View clinical trials related to Depressive Disorder.
Filter by:SUMMIT's (Scaling Up Maternal Mental health care by Increasing access to Treatment) overarching goal is to examine the scalability and patient-centered provision of brief, evidence-based psychological treatments for perinatal depression and anxiety (N=1226). Specifically, and through a multi-site, randomized, pragmatic trial, the trial examines whether a brief, behavioral activation (BA) treatment delivered via telemedicine is as effective as the same treatment delivered in person; and whether BA delivered by non-mental health providers (e.g., nurses), with appropriate training is as effective as when delivered by specialist providers (psychiatrists, psychologists and social workers) in reducing perinatal depressive and anxiety symptoms. The study will be conducted in Toronto, NorthShore University HealthSystem in Evanston and surrounding areas including Chicago, and North Carolina. The trial will also identify relevant underlying implementation processes and determine whether, and to what extent, these strategies work differentially for certain women over others.
Based on the mechanism hypothesis and clinical efficacy of VNS in treating refractory depression, this study will evaluate the safety and efficacy of VNS in treating refractory depression through a small sample of clinical trials
This study will examine the effects of smartphone-based lifestyle medicine (LM) for alleviating depressive and anxiety symptoms in Chinese population. Since a range of lifestyle factors are involved in the pathogenesis and progression of depression and anxiety, modifying different lifestyle factors simultaneously, for example, diet, exercise, stress and sleep which are empirically supported by previous reviews, may be effective to reduce depressive and anxiety symptoms. Traditional Chinese medicine concepts will be integrated into the app to increase the acceptability towards mental health treatment. Through this study, we aim to promote evidence-based patient care and to improve help-seeking and access to evidence-based interventions for depression and anxiety.
Major depressive disorder (MDD) is a debilitating disease characterized by depressed mood, diminished interests, impaired cognitive function and vegetative symptoms, such as disturbed sleep or appetite. MDD affects one in six adults in their lifetime. To date, decisions regarding specific treatment protocols for MDD are based on clinical experience and risk factors with limited data on outcome prediction. In addition, since it takes 8 weeks to assess if a treatment is successful, the long and often unsuccessful search for an effective antidepressant is accompanied by significant decrease in patients' quality of life, an increased risk of suicidal action, and decreased chance of response and remission with each attempt. This has led to examination of various markers (e.g., neuroimaging, electrophysiological, genetic and behavioral) in an attempt to predict the response to various forms of treatments, including pharmacotherapies and TMS (Transcranial Magnetic Stimulation) for depression. Elminda had developed a novel, non-invasive imaging EEG-based technology, Brain Network Analytics (BNA), for visualization and quantification of specific brain functions. The rationale of the study is to develop a reliable marker for MDD treatment outcome based on the BNA.
This study investigates the effects of two hormones called epinephrine and cortisol on how the brain processes emotional material using functional MRI to measure brain activity. The study hopes to learn how epinephrine and cortisol affect the brain differently in depressed and non-depressed individuals.
Targeted and individualized treatments for mental health disorders are critically needed. Repetitive transcranial magnetic stimulation (rTMS) represents the front-line of new and innovative approaches to normalizing dysfunctional brain networks in those with mental illness. rTMS is FDA-approved for depression and obsessive-compulsive disorder with clinical trials underway for PTSD and addiction, among others. However, remission rates are suboptimal and ideal stimulation parameters are unknown. We recently completed a randomized, double blind clinical trial and a depression severity biomarker that predicts clinical outcome. The overarching goal of this study is to develop the first broadly generalizable platform for real-time biomarker monitoring and personalized rTMS treatment. We plan to recruit patients with medication-resistant depression and in perform a four-phase, cross-over, double-blind, placebo-controlled trial to 1) identify how standard and optimized rTMS patterns engage the depression severity biomarker, and 2) determine the dose-response of these rTMS patterns. Findings from this study will provide the basis for a double-blind, randomized clinical trial comparing rTMS optimized to the individual against standard rTMS.
This study will test whether 7-10 day administration of the anti-inflammatory drug, tofacitinib, has positive effects on people experiencing treatment-resistant depression compared to placebo.
The purpose of this study is to investigate the effects of a type of non-invasive transcranial alternating current stimulation (tACS) on patients diagnosed with systemic lupus erythematosus (SLE) who are experiencing depression. Targeting depression in patients with SLE may provide benefit to these patients, as there is a clear relationship between chronic pain and depression. The investigators propose that a tACS stimulation montage that was previously used in depression could be beneficial to patients with SLE, resulting in reduced depression symptoms, thus resulting in reduced chronic pain and cognitive difficulties.
Depression is a common condition and is the leading cause of disability worldwide. Preventing or delaying the onset of depression is an important way to reduce the burden of depression. Some research suggests online methods may be effective in preventing depression, but to date, few studies have looked at the application of these methods in the UK. This study aims to assess the effects of an online self-help intervention (Moodbuster) on preventing depression in a primary care population, who are experiencing mild-moderate symptoms of depression, but do not meet the threshold for diagnosis. A randomised control design with a six-month and nine-month follow up will be used to compare Moodbuster to a wait-listed control group. Then, a qualitative process evaluation will be used to understand the barriers and facilitators of implementing the intervention. Eligible participants in Greater Manchester (individuals with mild to moderate symptoms of depression, who do not have a diagnosis of major depressive disorder and have access to the internet) will take part in a 6-week online self-help programme, accompanied by three telephone calls with a trained researcher to support them in their use of the programme. Researchers will follow-up with participants six and nine months after starting the programme to measure depression, anxiety, quality of life, and use of services. The process evaluation will involve qualitative interviews with participants and focus groups with practitioners who referred individuals to the study. This study will assess the effects of Moodbuster on preventing depression and barriers and facilitators of implementing such an intervention in a UK primary care population. It is hypothesised that the intervention group will display reduced depression symptoms and incidence, reduced service use, and improved quality of life, and the intervention will be acceptable to a UK primary care population.
Previous research has shown that modulation of a brain region in rodents, the ventral tegmental area (VTA), improves depressive symptoms. Human research has also shown that VTA self-modulation using 'biofeedback' is feasible and successful in healthy volunteers. This biofeedback procedure is a type of cognitive training that includes real-time feedback about brain signal levels from the VTA. Our question is whether VTA self-modulation with biofeedback can influence depression symptoms.