Self-tracking for fertility care: Collaborative support for a highly-personalized problem

by | Dec 1, 2017 | Research Publications

Author(s):

  • Figueiredo, Mayara Costa
  • Caldeira, Clara
  • Reynolds, Tera L.
  • Victory, Sean
  • Zheng, Kai
  • Chen, Yunan

Abstract:

Infertility is a global health concern that affects countless couples trying to conceive a child. Effective fertility treatment requires continuous monitoring of a wide range of health indicators through self-tracking. The process of collecting and interpreting data and information about fertility is complex, and much of the burden falls on women. In this study, we analyzed patient-generated content in a popular online health community dedicated to fertility issues. The objective was to understand the process in which women engage in tracking relevant information, and the challenges they face. Leveraging the Personal Informatics Model, we describe women’s self-tracking experiences during their fertility cycles. We discuss how a complex and highly personalized context leads to responsibility, pressure, and emotional burden on women performing self-tracking activities, as well as the role of collaboration in creating individualized solutions. Finally, we provide implications for technologies aiming to support women with fertility care needs.

Document:

https://dl.acm.org/doi/10.1145/3134671

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