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A mobile-based airway clearance care system using deep learning-based vision technology to support personalized home-based pulmonary rehabilitation for COAD patients: Development and usability testing
BACKGROUND: Excessive mucus secretion is a serious issue for patients with chronic obstructive airway disease (COAD), which can be effectively managed through postural drainage and percussion (PD + P) during pulmonary rehabilitation (PR). Home-based (H)-PR can be as effective as center-based PR but...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10571692/ https://www.ncbi.nlm.nih.gov/pubmed/37841513 http://dx.doi.org/10.1177/20552076231207206 |
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author | Su, Jun-Ming Chen, Kuan-Yuan Wu, Sheng-Ming Lee, Kang-Yun Ho, Shu-Chuan |
author_facet | Su, Jun-Ming Chen, Kuan-Yuan Wu, Sheng-Ming Lee, Kang-Yun Ho, Shu-Chuan |
author_sort | Su, Jun-Ming |
collection | PubMed |
description | BACKGROUND: Excessive mucus secretion is a serious issue for patients with chronic obstructive airway disease (COAD), which can be effectively managed through postural drainage and percussion (PD + P) during pulmonary rehabilitation (PR). Home-based (H)-PR can be as effective as center-based PR but lacks professional supervision and timely feedback, leading to low motivation and adherence. Telehealth home-based pulmonary (TH-PR) has emerged to assist H-PR, but video conferencing and telephone calls remain the main approaches for COAD patients. Therefore, research on effectively assisting patients in performing PD + P during TH-PR is limited. OBJECTIVE: This study developed a mobile-based airway clearance care for chronic obstructive airway disease (COAD-MoAcCare) system to support personalized TH-PR for COAD patients and evaluated its usability through expert validation. METHODS: The COAD-MoAcCare system uses a mobile device through deep learning-based vision technology to monitor, guide, and evaluate COAD patients’ PD + P operations in real time during TH-PR programs. Medical personnel can manage and monitor their personalized PD + P and operational statuses through the system to improve TH-PR performance. Respiratory therapists from different hospitals evaluated the system usability using system questionnaires based on the technology acceptance model, system usability scale (SUS), and task load index (NASA-TLX). RESULTS: Eleven participant therapists were highly satisfied with the COAD-MoAcCare system, rating it between 4.1 and 4.6 out of 5.0 on all scales. The system demonstrated good usability (SUS score of 74.1 out of 100) and a lower task load (NASA-TLX score of 30.0 out of 100). The overall accuracy of PD + P operations reached a high level of 97.5% by comparing evaluation results of the system by experts. CONCLUSIONS: The COAD-MoAcCare system is the first mobile-based method to assist COAD patients in conducting PD + P in TH-PR. It was proven to be usable by respiratory therapists, so it is expected to benefit medical personnel and COAD patients. It will be further evaluated through clinical trials. |
format | Online Article Text |
id | pubmed-10571692 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-105716922023-10-14 A mobile-based airway clearance care system using deep learning-based vision technology to support personalized home-based pulmonary rehabilitation for COAD patients: Development and usability testing Su, Jun-Ming Chen, Kuan-Yuan Wu, Sheng-Ming Lee, Kang-Yun Ho, Shu-Chuan Digit Health Original Research BACKGROUND: Excessive mucus secretion is a serious issue for patients with chronic obstructive airway disease (COAD), which can be effectively managed through postural drainage and percussion (PD + P) during pulmonary rehabilitation (PR). Home-based (H)-PR can be as effective as center-based PR but lacks professional supervision and timely feedback, leading to low motivation and adherence. Telehealth home-based pulmonary (TH-PR) has emerged to assist H-PR, but video conferencing and telephone calls remain the main approaches for COAD patients. Therefore, research on effectively assisting patients in performing PD + P during TH-PR is limited. OBJECTIVE: This study developed a mobile-based airway clearance care for chronic obstructive airway disease (COAD-MoAcCare) system to support personalized TH-PR for COAD patients and evaluated its usability through expert validation. METHODS: The COAD-MoAcCare system uses a mobile device through deep learning-based vision technology to monitor, guide, and evaluate COAD patients’ PD + P operations in real time during TH-PR programs. Medical personnel can manage and monitor their personalized PD + P and operational statuses through the system to improve TH-PR performance. Respiratory therapists from different hospitals evaluated the system usability using system questionnaires based on the technology acceptance model, system usability scale (SUS), and task load index (NASA-TLX). RESULTS: Eleven participant therapists were highly satisfied with the COAD-MoAcCare system, rating it between 4.1 and 4.6 out of 5.0 on all scales. The system demonstrated good usability (SUS score of 74.1 out of 100) and a lower task load (NASA-TLX score of 30.0 out of 100). The overall accuracy of PD + P operations reached a high level of 97.5% by comparing evaluation results of the system by experts. CONCLUSIONS: The COAD-MoAcCare system is the first mobile-based method to assist COAD patients in conducting PD + P in TH-PR. It was proven to be usable by respiratory therapists, so it is expected to benefit medical personnel and COAD patients. It will be further evaluated through clinical trials. SAGE Publications 2023-10-12 /pmc/articles/PMC10571692/ /pubmed/37841513 http://dx.doi.org/10.1177/20552076231207206 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
spellingShingle | Original Research Su, Jun-Ming Chen, Kuan-Yuan Wu, Sheng-Ming Lee, Kang-Yun Ho, Shu-Chuan A mobile-based airway clearance care system using deep learning-based vision technology to support personalized home-based pulmonary rehabilitation for COAD patients: Development and usability testing |
title | A mobile-based airway clearance care system using deep learning-based vision technology to support personalized home-based pulmonary rehabilitation for COAD patients: Development and usability testing |
title_full | A mobile-based airway clearance care system using deep learning-based vision technology to support personalized home-based pulmonary rehabilitation for COAD patients: Development and usability testing |
title_fullStr | A mobile-based airway clearance care system using deep learning-based vision technology to support personalized home-based pulmonary rehabilitation for COAD patients: Development and usability testing |
title_full_unstemmed | A mobile-based airway clearance care system using deep learning-based vision technology to support personalized home-based pulmonary rehabilitation for COAD patients: Development and usability testing |
title_short | A mobile-based airway clearance care system using deep learning-based vision technology to support personalized home-based pulmonary rehabilitation for COAD patients: Development and usability testing |
title_sort | mobile-based airway clearance care system using deep learning-based vision technology to support personalized home-based pulmonary rehabilitation for coad patients: development and usability testing |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10571692/ https://www.ncbi.nlm.nih.gov/pubmed/37841513 http://dx.doi.org/10.1177/20552076231207206 |
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