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Communicating Uncertainty in Fertility Prognosis

Published: 02 May 2019 Publication History

Abstract

Communicating uncertainty has been shown to provide positive effects on user understanding and decision-making. Surprisingly however, most personal health tracking applications fail to disclose the accuracy of their measurements and predictions. In the case of fertility tracking applications (FTAs), inaccurate predictions have already led to numerous unwanted pregnancies and law suits. However, integrating uncertainty into FTAs is challenging: Prediction accuracy is hard to understand and communicate, and its effect on users' trust and behavior is not well understood. We created a prototype for uncertainty visualizations for FTAs and evaluated it in a four-week field study with real users and their own data (N=9). Our results uncover far-reaching effects of communicating uncertainty: For example, users interpreted prediction accuracy as a proxy for their cycle health and as a security indicator for contraception. Displaying predicted and detected fertile phases next to each other helped users to understand uncertainty without negative emotional effects.

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cover image ACM Conferences
CHI '19: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
May 2019
9077 pages
ISBN:9781450359702
DOI:10.1145/3290605
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 02 May 2019

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Author Tags

  1. fertility tracking applications
  2. menstrual cycle
  3. personal informatics
  4. uncertainty visualization
  5. women's health

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CHI '19 Paper Acceptance Rate 703 of 2,958 submissions, 24%;
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