{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T07:20:18Z","timestamp":1770276018807,"version":"3.49.0"},"reference-count":55,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,12,22]],"date-time":"2021-12-22T00:00:00Z","timestamp":1640131200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Lundbeck (Denmark)","award":["N\/A"],"award-info":[{"award-number":["N\/A"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>People with Parkinson\u2019s disease (PD) experience significant impairments to gait and balance; as a result, the rate of falls in people with Parkinson\u2019s disease is much greater than that of the general population. Falls can have a catastrophic impact on quality of life, often resulting in serious injury and even death. The number (or rate) of falls is often used as a primary outcome in clinical trials on PD. However, falls data can be unreliable, expensive and time-consuming to collect. We sought to validate and test a novel digital biomarker for PD that uses wearable sensor data obtained during the Timed Up and Go (TUG) test to predict the number of falls that will be experienced by a person with PD. Three datasets, containing a total of 1057 (671 female) participants, including 71 previously diagnosed with PD, were included in the analysis. Two statistical approaches were considered in predicting falls counts: the first based on a previously reported falls risk assessment algorithm, and the second based on elastic net and ensemble regression models. A predictive model for falls counts in PD showed a mean R2 value of 0.43, mean error of 0.42 and a mean correlation of 30% when the results were averaged across two independent sets of PD data. The results also suggest a strong association between falls counts and a previously reported inertial sensor-based falls risk estimate. In addition, significant associations were observed between falls counts and a number of individual gait and mobility parameters. Our preliminary research suggests that the falls counts predicted from the inertial sensor data obtained during a simple walking task have the potential to be developed as a novel digital biomarker for PD, and this deserves further validation in the targeted clinical population.<\/jats:p>","DOI":"10.3390\/s22010054","type":"journal-article","created":{"date-parts":[[2021,12,23]],"date-time":"2021-12-23T02:02:57Z","timestamp":1640224977000},"page":"54","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Predicting Fall Counts Using Wearable Sensors: A Novel Digital Biomarker for Parkinson\u2019s Disease"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4445-9609","authenticated-orcid":false,"given":"Barry R.","family":"Greene","sequence":"first","affiliation":[{"name":"Kinesis Health Technologies Ltd., D04 V2N9 Dublin, Ireland"}]},{"given":"Isabella","family":"Premoli","sequence":"additional","affiliation":[{"name":"Biomarker Department, Division of Experimental Medicine, H. Lundbeck A\/S, 2500 Copenhagen, Denmark"},{"name":"Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King\u2032s College London, London SE5 9RX, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7156-4493","authenticated-orcid":false,"given":"Killian","family":"McManus","sequence":"additional","affiliation":[{"name":"Kinesis Health Technologies Ltd., D04 V2N9 Dublin, Ireland"},{"name":"Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland"}]},{"given":"Denise","family":"McGrath","sequence":"additional","affiliation":[{"name":"School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland"}]},{"given":"Brian","family":"Caulfield","sequence":"additional","affiliation":[{"name":"Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland"},{"name":"School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1016\/S1474-4422(06)70471-9","article-title":"Epidemiology of Parkinson\u2019s disease","volume":"5","author":"Breteler","year":"2006","journal-title":"Lancet Neurol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1449","DOI":"10.1002\/mds.20609","article-title":"Burden of illness in Parkinson\u2019s disease","volume":"20","author":"Huse","year":"2005","journal-title":"Mov. 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