View clinical trials related to Depressive Disorder, Major.
Filter by:In this randomized, single-blinded clinical trial, 58 patients with major depressive disorder were assigned to active and sham tDCS stimulation groups in a 1:1 ratio, and treatment responses were evaluated biweekly over six weeks.
Examine the safety and effectiveness of the Fisher Wallace Cranial Electrotherapy Stimulator Device on Major Depressive Disorder using two 20-minute per day treatment sessions over eight weeks.
The present study explored the effects of the implementation of the Pythagorean Self Awarenes Intervention (PSAI) on patients diagnosed with major depressive disorder. The primary aim was to evaluate the effectiveness of PSAI compared to the usual care provided for adults with major depressive disorder with respect to the reduction of depressive symptoms. Secondary aims of this study included reduction of stress and anxiety, enhancement of healthy lifestyle and improvement of affect, sleep quality and cognitive functions of patients.
The purpose of this study is to assess the efficacy of seltorexant compared with placebo as adjunctive therapy to an antidepressant in improving depressive symptoms in participants with major depressive disorder with insomnia symptoms (MDDIS) who have had an inadequate response to current antidepressant therapy with an selective serotonin reuptake inhibitor (SSRI) or serotonin-norepinephrine reuptake inhibitor (SNRI) in double-blind treatment phase and to assess the long-term safety and tolerability of seltorexant as adjunctive therapy to an antidepressant in participants with major depressive disorder (MDD) in open-label treatment phase.
The purpose of this study is to assess the efficacy of Seltorexant compared with placebo as adjunctive therapy to an antidepressant in improving depressive symptoms in participants with major depressive disorder with insomnia symptoms (MDDIS) who have had an inadequate response to current antidepressant therapy with a selective serotonin reuptake inhibitor (SSRI) or serotonin-norepinephrine reuptake inhibitor (SNRI).
Inflammation that is mediated by microglia activation plays an important role in the pathogenesis of depression. Microglia activation can lead to an increase in the levels of proinflammatory cytokines, including TNF-α, which leads to neuronal apoptosis in the specific neural circuits of some brain regions, abnormal cognition, and treatment-resistant depression (TRD). Protein kinase C (PKC) is a key regulator of the microglia activation process. The investigators assume that the abnormality in PKC might be the serum biomarkers of depression.
Several open-label trials have shown the therapeutic promise of deep brain stimulation (DBS) targeted to striatal and surrounding capsular areas in treatment-resistant depression (TRD). However, the results of placebo-controlled trials have been mixed, with one showing a large difference between active and sham DBS and another finding no difference. Main aim of this study is establishing whether active DBS results in more treatment responders than sham DBS. Secondary aims are establishing an adverse events profile, establishing effects on quality of life,neuropsychological and neuroimaging measures, and finding predictors of response.
This is a retrospective registration. The study was conducted at The Ohio State University from 2011 to 2012. This study was approved by an Institutional Review Board at The Ohio State University. The investigators retroactively registered this trial to facilitate publishing results in journal that now require registration. Any reference to the study's registration will make it clear that the registration was retroactive. Cognitive behavior therapy (CBT) has been shown to be an effective treatment for depression. However, a substantial number of patients do not respond to treatment or continue to be symptomatic at its conclusion. An important goal of ongoing research is to find ways to enhance treatment outcomes. One approach to doing this is to modifying existing treatments to individualize the approach to better meet the needs of individual patients. In this study, the investigators tested two main components of CBT to empirically evaluate patient characteristics that may predict differential response to these components. By using components of CBT, any suggestions about the strategies that are best suited to different patients are likely be easily implemented by therapists providing CBT. The two treatment components the investigators examined were: cognitive interventions (e.g., challenging negative automatic thoughts) and behavioral interventions (e.g., engaging in activities to promote a sense of pleasure or accomplishment). The investigators recruited adults with major depressive disorder and randomized them to a cognitive or behavioral intervention. After 8 weeks of treatment, patients were randomized again to a cognitive or behavioral intervention. Consequently, participants were offered a total of 16 weeks of treatment. Depressive symptoms were assessed with the Beck Depression Inventory-II (BDI-II) and the Hamilton Rating Scale for Depression (HRSD), with the latter being the primary outcome measure. Several variables that might serve to predict differential response to cognitive and behavioral treatments were also assessed. The results of this study may help to elucidate how cognitive or behavioral interventions might be selected so as to enhance overall treatment outcomes.
Major depressive disorder (MDD) characterized by high prevalence, high recurrence rate and high disability rate is a mental illness with the heaviest burden and has become a major public health issue in China and the world. Great challenges in diagnosis and treatment of depression consist of the complicated pathogenesis, a lack of objective diagnostic criteria, unsatisfactory treatment outcomes and poor treatment compliance. The previous studies of our research team showed that depression is affected by multiple factors. We could explore important markers for the diagnosis, treatment and prediction of treatment efficacy in depressed patients' data collected from different dimensions including immunometabolism, brain electrophysiology, brain structure and functional neural circuits, neuropsychology and psychophysiology. Our completed studies in the National Science and Technology Support Program and National Key Research and Development (R&D) Program of the 12th and 13th Five-Year Plan in China found that treatment designed for specific clinical subtypes can improve the treatment effect, and meanwhile, the application of electronic-measurement based care (e-MBC) combined with smart mobile terminals can effectively provide whole-course medical management for patients, improve patient compliance and increase the efficacy of clinical diagnosis and treatment. However, due to disease diagnosis based on clinical symptomatology without subtype distinction and lack of multi-scale biological data mining, multidimensional assessment and deep integration, the results of most previous studies can hardly be used in clinical practice. Therefore, there is a strong urge to carry out a systematic research in which multidimensional evaluation of clinical characteristics and a large scale of data collection and mining are needed to form clinical diagnosis and optimal treatment regimens for depression subtypes. To achieve the goal, patients with depression will be our research subject in this study. First, on the basis of the previous cohort study and the whole-course e-MBC, patients' data of movement, respiratory rate, heart rate and sleep will be further collected. With the help of artificial intelligence (AI) technology such as deep machine learning, the data integrated with EEG imaging and specific immunometabolic markers in blood will be analyzed with clinical characteristics. The model of diagnosis and classification will be established based on multidimensional clinical assessment and verified. Second, through a prospective multicenter randomized controlled trial, optimal treatment regimens for different depression subtypes and individualized magnetic stimulation physical intervention technology navigated by AI will be explored so as to establish a predictive model of curative effect. Finally, long-term follow-up and its regular data collection can be completed on the patient diagnosis and treatment platform which is linked to the e-MBC. Thus, a stable clinical cohort and an advanced database containing multidimensional information of depression will be set up. The whole course e-MBC management platform will be optimized and promoted to improve patient compliance, treatment efficiency and prognosis. This study can provide evidence for precise diagnosis and classification of depression and optimal treatment regimens for different subtypes.
The study team will use components of the Reach, Effectiveness, Adoption, Implementation, Maintenance (RE-AIM) framework to compare Cognitive Adaptation Training (CAT) to Remotely delivered Cognitive Adaptation Training (R-CAT) 1-9 within a managed care organization (MCO), targeting members with serious mental illness (SMI) needing assistance with the regular taking of medication.