Article Text

Quantifying Parkinson’s disease severity using mobile wearable devices and machine learning: the ParkApp pilot study protocol
  1. Gent Ymeri1,2,
  2. Dario Salvi1,2,
  3. Carl Magnus Olsson1,2,
  4. Myrthe Vivianne Wassenburg3,4,
  5. Athanasios Tsanas5,6,
  6. Per Svenningsson3,4
  1. 1Department of Computer Science and Media Technology (DVMT), Malmö University, Malmö, Sweden
  2. 2Internet of Things and People Research Center (IOTAP), Malmö University, Malmö, Sweden
  3. 3Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden
  4. 4Center for Neurology, Academic Specialist Center Torsplan, Region Stockholm, Sweden
  5. 5Usher Institute, Edinburgh Medical School, The University of Edinburgh, Edinburgh, UK
  6. 6Alan Turing Institute, London, UK
  1. Correspondence to Gent Ymeri; gent.ymeri{at}


Introduction 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.

Aim 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.

Methods and analysis In an ongoing clinical feasibility study, participants with a confirmed PD diagnosis are being recruited. Participants perform activity tests, including Timed Up and Go (TUG), tremor, finger tapping, drawing and vocalisation, once a week for 2 months using the Mobistudy smartphone app in their homes. Concurrently, participants wear the GENEActiv wrist device for 28 days to measure actigraphy continuously. In addition to using sensors, participants complete the Beck’s Depression Inventory, Non-Motor Symptoms Questionnaire (NMSQuest) and Parkinson’s Disease Questionnaire (PDQ-8) questionnaires at baseline, at 1 month and at the end of the study. Sleep disorders are assessed through the Parkinson’s Disease Sleep Scale-2 questionnaire (weekly) and a custom sleep quality daily questionnaire. User experience questionnaires, Technology Acceptance Model and User Version of the Mobile Application Rating Scale, are delivered at 1 month. Clinical assessment (Movement Disorder Society-Unified Parkinson Disease Rating Scale (MDS-UPDRS)) is performed at enrollment and the 2-month follow-up visit. During visits, a TUG test is performed using the smartphone and the G-Walk motion sensor as reference device. Signal processing and machine learning techniques will be employed to analyse the data collected from Mobistudy app and the GENEActiv and correlate them with the MDS-UPDRS. Compliance and user aspects will be informing the long-term feasibility.

Ethics and dissemination The study received ethical approval by the Swedish Ethical Review Authority (Etikprövningsmyndigheten), with application number 2022-02885-01. Results will be reported in peer-reviewed journals and conferences. Results will be shared with the study participants.

  • parkinson's disease
  • health informatics
  • telemedicine

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  • Contributors GY: methodology, software, data curation, formal analysis, investigation, writing—original draft, visualisation. DS: conceptualisation, methodology, software, writing—review and editing, supervision, project administration, funding acquisition. CMO: conceptualisation, methodology, writing—review and editing, supervision, funding acquisition. MVW: investigation, resources, data curation, writing—review and editing. AT: conceptualisation, methodology, writing—review and editing, supervision, funding acquisition. PS: conceptualisation, methodology, resources, writing—review and editing, supervision, funding acquisition.

  • Funding Mats Paulsson foundation, grant number: NA. Internet of Things and People (IOTAP) research center, grant number: NA. Malmö University, grant number: NA.

  • Competing interests None declared.

  • Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.