[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127864-en":3,"doc-seo-127864-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127864,2336474466712,"Maeve","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","LEVERAGING PASSIVELY-COLLECTED WEARABLE ACCELEROMETRY DATA COUPLED WITH MACHINE LEARNING TO LONGITUDINALLY DETECT AND PREDICT MAJOR DEPRESSIVE DISORDER - A Thesis","Major depressive disorder (MDD) is a debilitating, heterogeneous mental health condition defined by symptoms such as low mood, sleep problems, psychomotor difficulties, and fatigue. Given that MDD affects roughly one in twenty adults globally and has increased in the United States, effective screening, diagnosis, and treatment remain urgent. This thesis investigates whether longitudinal, passively collected wearable accelerometry can detect and predict MDD-related outcomes. Using nationally representative and clinical samples, unsupervised, supervised, and deep learning models characterize movement, sedentary, and sleep behaviors to model depression presence, symptom severity, and variability. The work supports real-world clinical potential.","Dartmouth College  \nDartmouth Digital Commons  \n\n| Dartmouth College Ph. D Dissertations | Theses and Dissertations |\n| --- | --- |\n| Spring 3-5-2024\u003Cbr>LEVERAGING PASSIVELY-COLLECTED WEARABLE ACCELEROMETRY DATA COUPLED WITH MACHINE LEARNING TO LONGITUDINALLY DETECT AND PREDICT MAJOR DEPRESSIVE DISORDER\u003Cbr>George D. Price\u003Cbr>Dartmouth College, [george.price.gr@dartmouth.edu](george.price.gr@dartmouth.edu)\u003Cbr>Follow this and additional works at: [https://digitalcommons.dartmouth.edu/dissertations](https://digitalcommons.dartmouth.edu/dissertations)\u003Cbr> Part of the Quantitative Psychology Commons |  |\n\nRecommended Citation  \nPrice, 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.  \n[https://digitalcommons.dartmouth.edu/dissertations/279](https://digitalcommons.dartmouth.edu/dissertations/279)  \nThis Thesis (Ph. D.) is brought to you for free and open access by the Theses and Dissertations at Dartmouth Digital Commons. It has been accepted for inclusion in Dartmouth College Ph. D Dissertations by an authorized administrator of Dartmouth Digital Commons. For more information, please contact [dartmouthdigitalcommons@groups.dartmouth.edu](dartmouthdigitalcommons@groups.dartmouth.edu).  \nLEVERAGING PASSIVELY-COLLECTED WEARABLE ACCELEROMETRY DATA COUPLED WITH MACHINE LEARNING TO LONGITUDINALLY DETECT AND PREDICT MAJOR DEPRESSIVE DISORDER  \nA Thesis  \nSubmitted to the Faculty  \nIn partial fulfillment of the requirements for the  \nDegree of  \nDoctor of Philosophy  \nin  \nQuantitative Biomedical Sciences  \nby George D. Price  \nGuarini School of Graduate and Advanced Studies  \nDartmouth College  \nHanover, New Hampshire  \nMarch 2024  \nExamining Committee:  \n\n| (chair) Nicholas Jacobson |\n| --- |\n| Jennifer Emond |\n| Lisa Marsch |\n\nVarun Mishra  \nF. Jon Kull, Ph.D.  \nDean of the Guarini School of Graduate and Advanced Studies  \nABSTRACT  \nMajor 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.  \nPREFACE  \nI was first introduced to academic research during my sophomore year at Boston College when I j","cbCais3gUTOhuTPQ","https://ap.wps.com/l/cbCais3gUTOhuTPQ","pdf",3582807,4,1,209,"English","en",105,"# Abstract\n# Preface\n# Methods and Data Sources\n## Machine Learning and Deep Learning Approaches\n## Outcomes: Detection and Prediction of MDD","[{\"question\":\"What is the main goal of this thesis?\",\"answer\":\"To evaluate how longitudinal, passively collected wearable accelerometry data can detect and predict outcomes related to major depressive disorder (MDD).\"},{\"question\":\"What data sources are used for the study?\",\"answer\":\"The primary data come from the nationally representative 2011–2014 National Health And Nutrition Examination Survey, supplemented by two separate clinical samples.\"},{\"question\":\"Which outcomes related to MDD are modeled?\",\"answer\":\"The thesis models depression presence, individual depressive symptoms, long-term depression variability, and acute depression variability.\"}]","LEVERAGING PASSIVELY-COLLECTED WEARABLE ACCELEROMETRY DATA COUPLED WITH MACHINE LEARNING TO LONGITUDINALLY DETECT AND PREDICT MAJOR DEPRESSIVE DISORDER - A Thesis | PDF",1785942413,527,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"leveraging-passively-collected-wearable-accelerometry-data-coupled-with-machine-learning-to-longitudinally-detect-and-predict-major-depressive-disorder-a-thesis","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":20},"https://docshare.wps.com/document/leveraging-passively-collected-wearable-accelerometry-data-coupled-with-machine-learning-to-longitudinally-detect-and-predict-major-depressive-disorder-a-thesis/127864/",{"url":53,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-25","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is the main goal of this thesis?","Question",{"text":76,"@type":77},"To evaluate how longitudinal, passively collected wearable accelerometry data can detect and predict outcomes related to major depressive disorder (MDD).","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What data sources are used for the study?",{"text":81,"@type":77},"The primary data come from the nationally representative 2011–2014 National Health And Nutrition Examination Survey, supplemented by two separate clinical samples.",{"name":83,"@type":74,"acceptedAnswer":84},"Which outcomes related to MDD are modeled?",{"text":85,"@type":77},"The thesis models depression presence, individual depressive symptoms, long-term depression variability, and acute depression variability.","https://schema.org",{"og:url":53,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]