Using Machine Learning to predict daily PTSD and cannabis use disorder symptoms among non-treatment seeking veterans

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PROJECT SUMMARY Posttraumatic stress disorder (PTSD) is the highest co-occurring disorder among veterans who report problematic cannabis use. However, many veterans fail to seek or engage with health care services for both conditions, and as a result, increases in symptom severity and corresponding risk may go undetected and unmanaged. Although there is increasing interest in reaching non-treatment-seeking veterans by delivering just-in-time interventions via mobile devices, such interventions require a clear understanding of when veterans with PTSD and problematic cannabis use are at heightened risk for escalating symptoms. Despite ongoing efforts to identify veterans who need support for mental health and substance use difficulties at the time of reintegration (upon return from deployment), these efforts have achieved minimal success. Machine learning-- a special form of artificial intelligence that aids in classifying individuals into risk profiles--may have promise in improving risk assessment and symptom escalation. Machine learning algorithms applied to passively- collected data from mobile and wearable devices (e.g., accelerometer data, time spent looking at screens, sleep data, exercise, GPS data) could be a promising, minimal-burden strategy to detect periods of risk and ultimately inform just-in-time interventions. Passive data from smartphones and wearable devices has been used in machine learning algorithms to predict risk for PTSD and other conditions (e.g., depression), but has not been applied to the prediction of PTSD and cannabis use or the understanding of the interplay between these conditions. Although past research has successfully engaged veterans in passive data collection and this strategy would be lower-burden than active data collection, it is unclear whether this is a feasible approach in clinical applications. Thus, the objective of this application is to understand the utility of passive data, in conjunction with self-report data or alone, in predicting clinically significant escalations in PTSD symptoms and problematic cannabis use among non-treatment seeking veterans who have recently discharged from the military. Seventy-five male and female non-treatment-seeking veterans with a history of trauma exposure and past-month cannabis use who are within six months of civilian reintegration will be recruited online. Participants will be given a FitBit and install the passive and active data collection app on their smartphone (HeadSmart). They will complete a baseline and three monthly follow-up surveys. Further, over the observation period, veterans will complete brief daily surveys of PTSD symptoms and cannabis use, and passive data will be recorded. Passive and daily diary data will be analyzed in machine learning algorithms to predict symptom escalation and future caseness (e.g., presence of clinically significant increase) (Aim 1) and understand daily/weekly symptom interplay (Aim 2). We will also assess the feasibility and acceptability of this approach (Aim 3). The results of this research will ultimately inform prevention or early intervention efforts among this high-need population of veterans.