View clinical trials related to Depressive Disorder.
Filter by:Background: The prevalence of depressive disorder, particularly major depressive disorder is on the rise globally and the use of antidepressant medication for treatment does not usually resulting in full remission. However, combination of antidepressant medication and psychotherapy for treatment of major depressive disorder (MDD) increase the rate of full remission compared to antidepressant monotherapy. This three-armed, parallel-group, double-blind, randomized control trial (RCT) aims to assess and compare the effects of combination of exergame and acceptance and commitment (e-ACT) training programme and ACT only on psychiatric symptoms (depression and anxiety), experiential avoidance (EA), quality of life (QoL), and biomarkers of depression (such as BDNF, CRP, and VEGF) among MDD patients. Methods: This RCT will recruit 120 patients diagnosed with MDD who will be randomized using stratified permuted block randomization into three groups, which are combined e-ACT training programme, ACT only and treatment-as-usual control groups in a 1:1:1 allocation ratio. The participants in the e-ACT and ACT only intervention groups will undergo once a week intervention sessions for 8 weeks. Assessments will be carried out through three time points, such as the first assessment before intervention began (t0), second assessment immediately after completion of intervention at 8 weeks (t1) and third assessment 24 weeks after completion of intervention (t2). During each assessment, the primary outcome assessed is the severity of depression symptoms, while the secondary outcomes assessed are severity of anxiety symptoms, QoL, EA, and biomarkers of depression.
There are 636,000 self-reported cases of sexual assault annually in Canada, and nine out of ten persons who have experienced sexual assault are women. Cognitive and behavioural therapies (CBT) are the treatment of choice for many psychological problems arising from sexual assault. However, accessing CBT is a significant challenge, especially for women who have experienced sexual assault who may be ashamed and not disclose the sexual assault. Online CBT is an effective option to circumvent these barriers. In addition to being accessible and less resource-intensive, studies report that patients are less inhibited and that the online environment provides greater emotional safety. There is also a growing body of evidence that online CBT programs requiring little or no contact with a mental health professional are effective, this having been demonstrated primarily with individuals with anxiety and mood disorders. But when it comes to treating the psychological symptoms of sexual assault in potentially vulnerable individuals, can we really suggest a self-care approach? There is no direct empirical evidence to support such a recommendation, and it is this important question that this project wishes to address. To compare the effectiveness, acceptability and user engagement in a self-managed treatment platform with or without the support of a therapist to reduce post-traumatic symptoms, depression and insomnia in people who have suffered one or more sexual assaults, 204 victims of sexual assault experiencing significant distress will be recruited and randomly assigned to either the self-managed or the therapist-assisted online treatment condition. Participants will complete measures assessing post-traumatic stress disorder, insomnia, depression, anxiety, and maladaptive beliefs before, during, after and 3 months after treatment. Secondary outcome will be and appreciation of the online treatment measures by a self-report questionnaire and a semi-structured interview. If effective in reducing symptoms, this treatment would offer the potential to support a self-care approach to treating a wide range of psychological symptoms resulting from sexual assault. The self-managed online platform would fill a service gap deplored by this population.
Many physical changes, as well as emotional disturbance, occur during pregnancy. In addition to external physical changes, some mental health problems such as depression, sleep and psychosis significantly increase during pregnancy. Aerobic exercises during pregnancy in primigravida females has a positive effect on depression and sleep as a primary and secondary preventive strategy. The study will be a randomized clinical trial and Setting will be children hospital and Mehmooda hospital sheikhupura. This study will be completed in ten months and convenient sampling technique will be used. Fourty two subjects will be assigned randomly by using lottery method into two groups. Group A will receive Aerobic exercises and Group B will receive Pilate exercises. Warm up (walking) and cool down period of ten minutes will be performed by both groups. Data will be collected from all participants at baseline and after 8 weeks of treatment by using CES-D and PSQI questionnaire. After assessing the data will be analyzed by using parametric or non-parametric test by using SPSS-25.
Repetitive transcranial magnetic stimulation (rTMS) and Theta burst stimulation (TBS) are approved by the US. Food and Drug administration (FDA) for the treatment of refractory major depression. TBS is more efficient than rTMS as it requires shorter stimulation time.Studies suggest that the efficacy of TBS could be enhanced and expedited by accelerated protocols (more than once daily sessions) with higher doses of stimulation (>600 TBS pulses up to 3600 pulses per session) and shorter duration of treatment (4-10days). The main objective of this study is to determine the clinical efficacy and safety of accelerated high dose bilateral TBS treatment for patients with treatment resistant depression in comparison to sham stimulation using a randomized double blind clinical trial design.
Clinical depression often includes a pessimistic view of things which have happened in the past and an impairment in the ability to experience pleasure or looking forward to things. A licensed drug called ketamine affects the levels of glutamate, a chemical messenger in the brain, and has been used as a treatment particularly for depression which hasn't got better with other types of medication. Glutamate plays a role in learning and memory so the investigators are interested in understanding how ketamine can affect how people with depression remember past negative and positive memories and how they experience reward. The investigators are conducting a study in depressed participants who did not improve with the standard antidepressant treatment to expand our understanding on how ketamine can influence memory, the way people understand emotions and learn from rewards and punishments. Study participants will undergo medical and psychiatric health screening, drug administration (ketamine or saline), questionnaires and computer tasks before and after the administration of the study drug, and an MRI scan after administration of the drug. MRI is a type of brain scan that allows us to see how the brain responds during for example memories of things which have happened in the past. This project will help us understand how NMDA antagonists may work in depression.
The purpose of this project is to determine if specific gut microbiome or gut-derived metabolites are associated with depression in patients with Multiple Sclerosis (pwMS). Mechanistically, the investigators further hypothesize that depression in pwMS is related to decreased abundance of gut bacteria with GABA-producing activities and/or with anti-inflammatory properties. To determine if the presence of depression in pwMS is associated with specific gut microbiome, gut-derived metabolites or peripheral blood immune profiles. The investigators will perform a cross-sectional study in clinically stable pwMS recruited at the John L. Trotter MS Center. The investigators will evaluate the presence of depression using the Quality of Life in Neurological Disorders (Neuro-Qol) depression scale, one of the 13 scales in the Neuro-Qol recently developed by the NIH using modern psychometric techniques and validated in pwMS. A total of 120 pwMS will be recruited: 60 with and 60 without depression based on the Neuro-Qol depression scale. At the study visit each participant will be asked to provide a stool sample for microbiome analyses and a blood sample for peripheral blood immunophenotyping. Potential confounders will be collected and treated as covariates in the analyses. These include: 1) degree of disability (EDSS); 2) treatment with anti-depressants and DMTs; 3) a 4-days food diary to evaluate diet composition; 4) weight and height to calculate the BMI; 5) fatigue; 6) level of physical activity; 7) sleep quality.
This study was planned to examine the effects of the Mindfulness-Based Stress Reduction Program, which is one of the popular concepts of the 21st century, on depression, psychological well-being and prenatal attachment, which are likely to occur as a result of increased stress during an important period that requires adjustment as a result of physical, mental and cognitive changes during pregnancy. It will be conducted in a randomized parallel controlled experimental design type using Consolidated Standards of Reporting Trials (CONSORT) guidelines. It is planned to be carried out with the ZOOM Cloud Meetings program, which is an online education platform, with pregnant women who apply to the Health Sciences University Ümraniye Training and Research Hospital pregnancy polyclinic in the Ümraniye district of Istanbul. The universe of the research will be the pregnant women who applied to the Health Sciences University Ümraniye Training and Research Hospital pregnant outpatient clinic. Primiparous pregnant women who meet the inclusion criteria and willingly agree to participate in the study will be included in the study. 54 people in the intervention group, 54 people in the control group, and a total of 108 people will be taken. While the Conscious Awareness-Based Stress Reduction Program adapted for pregnant women will be applied to the intervention group, routine follow-up will be applied to the control group within the scope of the outpatient clinic follow-up of the relevant institution. Informed Voluntary Consent Form, Descriptive Characteristics Information Form, Psychological Well-Being Scale, Beck Depression Scale, Prenatal Attachment Scale and Conscious Awareness Scale will be administered to the participants included in the study. As a result of the findings, the discussion and results of the study will be written.
This study is a multicenter, open-label, single-arm Phase 2 clinical trial. Approximately 15 female participants with clinically diagnosed postpartum depression (PPD) will be included in this study. The participants will receive an individualized dosing regimen (IDR) with at least one and up to three doses of GH001 administered within a single day.
Mindfulness-Based Cognitive Therapy (MBCT) is effective in reducing relapse rates and (residual) symptoms in major depressive disorder (MDD). However, the mechanisms underlying those MBCT-induced effects are far from clear. The goal of this study is to get more insight into the working mechanisms of MBCT. The main question to be answered is whether MBCT-induced reduction in depressive symptoms is mediated and/or moderated by repetitive negative thinking (RNT), or other factors hypothesized to be involved in the working mechanism of MBCT (e.g. mindfulness skills and self-compassion).
Based on robust evidence from literature, the investigators hypothesize the presence of disease-specific neurobiological underpinnings for bipolar and unipolar disorder, which may serve as biomarkers for differential diagnosis. However, the group comparison approaches adopted in psychiatric research fail to translate the emerging knowledge to the diagnostic routine. How can physicians predict differential diagnosis and treatment response by using cutting-edge knowledge obtained in the last decade? How can such extensive knowledge be useful and applicable in clinical practice? With this project, the investigators propose a solution to these challenges by developing a software tool that integrates the available clinical, biological, genetic and imaging data to predict diagnosis and outcome of new individual patients. The decision support platform will employ artificial intelligence, specifically machine learning techniques, which will be "trained" through data in order to predict the category to which a new observation belongs to. By doing this, existing and newly acquired multimodal datasets of bipolar and unipolar patients will be translated into predictors for personalized patient diagnosis and prognosis. The project can have a great impact on psychiatric community and healthcare system. Identifying predictive biomarkers for UD and BD will provide an essential tool in the early stages of the disease, ensuring accurate diagnosis, enhancing prognosis and limiting health care costs. The investigators will recruit 80 bipolar patients, 80 unipolar patients and 80 healthy controls for the MRI study. Clinical, genetic and inflammation data will be acquired from all subjects. The following data will be obtained: age, gender, number of episodes, recurrence, age of illness onset, lifetime psychosis, BD or UD familiarity, tempted suicide, medication, scores at HDRS, Beck Depression Inventory and BACS battery. MRI will be performed on 3.0 Tesla scanners. MRI acquisitions will include SE EPI DTI, T1-weighted 3D MPRAGE and fMRI sequences during resting state and a face matching paradigm, which previously allowed defining the connectivity in mood disorder. Blood samples samples will be collected and plasma will be extracted and stored at -80. Pro- and anti-inflammatory cytokines will be measured using the Bioplex human cytokines 27-plex. Genetic variants associated considered for differential diagnosis will be evaluated using the Infinium PsychArray-24 BeadChip. This cost-effective, high-density microarray was developed in collaboration with the Psychiatric Genomics Consortium for large-scale genetic studies focused on psychiatric predisposition and risk. The relevance of the single clinical, genetic, molecular and image-based features as bipolar and unipolar disorder signatures will be evaluated by considered the cutting-edge literature and estimated on a independent already existing dataset (30 subjects per group). General Linear Model analyses followed by two sided t-tests will be used to identify whether each parameter significantly differs among groups, while removing the contribution of age, gender, length of illness and other confounding factors. A multiple kernel learning (MKL) algorithm will project the multisource features to a higher-dimensional space where the three subject groups will be maximally separated. The selected features will be used both separately and in combination. The nuisance effects of age, gender, length of illness and MRI system will be corrected during the training phase of the algorithm. The MKL classifier will be tested using a k-fold nested cross-validation strategy with hyperparameter tuning. The training dataset is already made available and includes about 550 subjects. The software architecture will be designed in Matlab environment by integrating quantitative imaging methods, machine learning algorithm and statistical analyses as separate modules in a user-friendly interface, which will facilitate the sharing of computational resources in the clinical community.