View clinical trials related to Depressive Disorder.
Filter by:Perinatal depression is experienced by at least 14-20% of pregnant and postpartum women, and is recognized as the most common complication of childbirth. In this project, the investigators plan to complete the process of making MomMoodBooster (MMB), a web-based cognitive-behavioral depression intervention, into a commercial ready product, MMB 2.0, that fits the workflow and staffing of healthcare organizations and is designed for both prenatal and postpartum women who are depressed. The investigators will also conduct a 2-arm randomized controlled trial to evaluate the efficacy of treatment as usual plus MMB 2.0 compared to treatment as usual in a large healthcare setting.
Behavioural activation (BA) is widely accepted as an efficacious treatment for depression. It has been suggested that several depression treatments work via early changes in emotional processing (e.g. affective bias in the processing of facial expressions) and that these could help predict treatment success, but it has not yet been examined whether the same applies in behavioural interventions. The investigators will examine how BA affects early emotional information processing in participants who are currently experiencing low mood, to see whether this can predict eventual changes in mood and to gain a better understanding of the treatment mechanisms of BA. Participants will be in three groups undergoing either behavioural activation, or activity monitoring alone (active control) for 4 weeks, or they will be on a waiting list (passive control). The investigators will also examine whether other factors, such as anxiety, social support and environmental reward, can predict the success of BA. This could help us understand how BA works and who may be most suitable for this intervention.
The purpose of this research study is to use a specific type of non-invasive brain stimulation known as transcranial alternating current stimulation (tACS) to determine its effects on brain activity (measured with EEG) and mood in patients with Major Depressive Disorder (MDD).
PRECISE-D is a single site, randomized, open label 8-week clinical trial that will enroll 70 participants to evaluate if the level of inflammation in our body can predict how we will respond to antidepressants. C-reactive protein (CRP) is a substance in the body that is associated with inflammation. Previous research has suggested that people with high CRP (i.e., high inflammation levels) tend to have greater improvement of depressive symptoms with an antidepressant called bupropion, while individuals with low CRP (i.e., low inflammation levels) appear to have more benefit from selective serotonin reuptake inhibitors antidepressants (SSRI), such as escitalopram. However, it is not completely clear if CRP can predict your response to these two antidepressants. Participants will undergo a screening visit that includes a physical exam, overall health evaluation, assessment of mental health history, and a toxicology and pregnancy test. Once screening is complete, participants will be randomized to one of two groups that will determine whether their CRP levels will be used to select which antidepressant they will receive. Participants will then complete 4 follow up visits at weeks 2, 4, 6, and 8. A follow-up phone call from the study team will occur at week 12.
Depression is a prevalent non-motor symptom of Parkinson's disease (PD). Cognitive-behavioral therapy (CBT) has been shown to be an effective treatment for depression in PD. CBT is usually administered in-person in weekly sessions, but PD motor disability, stigma, and transportation issues may prevent attending such therapy sessions. CBT administered via live videoconference technology may allow the treatment of depression, while circumventing the barriers that deter those with PD from seeking psychological services. The investigators propose that videoconference CBT will improve mood in individuals with PD who have depression.
Individuals with Cystic Fibrosis (CF) are at high risk for depression and anxiety, with negative consequences for quality of life, ability to carry out daily CF treatments, and health. CF Foundation and European CF Society guidelines recommend routine screening, treatment, and preventative efforts for depression and anxiety. Cognitive-behavioral therapy (CBT) interventions focused on teaching coping skills have a large evidence-base for prevention and treatment of depression and anxiety, but there are barriers to accessing these interventions for individuals with CF. Drs. Friedman and Georgiopoulos at Massachusetts General Hospital (MGH) have developed a CF-specific CBT-based preventive intervention for depression and anxiety with input from adults with CF and CF healthcare team members, called CF-CBT: A cognitive-behavioral skills-based program to promote emotional well-being for adults with CF, along with a training and supervision program for CF team interventionists. CF-CBT consists of 8 45-minute modules that can be flexibly delivered in the outpatient CF clinic, on the inpatient unit, or by telephone, by multidisciplinary members of the CF care team, minimizing additional cost and burden of care to patients. The goal of this study is to test CF-CBT in 60 adults screening in the mild range on measures of depression and anxiety at 4 CF centers, in a prospective randomized clinical trial comparing the intervention to usual treatment. Participants will be randomized to receive the CF-CBT intervention immediately, or to a 3-month waitlist control followed by intervention. The study will measure depression, anxiety, quality of life, stress, and coping self-efficacy before and after the CF-CBT intervention, and also 3 and 6 months post-intervention.
The objective of this study is to analyze the physiological patterns of two groups of patients, one control and one with anxiety disorder and alcoholic abuse disorder using sensor data from mobile devices and wearables. This data will be compared to the data presented by three clinical questionnaires: State-trait Anxiety Inventory (STAI), the Alcohol Use Disorders Identification Test (AUDIT), and the Beck's Depression Inventory (BDI-II) in order to determine the feasibility of remote collected data.
Study of individualized accurate targeting rTMS intervention on motivational anhedonia of treatment resistant depression and brain network mechanism
The main goal is to design, develop and evaluate a personalized intervention to prevent the onset of depression based on Information and Communications Technology (ICTs), risk predictive algorithms and decision support systems (DSS) for patients and general practitioners (GPs). The specific goals are 1) to design and develop a DSS, called e-predictD-DSS, to elaborate personalized plans to prevent depression; 2) to design and develop an ICT solution that integrates the DSS on the web, a mobile application (App), the risk predictive algorithm, different intervention modules and a monitoring-feedback system; 3) to evaluate the usability and adherence of primary care patients and their GPs with the e-predictD intervention; 4) to evaluate the effectiveness of the e-predictD intervention to reduce the incidence of major depression, depression and anxiety symptoms and the probability of major depression next year; 5) to evaluate the cost-effectiveness and cost-utility of the e-predictD intervention to prevent depression. Methods: This is a randomized controlled trial with allocation by cluster (GPs), simple blind, two parallel arms (e-predictD vs "active m-Health control") and 1 year follow-up including 720 patients (360 in each arm) and 72 GPs (36 in each arm). Patients will be free of major depression at baseline and aged between 18 and 55 years old. Primary outcome will be the incidence of major depression at 12 months measured by CIDI. As secondary outcomes: depressive and anxiety symptomatology measured by PHQ-9 and GAD-7 and the risk probability of depression measured by predictD algorithm, as well as cost-effectiveness and cost-utility. The e-predictD intervention is multi-component and it is based on a DSS that helps the patients to elaborate their own personalized depression prevention plans, which the patient approves, and implements, and the system monitors offering feedback to the patient and to the GPs. It is an e-Health intervention because it is based on a web and m-Health because it is also implemented on the patient's smartphones through an App. In addition, it integrates a risk algorithm of depression, which is already validated (the predictD algorithm). It also includes an initial GP-patient interview and a specific training for the GP. Finally, a map of potentially useful local community resources to prevent depression will be integrated into the DSS.
The goal of this research is to bridge a significant "effectiveness" gap in the treatment of depression. The investigators have developed a chatbot which will assist in performing measurement-based care (MBC) via Facebook Messenger. Participants will be randomized to either Usual Care or Usual Care with additional Chatbot Care.