Category Archives: New paper updates

Towards Supporting Patient Decision-making In Online Diabetes Communities

Towards Supporting Patient Decision-making In Online Diabetes Communities

Authors: Jing Zhang, Rebecca Marmor, Jina Huh

Accepted at AMIA 2017, Washington DC


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.

Toward Predicting Social Support Needs in Online Health Social Networks

Our paper on identifying features critical for predicting social support needs in online health communities have been accepted to the Journal of Medical Internet Research.

Min-Je Choi, Sung-Hee Kim, Sukwon Lee, et al. 2017. Toward Predicting Social Support Needs in Online Health Social Networks. Journal of Medical Internet Research (forthcoming).


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 is 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 an online survey with 184 OHSN users. From this survey data, we extracted 34 features based on five categories: (1) demographics, (2) reading behavior, (3) posting behavior, (4) perceived roles in OHSNs, and (5) values sought in OHSNs. Features from the first four 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 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 discovered that users seeking emotional support tend to post more in OHSNs compared to others.

Conclusions: We developed an initial framework for automatically predicting social support needs in OHSNs using survey data. Future work should involve non-survey data to evaluate the feasibility of the framework. Our study contributes to providing personalized social support in OHSNs.

Increase in Contralateral Prophylactic Mastectomy Conversation Online Unrelated to Decision-Making

Our paper led by Rebecca Marmor has been accepted to the Journal of Surgical Research [impact factor: 2.198]:

Rebecca A. Marmor, MD, MAS; Wenrui Dai, PhD; Xiaoqian Jiang, PhD; Shuang Wang, PhD; Sarah L Blair, MD; Jina Huh, PhD

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=7,095), mastectomy (n=10,889) and lumpectomy (n=9,694). 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 *Revision version w/ Markings 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 the majority of 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 majority of posts mentioning multiple operations centered around the patients’ surgical decision-making process.

Conclusions: CPM-related conversation is increasing on this online breast cancer community, while 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.

FamilyLog: A Mobile System for Monitoring Family Mealtime Activities

Our work on acoustic sensing mechanism led by Chuongguang Bi and Guoliang Xing on the SHINE system has been published in 2017 IEEE Pervasive Computing. [link to the paper]

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.

“How Did We Get Here?”: Topic Drift in Online Health Discussions

Our paper,  ‘“How Did We Get Here?”: Topic Drift in Online Health Discussions’, has been accepted to the Journal of Medical Internet Research.

Albert Park, Andrea Hartzler, Jina Huh, Gary Hsieh, David McDonald, Wanda Pratt.“How Did We Get Here?”: Topic Drift in Online Health Discussions’. J Med Internet Res (forthcoming). doi:10.2196/jmir.6297


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 frequently occurring phenomenon that is 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 seven online health communities within WebMD to understand topic drift, members’ reaction towards topic drift, and their effort to counteract topic drift. Then, we developed an automated approach to detect topic drift and counteraction effort. 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 achieve an F1-score of .71 and .73 for detecting topic drift and counteracting effort 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.

Lessons learned for online health community moderator roles: A mixed methods study of moderators resigning from WebMD communities

Our mixed methods paper on examining what happened when all staff moderators left WebMD online health communities in 2013 has been accepted to the Journal of Medical Internet Research!

Jina Huh, Rebecca Marmor, and Xiaoqian Jiang. 2016. Lessons Learned for Online Health Community Moderator Roles: A Mixed-Methods Study of Moderators Resigning From WebMD Communities. Journal of medical Internet research 18, 9: e247. [pdf]


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 in receiving 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 OHC members’ perception of OHC moderators by studying their reactions towards 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 mixed methods to studying all 55 moderated WebMD communities’ posts by querying 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 disappeared moderators. We analyzed the number of posts and poster groups (members and moderators) over time to understand posting patterns around moderators’ departure.

Results: From 821 posts under 95 threads retrieved, a total of 166 open codes were generated. The codes were then grouped into two main themes with six total sub-themes. First, patients attempted to understand why moderators had left and what could be done to fill the void of the missing moderators. During these discussions, the posts revealed that patients believed moderators played critical roles in the communities by: making the communities vibrant and healthy, finding solutions, and giving medical information. Some patients felt personally tied with moderators, expressing they would cease their community participation. Patients also indicated that moderators were not useful or sometimes even harmful for peer interactions. The overall community’s posting activity analysis showed no significant difference before and after the moderators’ departure. The overall posting activities of the communities were declining well before the moderators’ departure. This declining posting activities might be the reason 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

Our paper on personas in online health communities have been accepted to the Journal of Biomedical Informatics! We will keep you updated once the paper has the final camera-ready version.

Huh J, Kwon BC, Kim S-H, et al. Personas In Online Health Communities. J Biomed Inform. 2016. In Press. doi:10.1016/j.jbi.2016.08.019.

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.