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.

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.

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.

Background: eHealth literacy is a necessary competency for individuals to achieve health self-management in the digital age, and the evaluation of eHealth literacy is an important foundation for clarifying individual eHealth literacy levels and implementing eHealth behavior interventions. Objective: This study reviews the research progress of eHealth literacy assessment instruments to offer suggestions for further development and improvement as well as to provide a reference to eHealth intervention. Methods: We reviewed papers on Web of Science, Scopus, PubMed, and EBSCO in English between 2006 and 2024 and included studies involving the development of eHealth literacy assessment instruments, which must be published in peer-reviewed journals. An analysis in terms of the development process, instrument characteristics, and assessment themes was conducted to reveal the content, features, and application of currently available eHealth literacy assessment instruments. Results: Searches yielded 2972 studies, of which 13 studies were included in the final analysis. The analysis of the 13 studies indicated that the development of instruments is improving constantly, as the concept of eHealth literacy evolves. In total, 9 of the 13 tools are subjective assessments, with eHealth Literacy Scale being the most widely used. In contrast, the remaining 4 comprehensive assessment tools incorporate objective evaluation criteria. The 13 instruments’ reliability ranged from 0.52 to 0.976. Validity was reported for 12 tools (excluding eHealth Literacy Scale), covering 5 types: content validity, structural validity, discriminant validity, external validity, and convergent validity. Regarding assessment themes, skill factors are involved in many instruments, but psychology factors and information factors are less concerned. Conclusions: The evaluation of the characteristics of existing eHealth literacy assessment tools in this paper can provide a reference for the selection of assessment tools. Overall, subjective and comprehensive assessment tools for eHealth literacy have their own advantages and disadvantages. Subjective assessment tools have a friendly evaluation method, but their test validity is relatively low. There is a risk of time-consuming and low recognition for comprehensive evaluation tools. Future research should be based on the deepening of eHealth literacy connotation, further verifying the effectiveness of existing eHealth literacy assessment tools and adding objective evaluation dimensions.

(1) Background: Online health communities (OHCs) serve as ecosystems connecting doctors, patients, and medical resources. Studying their deep network structure and impact mechanisms on medical service quality provides a comprehensive understanding of digital healthcare ecosystems and has guiding significance for platform service optimization. (2) Methods: Using the “Good Doctor Online” platform as the data source, we employed social network analysis methods to construct network models from the professional title and disease-type dimensions, and used multiple linear regression statistical analysis to identify the influencing factors of doctor recommendation rates. (3) Results: Our analysis found that depression doctors exhibit the highest network connectivity (average degree = 17.378), and chief physicians demonstrate significantly higher internal connectivity (average degree = 9.353) compared to resident physicians (average degree = 0.804). The doctor recommendation rate is significantly correlated with post-consultation evaluation (r = 0.602, p < 0.001) and shows a 45% variance explanation (R² = 0.450) in our regression model. (4) Conclusions: Different disease types in OHCs demonstrate distinct organizational patterns, with depression networks showing significantly denser connections than diabetes networks. Professional titles strongly influence network position, with chief physicians forming highly connected hubs while resident physicians remain peripheral. Recommendation rates emerge through multi-dimensional trust processes primarily driven by post-consultation evaluation quality.