Understanding Quantified-Selfers' Practices in Collecting and Exploring Personal Data

被引:399
作者
Choe, Eun Kyoung [1 ]
Lee, Nicole B. [2 ]
Lee, Bongshin [2 ]
Pratt, Wanda [1 ]
Kientz, Julie A. [1 ]
机构
[1] Univ Washington, Seattle, WA 98195 USA
[2] Microsoft Corp, Redmond, WA 98052 USA
来源
32ND ANNUAL ACM CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI 2014) | 2014年
基金
美国国家科学基金会;
关键词
Quantified Self; self-monitoring; self-tracking; health; personal informatics; personal analytics; self-experimentation; HEALTH-CARE;
D O I
10.1145/2556288.2557372
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Researchers have studied how people use self-tracking technologies and discovered a long list of barriers including lack of time and motivation as well as difficulty in data integration and interpretation. Despite the barriers, an increasing number of Quantified-Selfers diligently track many kinds of data about themselves, and some of them share their best practices and mistakes through Meetup talks, blogging, and conferences. In this work, we aim to gain insights from these "extreme users," who have used existing technologies and built their own workarounds to overcome different barriers. We conducted a qualitative and quantitative analysis of 52 video recordings of Quantified Self Meetup talks to understand what they did, how they did it, and what they learned. We highlight several common pitfalls to self-tracking, including tracking too many things, not tracking triggers and context, and insufficient scientific rigor. We identify future research efforts that could help make progress toward addressing these pitfalls. We also discuss how our findings can have broad implications in designing and developing self-tracking technologies.
引用
收藏
页码:1143 / 1152
页数:10
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