School of Communication and Design, Sun Yat-sen University, Guangzhou, China.
Department of Public and International Affairs, City University of Hong Kong, Hong Kong, China.
BMC Public Health. 2022 Dec 31;22(1):2457. doi: 10.1186/s12889-022-14918-8.
Countries across the globe have released many COVID-19 mobile apps. However, there is a lack of systematic empirical investigation into the factors affecting the adoption and evaluation of COVID-related apps. This study explores what factors at the country level and the app levels would influence the adoption and evaluation of COVID-19 apps.
We collected data on 267 COVID-19 apps in App Store and Google Play. The number of installs, ratings, reviews and rating scores were used as indicators of adoption and evaluation. Country-level predictors include the number of infected cases and the political system (i.e., democratic vs. non-democratic). App-level predictors include developer (i.e., government vs. non-government) and functions. Four app functions were coded for analysis: providing health information, contact tracing, home monitoring, and consultation. Negative binomial regression and OLS (Ordinary Least Square) regression were used to analyze the data.
Our analyses show that apps developed by countries with more infected cases (B = 0.079, CI (Confidence Interval) = 0.000, 0.158; P = .049) and by non-governmental institutions (B=-0.369, CI=-0.653, -0.083; P = .01) received more positive rating scores. Apps with home monitoring function received lower rating scores (B=-0.550, CI=-0.971, -0.129; P = .01). Regarding adoption, apps developed by governments were more likely to be installed (IRR (Incident Rate Ratio) = 8.156, CI = 3.389, 19.626; P < .001), to be rated (IRR = 26.036, CI = 7.331, 92.468; P < .001), and to receive user comments (IRR = 12.080, CI = 3.954, 37.568; p < .001). Apps with functions of contact tracing or consultation were more likely to be installed (IRR = 4.533, CI = 2.072, 9.918; p < .001; IRR = 4.885, CI = 1.970, 12.111; p < .001), to be rated (IRR = 11.634, CI = 3.486, 38.827; p < .001; IRR = 17.194, CI = 5.309, 55.680; p < .001), and to receive user comments (IRR = 5.688, CI = 2.052, 5.770; p < .001; IRR = 16.718, CI = 5.363, 52.113; p < .001). Apps with home monitoring functions were less likely to be rated (IRR = 0.206, CI = 0.047, 0.896; P = .04) but more likely to receive user comments (IRR = 3.874, CI = 1.044, 14.349; P = .04). Further analysis shows that apps developed in democratic countries (OR (Odd Ratio) = 3.650, CI = 1.238, 10.758; P = .02) or by governments (OR = 7.987, CI = 4.106, 15.534, P < .001) were more likely to include the function of contact tracing.
This study systematically investigates factors affecting the adoption and evaluation of COVID-19 apps. Evidence shows that government-developed apps and the inclusion of contact tracing and consultation app functions strongly predict app adoption.
全球各国发布了许多 COVID-19 移动应用程序。然而，对于影响 COVID 相关应用程序采用和评估的因素，缺乏系统的实证研究。本研究探讨了国家层面和应用程序层面的哪些因素会影响 COVID-19 应用程序的采用和评估。
我们收集了 App Store 和 Google Play 上 267 个 COVID-19 应用程序的数据。安装次数、评分、评论和评分分数被用作采用和评估的指标。国家层面的预测因素包括感染病例数和政治制度（即民主与非民主）。应用程序层面的预测因素包括开发者（即政府与非政府）和功能。分析中对四个应用程序功能进行了编码：提供健康信息、接触者追踪、家庭监测和咨询。使用负二项回归和普通最小二乘法（OLS）回归分析数据。
我们的分析表明，来自感染病例较多的国家（B=0.079，置信区间（CI）=0.000，0.158；P=0.049）和非政府机构（B=-0.369，CI=-0.653，-0.083；P=0.01）开发的应用程序获得了更多的正面评分分数。具有家庭监测功能的应用程序获得的评分分数较低（B=-0.550，CI=-0.971，-0.129；P=0.01）。关于采用情况，政府开发的应用程序更有可能被安装（发生率比（IRR）=8.156，CI=3.389，19.626；P<0.001）、被评分（IRR=26.036，CI=7.331，92.468；P<0.001）和收到用户评论（IRR=12.080，CI=3.954，37.568；P<0.001）。具有接触者追踪或咨询功能的应用程序更有可能被安装（IRR=4.533，CI=2.072，9.918；P<0.001；IRR=4.885，CI=1.970，12.111；P<0.001）、被评分（IRR=11.634，CI=3.486，38.827；P<0.001；IRR=17.194，CI=5.309，55.680；P<0.001）和收到用户评论（IRR=5.688，CI=2.052，5.770；P<0.001；IRR=16.718，CI=5.363，52.113；P<0.001）。具有家庭监测功能的应用程序更不可能被评分（IRR=0.206，CI=0.047，0.896；P=0.04），但更有可能收到用户评论（IRR=3.874，CI=1.044，14.349；P=0.04）。进一步分析表明，在民主国家（OR=3.650，CI=1.238，10.758；P=0.02）或由政府（OR=7.987，CI=4.106，15.534，P<0.001）开发的应用程序更有可能包含接触者追踪功能。
本研究系统地调查了影响 COVID-19 应用程序采用和评估的因素。证据表明，政府开发的应用程序和包含接触者追踪和咨询应用程序功能强烈预测了应用程序的采用。
