SleepExplorer: a visualization tool to make sense of correlations between personal sleep data and contextual factors

Abstract

Getting enough quality sleep is a key part of a healthy lifestyle. Many people are tracking their sleep through mobile and wearable technology, together with contextual information that may influence sleep quality, like exercise, diet, and stress. However, there is limited support to help people make sense of this wealth of data, i.e., to explore the relationship between sleep data and contextual data. We strive to bridge this gap between sleep-tracking and sense-making through the design of SleepExplorer, a web-based tool that helps individuals understand sleep quality through multi-dimensional sleep structure and explore correlations between sleep data and contextual information. Based on a two-week field study with 12 participants, this paper offers a rich understanding on how technology can support sense-making on personal sleep data: SleepExplorer organizes a flux of sleep data into sleep structure, guides sleep-tracking activities, highlights connections between sleep and contributing factors, and supports individuals in taking actions. We discuss challenges and opportunities to inform the work of researchers and designers creating data-driven health and well-being applications.

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Acknowledgments

This study was supported by Australian Government Endeavour Research Fellowship and Microsoft BizSpark. We would like to thank our study participants for contribution. We also appreciate feedback and support from Dr. Kathleen Gray, Manal Almalki, and our colleagues in the Microsoft Research Centre for Social NUI at the University of Melbourne.

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Correspondence to Zilu Liang.

Appendix

Appendix

See Table 5.

Table 5 Sleep contextual factors collected for users during field study

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Liang, Z., Ploderer, B., Liu, W. et al. SleepExplorer: a visualization tool to make sense of correlations between personal sleep data and contextual factors. Pers Ubiquit Comput 20, 985–1000 (2016). https://doi.org/10.1007/s00779-016-0960-6

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Keywords

  • Sleep
  • Health
  • Personal informatics
  • Self-tracking
  • Sense-making
  • HCI