Author ORCID Identifier
Date of Award
Spring 3-5-2024
Document Type
Thesis (Ph.D.)
Department or Program
Quantitative Biomedical Sciences
First Advisor
Dr. Nicholas Jacobson
Second Advisor
Dr. Jennifer Emond
Third Advisor
Dr. Lisa Marsch
Abstract
Major depressive disorder (MDD) is a debilitating and heterogenous mental health disorder that is characterized by symptoms including low mood, issues with sleep, psychomotor difficulties, and fatigue. MDD affects one in twenty adults worldwide and has shown increased prevalence in the United States over the past twenty years. As such, efforts to effectively screen, diagnose, and treat MDD are paramount. However, to address these concerns, an increased understanding of an individual’s daily behavior is required, which is not adequately captured by infrequent clinical visits. One such method for consistent, longitudinal observation, passively-collected accelerometry, serves as an observational method for unobtrusively capturing movement, sedentary and sleep behaviors in real-time. Therefore, the primary effort of this thesis work is to investigate the utility of leveraging longitudinal, passively-collected accelerometer information in detecting and predicting outcomes related to MDD. The primary data source for this work comes from the nationally representative 2011-2014 National Health And Nutrition Examination Survey and two separate clinical samples. A combination of unsupervised machine learning, supervised machine learning, and deep learning techniques were used to characterize movement, sedentary and sleep behaviors for individuals with MDD, as well as investigate depression presence, individual depressive symptoms, long-term depression variability, and acute depression variability. Taken together, this work highlights the utility of using solely passively-collected accelerometry to investigate outcomes related to MDD and seeks to provide insight into the utility of implementing such approaches in real-world clinical settings to both improve our understanding of, and opportunities for, clinical engagement for individuals with MDD.
Recommended Citation
Price, George D., "LEVERAGING PASSIVELY-COLLECTED WEARABLE ACCELEROMETRY DATA COUPLED WITH MACHINE LEARNING TO LONGITUDINALLY DETECT AND PREDICT MAJOR DEPRESSIVE DISORDER" (2024). Dartmouth College Ph.D Dissertations. 279.
https://digitalcommons.dartmouth.edu/dissertations/279
