University of Oulu

Kuosmanen E, Wolling F, Vega J, Kan V, Nishiyama Y, Harper S, Van Laerhoven K, Hosio S, Ferreira D, Smartphone-Based Monitoring of Parkinson Disease: Quasi-Experimental Study to Quantify Hand Tremor Severity and Medication Effectiveness, JMIR Mhealth Uhealth 2020;8(11):e21543, DOI: 10.2196/21543

Smartphone-based monitoring of Parkinson disease : quasi-experimental study to quantify hand tremor severity and medication effectiveness

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Author: Kuosmanen, Elina1; Wolling, Florian2; Vega, Julio3;
Organizations: 1University of Oulu, Oulu, Finland
2University of Siegen, Siegen, Germany
3University of Manchester, Manchester, United Kingdom
4University of Tokyo, Tokyo, Japan
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 1.5 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2020120899777
Language: English
Published: JMIR Publications, 2020
Publish Date: 2020-12-08
Description:

Abstract

Background: Hand tremor typically has a negative impact on a person’s ability to complete many common daily activities. Previous research has investigated how to quantify hand tremor with smartphones and wearable sensors, mainly under controlled data collection conditions. Solutions for daily real-life settings remain largely underexplored.

Objective: Our objective was to monitor and assess hand tremor severity in patients with Parkinson disease (PD), and to better understand the effects of PD medications in a naturalistic environment.

Methods: Using the Welch method, we generated periodograms of accelerometer data and computed signal features to compare patients with varying degrees of PD symptoms.

Results: We introduced and empirically evaluated the tremor intensity parameter (TIP), an accelerometer-based metric to quantify hand tremor severity in PD using smartphones. There was a statistically significant correlation between the TIP and self-assessed Unified Parkinson Disease Rating Scale (UPDRS) II tremor scores (Kendall rank correlation test: z=30.521, P<.001, τ=0.5367379; n=11). An analysis of the “before” and “after” medication intake conditions identified a significant difference in accelerometer signal characteristics among participants with different levels of rigidity and bradykinesia (Wilcoxon rank sum test, P<.05).

Conclusions: Our work demonstrates the potential use of smartphone inertial sensors as a systematic symptom severity assessment mechanism to monitor PD symptoms and to assess medication effectiveness remotely. Our smartphone-based monitoring app may also be relevant for other conditions where hand tremor is a prevalent symptom.

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Series: JMIR mHealth and uHealth
ISSN: 2291-5222
ISSN-E: 2291-5222
ISSN-L: 2291-5222
Volume: 8
Issue: 11
Article number: e21543
DOI: 10.2196/21543
OADOI: https://oadoi.org/10.2196/21543
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
217 Medical engineering
213 Electronic, automation and communications engineering, electronics
Subjects:
Funding: This work is partially funded by the Academy of Finland (Grants 313224-STOP, 316253-SENSATE, 320089-SENSATE, 318927-6Genesis Flagship, and 318930-GenZ), and by personal research grants awarded by the Finnish Parkinson Foundation, the Tauno Tönning Foundation, the Jenny and Antti Wihuri Foundation, the Nokia Foundation, and the Emil Aaltonen Foundation. The author Florian Wolling has been supported by the University of Siegen and the German Academic Exchange Service (DAAD), which enabled his research visit at the University of Oulu, Finland, Biomimetics and Intelligent Systems Group.
Academy of Finland Grant Number: 313224
316253
320089
318927
318930
Detailed Information: 313224 (Academy of Finland Funding decision)
316253 (Academy of Finland Funding decision)
320089 (Academy of Finland Funding decision)
318927 (Academy of Finland Funding decision)
318930 (Academy of Finland Funding decision)
Copyright information: ©Elina Kuosmanen, Florian Wolling, Julio Vega, Valerii Kan, Yuuki Nishiyama, Simon Harper, Kristof Van Laerhoven, Simo Hosio, Denzil Ferreira. Originally published in JMIR mHealth and uHealth (http://mhealth.jmir.org), 26.11.2020. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mHealth and uHealth, is properly cited. The complete bibliographic information, a link to the original publication on http://mhealth.jmir.org/, as well as this copyright and license information must be included.
  https://creativecommons.org/licenses/by/4.0/