Automatic Assessment of the 2-Minute Walk Distance for Remote Monitoring of People with Multiple Sclerosis

The aim of this study was to investigate the feasibility of automatically assessing the 2-Minute Walk Distance (2MWD) for monitoring people with multiple sclerosis (pwMS). For 154 pwMS, MS-related clinical outcomes as well as the 2MWDs as evaluated by clinicians and derived from accelerometer data w...

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Vydáno v:	Sensors (Basel, Switzerland) Ročník 23; číslo 13; s. 6017
Hlavní autoři:	Kontaxis, Spyridon, Laporta, Estela, Garcia, Esther, Martinis, Matteo, Leocani, Letizia, Roselli, Lucia, Buron, Mathias Due, Guerrero, Ana Isabel, Zabala, Ana, Cummins, Nicholas, Vairavan, Srinivasan, Hotopf, Matthew, Dobson, Richard J. B., Narayan, Vaibhav A., La Porta, Maria Libera, Costa, Gloria Dalla, Magyari, Melinda, Sørensen, Per Soelberg, Nos, Carlos, Bailon, Raquel, Comi, Giancarlo
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Switzerland MDPI AG 29.06.2023 MDPI
Témata:	accelerometer sensor Accelerometers Analysis Automation Clinical outcomes disability level Disease Fatigue fatigue severity Gait Hospitals Humans Medical equipment Multiple Sclerosis Smartphones Walk Test walk tests Walking Wearable computers wearable device United Kingdom Spain wearable device walk tests disability level fatigue severity accelerometer sensor
ISSN:	1424-8220, 1424-8220
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Abstract	The aim of this study was to investigate the feasibility of automatically assessing the 2-Minute Walk Distance (2MWD) for monitoring people with multiple sclerosis (pwMS). For 154 pwMS, MS-related clinical outcomes as well as the 2MWDs as evaluated by clinicians and derived from accelerometer data were collected from a total of 323 periodic clinical visits. Accelerometer data from a wearable device during 100 home-based 2MWD assessments were also acquired. The error in estimating the 2MWD was validated for walk tests performed at hospital, and then the correlation (r) between clinical outcomes and home-based 2MWD assessments was evaluated. Robust performance in estimating the 2MWD from the wearable device was obtained, yielding an error of less than 10% in about two-thirds of clinical visits. Correlation analysis showed that there is a strong association between the actual and the estimated 2MWD obtained either at hospital (r = 0.71) or at home (r = 0.58). Furthermore, the estimated 2MWD exhibits moderate-to-strong correlation with various MS-related clinical outcomes, including disability and fatigue severity scores. Automatic assessment of the 2MWD in pwMS is feasible with the usage of a consumer-friendly wearable device in clinical and non-clinical settings. Wearable devices can also enhance the assessment of MS-related clinical outcomes.
AbstractList	The aim of this study was to investigate the feasibility of automatically assessing the 2-Minute Walk Distance (2MWD) for monitoring people with multiple sclerosis (pwMS). For 154 pwMS, MS-related clinical outcomes as well as the 2MWDs as evaluated by clinicians and derived from accelerometer data were collected from a total of 323 periodic clinical visits. Accelerometer data from a wearable device during 100 home-based 2MWD assessments were also acquired. The error in estimating the 2MWD was validated for walk tests performed at hospital, and then the correlation (r) between clinical outcomes and home-based 2MWD assessments was evaluated. Robust performance in estimating the 2MWD from the wearable device was obtained, yielding an error of less than 10% in about two-thirds of clinical visits. Correlation analysis showed that there is a strong association between the actual and the estimated 2MWD obtained either at hospital (r = 0.71) or at home (r = 0.58). Furthermore, the estimated 2MWD exhibits moderate-to-strong correlation with various MS-related clinical outcomes, including disability and fatigue severity scores. Automatic assessment of the 2MWD in pwMS is feasible with the usage of a consumer-friendly wearable device in clinical and non-clinical settings. Wearable devices can also enhance the assessment of MS-related clinical outcomes. The aim of this study was to investigate the feasibility of automatically assessing the 2-Minute Walk Distance (2MWD) for monitoring people with multiple sclerosis (pwMS). For 154 pwMS, MS-related clinical outcomes as well as the 2MWDs as evaluated by clinicians and derived from accelerometer data were collected from a total of 323 periodic clinical visits. Accelerometer data from a wearable device during 100 home-based 2MWD assessments were also acquired. The error in estimating the 2MWD was validated for walk tests performed at hospital, and then the correlation (r) between clinical outcomes and home-based 2MWD assessments was evaluated. Robust performance in estimating the 2MWD from the wearable device was obtained, yielding an error of less than 10% in about two-thirds of clinical visits. Correlation analysis showed that there is a strong association between the actual and the estimated 2MWD obtained either at hospital (r = 0.71) or at home (r = 0.58). Furthermore, the estimated 2MWD exhibits moderate-to-strong correlation with various MS-related clinical outcomes, including disability and fatigue severity scores. Automatic assessment of the 2MWD in pwMS is feasible with the usage of a consumer-friendly wearable device in clinical and non-clinical settings. Wearable devices can also enhance the assessment of MS-related clinical outcomes.The aim of this study was to investigate the feasibility of automatically assessing the 2-Minute Walk Distance (2MWD) for monitoring people with multiple sclerosis (pwMS). For 154 pwMS, MS-related clinical outcomes as well as the 2MWDs as evaluated by clinicians and derived from accelerometer data were collected from a total of 323 periodic clinical visits. Accelerometer data from a wearable device during 100 home-based 2MWD assessments were also acquired. The error in estimating the 2MWD was validated for walk tests performed at hospital, and then the correlation (r) between clinical outcomes and home-based 2MWD assessments was evaluated. Robust performance in estimating the 2MWD from the wearable device was obtained, yielding an error of less than 10% in about two-thirds of clinical visits. Correlation analysis showed that there is a strong association between the actual and the estimated 2MWD obtained either at hospital (r = 0.71) or at home (r = 0.58). Furthermore, the estimated 2MWD exhibits moderate-to-strong correlation with various MS-related clinical outcomes, including disability and fatigue severity scores. Automatic assessment of the 2MWD in pwMS is feasible with the usage of a consumer-friendly wearable device in clinical and non-clinical settings. Wearable devices can also enhance the assessment of MS-related clinical outcomes.
Audience	Academic
Author	Garcia, Esther Cummins, Nicholas Nos, Carlos Laporta, Estela Zabala, Ana Hotopf, Matthew Martinis, Matteo Dobson, Richard J. B. Kontaxis, Spyridon Magyari, Melinda Vairavan, Srinivasan Sørensen, Per Soelberg Narayan, Vaibhav A. Roselli, Lucia Comi, Giancarlo Bailon, Raquel Leocani, Letizia Buron, Mathias Due Guerrero, Ana Isabel La Porta, Maria Libera Costa, Gloria Dalla
AuthorAffiliation	1 Laboratory of Biomedical Signal Interpretation and Computational Simulation (BSICoS), University of Zaragoza, 50018 Zaragoza, Spain 5 Danish Multiple Sclerosis Center, Department of Neurology, Copenhagen University Hospital Rigshospitalet, 2100 Copenhagen, Denmark 8 Janssen Research and Development, LLC, Titusville, NJ 08560, USA 10 Institute of Health Informatics, University College London, London NW1 2DA, UK 2 Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), 28006 Barcelona, Spain 4 Department of Medicine and Surgery, University Vita-Salute and Hospital San Raffaele, 20132 Milan, Italy 3 Department of Microelectronics and Electronic Systems, Autonomous University of Barcelona, 08193 Bellaterra, Spain 12 Casa di Cura del Policlinico, 20144 Milan, Italy 7 Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK 11 Davos Alzheimer’s Collaborative, Wayne,
AuthorAffiliation_xml	– name: 11 Davos Alzheimer’s Collaborative, Wayne, PA 19087, USA – name: 5 Danish Multiple Sclerosis Center, Department of Neurology, Copenhagen University Hospital Rigshospitalet, 2100 Copenhagen, Denmark – name: 7 Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK – name: 2 Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), 28006 Barcelona, Spain – name: 3 Department of Microelectronics and Electronic Systems, Autonomous University of Barcelona, 08193 Bellaterra, Spain – name: 6 Multiple Sclerosis Center of Catalonia (CEMCAT), Department of Neurology/Neuroimmunology, Hospital Universitari Vall d’Hebron, Universitat Autonoma de Barcelona, 08035 Barcelona, Spain – name: 10 Institute of Health Informatics, University College London, London NW1 2DA, UK – name: 1 Laboratory of Biomedical Signal Interpretation and Computational Simulation (BSICoS), University of Zaragoza, 50018 Zaragoza, Spain – name: 9 Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK – name: 12 Casa di Cura del Policlinico, 20144 Milan, Italy – name: 4 Department of Medicine and Surgery, University Vita-Salute and Hospital San Raffaele, 20132 Milan, Italy – name: 8 Janssen Research and Development, LLC, Titusville, NJ 08560, USA
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Snippet	The aim of this study was to investigate the feasibility of automatically assessing the 2-Minute Walk Distance (2MWD) for monitoring people with multiple...
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SubjectTerms	accelerometer sensor Accelerometers Analysis Automation Clinical outcomes disability level Disease Fatigue fatigue severity Gait Hospitals Humans Medical equipment Multiple Sclerosis Smartphones Walk Test walk tests Walking Wearable computers wearable device
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Title	Automatic Assessment of the 2-Minute Walk Distance for Remote Monitoring of People with Multiple Sclerosis
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