Quantified self-experimentation with personal health is a growing activity among health enthusiasts, biohackers, and patients with chronic conditions. By collecting and sharing their health data through self-tracking devices and health networking services, self-experimenters engage in a unique form of n=1 citizen science-style research. This data sharing altruism is constrained by limited data security, validity, and socioeconomic access. We will explore these issues as design challenges. The workshop invites various stakeholders (private, corporate, non-profit, academic) to engage in a discussion and a performative prototyping of a design framework for transparent and just health self-experimentation.
|Title of host publication||CHI 2017 Extended Abstracts - Proceedings of the 2017 ACM SIGCHI Conference on Human Factors in Computing Systems|
|Subtitle of host publication||Explore, Innovate, Inspire|
|Publisher||Association for Computing Machinery|
|Number of pages||8|
|State||Published - 6 May 2017|
|Event||2017 ACM SIGCHI Conference on Human Factors in Computing Systems, CHI EA 2017 - Denver, United States|
Duration: 6 May 2017 → 11 May 2017
|Name||Conference on Human Factors in Computing Systems - Proceedings|
|Conference||2017 ACM SIGCHI Conference on Human Factors in Computing Systems, CHI EA 2017|
|Period||6/05/17 → 11/05/17|
Bibliographical notePublisher Copyright:
Copyright © 2017 by the Association for Computing Machinery, Inc. (ACM).
- Citizen science
- Digital health
- Quantified self