A Patient-Centered Proposal for Bayesian Analysis of Self-Experiments for Health

Jessica Schroeder*, Ravi Karkar, James Fogarty, Julie A. Kientz, Sean A. Munson, Matthew Kay

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


The rise of affordable sensors and apps has enabled people to monitor various health indicators via self-tracking. This trend encourages self-experimentation, a subset of self-tracking in which a person systematically explores potential causal relationships to try to answer questions about their health. Although recent research has investigated how to support the data collection necessary for self-experiments, less research has considered the best way to analyze data resulting from these self-experiments. Most tools default to using traditional frequentist methods. However, the US Agency for Healthcare Research and Quality recommends using Bayesian analysis for n-of-1 studies, arguing from a statistical perspective. To develop a complementary patient-centered perspective on the potential benefits of Bayesian analysis, this paper describes types of questions people want to answer via self-experimentation, as informed by (1) our experiences engaging with irritable bowel syndrome patients and their healthcare providers and (2) a survey investigating what questions individuals want to answer about their health and wellness. We provide examples of how those questions might be answered using (1) frequentist null hypothesis significance testing, (2) frequentist estimation, and (3) Bayesian estimation and prediction. We then provide design recommendations for analyses and visualizations that could help people answer and interpret such questions. We find the majority of the questions people want to answer with self-experimentation data are better answered with Bayesian methods than with frequentist methods. Our results therefore provide patient-centered support for the use of Bayesian analysis for n-of-1 studies.

Original languageEnglish (US)
Pages (from-to)124-155
Number of pages32
JournalJournal of Healthcare Informatics Research
Issue number1
StatePublished - Mar 15 2019
Externally publishedYes


  • Bayesian analysis
  • Interface design
  • N-of-1
  • Self-experiment
  • Self-tracking
  • User-centered design

ASJC Scopus subject areas

  • Health Informatics
  • Computer Science Applications
  • Information Systems
  • Artificial Intelligence

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