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LITTLE REWARDS, BIG CHANGES: USING EXERCISE ANALYTICS TO
MOTIVATE SUSTAINABLE CHANGES IN PHYSICAL ACTIVITY
Kirk Plangger, King’s College London, UK
Colin Campbell, University of San Diego, USA
Karen Robson, University of Windsor, Canada
Matteo Montecchi, King’s College London, UK
* Corresponding Author: kirk.plangger@kcl.ac.uk
ABSTRACT
Even using simple techniques like taking the stairs, many individuals struggle to maintain the
motivation to be active. Health gamification systems can encourage activity by providing points
earned through exercise that are redeemable for tangible extrinsic rewards. Using self-
determination theory, we investigate rewards’ effectiveness to promote exercise considering
reward value, redemption frequency patterns, and fitness levels within a health gamification
system. We find that rewards do significantly increase activity levels, and this effect is larger for
advanced users who redeem multiple times for higher value rewards. We close by offering future
research avenues and advice to optimize reward portfolios.
Keywords: Health gamification system; Tangible extrinsic rewards; Reward portfolio design;
Reward effectiveness optimization; Redemption patterns; Fitness stage
Suggested Citation:
Plangger K, Campbell C, Robson K, & Montecchi M (In Press) Little rewards, big changes: Using
exercise analytics to motivate sustainable changes in physical activity. Information &
Management, volume and page numbers forthcoming.
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INTRODUCTION
Wearable fitness devices such as
smartwatches and fitness trackers are
becoming mainstream (Shin, 2017; Wu et al.,
2016) as users’ attempt to improve their
overall health through the tracking of their
physical activity (Attig & Franke, 2019;
Nelson et al., 2016). These devices produce
vast amounts of data that can enable socially
aware managers and organizations to develop
strategies to nudge wearable users to increase
and sustain physical activity levels. We
explore the effectiveness of rewards through
the lens of self-determination theory (Ryan &
Deci, 2000) to understand the combined
impacts of reward value, redemption
patterns, and fitness stage on physical
activity. We do so through a large health
gamification system that allows its users to
earn points for their physical activity that can
be redeemed for tangible extrinsic rewards.
From this analysis, we highlight new data
analytics and health gamification design
research avenues, as well as report actionable
insights for managers of gamification
systems.
Gamification systems are commonly
employed to change stakeholder behavior in
desirable ways (Aparicio, et al., 2019;
Koivisto & Hamari, 2019; Robson et al.,
2016). Robson et al. (2015) define
gamification as the application of game
design principles in non-game contexts.
Existing research shows that providing either
intangible or tangible rewards through a
health gamification system can improve
users’ motivation to engage in physical
activity (González et al., 2016; Pyky et al.,
2017; Royer et al., 2015). However, research
has yet to scrutinize the design of reward
portfolios in health gamification systems, and
specifically, the effectiveness of rewards of
different value and the impact of redemption
frequency patterns. Moreover, there is little
evidence of how rewards’ effectiveness is
impacted by heterogeneity in users’ fitness
levels. We examine how reward value and
redemption frequency patterns can motivate
users in distinct fitness stages to engage in
physical activity to optimize reward
portfolios.
Before developing hypotheses, we
examine the self-determination theory,
behavior reinforcement, and gamification
literatures to gain insights into how the
design of reward portfolios and health
gamification systems can more effectively
foster physical activity. Then, we report the
findings of an analysis of a health
gamification system that includes 3502 users’
observed physical activity over six months.
Next, we discuss the implications of these
results for researchers, managers, and
designers of health gamification systems. We
close by addressing several limitations of this
study and offering some concluding
thoughts.
LITERATURE REVIEW
Motivating Behavioral Changes
To design behavior change systems,
we must first understand users’ goals and
behavioral regulatory tendencies. Thus, we
adopt self-determination theory, which is an
influential conceptual lens that explains how
individual users achieve goals through
various types of motivation: intrinsic,
extrinsic, or amotivation (Deci & Ryan,
1985; Ryan & Deci, 2000; Ryan & Deci,
2017). Intrinsic motivation is experienced
when users engage in something for the
inherent satisfaction of activities, such as the
satisfaction derived from exploring or
learning. When activities are intrinsically
motivating, they likely satisfy needs related
to competence, autonomy, and relatedness.
Extrinsic motivation is experienced when
users seek outcomes that are detached from
the activities undertaken. These outcomes
can include money, peer recognition, gifts,
status symbols, other external rewards, or
even the avoidance of punishments. Finally,
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amotivation is experienced when users lack
the intention to act.
Self-determination theory is widely
applied to understand users’ motivations for
engaging in physical activity to improve
health (Ingeldew & Markland, 2008;
Buckworth et al., 2007). Studies adopting this
approach show that more autonomous
motivations (i.e., want to) compared to
nonautonomous motivations (i.e., have to)
have positive associations with exercise
behavior (Deci & Ryan, 2008; Teixeira et al.,
2012). Although physical activity is
motivated both intrinsically and extrinsically
(Ryan & Patrick, 2009), the dominance of
extrinsic versus intrinsic motivations is likely
to change as users progress through fitness
stages. In other words, extrinsic motivation is
likely critical in compelling users to first
adopt a fitness regime, while intrinsic
motivation is likely necessary to sustain long-
term commitment (Teixeira et al., 2012).
Extrinsic rewards that are desirable,
relevant, and administered appropriately can
be effective motivational instruments
(Charness & Gneezy, 2009; Ryan & Deci,
2000; Tong et al., 2013). Behaviors that are
positively reinforced tend to be repeated
more than nonreinforced behaviors
(Premack, 1959; Skinner, 1953), which can
ultimately lead to habit formation
(Rothschild & Gaidis, 1981). Behavioral
reinforcement occurs when users learn the
positive or negative associations between
reinforced behaviors and certain tangible or
intangible reinforcers (i.e., rewards or
punishments; Crutzen & Peters, 2018).
Although behavior change can be
reinforced successfully through extrinsic
rewards, rewards that are contingent on task
performance are known to undermine or
“crowd-out” intrinsic motivation (Acland &
Levy, 2015; Gneezy et al., 2011; Wu, 2019).
In addition, the “style” in which a reward is
given can also impact intrinsic motivation.
For example, rewards given in controlling
ways undermine intrinsic motivation (Ryan
et al., 1983), while rewards given in ways that
enhance autonomy, competence, or
relatedness may increase intrinsic motivation
(Lewis et al., 2016; Van Dyck et al., 2018).
Designing Health Gamification Systems
Gamification is an increasingly
popular technique that involves motivating
users to learn or change behaviors by taking
part in game-like experiences (Deterding et
al., 2011; Jin, 2013; Robson et al., 2015;
Dissanayake et al., 2018). Gamification
systems provide users with relevant and
desirable extrinsic rewards often in
autonomous ways, which can act as powerful
reinforcers and motivate behavior change
(Charness & Gneezy 2009; Lewis et al.,
2016). Gamifying an activity involves more
than only the use of rules and rewards, or
gamification mechanics. Gamification also
involves user dynamics (i.e., social and
interactive aspects) and the elicitation of
users’ emotions. Together, the successful
implementation of these building blocks –
mechanics, dynamics, and emotions – creates
a powerful information system that
stimulates users to change target behaviors
(Robson et al., 2015, 2016).
Health gamification systems motivate
users to change behaviors for the
improvement of health. These systems have
been designed to increase physical activity
(Chen & Pu, 2014; Chen et al., 2014;
Höchsmann et al., 2019), encourage healthy
eating habits (Jones et al., 2014), reduce
alcohol consumption (Boendermaker et al.,
2015), and enhance mental wellbeing
(Ludden et al., 2014). When designing
rewards to motivate healthy behaviors, there
are three key issues to consider: reward type,
redemption dynamics, and heterogeneity of
users (see Table 1 for selected studies on
health gamification and rewards).
Health gamification systems use
different types of rewards to reinforce
behavior changes (Lewis et al., 2016; Robson
3
et al., 2015). Types of rewards range from
simple accumulation of points (Nelson et al.,
2016), to digital badges and medals (Allam et
al., 2015; Hamari & Koivisto, 2014, 2015a,
2015b; Koivisto & Hamari, 2014), to digital
storyline artifacts (Höchsmann et al., 2019;
Kaczmarek et al., 2017; Pyky et al., 2017), to
monetary incentives (Boendermake et al.,
2015; Daryanto et al., 2010), and to tangibles
of assorted value (e.g., beverages, clothing,
or merchandise; Black et al., 2014; Mitchell
et al., 2018; Patel et al., 2018). Through
redeeming these rewards, users show not
only long-term positive effects on desired
health behaviors but also that these effects
potentially could persist even when rewards
are removed (Charness & Gneezy, 2009;
Acland & Levy, 2015). Additionally, user
dynamics can be fostered by including social
support features and messaging between
users that can augment the positive effect of
rewards (Allam et al., 2015; Chen & Pu,
2014; Chen et al., 2014). Emotions are
evoked in users when receiving these rewards
that are essential to sustaining users’
engagement with the health gamification
system (Robson et al., 2015; Nelson et al.,
2016).
Redemption provides a direct boost to
extrinsic motivation to sustain engagement
(Hu et al., 2010; Woolley & Fishbach, 2017).
Redemption dynamics influence observed
behaviors and can include users’ tenure in the
system (Black et al., 2014; Koivisto &
Hamari, 2014), goal achievement (Patel et
al., 2018; Mitchell et al., 2018), or frequency
patterns. As these dynamics have rarely been
the focus of gamification studies, the loyalty
literature provides additional insights into the
behavioral consequences of both saving and
redeeming for rewards, including
motivations for redeeming points (Smith &
Sparks, 2009a, b; Kivetz & Simonson, 2002;
Chan et al., 2016), saving points to obtain
higher value rewards (Stourm et al., 2015;
Chun & Hamilton, 2016), and letting points
expire (Dorotic et al., 2014). If point
collection occurs closer to the redemption,
users likely perceive greater system value
(Hu et al., 2010), experience reduced
opportunity costs (Woolley & Fishbach,
2017), and display more perseverance to
achieve long-term goals (Woolley &
Fishbach, 2017, 2018). Furthermore, reward
dynamics that result in highly effortful
redemptions influence both frequency
patterns and reward values sought by users
(Kivetz & Simonson, 2002; Smith & Sparks,
2009a, b).
The heterogeneity of users can pose a
troubling challenge to health gamification
system designers in crafting reward
portfolios that account for this diversity in
terms of demographics (e.g., age, gender,
location, education, employment, etc.; Black
et al., 2014; Boendermake et al., 2015;
Kaczmarek et al., 2017; Koivisto & Hamari,
2014; Mitchell et al., 2018; Pyky et al., 2017),
psychographics (e.g., personality traits,
psycholinguistic characteristics, regulatory
orientations, life satisfaction, etc.; Daryanto
et al., 2010; Pyky et al., 2017), health
indicators (e.g., weight, alcohol use, blood
pressure, etc.; Boendermake et al., 2015), and
observed behaviors (e.g., fitness level;
Mitchell et al., 2018; Patel et al., 2018).
While some of these characteristics are not
likely to change (e.g., demographics,
psychographics), other aspects of users
evolve through sustained engagement with
the health gamification system (e.g., health
indicators, observed behaviors). The
behavioral change successes of these systems
are linked to the ability to adapt reward types
and redemption dynamics to user
heterogeneity.
4
Table 1: Selected Studies on Health Gamification and Rewards
Study
Gamification intervention
Reward design issues investigated
Key insights
Reward
type
Redemption
dynamics
User
heterogeneity
Allam et al.
(2015)
Social support and web-
app for arthritis patients
Digital badges
and medals
-
-
Positive effect of social support and gamification
on fitness, empowerment, and reduced health visits
Black et al. (2014)
Youth center reward
program for HIV testing
Tangibles of
assorted value
System tenure
Demographics,
behaviors
Increase in HIV, pregnancy, and other health tests
from active participation in the reward program
Boendermake et
al. (2015)
Cognitive retraining of
automatic appetite process
Monetary
incentives
-
Demographics,
health
Social game elements increase aspects of user
experience and motivation to train
Daryanto et al.
(2010)
Health club fitness reward
program
Monetary
incentives
-
Psychographics
Regulatory fit impact on exercise intensity is
stronger over time than nonfit impact
Hamari &
Koivisto (2014)
Exercise support web app
Digital badges
and medals
-
-
Identified flow state (i.e., sustained engagement)
conditions and outcomes
Hamari &
Koivisto (2015a)
Exercise support web app
Digital badges
and medals
-
-
Extended social influence impact on individual
exercise behavior
Hamari &
Koivisto (2015b)
Exercise support web app
Digital badges
and medals
-
-
Relationship between utilitarian, hedonic, and
social benefits
Höchsmann et al.
(2019)
Fitness game app for Type
2 diabetes patients
Digital storyline
artifacts
-
-
Increase in intrinsic motivation to engage in
physical activity in the intervention group
Kaczmarek et al.
(2017)
App-based game
Digital storyline
artifacts
-
Demographics
Health motivation and social motivation for game
playing are related to health outcomes.
Koivisto &
Hamari (2014)
Exercise support web app
Digital badges
and medals
System tenure
Demographics,
behaviors
Perceived enjoyment and usefulness of the
gamification system decline with use tenure
Mitchell et al.
(2018)
Game app to earn loyalty
program points
Tangibles of
assorted value
Goal
achievement
Demographics,
behaviors
Increase in steps, especially for physically inactive
users
Nelson et al.
(2016)
Smart wristbands and
activity tracking
Accumulated
point balance
-
-
Activity trackers with gamification have strong
empowering capabilities
Patel et al. (2018)
Fitness app supporting
fitness incentive program
Tangibles of
assorted value
Goal
achievement
Behaviors
Effective behavioral change as a result of joining
the fitness incentive program
Pyky et al. (2017)
App service supporting
physical activity
Digital storyline
artifacts
-
Demographics,
psychographics
Significant improvement of life satisfaction,
especially for men with low life satisfaction
The current study
Points for exercise web
app
Tangibles of
assorted value
Frequency
patterns
Behaviors
Increase in fitness from redemption and combined
effect of reward value, redemption, and fitness
stage
5
As Table 1 shows, the current study adds to
the literature on health gamification systems
and focuses on all three reward design issues.
Specifically, in the next section, it
conceptualizes effects on reward
effectiveness from variations in reward value
and redemption frequency patterns across
three fitness stages.
Hypothesis Development
The design of health gamification
systems that promote increased or sustained
physical activity requires an understanding of
what we term the motivational utility of the
extrinsic rewards provided to users. A
reward’s motivational utility is based on the
perceived ability of that reward to influence
users’ levels of physical activity (Keeling et
al., 2013; Stourm et al., 2015; Smith &
Sparks, 2009). We argue that a reward’s
motivational utility is affected by reward
redemption, a user’s fitness stage, and the
value of that reward. First, reward
redemption provides extrinsic motivation to
engage in additional physical activity (Hu et
al., 2010; Woolley & Fishbach, 2017). When
users redeem for rewards, they receive
extrinsic motivational boosts that enhance
their ability to sustain higher levels of
physical activity (Woolley & Fishbach, 2017,
2018). For this reason, we expect any
redemption to increase exercise point
collection relative to users who do not
redeem. Formally:
H1: Users who make any reward
redemption will collect more
exercise points than users who do not
make a reward redemption.
A user’s fitness stage is likely to
affect the motivational utility of a reward.
Fitness stage can be calculated using average
daily fitness levels from observed physical
activity tracking data (as is common practice
in studies on exercise: e.g., Tappe et al.,
2013; O’Donovan et al., 2009; Watson &
Mock, 2004; Lee & Paffenbarger, 2000) and
is commonly categorized as novice,
intermediate, and advanced. “Novice” users
are characterized by a relatively low level of
daily physical activity and relatively lower
intrinsic motivation to engage in physical
activity. Compared to novices,
“intermediate” users have a higher level of
daily physical activity and related intrinsic
motivation to engage in it. At the extreme,
“advanced” users have the highest levels of
daily physical activity and demonstrate well-
developed fitness practices reflecting high
levels of intrinsic motivation. These
differences in intrinsic motivation are likely
to change how users react to reward
redemption. Specifically, because more
advanced users will likely have higher
intrinsic motivation than less advanced users
(Acland & Levy, 2015; Gneezy et al., 2011),
advanced users are more likely to exhibit the
“crowding out” of intrinsic motivation that
accompanies redemptions for extrinsic
rewards. However, this effect likely depends
on both the user’s number of redemptions and
the value of the reward.
Specifically, more redemptions are
likely to have a positive motivational effect
on novice and intermediate users’ physical
activity levels. A different effect is likely to
be observed with advanced users, although
we believe the effect is qualified by the value
of the reward that they redeem. As higher
value rewards require more exercise points
than lower value rewards, their redemption is
naturally spaced out over a longer period. We
argue that the delay will not affect novice and
intermediate users but will impact advanced
users. Novice and intermediate users, who
have relatively lower levels of intrinsic
motivation that can be crowded out, will
likely show boosts in exercise points
collected after redemptions, irrespective of
whether redemptions are for high- or low-
value rewards (Hu et al., 2010; Woolley &
Fishbach, 2017).
In contrast, advanced users are likely
to be more sensitive to the time delay
6
differences associated with low- versus high-
value rewards. Advanced users who redeem
for multiple low-value rewards are likely to
exhibit no change in exercise point collection
compared to users who redeem for a single
low-value reward because of crowding out of
intrinsic motivation (Acland & Levy, 2015;
Gneezy et al., 2011). However, the temporal
spacing afforded by high-value rewards will
enable advanced users to maintain those
rewards’ motivational utility even if
advanced users redeem them multiple times.
Hence, we propose a three-way interaction
between reward value (low or high), number
of redemptions (zero, one, or multiple), and
fitness stage (novice, intermediate, or
advanced), as follows:
H2a: Novice users who redeem for
multiple low-value rewards will
collect more points than novice users
who redeem for a single low-value
reward.
H2b: Novice users who redeem for
multiple high-value rewards will
collect more points than novice users
who redeem for a single high-value
reward.
H2c: Intermediate users who redeem for
multiple low-value rewards will
collect more points than intermediate
users who redeem for a single low-
value reward.
H2d: Intermediate users who redeem for
multiple high-value rewards will
collect more points than intermediate
users who redeem for a single high-
value reward.
H2e: Advanced users who redeem for
multiple low-value rewards will not
collect more points than advanced
users who redeem for a single low-
value reward.
H2f: Advanced users who redeem for
multiple high-value rewards will
collect more points than advanced
users who redeem for a single high-
value reward.
DATA, METHOD, AND RESULTS
Data and Method
To test the hypotheses developed
above, this paper analyzes a large-scale
health gamification system deployed at a
major university. The system is free of charge
to its users and aims to improve their activity
levels during daily life. Users earn points for
activity (e.g., walking steps, running,
swimming, skiing, bicycling, etc.) that is
recorded by users’ wearable fitness trackers
(e.g., Apple Watch, Fitbit, etc.) and
automatically entered into the system through
their trackers’ mobile applications. These
exercise points can be redeemed for lower
value rewards, such as hot beverages or
discounts at popular retailers, or can be saved
up for higher value rewards, such as
university-branded water bottles, hoodies, or
T-shirts.
The dataset reports activity and
redemption data for 3,502 users over a six-
month period, after removing inactive (i.e.,
those that earned no activity points during our
period) and outlier (i.e., ages below 18 and
above 65 years) user accounts. There were
900 new users within the dataset, but these
users were randomly distributed across the
variables of interest. The length of time in the
system was not a significant indicator of
exercise point collection. Table 2 details the
users’ age and gender statistics in our sample.
The study relied on four main
variables: exercise point collected, fitness
stage, number of redemptions, and reward
value. Exercise points collected records the
points that users earned for their activity
during the study period and was used as the
primary dependent variable (M = 1622.74,
S.D. = 2321.11). Users’ fitness stages were
calculated by dividing the “daily average
points collected” (M = 24.82, S.D. = 37.47)
into three categories to facilitate the
7
comparison of these stages and observed
exercise behaviors. Specifically, these stages
are classified as either “novice” (under mean,
n = 2071), “intermediate” (mean to +1
standard deviation, n= 1279), or “advanced”
(above +1 standard deviation, n = 152). We
acknowledge that this classification is
correlated with our dependent variable (i.e.,
exercise points collected), but the fitness
stage classification aids in the understanding
of different behaviors within the
classifications, especially comparing the least
and most active users, rather than comparing
physical activity levels across stages.
Furthermore, by defining fitness stage as we
have, we identify not only future research
avenues but also managerial implications for
the design and operation of reward-based
motivation systems that are sensitive to user
activity levels. Redemptions reports
redemption behavior and categorizes users as
having either zero redemptions (n = 2689),
one redemption (n = 360), or multiple
redemptions (n = 453). Reward value
accounts for differences in the utility derived
from the average redemption and categorizes
rewards as low value (i.e., 400 points and
under; e.g., coffee, coupons; n = 331) or high
value (i.e., over 400 points; e.g., branded
clothing, towels, water bottles; n = 482). The
next section reports exercise point collection
differences between the various categories of
fitness stage, number of redemptions, and
reward value.
Table 2: Sample Characteristics
Demographic variable
n
Sample percent
Gender
Male
1241
35.4
Female
1948
55.6
Undisclosed
313
8.9
Age
18-24
1970
56.3
24-34
916
26.1
34-49
240
6.7
50-65
57
1.6
Undisclosed
319
9.1
Results
To test H1, we first conducted a 3
(number of redemptions: zero, one, or
multiple) x 3 (fitness stage: new,
intermediate, and advanced) between-
subjects ANOVA with total exercise points
collected as the dependent variable. We
included fitness stage in our analysis of H1
because of the interaction effects that we
investigated later in H2a-f. Furthermore, we
conducted this analysis first because it uses
the wider sample of users who redeemed and
did not redeem, while our investigation of
H2a-f included only users who redeemed.
The analysis revealed several
significant main and interaction effects (see
Table 3). This included significant main
effects of redemptions (F(1, 3493) = 313.52,
p < .001) and fitness stage (F(2, 3493) =
296.22, p < .001). More importantly, a
significant two-way interaction between
fitness stage and number of redemptions
occurred (F (4, 3493) = 35.64, p < .001; see
Figure 1). Confirming H1, Bonferroni
adjusted post-hoc comparisons found that
across all fitness stages points collected were
significantly higher for users who made
multiple versus one redemption (p < 0.001),
as well as one versus zero redemptions (p <
0.001).
8
Table 3: Effect of Redemption on Exercise Points by Fitness Stage
Number of
Redemptions
Fitness Stage
Grand Mean
Novice
Intermediate
Advanced
Zero
553.75a,b
2359.07c,d
2601.16e,f
1140.70
One
1678.11a
3517.18c
3989.42e
2837.13
Multiple
2141.34b
4523.16d
8188.28f
4467.86
Grand Mean
712.23
3016.65
5127.16
1745.47
n
2071
1279
152
3502
Note: Superscripts indicate significant differences based on Bonferroni adjusted post-hoc pairwise comparisons with
a familywise error rate of 0.05.
A 2 (reward value: small vs. large) x
3 (number of redemptions: zero, one, or
multiple) x 3 (fitness stage: novice,
intermediate, and advanced) between-
subjects ANOVA with total exercise points
collected as the dependent variable revealed
significant main and interaction effects (see
Table 4). This included significant main
effects of reward value (F(1, 801) = 28.16, p
< .001), redemptions (F(1, 801) = 21.36, p <
.001), and fitness stage (F(2, 801) = 56.99, p
< .001). A significant two-way interaction
between the number of redemptions and
reward value occurred (F(1, 801) = 14.50, p
< .001). The other two-way interactions were
not significant. More importantly, a
significant three-way interaction occurred
between reward value, number of
redemptions, and fitness stage (F (2, 801) =
7.38, p = .001; see Figure 2).
For novice users, multiple versus
single redemptions had no effect on exercise
point collection for users who redeemed for
either low-value rewards (Mmultiple,low =
1749.12, Msingle,low = 1258.34, F(1, 801) =
1.13, p = .29) or high-value rewards
(Mmultiple,high = 2720.75, Msingle,high = 1991.61,
F(1, 801) = 2.29, p = .13). While we note that
effects are in the hypothesized directions, the
fact that the respective results are not
significant leads us to reject Hypotheses H2a
and H2b. Confirming H2c and H2d,
intermediate users who made multiple
redemptions collected significantly more
exercise points than those who redeemed
once, irrespective of the rewards being of low
value (Mmultiple,low = 3899.66, Msingle,low =
2774.91, F(1, 801) = 7.72, p < .001) or of
high value (Mmultiple,high = 5063.53, Msingle,high
= 3814.09, F(1, 801) = 17.51, p < .001).
Finally, for advanced users redeeming for
low-value rewards, multiple redemptions had
no effect on exercise point collection as
compared to single redemptions (Mmultiple,low
= 3434.93, Msingle,low = 4305.50, F(1, 801) =
.364, p = .56), confirming H2e. However,
advanced users making multiple redemptions
collected significantly more exercise points
than those who made a single redemption
when rewards were of high value (Mmultiple,high
= 9643.39, Msingle,high = 3905.13, F(1, 801) =
57.53, p < .001).
9
Figure 1: Effect of Redemption on Exercise Points Collected by Fitness Stage
Table 4: Effect of Reward Value and Redemption on Exercise Points by Fitness Stage
Reward Value
Number of
Redemptions
Fitness Stage
Grand
Mean
Novice
Intermediate
Advanced
Low
Single
1258.34
2774.91a
4305.50
2086.02
Multiple
1749.12
3899.66a
3434.93
3200.82
High
Single
1991.61
3814.09b
3905.13c
3217.39
Multiple
2720.75
5063.53b
9643.39c
5562.84
Grand Mean
1882.53
4100.36
7227.10
3745.77
n
247
483
83
813
Note: Superscripts indicate significant differences based on Bonferroni-adjusted post-hoc pairwise comparisons with
a familywise error rate of 0.05.
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
Novice Intermediate Advanced
Exercise(Points(Collected
Fitness(Stage
Zero:Redemptions
One:Redemption
Multiple
Redemptions
10
Figure 2: Three-Way Interaction Effect of Reward Value, Fitness Stage, and
Redemption Behavior on Exercise Points Collected
DISCUSSION AND IMPLICATIONS
Drawing on a dataset of the observed
physical activity levels of 3,502 users of a
health gamification system, we investigate
how reward value and redemption patterns
can motivate users in distinct stages of fitness
to engage in physical activity. A central goal
of this investigation is to better understand
how to optimize reward portfolio design to
maximize motivation to engage in physical
activity. With two exceptions in the case of
novices, our findings largely confirm our
hypotheses predicting the effectiveness of
different rewards and redemption behaviors
in motivating users of different fitness levels
to be physically active.
The results of our first analysis
confirm the motivational benefit of any
redemption within the system. Specifically,
users who redeem one or multiple times in the
system lead to significant increases in
exercise points collection as compared to
users who did not redeem. This finding
corroborates earlier studies that show that
rewards motivate increased physical activity
(González et al., 2016; Pyky et al., 2017;
Royer et al., 2015) and mirrors similar
findings in the loyalty program literature on
the positive compounding effect of
redeeming rewards multiple times (Smith &
Sparks, 2009a, b; Dorotic et al., 2014).
Our results also confirm that a
significant interaction between reward value
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
Novice Intermediate Advanced
Exercise(Points(Collected
Fitness(Stages
Low:Reward:Value
One:Redemption Multiple:Redemptions
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
Novice Intermediate Advanced
Exercise(Points(Collected
Fitness(Stages
High:Reward:Value
One:Redemption Multiple:Redemptions
11
(low versus high) and reward redemptions
(single versus multiple), and different fitness
stages (novice versus intermediate versus
advanced) exists. While the singular roles of
higher value rewards (Smith & Sparks,
2009a, b; Kivetz & Simonson, 2002),
multiple redemptions (Smith & Sparks,
2009a, b; Dorotic et al., 2014), and fitness
stage (Tappe et al., 2013; O’Donovan et al.,
2009; Watson & Mock, 2004; Lee &
Paffenbarger, 2000) are investigated in
several past studies, we find significant
patterns of observed physical activity in
different levels of these three variables. This
is a novel theoretical contribution to the
literature. Specifically, users are observed to
have increased physical activity levels if they
are classified as advanced and redeem
multiple times for higher value rewards.
While significant effects were not found for
novice users, intermediate users were found
to have increased physical activity levels
with multiple redemptions. These results
show that the marginal benefit in the physical
activity of multiple redemptions changes
based on fitness stage and reward value. Our
findings imply that researchers need to create
more complex analytical frames that account
for, and are sensitive to, the heterogeneity of
users in terms of both their behaviors and
other characteristics.
Research on the interplay between
external rewards and intrinsic motivation in
the context of health gamification (e.g.,
Lewis et al., 2016) reveals a complex
relationship between these factors. Findings
from the current research add to our
understanding of this complex relationship.
As the points and rewards studied in this
research are not contingent on completion of
any specific physical tasks but are rather
collected based on any physical activity, the
external rewards in this health gamification
system are not likely to diminish the feelings
of autonomy (c.f., Deci et al., 1999). Our
research also suggests that in the context of
health and fitness, users’ fitness stage must
also be taken into consideration, as points
redemptions provide a motivational boost to
more advanced users. We suggest that the
delay experienced in redeeming for high
value rewards serves to protect advanced
fitness users from the otherwise negative
effect that extrinsic rewards are known to
have on intrinsic motivation (Acland & Levy,
2015; Gneezy et al., 2011). These findings
imply that gamification researchers need to
incorporate this effect into their research
design or risk misinterpreting or even
demotivating more advanced users.
More broadly, this paper introduces
two important theoretical concepts. First, we
unpack the concept of rewards in
gamification by showing that lower and
higher value rewards can have differential
effects on user behavior. This is consistent
with research on gamification, which
suggests that rewards should be tailored to
different users in gamified experiences
(Robson et al., 2014), as well as research on
motivation that identifies contextual factors
as important in understanding what compels
users to behave in a certain way (Deci &
Ryan, 2012). Second, this research highlights
the issues surrounding user heterogeneity,
specifically fitness stages in this case, and its
impact on reward effectiveness. While other
research identifies motivational differences
based on users’ characteristics (e.g., Teixeira
et al., 2012), most studies treat users as
homogeneous, thus potentially not
discovering latent insights. By showing that
not all rewards are created equal and
identifying key factors in health gamification
systems that can predict physical activity, we
open the door to additional research that
deconstructs rewards in other dimensions and
contexts.
Managerial Implications
Our results provide evidence that
health gamification systems positively
12
impact the observed physical activity when
users redeem for tangible rewards, and in
doing so add to the body of evidence that
identifies gamification as an effective
approach to motivating behavior changes
related to health and wellbeing (Hamari &
Koivisto, 2015a; Johnson et al., 2016;
Koivisto & Hamari, 2019). These findings
have three main implications for designers
and managers of health gamification systems,
as well as for public policy officials tasked
with improving public health.
Inspire more redemption: We find
that users who redeem even once are
observably physically more active on average
and that this impact is generally compounded
when users redeem on multiple occasions.
Thus, health gamification systems need to be
designed in ways that boost users’
redemption behavior using a selection of
gamification mechanics, dynamics, or
emotions (Robson et al., 2015). The need for
careful development of mechanics that foster
redemption is underscored by research on
loyalty programs that reveals an inverse
relationship between the complexity of the
redemption process and the likelihood that
users will redeem their points (Smith &
Sparks, 2009a, b; Sharp & Sharp, 1997).
Given this, designers are advised to use
simple, easy-to-follow redemption rules
when constructing health gamification
systems. Gamification designers could be
directed to create affordances surrounding
rewards, even targeting specific low-hanging
fruit (i.e., low value) rewards, to inspire non-
redeeming users to redeem by showcasing
attractive, relevant, and desirable rewards.
They could also apply gamification dynamics
and emotions to redemption activities so that
the positive feelings of gaining rewards are
shared more widely in local social networks,
which might spur other users to redeem
themselves and potentially create redemption
norms.
Foster saving for larger rewards: Our
findings highlight the effectiveness of higher
value rewards in motivating physical activity,
especially for advanced users. That is, users
who save for higher value rewards are
generally more physically active than those
who redeem for lower value rewards. Health
gamification systems could encourage saving
behavior by applying gamification
mechanics (Robson et al., 2015), such as
periodic reports enabled by point collection
and redemption analytics that motivate users
to save their collected points for higher value
rewards, particularly in the case of advanced
users. Gamification designers could use
mechanics to encourage point saving
behavior by showing images of high-value
rewards as grayed out or locked and then
providing the point goals needed to redeem
for those rewards. This could be an effective
approach in helping redeeming users
persevere toward achieving the point balance
needed for high-value rewards.
While these first two managerial
implications seem to be in conflict, in fact,
they are complementary. For health
gamification systems to have an effective
impact on physical activity, users must first
redeem for rewards to receive an extrinsic
motivational boost. Then, to magnify
redemption effects and sustain the increasing
levels of physical activity, more advanced
users need to be inspired to save for higher
value rewards, which leads us to our final
recommendation.
Customize reward portfolios: While
we find that multiple redemptions and higher
value rewards are associated with the highest
level of observed physical activity, these
effects depend on users’ fitness stage.
Specifically, advanced users are associated
with significant boosts in physical activity
only if they redeem multiple times for high-
value rewards, whereas those who redeem for
low-value rewards multiple times are
associated with lower physical activity. In
13
contrast, intermediate users who redeem
multiple times for rewards are associated
with higher levels of physical activity than
those who redeemed only once, regardless of
whether they redeem for higher or lower
value rewards. Managers of health
gamification systems can use these findings
to present different portfolios of rewards to
users at different fitness stages to optimize
reward effectiveness. For example, designers
could build automatic notification systems
into health gamification systems that send
fitness stage-targeted messages to users who
promote rewards tailored to activity levels.
Reward portfolios can be customized to fit
different fitness stages and thus be more
effective at motivating users to increase or
sustain physical activity levels.
LIMITATIONS AND FUTURE
RESEARCH
As with any study, the current
research has several limitations, which we
discuss in the next several paragraphs. This
study explains physical activity patterns by
exploring the effect of tangible rewards’
value and redemption frequency patterns. As
a result, we use summary statistics that could
not account for changes in users’ physical
activity, redemption frequency patterns, or
fitness stages during the period of data
collection. Moreover, because this study
compares and contrasts different levels of
physical activity across different redemption
frequency patterns, high- versus low-value
rewards, and novice, intermediate, or
advanced fitness stages, we constructed
several categorical variables out of
continuous indicators to generate our results.
We, therefore, encourage researchers to
reinvestigate our findings using other
methods, including experimental or
interpretivist qualitative approaches.
Our research investigates a health
gamification system that is implemented at a
large university, and as a result, the users
consist of that university’s community
including students, faculty, and
administrative staff. While our analysis
reveals no effect of age on our key measures,
it is possible that the university setting (i.e.,
the large number of young, student users)
creates a context that limits the
generalizability of our findings. Future
research would benefit from exploring the
implementation of similar health
gamification systems in offices, hospitals, or
other important settings to determine whether
our findings depend on contextual factors not
tested or studied.
Our research identifies a variety of
exciting opportunities for future research that
can track users through a “fitness motivation
lifecycle” using more advanced statistical
techniques, such as difference-in-difference
analysis (e.g., Guo et al., 2018). Future
research could investigate the constructs of
interest using alternative theoretical lenses.
We rely on self-determination theory (Ryan
& Deci, 2017) as our main theoretical frame,
believing that this theoretical lens is the most
appropriate for investigating a health
gamification system that uses tangible
rewards to motivate physical activity. Yet,
other theoretical approaches, such as
regulatory modes (Higgins et al., 2003;
Mathman et al., 2017), may offer
complementary perspectives and provide
additional insight. Future researchers could
use field experiments to examine messaging
or other inventions based on alternative
regulatory or behavioral theories that could
not only provide further depth into physical
activity motivations but also improve health
outcomes for users.
The rewards in the studied health
gamification system are rewards to the users
(e.g., coffee, coupons, and clothing).
However, less is known about the
effectiveness of rewards that are shared
within a social group or team, or that go to
other individuals in need (e.g., redeem for
14
clothing for the homeless, cans for the food
bank, etc.). Future research could explore
these types of rewards as a means of not only
enhancing users’ physical activity levels but
also providing another social good in the
users’ local context. Furthermore, our study
does not investigate the user experience or
the design of the user interface, including the
presentation and promotion of rewards,
which present valuable avenues for future
researchers to explore.
CONCLUSIONS
This article investigates the exercise
point collection and reward redemption
behavior of users in a large health
gamification system. Specifically, we
investigate the combined impact of reward
value, reward redemption patterns, and
fitness stage to change the effectiveness of
tangible rewards in motivating increased
physical activity. Our findings offer several
contributions to research and practice. First,
we introduce the notion of users’ fitness stage
to the literature, showing that users can be
classified into outcome (i.e., physical
activity) levels as a means of comparison and
evaluation of gamification mechanics (i.e.,
different types of rewards). Second, we show
that tangible extrinsic rewards’ effectiveness
varies in conjunction with fitness stages,
reward value, and redemption patterns. We
identify boundary conditions of the
effectiveness of these rewards in a real-world
setting with users recording their physical
activity passively using connected wearable
fitness devices. Third, we uncover the
complex relationships between a number of
key variables (fitness stage, redemption
behavior, and reward value) in understanding
how health gamification systems can be used
to inspire and reinforce healthy habits and
behaviors. Fourth, at a practical level, we find
that novice users (i.e., those with a relatively
low level of physical activity) should be
encouraged to redeem regardless of the
rewards’ value or the frequency of
redemption. However, advanced users (i.e.,
those with a relatively high level of physical
activity) should be encouraged to save their
points to redeem for higher value rewards to
space out extrinsic motivational boosts that
may crowd out intrinsic motivation.
Our findings can be directly applied
by public policy officials or designers of
health gamification systems to nudge or
motivate users to improve their physical
fitness. As the physical health of global
populations deteriorates (Karnani et al.,
2016), research such as this can be used to
guide the implementations of gamified
interventions to inspire positive changes in
health and wellbeing for individuals around
the world. Ultimately, our results offer
insights into designing and developing more
successful and cost-efficient health
gamification systems to increase health
through sustained physical activity.
15
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