Depressed Mood

Depression is a leading cause of global disease burden, poor quality of life, disability and suicide, and commonly occurs in adolescence and early adulthood. Insomnia and circadian factors were regarded as potential targets for preventing worsening of depressive symptoms. Additionally, digital insomnia treatment reduces depressive symptoms but is insufficient for individuals with an evening chronotype. Circadian intervention is an adjunctive treatment for sleep disturbance and depression, but is often overlooked. In this study, we aim to evaluate the effect of guided digital insomnia and circadian intervention (dCBT-I + dCI) in reducing depressive symptoms in college students with insomnia and an evening chronotype compared with digital insomnia intervention alone (dCBT-I) and a health education group (dHE). We also aim to develop and evaluate multimodal prediction models to identify individuals who are more or less likely to respond to the interventions, using clinical, behavioral, circadian, and digital engagement data.

This study will evaluate the feasibility and clinical utility of developing predictive models of treatment response for patients with depressed mood using multimodal clinical data collected in real-world clinical settings. The study will examine outcomes among patients treated with interventions including esketamine, conventional transcranial magnetic stimulation (TMS), or Stanford Accelerated Intelligent Neuromodulation Therapy (SAINT). Retrospective clinical and research data from existing databases may also be incorporated, when available and authorized, to support model development and validation. The goal is to assess whether integrated clinical datasets can be used to support the development of predictive tools that may inform personalized treatment selection in depression.

This study aims to evaluate the impact of public health interventions on changes in healthy lifestyle habits over time and their subsequent effects on health outcomes. The investigators hypothesize that exposing at-risk populations to structured physical activity programs, education on healthy nutrition, promotion of the Mediterranean diet, and workshops focused on strengthening psychological resilience will lead to improvements in anthropometric, oxidative, metabolic, and psychological parameters. Anthropometric and laboratory measures will be collected at multiple time points throughout the study. The longitudinal follow-up will span 12 months. It is anticipated that sustained adherence to healthy lifestyle behaviors will result in positive lifestyle changes and enhanced health-related quality of life.

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.

The goal of this superiority clinical investigation, prospective, multicenter, controlled, randomized, open-label is to evaluate the clinical impact of the Dalia medical telemonitoring device on the management of depressive patients. The main question it aims to answer is: the percentage of patients with clinically significant improvement at 3 months. A clinically significant improvement is defined as a decrease of at least 5 points from the initial PHQ-9 score AND/OR a PHQ-9 score lower than 15. The threshold of 5 points is the Minimal Clinically Important Difference (MCID) of the PHQ-9 scale