Author ORCID Identifier
https://orcid.org/0000-0002-6360-2899
Date of Award
Spring 5-29-2024
Document Type
Thesis (Undergraduate)
Department
Computer Science
First Advisor
Nicholas C. Jacobson
Abstract
Emotional regulation and physical activity are known to be associated with depression; however, a deeper understanding of how emotional regulation may strengthen or weaken the bonds between depressive symptoms and physical activity may aid clinicians and researchers in developing cognitive behavioral therapy (CBT) for those adversely affected by depression. As part of the Tracking Depression Study, this analysis uses data collected from 306 participants diagnosed with Major Depressive Disorder. To study their behavior, we analyze actigraphy data, or longitudinal physical activity intensity data, as it relates to depression severity, quantified by the daily PHQ-9 questionnaires. We study these associations through deep learning models, including a novel state-of-the-art pre-trained actigraphy transformer model, to analyze and characterize the association between physical activity and depression severity for two different groups, low and high emotional regulation. The findings of this study indicate that emotional regulation significantly modulates the association between actigraphy and depression symptoms. Specifically, in the group with high emotional regulation, there was a moderate correlation between physical activity patterns and depression severity, particularly during the periods around sleep. However, no significant associations were observed in the group with low emotional regulation. Surprisingly, the overall magnitude of daytime physical activity did not correlate with depression levels, suggesting that specific patterns of activity rather than overall activity levels may be more relevant to understanding depression in the context of emotional regulation. This study highlights the potential of cognitive behavioral therapy that incorporates differences in physical activity patterns and emotional regulation among individuals.
Recommended Citation
Ruan, Franklin Ye, "Emotional Regulation on Modulating Associations between Depression and Physical Activity as Characterized via Deep Learning" (2024). Computer Science Senior Theses. 32.
https://digitalcommons.dartmouth.edu/cs_senior_theses/32
