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BACKGROUND: Peer support for chronic pain is increasingly taking place on social media via social networking communities. Several theories on the development and maintenance of chronic pain highlight how rumination, catastrophizing, and negative social interactions can contribute to poor health outcomes. However, little is known regarding the role web-based health discussions play in the development of negative versus positive health attitudes relevant to chronic pain. OBJECTIVE: This study aims to investigate how participation in online peer-to-peer support communities influenced pain expressions by examining how the sentiment of user language evolved in response to peer interactions. METHODS: We collected the comment histories of 199 randomly sampled Reddit (Reddit, Inc) users who were active in a popular peer-to-peer chronic pain support community over 10 years. A total of 2 separate natural language processing methods were compared to calculate the sentiment of user comments on the forum (N=73,876). We then modeled the trajectories of users' language sentiment using mixed-effects growth curve modeling and measured the degree to which users affectively synchronized with their peers using bivariate wavelet analysis. RESULTS: In comparison to a shuffled baseline, we found evidence that users entrained their language sentiment to match the language of community members they interacted with (t198=4.02; P<.001; Cohen d=0.40). This synchrony was most apparent in low-frequency sentiment changes unfolding over hundreds of interactions as opposed to reactionary changes occurring from comment to comment (F2,198=17.70; P<.001). We also observed a significant trend in sentiment across all users (ß=-.02; P=.003), with users increasingly using more negative language as they continued to interact with the community. Notably, there was a significant interaction between affective synchrony and community tenure (ß=.02; P=.02), such that greater affective synchrony was associated with negative sentiment trajectories among short-term users and positive sentiment trajectories among long-term users. CONCLUSIONS: Our results are consistent with the social communication model of pain, which describes how social interactions can influence the expression of pain symptoms. The difference in long-term versus short-term affective synchrony observed between community members suggests a process of emotional coregulation and social learning. Participating in health discussions on Reddit appears to be associated with both negative and positive changes in sentiment depending on how individual users interacted with their peers. Thus, in addition to characterizing the sentiment dynamics existing within online chronic pain communities, our work provides insight into the potential benefits and drawbacks of relying on support communities organized on social media platforms.

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BACKGROUND: This scoping review accompanies our research study "The Experience of Health Professionals With Misinformation and Its Impact on Their Job Practice: Qualitative Interview Study." It surveys online health misinformation and is intended to provide an understanding of the communication context in which health professionals must operate. OBJECTIVE: Our objective was to illustrate the impact of social media in introducing additional sources of misinformation that impact health practitioners' ability to communicate effectively with their patients. In addition, we considered how the level of knowledge of practitioners mitigated the effect of misinformation and additional stress factors associated with dealing with outbreaks, such as the COVID-19 pandemic, that affect communication with patients. METHODS: This study used a 5-step scoping review methodology following Arksey and O'Malley's methodology to map relevant literature published in English between January 2012 and March 2024, focusing on health misinformation on social media platforms. We defined health misinformation as a false or misleading health-related claim that is not based on valid evidence or scientific knowledge. Electronic searches were performed on PubMed, Scopus, Web of Science, and Google Scholar. We included studies on the extent and impact of health misinformation in social media, mitigation strategies, and health practitioners' experiences of confronting health misinformation. Our independent reviewers identified relevant articles for data extraction. RESULTS: Our review synthesized findings from 70 sources on online health misinformation. It revealed a consensus regarding the significant problem of health misinformation disseminated on social network platforms. While users seek trustworthy sources of health information, they often lack adequate health and digital literacies, which is exacerbated by social and economic inequalities. Cultural contexts influence the reception of such misinformation, and health practitioners may be vulnerable, too. The effectiveness of online mitigation strategies like user correction and automatic detection are complicated by malicious actors and politicization. The role of health practitioners in this context is a challenging one. Although they are still best placed to combat health misinformation, this review identified stressors that create barriers to their abilities to do this well. Investment in health information management at local and global levels could enhance their capacity for effective communication with patients. CONCLUSIONS: This scoping review underscores the significance of addressing online health misinformation, particularly in the postpandemic era. It highlights the necessity for a collaborative global interdisciplinary effort to ensure equitable access to accurate health information, thereby empowering health practitioners to effectively combat the impact of online health misinformation. Academic research will need to be disseminated into the public domain in a way that is accessible to the public. Without equipping populations with health and digital literacies, the prevalence of online health misinformation will continue to pose a threat to global public health efforts.

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As the global demand for healthcare services continues to grow, improving the efficiency and effectiveness of the healthcare ecosystem has become a pressing concern. Information systems are transforming the healthcare delivery process, shifting the focus of healthcare services from passive disease treatment to proactive health prevention and the healthcare management model from hospital-centric to patient-centric. This study focuses on reviewing research in IS journals on the topic of e-health and is dedicated to constructing a theoretical model of intelligent health to provide a research basis for future discussions in this field. In addition, as the innovation of intelligent healthcare services has led to changes in its elements (e.g., an increase in the number of stakeholders), there is an urgent need to sort out and analyze the existing research.

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STUDY OBJECTIVE: Use machine learning to characterize the content of endometriosis online community posts and comments. DESIGN: Retrospective Descriptive Study SETTING: Endometriosis online health communities (OHCs) on the platform Reddit. PARTICIPANTS: Users of the endometriosis online health communities r/Endo and r/endometriosis. INTERVENTIONS: Machine learning was used to analyze thousands of posts made to endometriosis OHCs. Content of posts and comments were interpreted using topic modeling, persona identification and intent labeling. Measurements included baseline characteristics of users, posts and comments to the OHCs. Machine learning techniques; topic modeling, intent labeling and persona identification were used to identify the most common topics of conversation, the intents behind the posts and the subjects of people discussed in posts. System performance was assessed via accuracy at F1 score. RESULTS: A total of 34,715 posts and 353,162 comments responding to posts were evaluated. The topics most likely to be a subject of a post were menstruation (8%), sharing symptoms (8%), medical appointments (8%), medical story (9%), and empathy (7%). The majority of posts were written with the intent of seeking information about endometriosis (49%) or seeking the experiences of others with endometriosis (29%). Users expressed a strong preference for surgeons performing excision rather than ablation of endometriosis. CONCLUSION: Endometriosis OHCs are mostly used to learn about symptoms of endometriosis and share one's medical experiences. Posts and comments from users highlight the need for more empathy in the clinical care of endometriosis and easier access for patients to high quality information about endometriosis.

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Women with domestic violence experiences often refuse to seek help face-to-face due to embarrassment. They begin to share their emotions and seek help from online health communities. Understanding and responding to these posts can be crucial in providing timely support to the victims. We proposed a fine-tuned large language model (LLM) capable of accurately predicting the informational need based on the content of postings. We fine-tuned the LAMMA2-7B-chat model based on the guidance of identifying the information need and a dataset comprising 273 posts from Reddit, which are manually annotated by domain experts. Furthermore, we evaluated the performance of our model using a random sample of 15 posts, and 66.6% were accurately predicted. The results demonstrate that our model can rapidly capture the information needs expressed in the posts, enabling healthcare providers to provide timely and useful support based on our predictions.

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Online medical teams (OMTs), a new mode of online healthcare service, have emerged in online health communities (OHCs) in China. This study attempts to explore the underlying mechanism of how OMTs' engagement influences patient satisfaction through the lens of semantic features. This study also scrutinizes the moderating effect of multiple specializations on the link between OMTs' engagement and semantic features. We utilized a linear model that had fixed effects controlled at the team level for analysis. A bootstrapping approach using 5000 samples was employed to test the mediation effects. The findings reveal that OMTs' engagement significantly improves language concreteness in online team consultations, which subsequently enhances patient satisfaction. OMT engagement has a negative impact on emotional intensity, ultimately decreasing patient satisfaction. Multiple specializations strengthen the impact of OMT engagement on both language concreteness and emotional intensity. This study contributes to the literature on OMTs and patient satisfaction, providing insights into patients' perceptions of OMTs' engagement during online team consultation. This study also generates several implications for the practice of OHCs and OMTs.

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PURPOSE: This study aimed to assess the different needs of patients with breast cancer and their families in online health communities at different treatment phases using a Latent Dirichlet Allocation (LDA) model. METHODS: Using Python, breast cancer-related posts were collected from two online health communities: patient-to-patient and patient-to-doctor. After data cleaning, eligible posts were categorized based on the treatment phase. Subsequently, an LDA model identifying the distinct need-related topics for each phase of treatment, including data preprocessing and LDA topic modeling, was established. Additionally, the demographic and interactive features of the posts were manually analyzed. RESULTS: We collected 84,043 posts, of which 9504 posts were included after data cleaning. Early diagnosis and rehabilitation treatment phases had the highest and lowest number of posts, respectively. LDA identified 11 topics: three in the initial diagnosis phase and two in each of the remaining treatment phases. The topics included disease outcomes, diagnosis analysis, treatment information, and emotional support in the initial diagnosis phase; surgical options and outcomes, postoperative care, and treatment planning in the perioperative treatment phase; treatment options and costs, side effects management, and disease prognosis assessment in the non-operative treatment phase; diagnosis and treatment options, disease prognosis, and emotional support in the relapse and metastasis treatment phase; and follow-up and recurrence concerns, physical symptoms, and lifestyle adjustments in the rehabilitation treatment phase. CONCLUSION: The needs of patients with breast cancer and their families differ across various phases of cancer therapy. Therefore, specific information or emotional assistance should be tailored to each phase of treatment based on the unique needs of patients and their families.

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Research on online pornography abstinence movements has predominantly focused on men's perspectives, often within the context of the broader manosphere. This focus has overshadowed the unique experiences and viewpoints of women in these movements. Our study aimed to fill this gap by exploring women-centric perspectives in pornography abstinence forums, particularly Porn Free Women (r/pornfreewomen). Using a mixed methods approach, this study examined the sexual scripts presented in women-dominated pornography abstinence communities. Our structural topic modeling analysis delineated the interplay of therapeutic, heteronormative, and empowerment themes that were evident in women's narratives and expressions. Further, our discourse analysis elucidated three specific scripts: the addiction script, the heterosexual script, and the liberation script. These interweaving narratives show that discussions of women's pornography abstinence are multifaceted and include a variety of perspectives to negotiate. These results contribute to a nuanced understanding of the values of health and well-being, sexual liberation, and feminism within women's pornography abstinence communities.

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Introduction: The use of online follow-up services (OFUS) is becoming an increasingly important supplement to hospital care. Through OFUS, patients can find their doctors in online health communities (OHCs) and receive remote medical follow-ups after hospital treatment. However, the rate of effective use of OFUS by current patients is still low, and there is an urgent need for research to investigate the online information factors that affect patients' effective use of OFUS. Methods: Based on the elaboration likelihood model (ELM) of persuasion and an analysis of a panel dataset including 3,672 doctors in a leading OHC in China, this study explores how online information from doctors' knowledge contributions and patient feedback influences patients' effective use of OFUS. Results: The results show that both doctors' knowledge contributions and patient feedback positively influence patients' effective use of OFUS. Doctors' paid knowledge contributions and patients' paid feedback have stronger persuasive effects than doctors' free knowledge contributions and patients' free feedback, respectively. Moreover, there is a substitutional relationship between doctors' paid and free knowledge contributions and between patients' paid and free feedback in influencing patients' effective use of OFUS. Discussion: The findings of this study suggest that OHC platforms and healthcare providers should account not only for the persuasive effects of doctors' knowledge contributions and patient feedback but also for influential differences and relationships between the types of doctors' knowledge contributions and patient feedback to better persuade patients to effectively use OFUS.

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BACKGROUND: Pediatric stroke is relatively rare and underresearched, and there is little awareness of its occurrence in wider society. There is a paucity of literature on the effectiveness of interventions to improve rehabilitation and the services available to survivors. Access to online health communities through the internet may be a means of support for patients with pediatric stroke and their families during recovery; however, little research has been done in this area. OBJECTIVE: This study aims to identify the types of social support provided by an online peer support group to survivors of pediatric stroke and their families. METHODS: This was a qualitative thematic analysis of posts from a pediatric stroke population on a UK online stroke community active between 2004 and 2011. The population was split into 2 groups based on whether stroke survivors were aged ≤18 years or aged >18 years at the time of posting. The posts were read by 2 authors who used the adapted Social Support Behavior Code to analyze the types of social support exchanged. RESULTS: A total of 52 participants who experienced a pediatric stroke were identified, who posted a total of 425 messages to the community. About 41 survivors were aged ≤18 years at the time of posting and were written about by others (31/35 were mothers), while 11 were aged >18 years and were writing about themselves. Survivors and their families joined together in discussion threads. Support was offered and received by all participants, regardless of age. Of all 425 posts, 193 (45.4%) contained at least 1 instance of social support. All 5 types of social support were identified: informational, emotional, network, esteem support, and tangible aid. Informational and emotional support were most commonly exchanged. Emotional support was offered more often than informational support among participants aged ≤18 years at the time of posting; this finding was reversed in the group aged >18 years. Network support and esteem support were less commonly exchanged. Notably, the access subcategory of network support was not exchanged with the community. Tangible aid was the least commonly offered type of support. The exchanged social support provided insight into rehabilitation interventions and the unmet needs of pediatric stroke survivors. CONCLUSIONS: We found evidence of engagement of childhood stroke survivors and their families in an online stroke community, with peer support being exchanged between both long- and short-term survivors of pediatric stroke. Engagement of long-term survivors of pediatric stroke through the online community was key, as they were able to offer informational support from lived experience. Further interventional research is needed to assess health and rehabilitation outcomes from engagement with online support groups. Research is also needed to ensure safe, nurturing online communities.

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BACKGROUND: With the growing availability of online health resources and the widespread use of social media to better understand health conditions, people are increasingly making sense of and managing their health conditions using resources beyond their health professionals and personal networks. However, where the condition is complex and poorly understood, this can involve extensive "patient work" to locate, interpret and test the information available. The overall purpose of this study was to investigate how women with polycystic ovary syndrome (PCOS) across two healthcare systems engage with online health resources and social media to better understand this complex and poorly understood lifelong endocrine disorder. METHODS: A semi-structured interview study was conducted with women from the US ( N = 8 ) and UK ( N = 7 ) who had been diagnosed with PCOS within the previous five years. Transcribed data was analysed using a reflexive thematic analysis method. RESULTS: We highlight the information needs and information-seeking strategies women use to make sense of how PCOS affects them, to gain emotional support, and to help them find an effective treatment. We also show how women with PCOS use online health and social media resources to compare themselves to women they view as "normal" and other women with PCOS, to find their sense of "normal for me" along a spectrum of this disorder. CONCLUSION: We draw on previous models of sense-making and finding normal for other complex and sensitive health conditions to capture the nuances of making sense of PCOS. We also discuss implications for the design and use of social media to support people managing PCOS.

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This study significantly contributes to both theory and practice by providing valuable insights into the role and value of healthcare in the context of online health communities. This study highlights the increasing dependence of patients and their families on online sources for health information and the potential of technology to support individuals with health information needs. This study develops a theoretical framework by analyzing data from a cross-sectional survey using partial least squares structural equation modeling and multi-group and importance-performance map analysis. The findings of this study identify the most beneficial technology-related issues, like ease of site navigation and interaction with other online members, which have important implications for the development and management of online health communities. Healthcare professionals can also use this information to disseminate relevant information to those with chronic illnesses effectively. This study recommends proactive engagement between forum admins and participants to improve technology use and interaction, highlighting the benefits of guidelines for effective technology use to enhance users' information-seeking processes. Overall, this study's significant contribution lies in its identification of factors that aid online health community participants in the information-seeking process, providing valuable information to professionals on using technology to disseminate information relevant to chronic illnesses like COPD.

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Background: Risky health behaviors place an enormous toll on public health systems. While relapse prevention support is integrated with most behavior modification programs, the results are suboptimal. Recent advances in artificial intelligence (AI) applications provide us with unique opportunities to develop just-in-time adaptive behavior change solutions. Methods: In this study, we present an innovative framework, grounded in behavioral theory, and enhanced with social media sequencing and communications scenario builder to architect a conversational agent (CA) specialized in the prevention of relapses in the context of tobacco cessation. We modeled peer interaction data (n = 1000) using the taxonomy of behavior change techniques (BCTs) and speech act (SA) theory to uncover the socio-behavioral and linguistic context embedded within the online social discourse. Further, we uncovered the sequential patterns of BCTs and SAs from social conversations (n = 339,067). We utilized grounded theory-based techniques for extracting the scenarios that best describe individuals' needs and mapped them into the architecture of the virtual CA. Results: The frequently occurring sequential patterns for BCTs were comparison of behavior and feedback and monitoring; for SAs were directive and assertion. Five cravings-related scenarios describing users' needs as they deal with nicotine cravings were identified along with the kinds of behavior change constructs that are being elicited within those scenarios. Conclusions: AI-led virtual CAs focusing on behavior change need to employ data-driven and theory-linked approaches to address issues related to engagement, sustainability, and acceptance. The sequential patterns of theory and intent manifestations need to be considered when developing effective behavior change CAs.

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The growth of online health communities and socially generated health-related content has the potential to provide considerable value for patients and healthcare providers alike. For example, members of the public can acquire medical knowledge and interact with others online. However, the volume of information-and the consequent 'noise' associated with large data volumes-can create difficulties for users. In this paper, we present a data-driven approach to better understand these data from multiple stakeholder perspectives. We utilise three techniques-sentiment analysis, content analysis, and topic analysis-to analyse user-generated medical content related to Lyme disease. We use a supervised feature-based model to identify sentiments, content analysis to identify concepts that predominate, and latent Dirichlet allocation strategy as an unsupervised generative model to identify topics represented in the discourse. We validate that applying three different analytic methods highlights differing aspects of the information different stakeholders will be interested in based on the goals of different stakeholders, expert opinion, and comparison with patient information leaflets.

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Purpose/significance: Humans understand, think, and express themselves through metaphors. The current paper emphasizes the importance of identifying the metaphorical language used in online health communities (OHC) to understand how users frame and make sense of their experiences, which can boost the effectiveness of counseling and interventions for this population. Methods/process: We used a web crawler to obtain a corpus of an online depression community. We introduced a three-stage procedure for metaphor identification in a Chinese Corpus: (1) combine MIPVU to identify metaphorical expressions (ME) bottom-up and formulate preliminary working hypotheses; (2) collect more ME top-down in the corpus by performing semantic domain analysis on identified ME; and (3) analyze ME and categorize conceptual metaphors using a reference list. In this way, we have gained a greater understanding of how depression sufferers conceptualize their experience metaphorically in an under-represented language in the literature (Chinese) of a new genre (online health community). Results/conclusion: Main conceptual metaphors for depression are classified into PERSONAL LIFE, INTERPERSONAL RELATIONSHIP, TIME, and CYBERCULTURE metaphors. Identifying depression metaphors in the Chinese corpus pinpoints the sociocultural environment people with depression are experiencing: lack of offline support, social stigmatization, and substitutability of offline support with online support. We confirm a number of depression metaphors found in other languages, providing a theoretical basis for researching, identifying, and treating depression in multilingual settings. Our study also identifies new metaphors with source-target connections based on embodied, sociocultural, and idiosyncratic levels. From these three levels, we analyze metaphor research's theoretical and practical implications, finding ways to emphasize its inherent cross-disciplinarity meaningfully.

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BACKGROUND: Intimate partner violence (IPV) is an underreported public health crisis primarily affecting women associated with severe health conditions and can lead to a high rate of homicide. Owing to the COVID-19 pandemic, more women with IPV experiences visited online health communities (OHCs) to seek help because of anonymity. However, little is known regarding whether their help requests were answered and whether the information provided was delivered in an appropriate manner. To understand the help-seeking information sought and given in OHCs, extraction of postings and linguistic features could be helpful to develop automated models to improve future help-seeking experiences. OBJECTIVE: The objective of this study was to examine the types and patterns (ie, communication styles) of the advice offered by OHC members and whether the information received from women matched their expressed needs in their initial postings. METHODS: We examined data from Reddit using data from subreddit community r/domesticviolence posts from November 14, 2020, through November 14, 2021, during the COVID-19 pandemic. We included posts from women aged ≥18 years who self-identified or described experiencing IPV and requested advice or help in this subreddit community. Posts from nonabused women and women aged <18 years, non-English posts, good news announcements, gratitude posts without any advice seeking, and posts related to advertisements were excluded. We developed a codebook and annotated the postings in an iterative manner. Initial posts were also quantified using Linguistic Inquiry and Word Count to categorize linguistic and posting features. Postings were then classified into 2 categories (ie, matched needs and unmatched needs) according to the types of help sought and received in OHCs to capture the help-seeking result. Nonparametric statistical analysis (ie, 2-tailed t test or Mann-Whitney U test) was used to compare the linguistic and posting features between matched and unmatched needs. RESULTS: Overall, 250 postings were included, and 200 (80%) posting response comments matched with the type of help requested in initial postings, with legal advice and IPV knowledge achieving the highest matching rate. Overall, 17 linguistic or posting features were found to be significantly different between the 2 groups (ie, matched help and unmatched help). Positive title sentiment and linguistic features in postings containing health and wellness wordings were associated with unmatched needs postings, whereas the other 14 features were associated with postings with matched needs. CONCLUSIONS: OHCs can extract the linguistic and posting features to understand the help-seeking result among women with IPV experiences. Features identified in this corpus reflected the differences found between the 2 groups. This is the first study that leveraged Linguistic Inquiry and Word Count to shed light on generating predictive features from unstructured text in OHCs, which could guide future algorithm development to detect help-seeking results within OHCs effectively.

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BACKGROUND: Research examining online health communities suggests that individuals affected by chronic health conditions can obtain valuable information and social support through participation in peer-to-peer web-based information exchanges, including information sharing and seeking behaviors. The risks and rewards of these same behaviors in the case of acute illnesses, such as COVID-19, are less well understood, though there is reason to believe that individuals with COVID-19 and other acute illnesses may accrue similar benefits. OBJECTIVE: This study examines the propensity of American adults to disclose and discuss their COVID-19 diagnosis and symptoms on social media while actively infected with the SARS-CoV-2 virus, as well as to engage in peer-to-peer information seeking in order to better understand the illness that they are experiencing. Additionally, this study seeks to identify the motivations for these behaviors as well as their subsequent impacts on perceived social connectedness and health anxiety in patients with COVID-19. METHODS: We conducted a representative survey of 2500 US-based adults using a sample purchased through an industry-leading market research provider. Participants were selected through a stratified quota sampling approach to ensure a representative sample of the US population. Balanced quotas were determined (by region of the country) for gender, age, race, ethnicity, and political affiliation. Responses were analyzed from 946 participants who reported having an active social media account and testing positive for COVID-19 at least once since the start of the pandemic. RESULTS: The results show that only a small portion of social media users (166/946, 18%) chose to disclose and discuss their COVID-19 diagnosis while infected with the virus. However, among those who did, an overwhelming majority (206/251, 82%) said that doing so helped them feel more connected and supported while infected with the virus. A larger percentage of the 946 respondents (n=319, 34%) engaged in peer-to-peer information seeking while infected with COVID-19. Among those who did, a large majority (301/319, 94%) said that doing so was "helpful," but more than one-third (115/319, 36%) said that reading about other people's experiences made them "more worried" about having COVID-19, while 33% (108/319) said that it made them "less worried." Illness severity and political affiliation were significant predictors of both information sharing and seeking. CONCLUSIONS: The findings suggest that the benefits (and risks) associated with online health communities are germane to patients with acute illnesses such as COVID-19. It is recommended that public health officials and health care providers take a proactive approach to cultivating professionally moderated forums supporting peer-to-peer engagement during future outbreaks of COVID-19 and other acute illnesses in order to improve patient outcomes and promote social support and connectedness among infected patients.

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Promoting online peer support beyond the informal sector to statutory health services requires ethical considerations and evidence-based knowledge about its impact on patients, health care professionals, and the wider health care system. Evidence on the effectiveness of digital interventions in primary care is sparse, and definitive guidance is lacking on the ethical concerns arising from the use of social media as a means for health-related interventions and research. Existing literature examining ethical issues with digital interventions in health care mainly focuses on apps, electronic health records, wearables, and telephone or video consultations, without necessarily covering digital social interventions, and does not always account for primary care settings specifically. Here we address the ethical and information governance aspects of undertaking research on the promotion of online peer support to patients by primary care clinicians, related to medical and public health ethics.

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As the economy and society develop and the standard of living improves, people's health awareness increases and the demand for health information grows. This study introduces an advanced BERT-LDA model to conduct topic-sentiment analysis within online health communities. It examines nine primary categories of user information requirements: causes, symptoms and manifestations, examination and diagnosis, treatment, self-management and regulation, impact, prevention, social life, and knowledge acquisition. By analyzing the distribution of positive and negative sentiments across each topic, the correlation between various health information demands and emotional expressions is investigated. The model established in this paper integrates BERT's semantic comprehension with LDA's topic modeling capabilities, enhancing the accuracy of topic identification and sentiment analysis while providing a more comprehensive evaluation of user information demands. This research furthers our understanding of users' emotional reactions and presents valuable insights for delivering personalized health information in online communities.

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Background: As a new type of medical service application for doctor-patient interaction, online health communities (OHCs) have alleviated the imbalance between the supply and demand of medical resources in different regions and the problems of "difficult and expensive access to medical care", but also raised the concern of patients about the risk of disclosure of their health privacy information. Methods: In this study, a dual-calculus model was developed to explore users' motivation and decision-making mechanism in disclosing privacy information in OHCs by combining risk calculus and privacy calculus theories. Results: In OHCs, users' trust in physicians and applications is a prerequisite for their willingness to disclose health information. Meanwhile, during the privacy calculation, users' perceived benefits in OHCs had a positive effect on both trust in doctors and trust in applications, while perceived risks had a negative effect on both trusts in doctors and trust in applications. Furthermore, in the risk calculation, the perceived threat assessment in OHCs had a significant positive effect on perceived risk, while the response assessment had a significant negative effect on perceived risk, and the effect of users' trust in physicians far exceeded the effect of trust in applications. Finally, users' trust in physicians/applications is a mediating effect between perceived benefits/risks and privacy disclosure intentions. Conclusion: We combine risk calculus and privacy calculus theories to construct a dual-calculus model, which divides trust into trust in physicians and trust in applications, in order to explore the intrinsic motivation and decision-making mechanism of users' participation in privacy disclosure in OHCs. On the one hand, this theoretically compensates for the fact that privacy computing often underestimates perceived risk, complements the research on trust in OHCs, and reveals the influencing factors and decision transmission mechanisms of user privacy disclosure in OHCs. On the other hand, it also provides guidance for developing reasonable privacy policies and health information protection mechanisms for platform developers of OHCs.

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