<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Ymeri G</submitter><funding>Mats Paulssons Stiftelse</funding><funding>Internet of Things and People (IOTAP) research center</funding><funding>Malmö University</funding><pagination>e077766</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC10759062</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>13(12)</volume><pubmed_abstract>&lt;h4>Introduction&lt;/h4>The clinical assessment of Parkinson's disease (PD) symptoms can present reliability issues and, with visits typically spaced apart 6 months, can hardly capture their frequent variability. Smartphones and smartwatches along with signal processing and machine learning can facilitate frequent, remote, reliable and objective assessments of PD from patients' homes.&lt;h4>Aim&lt;/h4>To investigate the feasibility, compliance and user experience of passively and actively measuring symptoms from home environments using data from sensors embedded in smartphones and a wrist-wearable device.&lt;h4>Methods and analysis&lt;/h4>In an ongoing clinical feasibility study, participants with a confirmed PD diagnosis are being recruited. Participants perform activity tests, including Timed Up and Go</pubmed_abstract><journal>BMJ open</journal><pubmed_title>Quantifying Parkinson's disease severity using mobile wearable devices and machine learning: the ParkApp pilot study protocol.</pubmed_title><pmcid>PMC10759062</pmcid><funding_grant_id>NA</funding_grant_id><pubmed_authors>Ymeri G</pubmed_authors><pubmed_authors>Salvi D</pubmed_authors><pubmed_authors>Olsson CM</pubmed_authors><pubmed_authors>Tsanas A</pubmed_authors><pubmed_authors>Svenningsson P</pubmed_authors><pubmed_authors>Wassenburg MV</pubmed_authors></additional><is_claimable>false</is_claimable><name>Quantifying Parkinson's disease severity using mobile wearable devices and machine learning: the ParkApp pilot study protocol.</name><description>&lt;h4>Introduction&lt;/h4>The clinical assessment of Parkinson's disease (PD) symptoms can present reliability issues and, with visits typically spaced apart 6 months, can hardly capture their frequent variability. Smartphones and smartwatches along with signal processing and machine learning can facilitate frequent, remote, reliable and objective assessments of PD from patients' homes.&lt;h4>Aim&lt;/h4>To investigate the feasibility, compliance and user experience of passively and actively measuring symptoms from home environments using data from sensors embedded in smartphones and a wrist-wearable device.&lt;h4>Methods and analysis&lt;/h4>In an ongoing clinical feasibility study, participants with a confirmed PD diagnosis are being recruited. Participants perform activity tests, including Timed Up and Go</description><dates><release>2023-01-01T00:00:00Z</release><publication>2023 Dec</publication><modification>2026-07-14T15:35:03.05Z</modification><creation>2025-02-18T23:23:56.233Z</creation></dates><accession>S-EPMC10759062</accession><cross_references><pubmed>38154904</pubmed><doi>10.1136/bmjopen-2023-077766</doi></cross_references></HashMap>