Theoretically Guided Iterative Design of the Sense2Quit App for Tobacco Cessation in Persons Living with HIV.
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2023-02
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The use of mobile health (mHealth technology) can be an effective intervention when considering chronic illnesses. Qualitative research methods were used to identify specific content and features for a mobile app for smoking cessation amongst people living with HIV (PWH). We conducted five focus group sessions followed by two Design Sessions with PWH who were or are currently chronic cigarette smokers. The first five groups focused on the perceived barriers and facilitators to smoking cessation amongst PWH. The two Design Sessions leveraged the findings from the focus group sessions and were used to determine the optimal features and user interface of a mobile app to support smoking cessation amongst PWH. Thematic analysis was conducted using the Health Belief Model and Fogg's Functional Triad. Seven themes emerged from our focus group sessions: history of smoking, triggers, consequences of quitting smoking, motivation to quit, messages to help quit, quitting strategies, and mental health-related challenges. Functional details of the app were identified during the Design Sessions and used to build a functional prototype.
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Schnall, Rebecca, Paul Trujillo, Gabriella Alvarez, Claudia L Michaels, Maeve Brin, Ming-Chun Huang, Huan Chen, Wenyao Xu, et al. (2023). Theoretically Guided Iterative Design of the Sense2Quit App for Tobacco Cessation in Persons Living with HIV. International journal of environmental research and public health, 20(5). p. 4219. 10.3390/ijerph20054219 Retrieved from https://hdl.handle.net/10161/31312.
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Ming-Chun Huang
Huang has a B.S (2007) in Electrical Engineering at Tsing Hua University, Taiwan, an M.S. (2010) in Electrical Engineering at the University of Southern California, and a Ph.D. (2014) in Computer Science at the University of California, Los Angeles. Prior to joining Duke Kunshan University in 2021, he was an Associate Professor at Case Western Reserve University (2014-2021). His research focus is the intersection among Precision Health and Medicine, Internet-of-Things, Machine Learning and Informatics, Motion and Physiological Signal Sensing. He had over 15 years of research experience conducting interdisciplinary scientific projects with researchers from distinct areas (e.g., Biomedical Engineering, Medicine, and Nursing). He had successfully administered past funded projects and productively published over a hundred peer-reviewed publications, 6 invention patents and software copyrights, and won 7 best paper awards/runner-up, 3000+ citations. His research has been reported in hundreds of high-impact media outlets. For the nature of richness and high impact of the research topics he was involved in, his research results in a plethora of new knowledge in aspects ranging from innovative IoT sensing technology, closed-loop AI analytics methodology, optimized clinical decision-making, and just-in-time patient risk assessment.
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