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Accurate Measurement of Handwash Quality Using Sensor Armbands: Instrument Validation Study

BACKGROUND: Hand hygiene is a crucial and cost-effective method to prevent health care–associated infections, and in 2009, the World Health Organization (WHO) issued guidelines to encourage and standardize hand hygiene procedures. However, a common challenge in health care settings is low adherence,...

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Autores principales: Wang, Chaofan, Sarsenbayeva, Zhanna, Chen, Xiuge, Dingler, Tilman, Goncalves, Jorge, Kostakos, Vassilis
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7146248/
https://www.ncbi.nlm.nih.gov/pubmed/32213469
http://dx.doi.org/10.2196/17001
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author Wang, Chaofan
Sarsenbayeva, Zhanna
Chen, Xiuge
Dingler, Tilman
Goncalves, Jorge
Kostakos, Vassilis
author_facet Wang, Chaofan
Sarsenbayeva, Zhanna
Chen, Xiuge
Dingler, Tilman
Goncalves, Jorge
Kostakos, Vassilis
author_sort Wang, Chaofan
collection PubMed
description BACKGROUND: Hand hygiene is a crucial and cost-effective method to prevent health care–associated infections, and in 2009, the World Health Organization (WHO) issued guidelines to encourage and standardize hand hygiene procedures. However, a common challenge in health care settings is low adherence, leading to low handwashing quality. Recent advances in machine learning and wearable sensing have made it possible to accurately measure handwashing quality for the purposes of training, feedback, or accreditation. OBJECTIVE: We measured the accuracy of a sensor armband (Myo armband) in detecting the steps and duration of the WHO procedures for handwashing and handrubbing. METHODS: We recruited 20 participants (10 females; mean age 26.5 years, SD 3.3). In a semistructured environment, we collected armband data (acceleration, gyroscope, orientation, and surface electromyography data) and video data from each participant during 15 handrub and 15 handwash sessions. We evaluated the detection accuracy for different armband placements, sensor configurations, user-dependent vs user-independent models, and the use of bootstrapping. RESULTS: Using a single armband, the accuracy was 96% (SD 0.01) for the user-dependent model and 82% (SD 0.08) for the user-independent model. This increased when using two armbands to 97% (SD 0.01) and 91% (SD 0.04), respectively. Performance increased when the armband was placed on the forearm (user dependent: 97%, SD 0.01; and user independent: 91%, SD 0.04) and decreased when placed on the arm (user dependent: 96%, SD 0.01; and user independent: 80%, SD 0.06). In terms of bootstrapping, user-dependent models can achieve more than 80% accuracy after six training sessions and 90% with 16 sessions. Finally, we found that the combination of accelerometer and gyroscope minimizes power consumption and cost while maximizing performance. CONCLUSIONS: A sensor armband can be used to measure hand hygiene quality relatively accurately, in terms of both handwashing and handrubbing. The performance is acceptable using a single armband worn in the upper arm but can substantially improve by placing the armband on the forearm or by using two armbands.
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spelling pubmed-71462482020-04-21 Accurate Measurement of Handwash Quality Using Sensor Armbands: Instrument Validation Study Wang, Chaofan Sarsenbayeva, Zhanna Chen, Xiuge Dingler, Tilman Goncalves, Jorge Kostakos, Vassilis JMIR Mhealth Uhealth Original Paper BACKGROUND: Hand hygiene is a crucial and cost-effective method to prevent health care–associated infections, and in 2009, the World Health Organization (WHO) issued guidelines to encourage and standardize hand hygiene procedures. However, a common challenge in health care settings is low adherence, leading to low handwashing quality. Recent advances in machine learning and wearable sensing have made it possible to accurately measure handwashing quality for the purposes of training, feedback, or accreditation. OBJECTIVE: We measured the accuracy of a sensor armband (Myo armband) in detecting the steps and duration of the WHO procedures for handwashing and handrubbing. METHODS: We recruited 20 participants (10 females; mean age 26.5 years, SD 3.3). In a semistructured environment, we collected armband data (acceleration, gyroscope, orientation, and surface electromyography data) and video data from each participant during 15 handrub and 15 handwash sessions. We evaluated the detection accuracy for different armband placements, sensor configurations, user-dependent vs user-independent models, and the use of bootstrapping. RESULTS: Using a single armband, the accuracy was 96% (SD 0.01) for the user-dependent model and 82% (SD 0.08) for the user-independent model. This increased when using two armbands to 97% (SD 0.01) and 91% (SD 0.04), respectively. Performance increased when the armband was placed on the forearm (user dependent: 97%, SD 0.01; and user independent: 91%, SD 0.04) and decreased when placed on the arm (user dependent: 96%, SD 0.01; and user independent: 80%, SD 0.06). In terms of bootstrapping, user-dependent models can achieve more than 80% accuracy after six training sessions and 90% with 16 sessions. Finally, we found that the combination of accelerometer and gyroscope minimizes power consumption and cost while maximizing performance. CONCLUSIONS: A sensor armband can be used to measure hand hygiene quality relatively accurately, in terms of both handwashing and handrubbing. The performance is acceptable using a single armband worn in the upper arm but can substantially improve by placing the armband on the forearm or by using two armbands. JMIR Publications 2020-03-26 /pmc/articles/PMC7146248/ /pubmed/32213469 http://dx.doi.org/10.2196/17001 Text en ©Chaofan Wang, Zhanna Sarsenbayeva, Xiuge Chen, Tilman Dingler, Jorge Goncalves, Vassilis Kostakos. Originally published in JMIR mHealth and uHealth (http://mhealth.jmir.org), 26.03.2020. https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mHealth and uHealth, is properly cited. The complete bibliographic information, a link to the original publication on http://mhealth.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Wang, Chaofan
Sarsenbayeva, Zhanna
Chen, Xiuge
Dingler, Tilman
Goncalves, Jorge
Kostakos, Vassilis
Accurate Measurement of Handwash Quality Using Sensor Armbands: Instrument Validation Study
title Accurate Measurement of Handwash Quality Using Sensor Armbands: Instrument Validation Study
title_full Accurate Measurement of Handwash Quality Using Sensor Armbands: Instrument Validation Study
title_fullStr Accurate Measurement of Handwash Quality Using Sensor Armbands: Instrument Validation Study
title_full_unstemmed Accurate Measurement of Handwash Quality Using Sensor Armbands: Instrument Validation Study
title_short Accurate Measurement of Handwash Quality Using Sensor Armbands: Instrument Validation Study
title_sort accurate measurement of handwash quality using sensor armbands: instrument validation study
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7146248/
https://www.ncbi.nlm.nih.gov/pubmed/32213469
http://dx.doi.org/10.2196/17001
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