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