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System Comparison for Gait and Balance Monitoring Used for the Evaluation of a Home-Based Training
There are currently no standard methods for evaluating gait and balance performance at home. Smartphones include acceleration sensors and may represent a promising and easily accessible tool for this purpose. We performed an interventional feasibility study and compared a smartphone-based approach w...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9269735/ https://www.ncbi.nlm.nih.gov/pubmed/35808470 http://dx.doi.org/10.3390/s22134975 |
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author | Rentz, Clara Far, Mehran Sahandi Boltes, Maik Schnitzler, Alfons Amunts, Katrin Dukart, Juergen Minnerop, Martina |
author_facet | Rentz, Clara Far, Mehran Sahandi Boltes, Maik Schnitzler, Alfons Amunts, Katrin Dukart, Juergen Minnerop, Martina |
author_sort | Rentz, Clara |
collection | PubMed |
description | There are currently no standard methods for evaluating gait and balance performance at home. Smartphones include acceleration sensors and may represent a promising and easily accessible tool for this purpose. We performed an interventional feasibility study and compared a smartphone-based approach with two standard gait analysis systems (force plate and motion capturing systems). Healthy adults (n = 25, 44.1 ± 18.4 years) completed two laboratory evaluations before and after a three-week gait and balance training at home. There was an excellent agreement between all systems for stride time and cadence during normal, tandem and backward gait, whereas correlations for gait velocity were lower. Balance variables of both standard systems were moderately intercorrelated across all stance tasks, but only few correlated with the corresponding smartphone measures. Significant differences over time were found for several force plate and mocap system-obtained gait variables of normal, backward and tandem gait. Changes in balance variables over time were more heterogeneous and not significant for any system. The smartphone seems to be a suitable method to measure cadence and stride time of different gait, but not balance, tasks in healthy adults. Additional optimizations in data evaluation and processing may further improve the agreement between the analysis systems. |
format | Online Article Text |
id | pubmed-9269735 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-92697352022-07-09 System Comparison for Gait and Balance Monitoring Used for the Evaluation of a Home-Based Training Rentz, Clara Far, Mehran Sahandi Boltes, Maik Schnitzler, Alfons Amunts, Katrin Dukart, Juergen Minnerop, Martina Sensors (Basel) Article There are currently no standard methods for evaluating gait and balance performance at home. Smartphones include acceleration sensors and may represent a promising and easily accessible tool for this purpose. We performed an interventional feasibility study and compared a smartphone-based approach with two standard gait analysis systems (force plate and motion capturing systems). Healthy adults (n = 25, 44.1 ± 18.4 years) completed two laboratory evaluations before and after a three-week gait and balance training at home. There was an excellent agreement between all systems for stride time and cadence during normal, tandem and backward gait, whereas correlations for gait velocity were lower. Balance variables of both standard systems were moderately intercorrelated across all stance tasks, but only few correlated with the corresponding smartphone measures. Significant differences over time were found for several force plate and mocap system-obtained gait variables of normal, backward and tandem gait. Changes in balance variables over time were more heterogeneous and not significant for any system. The smartphone seems to be a suitable method to measure cadence and stride time of different gait, but not balance, tasks in healthy adults. Additional optimizations in data evaluation and processing may further improve the agreement between the analysis systems. MDPI 2022-06-30 /pmc/articles/PMC9269735/ /pubmed/35808470 http://dx.doi.org/10.3390/s22134975 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Rentz, Clara Far, Mehran Sahandi Boltes, Maik Schnitzler, Alfons Amunts, Katrin Dukart, Juergen Minnerop, Martina System Comparison for Gait and Balance Monitoring Used for the Evaluation of a Home-Based Training |
title | System Comparison for Gait and Balance Monitoring Used for the Evaluation of a Home-Based Training |
title_full | System Comparison for Gait and Balance Monitoring Used for the Evaluation of a Home-Based Training |
title_fullStr | System Comparison for Gait and Balance Monitoring Used for the Evaluation of a Home-Based Training |
title_full_unstemmed | System Comparison for Gait and Balance Monitoring Used for the Evaluation of a Home-Based Training |
title_short | System Comparison for Gait and Balance Monitoring Used for the Evaluation of a Home-Based Training |
title_sort | system comparison for gait and balance monitoring used for the evaluation of a home-based training |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9269735/ https://www.ncbi.nlm.nih.gov/pubmed/35808470 http://dx.doi.org/10.3390/s22134975 |
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