Purpose/Significance: Mobile health applications provide a convenient way for users to obtain health information and services. Studying the factors that influence users’ acceptance and use of mobile health applications (apps or Apps) will help to improve users’ actual usage behavior. Method/Process: Based on the literature review method and using the Web of Science core database as the data source, this paper summarizes the relevant research results regarding the influencing factors of the acceptance and use behavior of mobile health application users and makes a systematic review of the influencing factors from the perspectives of the individual, society, and application (app or App) design. Result/Conclusion: In terms of the individual dimension, the users’ behavior is influenced by demographic characteristics and motivations. Social attributes, source credibility, and legal issues all affect user behavior in the social dimension. In the application design dimension, functionality, perceived ease of use and usefulness, security, and cost are the main factors. At the end of the paper, suggestions are given to improve the users’ acceptability of mobile health applications and improve their use behavior.

Objective: To sort out the research focuses in the field of e-health literacy, analyze its research topics and development trends, and provide a reference for relevant research in this field in the future. Methods: The literature search yielded a total of 431 articles retrieved from the core dataset of Web of Science using the keywords “ehealth literacy”, “E-health literacy” and “electronic health literacy”. A bibliometric analysis was performed by using CiteSpace to explore the development history, hot themes, and trends of future research in the field of e-health literacy. Results: The thematic evolution path in e-health literacy was divided into three stages. The research focuses were inspected from four aspects: evaluation, correlation with health-promotion behaviors, influencing factors, and intervention measures for improvement. Conclusion: E-health literacy research faces challenges such as the development of the connotation of the term, the objectivity of evaluation methods, and the long-term impact of interventions. Future research themes in e-health literacy will include the standardization of evaluation instruments and the individualization of therapeutic strategies.

[Background]: In recent years, aging has become a global social problem. Intelligent health management technology (IHMT) provides solutions for the elderly to deal with various health risks. However, the elderly are facing many difficulties in using IHMT. Studying the application types of IHMT and the influencing factors of the elderly’s acceptance of it will help to improve the use behavior of the elderly. [Methods]: This paper summarizes the application types of IHMT, identifies the influencing factors of the elderly’s adaption of IHMT, and makes a systematic comment on the influencing factors. [Results]: We divide the different functions of IHMT for the elderly into four types: self-monitoring, medical care, remote monitoring, and health education. The influencing factors are divided into three types: individual, social, and technology. [Conclusions]: This study finds that IHMT’s application covers all aspects of the health services of the elderly. Among these applications, self-monitoring is the most used. We divided the influencing factors of the elderly’s acceptance of IHMT into three categories and nine subcategories, having 25 variables.

Objective: To establish the areas of research focus in the application of VR in rehabilitation medicine, analyze its themes and trends, and offer a reference for future related research in this field. Methods: This paper provides an in-depth analysis of the development process, areas of research focus, and research trends in the field of the application of VR in rehabilitation medicine, using the Web of Science core dataset as the source and using a bibliometric analysis with CiteSpace. Results: The application of VR in rehabilitation medicine was composed of three stages, and the research topics were reviewed from five perspectives: neurological rehabilitation, psychological treatment, pain distraction, cardiopulmonary rehabilitation, and visual–spatial disorder. Limitations: The research data were sourced from the Web of Science core dataset only, and the data-sample size was not comprehensive. Conclusions: Overcoming VR-technology-induced vertigo, mental disorders from the overuse of VR, individualized treatments, and integration with traditional therapy are all challenges in the application of VR in rehabilitation medicine that require research. In addition, developing VR products with better experiences, constructing standardized guidelines, and conducting more high-quality clinical studies are all future research topics related to the application of VR in rehabilitation medicine.

Background. As a new medical service model, the online health community can integrate various medical resources to the maximum extent and improve the accessibility and utilization rate of hospital high-quality medical resources. Objective. Research based on the characteristics of doctors can enable doctors to display themselves on the network platform better, provide better services for patients, and improve the quality of medical services for doctors. Method. By crawling the characteristic data of doctors in Good Doctor Online, using dynamic analysis, correlation analysis and regression analysis, this study explores the relationships between each characteristic data and online consultation volume. Results. The doctor’s title and city level representing the static characteristics of the doctor have a weak impact on the doctor’s online consultation volume, and the doctor’s dynamic characteristics such as the number of patient completions, the number of gifts received, and the number of published articles can have a positive impact on the doctor’s online consultation volume. However, the recommended heat will negatively affect the online consultation volume, and the comment text has no significant impact on the doctor’s online consultation volume. Conclusion. Therefore, doctors should actively publicize and show their professional level and constantly optimize their dynamic characteristics, increasing the number of online consultations and thus improving their influence.

Depression is one of the most common psychological problems in adolescence. Familial and school-related factors are closely related to adolescents’ depression, but their combined effects need further examination. The purpose of this study was to explore the combined effects of risk/protective factors of depression in family and school domains using a sample of Chinese adolescents differing in gender, age group and left-behind status. A total of 2455 Chinese students in primary and secondary school participated in the cross-sectional survey and reported multiple risk/protective factors in family and school environments and depressive symptoms. Association rule mining, a machine learning method, was used in the data analyses to identify the correlation between risk/protective factor combinations and depression. We found that (1) Family cohesion, family conflict, peer support, and teacher support emerged as the strongest factors associated with adolescent depression; (2) The combination of these aforementioned factors further strengthened their association with depression; (3) Female gender, middle school students, and family socioeconomic disadvantages attenuated the protective effects of positive relational factors while exacerbating the deleterious effects of negative relational factors; (4) For individuals at risk, lack of mental health education resources at school intensified the negative impact; (5) The risk and protective factors of depression varied according to gender, age stage and left-behind status. In conclusion, the findings shed light on the identification of high-risk adolescents for depression and underscore the importance of tailored programs targeting specific subgroups based on gender, age, or left-behind status.