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A Lived Informatics Model of Personal Informatics.

Daniel A Epstein1, An Ping2, James Fogarty1

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Summary
This summary is machine-generated.

This study introduces lived informatics, a new model for personal informatics systems that goes beyond behavior change goals. It explores how individuals integrate self-tracking into daily life across various domains.

Keywords:
FinancesH.5.m. Information interfaces and presentation (e.g., HCI)LapsingLived InformaticsLocationPersonal InformaticsPhysical ActivitySelf-Tracking

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Area of Science:

  • Human-Computer Interaction
  • Personal Informatics
  • Behavioral Science

Background:

  • Existing personal informatics models primarily focus on behavior change goals.
  • These models inadequately capture how individuals integrate self-tracking into diverse aspects of daily life.
  • A new perspective is needed to understand the broader use of self-tracking tools.

Purpose of the Study:

  • To propose a novel model of personal informatics based on the concept of 'lived informatics'.
  • To characterize the integration of self-tracking into everyday life for individuals with varied goals.
  • To identify challenges and future research directions in personal informatics.

Main Methods:

  • Surveyed 105, 99, and 83 past/present trackers of physical activity, finances, and location.
  • Conducted interviews with 22 self-trackers to explore lived informatics experiences.
  • Developed a model based on empirical data from surveys and interviews.

Main Results:

  • The proposed lived informatics model details processes of deciding to track, selecting tools, collecting data, integrating information, and reflecting on tracked data.
  • The model encompasses tracking and acting, as well as the phenomena of lapsing and resuming tracking.
  • Identified underexplored challenges within the lived informatics framework.

Conclusions:

  • The lived informatics model offers a more comprehensive understanding of personal informatics use beyond behavior change.
  • This framework highlights the dynamic nature of self-tracking, including initiation, continuation, and cessation.
  • The findings provide valuable insights for designing more effective and user-centered personal informatics systems and guiding future research.