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Title: Self-E: Smartphone-Supported Guidance for Customizable Self-Experimentation
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.  more » « less
Award ID(s):
1656763
PAR ID:
10334575
Author(s) / Creator(s):
; ; ; ; ; ; ;
Date Published:
Journal Name:
ACM CHI Conference on Human Factors in Computing Systems
Page Range / eLocation ID:
1 to 13
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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