Clinical Research Directory

Heat and Mental Health - PART 2

The goal of this clinical trial is to learn how hot indoor temperatures affect mood in young adults with depression, with or without anxiety. It will also explore whether the time of day the heat happens matters, and whether changes in sleep, thinking, and daily activity help explain the effect. The main questions it aims to answer are: * Does heat during the day affect mood differently than heat at night? * Do changes in sleep, thinking, or activity help explain those effects? Researchers will compare a weekend that is hot during the day to a weekend that is hot at night to see how the timing of heat affects mood. Participants will take part for about four weeks. During that time, participants will: * Stay in a sleep lab for two weekends, two weeks apart. Each stay runs from Friday afternoon to Monday midday. * Spend one weekend in rooms that are hot (32°C, or 90°F) from 8 in the morning to 8 at night, and comfortable (22°C, or 72°F) overnight * Spend the other weekend in rooms that are comfortable (22°C, or 72°F) during the day and hot (32°C, or 90°F) overnight * Be woken up at 2 in the morning on the last night of each lab stay. Participants will answer questions and do short thinking tasks, then go back to sleep. * Wear sensors that track their sleep, heart rate, and body temperature, and swallow a small capsule that measures body temperature from the inside * Answer questions about their mood and do short thinking tasks, both in the lab and at home * Use a phone app for the whole four weeks of the study. The app asks about mood and sleep, and works with the sensors to track sleep, movement, heart rate, temperature, and humidity.

REM Sleep Disruption and Its Potential Uses in Depression Management

This randomized study evaluated whether brief wrist vibrations delivered through an Apple Watch during algorithm-identified rapid eye movement (REM) sleep could reduce depressive symptoms over approximately four weeks. Adults with clinically significant depressive symptoms were assigned to either REM-contingent haptic stimulation or a monitoring-only control condition. Both groups used the same iPhone and Apple Watch applications and received the same morning Smart Alarm function; only the intervention group received night-time haptic stimulation during probable REM sleep. Sleep stages were estimated in real time using wrist accelerometry and photoplethysmography. Depressive symptoms were assessed using clinician-rated and self-reported measures, with the Montgomery-Åsberg Depression Rating Scale (MADRS) as the primary clinical outcome and the Beck Depression Inventory-II (BDI-II) as a secondary outcome. Sleep architecture, treatment exposure, adherence, and tolerability were also evaluated.

An Interrupted Time-Series Pilot Trial of LGBTQ-Affirming Cognitive Processing Therapy for Interpersonal and Developmental Trauma

The researchers are testing a single-group cognitive-behavioral intervention for treating traumatic stress related to interpersonal and developmental trauma in LGBTQ adults. This study requires in-person participation weekly at the UAB campus in Birmingham, AL.

Behavioral Activation for Treatment of Depression in Autistic Adolescents With Below Average Intelligence

Autism spectrum disorder is characterized by differences in social communication/interaction and the presence of restricted/repetitive behaviors, with significant heterogeneity in terms of autistic features, language ability, and cognitive characteristics. About 2-3% of individuals in the United States are autistic, and most autistic individuals have co-occurring mental health conditions. Autistic people are 3-4 times more likely to experience depression than non-autistic people. Of the emerging depression treatment research with autistic people, none to date has focused on developing and testing a depression treatment for autistic youth with below average cognitive abilities - a group which comprises over 50% of the autistic community. Therefore, this study seeks to evaluate the feasibility (Aim 1), acceptability (Aim 2), and preliminary treatment effects (Aim 3) of an extension of Behavioral Activation for Depression in Autistic Adolescents (BA-A), adapted to meet the needs of cognitively lower functioning autistic youth.

Impact of Lifestyle on Health Maintenance: A Randomized Controlled Trial

This study aims to evaluate the impact of public health interventions on changes in healthy lifestyle habits over time and their subsequent effects on health outcomes. The investigators hypothesize that exposing at-risk populations to structured physical activity programs, education on healthy nutrition, promotion of the Mediterranean diet, and workshops focused on strengthening psychological resilience will lead to improvements in anthropometric, oxidative, metabolic, and psychological parameters. Anthropometric and laboratory measures will be collected at multiple time points throughout the study. The longitudinal follow-up will span 12 months. It is anticipated that sustained adherence to healthy lifestyle behaviors will result in positive lifestyle changes and enhanced health-related quality of life.

Digital Insomnia and Circadian Therapy for Reducing Depression Symptoms in College Students

Depression is a leading cause of global disease burden, poor quality of life, disability and suicide, and commonly occurs in adolescence and early adulthood. Insomnia and circadian factors were regarded as potential targets for preventing worsening of depressive symptoms. Additionally, digital insomnia treatment reduces depressive symptoms but is insufficient for individuals with an evening chronotype. Circadian intervention is an adjunctive treatment for sleep disturbance and depression, but is often overlooked. In this study, we aim to evaluate the effect of guided digital insomnia and circadian intervention (dCBT-I + dCI) in reducing depressive symptoms in college students with insomnia and an evening chronotype compared with digital insomnia intervention alone (dCBT-I) and a health education group (dHE). We also aim to develop and evaluate multimodal prediction models to identify individuals who are more or less likely to respond to the interventions, using clinical, behavioral, circadian, and digital engagement data.

Ecological Momentary Intervention (E-Breathing) to Reduce Psychological Distress in Adolescents

A two-arm randomized controlled trial (RCT) will be conducted to test a mobile-based intervention, E-breathing, for early detection and intervention to address psychological distress in at-risk adolescents in Hong Kong. E-breathing harnesses the science of real-time, in-situ assessment approach - Ecological Momentary Assessment (EMA) - to capture the psychological symptoms and needs of adolescents. Based on the real-time data collected via smartphone, the mobile application prompts the practice of slow-paced breathing as an Ecological Momentary Intervention (EMI) to reduce psychological distress. This RCT will recruit a total of 100 participants who will be randomly allocated to either (1) an intervention group (EMI group), where they will receive a feedback prompt when they score high on negative affect (i.e., E-breathing) or (2) the control group (EMA group), where they will only complete EMAs without feedback. This project aims to investigate the feasibility and effectiveness of E-breathing in improving psychological symptoms in Hong Kong adolescents. This project also examine the underlying physio-mechanisms of the effects of E-breathing on psychological outcomes in this target population.

ATLAS-1: Advanced Trial for Longitudinal Assessment in Salma 1

This study will evaluate the feasibility and clinical utility of developing predictive models of treatment response for patients with depressed mood using multimodal clinical data collected in real-world clinical settings. The study will examine outcomes among patients treated with interventions including esketamine, conventional transcranial magnetic stimulation (TMS), or Stanford Accelerated Intelligent Neuromodulation Therapy (SAINT). Retrospective clinical and research data from existing databases may also be incorporated, when available and authorized, to support model development and validation. The goal is to assess whether integrated clinical datasets can be used to support the development of predictive tools that may inform personalized treatment selection in depression.

Precision Subclassification of Mental Health in Diabetes: Digital Twins for Precision Mental Health to Track Subgroups

Mental conditions and disorders (e.g. distress, depressive, anxiety, and eating disorders) are more prevalent in people with diabetes (PWD) and associated with reduced quality of life and impaired glycaemic outcomes. Evidence supports a complex network between psychosocial factors and glycaemic control that can be highly variable between persons. It is assumed that subgroups exist that show different trajectories of glycaemia and mental health. Belonging to a particular subgroup may be linked with a higher risk of developing mental health problems compared to others. This suggests that it is possible to treat individuals in different subgroups in a manner that optimizes their treatment and can improve health outcomes. Accurate characterisation can inform more individualized care. This calls for a more personalised approach considering the idiosyncrasies of different subgroups. Over 3 years, the investigators have established the basis of a precision mental health approach for diabetes using n-of-1 analyses. By utilizing combined ecological momentary assessment (EMA: repeated daily sampling of psychosocial factors in everyday life) and continuous glucose monitoring (CGM), intensive longitudinal data per person could be collected. This enables the analysis of individual associations between glycaemic parameters and psychosocial variables and identification of individual sources of diabetes distress in each person. The objective of the present study is to use of the n-of-1 approach to identify subgroups of PWD who share common characteristics in the associations between glucose and psychosocial variables. The identified subgroups shall be used to develop a digital twin for precision mental health in diabetes. The digital twin serves as representation of a real person, allowing to make simulations and predictions of the course of mental health and glycaemia. These predictions can inform diabetes care and lead to more precise, personalised treatment decisions. To achieve this, a longitudinal panel including over 1,400 PWD who continuously complete EMA and questionnaire surveys and measure glucose levels using CGM was developed. Over 1000 clinical interviews to diagnose mental disorders have been conducted to identify major mental health conditions and map mental outcomes. To identify subgroups and develop the digital twin, the sampling will be expanded aiming at a total of 1,809 PWD. Incidence and remission of mental disorders will be determined via repeated interviews. The complex networks between clinical, metabolic, and psychosocial data will be analysed using machine learning, leading to new insights with the potential to shape future guidelines. These results will be used by the digital twin to predict courses of glycaemic control and mental health, translating the individual evidence into direct treatment suggestions.

Evaluation of Dalia Solution For Depressed Patient

The goal of this superiority clinical investigation, prospective, multicenter, controlled, randomized, open-label is to evaluate the clinical impact of the Dalia medical telemonitoring device on the management of depressive patients. The main question it aims to answer is: the percentage of patients with clinically significant improvement at 3 months. A clinically significant improvement is defined as a decrease of at least 5 points from the initial PHQ-9 score AND/OR a PHQ-9 score lower than 15. The threshold of 5 points is the Minimal Clinically Important Difference (MCID) of the PHQ-9 scale