Self-E: Smartphone-Supported Guidance for Customizable Self-Experimentation

by | May 3, 2021 | Research Publications

Author(s):

  • Nediyana Daskalova
  • Eindra Kyi
  • Kevin Ouyang
  • Arthur Borem
  • Sally Chen
  • Sung Hyun Park
  • Nicole Nugent
  • Jeff Huang

Abstract:

The ubiquity of self-tracking devices and smartphone apps has empowered people to collect data about themselves and try to self-improve. However, people with little to no personal analytics experience may not be able to analyze data or run experiments on their own (self-experiments). To lower the barrier to intervention-based self-experimentation, we developed an app called Self-E, which guides users through the experiment. We conducted a 2-week diary study with 16 participants from the local population and a second study with a more advanced group of users to investigate how they perceive and carry out self-experiments with the help of Self-E, and what challenges they face. We find that users are influenced by their preconceived notions of how healthy a given behavior is, making it difficult to follow Self-E’s directions and trusting its results. We present suggestions to overcome this challenge, such as by incorporating empathy and scaffolding in the system.

Documentation:

https://doi.org/10.1145/3411764.3445100

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