Publications

2025

1. CHI’25

   A Systematic Review and Meta-Analysis of Research on Goals for Behavior Change

   Jun Zhu, Sruzan Lolla, Meeshu Agnihotri, and 4 more authors

   In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, 2025

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   HCI research on goals and behavior change has significantly increased over the past decade. However, while emerging work has synthesized personal informatics goals, fewer efforts have focused on also integrating HCI research on behavior change to chart future research directions. We conducted a systematic review of 180 papers focused on goals and behavior change from over 10 years of SIGCHI journals and conference proceedings. We further analyzed 37 papers from the data set that included evaluations of interventions’ effectiveness in-the-wild. We also reported on the effectiveness of 76 of such technology-based interventions and the meta-analysis of 28 of these interventions. We find that most research has focused on goals in the health and wellbeing domains, centered on the individual, low intrinsic goals, and partial use of theoretical constructs in technology-based interventions. We highlight opportunities for supporting multiple-domain, social, high intrinsic, and qualitative goals in HCI research for behavior change, and for more effective technology-based interventions with stronger theoretical underpinning, supporting users’ awareness of deep motives for qualitative goals.

   @inproceedings{zhu2025systematic,
     author = {Zhu, Jun and Lolla, Sruzan and Agnihotri, Meeshu and Asgari Tappeh, Sahar and Guluzade, Lala and Agapie, Elena and Sas, Corina},
     title = {A Systematic Review and Meta-Analysis of Research on Goals for Behavior Change},
     pages = {1--25},
     year = {2025},
     publisher = {Association for Computing Machinery},
     url = {https://dl.acm.org/doi/full/10.1145/3706598.3714072},
     booktitle = {Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems},
     serise = {CHI'25},
   }

2. CSCW’25

   Living with Brain Data: Collaboration and Equity in Data-Intensive Brain Implants

   Jun Zhu, and Megh Marathe

   Proceedings of the ACM on Human-Computer Interaction, 2025

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   This paper examines the lived experience of implanted medical devices through the case of brain implants for epilepsy. These data-driven devices record brain signals to detect and interrupt seizures, introducing new forms of technology-mediated care. Drawing on interviews with 17 patients and caregivers, we examine how data-intensive implants reshape medical interactions and everyday life. Participants reported shifts in doctor-patient collaboration, including the integration of a new expert—an engineer responsible for device-related concerns—into clinical visits. The preparatory and ongoing work of data transfer posed challenges for participants who were low-income, aging, traveling, or busy. Participants expressed a strong desire to access implant data to better understand and manage their condition. They were satisfied with the device unless their medications and/or seizures increased. We discuss emerging considerations for collaborative care and design justice introduced by medical implants that, unlike wearables, deliver treatment and cannot be easily set aside.

   @article{zhu2025living,
     author = {Zhu, Jun and Marathe, Megh},
     title = {Living with Brain Data: Collaboration and Equity in Data-Intensive Brain Implants},
     journal = {Proceedings of the ACM on Human-Computer Interaction},
     volume = {9},
     number = {7},
     pages = {1--26},
     year = {2025},
     publisher = {Association for Computing Machinery},
     url = {https://doi.org/10.1145/3757625},
     series = {CSCW'25},
   }

3. CSCW’25

   Understanding How Personal Activities Are Shared In Short-form Videos

   Dennis Wang, Jun Zhu, and Daniel A Epstein

   Proceedings of the ACM on Human-Computer Interaction, 2025

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   Sharing activities that people do in everyday life, such as physical activity, health management, or hobbies, help people receive benefits like social support and positive self-presentation. Short-form videos present new opportunities for activity-sharing, which has traditionally been studied in static contexts like text- and image-sharing. We therefore aim to understand what information people incorporate into short-form activity videos, and how. We qualitatively analyzed 420 short-form activity videos on TikTok across three domains: running, studying, and sketching. We found people often present information before, during, and after activities, developing strategies for qualitatively and quantitatively incorporating activity-relevant information in each. We also uncover practices for aligning the sharing of activity-relevant information with the nature of short-form videos, such as modifying broader-scale goals into video-scale goals. We further discuss design opportunities and challenges for designers to create tools that support the practice, such as closer integration with tracking tools and encouraging narrative structure.

   @article{wang2025understanding,
     author = {Wang, Dennis and Zhu, Jun and Epstein, Daniel A},
     title = {Understanding How Personal Activities Are Shared In Short-form Videos},
     journal = {Proceedings of the ACM on Human-Computer Interaction},
     volume = {9},
     number = {7},
     pages = {1--30},
     year = {2025},
     publisher = {Association for Computing Machinery},
     url = {https://doi.org/10.1145/3757435},
     series = {CSCW'25}
   }

2024

1. CHI’24 - Workshop

   * The Potential of Generative AI in Personalized Nutrition

   Bruna Oewel, Lala Guluzade, Jun Zhu, and 1 more author

   In Designing (with) AI for Wellbeing, CHI 2024 Workshop, 2024

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   Advancements in Generative AI (GenAI) promise to deliver support for well-being. We conducted semi-structured interviews with 9 participants to gain insights into their expectations and requirements for personalized diets using ChatGPT. The study aimed to understand how ChatGPT and other GenAI tools could be leveraged to support individuals in achieving their personalized dietary goals. Our finding reveals that ChatGPT often failed to meet the participants’ personalized expectations and misinterpreted requests, thereby raising ethical concerns. We argue such concerns within the context of the four principles informed by healthcare ethics: autonomy, non-maleficence, beneficence, and justice.

   @inproceedings{oewel2024potential,
     author = {Oewel, Bruna and Guluzade, Lala and Zhu, Jun and Huang, Yuanhui},
     title = {* The Potential of Generative AI in Personalized Nutrition},
     year = {2024},
     publisher = {Association for Computing Machinery},
     url = {https://www.researchgate.net/profile/Lala-Guluzade-2/publication/379332664_The_Potential_of_Generative_AI_in_Personalized_Nutrition/links/660477a0b839e05a209d3ee7/The-Potential-of-Generative-AI-in-Personalized-Nutrition.pdf},
     booktitle = {Designing (with) AI for Wellbeing, CHI 2024 Workshop},
     serise = {CHI'24},
   }

2023

1. DIS’23 - Poster

   Smartphone Stories: Experiences of Blind and Low Vision Older Adults in Acquiring a Smartphone

   Isabela Figueira, Yoonha Cha, Jun Zhu, and 1 more author

   In Proceedings of the Designing Interactive Systems Conference - Lightly-Reviewed, 2023

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   Older adults experience challenges with adopting novel technology due to intrinsic and social factors. However, the mobile phone adoption experiences of blind and low vision (BLV) older adults have largely been unexplored. We shed light on the socio-technical aspects of mobile phone adoption based on deep qualitative interviews with five BLV adults aged 60 and above. We found that narratives of smartphone accessibility can be misleading, expert knowledge is needed although scarce, and although smartphones increase access to society, using a smartphone with accessibility features for BLV people can be isolating. We present design implications for smartphones in order to improve shared access to the phone environment and to support inclusion and independent learning following smartphone adoption.

   @inproceedings{10.1145/3563703.3596639,
     author = {Figueira, Isabela and Cha, Yoonha and Zhu, Jun and Branham, Stacy M},
     title = {Smartphone Stories: Experiences of Blind and Low Vision Older Adults in Acquiring a Smartphone},
     year = {2023},
     isbn = {9781450398985},
     publisher = {Association for Computing Machinery},
     address = {New York, NY, USA},
     url = {https://doi.org/10.1145/3563703.3596639},
     doi = {10.1145/3563703.3596639},
     booktitle = {Proceedings of the Designing Interactive Systems Conference - Lightly-Reviewed},
     pages = {156–159},
     numpages = {4},
     keywords = {technology adoption, social support, low vision, blind, assistive technology, accessibility},
     series = {DIS '23 Companion},
   }

2022

1. AMIA

   Public Opinions toward COVID-19 Vaccine Mandates: A Machine Learning-based Analysis of US Tweets

   Yawen Guo, Jun Zhu, Yicong Huang, and 4 more authors

   AMIA Annual Symposium proceedings AMIA Symposium, 2022

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   People often face barriers to selecting self-tracking tools that support their goals and needs, resulting in tools not meeting their expectations and ultimately abandonment. We therefore examine how people approach selecting self-tracking apps and investigate how technology can better support the process. Drawing on past literature on how people select and perceive the features of commercial and research tracking tools, we surface seven attributes people consider during selection, and design a low-fidelity prototype of an app store that highlights these attributes. We then conduct semi-structured interviews with 18 participants to further investigate what people consider during selection, how people select self-tracking apps, and how surfacing tracking-related attributes could better support selection. We find that people often prioritize features related to self-tracking during selection, such as approaches to collecting and reflecting on data, and trial apps to determine whether they would suit their needs. Our results also show potential for technology surfacing how apps support tracking to reduce barriers to selection. We discuss future opportunities for improving self-tracking app selection, such as ways to enhance existing self-tracking app distribution platforms to enable people to filter and search apps by desirable features.

   @article{guo2022public,
     author = {Guo, Yawen and Zhu, Jun and Huang, Yicong and He, Lu and He, Changyang and Li, Chen and Zheng, Kai},
     title = {Public Opinions toward COVID-19 Vaccine Mandates: A Machine Learning-based Analysis of US Tweets},
     journal = {AMIA Annual Symposium proceedings AMIA Symposium},
     pages = {502--511},
     year = {2022},
     publisher = {American Medical Informatics Association},
     url = {https://pubmed.ncbi.nlm.nih.gov/37128441/},
   }