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

      research-article
      , BSc, MSc 1 , , , BSc, MSc 1 , , BSc 1 , , BSc, MSc, PhD 1 , , BSc, MSc, PhD 1 , , BSc, PhD 1
      (Reviewer), (Reviewer)
      JMIR mHealth and uHealth
      JMIR Publications
      hand hygiene, wearable devices, machine learning

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          Abstract

          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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          Most cited references36

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          Role of hand hygiene in healthcare-associated infection prevention.

          Healthcare workers' hands are the most common vehicle for the transmission of healthcare-associated pathogens from patient to patient and within the healthcare environment. Hand hygiene is the leading measure for preventing the spread of antimicrobial resistance and reducing healthcare-associated infections (HCAIs), but healthcare worker compliance with optimal practices remains low in most settings. This paper reviews factors influencing hand hygiene compliance, the impact of hand hygiene promotion on healthcare-associated pathogen cross-transmission and infection rates, and challenging issues related to the universal adoption of alcohol-based hand rub as a critical system change for successful promotion. Available evidence highlights the fact that multimodal intervention strategies lead to improved hand hygiene and a reduction in HCAI. However, further research is needed to evaluate the relative efficacy of each strategy component and to identify the most successful interventions, particularly in settings with limited resources. The main objective of the First Global Patient Safety Challenge, launched by the World Health Organization (WHO), is to achieve an improvement in hand hygiene practices worldwide with the ultimate goal of promoting a strong patient safety culture. We also report considerations and solutions resulting from the implementation of the multimodal strategy proposed in the WHO Guidelines on Hand Hygiene in Health Care.
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            The European Centre for Disease Prevention and Control (ECDC) pilot point prevalence survey of healthcare-associated infections and antimicrobial use.

            A standardised methodology for a combined point prevalence survey (PPS) on healthcare-associated infections (HAIs) and antimicrobial use in European acute care hospitals developed by the European Centre for Disease Prevention and Control was piloted across Europe. Variables were collected at national, hospital and patient level in 66 hospitals from 23 countries. A patient-based and a unit-based protocol were available. Feasibility was assessed via national and hospital questionnaires. Of 19,888 surveyed patients, 7.1% had an HAI and 34.6% were receiving at least one antimicrobial agent. Prevalence results were highest in intensive care units, with 28.1% patients with HAI, and 61.4% patients with antimicrobial use. Pneumonia and other lower respiratory tract infections (2.0% of patients; 95% confidence interval (CI): 1.8–2.2%) represented the most common type (25.7%) of HAI. Surgical prophylaxis was the indication for 17.3% of used antimicrobials and exceeded one day in 60.7% of cases. Risk factors in the patient-based protocol were provided for 98% or more of the included patients and all were independently associated with both presence of HAI and receiving an antimicrobial agent. The patient-based protocol required more work than the unit-based protocol, but allowed collecting detailed data and analysis of risk factors for HAI and antimicrobial use.
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              Hand hygiene among physicians: performance, beliefs, and perceptions.

              Physician adherence to hand hygiene remains low in most hospitals. To identify risk factors for nonadherence and assess beliefs and perceptions associated with hand hygiene among physicians. Cross-sectional survey of physician practices, beliefs, and attitudes toward hand hygiene. Large university hospital. 163 physicians. Individual observation of physician hand hygiene practices during routine patient care with documentation of relevant risk factors; self-report questionnaire to measure beliefs and perceptions. Logistic regression identified variables independently associated with adherence. Adherence averaged 57% and varied markedly across medical specialties. In multivariate analysis, adherence was associated with the awareness of being observed, the belief of being a role model for other colleagues, a positive attitude toward hand hygiene after patient contact, and easy access to hand-rub solution. Conversely, high workload, activities associated with a high risk for cross-transmission, and certain technical medical specialties (surgery, anesthesiology, emergency medicine, and intensive care medicine) were risk factors for nonadherence. Direct observation of physicians may have influenced both adherence to hand hygiene and responses to the self-report questionnaire. Generalizability of study results requires additional testing in other health care settings and physician populations. Physician adherence to hand hygiene is associated with work and system constraints, as well as knowledge and cognitive factors. At the individual level, strengthening a positive attitude toward hand hygiene and reinforcing the conviction that each individual can influence the group behavior may improve adherence among physicians. Physicians who work in technical specialties should also be targeted for improvement.
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                Author and article information

                Contributors
                Journal
                JMIR Mhealth Uhealth
                JMIR Mhealth Uhealth
                JMU
                JMIR mHealth and uHealth
                JMIR Publications (Toronto, Canada )
                2291-5222
                March 2020
                26 March 2020
                : 8
                : 3
                : e17001
                Affiliations
                [1 ] School of Computing and Information Systems The University of Melbourne Parkville Australia
                Author notes
                Corresponding Author: Chaofan Wang chaofan.wang@ 123456unimelb.edu.au
                Author information
                https://orcid.org/0000-0001-8213-6582
                https://orcid.org/0000-0002-1247-6036
                https://orcid.org/0000-0002-2342-5795
                https://orcid.org/0000-0001-6180-7033
                https://orcid.org/0000-0002-0117-0322
                https://orcid.org/0000-0003-2804-6038
                Article
                v8i3e17001
                10.2196/17001
                7146248
                32213469
                d8edde69-196b-42d9-8ded-90ce264996cb
                ©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.

                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.

                History
                : 11 November 2019
                : 2 December 2019
                : 20 December 2019
                : 24 January 2020
                Categories
                Original Paper
                Original Paper

                hand hygiene,wearable devices,machine learning
                hand hygiene, wearable devices, machine learning

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