StoryMap: Using Social Modeling and Self-Modeling for Supporting Physical Activity Among Low-SES Families

要旨

Physical activity (PA) is crucial for reducing the risk of obesity, an epidemic that disproportionately burdens families of low-socioeconomic status (SES). While fitness tracking tools can increase PA awareness, more work is needed to examine (1) how such tools can help people benefit from their social environment, and (2) how reflections can help enhance PA attitudes. We investigated how fitness tracking tools for families can support social modeling and self-modeling (through reflection), two critical processes in Social Cognitive Theory. We developed StoryMap, a novel fitness tracking app for families aimed at supporting both modes of modeling. Then, we conducted a five-week qualitative study evaluating StoryMap with 16 low-SES families. Our findings contribute an understanding of how social and self-modeling can be implemented in fitness tracking tools and how both modes of modeling can enhance key PA attitudes: self-efficacy and outcome expectations. Finally, we propose design recommendations for social personal informatics tools.

受賞
Honorable Mention
著者
Herman Saksono
Harvard University, Cambridge, Massachusetts, United States
Carmen Castaneda-sceppa
Northeastern University, Boston, Massachusetts, United States
Jessica Hoffman
Northeastern University, Boston, Massachusetts, United States
Magy Seif El-Nasr
University of California at Santa Cruz, Santa Clara, California, United States
Andrea G. Parker
Georgia Tech, Atlanta, Georgia, United States
DOI

10.1145/3411764.3445087

論文URL

https://doi.org/10.1145/3411764.3445087

動画

会議: CHI 2021

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2021.acm.org/)

セッション: Personal Health Data

[A] Paper Room 07, 2021-05-10 17:00:00~2021-05-10 19:00:00 / [B] Paper Room 07, 2021-05-11 01:00:00~2021-05-11 03:00:00 / [C] Paper Room 07, 2021-05-11 09:00:00~2021-05-11 11:00:00
Paper Room 07
12 件の発表
2021-05-10 17:00:00
2021-05-10 19:00:00
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