Unintended effects of self-tracking
Elisabeth T. van Dijk
Eindhoven University of
Technology
Eindhoven, The Netherlands
[email protected]
Femke Beute
Eindhoven University of
Technology
Eindhoven, The Netherlands
[email protected]
Joyce H.D.M. Westerink
Eindhoven University of
Technology; Philips Research
Eindhoven, The Netherlands
[email protected]
Wijnand A. IJsselsteijn
Eindhoven University of
Technology
Eindhoven, The Netherlands
[email protected]
Abstract
Although self-tracking is generally thought of as a
self-improvement tool, it may also affect its users and the
social context in which it is employed in unexpected ways.
Here we make a first attempt at an inventory of known
and theorized unintended effects of self-tracking.
Although this inventory is mostly based on theory,
personal experience and anecdotal evidence, the impact
some of these unintended effects might have highlights
the need for empirical investigation of these matters.
Author Keywords
Personal Informatics, Quantified Self, Evaluation
ACM Classification Keywords
H.5.m [Information interfaces and presentation (e.g.,
HCI)]: Miscellaneous.
Introduction
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CHI’15, April 18–April 23, 2015, Seoul, South-Korea. Workshop on ‘Beyond
Personal Informatics: Designing for Experiences of Data’.
The idea of the ‘quantified self’ (QS) was initially devised
as a way to obtain more objective knowledge about
oneself, resulting in avenues for improvement tailored to
the individual. Thinking about – and, consequently,
design of – self-tracking currently seems to be dominated
by this conceptualization of self-tracking as a means of
gaining insight and improving oneself. This is reflected in
Li, Dey and Forlizzi’s stage-based model of personal
informatics [4], which describes how a person engaged in
self-tracking might progress through five stages: preparing
to track (setting goals, determining what and how to
track), collecting data, transforming that data into a
usable format, reflecting on the data and finally taking
action based on the lessons learned.
start living farther away from their workplaces, thus
yielding the opposite effect from the one that was
intended. Even though technologies may themselves be
deterministic, their patterns of adoption and use are not.
In the realm of self-tracking, such unintended effects may
also play a role.
In recent years, voluntary self-tracking has become more
widespread, and self-tracking technology more widely
available. In addition, there seems to be an increasing
interest in applying self-tracking as an intervention or a
way for third parties (e.g. physicians, therapists, sports
coaches) to gain rich information about those in their care
(also referred to as ‘pushed’ or even ‘imposed’
self-tracking [6]). However, as self-tracking technologies
become more common and gain traction beyond the
dedicated ranks of the QS movement, it has been found
that actual usage of self-tracking technology does not
always conform to the stage-based model of personal
informatics [9].
By their very nature, unintended effects are difficult to
predict. But with the increasing popularity of
self-tracking, it nevertheless seems worthwhile to explore
them. Even if such explorations are necessarily incomplete,
they still serve to stimulate scientific and public debate
and to inform design of self-tracking systems to better
support previously unexplored benefits and avoid possible
drawbacks. In what follows, we will present a selection of
known and theorized unintended effects of self-tracking.
People often use new technologies in unexpected ways,
finding unforeseen ways to interact with it and give it
meaning. And even when technology is used as intended,
it may affect its users and the social context in which it is
employed in unexpected ways. This resonates with a
social constructivist view of technology (see e.g. [7]):
individual choices and cultural forces shape the use and
eventual impact of technology. Indeed, as Edward Tenner
argues in his book ‘Why things bite back: Technology and
the revenge of unintended consequences’[10], there are
many examples of how the adoption of new technologies
has had unforeseen and unintended effects. For example,
governments may want to reduce traffic congestion by
building better roads. As an effect, however, better roads
may attract more traffic, and may stimulate people to
Awareness and self-focus
Self-tracking is generally intended to put its users on a
path of self-discovery, which involves gaining an increased
awareness of the parameters being tracked. In addition to
promoting awareness of tracked parameters, self-tracking
may also provide an increased awareness of other factors,
like time. Although such awareness may be useful if it
leads to actionable insights, excessive self-focus may be
detrimental as well. It has been argued that that there are
two kinds of attentiveness to one’s inner thoughts and
feelings [11]. One is a ruminative style, involving
evaluation or judgment, while the other is a
philosophically oriented self-reflection. The ruminative
style of self-attentiveness is thought to be maladaptive
while the reflective style is considered more adaptive [11].
Specifically, it has been found that abstract thinking,
about outcomes, meanings and implications tends to be
maladaptive, while concrete thinking about processes and
plans makes for better problem solving [13]. This suggests
that self-tracking systems should seek to promote the
latter while avoiding the former.
Reductionist assessment
The measurements used in self-tracking tend to be
relatively simple and limited compared to the real-world
phenomena they aim to represent (e.g. BMI vs.
healthiness, also see discussion in [8]). Such limited
representations of tracked phenomena may lead to
unnecessary or unproductive behavior changes. That is,
users may be prompted to change their routine to better
suit what the system can reliably track (e.g. avoiding
certain foods to suit the diet app’s capabilities [14], or
replacing one type of exercise with another because the
activity tracker cannot reliably recognize certain
activities). Such behavioral adaptations to technological
constraints have been identified by other scholars as well.
For example, Jaron Lanier, in his manifesto ‘You are Not a
Gadget’ [3], describes the limitations in musical
expressivity imposed by the MIDI standard, transforming
the notion of a musical note into a “rigid mandatory
structure you couldn’t avoid in the aspects of life that had
gone digital”.
The reductionist assessments often offered by self-tracking
systems may also cause users to optimize the tracked
parameter rather than the underlying concept, leading to
a kind of ‘cheating’. Examples might be shaking a step
counter (mentioned in [9]), switching on a GPS running
tracker during a bike ride or opportunistic use of preset
categories (‘ketchup’s mostly vegetables, right?’). In this
way, self-tracking may serve as a means of perpetuating
self-deceit rather than ameliorating it. Alternatively,
cheating may not be about self-deceit, but about gaining
rewards. As noted by Rooksby, Rost, Morrison and
Chalmers [9], many self-tracking systems offer digital
rewards like badges and other markers of achievement,
and different schemes already exist whereby getting good
self-tracking scores can lead to financial gains (e.g.
getting a discount on car insurance by tracking your
driving habits 1 , getting a bonus from your employer if you
walk a milion steps2 or getting paid to reach your fitness
goals, with money coming from less productive users3 ).
Some users report that the promise of such rewards
motivates them to keep using certain tracking systems [9].
Alternatively, though, some users might be tempted to
find ways to improve their scores without doing the actual
work, all the while still reaping the rewards.
Over-trust of data
Current automatic self-tracking systems often suffer from
reliability issues. Although the technology is continuously
advancing, automatic interpretation of raw sensor data is
still difficult and often unreliable. Physiological
parameters especially are notoriously difficult to interpret
due to a myriad of confounding variables as well as
individual differences. Nevertheless, self-tracking data is
often presented in a way that seems to reflect a
straightforward relationship with underlying behavior and
physiological processes. In addition, the quantitative,
numerical presentations often used tend to imply a higher
level of precision than can actually be achieved (e.g.
presenting calories as a simple number implies the number
is accurate down to a precision of 1 calorie). This way of
presenting data, combined with generally-held beliefs
about the capabilities of technology, seems to leave users
1 http://www.progressive.com/newsroom/article/2011/march/snapshotnational-launch/
2 http://hr.bpglobal.com/LifeBenefits/Sites/core/BP-Lifebenefits/Employee-benefits-handbook/BP-Medical-Program/Howthe-BP-Medical-Program-works/Health-Savings-OOA-Optionsummary-chart/BP-Wellness-Program.aspx
3 http://www.gym-pact.com/
convinced that data provided by self-tracking systems
offer a more truthful, reliable and objective view of things
than their own subjective experience, even when this may
not always be the case.
As a result of this over-trust, the use of self-tracking
systems may cause users to discount their own experience
in favor of the data provided by the technology. This in
turn might lead users to become dependent on their
self-tracking systems, relying on the system to tell them
how they are doing and feeling uneasy or under-informed
when that information is not available. In addition, users
may feel that things are only real if they are tracked, so
that achievements are not valuable unless they have been
objectively documented by an external system.
In addition, over-trust may cause feedback from
self-tracking systems to take on the role of self-fulfilling
prophecy. For instance, if a sleep tracker shows the user
they have not slept well, they might not only believe, but
internalize that view, feeling more tired and being less
productive as a result. The same line of reasoning may
lead to positive outcomes if the picture painted by the
self-tracking data is more positive than the user expected.
A recent study offers a preliminary indication that such a
‘good news effect’ may indeed occur when participants are
given feedback about their stress level [12].
To encourage users to be more mindful and critical in
their interpretations and use of self-tracking data,
ambiguity in design [2] could be used to reflect the
uncertainty of the underlying data and interpretation
thereof. As noted in [2], “Ambiguity of information impels
people to question for themselves the truth of a
situation”, which in the case of self-tracking systems may
serve to prevent some of the issues mentioned above.
Healthism and responsibility
Self-tracking seems to promote the idea that if something
can be tracked, it can be improved. This fits into the
ideology of ‘healthism’: being healthy is not only
important, but is a responsibility that we each must take
charge of [1]. As a result, people may feel obliged to try
self-tracking. Similarly, users of self-tracking technology
may feel a pressure to ‘perform’: to find self-knowledge
through self-tracking and report progress. If this does not
happen, users may hold themselves responsible, which
may in turn lead to feelings of guilt and inadequacy. This
is especially problematic since it is to be expected that
self-tracking will not always be effective, simply because
not all relevant factors are tracked, or can be controlled.
In addition, as noted by Lupton, not all individuals may be
willing or able to use self-tracking technology [5].
Conclusion
In the preceding sections several possible unintended
effects of self-tracking have been highlighted. Firstly,
self-tracking may foster excessive self-focus, which is not
always adaptive. Secondly, the reductionist assessments
used in self-tracking may lead users to alter their behavior
to suit the technology and may also lead to ‘cheating’.
Thirdly, when users over-trust the data gathered through
self-tracking, the data may turn into a self-fulfilling
prophecy and users may develop a kind of
data-dependency. Finally, self-tracking may inadvertently
turn into an obligation, pressuring users to keep changing
and improving even if, for whatever reason, they cannot.
This inventory of possible unintended effects of
self-tracking is by no means complete and is, as it stands,
mostly based on theory, personal experience and anecdotal
evidence. That being said, some of the possible effects
identified here might greatly influence the overall impact
of self-tracking. This highlights the need for empirical
investigation of these matters, both to inform design of
self-tracking systems and to stimulate discussion and more
mindful use of self-tracking.
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