Online Health Communities Research Articles

Value creation for online collaboration between doctors and medical institutions: empirical evidence from online health communities

PurposeDoctor–medical institution collaboration (DMIC) services are an emerging service mode in focal online health communities (OHCs). This new service mode is anticipated to affect user satisfaction and doctors' engagement behaviors. However, whether and how DMIC occurs is still ambiguous because the topic is rarely examined. To bridge this gap, this study explores doctors' participation in DMIC services and its effects on their online performance, as well as its effect on patients' evaluation of them on OHC platforms.Design/methodology/approachThe authors propose hypotheses based on structural holes theory. A unique dataset obtained from one of the most popular OHCs in China is used to test the hypotheses, and difference-in-differences estimation is adopted to test the causality of the relationship.FindingsThe results demonstrate that providing DMIC services improves doctors' online consultation performance and patients' evaluations of them but has no significant effect on doctors' knowledge-sharing performance on OHC platforms. Doctors' knowledge-sharing performance and consultation performance mediate the relationship between participation in DMIC services and patients' evaluation of doctors. Regarding doctors' participation in DMIC services, its impact on doctors' consultation performance and patients' evaluation of them is weaker for doctors with higher professional titles than for doctors with lower professional titles.Originality/valueThe findings clarify the value creation mechanisms of online collaboration between doctors and medical institutions and thereafter facilitate doctors' participation in DMIC services and enhance the sustainable development of OHCs.

Online health communities provide important support in China.

Online health communities provide important support in China.

Online Health Communities’ Portrayal of Obesity on Social Media Platforms in South Africa

The rapidly increasing prevalence of obesity in South Africa, intertwined with extensive changes in diet, life expectancy, and nutritional status has led to a complex framing of obesity on social media. This has prompted the prioritization of media-based social and behavior change communication interventions leveraging social media for obesity prevention. This study was conducted to understand how obesity is constructed and represented on social media in South Africa. A media review of Facebook and Twitter platforms in South Africa was conducted over a six-month period using Meltwater software for data collection. The search yielded 13 500 posts and tweets. Data were cleaned and coded in Microsoft Excel. Content and framing analysis were performed to add insight into the nature of obesity discourse on social media. Portrayals of obesity on social media were dominated by stigmatizing imagery blaming individuals for unhealthy lifestyles, poor diets, and lack of physical activity. Future media-based social and behavior change communication interventions for obesity prevention can leverage social media to reach the broader public and insights into media portrayals of obesity have the potential to influence the shape and development of these behavioral interventions.

Do the certainty tone and seniority of physicians matter in patients' information adoption in online health communities?

PurposeGiven the popularity of online health communities (OHCs) and medical question-and-answer (Q&A) services, it is increasingly important to understand what constitutes useful answers and user-adopted standards in healthcare domain. However, few studies provide insights into how health information characteristics, provider characteristics and recipient characteristics jointly influence user information adoption decisions. To fill this research gap, this study examines the combined effects of physicians' certainty tone as information characteristics, seniority as provider characteristics and disease severity as recipient characteristics on patients' health information adoption.Design/methodology/approachDrawing on dual-process theory and information adoption model, an extended information adoption model is established in this study to examine the effect of attitude certainty on patients' health information adoption, and the moderating effects of online seniority and offline seniority, as well as patient motivation level—disease severity. Utilizing logit regression models, the authors empirically tested the hypotheses based on 4,224 Q&A records from a popular Chinese OHC.FindingsThe results show that (1) attitude certainty has a significant positive impact on patients' health information adoption, (2) the relationship between attitude certainty and information adoption is negatively moderated by physicians' online seniority, but is positively moderated by offline seniority; (3) there is a negative three-way interaction effect of attitude certainty, online seniority and disease severity on patients' health information adoption.Originality/valueThis study extends the information adoption model to examine the two-way interaction between argument quality and source reliability, as well as the three-way interaction with user motivation level, especially for health information adoption in the healthcare field. These findings also provide direct practical applications for knowledge contributors and OHCs.

Multiplex Social Network Analysis to Understand the Social Engagement of Patients in Online Health Communities

Social network analysis has been widely used in various fields including online health communities. However, it is still a challenge to understand how patients’ individual characteristics and online behaviors impact the formation of online health social networks. Furthermore, patients discuss various health topics and form multiplex social networks covering different aspects of their illnesses, including symptoms, treatment experiences, resource sharing, emotional expression, and new friendships. Further research is needed to investigate whether the factors influencing the formation of these topic-based networks are different and explore potential interconnections between various types of social relationships in these networks. To address these issues, this study applied exponential random graph models to characterize multiplex health social networks and conducted empirical research in a Chinese online mental health community. An integrated social network and five separate health-related topic-specific networks were constructed, each with 773 users as network nodes. The empirical findings revealed that patients’ demographic attributes (e.g., age, gender) and online behavioral features (e.g., emotional expression, online influence, participation duration) have significant impacts on the formation of online health social networks, and these patient characteristics have significantly different effects on various types of social relationships within multiplex networks. Additionally, significant cross-network effects, including entrainment and exchange effects, were found among multiple health topic-specific networks, indicating strong interdependencies between them. This research provides theoretical contributions to social network analysis and practical insights for the development of online healthcare social networks.

Multiple-Perspective Data-Driven Analysis of Online Health Communities.

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.

Examining the Supports and Advice That Women With Intimate Partner Violence Experience Received in Online Health Communities: Text Mining Approach.

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. 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. 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. 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. 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.

"I am Young, Why Should I Vaccinate?" How empathetic and aggressive communication on social media impact young adults' attitudes toward COVID-19 vaccination.

To combat the current COVID-19 pandemic, high vaccination rates are of crucial value. However, young people in particular tend to be hesitant toward vaccination. On social media, young adults are often called to vaccinate in an aggressive tone, arguing that there is no choice than to vaccinate and that all else is wrong. In an experimental study (N = 410), we investigated the effects of (a) empathetic vs. aggressive communication styles in social media postings and (b) the origin of the communicator on young adults' supportive attitudes toward COVID-19 vaccinations. We treated the gender of the communicator as a moderator, and expectancy violation, psychological distance as well as the perceived credibility of the communicator as mediators. Findings showed that an aggressive communication style generally had a negative impact on young adults' COVID-19 vaccination attitudes, fully mediated by expectancy violation and perceived credibility of the communicator. Gender and the origin of the communicator did not moderate this mediation processes. Further implications for online health communication strategies are discussed.

A Grounded Theory and Collaborative Design Approach to Disability Storytelling on TikTok

This developing dissertation explores the use of TikTok as a platform for individual and collective storytelling and information creation practices – within a specific online health community of people experiencing painful, invisible, and difficult to diagnose central sensitivity syndromes (CSSs) – to understand and support these embodied, creative, and collective information behaviors. Ongoing data collection indicates that people with CSSs are using TikTok affordances to tell and scaffold complex micro-stories about their expertise and social experiences of disability: by employing iconographic elements to make disability visual; intimate cinematography; audio, visual, and community-specific mimetic options; and platform-specific novel feature use. The research design draws upon critical disabilities studies (CDS) sensitizing concepts. A constructivist grounded theory approach will be employed, by theoretically sampling TikTok micro-videos, their top comments, and, by the time of this presentation, conducting semi-structured interviews with CSS TikTok community members. This poster also discusses these preliminary results, as well as a novel initial sampling approach which addresses both the hashtag and algorithmic logics of the platform, and an implementation of feminist ethics of care in research methods. Then, three codesign workshops with individuals experiencing CSSs will develop creative storytelling materials that can be utilized in various contexts. These workshops promote the inclusion of disabled community members as co-designers and aim to co-design physical and digital storytelling resources such as prompts, templates, and TikTok features.These findings expand storytelling theory into the health domain, introduce and define algorithmically mediated online health communities, and promote critical disability studies perspectives in information science.

Why Doctors Participate in Teams of Online Health Communities? A Social Identity and Brand Resource Perspective

Why Doctors Participate in Teams of Online Health Communities? A Social Identity and Brand Resource Perspective

Evaluating Similarity Variables for Peer Matching in Digital Health Storytelling

Peer matching can enhance the impact of social health technologies. By matching similar peers, online health communities can optimally facilitate social modeling that supports positive health attitudes and moods. However, little work has examined how to operationalize similarities in digital health tools, thus limiting our ability to perform optimal peer matching. To address this gap, we conducted a factorial experiment to examine how three categories of similarity variables (i.e., Demographic, Ability, Experiential) can be used to perform peer matching that supports the social modeling of physical activity. We focus this study on physical activity because it is a health behavior that reduces the risk of chronic diseases. We also prioritized this study for single-caregiver mothers who often face substantial barriers to being active because of immense employment and household responsibilities, especially Black single-caregiver mothers. We recruited 309 single-caregiver mothers (49% Black, 51% white), then we asked them to listen to peer audio storytelling about family physical activity. We randomly matched/mismatched the storyteller's profile using the three categories of similarity variables. Our analyses demonstrated that matching by Demographic variables led to a significantly higher Physical Activity Intention. Furthermore, our subgroup analyses indicated that Black single-caregiver mothers experienced a significant and immediate effect of peer matching in Physical Activity Intention, Self-efficacy, and mood. In contrast, white single-caregiver mothers did not report any significant immediate effect. Collectively, our data suggest that peer matching in health storytelling is potentially beneficial for racially minoritized groups; and that having diverse representations in health technology is required for promoting health equity.

The Role of Social Support and Patient Empowerment Through Online Health Communities in Enhancing Patient–Physician Relationship

Purpose: The goal of this project is to investigate how online health communities can improve patient-physician relationships by providing social support and patient empowerment.&#x0D; &#x0D; Theoretical framework: Nowadays, on online health forums, patients can connect with other patients and healthcare professionals and share knowledge and experiences. However, there is a dearth of research on how patient-physician relationships might be improved via online health communities. &#x0D; &#x0D; Design/Methodology/Approach: In this analytical study, a sample of 61 individuals receiving treatment in Jordan's private hospitals were given a questionnaire via two of the most popular health communities' Facebook pages.&#x0D; &#x0D; Findings: The findings showed that while patient empowerment had no impact on the patient-physician connection, computer-mediated social support had an impact on that relationship. These findings support the need to suggest Jordanian online health community models that might strengthen this kind of engagement. &#x0D; &#x0D; Research, Practical and Social Implications: Online health community providers need to be aware of the tactics they should use in these communities to strengthen the patient-physician relationship and integrate these efforts into patient treatment. Additionally, these communities can help medical practitioners ensure that patients receive the appropriate information and type of support.&#x0D; &#x0D; Originality/Value: As online health groups grew, so did the number of patients who relied on them for information and emotional support. Although the majority of studies focused on this type of support, little is known about how it affects the patient-physician interaction.

Diagnosis Disclosure and Peer-to-Peer Information Seeking Among COVID-19-Infected Social Media Users: Survey of US-Based Adults.

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. 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. 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. 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. 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.

Words for the hearts: a corpus study of metaphors in online depression communities.

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. 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). 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.

The Effect of Information Exchange Activities on Literacy in Online Health Community: The Evidence from PatientsLikeMe

Online health communities (OHC) consist of individuals with shared health-related interests who exchange health-related information among themselves and for the benefit of others. Unfortunately, a notable issue within these communities is the dissemination of a substantial volume of inaccurate health information by various online health groups. Nevertheless, a dearth of research examining the impact of information-seeking activities within OHCs exists. This study aimed to examine the influence of direct and indirect health information-seeking behaviors, specifically among users diagnosed with Type 2 diabetes who have reported complications in OHC, also called claims. Employing association rule mining (ARM) techniques, user data from PatientsLikeMe were extracted to capture information on users’ reported complications subsequent to being diagnosed with Type 2 diabetes (N = 6371). Subsequently, we utilized zero-inflated negative binomial regression (ZINB) to evaluate the effect of direct and indirect information search activities on false notes, including their interaction of them. The outcomes of this investigation have the potential to offer patients valuable insights regarding the reliability and trustworthiness of information derived from OHCs.

Drug Analysis System Using Machine Learning Algorithms

Internet-based healthcare communities persist in providing an extensive array of valuable medical insights, serving medical professionals, system administrators, and patients alike. Our approach involves the real-time compilation of health-related posts from reputable online sources, where patients share their perspectives, encompassing their encounters and potential drug side-effects. We intend to execute user post summarization on a per-drug basis, facilitating succinct findings accessible to both the medical community and patient cohort instantaneously. Further, we propose to classify the users based on their `emotional state of mind'. Furthermore, our methodology encompasses knowledge exploration within user-generated content, facilitating the identification of valuable 'patterns' pertaining to the interconnected 'drugs-symptoms-medicine' triad through the application of Association Learning techniques. Index Terms: Online Health Communities, Drug Summarization, Emotional User Classification, Association Learning.

Experiences of Diagnosis, Symptoms, and Use of Reliever Inhalers in Patients With Asthma and Concurrent Inducible Laryngeal Obstruction or Breathing Pattern Disorder: Qualitative Analysis of a UK Asthma Online Community.

Breathing pattern disorders (BPDs) and inducible laryngeal obstruction (ILO) cause similar symptoms to asthma, including dyspnea and chest tightness, with an estimated prevalence of up to one-fifth of patients with asthma. Both conditions can be comorbid with asthma, and there is evidence that they are misdiagnosed and mistreated as asthma. This study aims to explore whether the symptoms of ILO and BPD were topics of discussion in a UK asthma online health community and patient experiences of diagnosis and treatment, in particular their use of reliever inhalers. A qualitative thematic analysis was performed with posts from an asthma community between 2018 and 2022. A list of key ILO or BPD symptoms was created from the literature. Posts were identified using the search terms "blue inhaler" and "breath" and included if describing key symptoms. Discussion threads of included posts were also analyzed. The search retrieved a total of 1127 relevant posts: 1069 written by 302 users and 58 posted anonymously. All participants were adults, except 2 who were parents writing about their children. Sex and age were only available for 1.66% (5/302; 3 females and 2 males) and 9.93% (30/302) of participants (27 to 73 years old), respectively. The average number of posts written by each participant was 3.54 (range 1-63). Seven participants wrote >20 posts each. Participants experiencing undiagnosed ILO or BPD symptoms, whether or not comorbid with asthma, expressed frustration with the "one-size-fits-all" approach to diagnosis, as many felt that their asthma diagnosis did not fully explain symptoms. Some suspected or were formally diagnosed with BPD or ILO, the latter reporting relief on receiving a diagnosis and appropriate management. Participants showed awareness of their inappropriate salbutamol use or overuse due to lack of effect on symptoms. BPD and ILO symptoms were frequently comorbid with asthma. The asthma online community was a valuable resource: engagement with peers not only brought comfort but also prompted action with some going back to their clinicians and reaching a diagnosis and appropriate management. Undiagnosed ILO and BPD symptoms and lack of effects of asthma treatment were topics of discussion in an asthma online community, caused distress and frustration in participants, and affected their relationship with health care professionals, showing that patients experiencing BPD and ILO have unmet needs. Clinicians' education on BPD and ILO diagnosis and management, as well as increased access to appropriate management options, such as respiratory physiotherapy and speech and language therapy, are warranted particularly in primary care. Qualitative evidence that engagement with the online community resulted in patients taking action going back to their clinicians and reaching a diagnosis of ILO and BPD prompts future research on online peer support from an established online health community as a self-management resource for patients.

Participation In Online Health Communities: Decoding the antecedents and outcomes

A significant number (49 %) of Indian users tend to rely on digital media to either access health-related information or use the internet as a precursor to visit a doctor [20]. According to a report by Raheja [48], "The Internet of Things” (IoT) connection has the potential to change the malfunctioning medical system into a comprehensive, effective, and individualised system. This will allow for a more proactive approach to wellness and overall health, reducing medical expenses through these inclusive practices. Online health communities in India demonstrate significant clout to transform the healthcare industry by empowering patients. They offer a platform to all key stakeholders, that are, the healthcare professionals, patients and even the caregivers alike, to come forth, share their experiences and develop remedies for various issues faced by the healthcare industry. This paper examines the antecedents involved in the empowerment of patients in Online Health Communities (OHCs) and the outcomes of this in the form of participant compliance. The research additionally examines the moderating impact of certain factors such as the patients’ e-health literacy and health locus of control (HLOC) and the physicians’ paternalism, in examining the relationship between patient empowerment and compliance. The findings of the research propose a construct or a theoretical model for the numerous factors and moderators associated with the patient’s participation in online health communities. The social support available to patients leads to more empowered patients, ultimately resulting in higher Patient compliance. Further, this patient empowerment, which comprises of sense of autonomy, competence and self-efficacy makes people more compliant. The theoretical construct between patient empowerment and patient compliance, is further moderated by the patients’ e-health literacy and health locus of control (HLOC) and the physicians’ paternalism.

Healthier together: Social support, self-regulation and goal management for chronic conditions in online health communities

We investigate the effectiveness of goal management in online health communities (OHCs) in light of social support and self-regulation for patients with chronic diseases. With a five-year longitudinal data set, we found that informational support contributes to both goal progress and self-reflection. Emotional support encourages patients to practice self-reflection if the goals are set for non-mental diseases. We also find that patients' self-reflection promotes goal progress. Finally, the lack of attention control negatively affects goal progress and self-reflection. Grounded in social cognitive theory, this study is among the first to empirically examine the effect of self-regulation and social support in OHCs.

A Study on Online Health Community Users' Information Demands Based on the BERT-LDA Model.

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.