Monitoring Affective State Change with Wearable Technology: Assessing Physiological and Behavioral Mechanisms in Premenstrual Dysphoric Disorder

2021 Award: $40,000

Real-time monitoring during the transition in to or out of depression (affective state change) is an important precursor to providing targeted interventions to reduce high-risk periods of suicide and early detection and treatment for depression relapse. In this project, we capitalize on widely used wearable technology that can passively collect such real-time data including variables known to be associated with affective state change. Premenstrual dysphoric disorder (PMDD) provides a depression model with monthly switching of affective state. Establishing the physiological variables related to mood transition in PMDD is a first critical step in identifying mechanisms of risk for depression and suicide to develop a prediction model that may be applied to other mental health conditions.

Need/Problem: Depression rates are continuing to rise from the COVID-19 pandemic. Up to 80% of individuals with depression are not receiving treatment and with the high risk of recurrence, many wait until after relapse to seek clinical attention making treatment far more challenging. In addition, periods of affective state change are high-risk periods for suicide. Real-time monitoring during these high-risk periods is critical for reducing suicide and early identification for depression relapse.

Grant Summary: Smartphone devices and wearable technology offer a unique opportunity to provide real-time monitoring with a variety of sensors that can passively collect data. We will use a depression subtype with a known, frequent, and regularly occurring (monthly) trigger, premenstrual dysphoric disorder (PMDD), as a model to investigate mechanisms of affective state switching using real-time monitoring data.

Goals and Projected Outcomes: Our results will provide important information for 1) feasibility of commercially available technology as a clinical tool to collect physiological and behavioral measures, and 2) understanding the underlying mechanism of affective state change.

Julianna Prim, PhD

Grant Details: Currently there is no way to monitor affective state changes besides subjective self-reported symptoms.  Our project capitalizes on commercially available wearable technology to provide passively-collected, moment-by-moment data that can be quantified on an individual level including variables associated with depression- physical activity, sleep, heart rate variability, and social media engagement. The PMDD model is ideal to examine the relationship of these physiological and behavioral variables with real time mood data and allow us to further elucidate affective state change mechanisms. Our results will serve as a critical first step in development of a prediction model that identifies variable that increase risk for depression and suicide leading to early detection and prevention prospectively.