{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T02:30:49Z","timestamp":1791426649876,"version":"4.3.3"},"reference-count":28,"publisher":"Wiley","issue":"12","license":[{"start":{"date-parts":[[2023,4,21]],"date-time":"2023-04-21T00:00:00Z","timestamp":1682035200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["The Laryngoscope"],"published-print":{"date-parts":[[2023,12]]},"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Machine learning (ML) analysis of biometric data in non\u2010controlled environments is underexplored.<\/jats:p><\/jats:sec><jats:sec><jats:title>Objective<\/jats:title><jats:p>To evaluate whether ML analysis of physical activity data can be employed to classify whether individuals have postural dysfunction in middle\u2010aged and older individuals.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>A 1\u2009week period of physical activity was measured by a waist\u2010worn uni\u2010axial accelerometer during the 2003\u20132004 National Health and Nutrition Examination Survey sampling period. Features of physical activity along with basic demographic information (42 variables) were paired with ML models to predict the success or failure of a standard 30\u2009s modified Romberg test during which participants had their eyes closed and stood upon a 3\u2010inch compliant surface. Model performance was evaluated by area under the receiver operating characteristic curve (AUC\u2010ROC), balanced accuracy, and F1\u2010score.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The cohort was comprised of 1625 participants \u226540\u2009years (median age 61, IQR 51\u201371). Approximately half (47%) were diagnosed with postural dysfunction having failed the binarized (pass\/fail) scoring mechanism of the modified Romberg exam. Five ML models were trained on the classification task, achieving AUC values ranging from 0.67 to 0.73. The support vector machine (SVM) and a gradient\u2010boosted model, XGBoost, achieved the highest AUC of 0.73 (SD 0.71\u20130.75). Age was the most important variable for SVM classification, followed by four features that evaluated accelerometer counts at various thresholds, including those delineating total, moderate, and moderate\u2010vigorous activity.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>ML analysis of accelerometer\u2010derived physical activity data to classify postural dysfunction in middle\u2010aged and older individuals is feasible in real\u2010world environments such as the home.<\/jats:p><\/jats:sec><jats:sec><jats:title>Level of Evidence<\/jats:title><jats:p>3 <jats:italic>Laryngoscope<\/jats:italic>, 133:3529\u20133533, 2023<\/jats:p><\/jats:sec>","DOI":"10.1002\/lary.30698","type":"journal-article","created":{"date-parts":[[2023,4,21]],"date-time":"2023-04-21T10:31:31Z","timestamp":1682073091000},"page":"3529-3533","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Machine Learning Analysis of Physical Activity Data to Classify Postural Dysfunction"],"prefix":"10.1002","volume":"133","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8052-4327","authenticated-orcid":false,"given":"Erik B.","family":"Vanstrum","sequence":"first","affiliation":[{"name":"Department of Head and Neck Surgery David Geffen School of Medicine at the University of California Los Angeles  Los Angeles California U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8840-9411","authenticated-orcid":false,"given":"Janet S.","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology University of Minnesota  Minneapolis Minnesota U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1635-8627","authenticated-orcid":false,"given":"Yael","family":"Bensoussan","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology\u2014Head and Neck Surgery University of South Florida  Tampa Florida U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alaina M.","family":"Bassett","sequence":"additional","affiliation":[{"name":"California State University  Los Angeles California U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9950-0985","authenticated-orcid":false,"given":"Matthew G.","family":"Crowson","sequence":"additional","affiliation":[{"name":"Massachusetts Eye and Ear  Boston Massachusetts U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter A.","family":"Chiarelli","sequence":"additional","affiliation":[{"name":"Division of Neurosurgery, Children's Hospital Los Angeles, Keck School of Medicine University of Southern California  Los Angeles California U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,4,21]]},"reference":[{"key":"e_1_2_6_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-021-00514-4"},{"key":"e_1_2_6_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-021-10116-x"},{"key":"e_1_2_6_4_1","doi-asserted-by":"publisher","DOI":"10.1093\/gerona\/glz193"},{"key":"e_1_2_6_5_1","doi-asserted-by":"publisher","DOI":"10.1136\/bjsports-2021-104050"},{"key":"e_1_2_6_6_1","doi-asserted-by":"publisher","DOI":"10.3390\/s22051799"},{"key":"e_1_2_6_7_1","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2012.00158"},{"key":"e_1_2_6_8_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12984-021-00854-y"},{"key":"e_1_2_6_9_1","doi-asserted-by":"publisher","DOI":"10.3390\/bios9010029"},{"key":"e_1_2_6_10_1","unstructured":"AlessandriniM MicarelliA VizianoA et al.Body\u2010worn triaxial accelerometer coherence and reliability related to static posturography in unilateral vestibular failure. 6."},{"key":"e_1_2_6_11_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamaoto.2021.1276"},{"key":"e_1_2_6_12_1","doi-asserted-by":"publisher","DOI":"10.3390\/s21186318"},{"key":"e_1_2_6_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00415-020-09931-z"},{"key":"e_1_2_6_14_1","unstructured":"NHANES.About the National Health and Nutrition Examination Survey. 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