Yasaman S. Sefidgar
ABSTRACT
Personal data can be instrumental for health and well-being. While it has become easier to obtain such data, realizing its benefits remains challenging. People overburden themselves by collecting too much data yet they may not collect the relevant data, especially as their needs evolve. They cannot usually handle the complexity of the data or they fail to connect the data to their needs. They additionally struggle to translate data insights to actions. My research addresses these challenges through 1) using technology probes around tools that help individuals understand their personal well-being, 2) building data-driven behavior planning systems that facilitate health behavior change, and 3) designing computational methods that characterize and quantify the impact of social adversities on mental and emotional state.
- Marit Bentvelzen, Paweł W. Woźniak, Pia S.F. Herbes, Evropi Stefanidi, and Jasmin Niess. 2022. Revisiting Reflection in HCI: Four Design Resources for Technologies That Support Reflection. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 6, 1, Article 2 (mar 2022), 27 pages. https://doi.org/10.1145/3517233Google Scholar
Digital Library
- Eun Kyoung Choe, Nicole B. Lee, Bongshin Lee, Wanda Pratt, and Julie A. Kientz. 2014. Understanding Quantified-Selfers’ Practices in Collecting and Exploring Personal Data. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Toronto, Ontario, Canada) (CHI ’14). Association for Computing Machinery, New York, NY, USA, 1143–1152. https://doi.org/10.1145/2556288.2557372Google ScholarDigital Library
- Chia-Fang Chung, Kristin Dew, Allison Cole, Jasmine Zia, James Fogarty, Julie A. Kientz, and Sean A. Munson. 2016. Boundary Negotiating Artifacts in Personal Informatics: Patient-Provider Collaboration with Patient-Generated Data. In Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing (San Francisco, California, USA) (CSCW ’16). Association for Computing Machinery, New York, NY, USA, 770–786. https://doi.org/10.1145/2818048.2819926Google ScholarDigital Library
- Daniel A. Epstein, An Ping, James Fogarty, and Sean A. Munson. 2015. A Lived Informatics Model of Personal Informatics. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (Osaka, Japan) (UbiComp ’15). Association for Computing Machinery, New York, NY, USA, 731–742. https://doi.org/10.1145/2750858.2804250Google ScholarDigital Library
- Ian Li, Anind Dey, and Jodi Forlizzi. 2010. A Stage-Based Model of Personal Informatics Systems. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Atlanta, Georgia, USA) (CHI ’10). Association for Computing Machinery, New York, NY, USA, 557–566. https://doi.org/10.1145/1753326.1753409Google ScholarDigital Library
- Gabriele Oettingen and Peter M Gollwitzer. 2017. Health behavior change by self-regulation of goal pursuit: Mental contrasting with implementation intentions. In The Routledge international handbook of self-control in health and well-being. Routledge, 418–430. https://doi.org/10.4324/9781315648576Google Scholar
- Elizabeth A Pascoe and Laura Smart Richman. 2009. Perceived discrimination and health: a meta-analytic review.Psychological Bulletin 135, 4 (2009), 531.Google Scholar
- Lindsey N Potter, Elizabeth Brondolo, and Joshua M Smyth. 2019. Biopsychosocial correlates of discrimination in daily life: A review.Stigma and Health 4, 1 (2019), 38.Google ScholarCross Ref
- Jessica Schroeder, Ravi Karkar, Natalia Murinova, James Fogarty, and Sean A. Munson. 2020. Examining Opportunities for Goal-Directed Self-Tracking to Support Chronic Condition Management. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 3, 4, Article 151 (sep 2020), 26 pages. https://doi.org/10.1145/3369809Google ScholarDigital Library
- Yasaman S. Sefidgar, Matthew Jörke, Jina Suh, Koustuv Saha, Shamsi Iqbal, Gonzalo Ramos, and Mary P Czerwinski. 2023. Improving Work-Nonwork Balance with Data-Driven Implementation Intention and Mental Contrasting. Proc. ACM Hum.-Comput. Interact. x, CSCW, Article xx (nov 2023), xx pages. https://doi.org/xxxGoogle Scholar
- Yasaman S. Sefidgar, Matthew Jörke, Jina Suh, Koustuv Saha, Shamsi Iqbal, Gonzalo Ramos, and Mary P Czerwinski. 2023. Lessons Learned for Data-Driven Implementation Intentions with Mental Contrasting. In Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems (Hamburg, Germany) (CHI EA ’23). Association for Computing Machinery, New York, NY, USA, Article 390, 7 pages. https://doi.org/10.1145/3544549.3573851Google ScholarDigital Library
- Yasaman S Sefidgar, Paula S Nurius, Amanda Baughan, Lisa A Elkin, Anind K Dey, Eve Riskin, Jennifer Mankoff, and Margaret E Morris. 2021. Examining needs and opportunities for supporting students who experience discrimination. arXiv preprint arXiv:2111.13266 (2021).Google Scholar
- Yasaman S. Sefidgar, Woosuk Seo, Kevin S. Kuehn, Tim Althoff, Anne Browning, Eve Riskin, Paula S. Nurius, Anind K. Dey, and Jennifer Mankoff. 2019. Passively-Sensed Behavioral Correlates of Discrimination Events in College Students. Proc. ACM Hum.-Comput. Interact. 3, CSCW, Article 114 (nov 2019), 29 pages. https://doi.org/10.1145/3359216Google ScholarDigital Library
Index Terms
- Tools to Support Health and Well-being with Personal Data
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