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This study examines the determinants that drive the behavior of sharing health information within online health communities. Leveraging the Theory of Planned Behavior, the Technology Acceptance Model, and the “Knowledge-Attitude-Practice” theory, a comprehensive model elucidating the key elements that sway the health information-sharing behavior among users of online health communities is designed. This model is validated through Structural Equation Modeling (SEM) and Fuzzy Set Qualitative Comparative Analysis (fsQCA). Findings derived from the SEM suggest that perceived ease of use, perceived usefulness, perceived trust, and perceived behavioral control exert a significant positive impact on attitudes towards health information sharing, the intention to share health information, and the actual health information-sharing behavior. The fsQCA unfolds two unique configuration path models that lead to the emergence of health information-sharing behavior: one predicated on perceived trust and sharing intention, and the other on perceived usefulness, behavioral control, and sharing attitude. This research provides invaluable insights, fostering a deeper comprehension of the dynamics involved in health information sharing within online communities, thereby directing the design of more effective health platforms to augment user engagement and enable informed health decisions.

The rising prevalence of diabetes and the increasing awareness of self-health management have resulted in a surge in diabetes patients seeking health information and emotional support in online health communities. Consequently, there is a vast database of patient consultation information in these online health communities. However, due to the heterogeneity and incompleteness of the content, mining medical information and patient health data from these communities can be a challenge. To address this issue, we built the RoBERTa-BiLSTM-CRF (RBC) model for identifying entities in the online health community of diabetes. We selected 1889 question–answer texts from the most active online health community in China, Good Doctor Online, and used these public data to identify five types of entities. In addition, we conducted a comparative evaluation with three other commonly used models to validate the performance of our proposed model, including RoBERTa-CRF (RC), BilSTM-CRF (BC), and RoBERTa-Softmax (RS). The results showed that the RBC model achieved excellent performance on the test set, with an accuracy of 81.2% and an F1 score of 80.7%, outperforming the performance of traditional entity recognition models in named entity recognition in online medical communities for doctors and diabetes patients. The high performance of entity recognition in online health communities will provide a crucial knowledge source for constructing medical knowledge graphs. This integration would help alleviate the growing demand for medical consultations and the strain on healthcare resources, while assisting healthcare professionals in making informed decisions and providing personalized services to patients.

Background: Online health communities (OHCs) provide a new channel for users to obtain more health-related information and support, playing an important role in alleviating hospital congestion and uneven medical resource distribution, especially during the COVID-19 pandemic in China. An in-depth study of users’ continuous usage is of great value for the long-term development of OHCs. Objective: The purpose of this study is to explore the factors that influence users’ continuous usage in online health communities based on the theory of planned behavior (TPB) and social cognitive theory (SCT). Methods: Data from 480 users with experience in online health communities were collected through a questionnaire survey, and structural equations were applied to verify the model hypotheses empirically. Results: Self-efficacy and controllability have significant effects on users’ continuous intention; attitude has a significant relationship with continuous intention; social norms have a positive effect on continuous intention. Moreover, the relationship between continuous intention and behavior is positive. Self-efficacy and outcome expectations have significant positive associations with continuous usage. Finally, system quality, information quality, and social interaction ties have significant and positive relationships to continuous usage. Conclusion: To improve the level of user’s continuous usage, online health service providers can improve the quality of the community by organizing the website’s page layout, navigation menus, and site elements to ensure users quickly search and find what they want meanwhile try to change people’s cognition gradually, in addition, decision and policymakers should provide more favorable policies to stimulate and help provider in building and managing strategic plans for sustaining a thriving online community. A supportive climate in society through public service advertisements and others for the sake of OHCs is necessary. Limitations: (1) This study collected data through a cross-sectional survey. Thus, it lacked the process of capturing the changes in participants’ attitudes toward all variables. (2) The environmental factors in SCT theory need to be more comprehensive, containing online factors without offline factors. (3) The dates were obtained from China, which neglects the different cultural content.

Today’s diverse health needs place greater demands on physicians. However, individual doctors have limited capabilities and may encounter many unsolvable medical problems. The physician online community provides a platform for physicians to communicate with each other and help each other. Physicians can post for help about problems they encounter at work. The number of responses to physicians’ posts is critical to whether or not the problem is resolved. This study collected information on 13,226 posts from a well-known physician online community in China to analyze the factors that influence the number of post replies. In the analysis of the post content of the physician online community, this study innovatively introduces word usage features in the medical field. TextMind was used to extract the rate of several types of words in posts that frequently appear when describing medical information. Ultimately, we found that the rate of time words, visual words, auditory words, and physiological process words used in posts had a positive and significant effect on the number of post responses. A series of new post features has been found to have an impact on the number of post replies in physician online communities. This finding is beneficial for physicians to quickly obtain peer assistance through online platforms, increasing the likelihood of solving workplace challenges and improving physician care, as well as the success of physician online communities.

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.

Online health communities (OHCs) have become a major source of social support for people with health problems. Members of OHCs interact online with others facing similar health problems and receive multiple types of social support, including but not limited to informational support, emotional support, and companionship. The aim of this study is to examine the differences in social support communication among people with different types of cancers. A novel approach is developed to better understand the types of social support embedded in OHC posts. Our approach, based on the word co-occurrence network analysis, preserves the semantic structures of the texts. Information extraction from the semantic structures is supported by the interplay of quantitative and qualitative analyses of the network structures. Our analysis shows that significant differences in social support exist across cancer types, and evidence for the differences across diseases in terms of communication preferences and language use is also identified. Overall, this study can establish a new venue for extracting and analyzing information, so as to inform social support for clinical care.

An online community is one of the important ways for people with mental disorders to receive assistance and obtain support. This study aims to help users with mental disorders to obtain more support and communication through online communities, and to provide community managers with the possible influence mechanisms based on the information adoption model. We obtained a total of 49,047 posts of an online mental health communities in China, over a 40-day period. Then we used a combination of text mining and empirical analysis. Topic and sentiment analysis were used to derive the key variables—the topic of posts that the users care about most, and the emotion scores contained in posts. We then constructed a theoretical model based on the information adoption model. As core independent variables of information quality, on online mental health communities, the topic of social experience in posts (0.368 ***), the topic of emotional expression (0.353 ***), and the sentiment contained in the text (0.002 *) all had significant positive relationships with the number of likes and reposts. This study found that the users of online mental health communities are more attentive to the topics of social experience and emotional expressions, while they also care about the non-linguistic information. This study highlights the importance of helping community users to post on community-related topics, and gives administrators possible ways to help users gain the communication and support they need.

In China, medical resources are unevenly distributed, and hospitals are very congested. Online health communities (OHCs) provide a new way for patients to communicate and obtain health-related information, thereby alleviating the pressure of treatment in hospitals. However, little is known about how to increase individuals’ use intention for OHCs from the perspective of physicians. This study aims to investigate the impact of physicians’ competence and warmth on chronic patients’ intention to use physician-centered OHCs based on the technology acceptance model. A formal investigation was anonymously conducted through a web-based questionnaire survey addressed to participants, and 710 valid responses were received. A research model was constructed and the hypotheses were tested by structural equation modeling. The findings suggest that competence and warmth positively affect chronic patients’ behavioral intention to use (BIU) OHCs through the mediation of perceived usefulness (PU) and perceived ease of use (PEOU). All hypotheses were supported at the 0.05 significant level. Compared with competence, warmth has a slightly stronger impact on PU and PEOU. PEOU has a stronger impact on chronic patients’ BIU OHCs than PU. This study provides a comprehensive understanding of the impacts of physicians’ characteristics in physician-driven OHCs. Compared with competence, physicians’ warmth should be paid more attention to motivate more chronic patients to use OHCs. Enhancing physicians’ warmth and the ease of use are the preferred ways to improve chronic patients’ intention to use OHCs.

Quality of life of parents of children with chronic disease is important for disease self-management. This study aimed to identify predictors of quality of life among mothers of children with type 1 diabetes. A cross-sectional study was conducted. A total of 208 mothers of children with type 1 diabetes were recruited from an online health community. Online health community collective empowerment and social support, diabetes self-efficacy, diabetes-related burden, and quality of life were measured. A multiple regression analysis was conducted to determine predictive factors for quality of life. Multiple regression analysis showed that diabetes-related burden and the child’s age were predictors of quality of life, and total variance explained by the model was 64.1% using two factors. In mothers of younger children, it is important to reduce the diabetes-related burden. Factors that increase the diabetes-related burden should be identified, and strategies to reduce the diabetes-related burden must be established.

This study examines the determinants that drive the behavior of sharing health information within online health communities. Leveraging the Theory of Planned Behavior, the Technology Acceptance Model, and the "Knowledge-Attitude-Practice" theory, a comprehensive model elucidating the key elements that sway the health information-sharing behavior among users of online health communities is designed. This model is validated through Structural Equation Modeling (SEM) and Fuzzy Set Qualitative Comparative Analysis (fsQCA). Findings derived from the SEM suggest that perceived ease of use, perceived usefulness, perceived trust, and perceived behavioral control exert a significant positive impact on attitudes towards health information sharing, the intention to share health information, and the actual health information-sharing behavior. The fsQCA unfolds two unique configuration path models that lead to the emergence of health information-sharing behavior: one predicated on perceived trust and sharing intention, and the other on perceived usefulness, behavioral control, and sharing attitude. This research provides invaluable insights, fostering a deeper comprehension of the dynamics involved in health information sharing within online communities, thereby directing the design of more effective health platforms to augment user engagement and enable informed health decisions.

The rising prevalence of diabetes and the increasing awareness of self-health management have resulted in a surge in diabetes patients seeking health information and emotional support in online health communities. Consequently, there is a vast database of patient consultation information in these online health communities. However, due to the heterogeneity and incompleteness of the content, mining medical information and patient health data from these communities can be a challenge. To address this issue, we built the RoBERTa-BiLSTM-CRF (RBC) model for identifying entities in the online health community of diabetes. We selected 1889 question-answer texts from the most active online health community in China, Good Doctor Online, and used these public data to identify five types of entities. In addition, we conducted a comparative evaluation with three other commonly used models to validate the performance of our proposed model, including RoBERTa-CRF (RC), BilSTM-CRF (BC), and RoBERTa-Softmax (RS). The results showed that the RBC model achieved excellent performance on the test set, with an accuracy of 81.2% and an F1 score of 80.7%, outperforming the performance of traditional entity recognition models in named entity recognition in online medical communities for doctors and diabetes patients. The high performance of entity recognition in online health communities will provide a crucial knowledge source for constructing medical knowledge graphs. This integration would help alleviate the growing demand for medical consultations and the strain on healthcare resources, while assisting healthcare professionals in making informed decisions and providing personalized services to patients.

Today's diverse health needs place greater demands on physicians. However, individual doctors have limited capabilities and may encounter many unsolvable medical problems. The physician online community provides a platform for physicians to communicate with each other and help each other. Physicians can post for help about problems they encounter at work. The number of responses to physicians' posts is critical to whether or not the problem is resolved. This study collected information on 13,226 posts from a well-known physician online community in China to analyze the factors that influence the number of post replies. In the analysis of the post content of the physician online community, this study innovatively introduces word usage features in the medical field. TextMind was used to extract the rate of several types of words in posts that frequently appear when describing medical information. Ultimately, we found that the rate of time words, visual words, auditory words, and physiological process words used in posts had a positive and significant effect on the number of post responses. A series of new post features has been found to have an impact on the number of post replies in physician online communities. This finding is beneficial for physicians to quickly obtain peer assistance through online platforms, increasing the likelihood of solving workplace challenges and improving physician care, as well as the success of physician online communities.

Background: Online health communities (OHCs) provide a new channel for users to obtain more health-related information and support, playing an important role in alleviating hospital congestion and uneven medical resource distribution, especially during the COVID-19 pandemic in China. An in-depth study of users' continuous usage is of great value for the long-term development of OHCs., Objective: The purpose of this study is to explore the factors that influence users' continuous usage in online health communities based on the theory of planned behavior (TPB) and social cognitive theory (SCT)., Methods: Data from 480 users with experience in online health communities were collected through a questionnaire survey, and structural equations were applied to verify the model hypotheses empirically., Results: Self-efficacy and controllability have significant effects on users' continuous intention; attitude has a significant relationship with continuous intention; social norms have a positive effect on continuous intention. Moreover, the relationship between continuous intention and behavior is positive. Self-efficacy and outcome expectations have significant positive associations with continuous usage. Finally, system quality, information quality, and social interaction ties have significant and positive relationships to continuous usage., Conclusion: To improve the level of user's continuous usage, online health service providers can improve the quality of the community by organizing the website's page layout, navigation menus, and site elements to ensure users quickly search and find what they want meanwhile try to change people's cognition gradually, in addition, decision and policymakers should provide more favorable policies to stimulate and help provider in building and managing strategic plans for sustaining a thriving online community. A supportive climate in society through public service advertisements and others for the sake of OHCs is necessary., Limitations: (1) This study collected data through a cross-sectional survey. Thus, it lacked the process of capturing the changes in participants' attitudes toward all variables. (2) The environmental factors in SCT theory need to be more comprehensive, containing online factors without offline factors. (3) The dates were obtained from China, which neglects the different cultural content.

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.

An online community is one of the important ways for people with mental disorders to receive assistance and obtain support. This study aims to help users with mental disorders to obtain more support and communication through online communities, and to provide community managers with the possible influence mechanisms based on the information adoption model. We obtained a total of 49,047 posts of an online mental health communities in China, over a 40-day period. Then we used a combination of text mining and empirical analysis. Topic and sentiment analysis were used to derive the key variables-the topic of posts that the users care about most, and the emotion scores contained in posts. We then constructed a theoretical model based on the information adoption model. As core independent variables of information quality, on online mental health communities, the topic of social experience in posts (0.368 ***), the topic of emotional expression (0.353 ***), and the sentiment contained in the text (0.002 *) all had significant positive relationships with the number of likes and reposts. This study found that the users of online mental health communities are more attentive to the topics of social experience and emotional expressions, while they also care about the non-linguistic information. This study highlights the importance of helping community users to post on community-related topics, and gives administrators possible ways to help users gain the communication and support they need.

In China, medical resources are unevenly distributed, and hospitals are very congested. Online health communities (OHCs) provide a new way for patients to communicate and obtain health-related information, thereby alleviating the pressure of treatment in hospitals. However, little is known about how to increase individuals' use intention for OHCs from the perspective of physicians. This study aims to investigate the impact of physicians' competence and warmth on chronic patients' intention to use physician-centered OHCs based on the technology acceptance model. A formal investigation was anonymously conducted through a web-based questionnaire survey addressed to participants, and 710 valid responses were received. A research model was constructed and the hypotheses were tested by structural equation modeling. The findings suggest that competence and warmth positively affect chronic patients' behavioral intention to use (BIU) OHCs through the mediation of perceived usefulness (PU) and perceived ease of use (PEOU). All hypotheses were supported at the 0.05 significant level. Compared with competence, warmth has a slightly stronger impact on PU and PEOU. PEOU has a stronger impact on chronic patients' BIU OHCs than PU. This study provides a comprehensive understanding of the impacts of physicians' characteristics in physician-driven OHCs. Compared with competence, physicians' warmth should be paid more attention to motivate more chronic patients to use OHCs. Enhancing physicians' warmth and the ease of use are the preferred ways to improve chronic patients' intention to use OHCs.

Quality of life of parents of children with chronic disease is important for disease self-management. This study aimed to identify predictors of quality of life among mothers of children with type 1 diabetes. A cross-sectional study was conducted. A total of 208 mothers of children with type 1 diabetes were recruited from an online health community. Online health community collective empowerment and social support, diabetes self-efficacy, diabetes-related burden, and quality of life were measured. A multiple regression analysis was conducted to determine predictive factors for quality of life. Multiple regression analysis showed that diabetes-related burden and the child's age were predictors of quality of life, and total variance explained by the model was 64.1% using two factors. In mothers of younger children, it is important to reduce the diabetes-related burden. Factors that increase the diabetes-related burden should be identified, and strategies to reduce the diabetes-related burden must be established., Competing Interests: There are no conflicts of interest to declare.