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Utilizing Passively Collected Data in Daily Life to Enhance the Assessment and Treatment of Mental Health

SEPTEMBER 23, 2022

Nicholas C. Jacobson, PhD
Assistant Professor of Biomedical Data Science and Psychiatry, Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College

About the Presentation: Dr. Jacobson’s talk will discuss the use of technology to passively assess mental health and foster personalized treatment of mental health at scale.

About the Presenter: Dr. Nicholas C. Jacobson is an Assistant Professor of Biomedical Data Science and Psychiatry at the Geisel School of Medicine at Dartmouth. He received his PhD in Clinical Psychology from the Pennsylvania State University and completed his clinical fellowship and post-doctoral fellowship at Massachusetts General Hospital/Harvard Medical School. Dr. Jacobson researches the use of technology to enhance both the assessment and treatment of anxiety and depression. His work has focused on (1) enhancing precision assessment of anxiety and depression using intensive longitudinal data, (2) conducting multimethod assessment utilizing passive sensor data from smartphones and wearable devices, and (3) providing scalable, personalized technology-based treatments utilizing smartphones. He has a strong interest in creating personalized just-in-time adaptive interventions and the quantitative tools that make this work possible. To date, Dr. Jacobson’s smartphone applications which assess and treat anxiety and depression have been downloaded and installed by more than 50,000 people in over 100 countries.

Additionally, Dr. Jacobson has a strong quantitative background in analyzing intensive longitudinal data. In his work, he employs many different types of analyses including structural equation modeling, multilevel modeling, time-series techniques, dynamical systems modeling, and machine learning. He created a novel modeling technique, entitled the Differential Time-Varying Effect Model (DTVEM), which allows researchers to discover and model optimal lag times in intensive longitudinal data.