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.

BACKGROUND User engagement is the key performance variable for e-health websites. However, most of the existing studies on user engagement either focus on a single website or depend on survey data. Thus far, we still lack an overview of user engagement on multiple e-health websites that is derived from objective data. Therefore, it is relevant to provide a holistic view of user engagement on multiple e-health websites based on cross-site clickstream data. OBJECTIVE The main objectives of this paper were to (1) describe the patterns of user engagement on e-health websites and (2) investigate how the platform, the channel, gender and income will influence user engagement on e-health websites. METHODS The data used in this study are the clickstream data of 1095 mobile users from a large telecom company in Shanghai, China. The observation period covers 8 months (Jan. 2017 – Aug. 2017). Descriptive statistics, t-tests, and ANOVA were used in the data analysis. RESULTS (1) The medical category accounts for most of the market share of e-health website visits (72.5%), followed by the lifestyle category (25.4%). The e-pharmacy category has the smallest market share, accounting for only 2.14% of the total visits. (2) E-health websites are characterized by very low visit penetration but relatively high user penetration. (3) The distribution of the engagement intensity follows a power law distribution. (4) Visits to e-health websites are highly concentrated. (5) User engagement is generally high on weekdays but low on weekends. Furthermore, user engagement increases gradually from morning to noon. After noon, user engagement declines until it reaches its lowest level at midnight. (6) Lifestyle websites, followed by medical websites, have the highest customer loyalty. E-pharmacy websites have the lowest customer loyalty. (7) Popular e-health websites such as medical websites can effectively provide referral traffic for lifestyle websites and e-pharmacy websites. However, the opposite is not true. (8) Android users are more engaged on e-health websites than are iOS users. (9) The engagement volume of app users is 4.85 times that of browser users, and the engagement intensity of app users is 4.22 times that of browser users. (10) Male users have higher engagement intensity than female users. (11) Income negatively moderates the influence of the platform (Android vs. iOS) on user engagement. Low-income Android users are the users who are the most engaged on e-health websites. Conversely, low-income iOS users are the users who are the least engaged on e-health websites. CONCLUSIONS Clickstream data provide a new way to derive an overview of user engagement patterns on e-health websites and to investigate the influence of factors (e.g., the platform, the channel, gender and income) 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 of the user engagement patterns or findings regarding the influential factors revealed by cross-site clickstream data herein have not been previously reported.