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Bran Knowles ( b.h.knowles1@lancaster.ac.uk) is a lecturer in data science at Lancaster University, Lancaster, U. K.

Alison Smith-Renner ( alison.smith@dac.us) leads the Machine Learning Visualization Lab at Decisive Analytics Corporation, Arlington, VA, USA, and is a Ph.D. candidate in computer science at the University of Maryland, College Park, MD, USA.

Forough Poursabzi-Sangdeh (forough.poursabzi@ microsoft.com) is a post-doctoral researcher at Microsoft Research NYC, USA.

Di Lu ( di.lu@pitt.edu) is a Ph. D. student in the School of Information Sciences at the University of Pittsburgh, Pittsburgh, PA, USA.

Halimat Alabi ( halabi@sfu.ca) is an adjunct in the Art Institute Online and a Ph.D. candidate in the School of Interactive Art and Technology at Simon Fraser University, Vancouver, BC, Canada.

that data from individuals’ self-tracking devices will be used as bedrock data in other systems from which a range of inferences are made. Ensuring uncertainties are preserved and communicated throughout a long chain of systems whose developers and interpreters might have different readings of these uncertainties and tolerances for them is challenging but necessary if the systems are to be interpretable at scale, as discussed by Meyer et al. 19 This requires development of mechanisms for ensuring important context is not lost, including, say, both the uncertainties and uncertainty tolerances at different points along the chain.

How to tailor communication of uncertainties. Designs must be flexible and/or customizable, presenting uncertainty information in ways that are understandable by the full range of end users with differing needs in data granularity and information presentation. Given that much of the value of health wearables for lay consumers comes from data being available at-a-glance, there is a need to balance important nuance with the interface usability, as discussed by Liu et al. 16 Still, there are moments when even lay consumers could require access to uncertainty information, with systems perhaps allowing them to delve more deeply, as required. Contextual information (such as users’ intended and ongoing use of their wearable data) might also be useful for determining what kinds of uncertainty information the system ought to communicate. At the same time, designers must be cognizant of user variability in cognitive and affective responses to uncertainty-related information to design systems that can identify, learn from, and adapt to these responses to inform health-related decision making most effectively.

These design implications can be considered an open challenge to the health-wearables community without suggesting precise mechanisms for realizing them through design.

Acknowledgments

This article resulted from group work
that was part of the CHI 2017 work-
shop “Designing for Uncertainty in
HCI: When Does Uncertainty Help?”;
http://visualization.ischool.uw.edu/
hci_uncertainty/
We wish to thank our fellow work-
shop participants and keynote speaker,
Susan Joslyn, for their feedback in de-
veloping these ideas. And in particular
we thank the workshop’s organizers—
Miriam Greis, Jessica Hullman, Mi-
chael Correll, Matthew Kay, and Orit
Shaer—for providing a forum for dis-
cussing these ideas and bringing this
team of authors together for ongoing
collaboration post workshop.

Finally, we also thank the anonymous reviewers for their help shaping and improving this article.

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