[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127146-en":3,"doc-seo-127146-105":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},127146,687207022233,"Riley","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",7,"Healthcare","Identifying major depressive disorder in older adults through naturalistic driving behaviors and machine learning","Depression in older adults is frequently underdiagnosed and linked to adverse outcomes, including motor vehicle crashes. With a growing population of older drivers in the United States, improved screening is needed to detect individuals at highest risk of decline. This study applies machine learning to real-world naturalistic driving data, analyzing two years of GPS data from 157 older adults to identify depression status and test whether demographics and medication information enhance model performance.","Washington University School of Medicine  \nDigital Commons@Becker  \n\n| 2020-Current year OA Pubs | Open Access Publications |\n| --- | --- |\n| 2-15-2025\u003Cbr>Identifying major depressive disorder in older adults through naturalistic driving behaviors and machine learning\u003Cbr>Chen Chen\u003Cbr>Washington University School of Medicine in St. Louis David C Brown\u003Cbr>Washington University School of Medicine in St. Louis Noor Al-Hammadi\u003Cbr>Washington University School of Medicine in St. Louis Sayeh Bayat\u003Cbr>University of Calgary\u003Cbr>Anne Dickerson\u003Cbr>East Carolina University\u003Cbr>See next page for additional authors\u003Cbr>Follow this and additional works at: [https://digitalcommons.wustl.edu/oa_4](https://digitalcommons.wustl.edu/oa_4)\u003Cbr> Part of the Medicine and Health Sciences Commons\u003Cbr>Please let us know how this document benefits you. |  |\n\nRecommended Citation  \nChen, Chen; Brown, David C; Al-Hammadi, Noor; Bayat, Sayeh; Dickerson, Anne; Vrkljan, Brenda; Blake, Matthew; Zhu, Yiqi; Trani, Jean-Francois; Lenze, Eric J; Carr, David B; and Babulal, Ganesh M, \"Identifying major depressive disorder in older adults through naturalistic driving behaviors and machine learning.\" npj Digital Medicine. 8, 1. 102 (2025) .  \n[https://digitalcommons.wustl.edu/oa_4/4867](https://digitalcommons.wustl.edu/oa_4/4867)  \nThis Open Access Publication is brought to you for free and open access by the Open Access Publications at Digital Commons@Becker. It has been accepted for inclusion in 2020-Current year OA Pubs by an authorized administrator of Digital Commons@Becker. For more information, [please contact](please contact vanam@wustl.edu)[ vanam@wustl.edu](please contact vanam@wustl.edu).  \nAuthors  \nChen Chen, David C Brown, Noor Al-Hammadi, Sayeh Bayat, Anne Dickerson, Brenda Vrkljan, Matthew Blake, Yiqi Zhu, Jean-Francois Trani, Eric J Lenze, David B Carr, and Ganesh M Babulal  \nThis open access publication is available at Digital Commons@Becker: [https://digitalcommons.wustl.edu/oa_4/4867](https://digitalcommons.wustl.edu/oa_4/4867)  \nnpj | digital medicine Article  \n\n| Published in partnership with Seoul National University Bundang Hospital |  |  |\n| --- | --- | --- |\n| [https://doi.org/10.1038/s41746-025-01500-w](https://doi.org/10.1038/s41746-025-01500-w) |  |  |\n| Identifying major depressive disorder in older adults through naturalistic driving behaviors and machine learning\u003Cbr> Check for updates |  |  |\n| Chen Chen1, David C. Brown1, NoorAl-Hammadi1, Sayeh Bayat2,3, Anne Dickerson4, Brenda Vrkljan5, Matthew Blake1, Yiqi Zhu1, Jean-FrancoisTrani6,7,8,9, Eric J. Lenze10, David B. Carr11 &Ganesh M. Babulal1,7,9  |  |  |\n| Depression in older adults is often underdiagnosed and has been linked to adverse outcomes, including motor vehicle crashes. With a growing population of older drivers in the United States, innovations in screening methods are needed to identify older adults at greatest risk of decline. This study used machine learning techniques to analyze real-world naturalistic driving data to identify depression status in older adults and examined whether specific demographics and medications improved model performance. We analyzed two years of GPS data from 157 older adults, including 81 with major depressive disorder, using XGBoost and logistic regression models. The top-performing model achieved an area under the curve of 0.86 with driving features combined with total medication use. These findings suggest that naturalistic driving data holds high potential as a functional digital neurobehavioral marker for AI identifying depression in older adults on a national scale, thereby ensuring equitable access to treatment. |  |  |\n| In 2022, depression prevalence in the United States (US) was 13%, with a resulting economic burden estimated at $233 billion (2016 dollars) and higher rates observed among women compared to men1,2. Major Depressive Disorder (MDD) affects 14.4% of older adults in the US, with 5.4%(2.2 million) having an active diagnosis of MDD,","cbCaiudHggfi8xMx","https://ap.wps.com/l/cbCaiudHggfi8xMx","pdf",747828,2,1,10,"English","en",105,"# Introduction\n## Clinical and diagnostic challenges in older adults\n## Driving safety and depression risk\n# Methods\n## Naturalistic driving data and modeling approach\n# Results\n## Model performance and key predictive features\n# Discussion\n## Implications as a digital neurobehavioral marker","[{\"question\":\"How does the study identify major depressive disorder in older adults?\",\"answer\":\"It uses machine learning models to analyze real-world naturalistic driving data, including GPS-derived driving features, to infer depression status.\"},{\"question\":\"What data and sample size were used in the analysis?\",\"answer\":\"The study analyzes two years of GPS data from 157 older adults, including individuals with major depressive disorder.\"},{\"question\":\"Which modeling approaches were evaluated?\",\"answer\":\"XGBoost and logistic regression models were used, and model performance was assessed using metrics reported in the article.\"}]","Identifying major depressive disorder in older adults through naturalistic driving behaviors and machine learning | 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