An Analysis of E-health Website User Engagement on Smartphones Based on Cross-site Clickstream Data (Preprint)

Background: 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. Objective: 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. Methods: 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. Results: 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. Conclusions: 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.