BackgroundAbout 6 million people search for health information on the Internet each day in the United States. Both patients and caregivers search for information about prescribed courses of treatments, unanswered questions after a visit to their providers, or diet and exercise regimens. Past literature has indicated potential challenges around quality in health information available on the Internet. However, diverse information exists on the Internet—ranging from government-initiated webpages to personal blog pages. Yet we do not fully understand the strengths and weaknesses of different types of information available on the Internet.ObjectiveThe objective of this research was to investigate the strengths and challenges of various types of health information available online and to suggest what information sources best fit various question types.MethodsWe collected questions posted to and the responses they received from an online diabetes community and classified them according to Rothwell’s classification of question types (fact, policy, or value questions). We selected 60 questions (20 each of fact, policy, and value) and the replies the questions received from the community. We then searched for responses to the same questions using a search engine and recorded theResultsCommunity responses answered more questions than did search results overall. Search results were most effective in answering value questions and least effective in answering policy questions. Community responses answered questions across question types at an equivalent rate, but most answered policy questions and the least answered fact questions. Value questions were most answered by community responses, but some of these answers provided by the community were incorrect. Fact question search results were the most clinically valid.ConclusionsThe Internet is a prevalent source of health information for people. The information quality people encounter online can have a large impact on them. We present what kinds of questions people ask online and the advantages and disadvantages of various information sources in getting answers to those questions. This study contributes to addressing people’s online health information needs.

Objectives Patients increasingly visit online health communities to get help on managing health. The large scale of these online communities makes it impossible for the moderators to engage in all conversations; yet, some conversations need their expertise. Our work explores low-cost text classification methods to this new domain of determining whether a thread in an online health forum needs moderators’ help. Methods We employed a binary classifier on WebMD’s online diabetes community data. To train the classifier, we considered three feature types: (1) word unigram, (2) sentiment analysis features, and (3) thread length. We applied feature selection methods based on χ2 statistics and under sampling to account for unbalanced data. We then performed a qualitative error analysis to investigate the appropriateness of the gold standard. Results Using sentiment analysis features, feature selection methods, and balanced training data increased the AUC value up to 0.75 and the F1-score up to 0.54 compared to the baseline of using word unigrams with no feature selection methods on unbalanced data (0.65 AUC and 0.40 F1-score). The error analysis uncovered additional reasons for why moderators respond to patients’ posts. Discussion We showed how feature selection methods and balanced training data can improve the overall classification performance. We present implications of weighing precision versus recall for assisting moderators of online health communities. Our error analysis uncovered social, legal, and ethical issues around addressing community members’ needs. We also note challenges in producing a gold standard, and discuss potential solutions for addressing these challenges. Conclusion Social media environments provide popular venues in which patients gain health-related information. Our work contributes to understanding scalable solutions for providing moderators’ expertise in these large-scale, social media environments.

Studies have shown positive impact of video blogs (vlogs) on patient education. However, we know little on how patient-initiated vlogs shape the relationships among vloggers and viewers. We qualitatively analyzed 72 vlogs on YouTube by users diagnosed with HIV, diabetes, or cancer and 1,274 comments posted to the vlogs to understand viewers’ perspectives on the vlogs. We found that the unique video medium allowed intense and enriched personal and contextual disclosure to the viewers, leading to strong community-building activities and social support among vloggers and commenters, both informationally and emotionally. Furthermore, the unique communication structure of the vlogs allowed ad hoc small groups to form, which showed different group behavior than typical text-based social media, such as online communities. We provide implications to the Health Care Industry (HCI) community on how future technologies for health vlogs could be designed to further support chronic illness management.

Health video blogs (vlogs) allow individuals with chronic illnesses to share their stories, experiences, and knowledge with the general public. Furthermore, health vlogs help in creating a connection between the vlogger and the viewers. In this work, we present a qualitative study examining the various methods that health vloggers use to establish a connection with their viewers. We found that vloggers used genres to express specific messages to their viewers while using the uniqueness of video to establish a deeper connection with their viewers. Health vloggers also explicitly sought interaction with their viewers. Based on these results, we present design implications to help facilitate and build sustainable communities for vloggers.

Coping with chronic illness disease is a long and lonely journey, because the burden of managing the illness on a daily basis is placed upon the patients themselves. In this paper, we present our findings for how diabetes patient support groups help one another find individualized strategies for managing diabetes. Through field observations of face-to-face diabetes support groups, content analysis of an online diabetes community, and interviews, we found several help interactions that are critical in helping patients in finding individualized solutions. Those are: (1) patients operationalize their experiences to easily contextualize and share executable strategies; (2) operationalization has to be done within the larger context of sharing illness trajectories; and (3) the support groups develop common understanding towards diabetes management. We further discuss how our findings translate into design implications for supporting chronic illness patients in online community settings.