University of Oulu

STOP : a smartphone-based game for Parkinson’s disease medication adherence

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Author: Kan, Valerii1
Organizations: 1University of Oulu, Faculty of Information Technology and Electrical Engineering, Department of Information Processing Science, Information Processing Science
Format: ebook
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 1.6 MB)
Persistent link:
Language: English
Published: Oulu : V. Kan, 2018
Publish Date: 2018-06-04
Thesis type: Master's thesis
Tutor: Rajanen, Dorina
Teixeira Ferreira, Denzil
Reviewer: Rajanen, Dorina
Teixeira Ferreira, Denzil
Parkinson’s disease (PD) is a second most common neurological disorder that affects up to 10 million people worldwide. However, in spite of this vast number of patients, there is still no universal and applicable for everyone type of treatment. The current way of PD handling is followed by semiannual clinical visits with observations on the place and the corresponding medication prescription. However, the problem is that PD has an evolving nature and its symptoms may vary several times per day. Thus, the current way of observation does not provide a full picture of the disease and makes the personal treatment plan customization impossible. This study aims at the development of a new way of patient observation via mobile devices that can increase the patients’ medication adherence. The outcome of the study is the mobile application that leverages smartphone’s inbuilt sensors in order to keep track of subject’s state of health during the day. In order to encourage patients to regularly follow health sampling, the application uses gamification approach: the sampling sessions are implemented as a short-term accelerometer-based game that asks patients to play it several times per day. Along with it, with the use of smartphones notifications, the application reminds patients to take medications on time and record the timestamps to the application medication journal. The designed application will be used as a tool for continuous observation on the PD patients. The combination of datasets collected with the application can be used in the future studies in order to estimate the correlation between the medication effect and the severity of PD during the day.
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Copyright information: © Valerii Kan, 2018. This publication is copyrighted. You may download, display and print it for your own personal use. Commercial use is prohibited.