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duce important consequences in the
future. We raise three concerns in
particular: First, advances in wearable
technology will enable measurement
of physiological data of which the user
has little or no access to verifiable evi-
dence (see the section in this article on
emergency medical intervention and
disease prevention). Second, low-level
uncertainties are compounded by the
interdependency between various data
systems and their implications (such
as for disease prevention, prediction,
and management) (see the section on
life coaching). And third, near-future
scenarios involving external use of
personal health data introduce new
stakeholders whose tolerance for and
ability to understand uncertainties
will vary, requiring deeper research
into ways to deal with uncertainties
(see the section on patient compliance
monitoring).

Known Uncertainties
of Consumer Wearables

For this purpose, we use the term “un-
certainty” to mean a lack of under-
standing about the reliability of a par-
ticular input, output, or function of a
system that could affect its trustworthi-
ness. With wearable activity trackers,
uncertainties arise in various forms
and affect user trust to varying degrees.

The consequences, while not always apparent to the user, also differ. Here, we explore some of the salient uncertainties that will be relevant to the discussion later in the article.

The old engineering principle says,
“garbage in, garbage out,” but it can
be difficult to know whether the data
coming into a system is sufficiently
accurate to produce meaningful out-
put—where “meaningful” is defined
in relation to the user’s needs; we call
it “input uncertainty.” Inaccuracies in
data can be introduced by wearable
users in various ways. For example,
diagnostic tracking, 20 may require us-
ers to manually record instances of
symptoms, food they have eaten, or
medications they have taken. In such
cases, the reliability of system outputs
depends on users’ ability to correctly
infer what data their tracker is capable
of automatically collecting23 and their
vigilance in manually collecting the
rest, as well as the degree they are able
to understand the standards for enter-
ing data and the importance of the pre-
cision of their input. Users often lack
knowledge of how algorithms process
their data and may thus fail to appreci-
ate how imprecision in a single input
could affect the overall system’s abil-
ity to make appropriate recommenda-
tions. Supporting users’ understand-
ing of these impacts is difficult, 18 as few
people have the requisite knowledge or
interest in interrogating an algorithm.
However, we suggest that supporting
understanding and reducing input in-
accuracies may be helped by following
three practical guidelines: enable users
to engage in a trial-interaction phase,
where they are able to play around with
different inputs to see the effects on
calculated outputs; provide simple tips
on the inputs that explain data-collec-
tion standards and the importance of
precision; and/or provide some win-
dow into the underlying model and cal-
culations.