@article{ref1,
title="Detecting major depressive disorder presence using passively-collected wearable movement data in a nationally-representative sample",
journal="Psychiatry research",
year="2023",
author="Price, George D. and Heinz, Michael V. and Collins, Amanda C. and Jacobson, Nicholas C.",
volume="332",
number="",
pages="e115693-e115693",
abstract="Major Depressive Disorder (MDD) is a heterogeneous disorder, resulting in challenges with early detection. However, changes in sleep and movement patterns may help improve detection. Thus, this study aimed to explore the utility of wrist-worn actigraphy data in combination with machine learning (ML) and deep learning techniques to detect MDD using a commonly used screening method: Patient Health Questionnaire-9 (PHQ-9). Participants (N = 8,378; MDD Screening = 766 participants) completed the and wore Actigraph GT3X+ for one week as part of the National Health and Nutrition Examination Survey (NHANES). Leveraging minute-level, actigraphy data, we evaluated the efficacy of two commonly used ML approaches and identified actigraphy-derived biomarkers indicative of MDD. We employed two ML modeling strategies: (1) a traditional ML approach with theory-driven feature derivation, and (2) a deep learning Convolutional Neural Network (CNN) approach, coupled with gramian angular field transformation. <br><br>FINDINGS revealed movement-related features to be the most influential in the traditional ML approach and nighttime movement to be the most influential in the CNN approach for detecting MDD. Using a large, nationally-representative sample, this study highlights the potential of using passively-collected, actigraphy data for understanding MDD to better improve diagnosing and treating MDD.<p /> <p>Language: en</p>",
language="en",
issn="0165-1781",
doi="10.1016/j.psychres.2023.115693",
url="http://dx.doi.org/10.1016/j.psychres.2023.115693"
}