Overview
Personalized Treatment Lab, where the frontier of digital mental health meets the precision of science to create a better future for all. Our mission is threefold, weaving together the advancement of scalable digital interventions, the fine-tuning of personalized treatment strategies, and the exploration of the temporal dynamics of mental health through intensive longitudinal data.
The unifying premise that runs through the lab’s entire program of research is that information about how and for whom treatments will work can be used to improve patient outcomes. We employ a variety of data-science approaches in pursuit of this knowledge, including clinical prediction modeling and digital phenotyping.
At the heart of our work is the development of a Personalized Digital Therapy Ecosystem, a pioneering initiative designed to deliver, evaluate, and adapt evidence-based psychological therapies tailored to individual needs, especially within underserved communities.
Under the umbrella of this ecosystem, we’ve launched a stratified system of care that stands as a testament to the potential of digital therapy in making psychological support more accessible and effective. Supported by the guidance of Dr. Michelle Craske and with the assistance of the team assembled at the Depression Grand Challenge (DGC), Dr. Cohen has meticulously crafted a suite of online therapy modules and smartphone tools targeting a spectrum of mental health issues, from depression and anxiety to trauma and sleep disturbances, building a research ecosystem optimized for the development, delivery, evaluation, and adaptation of personalized psychological therapies. This system supports the DGC’s Screening and Treatment of Anxiety and Depression (STAND) program, a stratified system of care that has been successfully piloted at UCLA and East LA College, where STAND is now the college’s primary mental health resource.
Research Interests
Personalized/Precision Treatment
Our research focuses on personalized and precision treatment in mental health, aiming to match individuals with optimal therapies based on predictive models. By integrating a variety of predictive factors, we enhance treatment selection and outcomes. This approach addresses the limitations of single-variable moderation, utilizing data from randomized controlled trials to develop robust, individualized treatment plans. Our work in this area has been validated through extensive studies and published in high-impact journals. We aim to refine these models further, ensuring they are applicable across diverse populations and clinical settings, ultimately improving mental health care efficacy.
Temporal Dynamics and Complex Networks
Our research explores the temporal dynamics and complex networks of mental health disorders, aiming to understand how symptoms evolve and interact over time. By using advanced statistical methods and data from mobile and wearable devices, we capture the intricate patterns of mental health symptoms. This approach allows us to identify critical periods for intervention and understand the underlying mechanisms of mental health conditions. Our goal is to develop more precise, timely, and effective treatments by mapping these dynamic interactions, ultimately improving outcomes for individuals with complex mental health disorders.
Digital Mental Health
We are advancing the field of digital mental health by developing and implementing innovative digital therapy ecosystems. Our flagship project, RainFrog, offers evidence-based online therapy modules and smartphone tools tailored to individual needs. Our research also leverages passive data from mobile and wearable devices to create personalized digital phenotypes, enhancing the precision and scalability of mental health assessments and treatments. These efforts aim to make mental health care more accessible and effective globally.