Nicholas C. Jacobson

  • Assistant Professor of Biomedical Data Science and Psychiatry

  • Adjunct Assistant Professor of Computer Science

Nick Jacobson is an assistant professor in the departments of Biomedical Data Science and Psychiatry, and an adjunct assistant professor in Computer science. Dr. Jacobson holds a leadership position in the the Center for Technology and Behavioral Health in the Geisel School of Medicine at Dartmouth College. He directs the AI and Mental Health: Innovation in Technology Guided Healthcare (AIM HIGH) Laboratory. 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. Dr. Jacobson is the principal investigator of an R01 Awarded from the National Institute of Mental Health studying the use of personalized deep learning models to predict rapid changes in major depressive disorder symptoms using passive sensor data from smartphones and wearable devices. 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. He recently developed an anxiety and depression monitoring application entitled Mood Triggers which helps users to learn the triggers of their anxiety and depression in their daily lives. He also created a statistical package (written in R) called the Differential Time-Varying Effect Model (DTVEM) which is used to explore optimal time lags in intensive longitudinal data.


HB 7255