{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T04:25:03Z","timestamp":1789705503691,"version":"4.0.1"},"reference-count":29,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2020,3,1]],"date-time":"2020-03-01T00:00:00Z","timestamp":1583020800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Telemedicine and e-Health"],"published-print":{"date-parts":[[2020,3]]},"abstract":"<jats:sec>\n                    <jats:title>Introduction:<\/jats:title>\n                    <jats:p>Parkinson's disease affects over 10 million people globally, and \u223c20% of patients with Parkinson's disease have not been diagnosed as such. The clinical diagnosis is costly: there are no specific tests or biomarkers and it can take days to diagnose as it relies on a holistic evaluation of the individual's symptoms. Existing research either predicts a Unified Parkinson Disease Rating Scale rating, uses other key Parkinsonian features such as tapping, gait, and tremor to diagnose an individual, or focuses on different audio features.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>In this article, we present a classification approach implemented as an iOS App to detect whether an individual has Parkinson's using 10-s audio clips of the individual saying \u201caaah\u201d into a smartphone.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>The 1,000 voice samples analyzed were obtained from the mPower (mobile Parkinson Disease) study, which collected 65,022 voice samples from 5,826 unique participants.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions:<\/jats:title>\n                    <jats:p>The experimental results comparing 12 different methods indicate that our approach achieves 99.0% accuracy in under a second, which significantly outperforms both prior diagnosis methods in the accuracy achieved and the efficiency of clinical diagnoses.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1089\/tmj.2018.0271","type":"journal-article","created":{"date-parts":[[2019,4,29]],"date-time":"2019-04-29T10:24:17Z","timestamp":1556533457000},"page":"327-334","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":63,"title":["Robust Detection of Parkinson's Disease Using Harvested Smartphone Voice Data: A Telemedicine Approach"],"prefix":"10.1177","volume":"26","author":[{"given":"Sanjana","family":"Singh","sequence":"first","affiliation":[{"name":"McLean High School, McLean, Virginia."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenyao","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, State University of New York at Buffalo, Buffalo, New York."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2020,3,4]]},"reference":[{"key":"e_1_3_3_2_2","unstructured":"Parkinson's Foundation. 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