Clinical Research Directory

Right-sided 1-Hz Repetitive Transcranial Magnetic Stimulation (rTMS) Versus Left-sided Intermittent Theta Burst Stimulation (iTBS) in Patients With Depression

Aim: The purpose of the study is to establish the non-inferiority of right-sided inhibitory 1 Hz stimulation compared to left-sided intermittent theta burst stimulation (iTBS) in unipolar and bipolar depression. Design: A national, non-inferiority, register-based, randomized trial, unmasked, with two treatment arms. Primary objective: The primary objective is to determine if right-sided inhibitory 1-Hz stimulation to dorsolateral prefrontal cortex (DLPFC) is non inferior to iTBS in treating unipolar and bipolar depression by measuring reduction in Montgomery-Åsberg Depression Rating Scale, self-assessed version (MADRS-S) from baseline to end of treatment. Secondary objectives: Include testing for differences in: * Observer rated response according to Clinical Global Impression Scale-Improvement (≥2 point reduction CGI). * Response to treatment (a decrease of 50% on MADRS-S) * Self-rated global health measured with the EuroQual-group 5 Dimensions Scale Visual Analogue Scale (EQ-5D-VAS). * Drop-out from treatment. * Stimulation site pain measured with the Numerical Rating Scales (NRS). * Adverse events. * Admission and suicides within 6 months. * New treatment course of rTMS or ECT within 6 months * Remission (score \< 11 on the MADRS-S) * Memory impairment measured with the Comprehensive Psychopathological Rating Scale (CPRS). Study population: Patients with unipolar or bipolar depression. Sample size: 350 patients. Inclusion criteria: * At least 18 years of age at the time of inclusion. * A clinical diagnosis of unipolar or bipolar depression according to ICD-10. * Acceptance of rTMS. * A Swedish personal identity number. * Capable of giving informed consent. Exclusion criteria: • If the investigator judges one of the two treatment protocols inappropriate for the patient. Inclusion time: 2025-07-01 to 2029-01-01

Precision Subclassification of Mental Health in Diabetes: Digital Twins for Precision Mental Health to Track Subgroups

Mental conditions and disorders (e.g. distress, depressive, anxiety, and eating disorders) are more prevalent in people with diabetes (PWD) and associated with reduced quality of life and impaired glycaemic outcomes. Evidence supports a complex network between psychosocial factors and glycaemic control that can be highly variable between persons. It is assumed that subgroups exist that show different trajectories of glycaemia and mental health. Belonging to a particular subgroup may be linked with a higher risk of developing mental health problems compared to others. This suggests that it is possible to treat individuals in different subgroups in a manner that optimizes their treatment and can improve health outcomes. Accurate characterisation can inform more individualized care. This calls for a more personalised approach considering the idiosyncrasies of different subgroups. Over 3 years, the investigators have established the basis of a precision mental health approach for diabetes using n-of-1 analyses. By utilizing combined ecological momentary assessment (EMA: repeated daily sampling of psychosocial factors in everyday life) and continuous glucose monitoring (CGM), intensive longitudinal data per person could be collected. This enables the analysis of individual associations between glycaemic parameters and psychosocial variables and identification of individual sources of diabetes distress in each person. The objective of the present study is to use of the n-of-1 approach to identify subgroups of PWD who share common characteristics in the associations between glucose and psychosocial variables. The identified subgroups shall be used to develop a digital twin for precision mental health in diabetes. The digital twin serves as representation of a real person, allowing to make simulations and predictions of the course of mental health and glycaemia. These predictions can inform diabetes care and lead to more precise, personalised treatment decisions. To achieve this, a longitudinal panel including over 1,400 PWD who continuously complete EMA and questionnaire surveys and measure glucose levels using CGM was developed. Over 1000 clinical interviews to diagnose mental disorders have been conducted to identify major mental health conditions and map mental outcomes. To identify subgroups and develop the digital twin, the sampling will be expanded aiming at a total of 1,809 PWD. Incidence and remission of mental disorders will be determined via repeated interviews. The complex networks between clinical, metabolic, and psychosocial data will be analysed using machine learning, leading to new insights with the potential to shape future guidelines. These results will be used by the digital twin to predict courses of glycaemic control and mental health, translating the individual evidence into direct treatment suggestions.

Mental Health Mission Mood Disorder Cohort Study

The current study aims to understand why some people with depression respond to treatment and others do not, using markers of clinical symptoms, both clinician reported outcome measures and patient reported outcome measures, demographic information, cognitive function, genetic sequence information (genomic), chemical measures of metabolism (metabolomic), protein makeup (proteomic) and the body's natural defence system (immune/inflammatory markers) together with collections of cells that will facilitate new research to drive improvements in diagnosis and treatment of mood disorders that may be proving difficult to treat. This will allow future clinical trials within the NHS, academia and industry to drive forward new approaches and treatments. Participants who provide consent for re-contact for future treatment trials and other research studies have the potential to benefit from this with participation in experimental studies and clinical trials associated with improved patient outcomes. Overall, the cohort will generally support greater access to research opportunities for a wider population of people.

Digital Phenotypes for Predicting Depression

This longitudinal study aims to identify and validate digital phenotypes that can predict recurrence of major depressive episodes using passively collected, real-time sensing data from smartphones and wearable devices. Over a 12-month period, 540 participants-including patients with mood disorders and healthy or high-risk controls-will complete five clinical assessments at 3-month intervals, wear a Fitbit device daily, and log daily mood ratings via a mobile app. The study includes the development of AI-based predictive models and the construction of an anonymized wearable big-data repository for mood disorders.