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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

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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. / Jauhiainen, Milla; Puustinen, Juha; Mehrang, Saeed; Ruokolainen, Jari; Holm, Anu; Vehkaoja, Antti; Nieminen, Hannu.

julkaisussa: Journal of Medical Internet Research, Vuosikerta 21, Nro 3, e12808, 01.03.2019.

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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. Julkaisussa: Journal of Medical Internet Research. 2019 ; Vuosikerta 21, Nro 3.

Bibtex - Lataa

@article{b7536414f9b94692ae845b29552529f5,
title = "Identification of motor symptoms related to Parkinson disease using motion-tracking sensors at home (K{\"A}VELI): Protocol for an observational case-control study",
abstract = "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 andoutside 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.",
keywords = "Gait, Home monitoring, Mobile phone, Movement analysis, Parkinson disease, Smartphone, Wearable sensors",
author = "Milla Jauhiainen and Juha Puustinen and Saeed Mehrang and Jari Ruokolainen and Anu Holm and Antti Vehkaoja and Hannu Nieminen",
year = "2019",
month = "3",
day = "1",
doi = "10.2196/12808",
language = "English",
volume = "21",
journal = "Journal of Medical Internet Research",
issn = "1439-4456",
publisher = "Journal of Medical Internet Research",
number = "3",

}

RIS (suitable for import to EndNote) - Lataa

TY - JOUR

T1 - Identification of motor symptoms related to Parkinson disease using motion-tracking sensors at home (KÄVELI)

T2 - Protocol for an observational case-control study

AU - Jauhiainen, Milla

AU - Puustinen, Juha

AU - Mehrang, Saeed

AU - Ruokolainen, Jari

AU - Holm, Anu

AU - Vehkaoja, Antti

AU - Nieminen, Hannu

PY - 2019/3/1

Y1 - 2019/3/1

N2 - 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 andoutside 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.

AB - 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 andoutside 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.

KW - Gait

KW - Home monitoring

KW - Mobile phone

KW - Movement analysis

KW - Parkinson disease

KW - Smartphone

KW - Wearable sensors

U2 - 10.2196/12808

DO - 10.2196/12808

M3 - Article

VL - 21

JO - Journal of Medical Internet Research

JF - Journal of Medical Internet Research

SN - 1439-4456

IS - 3

M1 - e12808

ER -