INTEGRATING EXPOSURE AND RESPONSE PREVENTION AND HABIT REVERSAL TECHNIQUES TO TREAT A TOURETTIC SUBTYPE OF OBSESSIVE-COMPULSIVE DISORDER (TOURETTIC OCD).

Conditions: Obsessive – Compulsive Disorder; Tic Disorder, Chronic Motor or Vocal; Tic Disorders; Tic Disorder, Combined Vocal and Multiple Motor; Tourette Disease; Tourette Disorder; Tourettes Syndrome; Gilles de la Tourette Syndrome

Interventions: Behavioral: Exposure and Response Prevention (ERP); Behavioral: Habit Reversal Training (HbRT; Behavioral: Psychoeducation and Supportive Intervention

Sponsors: University College London Hospitals

Recruiting

Changes in depression, anxiety, and post-traumatic stress symptoms among children and adolescents exposed to adverse childhood experiences following participation in the PROACT intervention in Nairobi, Kenya

IntroductionGlobally, children and adolescents exposed to Adverse Childhood Experiences (ACEs) face an increased risk of developing mental health disorders. The prevalence of these mental disorders is further amplified by the lack of access to specialised mental health treatment especially in low resource settings. There is an urgent need for scalable mental health interventions that can effectively address the needs of these vulnerable populations. Non-specialist-delivered interventions, such as PROACT (Psychoeducation, Relaxation, Problem-solving, Activation, and Cognitive Coping Therapy), represent a promising scalable approach that could help bridge the existing mental health treatment gap in low-resource settings.ObjectivesThis study aimed to assess changes in depression, anxiety, and post-traumatic stress symptoms among children and adolescents exposed to adverse childhood experiences following participation in the PROACT intervention delivered by trained social workers in Nairobi, Kenya.MethodologyMixed-methods pre-post study design was employed. Twenty purposively selected sites across Nairobi County each contributed one social worker (N = 20), who received training to deliver the intervention. A total of 40 children participated and received 4–6 PROACT sessions. Quantitative data were analysed using STATA version 17. Paired t-tests were used to compare baseline and endline scores, while mixed-effects linear regression models with participant ID as a random effect were fitted to estimate changes in outcomes over time and account for repeated measures. Statistically significant improvements were observed across all mental health outcomes. Mean anxiety scores decreased from 6.2 at baseline to 2.7 at endline (mean difference: −3.5; 95% CI: −4.7 to −2.2; p < 0.001), while mean depression scores decreased from 6.4 to 2.9 (mean difference: −3.6; 95% CI: −4.9 to −2.3; p < 0.001). Mean PTSD scores decreased from 16.7 (95% CI: 12.8–20.5) at baseline to 7.3 (95% CI: 4.4–10.1) at endline (mean difference: −9.4; 95% CI: −14.6 to −9.3; p < 0.001). Mixed-effects linear regression analyses corroborated these findings, demonstrating significant reductions in PTSD (β = −9.44), anxiety (β = −3.48), and depression (β = −3.59) symptoms (all p < 0.001).ConclusionThe PROACT intervention was feasible and acceptable when delivered by social workers in Nairobi primary healthcare facilities and was associated with improvements in mental health outcomes among children and adolescents. These findings highlight the potential of task-sharing approaches to expand access to mental healthcare in low- and middle-income countries (LMICs) and warrant further evaluation in controlled studies.

From promise to practice: artificial intelligence in mental health care in the MENA region

Mental health disorders represent a growing burden across the Middle East and North Africa (MENA) region, where depression and anxiety are highly prevalent amid conflict, displacement, and socioeconomic strain, affecting up to 40 percent of adults, yet treatment gaps remain at 80-95% due to provider shortages, financial strain, and cultural barriers. In this context, artificial intelligence (AI), in the form of large language models (LLMs) and specialized psychotherapy chatbots, may offer a scalable adjunct to help address these gaps through anonymous screening, predictive risk modeling, psychoeducation, and brief interventions. This narrative review examines current evidence of AI-driven conversational tools in mental health with a specific focus on their application, acceptance, and limitations within the MENA region. To do so, A structured search of MEDLINE and Embase (2000–2026) identified studies on conversational AI in mental health, prioritizing evidence from the MENA region and supplemented by relevant global literature. Overall, findings suggest that while these tools offer high accessibility and user engagement, particularly for low-intensity support, their effectiveness is limited by linguistic and cultural mismatches, including Arabic diglossia and poor alignment with locally grounded expressions of distress. At the same time, user acceptance reflects a paradox in which stigma and privacy concerns drive reliance on anonymous AI tools while simultaneously limiting trust in their clinical reliability, reinforcing a preference for hybrid models with human oversight. Taken together, these findings indicate that current systems remain insufficiently adapted to the MENA context, underscoring the need for culturally grounded, dialect-sensitive, and clinically supervised approaches to ensure safe and effective integration.

VR-Based Behavioral Activation in Adults

Conditions: Depression – Major Depressive Disorder; Behavioral Activation; Virtual Reality Therapy; Behavioral Inhibition

Interventions: Device: Virtual Reality Behavioral Activation Software; Behavioral: Waitlist Control and Psychoeducation

Sponsors: KTO Karatay University

Completed