Helping People to Control their Everyday Data for Care: A Scenario-Based Study

by | Mar 23, 2022 | Research Publications

Author(s):

  • Pei-Yao Hung
  • Mark S. Ackerman

Abstract:

With the advent of pervasive sensing devices, data captured about one’s everyday life (e.g., heart rate, sleep quality, emotion, or social activity) offers enormous possibilities for promoting in-home health care for severe chronic care, such as can be found in Spinal Cord Injury or Disorders or the like. Sharing these Everyday Data for Care (EDC) allows care team personnel (e.g., caregivers and clinicians) to assist with health monitoring and decision-making, but will also create tension and concerns (e.g., privacy) for people with health conditions due to the detailed nature of the data. Resolving these tensions and concerns is critical for the adoption and use of a pervasive healthcare environment.

We examine data sharing of EDC to determine how we can better manage the tradeoffs between privacy on one hand and the pro-active sharing of data that one needs for better care. In this paper, we target one critical aspect of using EDC, the problem of sharing an overwhelming number of sensor outputs with numerous care team recipients. We report the results of a scenario-based study that examined ways to reduce the burden of setting policies or rules to manage both the pro-active data sharing and the privacy aspects of care with EDC. In summary, we found that our participants were able to use self-generated groupings of EDC data, and more importantly, largely kept those groupings when creating to share data with potential recipients and when dealing with changes in their health trajectory. These findings offer hope that we can reduce the burden of authoring and maintaining data sharing and privacy policies through semi-automatic mechanisms, where the system suggests policies that are consistent with the users’ preferences – especially as health changes.

Documentation:

https://doi.org/10.1007/978-3-030-99194-4_18

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