Optimizing outpatient treatment outcomes among methamphetamine-using gay and bisexual men through a computerized depression intervention

Cognitive behavioural therapy for internalizing symptoms in LGBTQ+ people: a preliminary meta-analysis

Internalizing disorders are common in lesbian, gay, bisexual, transgender, queer, questioning, and otherwise non-heterosexual or non-cisgender (LGBTQ+) people. Few studies have evaluated the efficacy of cognitive behavior therapy (CBT), a well-established treatment for internalizing disorders, in LGBTQ+ people. The current study quantitatively synthesized outcomes from existing trials of CBT for internalizing disorders in LGBTQ+ people. Seven databases were searched, identifying 14 relevant studies with a total of 414 participants. A medium within-group effect size was found for depressive symptoms from pre-treatment to post-treatment (k = 14; g = 0.60; 95% CI: 0.44-0.76; I2 = 71.59) and pre-treatment to 2-6-month follow-up (k = 7; g = 0.63; 95% CI: 0.40-0.86; I2 = 71.59). For anxiety and related disorder symptoms, a medium within-group effect size was found from both pre-treatment to post-treatment (k = 10; g = 0.73; 95% CI: 0.47-0.99; I2 = 71.59) and to 3-9-month follow-up (k = 5; g = 0.70; 95% CI: 0.54-0.87; I2 = 36.04). Exploratory analyses indicated small between-group effects at post-treatment between intervention and control groups. Effect sizes were comparable to those in the general population, indicating preliminary support for treating internalizing disorders in LGBTQ+ people with CBT.

The efficacy of InterRhythmic care for depression: A randomized control trial

OBJECTIVE

This study investigates the efficacy of an InterRhythmic Care (IRC) for major depressive disorder (MDD). There is a lack of clinical studies on its effect on depression.

METHODS

In this eight-week, randomized, single-blind, controlled trial, 120 patients with MDD were randomly assigned to receive IRC or Internet general psychoeducation (IGP). Participants' depressive and anxiety symptoms, interpersonal relationships, social function, and biological rhythms were assessed using the 17-item Hamilton Depression Rating Scale (HAMD-17), Hamilton Anxiety Scale (HAMA), Interpersonal Comprehensive Diagnostic Scale (ICDS), Sheehan Disability Scale (SDS), and Morning and Evening Questionnaire (MEQ) at baseline and the 8th week.

RESULTS

Compared to participants in IGP, participants in IRC had lower HAMD total scores, anxiety/somatization, weight, cognitive disturbance, retardation, and sleep disturbance subscores in patients with MDD (F = 190.94, p Bonferroni < 0.001; F = 83.13, p Bonferroni < 0.001; F = 4.15, p Bonferroni = 0.048; F = 65.42, p Bonferroni < 0.001; F = 53.15, p Bonferroni < 0.001; F = 67.76, p Bonferroni < 0.001, respectively); HAMA total score, somatic anxiety subscore, psychogenic anxiety subscore (F = 142.97, p Bonferroni < 0.001; F = 111.06, p Bonferroni < 0.001; F = 128.04, p Bonferroni < 0.001); ICDS total score and subscores for conversation, making friends, manners; and SDS subscores for work/school, social life, family life, and days underproductive (F = 17.38, p Bonferroni <0.001; F = 14.61, p Bonferroni < 0.001; F = 10.97, p Bonferroni = 0.001; F = 11.74, p Bonferroni = 0.001; F = 4.85, p Bonferroni = 0.031; F = 16.29, p Bonferroni < 0.001; F = 12.11, p Bonferroni = 0.001; F = 8.3, p Bonferroni = 0.005) at the end of the intervention period.

CONCLUSIONS

IRC helped patients with MDD improve clinical symptoms, including depressive and anxiety symptoms, interpersonal problems, and social function.

Mapping a psychosocial syndemic among methamphetamine-using sexual minority men living with HIV

INTRODUCTION

While research with sexual minority men (SMM) has focused on disparities related to HIV, substance use and mental health, synergistic psychosocial pathways driving these epidemics remain underexplored. We used syndemic theory to assess how psychosocial factors sustain methamphetamine use and hinder recovery efforts for SMM living with HIV.

METHODS

A triangulation of network analyses and constructivist grounded theory approaches is utilised to elucidate pathways through which psychosocial factors influence methamphetamine use among this population. Survey data (N = 129) are used for quantitative analyses and a purposive sub-sample (n = 24) was recruited for semi-structured interviews for qualitative analyses.

FINDINGS

The network analysis revealed two statistically significant bivariate associations: between post-traumatic stress disorder and depression symptoms (b = 0.37, SD = 0.07, 95% confidence interval [0.23, 0.49]) and between depression symptoms and negative affect (b = 0.26, SD = 0.07, 95% confidence interval [0.12, 0.38]). Findings from the constructivist grounded theory analysis supplement the network analysis by offering a nuanced take on how negative affect, post-traumatic stress disorder, and depression symptoms operate synergistically to promote methamphetamine use and impede recovery efforts.

DISCUSSION AND CONCLUSIONS

Participants relay experiences of using methamphetamine to cope with these psychosocial factors through avoidance, escapism, mood elevation, and numbing of emotions. Findings suggest that centring these psychosocial factors may inform more effective, holistic interventions for this high-priority population.

Digital mental health interventions: A narrative review of what is important from the perspective of LGBTQIA+ people

OBJECTIVES

Digital mental health interventions are a promising therapeutic modality to provide psychological support to LGBTQIA+ (lesbian, gay, bisexual, trans, Queer, intersex, asexual, plus other gender, sexual, and romantic minority identities) people. The aim of this narrative review is to explore how the LGBTQIA+ community has been engaged in the design of digital mental health interventions, how content has been tailored to the LGBTQIA+ community, and features identified as important by LGBTQIA+ participants.

METHODS

A total of 33 studies were included in this review from a larger yield of 1933 identified from systematic searches of five databases (PsycINFO, PubMed, Scopus, CINAHAL, and Medline). Data were analyzed narratively and using content analysis.

RESULTS

Only half of the studies reported engaging the LGBTQIA+ community in intervention designs. Interventions have been tailored in a variety of ways to support LGBTQIA+ individuals-such as through affirming imagery, recruitment through LGBTQIA+ networks, and designing content to focus specifically on LGBTQIA+ issues. A range of features were identified as important for participants, namely how content was tailored to LGBTQIA+ experiences, providing connection to community, and links to other relevant LGBTQIA+ resources. While not a primary aim, results also showed that a wide range of digital modalities can significantly improve a range of mental health problems.

CONCLUSION

Digital interventions are an acceptable and effective form of therapeutic intervention, but future research needs to focus on meaningful engagement of community members to inform design and implementation.

Optimizing Substance Use Treatment Selection Using Reinforcement Learning

Substance use disorder (SUD) exacts a substantial economic and social cost in the United States, and it is crucial for SUD treatment providers to match patients with feasible, effective, and affordable treatment plans. The availability of large SUD patient datasets allows for machine learning techniques to predict patient-level SUD outcomes, yet there has been almost no research on whether machine learning can be used to optimize or personalize which treatment plans SUD patients receive. We use contextual bandits (a reinforcement learning technique) to optimally map patients to SUD treatment plans, based on dozens of patient-level and geographic covariates. We also use near-optimal policies to incorporate treatments’ time-intensiveness and cost into our recommendations, to aid treatment providers and policymakers in allocating treatment resources. Our personalized treatment recommendation policies are estimated to yield higher remission rates than observed in our original dataset, and they suggest clinical insights to inform future research on data-driven SUD treatment matching.