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This dissertation research investigates the social implications of computing artifacts that make use of sensor driven self-quantification to implicitly or explicitly direct user behaviors. These technologies are referred to here

This dissertation research investigates the social implications of computing artifacts that make use of sensor driven self-quantification to implicitly or explicitly direct user behaviors. These technologies are referred to here as self-sensoring prescriptive applications (SSPA’s). This genre of technological application has a strong presence in healthcare as a means to monitor health, modify behavior, improve health outcomes, and reduce medical costs. However, the commercial sector is quickly adopting SSPA’s as a means to monitor and/or modify consumer behaviors as well (Swan, 2013).

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    Date Created
    • 2016
    Resource Type
  • Text
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    Note
    • Partial requirement for: Ph. D., Arizona State University, 2016
      Note type
      thesis
    • Includes bibliographical references (pages 83-96)
      Note type
      bibliography
    • Field of study: Psychology

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    by Denise A. Baker

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