstructs that can collectively account for users' engagement and experience trajectories in the context of digital therapy. Although users' engagement trajectories in digital therapy varied widely, they were principally informed by intrapsychic factors (eg, self-efficacy and outcome expectancy), interpersonal factors (eg, the therapeutic alliance and its rupture), and external factors (eg, treatment costs and social support). These constructs were organized into a proposed Integrative Engagement Model of Digital Psychotherapy. Notably, every participant in the focus groups indicated that their ability to connect with their therapist was among the most important factors that were considered in continuing or terminating treatment. CONCLUSIONS: Engagement in messaging therapy may be usefully approached through an interdisciplinary lens, linking constructs from health science, human-computer interaction studies, and clinical science in an integrative engagement framework. Taken together, our results suggest that users may not view the digital psychotherapy platform itself as a treatment so much as a means of gaining access to a helping provider, that is, users did not see themselves as engaging with a platform but instead viewed their experience as a healing relationship. The findings of this study suggest that a better understanding of user engagement is crucial for enhancing the effectiveness of digital mental health interventions, and future research should continue to explore the underlying factors that contribute to engagement in digital mental health interventions. TRIAL REGISTRATION: ClinicalTrials.gov NCT04507360; https://clinicaltrials.gov/ct2/show/NCT04507360.

Effects of cognitive behavioral techniques for gambling on recovery defined by gambling, psychological functioning, and quality of life: A systematic review and meta-analysis.

OBJECTIVE: Individuals who experience gambling harms report that sustained recovery involves changing both gambling behaviors and psychological symptoms, as well as building a meaningful life. However, there is limited understanding about the effect of cognitive behavioral (CB) techniques on psychological symptoms and quality of life. The purpose of the present study was to examine the effect of CB techniques for gambling-related harms on broader recovery outcomes such as psychological symptoms and quality of life. METHOD: A systematic article search was conducted to identify randomized controlled trials of CB techniques with nonactive and minimal treatment control groups that assessed psychological symptoms or quality of life as outcomes. Random-effects meta-analysis was used to examine the effect of CB techniques relative to nonactive and minimal treatment control groups. RESULTS: A total of nine studies representing 658 participants were included. Eight studies reported outcomes on depression and anxiety, three on substance use, and six on quality of life. CB techniques significantly reduced anxiety (g = -0.44) and depression (g = -0.35) at posttreatment, but not substance use. CB techniques also significantly increased quality of life at posttreatment (g = 0.40). There was a large amount of heterogeneity suggesting the magnitude of effects could vary significantly in future randomized trials. CONCLUSIONS: Future studies should examine the longitudinal associations between gambling harms, psychological symptoms, and quality of life and to assess whether changes in gambling harms throughout treatment precede or are a consequence of changes in psychological symptoms and quality of life. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Automatic rating of therapist facilitative interpersonal skills in text: A natural language processing application.

Background: While message-based therapy has been shown to be effective in treating a range of mood disorders, it is critical to ensure that providers are meeting a consistently high standard of care over this medium. One recently developed measure of messaging quality-The Facilitative Interpersonal Skills Task for Text (FIS-T)-provides estimates of therapists' demonstrated ability to convey psychotherapy's common factors (e.g., hopefulness, warmth, persuasiveness) over text. However, the FIS-T's scoring procedure relies on trained human coders to manually code responses, thereby rendering the FIS-T an unscalable quality control tool for large messaging therapy platforms. Objective: In the present study, researchers developed two algorithms to automatically score therapist performance on the FIS-T task. Methods: The FIS-T was administered to 978 messaging therapists, whose responses were then manually scored by a trained team of raters. Two machine learning algorithms were then trained on task-taker messages and coder scores: a support vector regressor (SVR) and a transformer-based neural network (DistilBERT). Results: The DistilBERT model had superior performance on the prediction task while providing a distribution of ratings that was more closely aligned with those of human raters, versus SVR. Specifically, the DistilBERT model was able to explain 58.8% of the variance (R 2 = 0.588) in human-derived ratings and realized a prediction mean absolute error of 0.134 on a 1-5 scale. Conclusions: Algorithms can be effectively used to ensure that digital providers meet a consistently high standard of interactions in the course of messaging therapy. Natural language processing can be applied to develop new quality assurance systems in message-based digital psychotherapy.