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Patient-led development of digital endpoints and the use of computer vision analysis in assessment of motor function in rare diseases

Digital health technologies are transforming the way health outcomes are captured and measured. Digital biomarkers may provide more objective measurements than traditional approaches as they encompass continuous and longitudinal data collection and use of automated analysis for data interpretation....

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Autores principales: Ferrer-Mallol, Elisa, Matthews, Clare, Stoodley, Madeline, Gaeta, Alessandra, George, Elinor, Reuben, Emily, Johnson, Alex, Davies, Elin Haf
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9510779/
https://www.ncbi.nlm.nih.gov/pubmed/36172196
http://dx.doi.org/10.3389/fphar.2022.916714
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author Ferrer-Mallol, Elisa
Matthews, Clare
Stoodley, Madeline
Gaeta, Alessandra
George, Elinor
Reuben, Emily
Johnson, Alex
Davies, Elin Haf
author_facet Ferrer-Mallol, Elisa
Matthews, Clare
Stoodley, Madeline
Gaeta, Alessandra
George, Elinor
Reuben, Emily
Johnson, Alex
Davies, Elin Haf
author_sort Ferrer-Mallol, Elisa
collection PubMed
description Digital health technologies are transforming the way health outcomes are captured and measured. Digital biomarkers may provide more objective measurements than traditional approaches as they encompass continuous and longitudinal data collection and use of automated analysis for data interpretation. In addition, the use of digital health technology allows for home-based disease assessments, which in addition to reducing patient burden from on-site hospital visits, provides a more holistic picture of how the patient feels and functions in the real world. Tools that can robustly capture drug efficacy based on disease-specific outcomes that are meaningful to patients, are going to be key to the successful development of new treatments. This is particularly important for people living with rare and chronic complex conditions, where therapeutic options are limited and need to be developed using a patient-focused approach to achieve the biggest impact. Working in partnership with patient Organisation Duchenne UK, we co-developed a video-based approach, delivered through a new mobile health platform (DMD Home), to assess motor function in patients with Duchenne muscular dystrophy (DMD), a genetic, rare, muscular disease characterized by the progressive loss of muscle function and strength. Motor function tasks were selected to reflect the “transfer stage” of the disease, when patients are no longer able to walk independently but can stand and weight-bear to transfer. This stage is important for patients and families as it represents a significant milestone in the progression of DMD but it is not routinely captured and/or scored by standard DMD clinical and physiotherapy assessments. A total of 62 videos were submitted by eight out of eleven participants who onboarded the app and were analysed with pose estimation software (OpenPose) that led to the extraction of objective, quantitative measures, including time, pattern of movement trajectory, and smoothness and symmetry of movement. Computer vision analysis of video tasks to identify voluntary or compensatory movements within the transfer stage merits further investigation. Longitudinal studies to validate DMD home as a new methodology to predict progression to the non-ambulant stage will be pursued.
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spelling pubmed-95107792022-09-27 Patient-led development of digital endpoints and the use of computer vision analysis in assessment of motor function in rare diseases Ferrer-Mallol, Elisa Matthews, Clare Stoodley, Madeline Gaeta, Alessandra George, Elinor Reuben, Emily Johnson, Alex Davies, Elin Haf Front Pharmacol Pharmacology Digital health technologies are transforming the way health outcomes are captured and measured. Digital biomarkers may provide more objective measurements than traditional approaches as they encompass continuous and longitudinal data collection and use of automated analysis for data interpretation. In addition, the use of digital health technology allows for home-based disease assessments, which in addition to reducing patient burden from on-site hospital visits, provides a more holistic picture of how the patient feels and functions in the real world. Tools that can robustly capture drug efficacy based on disease-specific outcomes that are meaningful to patients, are going to be key to the successful development of new treatments. This is particularly important for people living with rare and chronic complex conditions, where therapeutic options are limited and need to be developed using a patient-focused approach to achieve the biggest impact. Working in partnership with patient Organisation Duchenne UK, we co-developed a video-based approach, delivered through a new mobile health platform (DMD Home), to assess motor function in patients with Duchenne muscular dystrophy (DMD), a genetic, rare, muscular disease characterized by the progressive loss of muscle function and strength. Motor function tasks were selected to reflect the “transfer stage” of the disease, when patients are no longer able to walk independently but can stand and weight-bear to transfer. This stage is important for patients and families as it represents a significant milestone in the progression of DMD but it is not routinely captured and/or scored by standard DMD clinical and physiotherapy assessments. A total of 62 videos were submitted by eight out of eleven participants who onboarded the app and were analysed with pose estimation software (OpenPose) that led to the extraction of objective, quantitative measures, including time, pattern of movement trajectory, and smoothness and symmetry of movement. Computer vision analysis of video tasks to identify voluntary or compensatory movements within the transfer stage merits further investigation. Longitudinal studies to validate DMD home as a new methodology to predict progression to the non-ambulant stage will be pursued. Frontiers Media S.A. 2022-09-12 /pmc/articles/PMC9510779/ /pubmed/36172196 http://dx.doi.org/10.3389/fphar.2022.916714 Text en Copyright © 2022 Ferrer-Mallol, Matthews, Stoodley, Gaeta, George, Reuben, Johnson and Davies. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Pharmacology
Ferrer-Mallol, Elisa
Matthews, Clare
Stoodley, Madeline
Gaeta, Alessandra
George, Elinor
Reuben, Emily
Johnson, Alex
Davies, Elin Haf
Patient-led development of digital endpoints and the use of computer vision analysis in assessment of motor function in rare diseases
title Patient-led development of digital endpoints and the use of computer vision analysis in assessment of motor function in rare diseases
title_full Patient-led development of digital endpoints and the use of computer vision analysis in assessment of motor function in rare diseases
title_fullStr Patient-led development of digital endpoints and the use of computer vision analysis in assessment of motor function in rare diseases
title_full_unstemmed Patient-led development of digital endpoints and the use of computer vision analysis in assessment of motor function in rare diseases
title_short Patient-led development of digital endpoints and the use of computer vision analysis in assessment of motor function in rare diseases
title_sort patient-led development of digital endpoints and the use of computer vision analysis in assessment of motor function in rare diseases
topic Pharmacology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9510779/
https://www.ncbi.nlm.nih.gov/pubmed/36172196
http://dx.doi.org/10.3389/fphar.2022.916714
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