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This study investigates two tests from the Neurological Functional Tests Suite (NeuFun-TS) smartphone application, the \u201cPostural Sway\u201d and \u201cPronator Drift\u201d tests. These tests capture different domains of postural control and motoric dysfunction in healthy volunteers (\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u200913) and people with neurological disorders (n\u2009=\u200968 relapsing\u2013remitting multiple sclerosis [MS];\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u200921 secondary progressive MS;\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u200923 primary progressive MS;\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u200913 other inflammatory neurological diseases;\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u200921 non-inflammatory neurological diseases;\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u20094 clinically isolated syndrome;\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u20091 radiologically isolated syndrome). Smartphone accelerometer data was transformed into digital biomarkers, which were filtered in the training cohort (~\u200980% of subjects) for test\u2013retest reproducibility and correlations with subdomains of neurological examinations and validated imaging biomarkers. The independent validation cohort (~\u200920%) determined whether biomarker models outperformed the best single digital biomarkers. Postural sway acceleration magnitude in the eyes closed and feet together stance demonstrated the highest reliability (ICC\u2009=\u20090.706), strongest correlations with age (Pearson r\u2009&lt;=\u20090.82) and clinical and imaging outcomes (r\u2009&lt;=\u20090.65,\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u20090.001) and stronger predictive value for sway-relevant neurological disability outcomes than models that aggregated multiple biomarkers (coefficient of determination R\n                    <jats:sup>2<\/jats:sup>\n                    \u2009=\u20090.46 vs 0.38). The pronator drift test only captured cerebellar dysfunction, had less reproducible biomarkers, but provided additive value when combined with postural sway biomarkers into models predicting global scales of neurological disability. In conclusion, a simple 1-min postural sway test accurately measures body oscillations that increase with natural aging and differentiates them from abnormally increased body oscillations in people with neurological disabilities.\n                  <\/jats:p>","DOI":"10.1186\/s12883-025-04038-2","type":"journal-article","created":{"date-parts":[[2025,2,5]],"date-time":"2025-02-05T05:06:21Z","timestamp":1738731981000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Smartphone postural sway and pronator drift tests as measures of neurological disability"],"prefix":"10.1186","volume":"25","author":[{"given":"Michael","family":"Calcagni","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter","family":"Kosa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bibi","family":"Bielekova","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,2,5]]},"reference":[{"key":"4038_CR1","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1016\/j.gaitpost.2018.10.005","volume":"67","author":"KL Hsieh","year":"2019","unstructured":"Hsieh KL, Roach KL, Wajda DA, Sosnoff JJ. 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