The candidate requests support for a four-year program of training and research to better understand how smartphone based digital phenotyping and computational methods can predict relapse and create digital phenotypes of symptoms and clinical outcomes in early course psychosis. In the proposed training plan, the candidate will build upon his previous experiences in engineering, clinical informatics, and clinical psychiatry to perform a multidisciplinary project at Beth Israel Deaconess Medical Center. His training plan includes training in: 1) statistical methods for multivariate longitudinal analysis and predictive inference 2) the neuropsychiatric assessment of schizophrenia 3) longitudinal clinical research methodology with a focus on mobile technologies, and 4) the responsible conduct of research. Even with appropriate care, relapse is common in early course psychosis and each episode is associated higher costs of care, poorer lifetime outcomes, and chronicity of the disease. There is a need to learn more about the personal factors associated with relapse for individual patients in order to improve risk predictions, ensure appropriate early interventions, and support coordinated specialty care services for schizophrenia. This study proposes that smartphones sensors eg (GPS, accelerometer), wearable devices like smartwatches collecting physiology, and smartphone based surveys and cognitive tests, when combined with appropriate statistical methods, can capture digital biomarkers, refereed to here as digital phenotypes, of early course psychosis that can offer personalized relapse prediction and augment population level risk factors. This candidate's research plan seeks to: 1) propose digital phenotypes and relapse models of early course psychosis captured in an affordable and scalable manner from subject's personal smartphones as well as a wearable sensor in order to automatically collect self-report of symptoms, behaviors, cognition, and physiology 2) and evaluate the accuracy of digital phenotypes and the relapse prediction models. This study proposes to address this hypothesis by utilizing smartphone based digital phenotyping methods, primarily through running the Beiwe app on subjects' own smartphones, to capture longitudinal data on symptoms, behaviors, cognition, and physiology across subjects' natural environments. These studies will be performed across 3.5 years in subjects with early course psychosis and range between 6 to 12 months. The broader aim of this research is to understand the systems and processes, both personal and environmental, which contribute to relapse in early course psychosis. An understanding of the computational basis of relapse will inform better nosology, allow development of biomarkers of illness that may offer better targets for biological research, inform development of personalized interventions for psychotic illnesses, and help support early interventions for schizophrenia.
Schizophrenia is a chronic and disabling disorder that is characterized by episodes of relapse, and is thought to impact ~1.5% of the population. Our current treatments and ability to predict relapse early are limited by our understanding of the longitudinal course, environmental factors, and variability of clinical presentation. This study seeks to model this complexity using computational methods and digital phenotyping so that we may design more effective early interventions for this disease.
|Torous, John; Bauer, Amy; Chan, Steven et al. (2018) Smart Steps for Psychiatric Education: Approaching Smartphone Apps for Learning and Care. Acad Psychiatry 42:791-795|