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Age and environment-related differences in gait in healthy adults using wearables

Technological advances in multimodal wearable and connected devices have enabled the measurement of human movement and physiology in naturalistic settings. The ability to collect continuous activity monitoring data with digital devices in real-world environments has opened unprecedented opportunity...

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Autores principales: Czech, Matthew D., Psaltos, Dimitrios, Zhang, Hao, Adamusiak, Tomasz, Calicchio, Monica, Kelekar, Amey, Messere, Andrew, Van Dijk, Koene R. A., Ramos, Vesper, Demanuele, Charmaine, Cai, Xuemei, Santamaria, Mar, Patel, Shyamal, Karahanoglu, F. Isik
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7528045/
https://www.ncbi.nlm.nih.gov/pubmed/33083562
http://dx.doi.org/10.1038/s41746-020-00334-y
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author Czech, Matthew D.
Psaltos, Dimitrios
Zhang, Hao
Adamusiak, Tomasz
Calicchio, Monica
Kelekar, Amey
Messere, Andrew
Van Dijk, Koene R. A.
Ramos, Vesper
Demanuele, Charmaine
Cai, Xuemei
Santamaria, Mar
Patel, Shyamal
Karahanoglu, F. Isik
author_facet Czech, Matthew D.
Psaltos, Dimitrios
Zhang, Hao
Adamusiak, Tomasz
Calicchio, Monica
Kelekar, Amey
Messere, Andrew
Van Dijk, Koene R. A.
Ramos, Vesper
Demanuele, Charmaine
Cai, Xuemei
Santamaria, Mar
Patel, Shyamal
Karahanoglu, F. Isik
author_sort Czech, Matthew D.
collection PubMed
description Technological advances in multimodal wearable and connected devices have enabled the measurement of human movement and physiology in naturalistic settings. The ability to collect continuous activity monitoring data with digital devices in real-world environments has opened unprecedented opportunity to establish clinical digital phenotypes across diseases. Many traditional assessments of physical function utilized in clinical trials are limited because they are episodic, therefore, cannot capture the day-to-day temporal fluctuations and longitudinal changes in activity that individuals experience. In order to understand the sensitivity of gait speed as a potential endpoint for clinical trials, we investigated the use of digital devices during traditional clinical assessments and in real-world environments in a group of healthy younger (n = 33, 18–40 years) and older (n = 32, 65–85 years) adults. We observed good agreement between gait speed estimated using a lumbar-mounted accelerometer and gold standard system during the performance of traditional gait assessment task in-lab, and saw discrepancies between in-lab and at-home gait speed. We found that gait speed estimated in-lab, with or without digital devices, failed to differentiate between the age groups, whereas gait speed derived during at-home monitoring was able to distinguish the age groups. Furthermore, we found that only three days of at-home monitoring was sufficient to reliably estimate gait speed in our population, and still capture age-related group differences. Our results suggest that gait speed derived from activities during daily life using data from wearable devices may have the potential to transform clinical trials by non-invasively and unobtrusively providing a more objective and naturalistic measure of functional ability.
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spelling pubmed-75280452020-10-19 Age and environment-related differences in gait in healthy adults using wearables Czech, Matthew D. Psaltos, Dimitrios Zhang, Hao Adamusiak, Tomasz Calicchio, Monica Kelekar, Amey Messere, Andrew Van Dijk, Koene R. A. Ramos, Vesper Demanuele, Charmaine Cai, Xuemei Santamaria, Mar Patel, Shyamal Karahanoglu, F. Isik NPJ Digit Med Article Technological advances in multimodal wearable and connected devices have enabled the measurement of human movement and physiology in naturalistic settings. The ability to collect continuous activity monitoring data with digital devices in real-world environments has opened unprecedented opportunity to establish clinical digital phenotypes across diseases. Many traditional assessments of physical function utilized in clinical trials are limited because they are episodic, therefore, cannot capture the day-to-day temporal fluctuations and longitudinal changes in activity that individuals experience. In order to understand the sensitivity of gait speed as a potential endpoint for clinical trials, we investigated the use of digital devices during traditional clinical assessments and in real-world environments in a group of healthy younger (n = 33, 18–40 years) and older (n = 32, 65–85 years) adults. We observed good agreement between gait speed estimated using a lumbar-mounted accelerometer and gold standard system during the performance of traditional gait assessment task in-lab, and saw discrepancies between in-lab and at-home gait speed. We found that gait speed estimated in-lab, with or without digital devices, failed to differentiate between the age groups, whereas gait speed derived during at-home monitoring was able to distinguish the age groups. Furthermore, we found that only three days of at-home monitoring was sufficient to reliably estimate gait speed in our population, and still capture age-related group differences. Our results suggest that gait speed derived from activities during daily life using data from wearable devices may have the potential to transform clinical trials by non-invasively and unobtrusively providing a more objective and naturalistic measure of functional ability. Nature Publishing Group UK 2020-09-30 /pmc/articles/PMC7528045/ /pubmed/33083562 http://dx.doi.org/10.1038/s41746-020-00334-y Text en © The Author(s) 2020 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Czech, Matthew D.
Psaltos, Dimitrios
Zhang, Hao
Adamusiak, Tomasz
Calicchio, Monica
Kelekar, Amey
Messere, Andrew
Van Dijk, Koene R. A.
Ramos, Vesper
Demanuele, Charmaine
Cai, Xuemei
Santamaria, Mar
Patel, Shyamal
Karahanoglu, F. Isik
Age and environment-related differences in gait in healthy adults using wearables
title Age and environment-related differences in gait in healthy adults using wearables
title_full Age and environment-related differences in gait in healthy adults using wearables
title_fullStr Age and environment-related differences in gait in healthy adults using wearables
title_full_unstemmed Age and environment-related differences in gait in healthy adults using wearables
title_short Age and environment-related differences in gait in healthy adults using wearables
title_sort age and environment-related differences in gait in healthy adults using wearables
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7528045/
https://www.ncbi.nlm.nih.gov/pubmed/33083562
http://dx.doi.org/10.1038/s41746-020-00334-y
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