View clinical trials related to Depressive Disorder.
Filter by:While effective interventions for depression exist, their success rates are unsatisfactory and their provision is haphazard. The Canadian Depression Research and Intervention Network (CDRIN) Maritimes Depression Hub will improve the delivery of care and the quality of outcomes for youths, adults and seniors with depression across the Maritimes. The investigators will establish an integrated system of assessment, treatment, research and education related to depression with the active involvement of those with lived experience. The establishment of a patient registry is a key step that will facilitate evaluation and reform of current services, integration of patient choice and community resources into treatment programs, monitoring long-term outcomes, and development of more effective treatment approaches through research. The registry will facilitate research that will include validation of new diagnostic and outcome measurement tools, low-cost clinical trials and collaborative projects with national and international partners. Educational programs will involve training the next generation of researchers, those with lived experience, clinicians, and health system managers in critical appraisal and will facilitate their involvement in research. The registry, the proposed systematic measurement of outcomes and the broad dissemination of information and skills will improve the quality of research and of care as well as the experience of patients and their families. The need for a registry: It is increasingly recognized that major advances in the treatment of mental disorders will require large scale clinical research. Recently demonstrated ways of completing large-scale research with finite resources include the routine use of electronic health records (EHR), data linkage and randomized registry trial. Use of EHR is the most efficient way of rapidly obtaining large amounts of information. However, EHR cannot completely exclude confounding by indication and other unmeasured variables. Therefore, tests of treatment effects require experimental designs that cannot be replaced by routine health records data. The gold standard for testing the effects of treatment in an unbiased way is the randomized controlled trial (RCT), where measured and unmeasured confounders are balanced through the randomization process and any remaining confounding is due to chance alone. RCTs are valued as the highest level of evidence, but are costly and take significant time to be completed, partly because of the need to screen a large group of individuals to identify eligible participants. The most efficient unbiased test of interventions, new treatment modalities and novel ways of treatment delivery is a method that combines EHR use with the randomized controlled trial (RCT) methodology: the randomized registry trial (RRT). The RRT takes advantage of a registry of individuals with available information to identify a large number of individuals suitable for an RCT. The RRT approach is efficient especially if the same information (e.g. diagnosis and treatment history) is used repeatedly for different purposes. The same information can be used for clinical purposes, service improvement and multiple research projects. RRT will allow obtaining answers about the efficacy of new treatments and management strategies significantly faster and at a much lower cost than traditional RCTs. Therefore, the investigators propose to establish a registry that has the capacity to conduct RRTs. The proposed registry will be integrated with similar efforts across Canada. Jointly, this collaborative network of registries will facilitate fast and economical testing of new treatments, which is urgently needed to advance the therapeutic options for people with depression and related conditions.
The objective of this study is to identification of genetic markers and predictors of antidepressant-induced suicidality in youth depression. Participants who take the standardized pharmacotherapy (bupropion or lamotrigine) for depression will be observed for 8 weeks. They will do several scales and genetic tests at visit 1 (week 0), visit 3 (week 4) and visit 4 (week 8)
This study expect to investigate psychological intervention (Baduanjin qigong) in COPD patients combined with anxiety and/or depression.
number of center : 1 - duration of study : 24 months - recruitement time : 23 months - Aim :Principal Evaluate the interest of maintenance rTMS in a one-year double blind randomized controlled study for TRD patients. Secondary Evaluate the impact of rTMS on cognitive functions.
This research focuses on disorders of motivation, responsible for a disability that patients experience daily. It is a disorder that affects behavior, especially social. Mechanisms, which result in these disorders, are poorly understood. This ignorance is responsible for the lack of effective therapy. The investigators realize this work in order to better understand the motivational deficits. The objective of the study is to characterize the cognitive mechanisms of motivational deficits in schizophrenia and depression. To answer the question posed in the research, it is planned to include 35 people with schizophrenia, 35 people with depression and 70 heathy volunteers in the Hospital of Sainte-Anne .
The aim of the present study is to compare an intervention consisting of Family Psychoeducation (FPE) to an active control intervention of social support for relatives of patients with a diagnosis of major depression.
There is heterogeneity in patients with depression. Many scholars propose that individualization of antidepressant achieves better outcomes. However, the scientific theoretical basis of individualized treatment is still quite weak. Different clinical subtypes of depression and their possible biomarkers are critically needed to provide the individualization with theoretical base. Diagnostic types of major depression disorder (MDD) based on the Theory of Traditional Chinese Medicine (TCM) and possible differentiations in neurobiochemistry, metabonomics and neuroimaging could be one of ways to explore the biomarkers and support the theory of the individualized treatment. The hypothesized results will be of help to clarify the biological basis of MDD with LDQS and with DBHS, to provide the TCM with further scientific evidence, to explore the pathogenesis of depression, to improve the objective diagnosis of depression, and to promote targeted interventions by Western medicine, TCM or both.
Despite significant advances in pharmacological treatment, the global burden of depression is increasing worldwide. The major challenge in antidepressant treatment is the clinicians' inability to predict the variability in individual response to the treatment. The development of biomarkers to predict treatment outcomes would enable clinician to find the right medication for a particular patient at the early stage of the treatment and thus could reduce prolonged suffering and ineffective protracted treatment. Brain imaging studies that examined brain predictors of treatment response based on group comparisons have limited value in classifying individuals as responders or non-responders. Machine learning classification techniques such as the support vector machine (SVM) method have proven useful in the classification of individual brain image observations into distinct groups or classes. However, studies that have applied the SVM method to structural and functional magnetic resonance scans (fMRI) involved small sample sizes and were confounded by placebo responses. Furthermore, a recent meta-analysis of clinical trials and EEG studies have shown that early clinical responses and brain changes at the early phase of antidepressant treatment may predict later clinical outcomes suggesting that neural markers measured in the early phase of antidepressant treatment may improve predictive accuracy. However, there is no fMRI study to date that has examined the predictive accuracy of data obtained in early phase of the treatment. We have preliminary fMRI data relating to early treatment response that form the basis of this proposed study. The main objective of this study is to use machine learning method to examine the predictive value (sensitivity, specificity, accuracy) of resting state and emotional task-related fMRI data collected at pre-treatment baseline (week 0) and in the early phase of antidepressant treatment (week 2) in the classification of remitters (< 10 MADRS scores after 12 weeks of treatment) and non-remitters in patients with major depressive disorder (MDD). A secondary objective is to determine which data set (week 0 or week 2) gives the best predictive value.
iFighDepression is an online self-help programme based on cognitive-behavioral therapy that could be useful for the treatment of mild to moderate depression
Among antidepressant treatments, electroconvulsive therapy (ECT) remains the most effective. However, patient concerns with cognitive side effects have encouraged trials of new, non-convulsive forms of mild brain stimulation such as transcranial Direct Current Stimulation (tDCS). Our past and present studies of tDCS suggest that it has antidepressant effects and is safe, painless and well tolerated. However, not all patients may have an adequate response, raising the need to find ways of optimising efficacy. This clinical pilot study will examine the feasibility and safety of combining tDCS with a cognitive training task which engages the same brain region targeted by tDCS for treatment of depression.