Paola Pedrelli, Ph.D. - US grants

DESCRIPTION (provided by applicant): This application for a Mentored Patient-Oriented Research Career Award is designed to prepare the candidate, Paola Pedrelli, Ph.D., for a career in patient-oriented research on Alcohol Use Disorders (AUDs) and depressive symptoms. College students with binge drinking and depressive symptoms appear an ideal population with which begin this endeavor given that the co-occurrence of these conditions represent a serious and common public health problem that remarkably has been largely unaddressed in this younger population. Objectives for this training grant are for the candidate to: 1) Develop expertise in AUDs treatment research;2) Develop advanced skills in complex statistics;and 3) Develop RO1 level skills in research ethics for individuals with SUDs co-occurring with depressive symptoms. The stated objectives will be achieved through: 1) Resources at Massachusetts General Hospital and Harvard Medical School;2) Mentoring from co-primary mentors Drs. Maurizio Fava and Roger Weiss, mentors Drs. John Kelly and Brian Borsari and consultants Drs. Hang Lee and Jonathan Alpert;2) Targeted advanced coursework, seminars as well as supervised clinical experiences;and 3) Implementation of the proposed research project. The primary aim of the proposed research project is to systematically examine the efficacy in reducing alcohol consumption of an empirically supported treatment for depression, Cognitive Behavioral Therapy (CBT), enhanced with an empirically supported treatment for substance use, Motivational Interviewing (MI), among college students with binge drinking and depressive symptoms. Secondary aims of the proposal will include exploratory investigation of the role of depressive symptoms on course of alcohol use behavior and of mediators of alcohol outcome. Results of the proposed project will provide preliminary data for an RO1 application, and will lay the foundation for a career-long research program focused on developing treatment protocols for co-occurring AUDs and depressive symptoms. The candidate has demonstrated a strong commitment to a research career and excellence in her clinical and academic endeavors thus far. This training program will assist Dr. Pedrelli in making the transition to independent investigator and to become an expert in treatment for AUDs and co-occurring depressive symptoms. PUBLIC HEALTH RELEVANCE: Among college students, binge drinking is associated with severe negative consequences, and the presence of depressive symptoms leads to an even higher risk of harmful outcome. The study will examine the efficacy in reducing alcohol consumption, as well as depressive symptoms and alcohol related negative consequences of an empirically supported treatment for depression, Cognitive Behavioral Therapy (CBT), enhanced with an empirically supported treatment for substance use, Motivational Interviewing (MI), among college students with these co-occurring symptoms. Identifying an effective intervention for these serious and commonly co-occurring symptoms will have extraordinary public health significance.

Depression is one of the leading causes of disability worldwide, affecting an estimated 300 million people. Evidence-based treatments are available and measurement-based care has been described as the gold standard. Monitoring of depressive symptoms is currently performed with self-administered and interview- based assessment methods conducted by clinicians in their offices. However, the shortage of mental health specialists and the limited resources available to primary care physicians who often manage patients with depression, prevent close monitoring of symptoms delaying optimal treatment potentially prolonging suffering. Passive recording of behavioral data (gathering information without individual's direct input) has been identified as a potentially feasible method for long-term monitoring of depression. To date, most studies have collected passive behavioral data in real time through mobile apps (i.e. accelerometer, phone clicks) with the goal of identifying potential markers of depression. However, this method lacks critical biological indicators of depression, including sleep, arousal, and motion. Recent development in wristband sensor technology developed by out lab has allowed to measure physiological parameters like gait, heart rate variability (HRV) and electrodermal activity (EDA) continuously in ?real time?, allowing a broader anatomical and neurophysiological understanding of emotion, behavior, and cognition in mood disorders as they occur during routine activity. During the past decade, along with the development of sensors, we have seen the progressive use of machine learning, a branch of artificial intelligence that enables the detection of complex patterns in multimodal data, allowing the development of complex models. The combination of sensor technology and machine learning allows detailed measurement in real time of a wealth of behaviors predicting mood variation. Over the past 2 years, our interdisciplinary team, including one of the leading lab on depression research, and one of the most innovative lab on affective computing, has conducted a study applying machine learning analytics to create a model combining wristband sensors data and phone- based passive measurements to assess severity of depressive symptoms. In our pilot study with depressed patients monitored over 8 weeks, we found that an algorithm based on biological and behavioral sensor data could estimate depression severity evaluated by a clinician with high accuracy. The proposed study will further refine our model in a sample of 100 adults with depression, assessed over 12 weeks. We anticipate that the proposed study will enable the development of an objective, passive, sensor-based algorithm able to measure depressive symptom severity. The identification of reliable, objective, passive assessment of depressive symptoms with biosensors will have significant ramifications for the monitoring of depression, early detection of response, remission and relapse and ultimately contribute to the advancement of precision medicine.