the sidebar “Understanding Health
Wearables Data”). For example, many
users of activity trackers struggle to
understand how they compare with
others (such as whether their readings
are normal, exceptional, or worrying) 16
or whether they can claim to be “fit.” 14
Even if users are able to determine their
readings are outside what would be
considered by medical doctors in the
normal range, they routinely ask for
guidance about what to do with the information. 14, 15 Current tools do not provide the support users need to understand the significance of their data3 and
without it cannot determine the significance of uncertainties in that data.
While some evidence suggests pro-
viding users information about why a
system behaved a certain way can in-
crease trust17 and not doing so (such
as not providing uncertainty informa-
tion) can lead to reduced trust, 11 a re-
cent study found algorithm and system
transparency does not necessarily yield
more trust21 and greater intelligibil-
ity tends to reduce trust when there
are significant output uncertainties. 17
These points suggest questions that
deserve further research; for example,
when—or indeed for what users—is it
appropriate to communicate how the
systems collect and process data and
how confident the systems are in their
outputs? And, moreover, how should
these uncertainties be communicated
to maximize user trust?
A final notable concern is what
we call “functional uncertainty” that
emerges when users are unable to understand how, why, and by whom their
data is being used. Concerns about privacy and security are manifestations of
this uncertainty. It is not always apparent to users exactly what data is being
collected from their devices, as well as
the duration, location, or security level
of their storage. For example, Epstein
et al. 7 found that nearly half of the participants in their study turned off location tracking, fearing friends might be
able to see where they were at all times
or their location information might be
sold to companies to better target ads.
In certain contexts, a lack of location
information might reduce the precision of other calculated metrics that
depend on it. Further, consent terms
and conditions being notoriously verbose and inaccessible, consumers may
not fully understand the implications
of the consent given when signing up
with their devices. 1 This, in turn, can
influence user compliance with recommended usage, introducing further input uncertainties.
We argue that for general fitness
and well-being, the effect of the uncertainties we have just outlined are
limited. They may contribute to loss of
trust and high rates of device abandonment, 5 but while these consequences
may be a concern for companies producing the gadgets, it is not especially
problematic otherwise. However, our
interest throughout the rest of this article is how the effect of these uncertainties could intensify in more ambitious
uses of health-wearables data.
Uncertainties in Future Uses
Here, we introduce three areas where
we anticipate increased use of commercial activity-tracker data and explore
how they may further affect uncertainty
tolerance and thus implications in designing for uncertainty. We focus on
these scenarios as a way to draw out
three distinct concerns that require attending to in future research:
Emergency medical intervention
and disease prevention. Health wear-
The reliability of fitness-tracker data
has long been a source of concern in
human-computer Interaction (HCI),
and comparative evaluations of activity
tracker brands reveal minimal though
potentially significant differences in
reliability. 3 While users of these tools
are highly cognizant of their lack of
reliability (such as with step count-
ing6 and sleep monitoring16), attempts
to test devices for inaccuracies and
calibrate use accordingly often fail. 18
Prevailing advice from designers is
to enable users to annotate or amend
their data if deemed inaccurate, 6, 20 but
users’ ability to correct sensor errors is
limited only to readings they are able to
verify independently. As wearables be-
gin to measure physiological data (such
as heart strain) not otherwise accessible
to the user, new design solutions will be
needed to address input uncertainties.
Another type of uncertainty we call
“output uncertainty” is apparent when
users are unable to determine the significance of the inferences or recommendations produced by a system (see
The virtually limitless opportunities for passive data collection through wearables
mean any user has potentially large amounts of multidimensional data with
which to make health-related decisions. To do so effectively, they must make sense
of patterns within that data. This challenge is endemic to personal informatics,
or “lived informatics,” 22 more generally, the goal being to “help people collect
personally relevant information for the purpose of self-reflection and self-knowledge.” 15 In the context of health wearables specifically, systems are typically
designed to help users understand the effect of a range of contextual factors on a
desired health outcome (such as well-being). 2
Enabling user health revelations poses a significant information-presentation
challenge. For example, users demonstrate poor graph literacy, 2 yet commercial-brand
wearables interfaces are predominantly graph-based. These interfaces also tend to
prioritize time-based views of data, smoothing out peaks and troughs and obscuring
the most salient contexts around which they occur—information that would ostensibly
lead to greatest user insight. 2, 15 It is also often not readily apparent to users how the
complexities of interactions between factors is negotiated by the system’s algorithms, 2
nor whether such decision making is rooted in robust science. Complicating matters
further, users generally have poor conceptual grounding for such concepts as “health,”
“well-being,” and “fitness”; for example, Kay et al. 11 showed users are poorly equipped
to determine the clinical relevance of weight-fluctuation data.
A growing body of work in HCI explores strategies for supporting intelligibility of
data collected by health wearables (such as Bentley et al., 2 Consolvo et al., 6 Kay et al., 11
Kay et al., 12 Li et al., 15 and Liu et al. 16). This work is fundamental to attending to the
challenges of uncertainty for health wearables, as it is indeed the basis for providing
users insight into both when inaccuracies occur and the effect of inaccuracies in a
reading or output relative to their intended use of the device.
Understanding Health
Wearables Data