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Identification of Motor Symptoms Related to Parkinson Disease Using Motion-Tracking Sensors at Home (KÄVELI): Protocol for an Observational Case-Control Study

BACKGROUND: Clinical characterization of motion in patients with Parkinson disease (PD) is challenging: symptom progression, suitability of medication, and level of independence in the home environment can vary across time and patients. Appointments at the neurological outpatient clinic provide a li...

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Autores principales: Jauhiainen, Milla, Puustinen, Juha, Mehrang, Saeed, Ruokolainen, Jari, Holm, Anu, Vehkaoja, Antti, Nieminen, Hannu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6456828/
https://www.ncbi.nlm.nih.gov/pubmed/30916665
http://dx.doi.org/10.2196/12808
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author Jauhiainen, Milla
Puustinen, Juha
Mehrang, Saeed
Ruokolainen, Jari
Holm, Anu
Vehkaoja, Antti
Nieminen, Hannu
author_facet Jauhiainen, Milla
Puustinen, Juha
Mehrang, Saeed
Ruokolainen, Jari
Holm, Anu
Vehkaoja, Antti
Nieminen, Hannu
author_sort Jauhiainen, Milla
collection PubMed
description BACKGROUND: Clinical characterization of motion in patients with Parkinson disease (PD) is challenging: symptom progression, suitability of medication, and level of independence in the home environment can vary across time and patients. Appointments at the neurological outpatient clinic provide a limited understanding of the overall situation. In order to follow up these variations, longer-term measurements performed outside of the clinic setting could help optimize and personalize therapies. Several wearable sensors have been used to estimate the severity of symptoms in PD; however, longitudinal recordings, even for a short duration of a few days, are rare. Home recordings have the potential benefit of providing a more thorough and objective follow-up of the disease while providing more information about the possible need to change medications or consider invasive treatments. OBJECTIVE: The primary objective of this study is to collect a dataset for developing methods to detect PD-related symptoms that are visible in walking patterns at home. The movement data are collected continuously and remotely at home during the normal lives of patients with PD as well as controls. The secondary objective is to use the dataset to study whether the registered medication intakes can be identified from the collected movement data by looking for and analyzing short-term changes in walking patterns. METHODS: This paper described the protocol for an observational case-control study that measures activity using three different devices: (1) a smartphone with a built-in accelerometer, gyroscope, and phone orientation sensor, (2) a Movesense smart sensor to measure movement data from the wrist, and (3) a Forciot smart insole to measure the forces applied on the feet. The measurements are first collected during the appointment at the clinic conducted by a trained clinical physiotherapist. Subsequently, the subjects wear the smartphone at home for 3 consecutive days. Wrist and insole sensors are not used in the home recordings. RESULTS: Data collection began in March 2018. Subject recruitment and data collection will continue in spring 2019. The intended sample size was 150 subjects. In 2018, we collected a sample of 103 subjects, 66 of whom were diagnosed with PD. CONCLUSIONS: This study aims to produce an extensive movement-sensor dataset recorded from patients with PD in various phases of the disease as well as from a group of control subjects for effective and impactful comparison studies. The study also aims to develop data analysis methods to monitor PD symptoms and the effects of medication intake during normal life and outside of the clinic setting. Further applications of these methods may include using them as tools for health care professionals to monitor PD remotely and applying them to other movement disorders. TRIAL REGISTRATION: ClinicalTrials.gov NCT03366558; https://clinicaltrials.gov/ct2/show/NCT03366558  INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/12808
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spelling pubmed-64568282019-04-26 Identification of Motor Symptoms Related to Parkinson Disease Using Motion-Tracking Sensors at Home (KÄVELI): Protocol for an Observational Case-Control Study Jauhiainen, Milla Puustinen, Juha Mehrang, Saeed Ruokolainen, Jari Holm, Anu Vehkaoja, Antti Nieminen, Hannu JMIR Res Protoc Protocol BACKGROUND: Clinical characterization of motion in patients with Parkinson disease (PD) is challenging: symptom progression, suitability of medication, and level of independence in the home environment can vary across time and patients. Appointments at the neurological outpatient clinic provide a limited understanding of the overall situation. In order to follow up these variations, longer-term measurements performed outside of the clinic setting could help optimize and personalize therapies. Several wearable sensors have been used to estimate the severity of symptoms in PD; however, longitudinal recordings, even for a short duration of a few days, are rare. Home recordings have the potential benefit of providing a more thorough and objective follow-up of the disease while providing more information about the possible need to change medications or consider invasive treatments. OBJECTIVE: The primary objective of this study is to collect a dataset for developing methods to detect PD-related symptoms that are visible in walking patterns at home. The movement data are collected continuously and remotely at home during the normal lives of patients with PD as well as controls. The secondary objective is to use the dataset to study whether the registered medication intakes can be identified from the collected movement data by looking for and analyzing short-term changes in walking patterns. METHODS: This paper described the protocol for an observational case-control study that measures activity using three different devices: (1) a smartphone with a built-in accelerometer, gyroscope, and phone orientation sensor, (2) a Movesense smart sensor to measure movement data from the wrist, and (3) a Forciot smart insole to measure the forces applied on the feet. The measurements are first collected during the appointment at the clinic conducted by a trained clinical physiotherapist. Subsequently, the subjects wear the smartphone at home for 3 consecutive days. Wrist and insole sensors are not used in the home recordings. RESULTS: Data collection began in March 2018. Subject recruitment and data collection will continue in spring 2019. The intended sample size was 150 subjects. In 2018, we collected a sample of 103 subjects, 66 of whom were diagnosed with PD. CONCLUSIONS: This study aims to produce an extensive movement-sensor dataset recorded from patients with PD in various phases of the disease as well as from a group of control subjects for effective and impactful comparison studies. The study also aims to develop data analysis methods to monitor PD symptoms and the effects of medication intake during normal life and outside of the clinic setting. Further applications of these methods may include using them as tools for health care professionals to monitor PD remotely and applying them to other movement disorders. TRIAL REGISTRATION: ClinicalTrials.gov NCT03366558; https://clinicaltrials.gov/ct2/show/NCT03366558  INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/12808 JMIR Publications 2019-03-27 /pmc/articles/PMC6456828/ /pubmed/30916665 http://dx.doi.org/10.2196/12808 Text en ©Milla Jauhiainen, Juha Puustinen, Saeed Mehrang, Jari Ruokolainen, Anu Holm, Antti Vehkaoja, Hannu Nieminen. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 27.03.2019. https://creativecommons.org/licenses/by/4.0/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 Research Protocols, is properly cited. The complete bibliographic information, a link to the original publication on http://www.researchprotocols.org, as well as this copyright and license information must be included.
spellingShingle Protocol
Jauhiainen, Milla
Puustinen, Juha
Mehrang, Saeed
Ruokolainen, Jari
Holm, Anu
Vehkaoja, Antti
Nieminen, Hannu
Identification of Motor Symptoms Related to Parkinson Disease Using Motion-Tracking Sensors at Home (KÄVELI): Protocol for an Observational Case-Control Study
title Identification of Motor Symptoms Related to Parkinson Disease Using Motion-Tracking Sensors at Home (KÄVELI): Protocol for an Observational Case-Control Study
title_full Identification of Motor Symptoms Related to Parkinson Disease Using Motion-Tracking Sensors at Home (KÄVELI): Protocol for an Observational Case-Control Study
title_fullStr Identification of Motor Symptoms Related to Parkinson Disease Using Motion-Tracking Sensors at Home (KÄVELI): Protocol for an Observational Case-Control Study
title_full_unstemmed Identification of Motor Symptoms Related to Parkinson Disease Using Motion-Tracking Sensors at Home (KÄVELI): Protocol for an Observational Case-Control Study
title_short Identification of Motor Symptoms Related to Parkinson Disease Using Motion-Tracking Sensors at Home (KÄVELI): Protocol for an Observational Case-Control Study
title_sort identification of motor symptoms related to parkinson disease using motion-tracking sensors at home (käveli): protocol for an observational case-control study
topic Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6456828/
https://www.ncbi.nlm.nih.gov/pubmed/30916665
http://dx.doi.org/10.2196/12808
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