School of Business, East China University of Science and Technology, Shanghai, China.
Xi'an Research Institute of High Technology, Xi'an, China.
J Med Internet Res. 2021 Aug 13;23(8):e29299. doi: 10.2196/29299.
User engagement is a key performance variable for eHealth websites. However, most existing studies on user engagement either focus on a single website or depend on survey data. To date, we still lack an overview of user engagement on multiple eHealth websites derived from objective data. Therefore, it is relevant to provide a holistic view of user engagement on multiple eHealth websites based on cross-site clickstream data.
This study aims to describe the patterns of user engagement on eHealth websites and investigate how platforms, channels, sex, and income influence user engagement on eHealth websites.
The data used in this study were the clickstream data of 1095 mobile users, which were obtained from a large telecom company in Shanghai, China. The observation period covered 8 months (January 2017 to August 2017). Descriptive statistics, two-tailed t tests, and an analysis of variance were used for data analysis.
The medical category accounted for most of the market share of eHealth website visits (134,009/184,826, 72.51%), followed by the lifestyle category (46,870/184,826, 25.36%). The e-pharmacy category had the smallest market share, accounting for only 2.14% (3947/184,826) of the total visits. eHealth websites were characterized by very low visit penetration and relatively high user penetration. The distribution of engagement intensity followed a power law distribution. Visits to eHealth websites were highly concentrated. User engagement was generally high on weekdays but low on weekends. Furthermore, user engagement gradually increased from morning to noon. After noon, user engagement declined until it reached its lowest level at midnight. Lifestyle websites, followed by medical websites, had the highest customer loyalty. e-Pharmacy websites had the lowest customer loyalty. Popular eHealth websites, such as medical websites, can effectively provide referral traffic for lifestyle and e-pharmacy websites. However, the opposite is also true. Android users were more engaged in eHealth websites than iOS users. The engagement volume of app users was 4.85 times that of browser users, and the engagement intensity of app users was 4.22 times that of browser users. Male users had a higher engagement intensity than female users. Income negatively moderated the influence that platforms (Android vs iOS) had on user engagement. Low-income Android users were the most engaged in eHealth websites. Conversely, low-income iOS users were the least engaged in eHealth websites.
Clickstream data provide a new way to derive an overview of user engagement patterns on eHealth websites and investigate the influence that various factors (eg, platform, channel, sex, and income) have on engagement behavior. Compared with self-reported data from a questionnaire, cross-site clickstream data are more objective, accurate, and appropriate for pattern discovery. Many user engagement patterns and findings regarding the influential factors revealed by cross-site clickstream data have not been previously reported.
用户参与度是电子健康网站的关键绩效变量。然而，大多数关于用户参与度的现有研究要么只关注单个网站，要么依赖于调查数据。迄今为止，我们仍然缺乏基于跨站点点击流数据的对多个电子健康网站的用户参与度的概述。因此，基于跨站点点击流数据提供对多个电子健康网站的用户参与度的全面视图是相关的。
本研究旨在描述电子健康网站的用户参与模式，并研究平台、渠道、性别和收入如何影响电子健康网站的用户参与度。
本研究使用的数据来自中国上海一家大型电信公司的 1095 名移动用户的点击流数据。观察期涵盖 8 个月（2017 年 1 月至 2017 年 8 月）。使用描述性统计、双尾 t 检验和方差分析进行数据分析。
医疗类在电子健康网站访问量中占据最大的市场份额（134009/184826，72.51%），其次是生活方式类（46870/184826，25.36%）。电子药房类的市场份额最小，仅占总访问量的 2.14%（3947/184826）。电子健康网站的特点是访问渗透率非常低，用户渗透率相对较高。参与强度的分布遵循幂律分布。电子健康网站的访问量高度集中。用户参与度通常在工作日较高，而在周末较低。此外，用户参与度从早上到中午逐渐增加。中午过后，用户参与度下降，直到午夜达到最低水平。生活方式网站，其次是医疗网站，拥有最高的客户忠诚度。电子药房网站的客户忠诚度最低。受欢迎的电子健康网站，如医疗网站，可以为生活方式和电子药房网站有效地提供推荐流量。然而，情况正好相反。Android 用户比 iOS 用户更关注电子健康网站。应用程序用户的参与量是浏览器用户的 4.85 倍，应用程序用户的参与强度是浏览器用户的 4.22 倍。男性用户的参与强度高于女性用户。收入负向调节平台（Android 与 iOS）对用户参与度的影响。低收入的 Android 用户最关注电子健康网站。相反，低收入的 iOS 用户最不关注电子健康网站。
点击流数据为获取电子健康网站用户参与模式概述以及研究各种因素（例如平台、渠道、性别和收入）对参与行为的影响提供了一种新方法。与来自问卷调查的自我报告数据相比，跨站点点击流数据更客观、更准确、更适合模式发现。跨站点点击流数据揭示的许多用户参与模式和关于影响因素的发现以前都没有报道过。
