Acceptance of a computer vision facilitated protocol to measure adherence to face mask use: a single-site, observational cohort study among hospital staff
Giovanni Traverso,
Adam B Landman,
Peter R Chai,
Hen-Wei Huang,
Jack Chen,
Farah Dadabhoy,
Phillip Rupp,
Clint Vaz,
Anjali Sinha,
Claas Ehmke,
Akhil Thomas,
Jia Y Liang,
George Player,
Kevin Slattery
Affiliations
Giovanni Traverso
Division of Gastroenterology, Brigham and Women`s Hospital, Harvard Medical School, Boston, Massachusetts, USA
Adam B Landman
3 Brigham and Women’s Hospital Department of Medicine, Boston, Massachusetts, USA
Peter R Chai
Department of Emergency Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, USA
Hen-Wei Huang
David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
Jack Chen
Simpson Centre for Health Services Research, South Western Sydney Clinical School & Australian Institute of Health Innovation, University of New South Wales, Sydney, New South Wales, Australia
Farah Dadabhoy
Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
Phillip Rupp
Massachusetts Institute of Technology Koch Institute for Integrative Cancer Research, Cambridge, Massachusetts, USA
Clint Vaz
Emergency Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, USA
Anjali Sinha
Massachusetts Institute of Technology Koch Institute for Integrative Cancer Research, Cambridge, Massachusetts, USA
Claas Ehmke
Massachusetts Institute of Technology Koch Institute for Integrative Cancer Research, Cambridge, Massachusetts, USA
Akhil Thomas
Massachusetts Institute of Technology Koch Institute for Integrative Cancer Research, Cambridge, Massachusetts, USA
Jia Y Liang
Massachusetts Institute of Technology Koch Institute for Integrative Cancer Research, Cambridge, Massachusetts, USA
George Player
Facilities, Brigham and Women`s Hospital, Boston, Massachusetts, USA
Kevin Slattery
Security, Safety and Parking, Brigham and Women`s Hospital, Boston, Massachusetts, USA
Objectives Mask adherence continues to be a critical public health measure to prevent transmission of aerosol pathogens, such as SARS-CoV-2. We aimed to develop and deploy a computer vision algorithm to provide real-time feedback of mask wearing among staff in a hospital.Design Single-site, observational cohort study.Setting An urban, academic hospital in Boston, Massachusetts, USA.Participants We enrolled adult hospital staff entering the hospital at a key ingress point.Interventions Consenting participants entering the hospital were invited to experience the computer vision mask detection system. Key aspects of the detection algorithm and feedback were described to participants, who then completed a quantitative assessment to understand their perceptions and acceptance of interacting with the system to detect their mask adherence.Outcome measures Primary outcomes were willingness to interact with the mask system, and the degree of comfort participants felt in interacting with a public facing computer vision mask algorithm.Results One hundred and eleven participants with mean age 40 (SD15.5) were enrolled in the study. Males (47.7%) and females (52.3%) were equally represented, and the majority identified as white (N=54, 49%). Most participants (N=97, 87.3%) reported acceptance of the system and most participants (N=84, 75.7%) were accepting of deployment of the system to reinforce mask adherence in public places. One third of participants (N=36) felt that a public facing computer vision system would be an intrusion into personal privacy.Public-facing computer vision software to detect and provide feedback around mask adherence may be acceptable in the hospital setting. Similar systems may be considered for deployment in locations where mask adherence is important.
