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Feasibility and patient acceptability of a commercially available wearable and a smart phone application in identification of motor states in parkinson’s disease

In the quantification of symptoms of Parkinson’s disease (PD), healthcare professional assessments, patient reported outcomes (PRO), and medical device grade wearables are currently used. Recently, also commercially available smartphones and wearable devices have been actively researched in the dete...

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Autores principales: Liikkanen, Sammeli, Sinkkonen, Janne, Suorsa, Joni, Kaasinen, Valtteri, Pekkonen, Eero, Kärppä, Mikko, Scheperjans, Filip, Huttunen, Teppo, Sarapohja, Toni, Pesonen, Ullamari, Kuoppamäki, Mikko, Keränen, Tapani
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10081770/
https://www.ncbi.nlm.nih.gov/pubmed/37027348
http://dx.doi.org/10.1371/journal.pdig.0000225
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author Liikkanen, Sammeli
Sinkkonen, Janne
Suorsa, Joni
Kaasinen, Valtteri
Pekkonen, Eero
Kärppä, Mikko
Scheperjans, Filip
Huttunen, Teppo
Sarapohja, Toni
Pesonen, Ullamari
Kuoppamäki, Mikko
Keränen, Tapani
author_facet Liikkanen, Sammeli
Sinkkonen, Janne
Suorsa, Joni
Kaasinen, Valtteri
Pekkonen, Eero
Kärppä, Mikko
Scheperjans, Filip
Huttunen, Teppo
Sarapohja, Toni
Pesonen, Ullamari
Kuoppamäki, Mikko
Keränen, Tapani
author_sort Liikkanen, Sammeli
collection PubMed
description In the quantification of symptoms of Parkinson’s disease (PD), healthcare professional assessments, patient reported outcomes (PRO), and medical device grade wearables are currently used. Recently, also commercially available smartphones and wearable devices have been actively researched in the detection of PD symptoms. The continuous, longitudinal, and automated detection of motor and especially non-motor symptoms with these devices is still a challenge that requires more research. The data collected from everyday life can be noisy and frequently contains artefacts, and novel detection methods and algorithms are therefore needed. 42 PD patients and 23 control subjects were monitored with Garmin Vivosmart 4 wearable device and asked to fill a symptom and medication diary with a mobile application, at home, for about four weeks. Subsequent analyses are based on continuous accelerometer data from the device. Accelerometer data from the Levodopa Response Study (MJFFd) were reanalyzed, with symptoms quantified with linear spectral models trained on expert evaluations present in the data. Variational autoencoders (VAE) were trained on both our study accelerometer data and on MJFFd to detect movement states (e.g., walking, standing). A total of 7590 self-reported symptoms were recorded during the study. 88.9% (32/36) of PD patients, 80.0% (4/5) of DBS PD patients and 95.5% (21/22) of control subjects reported that using the wearable device was very easy or easy. Recording a symptom at the time of the event was assessed as very easy or easy by 70.1% (29/41) of subjects with PD. Aggregated spectrograms of the collected accelerometer data show relative attenuation of low (<5Hz) frequencies in patients. Similar spectral patterns also separate symptom periods from immediately adjacent non-symptomatic periods. Discriminative power of linear models to separate symptoms from adjacent periods is weak, but aggregates show partial separability of patients vs. controls. The analysis reveals differential symptom detectability across movement tasks, motivating the third part of the study. VAEs trained on either dataset produced embedding from which movement states in MJFFd could be predicted. A VAE model was able to detect the movement states. Thus, a pre-detection of these states with a VAE from accelerometer data with good S/N ratio, and subsequent quantification of PD symptoms is a feasible strategy. The usability of the data collection method is important to enable the collection of self-reported symptom data by PD patients. Finally, the usability of the data collection method is important to enable the collection of self-reported symptom data by PD patients.
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spelling pubmed-100817702023-04-08 Feasibility and patient acceptability of a commercially available wearable and a smart phone application in identification of motor states in parkinson’s disease Liikkanen, Sammeli Sinkkonen, Janne Suorsa, Joni Kaasinen, Valtteri Pekkonen, Eero Kärppä, Mikko Scheperjans, Filip Huttunen, Teppo Sarapohja, Toni Pesonen, Ullamari Kuoppamäki, Mikko Keränen, Tapani PLOS Digit Health Research Article In the quantification of symptoms of Parkinson’s disease (PD), healthcare professional assessments, patient reported outcomes (PRO), and medical device grade wearables are currently used. Recently, also commercially available smartphones and wearable devices have been actively researched in the detection of PD symptoms. The continuous, longitudinal, and automated detection of motor and especially non-motor symptoms with these devices is still a challenge that requires more research. The data collected from everyday life can be noisy and frequently contains artefacts, and novel detection methods and algorithms are therefore needed. 42 PD patients and 23 control subjects were monitored with Garmin Vivosmart 4 wearable device and asked to fill a symptom and medication diary with a mobile application, at home, for about four weeks. Subsequent analyses are based on continuous accelerometer data from the device. Accelerometer data from the Levodopa Response Study (MJFFd) were reanalyzed, with symptoms quantified with linear spectral models trained on expert evaluations present in the data. Variational autoencoders (VAE) were trained on both our study accelerometer data and on MJFFd to detect movement states (e.g., walking, standing). A total of 7590 self-reported symptoms were recorded during the study. 88.9% (32/36) of PD patients, 80.0% (4/5) of DBS PD patients and 95.5% (21/22) of control subjects reported that using the wearable device was very easy or easy. Recording a symptom at the time of the event was assessed as very easy or easy by 70.1% (29/41) of subjects with PD. Aggregated spectrograms of the collected accelerometer data show relative attenuation of low (<5Hz) frequencies in patients. Similar spectral patterns also separate symptom periods from immediately adjacent non-symptomatic periods. Discriminative power of linear models to separate symptoms from adjacent periods is weak, but aggregates show partial separability of patients vs. controls. The analysis reveals differential symptom detectability across movement tasks, motivating the third part of the study. VAEs trained on either dataset produced embedding from which movement states in MJFFd could be predicted. A VAE model was able to detect the movement states. Thus, a pre-detection of these states with a VAE from accelerometer data with good S/N ratio, and subsequent quantification of PD symptoms is a feasible strategy. The usability of the data collection method is important to enable the collection of self-reported symptom data by PD patients. Finally, the usability of the data collection method is important to enable the collection of self-reported symptom data by PD patients. Public Library of Science 2023-04-07 /pmc/articles/PMC10081770/ /pubmed/37027348 http://dx.doi.org/10.1371/journal.pdig.0000225 Text en © 2023 Liikkanen et al 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 author and source are credited.
spellingShingle Research Article
Liikkanen, Sammeli
Sinkkonen, Janne
Suorsa, Joni
Kaasinen, Valtteri
Pekkonen, Eero
Kärppä, Mikko
Scheperjans, Filip
Huttunen, Teppo
Sarapohja, Toni
Pesonen, Ullamari
Kuoppamäki, Mikko
Keränen, Tapani
Feasibility and patient acceptability of a commercially available wearable and a smart phone application in identification of motor states in parkinson’s disease
title Feasibility and patient acceptability of a commercially available wearable and a smart phone application in identification of motor states in parkinson’s disease
title_full Feasibility and patient acceptability of a commercially available wearable and a smart phone application in identification of motor states in parkinson’s disease
title_fullStr Feasibility and patient acceptability of a commercially available wearable and a smart phone application in identification of motor states in parkinson’s disease
title_full_unstemmed Feasibility and patient acceptability of a commercially available wearable and a smart phone application in identification of motor states in parkinson’s disease
title_short Feasibility and patient acceptability of a commercially available wearable and a smart phone application in identification of motor states in parkinson’s disease
title_sort feasibility and patient acceptability of a commercially available wearable and a smart phone application in identification of motor states in parkinson’s disease
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10081770/
https://www.ncbi.nlm.nih.gov/pubmed/37027348
http://dx.doi.org/10.1371/journal.pdig.0000225
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