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Session: Activity Recognition
CHI 2014, One of a CHInd, Toronto, ON, Canada
Persuasive Technology in the Real World: A Study of
Long-Term Use of Activity Sensing Devices for Fitness
Thomas Fritz, Elaine M. Huang
Univeristy of Zurich
{fritz,huang}@ifi.uzh.ch
Gail C. Murphy
University of British Columbia
murphy@cs.ubc.ca
ABSTRACT
Thomas Zimmermann
Microsoft Research
tzimmer@microsoft.com
activities and provide awareness [7, 13], how feedback is
understood and used [12], and how such technologies
should be evaluated [11].
Persuasive technology to motivate healthy behavior is a
growing area of research within HCI and ubiquitous
computing. The emergence of commercial wearable devices
for tracking health- and fitness-related activities arguably
represents the first widespread adoption of dedicated
ubiquitous persuasive technology. The recent ubiquity of
commercial systems allows us to learn about their value and
use in truly “in the wild” contexts and understand how
practices evolve over long-term, naturalistic use. We
present a study with 30 participants who had adopted
wearable activity-tracking devices of their own volition and
had continued to use them for between 3 and 54 months.
The findings, which both support and contrast with those of
previous research, paint a picture of the evolving benefits
and practices surrounding these emerging technologies over
long periods of use. They also serve as the basis for design
implications for personal informatics technologies for longterm health and fitness support.
We aim to learn what value these systems provide even
after months or years of use, whether and how this value
changes over time, and how persuasive personal informatics
technologies might be better designed to provide long-term
support. We build upon and extend the findings of previous
research in the area by contributing a study of in-the-wild
use of activity monitoring devices by long-term users in a
non-experimental context. Klasnja et al. have identified
field studies as an important approach for understanding the
impacts of these types of technologies [11]. The recent
emergence of commercial wearable devices designed to
track and motivate physical activity, such as the Fitbit
(fitbit.com) and Nike+ FuelBand (nike.com/fuelband),
provides a valuable opportunity to study the naturalistic use
of these technologies, and the practices and perspectives
that emerge over continued long-term use.
Author Keywords
Although not everyone who tries a wearable activity
monitor continues to use it in the long term, we focus on
those who have done so and integrated it into their daily
practices. Investigating the experiences of people who have
adopted these technologies “organically” and continued to
use them over time offers the opportunity to study certain
contexts and aspects of use not possible in shorter-term
experimental deployments. It also affords the opportunity to
see how findings of previous shorter studies hold over
longer-term use. In particular, we visit such issues as the
value of system-provided metrics as people’s fitness goals
and activities change, the challenges of data sharing and
identifying relevant social networks, and the ways in which
the design of self-monitoring systems influence people’s
activities and conceptualizations of healthy behavior.
Personal informatics; persuasive technology; activity
monitoring; wearable sensing; health; behavior change
ACM Classification Keywords
H.5.2 User Interfaces, H5.m Miscellaneous.
General Terms
Design, Human Factors
INTRODUCTION
Emerging persuasive technology and ubiquitous wearable
sensors offer much promise for improving health and
fitness practices. Commercial and research personal
informatics systems that employ these sensors enable the
automated tracking of personal information and activities,
such as sleep and physical activity. Along with
developments in the sensing technology itself, research has
also made great strides in understanding other aspects of
these technologies, such as how their design affects activity
and behavior [9, 24], how to use visualization to motivate
This work offers several contributions to the body of
knowledge on personal informatics and persuasive
technology. First, it provides a rich understanding of the
influence and role of wearable persuasive technology for
activity tracking. Second, it reveals perspectives and
practices of long-term use that add depth to the findings of
previous experimental and shorter term deployments of
related technologies. Although in some cases our findings
contrasted with those of previous work, the bulk of our
findings confirm them, thereby adding richness to our
understanding of such technologies as well as lending
credence to the findings of shorter term studies. Finally, our
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CHI 2014, April 26 – May 01 2014, Toronto, ON, Canada
Copyright is held by the owner/author(s). Publication rights licensed to ACM.
ACM 978-1-4503-2473-1/14/04…$15.00.
http://dx.doi.org/10.1145/2556288.2557383
487
Session: Activity Recognition
CHI 2014, One of a CHInd, Toronto, ON, Canada
work offers a set of targeted implications for designing
personal informatics technologies intended for long-term
use.
[23]. Fish ‘n’ Steps compared shared and individual use of
a fish tank visualization of activity, and found no significant
differences in activity levels between the conditions during
the deployment [12]. Houston, which combined journaling
with automated activity tracking was deployed and
evaluated, leading to design implications regarding social
pressure and support [6]. Prasad et al. investigated related
issues surrounding the use of Fitbit in a one-week study,
specifically looking privacy concerns related to data sharing
[19]. The findings suggested that people are less willing to
share personal demographic information than information
collected by the device. Such concerns are consistent with
the tension Newman et. al note between sharing health and
behavior goals with others in online forums and the need to
maintain a positive impression of one’s self to the social
community [17]. It should be noted that the various trials
and deployments of previous technologies described here
were short to moderate in length, ranging from 6 days
(Chick Clique) to 14 weeks (Houston) in duration. Our
study builds upon and generally confirms the findings of
these previous deployments, considering similar issues of
motivation, practices and social use, with a particular focus
they change or persist over long-term use.
RELATED WORK
Personal informatics systems to support health and fitness
are a growing area of interest. Research and commercial
systems have been developed to track and monitor
information such as weight, steps, sleep, and overall
activity. Munson proposes a spectrum of personal
informatics
applications
ranging
from
reflective
technologies intended to support insight into one’s own
behavior to persuasive technologies [15]. As Michie et. al.
discuss, there is a wide range of approaches to achieve
behavior change [14]. Persuasive technologies employ
varied strategies for influencing behavior and activities,
such as those described by Fogg, most notably selfmonitoring and conditioning [10]. Self-monitoring is one of
the most prevalent persuasive technology strategies [11],
but technologies often employ multiple strategies.
A variety of monitoring devices have been studied and
analyzed for their persuasive influence on practices and
behavior. For example Tudor-Locke et al. looked at the use
of simple pedometers for measuring and motivating activity
[24]. Based on a quantitative synthesis of literature, Bravata
et al. found that pedometers in combination with a step goal
can significantly increase physical activity [5]. The average
duration of the synthesized studies was 18 weeks and
Bravata et al. point out that the long-term durability of these
changes is unknown.
Monitoring devices are often limited in the activity that can
be sensed, leading to the need for integrating data from
multiple sources to get a broader view on health and fitness.
Systems that require more effort on the part of users for
tracking activities are less likely to be successfully adopted.
For example, research by Ahtinen et al. showed that manual
entering of health data was burdensome and led to declining
use of wellness applications [1]. Tollmar et al. looked at the
effects of health information “mashups” that integrate data
from multiple sensors and sources and discovered that these
combinations allowed people to gain novel insights about
their wellness [22].
In recent years, HCI and ubiquitous computing research has
produced more sophisticated devices that attempt to
persuade using various representations of sensed activity
data. The most prominent example is UbiFit, which
combined activity sensing with a glanceable visualization
of activity [7, 9]. In a field deployment of the system, the
researchers found that the visualization helped participants
maintain activity levels by providing positive feedback.
Other systems attempt to persuade through coaching and
advising metaphors. For example, Flowie, a persuasive
virtual coach intended to motivate elderly people to walk
more was deployed to two participants in an 11-day study
and identified types of feedback that were most promising
for motivation. Laura, a system with a similar goal, used an
animated relational agent as an exercise advisor [4].
Participants increased their walking by 215% during the
trial period. Li explored the use of contextual information
as a supplement to performance data in the IMPACT
system. A multi-week evaluation of the system revealed
that contextual information is useful for retrospective
interpretation of activities [12].
In general, persuasive technologies intended to spur
behavior change are challenging to evaluate because change
can only be proven if it persists over a long period of time.
Klasjna et al. therefore propose other approaches for
evaluating the role of these technologies, including field
deployments to understand their use [11]. As further
motivation for our research, a recent survey of fifty-four
people indicated a high value for long-term information that
is often not considered by studies with limited timespan [3].
We therefore believe that studying the long-term use and
influence of these devices is necessary for building a more
complete understanding of the value of persuasive
technology for motivating activity.
BACKGROUND
In addition to the research prototypes mentioned, several
commercial wearable activity trackers have been released in
recent years. Our study population included users of many
such devices, including Fitbit, the Nike+ Fuelband,
Jawbone UP (jawbone.com), Striiv (striiv.com), and
Bodybugg (bodybugg.com), and many of the participants
had tried multiple types of devices or multiple versions of
Researchers have also considered social aspects of
persuasive monitoring technologies. Chick Clique, a system
geared towards sharing health information among teenage
girls, was evaluated in a 6-day deployment, which led to the
finding that data sharing could be a powerful motivator
488
Session: Activity Recognition
CHI 2014, One of a CHInd, Toronto, ON, Canada
60s who had been using such a device for at least three
months (Table 1.) We chose three months as the minimum
length of use as previous experimental wearable activity
sensors for fitness had typically considered use periods of
three months or less. Most participants had been using their
devices for substantially longer; our longest-use participant
had been using activity tracking devices for 54 months at
the time of interview, and the overall mean length of use
across participants was 14.8 months.
Participants were recruited through a variety of approaches
including snowball sampling, recruiting emails, and posts to
online forums such as the Fitbit community. Participants
came from a variety of professions, including a business
development expert, a teacher’s aid, an attorney, and
students, however, overall our interview population slanted
towards people in technical professions, such as software
developers, and software project managers (Table 1). This
proportion may be partly attributable to the snowball
sampling approach through which we recruited participants,
but also to the fact that the early adopters of these types of
devices are likely to be tech-interested individuals.
Figure 1. Nike+ Fuelband (left) and Fitbit Ultra (right)
the same device. Although there are variations in what these
devices sense, record, and display, they share certain
commonalities.
The devices vary in form factor, including arm bands, wrist
bands, and or clip-on models (Fig. 1). Some have passive or
interactive displays that can show limited representations of
the wearer’s data. Several of the systems also provide
support for other aspects of health and wellness, such as
sleep tracking and food logging. In this work, however, we
focus solely on the activity tracking functions.
Twenty-four participants were using Fitbit at the time of
interview, four participants used a FuelBand, one
participant used a Jawbone and one participant used a
Striiv. Five participants had previously used other types of
activity monitoring technologies (see Table 1.) It is
important to note that our participants’ experiences are not
necessarily representative of experiences with wearable
activity sensing in general, as we are focusing specifically
on people who have continued to use the technology for
long periods of time. We did not include people who had
stopped using these devices and this work is therefore
limited in the insight it can offer about why these
technologies might fail to be adopted. In addition, as some
of our participants were recruited from online forums
focused on these technologies, it is likely that our
population overall is more active and enthusiastic about
their devices than the general population of wearers.
The devices make use of different sensing technologies,
such as accelerometers and altimeters to track movement or
activity. They also provide ways of viewing the information
through various visualizations on websites, mobile apps, or
on the devices themselves. Most systems provide multiple
representations of the activity, including concrete measures
such as step count, distance traveled, or flights of stairs
climbed, as well as abstracted compound representations
such as Nike’s “fuel” points or FitBit’s “activity score.”
With respect to persuasion strategies identified by Fogg
[10], all of these technologies can be classified primarily as
self-monitoring technologies. Nearly all of them also
employ Fogg’s notion of conditioning and Michie et al’s
notion of contingent rewards [14] to some extent in the
form of rewards and motivational messages displayed to the
user. These features vary in their degree of explicit
persuasion, ranging from badges that reflect achievements
to fitness challenges and competitions offered to the wearer.
Most of them also allow the wearer to set explicit goals, an
important strategy identified by Consolvo et al. [8] Nearly
all of the systems support data sharing through online
communities and social networking features on websites or
apps, another potentially valuable strategy identified by
Munson et al. [16] and Michie et al. [14].
Interviews lasted between 25 and 45 minutes in duration,
and were conducted in person when possible (18), and
otherwise over the phone or Skype (12). The general
interview format commenced with basic questions about the
participant’s reasons for acquiring or using their device, and
the length of time that they had had it. This was followed
by more open questions geared towards eliciting anecdotes
and experiences. We explored topics such as individuals’
daily practices with the technologies, how they used the
data the devices provided, their experiences with social
aspects of the system, and how their activities and use of
the devices had changed over time.
METHOD AND PARTICIPANTS
To learn about the use and influence of these technologies
on people’s activity, we conducted a study using in-depth
semi-structured interviews. We recruited 30 participants (16
female, 14 male) in various cities in North America (23),
Europe (6), and Asia (1) between their early 20s and mid
Interviews were audio recorded and transcribed. To analyze
the data, we used a combination of inductive open coding
and closed coding with codes derived from concepts in
related literature to identify important themes. In this work,
we present some of the most prominent emerging themes,
489
Session: Activity Recognition
CHI 2014, One of a CHInd, Toronto, ON, Canada
Table 1. Summary of participant information.
Participant
Gender
P1
P2
male
female
Activity Sensing
Devices Used
(Current Bold)
Fitbit
Fitbit
P3
female
Fitbit
6
P4
male
pedometer,3Fitbits
10
P5
P6
P7
P8
P9
female
female
female
female
female
bodybugg, 2Fitbits
pedometer, Fitbit
2 Fitbits
FuelBand
3 Fitbits
18
7
18
4.5
11
P10
male
2 Fitbits
11
P11
P12
P13
P14
P15
P16
male
male
female
male
male
male
5 Fitbits
FuelBand
5 Fitbits, Striiv
Fitbit
FuelBand
Fitbit
11
3.5
9
16
5
7
P17
male
Fitbit
6
P18
female
12
P19
male
P20
male
P21
female
4 Fitbits
3 bodybuggs,
1 Jawbone Up,
5 Fitbits
FuelBand
Fitbug, Striiv,
3 Fitbits
P22
male
4 Fitbits
21
P23
female
Fitbit
12
P24
male
Jawbone UP
5
P25
female
Fitbit
21
P26
female
3 Fitbits
42
P27
female
2 Fitbits
42
P28
male
Fitbit
8
P29
female
2 Fitbits
18
P30
female
2 Fitbits
22
Months
of Use
3
3
Occupation
Usability
Professor
Operations
Specialist
Software
Developer
Student
Director
Professor
Student
Student
Program
Manager
Director
Researcher
Attorney
Director
Director
Researcher
Service
Engineer
Researcher
54
Patent
litigater
8
Retail
16
Psychologist
Military
Personnel
Teacher’s
Aid
Retail
Web
Designer
Business
Developer
Volunteer
Coordinator
Finance
Counselor
Health
Professional
Loss
Mitigation
Manager
most notably those that illustrate the dynamic use and
influence of these the technologies over time.
FINDINGS
influence fitness and activity among long-term users. We
unpack several phenomena that illustrate how people derive
motivation and value from these systems, despite changes
in practices and needs that arise over long-term use. In
particular we focus on the ways in which metrics, data, and
social networking features provided by the system influence
people’s engagement with their personal fitness and the
technologies.
General effects
Most of the participants had integrated the devices deeply
into their routines and daily practices, wearing them either
all of the time or putting them on first thing in the morning
and taking them off just before bed. They described strong
attachments to them:
“I’m a little obsessed with it. I look at it all the time … I’m always
curious, like where I am at what point of the day.” (P30, 22
months)
“I feel I find it hard not to wear it.” (P29, 18 months)
“There’s also I think, some degree of psychological attachment to
the thing, [losing it is] kind of like when you lose your cell phone.
It’s weird to have something that you have with you all the time
and you’re constantly sort of playing …
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