Consumer Health Informatics Adoption among Underserved Populations: Thinking beyond the Digital Divide.

OBJECTIVES: Underserved populations can benefit from consumer health informatics (CHI) that promotes self-management at a lower cost. However, prior literature suggested that the digital divide and low motivation constituted barriers to CHI adoption. Despite increased Internet use, underserved populations continue to show slow CHI uptake. The aim of the paper is to revisit barriers and facilitators that may impact CHI adoption among underserved populations. METHODS: We surveyed the past five years of literature. We searched PubMed for articles published between 2012 and 2017 that describe empirical evaluations involving CHI use by underserved populations. We abstracted and summarized data about facilitators and barriers impacting CHI adoption. RESULTS: From 645 search results, after abstract and full-text screening, 13 publications met the inclusion criteria of identifying barriers to and facilitators of underserved populations' CHI adoption. Contrary to earlier literature, the studies suggested that the motivation to improve health literacy and adopt technology was high among studied populations. Beyond the digital divide, barriers included: low health and computer literacy, challenges in accepting the presented information, poor usability, and unclear content. Factors associated with increased use were: user needs for information, user-access mediated by a proxy person, and early user engagement in system design. CONCLUSIONS: While the digital divide remains a barrier, newer studies show that high motivation for CHI use exists. However, simply gaining access to technology is not sufficient to improve adoption unless CHI technology is tailored to address user needs. Future interventions should consider building larger empirical evidence on identifying CHI barriers and facilitators.

Increase in contralateral prophylactic mastectomy conversation online unrelated to decision-making.

BACKGROUND: The increased uptake of contralateral prophylactic mastectomy (CPM) among breast cancer patients remains poorly understood. We hypothesized that the increased rate of CPM is represented in conversations on an online breast cancer community and may contribute to patients choosing this operation. METHODS: We downloaded 328,763 posts and their dates of creation from an online breast cancer community from August 1, 2000, to May 22, 2016. We then performed a keyword search to identify posts which mentioned breast cancer surgeries: contralateral prophylactic mastectomy (n = 7095), mastectomy (n = 10,889), and lumpectomy (n = 9694). We graphed the percentage of CPM-related, lumpectomy-related, and mastectomy-related conversations over time. We also graphed the frequency of posts which mentioned multiple operations over time. Finally, we performed a qualitative study to identify factors influencing the observed trends. RESULTS: Surgically related posts (e.g., mentioning at least one operation) made up a small percentage (n = 27,678; 8.4%) of all posts on this community. The percentage of surgically related posts mentioning CPM was found to increase over time, whereas the percentage of surgically related posts mentioning mastectomy decreased over time. Among posts that mentioned more than one operation, mastectomy and lumpectomy were the procedures most commonly mentioned together, followed by mastectomy and CPM. There was no change over time in the frequency of posts that mentioned more than one operation. Our qualitative review found that most posts mentioning a single operation were unrelated to surgical decision-making; rather the operation was mentioned only in the context of the patient's cancer history. Conversely, the most posts mentioning multiple operations centered around the patients' surgical decision-making process. CONCLUSIONS: CPM-related conversation is increasing on this online breast cancer community, whereas mastectomy-related conversation is decreasing. These results appear to be primarily informed by patients reporting the types of operations they have undergone, and thus appear to correspond to the known increased uptake of CPM.

Detecting clinically related content in online patient posts.

Patients with chronic health conditions use online health communities to seek support and information to help manage their condition. For clinically related topics, patients can benefit from getting opinions from clinical experts, and many are concerned about misinformation and biased information being spread online. However, a large volume of community posts makes it challenging for moderators and clinical experts, if there are any, to provide necessary information. Automatically identifying forum posts that need validated clinical resources can help online health communities efficiently manage content exchange. This automation can also assist patients in need of clinical expertise by getting proper help. We present our results on testing text classification models that efficiently and accurately identify community posts containing clinical topics. We annotated 1817 posts comprised of 4966 sentences of an existing online diabetes community. We found that our classifier performed the best (F-measure: 0.83, Precision: 0.79, Recall:0.86) when using Naïve Bayes algorithm, unigrams, bigrams, trigrams, and MetaMap Symantic Types. Training took 5 s. The classification process took a fraction of 1 s. We applied our classifier to another online diabetes community, and the results were: F-measure: 0.63, Precision: 0.57, Recall: 0.71. Our results show our model is feasible to scale to other forums on identifying posts containing clinical topic with common errors properly addressed.

Toward Predicting Social Support Needs in Online Health Social Networks.

BACKGROUND: While online health social networks (OHSNs) serve as an effective platform for patients to fulfill their various social support needs, predicting the needs of users and providing tailored information remains a challenge. OBJECTIVE: The objective of this study was to discriminate important features for identifying users' social support needs based on knowledge gathered from survey data. This study also provides guidelines for a technical framework, which can be used to predict users' social support needs based on raw data collected from OHSNs. METHODS: We initially conducted a Web-based survey with 184 OHSN users. From this survey data, we extracted 34 features based on 5 categories: (1) demographics, (2) reading behavior, (3) posting behavior, (4) perceived roles in OHSNs, and (5) values sought in OHSNs. Features from the first 4 categories were used as variables for binary classification. For the prediction outcomes, we used features from the last category: the needs for emotional support, experience-based information, unconventional information, and medical facts. We compared 5 binary classifier algorithms: gradient boosting tree, random forest, decision tree, support vector machines, and logistic regression. We then calculated the scores of the area under the receiver operating characteristic (ROC) curve (AUC) to understand the comparative effectiveness of the used features. RESULTS: The best performance was AUC scores of 0.89 for predicting users seeking emotional support, 0.86 for experience-based information, 0.80 for unconventional information, and 0.83 for medical facts. With the gradient boosting tree as our best performing model, we analyzed the strength of individual features in predicting one's social support need. Among other discoveries, we found that users seeking emotional support tend to post more in OHSNs compared with others. CONCLUSIONS: We developed an initial framework for automatically predicting social support needs in OHSNs using survey data. Future work should involve nonsurvey data to evaluate the feasibility of the framework. Our study contributes to providing personalized social support in OHSNs.

FamilyLog: A Mobile System for Monitoring Family Mealtime Activities.

Research has shown that family mealtime plays a critical role in establishing good relationships among family members and maintaining their physical and mental health. In particular, regularly eating dinner as a family significantly reduces prevalence of obesity. However, American families with children spend only 1 hour on family meals while three hours watching TV on an average work day. Fine-grained activity-logging is proven effective for increasing self-awareness and motivating people to modify their life styles for improved wellness. This paper presents FamilyLog - a practical system to log family mealtime activities using smartphones and smartwatches. FamilyLog automatically detects and logs details of activities during the mealtime, including occurrence and duration of meal, conversations, participants, TV viewing etc., in an unobtrusive manner. Based on the sensor data collected from real families, we carefully design robust yet lightweight signal features from a set of complex activities during the meal, including clattering sound, arm gestures of eating, human voice, TV sound, etc. Moreover, FamilyLog opportunistically fuses data from built-in sensors of multiple mobile devices available in a family through an HMM-based classifier. To evaluate the real-world performance of FamilyLog, we perform extensive experiments that consist of 77 days of sensor data from 37 subjects in 8 families with children. Our results show that FamilyLog can detect those events with high accuracy across different families and home environments.

Towards Supporting Patient Decision-making In Online Diabetes Communities.

As of 2014, 29.1 million people in the US have diabetes. Patients with diabetes have evolving information needs around complex lifestyle and medical decisions. As their conditions progress, patients need to sporadically make decisions by understanding alternatives and comparing options. These moments along the decision-making process present a valuable opportunity to support their information needs. An increasing number of patients visit online diabetes communities to fulfill their information needs. To understand how patients attempt to fulfill the information needs around decision-making in online communities, we reviewed 801 posts from an online diabetes community and included 79 posts for in-depth content analysis. The findings revealed motivations for posters' inquiries related to decision-making including the changes in disease state, increased self-awareness, and conflict of information received. Medication and food were the among the most popular topics discussed as part of their decision-making inquiries. Additionally, We present insights for automatically identifying those decision-making inquiries to efficiently support information needs presented in online health communities.

"How Did We Get Here?": Topic Drift in Online Health Discussions.

BACKGROUND: Patients increasingly use online health communities to exchange health information and peer support. During the progression of health discussions, a change of topic-topic drift-can occur. Topic drift is a frequent phenomenon linked to incoherence and frustration in online communities and other forms of computer-mediated communication. For sensitive topics, such as health, such drift could have life-altering repercussions, yet topic drift has not been studied in these contexts. OBJECTIVE: Our goals were to understand topic drift in online health communities and then to develop and evaluate an automated approach to detect both topic drift and efforts of community members to counteract such drift. METHODS: We manually analyzed 721 posts from 184 threads from 7 online health communities within WebMD to understand topic drift, members' reaction towards topic drift, and their efforts to counteract topic drift. Then, we developed an automated approach to detect topic drift and counteraction efforts. We detected topic drift by calculating cosine similarity between 229,156 posts from 37,805 threads and measuring change of cosine similarity scores from the threads' first posts to their sequential posts. Using a similar approach, we detected counteractions to topic drift in threads by focusing on the irregular increase of similarity scores compared to the previous post in threads. Finally, we evaluated the performance of our automated approaches to detect topic drift and counteracting efforts by using a manually developed gold standard. RESULTS: Our qualitative analyses revealed that in threads of online health communities, topics change gradually, but usually stay within the global frame of topics for the specific community. Members showed frustration when topic drift occurred in the middle of threads but reacted positively to off-topic stories shared as separate threads. Although all types of members helped to counteract topic drift, original posters provided the most effort to keep threads on topic. Cosine similarity scores show promise for automatically detecting topical changes in online health discussions. In our manual evaluation, we achieved an F1 score of .71 and .73 for detecting topic drift and counteracting efforts to stay on topic, respectively. CONCLUSIONS: Our analyses expand our understanding of topic drift in a health context and highlight practical implications, such as promoting off-topic discussions as a function of building rapport in online health communities. Furthermore, the quantitative findings suggest that an automated tool could help detect topic drift, support counteraction efforts to bring the conversation back on topic, and improve communication in these important communities. Findings from this study have the potential to reduce topic drift and improve online health community members' experience of computer-mediated communication. Improved communication could enhance the personal health management of members who seek essential information and support during times of difficulty.

Lessons Learned for Online Health Community Moderator Roles: A Mixed-Methods Study of Moderators Resigning From WebMD Communities.

BACKGROUND: Online health community (OHC) moderators help facilitate conversations and provide information to members. However, the necessity of the moderator in helping members achieve goals by providing the support they need remains unclear, with some prior research suggesting that moderation is unnecessary or even harmful for close-knit OHCs. Similarly, members' perceptions of moderator roles are underexplored. Starting January of 2013, WebMD moderators stopped working for WebMD communities. This event provided an opportunity for us to study the perceived role of moderators in OHCs. OBJECTIVE: We examine the OHC members' perception on OHC moderators by studying their reactions toward the departure of moderators in their communities. We also analyzed the relative posting activity on OHCs before and after the departure of moderators from the communities among all members and those who discussed moderators' departures. METHODS: We applied a mixed-methods approach to study the posts of all 55 moderated WebMD communities by querying the terms relating to discussions surrounding moderators' disappearance from the WebMD community. We performed open and axial coding and affinity diagramming to thematically analyze patients' reactions to the disappeared moderators. The number of posts and poster groups (members and moderators) were analyzed over time to understand posting patterns around moderators' departure. RESULTS: Of 821 posts retrieved under 95 threads, a total of 166 open codes were generated. The codes were then grouped into 2 main themes with 6 total subthemes. First, patients attempted to understand why moderators had left and what could be done to fill the void left by the missing moderators. During these discussions, the posts revealed that patients believed that moderators played critical roles in the communities by making the communities vibrant and healthy, finding solutions, and giving medical information. Some patients felt personally attached with moderators, expressing they would cease their community participation. On the other hand, patients also indicated that moderators were not useful or sometimes even harmful for peer interactions. The overall communities' posting activity, which was already in decline, showed no significant difference before and after the moderators' departure. In fact, the overall posting activities of the communities were declining well before the moderators' departure. These declining posting activities might be the reason why WebMD removed the moderators. CONCLUSION: Compassionate moderators who provide medical expertise, control destructive member posts, and help answer questions can provide important support for patient engagement in OHCs. Moderators are in general received positively by community members and do not appear to interfere with peer interactions. Members are well aware of the possibility of misinformation spreading in OHCs. Further investigation into the attitudes of less vocal community members should be conducted.

Personas in online health communities.

Many researchers and practitioners use online health communities (OHCs) to influence health behavior and provide patients with social support. One of the biggest challenges in this approach, however, is the rate of attrition. OHCs face similar problems as other social media platforms where user migration happens unless tailored content and appropriate socialization is supported. To provide tailored support for each OHC user, we developed personas in OHCs illustrating users' needs and requirements in OHC use. To develop OHC personas, we first interviewed 16 OHC users and administrators to qualitatively understand varying user needs in OHC. Based on their responses, we developed an online survey to systematically investigate OHC personas. We received 184 survey responses from OHC users, which informed their values and their OHC use patterns. We performed open coding analysis with the interview data and cluster analysis with the survey data and consolidated the analyses of the two datasets. Four personas emerged-Caretakers, Opportunists, Scientists, and Adventurers. The results inform users' interaction behavior and attitude patterns with OHCs. We discuss implications for how these personas inform OHCs in delivering personalized informational and emotional support.

BeUpright: Posture Correction Using Relational Norm Intervention.

Research shows the critical role of social relationships in behavior change, and the advancement of mobile technologies brings new opportunities of using online social support for persuasive applications. In this paper, we propose Relational Norm Intervention (RNI) model for behavior change, which involves two individuals as a target user and a helper respectively. RNI model uses Negative Reinforcement and Other-Regarding Preferences as motivating factors for behavior change. The model features the passive participation of a helper who will undergo artificially generated discomforts (e.g., limited access to a mobile device) when a target user performs against a target behavior. Based on in-depth discussions from a two-phase design workshop, we designed and implemented BeUpright, a mobile application employing RNI model to correct sitting posture of a target user. Also, we conducted a two-week study to evaluate the effectiveness and user experience of BeUpright. The study showed that RNI model has a potential to increase efficacy, in terms of behavior change, compared to conventional notification approaches. The most influential factor of RNI model in the changing the behavior of target users was the intention to avoid discomforting their helpers. RNI model also showed a potential to help unmotivated individuals in behavior change. We discuss the mechanism of RNI model in relation to prior literature on behavior change and implications of exploiting discomfort in mobile behavior change services.

Answers to Health Questions: Internet Search Results Versus Online Health Community Responses.

BACKGROUND: About 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. OBJECTIVE: The 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. METHODS: We 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 the RESULTS: Community 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. CONCLUSIONS: The 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.

Website Sharing in Online Health Communities: A Descriptive Analysis.

BACKGROUND: An increasing number of people visit online health communities to seek health information. In these communities, people share experiences and information with others, often complemented with links to different websites. Understanding how people share websites can help us understand patients' needs in online health communities and improve how peer patients share health information online. OBJECTIVE: Our goal was to understand (1) what kinds of websites are shared, (2) information quality of the shared websites, (3) who shares websites, (4) community differences in website-sharing behavior, and (5) the contexts in which patients share websites. We aimed to find practical applications and implications of website-sharing practices in online health communities. METHODS: We used regular expressions to extract URLs from 10 WebMD online health communities. We then categorized the URLs based on their top-level domains. We counted the number of trust codes (eg, accredited agencies' formal evaluation and PubMed authors' institutions) for each website to assess information quality. We used descriptive statistics to determine website-sharing activities. To understand the context of the URL being discussed, we conducted a simple random selection of 5 threads that contained at least one post with URLs from each community. Gathering all other posts in these threads resulted in 387 posts for open coding analysis with the goal of understanding motivations and situations in which website sharing occurred. RESULTS: We extracted a total of 25,448 websites. The majority of the shared websites were .com (59.16%, 15,056/25,448) and WebMD internal (23.2%, 5905/25,448) websites; the least shared websites were social media websites (0.15%, 39/25,448). High-posting community members and moderators posted more websites with trust codes than low-posting community members did. The heart disease community had the highest percentage of websites containing trust codes compared to other communities. Members used websites to disseminate information, supportive evidence, resources for social support, and other ways to communicate. CONCLUSIONS: Online health communities can be used as important health care information resources for patients and caregivers. Our findings inform patients' health information-sharing activities. This information assists health care providers, informaticians, and online health information entrepreneurs and developers in helping patients and caregivers make informed choices.

VisOHC: Designing Visual Analytics for Online Health Communities.

Through online health communities (OHCs), patients and caregivers exchange their illness experiences and strategies for overcoming the illness, and provide emotional support. To facilitate healthy and lively conversations in these communities, their members should be continuously monitored and nurtured by OHC administrators. The main challenge of OHC administrators' tasks lies in understanding the diverse dimensions of conversation threads that lead to productive discussions in their communities. In this paper, we present a design study in which three domain expert groups participated, an OHC researcher and two OHC administrators of online health communities, which was conducted to find with a visual analytic solution. Through our design study, we characterized the domain goals of OHC administrators and derived tasks to achieve these goals. As a result of this study, we propose a system called VisOHC, which visualizes individual OHC conversation threads as collapsed boxes-a visual metaphor of conversation threads. In addition, we augmented the posters' reply authorship network with marks and/or beams to show conversation dynamics within threads. We also developed unique measures tailored to the characteristics of OHCs, which can be encoded for thread visualizations at the users' requests. Our observation of the two administrators while using VisOHC showed that it supports their tasks and reveals interesting insights into online health communities. Finally, we share our methodological lessons on probing visual designs together with domain experts by allowing them to freely encode measurements into visual variables.

Automatically Detecting Failures in Natural Language Processing Tools for Online Community Text.

BACKGROUND: The prevalence and value of patient-generated health text are increasing, but processing such text remains problematic. Although existing biomedical natural language processing (NLP) tools are appealing, most were developed to process clinician- or researcher-generated text, such as clinical notes or journal articles. In addition to being constructed for different types of text, other challenges of using existing NLP include constantly changing technologies, source vocabularies, and characteristics of text. These continuously evolving challenges warrant the need for applying low-cost systematic assessment. However, the primarily accepted evaluation method in NLP, manual annotation, requires tremendous effort and time. OBJECTIVE: The primary objective of this study is to explore an alternative approach-using low-cost, automated methods to detect failures (eg, incorrect boundaries, missed terms, mismapped concepts) when processing patient-generated text with existing biomedical NLP tools. We first characterize common failures that NLP tools can make in processing online community text. We then demonstrate the feasibility of our automated approach in detecting these common failures using one of the most popular biomedical NLP tools, MetaMap. METHODS: Using 9657 posts from an online cancer community, we explored our automated failure detection approach in two steps: (1) to characterize the failure types, we first manually reviewed MetaMap's commonly occurring failures, grouped the inaccurate mappings into failure types, and then identified causes of the failures through iterative rounds of manual review using open coding, and (2) to automatically detect these failure types, we then explored combinations of existing NLP techniques and dictionary-based matching for each failure cause. Finally, we manually evaluated the automatically detected failures. RESULTS: From our manual review, we characterized three types of failure: (1) boundary failures, (2) missed term failures, and (3) word ambiguity failures. Within these three failure types, we discovered 12 causes of inaccurate mappings of concepts. We used automated methods to detect almost half of 383,572 MetaMap's mappings as problematic. Word sense ambiguity failure was the most widely occurring, comprising 82.22% of failures. Boundary failure was the second most frequent, amounting to 15.90% of failures, while missed term failures were the least common, making up 1.88% of failures. The automated failure detection achieved precision, recall, accuracy, and F1 score of 83.00%, 92.57%, 88.17%, and 87.52%, respectively. CONCLUSIONS: We illustrate the challenges of processing patient-generated online health community text and characterize failures of NLP tools on this patient-generated health text, demonstrating the feasibility of our low-cost approach to automatically detect those failures. Our approach shows the potential for scalable and effective solutions to automatically assess the constantly evolving NLP tools and source vocabularies to process patient-generated text.

Clinical Questions in Online Health Communities: The Case of "See your doctor" Threads.

Online health communities are known to provide psychosocial support. However, concerns for misinformation being shared around clinical information persist. An existing practice addressing this concern includes monitoring and, as needed, discouraging asking clinical questions in the community. In this paper, I examine such practice where moderators redirected patients to see their health care providers instead of consulting the community. I observed that, contrary to common beliefs, community members provided constructive tips and persuaded the patients to see doctors rather than attempting to make a diagnosis or give medical advice. Moderators' posts on redirecting patients to see their providers were highly associated with no more follow up replies, potentially hindering active community dynamic. The findings showed what is previously thought of as a solution-quality control through moderation-might not be best and that the community, in coordination with moderators, can provide critical help in addressing clinical questions and building constructive information sharing community environment.

Videos Influence Behavior Change Measures for Voice and Speech in Individuals with Parkinson's Disease.

The majority of individuals with Parkinson's disease (PD) experience voice and speech difficulties at some point over the course of the disease. Voice therapy has been found to help improve voice and speech in individuals with PD, but the majority of these individuals do not enroll in voice therapy. The purpose of this study was to determine whether watching short videos about voice symptoms and treatment in Parkinson's disease influences readiness to change, stages of change, and self-efficacy in individuals with PD. Eight individuals with PD participated in the study. Fifteen videos were chosen, three representing each of the five stages of change. We chose videos from YouTube that represented variety in speakers, content, and genre. We found that readiness to change significantly increased after watching videos, suggesting that watching videos helped these individuals move closer to actively improving their voice and speech. In addition, five of the eight participants showed forward movement in stages of change. Finally, self-efficacy demonstrated a positive trend following video watching. Overall, our results demonstrate that watching videos available on the internet can influence individuals with Parkinson's disease in changing vocal behavior. Implications for future wireless health applications are described.

Homophily of Vocabulary Usage: Beneficial Effects of Vocabulary Similarity on Online Health Communities Participation.

Online health communities provide popular platforms for individuals to exchange psychosocial support and form ties. Although regular active participation (i.e., posting to interact with other members) in online health communities can provide important benefits, sustained active participation remains challenging for these communities. Leveraging previous literature on homophily (i.e., "love of those who are like themselves"), we examined the relationship between vocabulary similarity (i.e., homophily of word usage) of thread posts and members' future interaction in online health communities. We quantitatively measured vocabulary similarity by calculating, in a vector space model, cosine similarity between the original post and the first reply in 20,499 threads. Our findings across five online health communities suggest that vocabulary similarity is a significant predictor of members' future interaction in online health communities. These findings carry practical implications for facilitating and sustaining online community participation through beneficial effects of homophily in the vocabulary of essential peer support.

Evaluating health interest profiles extracted from patient-generated data.

Patient-generated health data (PGHD) offers a promising resource for shaping patient care, self-management, population health, and health policy. Although emerging technologies bolster opportunities to extract PGHD and profile the needs and experiences of patients, few efforts examine the validity and use of such profiles from the patient's perspective. To address this gap, we explore health interest profiles built automatically from online community posts. Through a user evaluation with community members, we found that extracted profiles not only align with members' stated health interests, but also expand upon those manually entered interests with little user effort. Community members express positive attitudes toward the use and expansion of profiles to connect with peers for support. Despite this promising approach, findings also point to improvements required of biomedical text processing tools to effectively process PGHD. Findings demonstrate opportunities to leverage the wealth of unstructured PGHD available in emerging technologies that patients regularly use.

Weaving Clinical Expertise in Online Health Communities.

Many patients visit online health communities to receive support. In face-to-face support groups, health professionals facilitate peer-patients exchanging experience while adding their clinical expertise when necessary. However, the large scale of online health communities makes it challenging for such health professional moderators' involvement to happen. To address this challenge of delivering clinical expertise to where patients need them, we explore the idea of semi-automatically providing clinical expertise in online health communities. We interviewed 14 clinicians showing them example peer-patient conversation threads. From the interviews, we examined the ideal practice of clinicians providing expertise to patients. The clinicians continuously assessed when peer-patients were providing appropriate support, what kinds of clinical help they could give online, and when to defer to patients' healthcare providers. The findings inform requirements for building a semi-automated system delivering clinical expertise in online health communities.

Health Vlogs as Social Support for Chronic Illness Management.

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.