[Purpose/significance] Privacy protection in mobile health environment is an important issue that needs to be solved urgently. Research on the reading effect of privacy policies of mobile health apps can stimulate users’ willingness to read the privacy policies and improve users’ awareness of privacy protection, which is conducive to the long-term development and application of the mobile health apps. [Method/process]z This paper attempts to measure the subjective cognitive load and objective performance of different types of users when reading different forms of privacy policies from the perspective of user-friendly reading and understanding, based on the cognitive load theory, through virtual experiments and multi factor analysis of variance, and to study the influence of information representation on the reading effect of privacy policies of mobile medical app. [Result/conclusion] The results showed that compared with the textual privacy policy, the graphical privacy policy is more readable and comprehensible, which can effectively reduce the cognitive load of visual users, and can significantly improve users’ reading achievement.

[Purpose/significance] The effective identification of high-impact users in online health communities is helpful for demanders to find valuable health information, which is of great significance for reducing the cost of health information search and improving the effectiveness of health behavior decision-making. [Method/process] This study was from the perspective of interactivity of users and emotional tendency of comments, using PageRank and SVM algorithm to build a method to measure the users’ influence in online health community, and took the medical network as experimental object, from the angle of content use value, further calculated the comprehensive influence of users in the community, and in case the user is analyzed. [Result/conclusion] The results show that the algorithm is reasonable and can optimize the calculation results of PageRank algorithm. At the same time, the TF-IDF and Mutual Information algorithm are used to reveal that the information content published by high comprehensive influence users is basically consistent with content topics of other user groups in the community, and such users play a certain role in guiding the theme direction of the community. Therefore, the method constructed in this study can effectively and reasonably identify high-impact users, which is helpful for health demanders to find the required information timely and accurately, improving the effect of using health information, so as to enrich the theoretical and practical research on the information behavior of users in online health communities.

[Purpose/significance] Recently, health information has become prevalent on the Internet. The extensive information volume and heterogeneity in information quality increased the probability of encountering conflicting information in users’ health information seeking. However, it has been insufficiently explored how conflicting health information may impact users. This study departs from the relationship between information seeking and uncertainty reduction, as indicated by many classic information behavior theories. We raised a research question of whether the hypothesized relationship still stands in conflicting health information contexts. [Method/process] By employing the experimental approach and one-way analysis of variance (ANOVA), we examine the effects of conflicting health information on individuals regarding knowledge states, confusion, and health beliefs. [Result/conclusion] The results suggested that conflicting health information blurred knowledge states, increased confusion, and decreased health belief. Thus, we inferred that uncertainty increased when they encountered conflicting health information. Based on the results, several theoretical and practical implications were discussed.

[Purpose/significance] Medical team services are emerging form of services provided by online health communities, which have greatly improved the development of online medical services. This paper attempts to explore factors that affect the sales of medical team services in online health communities, the aim is to put forward practical suggestions for the development of online health community and promote the development of online medical service. [Method/process] Based on a dataset including 2784 teams drawn from a leading online health community in China, we built models to examine the influencing factors that affect patients’ choice of online medical team services. In particular, we studied how factors including team reputation, team size, team response speed and team login behavior affect patients’ choice of online medical team services using regression analysis and bootstrap. In addition, we explored the meditating role of prices. [Result/conclusion] Results show that team reputation, team size, team response speed and team login behavior had positive effects on patients’ choices. Team prices had a suppressing effect between team reputation, team size acts and patients’ choices, but acted as a partial mediator between the team response speed and patients’ choice of online medical team services.

[Purpose/significance] Combined with the current social background of the aging population, this paper explores the behavior mechanism of online health information search of elderly users in China from the perspective of mobile terminals, to enrich the current research results of information search behavior of elderly users and guide elderly users to effectively search for health information and optimize the work of the health information service department. [Method/process] This paper used the semi-structured interview method to collect online health information search experiences of 24 elderly users, utilized grounded theory to extract five influencing factors of online health information search behavior of elderly users. And it constructed a theoretical model of factors affecting online health information search behavior of elderly users.[Result/conclusion] Finally, it’s found that network environment factors, individual characteristic factors, social support factors, mobile terminal objective factors, and individual cognitive factors are the key factors that affect the online health information search behavior of elderly users, and the influencing mechanism among these factors is explained in depth. This paper also puts forward some feasible suggestions to the government, medical institutions, APP software and other health information providers based on the theoretical model.