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Browse 605 clinical trials for bipolar disorder. Find studies that match your criteria and connect with research centers.
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NCT07167563
The aim of the present study is to study compliance with lithium therapy and patterns of lithium intake among a representative group of patients with bipolar disorder in a real-world clinical setting. This will be studied via a pill box that registers the daily time when the participant took the lithium tablet out of the pill box, representing a proxy for the actual lithium intake. Participants will receive a weekly phone call asking for several aspects of lithium intake.
NCT06809881
To compare olfactory performance between patients with BD and HC and correlate their performances to neuroimaging data
NCT05408637
Transcranial light therapy, or transcranial photobiomodulation (tPBM), is a treatment that stimulates the brain by applying near-infrared light to the forehead. Transcranial light therapy has been found to promote brain metabolism, which may help improve executive function in people with bipolar disorder. The research team proposes a novel approach to treating bipolar disorder by using transcranial light therapy.
NCT06204705
Veterans with bipolar disorders (BD) experience recurrent and seemingly unpredictable periods of severe impairments in psychosocial functioning, such as participation in social roles and activities. Many effective treatments for BD emphasize early detection of bipolar episodes, in order to make necessary treatment adjustments and prevent psychosocial impairments associated with acute mood episodes. Unfortunately, acute mood episodes in BD are also associated with a decrease in a patient's insight into their own symptoms, which can prevent one's ability to self-report first signs of symptoms and functional declines. Moreover, routine care visits for BD are typically too infrequent to capture and effectively monitor day-to-day changes in a patient's mood and functioning. Objective, low-effort, and continuous methods of tracking symptoms and social participation of Veterans with BD in real-time and in-situ are needed to provide early (i.e., days in advance) warning signs of acute bipolar episodes and functional declines, which in turn would enable well-timed interventions to prevent poor psychosocial outcomes. mHealth refers to the use of mobile and wireless devices as part of patient care and offers many potential opportunities for early detection of and intervention for acute mood states in this population. However, these mHealth approaches have not been investigated in Veterans with BD. In a Small Projects in Rehabilitation Research (SPiRE)-funded pilot study, the investigator team established high feasibility and acceptability of one such innovative passive mHealth approach using a smartphone program, or an app, in a small sample of Veterans with BD to track their smartphone's GPS/location. The pilot study used a priori location context ratings of visited places (e.g., a priori ratings on types of activities usually engaged in at a frequently visited location) to derive unobtrusive measures of social participation (e.g., time spent at work-related locations). The goal of this Merit Review proposal is to establish reliable and valid machine-learning algorithms using the same types of mHealth data to prospectively (days in advance) detect declines in social participation and prospective onset of mania and depression in Veterans with BD. This proposal has three aims: Aim 1. To establish a machine learning algorithm using GPS/location data for predicting prospective declines in social participation in Veterans with BD. Aim 2. To establish machine learning algorithms using GPS/location data for predicting prospective acute BD clinical states. The investigators will explore whether adding more burdensome daily self-report and voice diaries' speech analysis features improves the models' precision using statistical indices of prediction precision or accuracy. Aim 3. To explore clinical implementation of the mHealth-based algorithms in treatment of BD. Focus groups of VA providers and administrators will assess feasibility of algorithms' implementation in clinical care.