ables allow users to make sense of past events—what activities they have done and what effect they are likely to have on their well-being—to prompt positive behavior change, as discussed by Fritz et al. 8 The next stage of development might be for health wearables to predict health crises; examples include alerting a hospital of early signs of a heart attack or warning users of how likely it is they will develop, say, breast cancer.

The scenario involving predictive
emergency medical intervention raises
the question of who ought to have ac-
cess to personal health data. While it
would be helpful to link one’s health
data directly to the closest hospital in
order to set the long chain of care in
motion as early as possible in an emer-
gency, there would be highly sensible
consumer pushback around the access
various parties might want to have to
personal health data and that function-
al uncertainty in this arena would likely
not be tolerated. Alternatively, if a wear-
able device alerted a user to hurry to a
hospital at the start of a possible medi-
cal crisis, how certain does the device
have to be? Should gadgets err on the
side of caution, possibly provoking a
false alarm? While not alerting a user
due to insufficient certainty may lead
to preventable deaths, so might caus-
ing alarm when alarm is not absolutely
necessary, thus leading users to ignore
or even reject subsequent alerts, with
the gadget turning the user into “the
boy who cried wolf.”
The very notion of a health wear-
able alerting a user to an otherwise
imperceptible impending crisis dem-
onstrates the insufficiency of solutions
for addressing uncertainty that rely on
manual data correction by the user, as
suggested by Consolvo et al. 6 and Pack-
er et al. 20 Explaining the data collected
and the ways it is processed by the al-
gorithm may be more appropriate for
assisting a user determining whether
the device output is certain enough to
warrant seeking medical attention. At
the same time, this information must
be delivered in ways that can be evalu-
ated rationally by a person who just re-
ceived an anxiety-provoking output (see
the sidebar “Communicating Uncer-
tainty”). Both parts of this solution are
non-trivial and require further research.

Life coaching. Tracking data points
the individual user, affecting others
in the user’s social circle who did not
necessarily consent to such analysis.
Additionally, the consequences to in-
dividuals deciding to cut a person out
of their lives are not necessarily know-
able to a system (such as how cutting
ties might introduce undue financial
instability into their lives). How certain
would one have to be of the toxicity of
a relationship to be willing to end it? It
might indeed be the case that people
would more readily accept diagno-
ses of their problems in the form of a
scapegoat than that their unhappiness
is a result of their own behaviors they
find difficult to change. This is all the
more reason why tools that claim deep
insight into users’ lives must be very
clear about the uncertainties they are
juggling in their algorithms.
For advanced diagnostic tracking
in the form of life coaching, new tech-
niques are needed to identify potential
triggers from relevant contextual infor-
mation; and to the extent that doing so
entails drawing data from other perva-
sive devices, such a filter might intro-
duce further uncertainties that need to
be reflected in overall measures of un-
certainty. Additional research is need-
ed to understand how best to commu-
nicate these uncertainties to users. In
particular, tools are needed for captur-
ing users’ cognitive and affective re-
sponses to these uncertainties (as cov-
ered in the sidebar “Communicating
through one’s personal history is of
limited value for individuals seeking
improvements in and maintenance of
their well-being, in contrast to infor-
mation about dependencies and cor-
relations among multiple variables5

(such as the effect of certain foods on an individual’s blood sugars). Given that users are often not rational data scientists22 and consistent in asking for greater analytical capabilities than their devices are capable of providing, it seems inevitable that device manufacturers will introduce systems that purport to provide more definitive answers for users. The danger would be doing so without properly attending to the uncertainties highlighted earlier.

Users’ inability to appreciate uncertainty is made especially clear in the case of wearables that claim to identify correlations between mood and activities (such as ZENTA, https://www.indi-egogo.com/projects/zenta-stress-emo-tion-management-on-your-wrist). It is conceivable that wearable life coaches may soon draw from other pervasive technologies to provide indications of, say, toxic relationships between the user and other individuals and encouraging them to cut unhealthy social ties. While such revelations could have benefits, the implications of inaccuracies of one’s data or of the data being drawn from other sources to determine correlations would begin to extend beyond

 

Experimental psychology studies, including those undertaken by Susan Joslyn and her colleagues at the University of Washington in Seattle (http://depts.washington. edu/forecast/), have shown that providing information about uncertainty can lead to greater trust in system models and better decision making. These benefits are far from assured, however, as studies have also shown that non-expert end users have great difficulty interpreting information about uncertainty. 12

One presumed reason for this difficulty is that uncertainty increases the cognitive load individuals need to manage while making any kind of decision. It requires that individuals engage in slow and methodical thinking, as opposed to more quick and heuristic thinking. Verbal and numerical expressions of uncertainty create potential complications for decision makers—the verbal expressions being open to more subjective interpretative variability, and the numerical expressions often being more difficult to decipher. 9 Both forms are potentially subject to framing effects that influence people’s processing of the information. Research has also uncovered “deterministic construal errors,” 9 or the tendency to interpret uncertain information as deterministic; for example, people frequently interpret— incorrectly—the “cone of uncertainty” in hurricane forecasts as the extent of the wind field, while, in fact, it represents the extent of all possible hurricane trajectories. All such factors require careful consideration when designing representations of uncertainty information. Communicating Uncertainty