Predictors of Online Health Information Seeking Among Women with Breast Cancer: The Role of Social Support Perception and Emotional Well-Being

Kim, Sojung Claire; Shah, Dhavan V.; Namkoong, Kang; McTavish, Fiona; Gustafson, David H.

doi:10.1111/jcc4.12002

Cited by 72 publications

(49 citation statements)

References 51 publications

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“…To enable reliable and useful annotation of topics, we established a coding schema of discussion topics through a literature review of information needs in online health communities, with an emphasis on breast cancer communities (Meier et al, 2007b; Civan and Pratt, 2007; Blank et al, 2010; Skeels et al, 2010; Wen et al, 2011; Bender et al, 2013; Kim et al, 2013). Our objectives were (i) to devise a coding scheme that is both relevant to describing the information needs of community members as well as applicable to and robust enough for automatic topic classification; and (ii) to design a coding scheme that can be applied to characterizing topics of discussion for either an entire post or its individual sentences.…”

Section: Methodsmentioning

confidence: 99%

Longitudinal analysis of discussion topics in an online breast cancer community using convolutional neural networks

Zhang

Grave

Sklar

et al. 2017

Journal of Biomedical Informatics

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Identifying topics of discussions in online health communities (OHC) is critical to various applications, but can be difficult because topics of OHC content are usually heterogeneous and domain-dependent. In this paper, we provide a multi-class schema, an annotated dataset, and supervised classifiers based on convolutional neural network (CNN) and other models for the task of classifying discussion topics. We apply the CNN classifier to the most popular breast cancer online community, and carry out a longitudinal analysis to show topic distributions and topic changes throughout members' participation. Our experimental results suggest that CNN outperforms other classifiers in the task of topic classification, and that certain trajectories can be detected with respect to topic changes.

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Section: Methodsmentioning

confidence: 99%

Longitudinal analysis of discussion topics in an online breast cancer community using convolutional neural networks

Zhang

Grave

Sklar

et al. 2017

Journal of Biomedical Informatics

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“…Apart from these traditional media outlets, the Internet provides easy access to a wealth of breast cancer information. Thus, more women are turning to online health sources (Kim, Shah, Namkoong, McTavish, & Gustafson, 2013), especially among those with a higher risk perception of cancer (Shim, 2008).…”

Section: Risk Perception As a Motivator Of Media Attention And Interpmentioning

confidence: 99%

The Augmented Cognitive Mediation Model: Examining Antecedents of Factual and Structural Breast Cancer Knowledge Among Singaporean Women

Lee

Shin

Kawaja

et al. 2016

Journal of Health Communication

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As knowledge acquisition is an important component of health communication research, this study examines factors associated with Singaporean women's breast cancer knowledge using an augmented cognitive mediation model. We conducted a nationally representative study that surveyed 802 women between the ages of 30 and 70 using random-digit dialing. The results supported the augmented cognitive mediation model, which proposes the inclusion of risk perception as a motivator of health information seeking and structural knowledge as an additional knowledge dimension. There was adequate support for the hypothesized paths in the model. Risk perception was positively associated with attention to newspaper, television, Internet, and interpersonal communication. Attention to the three media channels was associated with interpersonal communication, but only newspaper and television attention were associated with elaboration. Interpersonal communication was positively associated with structural knowledge, whereas elaboration was associated with both factual and structural knowledge. Differential indirect effects between media attention and knowledge dimensions via interpersonal communication and elaboration were found. Theoretical and practical implications are discussed.

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“…18,19 Recent research suggests that cancer survivors who are more disinclined to use social networks and resources have worse health-related quality of life, 20 and those with low social support may be the most likely to turn to the Internet to seek out health information and support. 21 In a large sample of the impact of social networking on breast cancer survivors, each additional member in a given person's social network independently predicted higher quality of life. 22 Social networking components are likely to motivate those with cancer to participate in online communities [23][24][25] and have been linked with positive health outcomes.…”

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confidence: 99%

Characterizing Social Networks and Communication Channels in a Web-Based Peer Support Intervention

Owen

Curran

Bantum

et al. 2016

Cyberpsychology, Behavior, and Social Networking

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Web and mobile (mHealth) interventions have promise for improving health outcomes, but engagement and attrition may be reducing effect sizes. Because social networks can improve engagement, which is a key mechanism of action, understanding the structure and potential impact of social networks could be key to improving mHealth effects. This study (a) evaluates social network characteristics of four distinct communication channels (discussion board, chat, e-mail, and blog) in a large social networking intervention, (b) predicts membership in online communities, and (c) evaluates whether community membership impacts engagement. Participants were 299 cancer survivors with significant distress using the 12-week health-space.net intervention. Social networking attributes (e.g., density and clustering) were identified separately for each type of network communication (i.e., discussion board, blog, web mail, and chat). Each channel demonstrated high levels of clustering, and being a community member in one communication channel was associated with being in the same community in each of the other channels (u = 0.56-0.89, ps < 0.05). Predictors of community membership differed across communication channels, suggesting that each channel reached distinct types of users. Finally, membership in a discussion board, chat, or blog community was strongly associated with time spent engaging with coping skills exercises (Ds = 1.08-1.84, ps < 0.001) and total time of intervention (Ds = 1.13-1.80, ps < 0.001). mHealth interventions that offer multiple channels for communication allow participants to expand the number of individuals with whom they are communicating, create opportunities for communicating with different individuals in distinct channels, and likely enhance overall engagement.

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Predictors of Online Health Information Seeking Among Women with Breast Cancer: The Role of Social Support Perception and Emotional Well-Being

Cited by 72 publications

References 51 publications

Longitudinal analysis of discussion topics in an online breast cancer community using convolutional neural networks

Longitudinal analysis of discussion topics in an online breast cancer community using convolutional neural networks

The Augmented Cognitive Mediation Model: Examining Antecedents of Factual and Structural Breast Cancer Knowledge Among Singaporean Women

Characterizing Social Networks and Communication Channels in a Web-Based Peer Support Intervention

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