Smartphone-based digital phenotyping to detect high-risk affect states in body dysmorphic disorder

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Body dysmorphic disorder (BDD) is associated with extremely high risk for suicide attempts (22-28%) and substance use disorders (49%), underscoring the critical importance of risk detection in BDD. Negative affect states - particularly anxiety and shame - are well-documented risk factors for suicide and substance use in BDD, offering clear targets for risk detection and intervention. This K23 aims to develop and validate unobtrusive, time-sensitive, and ecologically valid measures of anxiety, shame, and general negative affect states in BDD, using smartphone-based digital phenotyping. Passive (i.e., unobtrusive) smartphone measurement of negative affect states will be based on GPS, accelerometer, and communication logs, used to detect behavioral features of anxiety (avoidance, rituals), shame (social withdrawal, isolation), and general negative affect (aggregated avoidance, rituals, withdrawal, and isolation features). We will collect passive and active (i.e., ecological momentary assessment [EMA]) smartphone data in 85 adults with BDD and will use EMA ratings of negative affect as outcomes, to build and validate predictive statistical models from passive data. We will also test the hypotheses that passive smartphone measures of negative affect states can significantly predict next-day suicidal ideation and substance use in BDD, above and beyond common clinical indices of risk. This project synthesizes the Candidate’s expertise in emotion-based risk for suicide in BDD with her experience conducting smartphone research. Building from this foundation, this K23 will provide critical new training in key areas to launch the Candidate’s independent research career: (1) digital phenotyping, including statistical learning and longitudinal analysis; (2) EMA methods; (3) assessment of suicide and substance use; (4) career development, including R01 writing; and (5) ethics of technology-based suicide and substance use research. Training goals will be accomplished with stellar mentorship and institutional support at Massachusetts General Hospital and Harvard Medical School. Dr. Sabine Wilhelm, a leader in BDD and clinical research, will serve as the primary mentor. Dr. Jukka-Pekka Onnela, an expert in digital phenotyping and its statistical approaches, and Dr. Michael Armey, an expert in EMA research of emotions and suicide, will serve as co-mentors. Complementary guidance in EMA and substance use will be provided by the advisory team: Drs. Bettina Hoeppner and A. Eden Evins. In line with NIMH Strategic Objective 2, this K23 will yield scalable, unobtrusive tools to detect acute, modifiable risk factors for suicide and substance use in a high-risk population. Moreover, negative affect states are transdiagnostic risk factors. As a next step to this proof-of- concept K23, the Candidate will apply for an R01 to further validate passive mobile detection of negative affect states and their ability to predict risk transdiagnostically. This program of research can enable (1) personalized just-in-time interventions targeting high-risk affect states, to reduce suicide and substance use; (2) unobtrusive monitoring of changes in risk; and (3) large-scale, ecologically-valid longitudinal research of risk processes.