Longitudinal Trend Monitoring of Multiple Sclerosis Ambulation Using Smartphones
<italic>Goal:</italic> Smartphone and wearable devices may act as powerful tools to remotely monitor physical function in people with neurodegenerative and autoimmune diseases from out-of-clinic environments. Detection of progression onset or worsening of symptoms is especially important...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Open Journal of Engineering in Medicine and Biology |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9944841/ |
| _version_ | 1797893818007158784 |
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| author | Andrew P. Creagh Frank Dondelinger Florian Lipsmeier Michael Lindemann Maarten De Vos |
| author_facet | Andrew P. Creagh Frank Dondelinger Florian Lipsmeier Michael Lindemann Maarten De Vos |
| author_sort | Andrew P. Creagh |
| collection | DOAJ |
| description | <italic>Goal:</italic> Smartphone and wearable devices may act as powerful tools to remotely monitor physical function in people with neurodegenerative and autoimmune diseases from out-of-clinic environments. Detection of progression onset or worsening of symptoms is especially important in people living with multiple sclerosis (PwMS) in order to enable optimally adapted therapeutic strategies. MS symptoms typically follow subtle and fluctuating disease courses, patient-to-patient, and over time. Current in-clinic assessments are often too infrequently administered to reflect longitudinal changes in MS impairment that impact daily life. This work, therefore, explores how smartphones can administer daily two-minute walking assessments to monitor PwMS physical function at home. <italic>Methods:</italic> Remotely collected smartphone inertial sensor data was transformed through state-of-the-art Deep Convolutional Neural Networks, to estimate a participant's daily ambulatory-related disease severity, longitudinally over a 24-week study. <italic>Results:</italic> This study demonstrated that smartphone-based ambulatory severity outcomes could accurately estimate MS level of disability, as measured by the EDSS score (<inline-formula><tex-math notation="LaTeX">$r^{2}$</tex-math></inline-formula>: 0.56,<inline-formula><tex-math notation="LaTeX">$p< $</tex-math></inline-formula>0.001). Furthermore, longitudinal severity outcomes were shown to accurately reflect individual participants' level of disability over the study duration. <italic>Conclusion:</italic> Smartphone-based assessments, that can be performed by patients from their home environments, could greatly augment standard in-clinic outcomes for neurodegenerative diseases. The ability to understand the impact of disease on daily-life between clinical visits, through objective digital outcomes, paves the way forward to better measure and identify signs of disease progression that may be occurring out-of-clinic, to monitor how different patients respond to various treatments, and to ultimately enable the development of better, and more personalised care. |
| first_indexed | 2024-04-10T06:59:05Z |
| format | Article |
| id | doaj.art-0db203594df34e28bd1060480713f08c |
| institution | Directory Open Access Journal |
| issn | 2644-1276 |
| language | English |
| last_indexed | 2024-04-10T06:59:05Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Engineering in Medicine and Biology |
| spelling | doaj.art-0db203594df34e28bd1060480713f08c2023-02-28T00:01:12ZengIEEEIEEE Open Journal of Engineering in Medicine and Biology2644-12762022-01-01320221010.1109/OJEMB.2022.32213069944841Longitudinal Trend Monitoring of Multiple Sclerosis Ambulation Using SmartphonesAndrew P. Creagh0https://orcid.org/0000-0002-6086-6098Frank Dondelinger1Florian Lipsmeier2https://orcid.org/0000-0002-8663-819XMichael Lindemann3Maarten De Vos4Institute of Biomedical Engineering, University of Oxford, Oxford, U.K.F. Hoffmann-La Roche Ltd, Basel, SwitzerlandF. Hoffmann-La Roche Ltd, Basel, SwitzerlandF. Hoffmann-La Roche Ltd, Basel, SwitzerlandDepartment of Electrical Engineering, Katholieke Universiteit Leuven, Leuven, Belgium<italic>Goal:</italic> Smartphone and wearable devices may act as powerful tools to remotely monitor physical function in people with neurodegenerative and autoimmune diseases from out-of-clinic environments. Detection of progression onset or worsening of symptoms is especially important in people living with multiple sclerosis (PwMS) in order to enable optimally adapted therapeutic strategies. MS symptoms typically follow subtle and fluctuating disease courses, patient-to-patient, and over time. Current in-clinic assessments are often too infrequently administered to reflect longitudinal changes in MS impairment that impact daily life. This work, therefore, explores how smartphones can administer daily two-minute walking assessments to monitor PwMS physical function at home. <italic>Methods:</italic> Remotely collected smartphone inertial sensor data was transformed through state-of-the-art Deep Convolutional Neural Networks, to estimate a participant's daily ambulatory-related disease severity, longitudinally over a 24-week study. <italic>Results:</italic> This study demonstrated that smartphone-based ambulatory severity outcomes could accurately estimate MS level of disability, as measured by the EDSS score (<inline-formula><tex-math notation="LaTeX">$r^{2}$</tex-math></inline-formula>: 0.56,<inline-formula><tex-math notation="LaTeX">$p< $</tex-math></inline-formula>0.001). Furthermore, longitudinal severity outcomes were shown to accurately reflect individual participants' level of disability over the study duration. <italic>Conclusion:</italic> Smartphone-based assessments, that can be performed by patients from their home environments, could greatly augment standard in-clinic outcomes for neurodegenerative diseases. The ability to understand the impact of disease on daily-life between clinical visits, through objective digital outcomes, paves the way forward to better measure and identify signs of disease progression that may be occurring out-of-clinic, to monitor how different patients respond to various treatments, and to ultimately enable the development of better, and more personalised care.https://ieeexplore.ieee.org/document/9944841/Deep learningdigital biomarkersgaitmultiple sclerosissmartphones |
| spellingShingle | Andrew P. Creagh Frank Dondelinger Florian Lipsmeier Michael Lindemann Maarten De Vos Longitudinal Trend Monitoring of Multiple Sclerosis Ambulation Using Smartphones IEEE Open Journal of Engineering in Medicine and Biology Deep learning digital biomarkers gait multiple sclerosis smartphones |
| title | Longitudinal Trend Monitoring of Multiple Sclerosis Ambulation Using Smartphones |
| title_full | Longitudinal Trend Monitoring of Multiple Sclerosis Ambulation Using Smartphones |
| title_fullStr | Longitudinal Trend Monitoring of Multiple Sclerosis Ambulation Using Smartphones |
| title_full_unstemmed | Longitudinal Trend Monitoring of Multiple Sclerosis Ambulation Using Smartphones |
| title_short | Longitudinal Trend Monitoring of Multiple Sclerosis Ambulation Using Smartphones |
| title_sort | longitudinal trend monitoring of multiple sclerosis ambulation using smartphones |
| topic | Deep learning digital biomarkers gait multiple sclerosis smartphones |
| url | https://ieeexplore.ieee.org/document/9944841/ |
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