@文章{信息:doi/10.2196/33951，作者="Sousa, Sonia and Kalju, Tiina"，标题="基于人机信任量表的COVID-19接触追踪应用程序信任建模:在线调查研究"，期刊="JMIR Hum Factors"，年="2022"，月="Jun"，日="13"，卷="9"，数="2"，页数="e33951"，关键词="人机交互;COVID-19;人为因素;值得信赖的人工智能;接触者追踪;应用程序;安全;信任;人工智能;爱沙尼亚; case study; monitoring; surveillance; perspective; awareness; design; covid; mobile app; mHealth; mobile health", abstract="Background: The COVID-19 pandemic has caused changes in technology use worldwide, both socially and economically. This pandemic crisis has brought additional measures such as contact-tracing apps (CTAs) to help fight against spread of the virus. Unfortunately, the low adoption rate of these apps affected their success. There could be many reasons for the low adoption, including concerns of security and privacy, along with reported issues of trust in CTAs. Some concerns are related with how CTAs could be used as surveillance tools or their potential threats to privacy as they involve health data. For example, in Estonia, the CTA named HOIA had approximately 250,000 downloads in the middle of January 2021. However, in 2021, only 4.7{\%} of the population used HOIA as a COVID-19 CTA. The reasons for the low adoption include lack of competency, and privacy and security concerns. This lower adoption and the lack of trustworthiness persist despite efforts of the European Union in building ethics and trustworthy artificial intelligence (AI)-based apps. Objective: The aim of this study was to understand how to measure trust in health technologies. Specifically, we assessed the usefulness of the Human-Computer Trust Scale (HCTS) to measure Estonians' trust in the HOIA app and the causes for this lack of trust. Methods: The main research question was: Can the HCTS be used to assess citizens' perception of trust in health technologies? We established four hypotheses that were tested with a survey. We used a convenience sample for data collection, including sharing the questionnaire on social network sites and using the snowball method to reach all potential HOIA users in the Estonian population. Results: Among the 78 respondents, 61 had downloaded the HOIA app with data on usage patterns. However, 20 of those who downloaded the app admitted that it was never opened despite most claiming to regularly use mobile apps. The main reasons included not understanding how it works, and privacy and security concerns. Significant correlations were found between participants' trust in CTAs in general and their perceived trust in the HOIA app regarding three attributes: competency (P<.001), risk perception (P<.001), and reciprocity (P=.01). Conclusions: This study shows that trust in the HOIA app among Estonian residents did affect their predisposition to use the app. Participants did not generally believe that HOIA could help to control the spread of the virus. The result of this work is limited to HOIA and health apps that use similar contact-tracing methods. However, the findings can contribute to gaining a broader understanding and awareness of the need for designing trustworthy technologies. Moreover, this work can help to provide design recommendations that ensure trustworthiness in CTAs, and the ability of AI to use highly sensitive data and serve society. ", issn="2292-9495", doi="10.2196/33951", url="https://humanfactors.www.mybigtv.com/2022/2/e33951", url="https://doi.org/10.2196/33951", url="http://www.ncbi.nlm.nih.gov/pubmed/35699973" }