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Prediction of setup times for an advanced upper limb functional electrical stimulation system
INTRODUCTION: Rehabilitation devices take time to don, and longer or unpredictable setup time impacts on usage. This paper reports on the development of a model to predict setup time for upper limb functional electrical stimulation. METHODS: Participants’ level of impairment (Fugl Meyer-Upper Extrem...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6531802/ https://www.ncbi.nlm.nih.gov/pubmed/31191957 http://dx.doi.org/10.1177/2055668318802561 |
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author | Smith, Christine Kenney, Laurence Howard, David Waring, Karen Sun, Minxgu Luckie, Helen Hardiker, Nicholas Cotterill, Sarah |
author_facet | Smith, Christine Kenney, Laurence Howard, David Waring, Karen Sun, Minxgu Luckie, Helen Hardiker, Nicholas Cotterill, Sarah |
author_sort | Smith, Christine |
collection | PubMed |
description | INTRODUCTION: Rehabilitation devices take time to don, and longer or unpredictable setup time impacts on usage. This paper reports on the development of a model to predict setup time for upper limb functional electrical stimulation. METHODS: Participants’ level of impairment (Fugl Meyer-Upper Extremity Scale), function (Action Research Arm Test) and mental status (Mini Mental Scale) were measured. Setup times for each stage of the setup process and total setup times were recorded. A predictive model of setup time was devised using upper limb impairment and task complexity. RESULTS: Six participants with stroke were recruited, mean age 60 (±17) years and mean time since stroke 9.8 (±9.6) years. Mean Fugl Meyer-Upper Extremity score was 31.1 (±6), Action Research Arm Test 10.4 (±7.9) and Mini Mental Scale 26.1 (±2.7). Linear regression analysis showed that upper limb impairment and task complexity most effectively predicted setup time (51% as compared with 39%) (F(2,21) = 12.782, adjusted R(2) = 0.506; p < .05). CONCLUSIONS: A model to predict setup time based on upper limb impairment and task complexity accounted for 51% of the variation in setup time. Further studies are required to test the model in real-world settings and to identify other contributing factors. |
format | Online Article Text |
id | pubmed-6531802 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-65318022019-06-12 Prediction of setup times for an advanced upper limb functional electrical stimulation system Smith, Christine Kenney, Laurence Howard, David Waring, Karen Sun, Minxgu Luckie, Helen Hardiker, Nicholas Cotterill, Sarah J Rehabil Assist Technol Eng Original Article INTRODUCTION: Rehabilitation devices take time to don, and longer or unpredictable setup time impacts on usage. This paper reports on the development of a model to predict setup time for upper limb functional electrical stimulation. METHODS: Participants’ level of impairment (Fugl Meyer-Upper Extremity Scale), function (Action Research Arm Test) and mental status (Mini Mental Scale) were measured. Setup times for each stage of the setup process and total setup times were recorded. A predictive model of setup time was devised using upper limb impairment and task complexity. RESULTS: Six participants with stroke were recruited, mean age 60 (±17) years and mean time since stroke 9.8 (±9.6) years. Mean Fugl Meyer-Upper Extremity score was 31.1 (±6), Action Research Arm Test 10.4 (±7.9) and Mini Mental Scale 26.1 (±2.7). Linear regression analysis showed that upper limb impairment and task complexity most effectively predicted setup time (51% as compared with 39%) (F(2,21) = 12.782, adjusted R(2) = 0.506; p < .05). CONCLUSIONS: A model to predict setup time based on upper limb impairment and task complexity accounted for 51% of the variation in setup time. Further studies are required to test the model in real-world settings and to identify other contributing factors. SAGE Publications 2018-11-18 /pmc/articles/PMC6531802/ /pubmed/31191957 http://dx.doi.org/10.1177/2055668318802561 Text en © The Author(s) 2018 http://creativecommons.org/licenses/by/4.0/ Creative Commons CC-BY: This article is distributed under the terms of the Creative Commons Attribution 4.0 License (http://www.creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
spellingShingle | Original Article Smith, Christine Kenney, Laurence Howard, David Waring, Karen Sun, Minxgu Luckie, Helen Hardiker, Nicholas Cotterill, Sarah Prediction of setup times for an advanced upper limb functional electrical stimulation system |
title | Prediction of setup times for an advanced upper limb functional
electrical stimulation system |
title_full | Prediction of setup times for an advanced upper limb functional
electrical stimulation system |
title_fullStr | Prediction of setup times for an advanced upper limb functional
electrical stimulation system |
title_full_unstemmed | Prediction of setup times for an advanced upper limb functional
electrical stimulation system |
title_short | Prediction of setup times for an advanced upper limb functional
electrical stimulation system |
title_sort | prediction of setup times for an advanced upper limb functional
electrical stimulation system |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6531802/ https://www.ncbi.nlm.nih.gov/pubmed/31191957 http://dx.doi.org/10.1177/2055668318802561 |
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