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Usability of a Mobile Application for Patients with Parkinson’s Disease
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, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
Karolinska Institute, Department of Clinical Neuroscience, Stockholm, Sweden.
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
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2024 (English)In: 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Institute of Electrical and Electronics Engineers (IEEE), 2024, p. 1-6Conference paper, Published paper (Refereed)
Abstract [en]

This paper investigates usability aspects of a mobile application aimed at monitoring symptoms of Parkinson’s disease (PD) patients. Thirty PD patients collected data through mobile-based questionnaires and activity tasks aimed at measuring motor and non-motor symptoms for a duration of two months. We report the results about usability conducted within this study. A combination of methods consisting of the uMARS questionnaire and interviews with PD patients inform the usability aspects of the mobile application. Results indicate that the app is overall received well and is usable (median uMARS score=4). Interviews reveal usability issues related to the size of textual instructions and buttons, and to the context of use of the app, particularly when the phone is used as a sensor. These findings highlight the need of co-design and preliminary testing when developing apps for PD.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2024. p. 1-6
Series
Annual International Conference of the IEEE Engineering in Medicine and Biology Society, ISSN 2375-7477, E-ISSN 2694-0604
Keywords [en]
Parkinson’s disease, mobile app, usability, uMARS
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:mau:diva-72836DOI: 10.1109/embc53108.2024.10782276Scopus ID: 2-s2.0-85214996203ISBN: 979-8-3503-7149-9 (electronic)OAI: oai:DiVA.org:mau-72836DiVA, id: diva2:1922927
Conference
46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA , 15-19 July 2024
Funder
Knowledge FoundationAvailable from: 2024-12-19 Created: 2024-12-19 Last updated: 2025-01-27Bibliographically 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)
Opponent
Supervisors
Note

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: 2024-12-19Bibliographically approved

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Ymeri, GentMaus, BenjaminOlsson, Carl MagnusSalvi, Dario

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