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Quantifying Parkinson's disease severity using mobile wearable devices and machine learning: the ParkApp pilot study protocol
Malmö University, Internet of Things and People (IOTAP). Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).ORCID iD: 0000-0002-7102-083X
Malmö University, Internet of Things and People (IOTAP). Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).ORCID iD: 0000-0002-9203-1124
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).ORCID iD: 0000-0002-4261-281X
Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden; Center for Neurology, Academic Specialist Center Torsplan, Region Stockholm, Sweden.
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2023 (English)In: BMJ Open, E-ISSN 2044-6055, Vol. 13, no 12, article id e077766Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
BMJ Publishing Group Ltd, 2023. Vol. 13, no 12, article id e077766
Keywords [en]
health informatics, parkinson's disease, telemedicine
National Category
Neurology
Identifiers
URN: urn:nbn:se:mau:diva-64860DOI: 10.1136/bmjopen-2023-077766ISI: 001134943800008PubMedID: 38154904Scopus ID: 2-s2.0-85181165016OAI: oai:DiVA.org:mau-64860DiVA, id: diva2:1824861
Available from: 2024-01-08 Created: 2024-01-08 Last updated: 2024-10-30Bibliographically approved
In thesis
1. Machine Learning-Driven Analysis of Sensor Data for Objective Assessment of Parkinson's Disease Motor Symptoms in Home Environments
Open this publication in new window or tab >>Machine Learning-Driven Analysis of Sensor Data for Objective Assessment of Parkinson's Disease Motor Symptoms in Home Environments
2024 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Parkinson’s disease (PD) is a progressive neurodegenerative brain disorder that signifi- cantly impacts quality of life for those who are affected. It is a rapidly growing condition affecting millions of people worldwide, where treatments focus on managing symptoms and slowing the degenerative process, as there are no validated treatments that can stop its progression or preemptively prevent it. Effective management of the disease relies on accurate and timely assessment of symptoms based on clinical ratings, traditionally performed through clinical examinations using the Movement Disorder Society-Unified Parkinson’s Disease Rating Scale (MDS-UPDRS). However, in-clinic assessments are infrequent and may not capture the full spectrum of symptom fluctuations in daily life. While existing literature has focused on diagnosing PD, the current understanding falls short in terms of objectively quantifying its symptoms in daily-living conditions.

Following a design science research methodology, this thesis responds to this research gap by exploring the feasibility of using smartphones to quantify PD symptoms in a real- world, at-home setting. The research presents a cross-platform mobile application de- veloped for data collection from PD patients with the aim to identify promising system components and data types for capturing PD symptoms. Using data mining and machine learning techniques, the research explores if it is feasible to estimate the MDS-UPDRS scale based on objective measurements from smartphone-collected data. Additionally, it investigates the usability of the proposed mobile application for PD patients. By de- veloping and validating a cross-platform mobile application for symptom capturing, this thesis contributes both in terms of research results communicated in the associated peer- reviewed papers, and by providing an open source based app which makes PD symptom assessments more accessible, objective, and patient-centric.

Place, publisher, year, edition, pages
Malmö: Malmö University Press, 2024. p. 47
Series
Studies in Computer Science ; 27
National Category
Computer Sciences
Identifiers
urn:nbn:se:mau:diva-71851 (URN)10.24834/isbn.9789178774913 (DOI)9789178774906 (ISBN)9789178774913 (ISBN)
Presentation
2024-10-16, Niagara, hörsal B2, Nordenskiöldsgatan 1, Malmö, 13:00 (English)
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Note: The papers are not included in the fulltext online.

Paper V in dissertation as manuscript.

Available from: 2024-11-04 Created: 2024-10-30 Last updated: 2024-11-04Bibliographically approved

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Ymeri, GentSalvi, DarioOlsson, Carl Magnus

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