cc5dec015c0d85e8817cc11010d3c339 pmc_10346544.pdf 7bafe0d27cfe1a638d0ab446b316b59b05ff0851 pmc_10346544.pdf 9aa501989255a209f7368d13b33c415f2184bd8d7e7cdedf8882991a39323132 pmc_10346544.pdf Title: Automatic Assessment of the 2-Minute Walk Distance for Remote Monitoring of People with Multiple Sclerosis Subject: 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. Keywords: wearable device; accelerometer sensor; walk tests; disability level; fatigue severity Author: Spyridon Kontaxis, Estela Laporta, Esther Garcia, Matteo Martinis, Letizia Leocani, Lucia Roselli, Mathias Due Buron, Ana Isabel Guerrero, Ana Zabala, Nicholas Cummins, Srinivasan Vairavan, Matthew Hotopf, Richard JB Dobson, Vaibhav A Narayan, Maria Libera La Porta, Gloria Dalla Costa, Melinda Magyari, Per Soelberg Sørensen, Carlos Nos, Raquel Bailon, Giancarlo Comi Creator: LaTeX with hyperref Producer: pdfTeX-1.40.21 CreationDate: Thu Jun 29 08:42:01 2023 CEST ModDate: Thu Jun 29 08:47:30 2023 CEST Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 11 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 750017 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- AKEGRH+URWPalladioL-Bold Type 1 Custom yes yes no 10 0 YVKPEY+URWPalladioL-Roma Type 1 Custom yes yes no 15 0 JXFZIR+URWPalladioL-Ital Type 1 Custom yes yes no 19 0 RVSEMT+TimesNewRomanPS-BoldItalicMT Type 1C WinAnsi yes yes no 37 0 NQNFVE+CMSY10 Type 1 Builtin yes yes no 62 0 WBREXZ+PazoMath-Italic Type 1 Builtin yes yes no 69 0 JHYTSG+CMR10 Type 1 Builtin yes yes no 73 0 SYFPBV+CMMI10 Type 1 Builtin yes yes no 80 0 USQVZN+URWPalladioL-BoldItal Type 1 Custom yes yes no 90 0 Jhove (Rel. 1.22.1, 2019-04-17) Date: 2023-11-17 02:35:38 CET RepresentationInformation: pmc_10346544.pdf ReportingModule: PDF-hul, Rel. 1.12.1 (2019-04-17) LastModified: 2023-11-14 02:22:45 CET Size: 750017 Format: PDF Version: 1.7 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 286 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@4d3167f4 Item: Title: Materials and Methods Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@ed9d034 Children: Item: Title: Dataset and Study Design Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@6121c9d6 Item: Title: Estimation of the 2MWD from Accelerometer Signals Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@87f383f Item: Title: Performance Evaluation and Correlation Analysis with Clinical Outcomes Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@4eb7f003 Item: Title: Results Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@eafc191 Item: Title: Discussion Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@612fc6eb Item: Title: Conclusions Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@1060b431 Item: Title: References Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@612679d6 Info: Title: Automatic Assessment of the 2-Minute Walk Distance for Remote Monitoring of People with Multiple Sclerosis Author: Spyridon Kontaxis, Estela Laporta, Esther Garcia, Matteo Martinis, Letizia Leocani, Lucia Roselli, Mathias Due Buron, Ana Isabel Guerrero, Ana Zabala, Nicholas Cummins, Srinivasan Vairavan, Matthew Hotopf, Richard JB Dobson, Vaibhav A Narayan, Maria Libera La Porta, Gloria Dalla Costa, Melinda Magyari, Per Soelberg Sørensen, Carlos Nos, Raquel Bailon, Giancarlo Comi Subject: 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. 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