Contextual Acceptance of COVID-19 Mitigation Mobile Apps in the United States: Mixed Methods Survey Study on Postpandemic Data Privacy

被引:0
作者
Feng, Yuanyuan [1 ]
Stenger, Brad [1 ]
Zhan, Shikun [2 ]
机构
[1] Univ Vermont, Dept Comp Sci, 85 South Prospect St, Burlington, VT 05405 USA
[2] Carnegie Mellon Univ, Sch Comp Sci, Pittsburgh, PA USA
关键词
data privacy; health privacy; COVID-19; mobile apps; contextual integrity; respiratory; infectious; pulmonary; pandemic; mobileapp; app; apps; digital health; digital technology; digital intervention; digital interventions; smartphone; smartphones; mobilephone; DATA RETENTION; VIGNETTE;
D O I
10.2196/57309
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
Background: The COVID-19 pandemic gave rise to countless user-facing mobile apps to help fight the pandemic ("COVID-19mitigation apps"). These apps have been at the center of data privacy discussions because they collect, use, and even retain sensitive personal data from their users (eg, medical records and location data). The US government ended its COVID-19emergency declaration in May 2023, marking a unique time to comprehensively investigate how data privacy impacted people's acceptance of various COVID-19 mitigation apps deployed throughout the pandemic. Objective: This research aims to provide insights into health data privacy regarding COVID-19 mitigation apps and policy recommendations for future deployment of public health mobile apps through the lens of data privacy. This research explorespeople's contextual acceptance of different types of COVID-19 mitigation apps by applying the privacy framework of contextualintegrity. Specifically, this research seeks to identify the factors that impact people's acceptance of data sharing and data retention practices in various social contexts. Methods: A mixed methods web-based survey study was conducted by recruiting a simple US representative sample (N=674)on Prolific in February 2023. The survey includes a total of 60 vignette scenarios representing realistic social contexts thatCOVID-19 mitigation apps could be used. Each survey respondent answered questions about their acceptance of 10 randomly selected scenarios. Three contextual integrity parameters (attribute, recipient, and transmission principle) and respondents' basic demographics are controlled as independent variables. Regression analysis was performed to determine the factors impacting people's acceptance of initial data sharing and data retention practices via these apps. Qualitative data from the survey were analyzed to support the statistical results. Results: Many contextual integrity parameter values, pairwise combinations of contextual integrity parameter values, and some demographic features of respondents have a significant impact on their acceptance of using COVID-19 mitigation apps in various social contexts. Respondents' acceptance of data retention practices diverged from their acceptance of initial data sharing practices in some scenarios. Conclusions: This study showed that people's acceptance of using various COVID-19 mitigation apps depends on specific social contexts, including the type of data (attribute), the recipients of the data (recipient), and the purpose of data use (transmissionprinciple). Such acceptance may differ between the initial data sharing and data retention practices, even in the same context. Study findings generated rich implications for future pandemic mitigation apps and the broader public health mobile apps regarding data privacy and deployment considerations.
引用
收藏
页数:19
相关论文
共 50 条
  • [21] A comprehensive survey on exploring and analyzing COVID-19 mobile apps: Meta and exploratory analysis
    Khan, Habib Ullah
    Ali, Yasir
    Akbar, Muhammad Azeem
    Khan, Faheem
    HELIYON, 2024, 10 (15)
  • [22] Losing Control to Data-Hungry Apps: A Mixed-Methods Approach to Mobile App Privacy
    Brandtzaeg, Petter Bae
    Pultier, Antoine
    Moen, Gro Mette
    SOCIAL SCIENCE COMPUTER REVIEW, 2019, 37 (04) : 466 - 488
  • [23] COVID-19 vaccine acceptance and coverage among pregnant persons in the United States
    Regan, Annette K.
    Kaur, Ravneet
    Nosek, Marcianna
    Swathi, Pallavi A.
    Gu, Ning Y.
    PREVENTIVE MEDICINE REPORTS, 2022, 29
  • [24] COVID-19 Vaccine Acceptance among Health Care Workers in the United States
    Shekhar, Rahul
    Sheikh, Abu Baker
    Upadhyay, Shubhra
    Singh, Mriganka
    Kottewar, Saket
    Mir, Hamza
    Barrett, Eileen
    Pal, Suman
    VACCINES, 2021, 9 (02) : 1 - 18
  • [25] Web-Based COVID-19 Dashboards and Trackers in the United States: Survey Study
    Clarkson, Melissa D.
    JMIR HUMAN FACTORS, 2023, 10
  • [26] COVID-19 Mobile Applications: A Study of Trackers and Data Leaks
    Serrano, Nicolas
    Betarte, Gustavo
    Campo, Juan Diego
    JOURNAL OF INTERNET SERVICES AND APPLICATIONS, 2024, 15 (01) : 139 - 159
  • [27] Survey data for COVID-19 vaccine preference analysis in the United Arab Emirates
    Muqattash, Riham
    Niankara, Ibrahim
    Traoret, Rachidatou I.
    DATA IN BRIEF, 2020, 33
  • [28] Attitudes About COVID-19 and Health (ATTACH): Online Survey and Mixed Methods Study
    Hood, Anna M.
    Stotesbury, Hanne
    Murphy, Jennifer
    Kolbel, Melanie
    Slee, April
    Springall, Charlie
    Paradis, Matthew
    Corral-Frias, Nadia Sarai
    Reyes-Aguilar, Azalea
    Barboza, Alfredo B. Cuellar
    Noser, Amy E.
    Gomes, Stacey
    Mitchell, Monica
    Watkins, Sharon M.
    Kovacic, Melinda Butsch
    Kirkham, Fenella J.
    Crosby, Lori E.
    JMIR MENTAL HEALTH, 2021, 8 (10):
  • [29] Survey of Vitreoretinal Specialists in the United States Regarding Telemedicine During the COVID-19 Pandemic
    Cohen, Michael N.
    Ammar, Michael J.
    Mahmoudzadeh, Raziyeh
    Salabati, Mirataollah
    Gruver, Rachel S.
    Starr, Matthew R.
    Patel, Luv G.
    Klufas, Michael A.
    Garg, Sunir J.
    Yonekawa, Yoshihiro
    Kuriyan, Ajay E.
    Khan, M. Ali
    TELEMEDICINE AND E-HEALTH, 2022, 28 (12) : 1817 - 1822
  • [30] Change Point Modeling of Covid-19 Data in the United States
    Zhang, Sheng
    Xu, Ziyue
    Peng, Hanxiang
    STATISTICS AND APPLICATIONS, 2020, 18 (01): : 307 - 318