to acceptability of accuracy. In Proceedings of the
33rd Annual ACM Conference on Human Factors in
Computing Systems (Seoul, Republic of Korea, Apr.
18–23). ACM Press, New York, 2015, 347–356.
13. Knowles, B. Emerging trust implications of data-rich
systems. IEEE Pervasive Computing 15, 4 (Oct. 2016),
14. Lazar, A., Koehler, C., Tanenbaum, J., and Nguyen,
D.H. Why we use and abandon smart devices. In
Proceedings of the 2015 ACM International Joint
Conference on Pervasive and Ubiquitous Computing
(Osaka, Japan, Sept. 7–11). ACM Press, New York,
15. Li, I., Dey, A., and Forlizzi, J. A stage-based model of
personal informatics systems. In Proceedings of the
SIGCHI Conference on Human Factors in Computing
Systems (Atlanta, GA, Apr. 10–15). ACM Press, New
York, 2010, 557–566.
16. Liu, W., Ploderer, B., and Hoang, T. In bed with
technology: Challenges and opportunities for sleep
tracking. In Proceedings of the Annual Meeting of
the Australian Special Interest Group for Computer
Human Interaction (Parkville, VIC, Australia, Dec.
7–10). ACM Press, New York, 2015, 142–151.
17. Lim, B. Y., Dey, A.K., and Avrahami, D. Why and why not
explanations improve the intelligibility of context-aware intelligent systems. In Proceedings of the
SIGCHI Conference on Human Factors in Computing
Systems (Boston, MA, Apr. 4–9). ACM Press, New
York, 2009, 2119–2128.
18. Mackinlay, M.Z. Phases of accuracy diagnosis: (In)
visibility of system status in the FitBit. Intersect: The
Stanford Journal of Science, Technology and Society 6,
2 (June 2013).
19. Meyer, J., Wasmann, M., Heuten, W., El Ali, A., and Boll,
S.C. Identification and classification of usage patterns
in long-term activity tracking. In Proceedings of the
2017 CHI Conference on Human Factors in Computing
Systems (Denver, CO, May 6–11). ACM Press, New
York, 2017, 667–678.
20. Packer, H. S., Buzogany, G., Smith, D.A., Dragan, L.,
Van Kleek, M., and Shadbolt, N.R. The editable self: A
workbench for personal activity data. In Proceedings
of CHI 2014 Extended Abstracts on Human Factors in
Computing Systems (Toronto, ON, Canada, Apr. 26–
May 1). ACM Press, New York, 2014, 2185–2190.
21. Poursabzi-Sangdeh, F., Goldstein D.G., Hofman J.M.,
Wortman Vaughan, J., and Wallach H. Manipulating
and measuring model interpretability. arXiv preprint,
22. Rooksby, J., Rost, M., Morrison, A., and Chalmers, M.C.
Personal tracking as lived informatics. In Proceedings
of the 32nd annual ACM Conference on Human Factors
in Computing Systems ( Toronto, ON, Canada, Apr. 26–
May 1). ACM Press, New York, 2014, 1163–1172.
23. Shih, P.C., Han, K., Poole, E.S., Rosson, M.B., and
Carroll, J. M. Use and adoption challenges of
wearable activity trackers. IConference Proceedings
24. Swan, M. Emerging patient-driven health care models:
An examination of health social networks, consumer
personalized medicine and quantified self-tracking.
International Journal of Environmental Research and
Public Health 6, 2 (Feb. 2009), 492–525.
Bran Knowles ( email@example.com) is a lecturer
in data science at Lancaster University, Lancaster, U. K.
Alison Smith-Renner ( firstname.lastname@example.org) 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 ( email@example.com) is a Ph. D. student in the School
of Information Sciences at the University of Pittsburgh,
Pittsburgh, PA, USA.
Halimat Alabi ( firstname.lastname@example.org) 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.
© 2018 ACM 0001-0782/18/12 $15.00
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.
This article resulted from group work
that was part of the CHI 2017 work-
shop “Designing for Uncertainty in
HCI: When Does Uncertainty Help?”;
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.
1. Barcena, M.B., Wueest, C., and Lau, H. How safe is your
quantified self? Symantec, Inc., 2014; https://www.
2. Bentley, F., Tollmar, K., Stephenson, P., Levy, L.,
Jones, B., Robertson, S., Price, E., Catrambone, R.,
and Wilson, J. Health mashups: Presenting statistical
patterns between well-being data and context in
natural language to promote behavior change. ACM
Transactions on Computer-Human Interactions 20, 5
(Nov. 2013), 1–27.
3. Case, M. A., Bur wick, H. A., Volpp, K.G., and Patel, M.S.
Accuracy of smartphone applications and wearable
devices for tracking physical activity data. Journal of
the American Medical Association 313, 6 (Feb. 2015),
4. Choe, E.K., Lee, N. B., Lee, B., Pratt, W., and Kientz,
J. A. Understanding quantified-selfers’ practices in
collecting and exploring personal data. In Proceedings
of the 32nd Annual ACM Conference on Human Factors
in Computing Systems (Toronto, ON, Canada, Apr. 26–
May 1). ACM Press, New York, 2014, 1143–1152.
5. Clawson, J., Pater, J.A., Miller, A.D., Mynatt, E.D.,
and Mamykina, L. No longer wearing: Investigating
the abandonment of personal health-tracking
technologies on Craigslist. In Proceedings of the 2015
ACM International Joint Conference on Pervasive and
Ubiquitous Computing (Osaka, Japan, Sept. 7–11).
ACM Press, New York, 2015, 647–658.
6. Consolvo, S., McDonald, D. W., Toscos, T., Chen, M. Y.,
Froehlich, J., Harrison, B., Klasnja, P., LaMarca, A.,
LeGrand, L., Libby, R., Smith, I., and Landay, J. Activity
sensing in the wild: A field trial of UbiFit Garden. In
Proceedings of the SIGCHI Conference on Human
Factors in Computing Systems (Florence, Italy, Apr.
5–10). ACM Press, New York, 2008, 1797–1806.
7. Epstein, D.A., Caraway, M., Johnston, C., Ping, A.,
Fogarty, J., and Munson, S. A. Beyond abandonment to
next steps: Understanding and designing for life after
personal informatics tool use. In Proceedings of the
2016 CHI Conference on Human Factors in Computing
Systems (San Jose, CA, May 7–12). ACM Press, New
York, 2016, 1109–1113.
8. Fritz, T., Huang, E. M., Murphy, G. C., and Zimmermann,
T. Persuasive technology in the real world: A study of
long-term use of activity sensing devices for fitness.
In Proceedings of the SIGCHI Conference on Human
Factors in Computing Systems (Toronto, ON, Canada,
Apr. 26–May 1). ACM Press, New York, 2014, 487–496.
9. Grounds, M.A., Joslyn, S., and Otsuka, K. Probabilistic
interval forecasts: An individual differences approach
to understanding forecast communication. Advances
in Meteorology (2017).
10. Herz, J. Wearables are totally failing the people who
need them most. Wired (Nov. 6, 2014); https://www.
11. Kay, M., Morris, D., and Kientz, J.A. There’s no such
thing as gaining a pound: Reconsidering the bathroom
scale user interface. In Proceedings of the 2013 ACM
International Joint Conference on Pervasive and
Ubiquitous Computing (Zurich, Switzerland, Sept.
8–12). ACM Press, New York, 2013, 401–410.
12. Kay, M., Patel, S. N., and Kientz, J.A. How good is
85%? A survey tool to connect classifier evaluation