Identifying predictors and indicators of emerging mental illness is a high public health priority. Early intervention will be most effectively implemented if we know who and what to target. Current predictors and risk calculators consist of static measures such as sociodemographics, symptom levels, and cognitive functioning. Yet substantial evidence suggests that the emergence of mental illness occurs in a dynamic interaction with the environment, with both continuity and discontinuity of symptoms over time, and that subtle changes precede most acute episodes. The earliest symptoms are often ?nonspecific? as they are precursors to a range of diagnostic outcomes and trajectories. Although there are significant efforts to capture dynamic genetic, structural, electrophysiological, and other brain-based and biological changes accompanying the onset of psychosis, there has been very little characterization of symptom dynamics predicting clinical course, including the temporal sequencing of symptoms during the early phases of major mental illness. The proposed project is a preliminary step in a line of research intended to address this gap of knowledge. The study uses a strategy designed for collecting dynamic data, Experience Sampling Methods (ESM), in a high priority population: adolescents and young adults at elevated risk for psychosis or within the initial five years following a first episode of psychosis (FEP).
The aim i s to measure a novel target for this population, temporal variability in affect, and examine its relationship to symptoms of particular relevance to poorer outcomes: psychotic-spectrum symptoms and thoughts of self-harm. The proposed study is designed to test initial hypotheses that greater affect variability will be associated with elevations in these symptoms and to explore the potential roles of age and social context in these dynamics. These preliminary data are expected to inform a highly promising line of research into dynamic predictors of critical events and transitions during the early course of major mental illness, features that may also predict long-term trajectories. Early course symptom dynamics are also expected to inform both the understanding of mechanisms of illness progression and novel, including personalized and mobile, interventions to interrupt pathological sequences and improve functional outcomes. Thus the goals of this proposal are consistent with the R21 guidelines emphasizing novelty and innovation, as well as NIMH strategic objectives to chart mental illness trajectories to identify when, where, and how to intervene, and to identify clinically useful dimensions of behavior and behavioral predictors of change.
Most major mental illnesses become diagnosable in adolescence and early adulthood, after the initial years of symptom emergence and changing social context have led to a myriad of diagnoses and ineffective treatments. Almost all of current early intervention is based on static predictors, with minimal understanding of the relationship of dynamic changes in symptoms and social context to illness trajectories. Using a smartphone app, we will collect 8 symptom reports a day for 21 days in youth ages 15-25 with psychotic symptoms to better characterize the dynamic interaction of affect, psychosis, social context, and thoughts of self-harm during the early course of major mental illness.
