{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Ymeri G"],"funding":["Mats Paulssons Stiftelse","Internet of Things and People (IOTAP) research center","Malmö University"],"pagination":["e077766"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10759062"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["13(12)"],"pubmed_abstract":["<h4>Introduction</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.<h4>Aim</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.<h4>Methods and analysis</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"],"journal":["BMJ open"],"pubmed_title":["Quantifying Parkinson's disease severity using mobile wearable devices and machine learning: the ParkApp pilot study protocol."],"pmcid":["PMC10759062"],"funding_grant_id":["NA"],"pubmed_authors":["Ymeri G","Salvi D","Olsson CM","Tsanas A","Svenningsson P","Wassenburg MV"],"additional_accession":[]},"is_claimable":false,"name":"Quantifying Parkinson's disease severity using mobile wearable devices and machine learning: the ParkApp pilot study protocol.","description":"<h4>Introduction</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.<h4>Aim</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.<h4>Methods and analysis</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","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Dec","modification":"2026-07-14T15:35:03.05Z","creation":"2025-02-18T23:23:56.233Z"},"accession":"S-EPMC10759062","cross_references":{"pubmed":["38154904"],"doi":["10.1136/bmjopen-2023-077766"]}}