All posts by huhjina

NSF postdoc position

***Call for a postdoctoral position in behavior and technology

We are hiring a postdoctoral fellow for a project funded by the National Science Foundation Smart and Connected Health, “SCH: INT: Collaborative Research: Unobtrusive Sensing and Motivational Feedback for Family Wellness” (https://www.nsf.gov/awardsearch/showAward?AWD_ID=1622626&HistoricalAwards=false). The candidate will work full time for one year with a possibility of renewing for two more years. The candidate will be housed within the Division of Biomedical Informatics at the University of California San Diego Department of Medicine. The earliest starting date is between November, 2017 and January, 2018. The deadline for the application is on a rolling basis. The search will continue until the ideal candidate has been selected.

The candidate will be expected to carry out the design, development, and evaluation of the SHINE system, a system that senses and visualizes to families their behavioral routines that impact child obesity. The following are expected roles:
* Project management: Organize milestones of the project, coordinate communication among collaborators, manage student research volunteers, purchase project materials and participate in budget management, work with the IRB
* Research activities: Contribute to refining and optimizing proposed research design, conduct qualitative and quantitative research, conduct literature reviews, generate publications, write grants
* Work with the Latino community in San Diego: Work closely with our Latino community partners to build rapport with the community, recruit participants for the study, and engage our Latino community advisory board as a major stakeholder throughout the project

The candidate is expected to:
* Be a fluent bilingual speaker and writer of English and Spanish
* Have strong social science research training: Basic theoretical understanding of various research methods including qualitative research, community-based participatory research, field observations, randomized controlled trials, and meta-reviews
* Have strong expertise in at least one behavioral health area with interest in technology: public health, behavior change, health informatics, health communication, epidemiology, family medicine, psychology, sociology, or others related to technology and health
* Maintain strong work ethic, professionalism, and motivation and be able to work independently
* Have great communication and organizational skills: Prompt communication, effective goal setting, and execution of tasks
* Enjoy working with people: Know how to utilize the various skillsets of volunteer students to accomplish goals
* Have strong writing skills (from emails to journal articles) and solid history of publications in her/his field

Degrees required:
PhD or equivalent in the areas of behavioral health and technology (e.g., public health, behavior change, health informatics, health communication, epidemiology, family medicine, psychology, sociology)

Salary:
The salary will be in accordance with UC San Diego’s salary scale relevant to the candidate’s qualifications:
http://academicaffairs.ucsd.edu/aps/compensation/salary-scales.html

Applying:
Interested candidates should email dbmipostdoc@ucsd.edu a cover letter and a CV. In the email title, add “[SHINE]” to make sure the email is forwarded to this position call. Feel free to email Jina Huh (jinahuh@ucsd.edu) for any questions regarding specific questions about the project.

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

Abstract:

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). http://doi.org/10.2196/jmir.7660

Abstract

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]:

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

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

Abstract
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]

Title
FamilyLog: A Mobile System for Monitoring Family Mealtime Activities

Abstract
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.

New seed grant received on expressive art therapy in inpatient services

We received $10,000 to develop the project, ‘Assessing efficacy of passive and active forms of expressive art therapy in inpatient services’, through the UCSD Health Sciences Academic Senate Research Grant Program.

Research questions and hypotheses
Study 1. Investigate efficacy and feasibility of passive expressive art therapy (PEAT)
Research Question. How is PEAT used at the JMC hospital?

Hypotheses: We hypothesize that patients who use the PEAT application will demonstrate reduced pain (Primary). We hypothesize that prediction models among patients will be able to identify patient cohorts that are more likely to use the PEAT application (Secondary).

Study 2. Evaluate the feasibility and efficacy of passive and active expressive art therapy
Research Question. What is the feasibility of recruitment, assessment, retention, compliance, and patient satisfaction?

Hypotheses: Expressive art therapy (both, active and passive) will improve reported pain among inpatients compared to usual care (Primary). Expressive art therapy will improve anxiety among inpatients compared to usual care (Secondary).

With Jejo Koola, MD, at Biomedical Informatics, Jina Huh will collaborate with the following people for this project: Chief Information Officer of UC San Diego Health (UCSDH) Chris Longhurst’s office, Steven Hickman’s group at the Mindfulness Institute at UCSDH, the Expressive Arts Institute of San Diego, CEO of UCSDH Thomas Savides, CMIO of Inpatient and Hopsital Affiliations at UCSDH, Paul Mills at CTRI UCSD, Rebecca Marmor, Kevin Ramotar at UCSD CAPS, and palliative care clinician Jeremy Hirst and alternative medicine researcher Erik Groessl.

 

Hiring 2 postdocs at UCSD DBMI: NOW CLOSED

Update 12/1/2016: POSITION CLOSED

The Division of Biomedical Informatics (DBMI) in the Department of Medicine at the University of California San Diego is looking for 2 postdoctoral candidates with a background in Human-Computer Interaction (HCI) and usability. The position will start immediately, and the duration of the postdoc is one year with a possibility of extending for another year.

DBMI leads multiple federally and non-profit funded biomedical research projects, which are focused on the integration, analysis, and sharing of biomedical and health care data for the scientific community, as well as patient-centered research. In addition, the department runs a complex computational infrastructure that allows privacy preserved access and analysis of these data.

The candidates will work as part of an NIH BD2K project (https://biocaddie.org/) and conduct contextual inquiry and usability testing for improving and expanding DataMed (https://datamed.org/), which is a data access tool for biomedical researchers.

* Salary and stipends

The salaries follow standard University of California rate:

http://postdoc.ucsd.edu/appointment-guidelines/index.html

As of December, 2016, postdocs salaries will be at a minimum at level 2. Depending on the total number of years worked as a postdoc, candidates will receive higher-level salaries according to the salary table.

The application review will begin immediately. To apply, please email jinahuh@ucsd.edu your CV, 2 references, a one-page research statement describing your responses to this call with the title starting, “[DataMed postdoc].” Please feel free to email any questions before you apply.

“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 http://dx.doi.org/10.2196/jmir.6297

Abstract

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.