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Little rewards, big changes: Using exercise analytics to motivate sustainable changes in physical activity

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Even using simple techniques like taking the stairs, many individuals struggle to maintain the motivation to be physically active. Health gamification systems can aid this goal by providing points earned through exercise that are redeemable for tangible extrinsic rewards. Using self-determination theory, we conduct research on one such system and investigate rewards’ effectiveness to promote exercise considering reward value, redemption frequency patterns, and fitness levels. 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.
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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
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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.
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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
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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
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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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... Huang and Ren (2010) evaluated how HA functions, such as instruction provision, goal attainment, self-monitoring and self-regulation affect perceived usefulness.. Studies investigating the behavioral outcomes of MHA presented the positive result of their usage in terms of increased physical activity (Barkley et al., 2020;Glynn et al., 2014), better body shape (Fjeldsoe et al., 2016), more effective exercising routine (Walsh et al., 2016) and patients' wellbeing (Li et al., 2020). Research also indicates that the rewards feature of HA significantly increase users' activity levels (Plangger et al., 2019) As we can see from the literature review above, research has analysed medical MHA connecting patients with their doctors, such as consulting, clinical and public health, chronic disease management (Akter et al., 2013;Sezgin et al., 2017;Storni, 2014;Zhang et al., 2014). ...
... Hence, previous studies on MHA mostly focused on general health and wellness apps (Balapour et al., 2019;Chiu et al., 2020;Cho et al., 2014;Cho, 2016;Dwivedi et al., 2016;Gowin et al., 2015). Scholars call for additional research on exploring the determinants of FA adoption intention and use for exercising (Plangger et al., 2019). Furthermore, the majority of studies have focused on Asian users and scholars call for research on eHelath other cultures (Gao et al., 2015). ...
... hence, a growing number of them download fitness apps, which are among the most downloaded ones (Beldad & Hegner, 2018;Huang & Ren, 2020). This study has investigated the determinants of intention to use FAs -a category of HAs -responding to a call for further research on the determinants of fitness app's adoption intention and use (Plangger et al., 2019). ...
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... Currently, various types of m-health devices are playing an increasingly vital role in managing people's daily health (Kay et al., 2011;Li and Chang, 2021;Plangger et al., 2019). A recent report shows that there are totally 325,000 health-related apps in major app stores . ...
... Moreover, only a limited number of studies have investigated some types of incentive factors in MFAs, but their effects are usually separately explored (Edney et al., 2020;Song et al., 2019;Wang et al., 2017). Actually, users' PA is often influenced simultaneously by these incentive factors during their use of MFAs (Edney et al., 2020;Plangger et al., 2019), and the sizes of their effects may vary (Stragier et al., 2016;Tu et al., 2019). Thus, in this paper, we include different incentive factors in one study, and empirically identify their effects respectively. ...
... Currently, most MFAs usually adopt various BCTs to design incentive factors for improving users' health behaviors and health conditions (Mao et al., 2020;Plangger et al., 2019;Wang et al., 2020). BCT refers to a systematic procedure, which is an active part of interventions aimed at changing individual behavior (Mao et al., 2020). ...
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... In fact, the social influence of others and the desire to conform to social norms have been known to encourage participation in physical activity (Coleman et al., 2008;Priebe & Spink, 2012). In addition, recent studies have shown that the intervention of an external reward through gamification can positively influence the level of physical activity (Ahn et al., 2019;Plangger et al., 2019). Thus, in the COVID-19 context, it may be effective to have interventions that focus on psychosocial benefits (e.g., meeting the expectations of others, health promotion, etc.) accompanying physical activity participation. ...
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... Exercising for non-survival-related purposes may force the system to identify other potential rewards as a means by which to justify the high levels of energy expenditure (Peters et al., 2004). During fatiguing and exhaustive exercise tasks, the human brain will engage in a continuous evaluation process of such potential rewards (Plangger et al., 2019). The presence of fatigue-related symptoms appears to influence this evaluation process and, as a consequence, the neural control of the musculature (see McMorris et al., 2018). ...
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... Applications that allow individuals to track their health choices, categorize them into investing or divesting health behaviors, and quantify them based on scientific evidence would also help people to better visualize their available health reserves. Principles of gamification (e.g., Plangger et al., 2019;Robson et al., 2015) could then be used to help motivate individuals based on the state of their health reservoir and take actions to balance their health behaviors. ...
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... Recent studies have confirmed that systems with immediate virtual rewards positively affect continuous data collection [98], [99]. However, we evaluated the validity of a reward distribution system that gives rewards to participants in the future, rather than immediately. ...
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Customer loyalty is central to both the study and practice of marketing. Some marketing managers have designed programs to create, maintain, and build customer loyalty in a game-like nature, which can be described as gamification. Built upon the conceptual foundations of customer loyalty, this chapter discusses how gamification, and its underlying principles, can be applied to the customer loyalty context. We outline guidelines for designing gamification strategies to enhance customer loyalty outcomes by looking closely at some real-world cases where marketers have had mixed success applying gamification to their loyalty programs. The chapter closes by suggesting potentially interesting directions for future research.
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Purpose The purpose of this paper is to design and incorporate gamebased pedagogy grounded in self-determination theory (SDT) for a higher education course in a business school program. Design/methodology/approach The study evaluates the learning outcomes of students from higher education in gamified and non-gamified contexts. The study was conducted over a period of two years in a management institute in the National Capital Region of India. The participants of the gamification study were the students in the age group 22–25 years with 0–3 years of work experience. Findings In general, findings of this study suggest that the group that underwent the course with the game-based pedagogy had better learning outcomes. In the game design, this study found that the addition of “meaningfulness” to the game elements improved the engagement with the gamification process for the learners. Consequently, this study found that “meaningfulness” played an important role in engaging the students, thereby, leading to improved learning outcomes. Research limitations/implications The study suggests that when the game design is rooted in theory, it is likely that the desired results from gamification will be achieved. The evaluation of the courses was done by the researcher themselves. An external evaluation is required to confirm the results of the gamification elements used in the course as enumerated in the paper. Practical implications All the game elements used in the game design were underpinned by SDT which suggests that if the three innate needs of competence, relatedness and autonomy in individuals are met, the desired learning outcomes is likely to follow. Social implications Due to the use of an online environment for the conduct of the evaluations, the study permitted the students to receive and have access to constant feedback enabling them to improve and enhance their learning. Originality/value Existing research shows inconsistent results with the use of gamification in the learning process. This study suggests that by grounding the gamification design in learning theory is more likely to achieve favourable results. In addition, if the game elements provide meaningfulness to the participants, the gamification process is more likely to succeed.
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Virtual teaching modalities urgently implemented during the Covid-19 pandemic require strategies to motivate students to participate actively in higher education. Our study found that gamification using a reward-based system is a strategy that can improve the educational experience under exceptional circumstances. This article reports the results of two gamified undergraduate courses (Calculus and Development of Transversal Competencies) designed with a reward system. The results derived from analyses of online surveys, the final grades, and their correlations revealed that gamification helped motivate students to participate actively and improved their academic performance, in a setting where the mode of instruction was remote, synchronous, and online. From the results we conclude that gamification favours the relationship between attention, participation, and performance, while promoting the humanisation of virtual environments created during academic confinement. Implications for practice or policy: Gamification using a reward-based system promoted active class participation and improved student performance after the transition from face-to-face to virtual instruction required as a result of the global pandemic. Systemic recognition in a reward-based system improved the participants' emotional states, reducing their anxiety and the feeling of isolation caused by the pandemic, and leading to student engagement with . Gamification works as an accompaniment for students to help the increasement of teacher-student and student-student interactions.
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This research explores online reviewers’ motivations and how different motives interact with one another. Through in-depth interviews with Amazon reviewers and a six-month observation of the reviewers’ forums, the study found that extrinsic motivation may crowd out or crowd in intrinsic motivation in different scenarios. If a reviewer becomes driven mostly by status recognition and reciprocal obligation, their initial intrinsic enjoyment may suffer a crowding-out effect. The reviewer’s motivation mix can also be in a state of flux as they rise through the ranks. This research sheds new light on motivation crowding and offers implications for online review management.
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Background Compared to traditional persuasive technology and health games, gamification is posited to offer several advantages for motivating behaviour change for health and well-being, and increasingly used. Yet little is known about its effectiveness. Aims We aimed to assess the amount and quality of empirical support for the advantages and effectiveness of gamification applied to health and well-being. Methods We identified seven potential advantages of gamification from existing research and conducted a systematic literature review of empirical studies on gamification for health and well-being, assessing quality of evidence, effect type, and application domain. Results We identified 19 papers that report empirical evidence on the effect of gamification on health and well-being. 59% reported positive, 41% mixed effects, with mostly moderate or lower quality of evidence provided. Results were clear for health-related behaviours, but mixed for cognitive outcomes. Conclusions The current state of evidence supports that gamification can have a positive impact in health and wellbeing, particularly for health behaviours. However several studies report mixed or neutral effect. Findings need to be interpreted with caution due to the relatively small number of studies and methodological limitations of many studies (e.g., a lack of comparison of gamified interventions to non-gamified versions of the intervention).
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Today, our reality and lives are increasingly game-like, not only because games have become a pervasive part of our lives, but also because activities, systems and services are increasingly gamified. Gamification refers to designing information systems to afford similar experiences and motivations as games do, and consequently, attempting to affect user behavior. In recent years, popularity of gamification has skyrocketed and manifested in growing numbers of gamified applications, as well as a rapidly increasing amount of research. However, this vein of research has mainly advanced without an agenda, theoretical guidance or a clear picture of the field. To make the picture more coherent, we provide a comprehensive review of the gamification research (N = 819 studies) and analyze the research models and results in empirical studies on gamification. While the results in general lean towards positive findings about the effectiveness of gamification, the amount of mixed results is remarkable. Furthermore, education, health and crowdsourcing as well as points, badges and leaderboards persist as the most common contexts and ways of implementing gamification. Concurrently, gamification research still lacks coherence in research models, and a consistency in the variables and theoretical foundations. As a final contribution of the review, we provide a comprehensive discussion, consisting of 15 future research trajectories, on future agenda for the growing vein of literature on gamification and gameful systems within the information system science field.
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Background: The Carrot Rewards application ('app') was developed as part of an innovative public-private partnership to reward Canadians with loyalty points, exchangeable for retail goods, travel rewards and groceries, for engaging in healthy behaviors such as walking. Objective: The purpose of this study was to examine whether a multi-component intervention including goal setting, graded tasks, biofeedback and very small incentives tied to daily step goal achievement (assessed by built-in smartphone accelerometers) could increase physical activity in two Canadian provinces, British Columbia (BC) and Newfoundland and Labrador (NL). Methods: A 12-week quasi-experimental (single group pre/post) study was conducted. Among eligible participants (n=78,882), 44.39% (n=35,014) enrolled in the Carrot Rewards "Steps" walking program during the recruitment period (June 13th - July 10th 2016). During the two-week baseline (or 'run-in') period, mean steps/day were calculated for participants. Thereafter, participants earned incentives in the form of loyalty points (worth $0.04 CAD) every day they reached their personalized daily step goal (i.e. baseline mean + 1,000 steps = level of first daily step goal). Participants earned additional points (worth $0.40 CAD) for meeting their step goal 10+ non-consecutive times in a 14-day period (called a "Step Up Challenge"). Participants could earn up to $5.00 CAD during the 12-week evaluation period. Upon meeting the 10-day contingency, participants could increase their daily goal by 500 steps, with the objective of gradually increasing the number of steps participants take each day by 3,000. Only participants with five or more valid days (days with step counts between 1,000 and 40,000) during the baseline period were included in the analysis (n=32,229).The primary study outcome was mean steps/day (by week), and was analyzed using linear mixed-effects models. Results: Of the 32,229 participants with valid baseline data, the mean age was 33.7 ± 11.6 years and 66.11% (21,306/32,229) were female. The mean daily step count at baseline was 6,511.22. Just over half of users (50.69%, 16,336/32,229) were categorized as "physically inactive", accumulating less than 5,000 daily steps at baseline. Results from the mixed-effects models revealed statistically significant increases in mean daily step counts when comparing baseline with each Study Week (P<.0001). Compared to baseline, participants walked 115.70 more steps (95% CI: 74.59,156.81; P<.0001) at Study Week 12. Users classified as "high engagers" (app engagement above the sample median; 48.13%, 15,511/32,229) in BC and NL walked 738.70 (95% CI: 673.81, 803.54; P<.0001) and 346.00 (95% CI: 239.26, 452.74; P<.0001) more steps, respectively. Among physically inactive, high engagers (21.08%; 7,022/32,229) an average increase of 1,224.66 steps per day (95% CI: 1160.69, 1288.63; P<.0001) was observed. Effect sizes were modest. Conclusions: Providing very small but immediate rewards for personalized daily step goal achievement as part of a multi-component intervention increased daily step counts on a population-scale, especially for physically inactive individuals and individuals who engaged more with the walking program. Positive effects in both BC and NL provide evidence of replicability.
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Can immediate (vs. delayed) rewards increase intrinsic motivation? Prior research compared the presence versus absence of rewards. By contrast, this research compared immediate versus delayed rewards, predicting that more immediate rewards increase intrinsic motivation by creating a perceptual fusion between the activity and its goal (i.e., the reward). In support of the hypothesis, framing a reward from watching a news program as more immediate (vs. delayed) increased intrinsic motivation to watch the program (Study 1), and receiving more immediate bonus (vs. delayed, Study 2; and vs. delayed and no bonus, Study 3) increased intrinsic motivation in an experimental task. The effect of reward timing was mediated by the strength of the association between an activity and a reward, and was specific to intrinsic (vs. extrinsic) motivation-immediacy influenced the positive experience of an activity, but not perceived outcome importance (Study 4). In addition, the effect of the timing of rewards was independent of the effect of the magnitude of the rewards (Study 5).
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BACKGROUND: Regular physical activity (PA) is an essential component of a successful type 2 diabetes treatment. However, despite the manifest evidence for the numerous health benefits of regular PA, most patients with type 2 diabetes remain inactive, often due to low motivation and lack of PA enjoyment. A recent and promising approach to help overcome these PA barriers and motivate inactive individuals to change their PA behavior is PA-promoting smartphone games. While short-term results of these games are encouraging, the long-term success in effectively changing PA behavior has to date not been confirmed. It is possible that an insufficient incorporation of motivational elements or flaws in gameplay and storyline in these games affect the long-term motivation to play and thereby prevent sustained changes in PA behavior. We aimed to address these design challenges by developing a PA-promoting smartphone game that incorporates established behavior change techniques and specifically targets inactive type 2 diabetes patients. OBJECTIVE: To investigate if a self-developed, behavior change technique-based smartphone game designed by an interdisciplinary team is able to motivate inactive individuals with type 2 diabetes for regular use and thereby increase their intrinsic PA motivation. METHODS: Thirty-six inactive, overweight type 2 diabetes patients (45-70 years of age) were randomly assigned to either the intervention group or the control group (one-time lifestyle counseling). Participants were instructed to play the smartphone game or to implement the recommendations from the lifestyle counseling autonomously during the 24-week intervention period. Intrinsic PA motivation was assessed with an abridged 12-item version of the Intrinsic Motivation Inventory (IMI) before and after the intervention. In addition, adherence to the game-proposed PA recommendations during the intervention period was assessed in the intervention group via the phone-recorded game usage data. RESULTS: Intrinsic PA motivation (IMI total score) increased significantly in the intervention group (+6.4 (SD 4.2; P<.001) points) while it decreased by 1.9 (SD 16.5; P=.623) points in the control group. The adjusted difference between both groups was 8.1 (95% CI 0.9, 15.4; P=.029) points. The subscales “interest/enjoyment” (+2.0 (SD 1.9) points, P<.001) and “perceived competence” (+2.4 (SD 2.4) points, P<.001) likewise increased significantly in the intervention group while they did not change significantly in the control group. The usage data revealed that participants in the intervention group used the game for an average of 131.1 (SD 48.7) minutes of in-game walking and for an average of 15.3 (SD 24.6) minutes of strength training per week. We found a significant positive association between total in-game training (min) and change in IMI total score (beta=0.0028; 95% CI 0.0007-0.0049; P=.01). CONCLUSIONS: In inactive individuals with type 2 diabetes, a novel smartphone game incorporating established motivational elements and personalized PA recommendations elicits significant increases in intrinsic PA motivation that are accompanied by de-facto improvements in PA adherence over 24 weeks.
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Behavioral learning theory has been generally overlooked in the development of marketing thought. The central concept states that behavior that is positively reinforced is more likely to recur than nonreinforced behavior. This runs parallel to the marketing concept and may be a sufficient model for dealing with most low involvement purchase situations. Its greatest value may be in the development of promotional strategies. This paper extends some of the ideas presented in an earlier paper in this journal.
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Feasible and effective interventions to promote physical activity among vocational school‐aged adolescents are strongly needed. Text messaging and Facebook are feasible and acceptable delivery modes for PA interventions among youth. However, little is known about the opinion of vocational school‐aged adolescents regarding behavioural change techniques that can be applied through Facebook or text messaging. Therefore, our aim was to gain insight into the opinions of vocational school‐aged adolescents towards the use of different behaviour change techniques and towards Facebook/text messaging as a delivery mode for PA interventions. Six focus groups were conducted with 41 adolescents from the first grade (12–14 years) of secondary vocational schools in Flanders (Belgium). In total 41 adolescents participated and completed a questionnaire about their text messaging and Facebook use prior to group discussions. Focus group discussions were audio‐recorded and analysed using a thematic analysis method in Nvivo. Participants thought that different behaviour change techniques (e.g., providing feedback, goal setting, self‐monitoring, social comparison) could be integrated in a PA intervention using text messaging and Facebook and were enthusiastic about participating in such an intervention. They indicated that text messages are an easy way to receive information about PA, and that a group page on Facebook is ideal to share information with others. Participants deemed it very important that the group page on Facebook would only include peers with whom they also share an offline connection. Furthermore, adolescents stressed the importance of having autonomy (e.g., to determine their personal activity goals, to self‐monitor their behaviour) and of being active together with friends. This qualitative study revealed that the use of Facebook and text messaging is promising as a delivery method for PA interventions among vocational school‐aged adolescents. The adolescents were keen to participate in an intervention that integrates behaviour change techniques using text messaging or Facebook.
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Massive open online courses (MOOCs), contribute significantly to individual empowerment because they can help people learn about a wide range of topics. To realize the full potential of MOOCs, we need to understand their factors of success, here defined as the use, user satisfaction, along the individual and organizational performance resulting from the user involvement. We propose a theoretical framework to identify the determinants of successful MOOCs, and empirically measure these factors in a real MOOC context. We put forward the role of gamification and suggest that, together with information system (IS) theory, gamification proved to play a crucial role in the success of MOOCs.