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Potential and effects of personalizing gameful fitness applications using behavior change intentions and Hexad user types

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Personalizing gameful applications is essential to account for interpersonal differences in the perception of gameful design elements. Considering that an increasing number of people lead sedentary lifestyles, using personalized gameful applications to encourage physical activity is a particularly relevant domain. In this article, we investigate behavior change intentions and Hexad user types as factors to personalize gameful fitness applications. We first explored the potential of these two factors by analyzing differences in the perceived persuasiveness of gameful design elements using a storyboards-based online study (N=178). Our results show several significant effects regarding both factors and thus support the usefulness of them in explaining perceptual differences. Based on these findings, we implemented “Endless Universe,” a personalized gameful application encouraging physical activity on a treadmill. We used the system in a laboratory study (N=20) to study actual effects of personalization on the users’ performance, enjoyment and affective experiences. While we did not find effects on the immediate performance of users, positive effects on user experience-related measures were found. The results of this study support the relevance of behavior change intentions and Hexad user types for personalizing gameful fitness systems further.
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Vol.:(0123456789)
User Modeling and User-Adapted Interaction (2021) 31:675–712
https://doi.org/10.1007/s11257-021-09288-6
1 3
Potential andeffects ofpersonalizing gameful fitness
applications using behavior change intentions andHexad
user types
MaximilianAltmeyer1,2 · PascalLessel1,2· SubhashiniJantwal2·
LindaMuller2· FlorianDaiber1,2· AntonioKrüger1,2
Received: 16 June 2020 / Accepted in revised form: 6 January 2021 / Published online: 1 February 2021
© The Author(s) 2021
Abstract
Personalizing gameful applications is essential to account for interpersonal differ-
ences in the perception of gameful design elements. Considering that an increasing
number of people lead sedentary lifestyles, using personalized gameful applications
to encourage physical activity is a particularly relevant domain. In this article, we
investigate behavior change intentions and Hexad user types as factors to personal-
ize gameful fitness applications. We first explored the potential of these two fac-
tors by analyzing differences in the perceived persuasiveness of gameful design ele-
ments using a storyboards-based online study (
N=178
). Our results show several
significant effects regarding both factors and thus support the usefulness of them in
explaining perceptual differences. Based on these findings, we implemented “End-
less Universe,” a personalized gameful application encouraging physical activity
on a treadmill. We used the system in a laboratory study (
N=20
) to study actual
effects of personalization on the users’ performance, enjoyment and affective experi-
ences. While we did not find effects on the immediate performance of users, positive
effects on user experience-related measures were found. The results of this study
support the relevance of behavior change intentions and Hexad user types for per-
sonalizing gameful fitness systems further.
Keywords Gamification· Persuasive technology· Behavior change·
Personalization· Hexad user types· Stage of change· TTM· Running· Sports· Step
count· Physical activity
* Maximilian Altmeyer
maximilian.altmeyer@dfki.de
1 German Research Center forArtificial Intelligence (DFKI), Saarbrücken, Germany
2 Saarland Informatics Campus, Saarbrücken, Germany
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1 Introduction
Our daily life is more and more susceptible to physical inactivity caused by an
increasing number of people leading sedentary lifestyles(Rajaratnam and Arendt
2001). This lack of physical activity leads to numerous health issues, including
cardiovascular diseases, obesity and many other chronic illnesses(Bravata etal.
2007). Therefore, motivating people to lead an active lifestyle is important for
public and private health and has been targeted by several interventions in the
past (Aldenaini et al. 2020). Often, such interventions employ gameful design
elements by using gamification, the use of game design elements in non-game
contexts(Deterding etal. 2011). Mostly, a “one-size-fits-all” approach (i.e., using
a static set of gamification elements) is used(Hamari and Sarsa 2014; Jia etal.
2016; Seaborn and Fels 2015). However, previous research has shown that there
are interpersonal differences in the perception of gameful design elements(Ton-
dello et al. 2016), which poses a threat to such static gamification approaches.
Consequently, research has been carried out to investigate which factors moderate
the perception of gameful design elements or persuasive strategies. For instance,
demographic factors such as age(Birk et al. 2017), gender(Orji etal. 2015) or
personality traits(Jia etal. 2016) have been shown to play a role in this context.
However, none of these factors is particularly suitable or has been specifically
developed personalizing gameful systems and maximizing their motivational
impact. To bridge this gap, Marczewski(Marczewski 2015) proposed the Hexad
user type model—a model that has been developed to explain user preferences
in gameful systems(Orji etal. 2018; Tondello etal. 2017). It consists of six user
types, which differ in the degree to which they are driven by autonomy, related-
ness and competence, which are core aspects of Self-Determination Theory(Ryan
and Deci 2000). Although the Hexad model has been subsequently used success-
fully in various domains including education(Mora et al. 2018), energy conser-
vation(Kotsopoulos etal. 2018) or alcohol consumption(Orji et al. 2018), the
applicability in the fitness context has not been shown, as far as we know.
Moreover, most aforementioned factors (including the Hexad model) are static,
i.e., they usually do not change over time. Considering that research has demon-
strated that goal completion and motivation is affected by task-related self-effi-
cacy and an individual’s belief that the goal can be achieved(Cham etal. 2019;
Locke and Latham 2002), considering dynamic factors to personalize gameful
systems encouraging physical activity is important.
Ultimately, previous studies investigating factors for personalization were
survey-based, which means that participants did not have the chance to interact
with applications but instead rated their perception by imagining how the game-
ful design elements would look like in a real system. This survey methodology is
appropriate to recruit a large amount of participants and investigate the potential
of such factors for personalization(Orji etal. 2018). However, it is quintessential
to also investigate the actual effect of these factors.
We contribute to the aforementioned research gaps. First, we show the appli-
cability of the Hexad model in the fitness context and replicate previously found
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Potential andeffects ofpersonalizing gameful fitness…
correlations between gameful design elements and Hexad user types in other
domains, supporting the usefulness of the Hexad model. Second, we demonstrate
that behavior change intentions have an impact on the perception of gameful
design elements. This shows the importance of dynamic factors in the context of
tailored gameful design for behavior change. While we use a storyboards-based
approach to investigate the potential of Hexad user types and behavior change
intentions (
N=178
), we contribute to the third aspect by applying our findings
in the context of “Endless Universe,” a gameful application encouraging physi-
cal activity on a treadmill. In a laboratory experiment (
N=20
), we show that
in general, Endless Universe significantly increased the performance of users,
supporting its validity. While we found no immediate effects on performance
improvement when personalizing Endless Universe based on Hexad user types or
behavior change intentions, improvements on user experience-related measures
were found. Our results show that adapting gameful applications to the behavio-
ral intention of users leads to stronger affective experiences. Also, we show that
tailoring for Hexad user types has a positive effect on users’ motivation to run,
whereas counter-tailoring has detrimental effects. Summing up the findings from
both user studies, we demonstrate that Hexad user types and behavior change
intentions are important factors for personalizing gameful applications encourag-
ing physical activity.
This article is structured as follows: In Sect. 2, we introduce the Hexad user
types model and the concept of behavior change intentions, utilizing the “stages
of change” theory of the transtheoretical model by Prochaska and Velicer (1997).
Next, we present related work and frame our contribution in Sect.3. Sections 4.1
and 5 explain the storyboards-based approach we followed to investigate interper-
sonal differences in the perception of gameful design elements in the course of an
online study. Sections4.1 and 5 are based on our previously published work(Alt-
meyer etal. 2019). In Sect. 6, we describe the design of a personalized gameful
application (Fig.1). This application is used to investigate the effects of personaliza-
tion in Sect.7. Both the storyboards-based online study and the laboratory study are
discussed in Sect.8. Finally, we summarize our findings and outline directions for
future work in Sect.9.
2 Background
Before presenting relevant literature in the context of encouraging physical activity
and personalization of gameful systems, we explain and define the two factors that
we are considering in this article.
2.1 Hexad user type model
The Hexad user types model (Marczewski 2015) was specifically developed to
understand and explain user preferences within gameful systems(Orji etal. 2018;
Tondello etal. 2016). It consists of six user types that differ in the degree to which
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they are driven by their needs for autonomy, relatedness and competence as defined
by the Self-Determination Theory (SDT) (Ryan and Deci 2000). In HCI research,
SDT is widely used to explain motivation and behavior when interacting with tech-
nology(Tyack and Mekler 2020). According to SDT, the motivation to engage in
a task is located on a spectrum ranging from extrinsic (the task is pursued because
of factors outside of the task) to intrinsic (the task is enjoyable on its own) motiva-
tion. SDT further posits that a task is more enjoyable (and thus more intrinsically
motivating), when three basic psychological human needs are fulfilled: competence,
the feeling of acting skillfully and having an effect; autonomy, a feeling of being in
control and that actions are self-endorsed; and relatedness, a sense of belonging and
a feeling of involvement with others. Based on the type of motivation and on needs
satisfaction, the Hexad model establishes the following user types:
Philanthropists (“PH”) Are socially minded, like to bear responsibility and share
knowledge with other users. They are driven by purpose.
Socialisers (“SO”) Are also socially minded but are more driven by interact-
ing with other users. Therefore, relatedness is their main
motivation.
Fig. 1 Gamification user types Hexad, taken fromMarczewski (2015). Copyright Andrzej Marczewski
(CC BY-NC-ND)
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Potential andeffects ofpersonalizing gameful fitness…
Free Spirits (“FS”) Are satisfied when acting without external control, with
autonomy being most important for them.
Achievers (“AC”) Are driven by overcoming obstacles and mastering diffi-
cult challenges. Competence is most important for them.
Players (“PL”) Are out for their own benefits and will do their best to
earn rewards. Hence, extrinsic rewards are most impor-
tant for them.
Disruptors (“DI”) Like to test a system’s boundaries and are driven by trig-
gering change, either positive or negative.
Tondello etal. (2016) developed a questionnaire to assess Hexad user types, and
more recently, the authors (Tondello et al. 2018) made slight adjustments to it and
showed its reliability and validity. It should be noted that users do not have one specific
user type but that the Hexad model is a traits model, which means that users are char-
acterized by their distribution of scores across the six user types(Tondello etal. 2016).
2.2 Behavior change intentions
To formalize the intention to change behavior of users, we utilized the “stages of
change” concept of the Transtheoretical Model by Prochaska and Velicer (1997).
It describes the process of intentional behavior change, stating that behavior change
involves progress through five so-called stages of change. These stages are character-
ized in the following:
Precontemplation The subject has no intention to take action in the foreseeable
future (usually 6 months).
Contemplation The subject intends to take action within the foreseeable future
(6 months).
Preparation The subject intends to take action in the immediate future
(usually 30 days) and has taken some behavioral steps in this
direction.
Action The subject has changed their behavior for less than 6 months.
Maintenance The subject has changed their behavior for more than 6 months.
When individuals progress through these stages, their motivation becomes more
intrinsic as behavioral regulation becomes more self-determined(Mullan and Markland
1997). We expect that this has an effect on the perception of gameful design elements
and on aspects related to the user experience and motivation within gameful systems.
3 Related work
We contribute to the fields of physical activity encouragement and individualiza-
tion of gameful applications for behavior change. Therefore, we start by present-
ing relevant research that has been carried out in the field of gameful applications
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encouraging physical activity. Next, we discuss why personalization is essential to
gameful systems and which factors have been considered. We conclude the related
work section by summarizing the key findings and by framing the contribution of
this article.
3.1 Encouraging physical activity throughgameful design
Encouraging people leading an active lifestyle has been the goal of numerous inter-
ventions in the past and is an ongoing research field(Aldenaini etal. 2020; Hamari
and Sarsa 2014; Seaborn and Fels 2015). There is a wide spectrum of approaches
regarding how to motivate people being more physically active using gameful
design (Aldenaini et al. 2020; Hamari et al. 2014; Hamari and Sarsa 2014). For
instance, UbiFit Garden (Consolvo etal. 2008a, b), an application showing a vir-
tual garden on participants’ mobile phones, has been shown to increase their activity
levels. The system uses activity goals and conveys progress through flowers and but-
terflies growing and appearing. Similarly, goals and progression are used as moti-
vational affordances in a system investigated by Consolvo etal. (2006). The authors
present “Houston,” a fitness app available in two versions. The “personal” version
uses daily step goals and visualizes progression towards these goals for the last
seven days. In the “sharing” version, users are additionally able to see progress made
towards goals by others. Results demonstrated that participants in the “sharing” ver-
sion were more likely to reach their daily step goal. Similarly, Zuckerman and Gal-
Oz (2014) developed a research prototype called “StepByStep” to motivate people
to walk more. In contrast to Consolvo etal. (2006), the study comparing two gami-
fied versions against a non-gamified version revealed that the gamified versions were
just as successful as non-gamified one. The authors state that social comparison was
effective for some, but not all participants and that interpersonal differences might
explain the absence of effects in the gamified conditions. This underlines the need to
understand which factors explain such interpersonal differences, to which this article
contributes. StepStream (Miller and Mynatt 2014) establishes goals based on the
performance of other users. The system uses a social stream on a website, showing
achievements when users reach their daily step goals. The user study revealed that
the system did not lead to an increase in step counts. As reported by the authors,
participants were living in an urban community with low walkability. Thus, their
intention to perform physical activity might have been low and social comparison
might have been unsuitable to motivate this population effectively.
To better understand user behavior in different group settings within game-
ful applications, Chen and Pu (2014) investigate the effectiveness of using social
collaboration, competition or hybrid settings to encourage physical activity. They
developed “HealthyTogether,” a smartphone application that pairs users to exer-
cise together. Differing from the findings before, the results show that collaboration
and hybrid settings outperformed competition. Similarly, Gui et al. (2017) inves-
tigate social comparison strategies in preexisting social networks. Instead of pair-
ing unknown users, the authors analyzed whether existing social peers stimulate an
engaging environment motivating physical activity. Their results show that sharing
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Potential andeffects ofpersonalizing gameful fitness…
fitness data with established social networks motivates users to keep tracking their
steps and has the potential to improve their social relationships. The impact of social
game design elements on walking behavior was also part of “Active2Gether”(Klein
et al. 2017), a smartphone application using social comparison, tailored coaching
messages and self-monitoring. In a user study by Middelweerd etal. (2020), three
conditions were compared. In one condition, the full range of game design elements
was used, whereas only self-monitoring and social comparison were used in a sec-
ond condition. In the third condition, participants were given a commercially availa-
ble fitness application using self-monitoring only. When comparing both versions of
the system against the commercially available application, the effect sizes for active
minutes per day were larger in the second condition and smaller in the first condi-
tion. However, no significant differences were found between the conditions. As a
result, understanding behavioral determinants and studying personalized interven-
tions to increase physical activity is explicitly stated as important future work.
Further investigating the role of social factors in public spaces, Cercos and Muel-
ler (2013) report findings from a public display visualizing each participant’s step
count in a graph. It was found that participants started socializing and that the
public display led to an increased usage of the pedometers and more motivation.
These results are similar to a more recent study by Altmeyer et al. (2018b) who
investigated the effect of showing gameful feedback about step counts publicly in
addition to showing them in a mobile application. They found that showing each
users’ progress toward step goals publicly led to a significant increase in step counts.
The acceptance of a public system to encourage stair climbing was investigated by
Meyer etal. (2018). They developed the “ActiStairs” system and found that it was
successful in increasing awareness for stair climbing. Fish’n’Steps(Lin etal. 2006)
links users’ step counts to the growth and emotional state of a virtual fish to encour-
age them to walk more. In a user study in an office environment, all participants
were able to see their personal fish tank, while half of the participants additionally
were grouped in teams. Teams have their own fish tanks, in which the virtual fish of
all team members are living. These team fish tanks were shown on a public display,
thus introducing social comparison. The study revealed that there were no differ-
ences in the amount of steps walked between these two conditions. As stated by the
authors, this might have been due to the fact that participants had little chance to
socialize. Nakajima and Lehdonvirta (2013) investigated virtual ambient paintings
which change their appearance based on the amount of physical activity a user per-
forms. In two user studies, no effects could have been found. The authors speculate
about the role of behavior change intentions in this context and state that the type of
motivational affordance might need to be tailored to the stage of behavior change of
a users, which motivates the relevance of our research.
While the aforementioned approaches are build upon static goals, research has
demonstrated that designing for dynamic goals, i.e., goals which may change over
time, is important in the context of encouraging physical activity. This motivates
investigating ways to formalize these dynamic goal adjustments, to which we con-
tribute by analyzing the role of behavior change intentions as a factor for person-
alization. As such, Niess and Woźniak (2018) emphasize that fitness tracker goals
are evolving. To explain these dynamic transition of goals, they define the “Tracker
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Goal Evolution Model.” It states that qualitative goals (doing more sports) are built
upon internalized user needs, which can be translated into quantitative fitness goals.
Similar to this, Epstein etal. (2015) proposed a lived model of personal informat-
ics. The model supports the fact that the motivations, goals and needs, while self-
tracking dynamically changes over time. Also, Li etal. (2010) emphasize that fitness
tracker users progress through five phases, which pose different challenges to the
user. In line with Epstein etal. (2015) and Niess and Woźniak (2018), they state that
the motivation of users changes when progressing through these stages. The fact that
these stages are based on the Transtheoretical Model of Behavior change supports
the relevance of considering behavior change intentions as potential moderators of
how certain gameful design elements are perceived.
3.2 Personalization ofgameful applications
The previous section has demonstrated that interventions aiming at encouraging
physical activity lead to a wide spectrum of positive, neutral or even negative out-
comes. A recent literature review by Aldenaini etal. (2020) supports this finding.
The authors reviewed 170 papers regarding the effectiveness of gameful interven-
tions in encouraging physical activity and found that 49% of them were partially
successful or even unsuccessful. Therefore, understanding which factors influence
the perception and effectiveness of interventions encouraging physical activity is
important.
Jia etal. (2016) investigated the influence of personality traits on the perception
of gameful design elements. They used videos of a researcher interacting with game-
ful design elements, provided textual descriptions of the presented gameful design
elements and asked participants to rate their perception in a survey. Their results
show that personality traits influence the perception of certain gameful design ele-
ments, e.g., the authors found that “extroversion” positively impacts the perception
of points and levels. In a follow-up work(Jia etal. 2017), the authors demonstrate
that the perception of several ways to represent leaderboards is moderated by person-
ality traits. They use storyboards to explain the different types of leaderboards and
ask participants to rate their perceived enjoyment. Besides other results, they found
that more extroverted users perceived leaderboards more positively, independent of
their ranking. Also, Orji et al. (2017) investigated the role of personality traits to
explain the perceived persuasiveness (defined as “an individual’s favorable impres-
sions toward the system”(Drozd etal. 2012)) of 11 persuasive strategies including
social comparison, rewards or goal setting. The authors created storyboards explain-
ing each strategy in the context of unhealthy alcohol behavior and found simi-
lar effects as Jia etal. (2016). In another work by Orji etal. (2014), the impact of
BrainHex(Nacke etal. 2014) player types on the perception of persuasive strategies
was investigated and several correlations were found. However, subsequent research
revealed severe issues regarding the reliability and validity of BrainHex(Busch etal.
2016b) and the effectiveness of personalizing persuasive systems using BrainHex
has been questioned(Busch etal. 2016a). Therefore, BrainHex should not be used
for personalization purposes, especially for gameful systems(Hallifax etal. 2019).
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Potential andeffects ofpersonalizing gameful fitness…
In line with Orji etal. (2017), Halko and Kientz (2010) used storyboards explaining
persuasive strategies to investigate potential relationships between personality traits
and users’ perceived enjoyment. The authors focused on the domain of encouraging
physical activity by using mobile devices. Their results revealed significant correla-
tions between the factors of the Big-5 personality traits and the perception of the
persuasive strategies, further emphasizing the importance of personalization of sys-
tems encouraging physical activity.
In addition to personality, research has been carried out to understand age as a
potential factor for personalization. As such, Birk etal. (2017) investigated play hab-
its and play preferences among older adults. They found changes in these aspects,
i.e., that with increasing age participants focus more on enjoyment instead of per-
formance. This is supported by Altmeyer and Lessel (2017), showing that the main
reason to play is that older adults enjoy spending time with other people and focus
less on performance in games. In a follow-up study with participants being older
than 75 years, Altmeyer etal. (2018a) use storyboards to explain commonly used
gameful design elements to older adults. They used this approach to investigate the
perception of these gameful design elements and found that the most commonly
used elements—points, badges and leaderboards—are perceived negatively among
older adults. Similarly, Kappen etal. (2016) focused on barriers and challenges in
designing gameful applications encouraging physical activity among older adults.
They conclude that personalizing gameful applications to support physical activ-
ity is important, as age-specific challenges need to be considered. In addition, the
impacts of age and gender on the perception of Cialdini’s persuasion strategies have
been investigated by Orji etal. (2015). Regarding age, they found that the principle
of scarcity is more valuable to younger people, while older adults are more driven
by consistent commitment. Regarding gender, their results indicate that females
are more responsive to most of the strategies. Gender-wise differences have also
been demonstrated by Oyibo etal. (2017), who found that competition and virtual
rewards are perceived as more persuasive by male participants. Furthermore, Oyibo
and Vassileva (2019) investigated whether there are differences between collectivist
and individualist cultures regarding the relevance of persuasive features in the physi-
cal activity domain. Their results show that collectivist cultures are more susceptible
to persuasive features in general, whereas individualist cultures are more affected by
personal persuasive features.
Albeit showing that personalization is essential for gameful applications encour-
aging physical activity, none of the factors presented above (personality traits, age
and gender) is particularly suitable or was specifically developed for the purpose of
personalizing gameful applications. The Hexad user-type model(Marczewski 2015)
bridges this gap. It was specifically developed to cluster users of gameful systems
and personalize the gameful design elements of a system. Establishing the basis for
further research, Tondello etal. (2016) created a questionnaire to assess Hexad user
types, which has been slightly adjusted and shown to be reliable and valid more
recently(Tondello etal. 2018). In addition, the practicability of the Hexad model for
personalizing gameful systems has been demonstrated by Tondello (2019),chapter3.
Here, a method for personalized gameful design based on the Hexad user types to
select which gameful design elements to use was proposed. Consequently, the Hexad
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user-type model has been utilized across different contexts and domains, showing
that it is able to explain preferences for and perceptions of gameful design elements.
In the health domain, Orji etal. (2018) examined the suitability of the Hexad model
in the context of unhealthy alcohol consumption. The authors found that a users
Hexad type influences the perceived persuasiveness of persuasive strategies. Their
results are in line with the Hexad user type definitions and support the applicability
of the Hexad model in the health domain. The applicability of the Hexad model has
also been demonstrated in an educational context by Mora etal. (2018). The authors
investigated the potential of using the Hexad model to personalize learning experi-
ences in order to motivate and engage students. They found that the approach that
utilized the Hexad model to personalize the game design elements yielded higher
engagement of the students. This underlines the usefulness of the Hexad model
for tailoring gameful systems. In the context of energy efficiency at the workplace,
Kotsopoulos etal. (2018) investigated the perception of certain gameful design ele-
ments and correlations to Hexad user types. As such, the authors showed the validity
of the Hexad user model in another domain since they found similar correlations
between gameful design elements and user types as Tondello et al. (2016). Ton-
dello etal. (2017) proposed a conceptual framework based on an exploratory fac-
tor analysis of people’s preferences in a general context, which allows to classify
game design elements systematically. In line with previous results, expected correla-
tions to the Hexad user types have been found. Further supporting the suitability of
the Hexad model for explaining user preferences in gameful systems, Hallifax etal.
(2019) found that the Hexad model is the most suitable typology for this purpose.
They investigated which user models should be used and compared the BrainHex
model, the Hexad model and the Big-5 personality model(McCrae and John 1992).
They ran a study utilizing storyboards to explain game design elements to partici-
pants and found that most of the results that were found by the authors are in line
with the definitions of the Hexad user types. The authors state that this is potentially
because the Hexad model was specifically designed for gamification (which is not
the case for other factors), and most of its user types are based on the well-estab-
lished SDT(Ryan and Deci 2000).
3.3 Summary
Related work shows that there is increasing evidence that gameful design elements
contribute positively to motivational and behavioral aspects in the context of physi-
cal activity(Aldenaini et al. 2020). However, research has also shown that roughly
half of the interventions relying on a “one-size-fits-all” approach are only partially
successful or even unsuccessful (Aldenaini et al. 2020). Similarly, the results of
the presented papers show that the success of different game design elements dif-
fers substantially across interventions. Such contradictory findings pose the ques-
tion of which factors moderate the perception of gameful interventions to encourage
physical activity, to which we contribute in this article. It has been shown that static
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Potential andeffects ofpersonalizing gameful fitness…
factors such as personality traits(Halko and Kientz 2010; Jia etal. 2016, 2017; Orji
etal. 2017) or demographic data such as age, culture or gender(Birk etal. 2017; Alt-
meyer and Lessel 2017; Altmeyer etal. 2018a; Kappen etal. 2016; Orji etal. 2015;
Oyibo etal. 2017; Oyibo and Vassileva 2019) play a role in the perception of game-
ful design elements. However, none of these factors has been specifically developed
for the purpose of personalizing gameful applications. In fact, the Hexad user-type
model is the only model specifically designed for this purposeOrji etal. (2018). It
has been shown to be reliable across various domains(Tondello etal. 2016; Mora
etal. 2018; Orji et al. 2018; Kotsopoulos etal. 2018; Tondello etal. 2017), which
emphasizes its relevance for personalizing gameful systems. We contribute the first
investigation of the Hexad user-type model in the domain of encouraging physical
activity, as far as we know.
Also, the aforementioned factors are static, i.e., do not change over time. This
is contrary to findings by Niess and Woźniak 2018; Li et al. 2010; Epstein et al.
2015, who consistently provided evidence for the dynamic nature of goals and moti-
vations. Therefore, it is important to find a way to formalize these dynamic pro-
cesses and integrate them into a personalization approach when aiming at encourag-
ing physical activity through gameful design. We contribute to this by investigating
behavior change intentions as one way to deal with these dynamics, which has not
been investigated before.
Furthermore, previous research has considered self-reported preferences based on
storyboards (Oyibo and Vassileva 2019; Orji etal. 2018; Halko and Kientz 2010;
Jia etal. 2017; Altmeyer etal. 2018a), textual descriptions (Tondello etal. 2016;
Kotsopoulos etal. 2018) or videos(Jia etal. 2016). Since most previous studies used
storyboards successfully to assess perceived preferences for game design elements,
we follow a similar approach to investigate the potential of behavior change inten-
tions and Hexad user types as factors for personalization in the context of physi-
cal activity. Using storyboards allows to recruit a large amount of participants from
diverse populations as well as provides a common visual language that is easy to
understand(Orji etal. 2018). However, in contrast to previous work, we additionally
investigate whether personalizing a real gameful application based on the findings
of the first, storyboards-based study, has an effect on affective experiences, user per-
formance and enjoyment. This is an important contribution, as none of the previ-
ous works (also outside the physical activity context) allowed participants to interact
with an implemented, personalized application.
4 Storyboards forgameful design elements
To investigate the potential of behavior change intentions and Hexad user types as
factors for personalizing gameful applications encouraging physical activity, we fol-
low the approach of illustrating gameful design elements by using storyboards(Orji
etal. 2018; Halko and Kientz 2010).
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4.1 Selection ofgameful design elements
We selected commonly used gameful design elements based on the literature
reviews by Seaborn and Fels (2015), Hamari and Sarsa (2014) and ensured to
include at least one gameful design element for each Hexad user type, based on the
correlations established by Marczewski (2015), Tondello etal. (2016). We ended up
with a selection of twelve commonly used gameful design elements and created sto-
ryboards for each one, illustrating the respective elements as explained in Table1.
The design process of the storyboards followed the guidelines established by Truong
etal. (2006), i.e., we used short texts to demonstrate novel aspects, included people
to explain the interactive experience, indicated the passage of time only when nec-
essary, and used the minimum level of detail required to understand the gameful
design elements. We used walking as a concrete contextualization of physical activ-
ity in the storyboards and focused on encouraging a users’ step count. The context of
step counting was used because it is among the most frequently used ones(Koivisto
and Hamari 2019; Aldenaini etal. 2020) and is relevant to the general public(King
etal. 2009). In the storyboards, a character was shown, interacting with a gameful
application employing the specific gameful design element. Two exemplary story-
boards (for Badges and Social Competition) can be seen in Fig.2. All created story-
boards are freely available on figshare.1
Table 1 Gameful design elements, a short textual description explaining what is depicted in the corre-
sponding storyboard and the user types (“UT”) that we expect to be positively correlated to their per-
ceived persuasiveness based on Marczewski (2015), Tondello etal. (2016)
Gamef. Des. Elem. Short storyboard description Expected UT
Virtual character The appearance of a virtual character is linked to the amount of
steps walked
AC, PL
Custom goal The user sets herself a custom step goal AC, FS
Personalized goal The system personalizes the users’ step goal AC
Challenge The user manages to reach a demanding goal AC
Badges The user reaches her goal three times, unlocking a new badge AC, PL
Points The system rewards the user with points for walking steps PL, AC
Rewards After reaching the step goal three times, the user receives a coupon
code
PL
Knowledge sharing The user helps another user in a forum by answering a question PH
Unlockable content After reaching the step goal three times, the app unlocks a new
feature
FS
Cheating The user decides to cheat by driving a car to reach her step goal DI
Social collaboration A group of users have to collaborate, to reach their shared step goal SO
Social competition A group of users are shown on a leaderboard, competing for the top
position
SO, PL
1 https ://doi.org/10.6084/m9.figsh are.73809 02.v1.
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4.2 Storyboard validation
Before using the storyboards in the online study, we wanted to ensure that they actu-
ally explain the intended gameful design elements and are understandable to partici-
pants. Therefore, we conducted a qualitative pre-study in the laboratory.
4.2.1 Method
After answering demographic questions, the printed storyboards were shown to
participants in random order. To understand whether users have problems under-
standing which gameful design element is illustrated by the storyboards, we con-
ducted semi-structured interviews. The interview sessions were conducted by one
researcher, and audio recordings were made. As a first step, participants were asked
to describe the storyboards in their own words. When necessary, the interviewer
asked questions to prompt participants to state which activities are shown in the sto-
ryboards. Questions included: “What is the character’s goal?” and “What means
does the character use to achieve her goal?”. Afterwards, participants were given
a short printed textual summary of each gameful design element. They were asked
to assign these printed statements to each of the storyboards by placing them next
to the respective storyboard. This was done to investigate whether the storyboards
can be mapped to the respective gameful design elements and thus are successful in
conveying them.
Finally, interviews were transcribed and analyzed by two independent raters
(“R1,” “R2”). The raters received the transcriptions for each storyboard, without
revealing which gameful design element was described by the participants. Their
task was to evaluate which element was being described. This was also done to
ensure that the storyboards explain the intended gameful design elements. Also,
Fig. 2 Storyboards for gameful design elements
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raters were asked to rate how well the gameful design element was understood,
based on the explanation provided by the participant, on a 5-point scale (1—very
poor to 5—very well).
4.2.2 Results
Eight German participants took part (four females, average age 21.75). To ensure
that the ratings can be interpreted objectively, we calculated the inter-rater agree-
ment and found it to be Kappa=0.75, which is considered as substantial(McHugh
2012). Analyzing the ratings of the two independent raters, we found that the par-
ticipants understood the storyboards very well (MR1 = 4.90, MinR1 = 4; MR2 = 4.86,
MinR2 = 4). This was supported by the fact that both raters successfully assigned
the correct game element based on participants’ storyboard descriptions. Regarding
users assigning the textual summaries to the respective storyboard, only one assign-
ment was incorrect. However, this wrong assignment was not due to a misunder-
standing of the game element, but due to the participant misreading the descrip-
tions of one of the game elements. The participant assured us that the storyboard
and respective game element were clear to him.
5 Online study: potential ofbehavior change intentions andHexad
user types
After showing that the storyboards that were created for the twelve commonly used
gameful design elements are comprehensible and successfully explain the intended
gameful design elements, we used them to conduct an online study. Here, we were
interested in the perceived persuasiveness of each gameful design element and
potential differences related to behavior change intentions and Hexad user types.
The results presented in this section were already published in a previous paper that
we authored(Altmeyer etal. 2019).
5.1 Procedure andmethod
The online survey was available in English and German. Participants were recruited
via social media and Academic Prolific (paid £1.50 GBP). The study took between
10 and 15 minutes to complete and has been reviewed and received ethics clear-
ance through an institutional Research Ethics Committee (#18-6-4).2 After giving
informed consent, participants were asked to provide demographic data and rate
their gaming behavior on 5-point Likert scales (5=strong agreement). Behavioral
intentions were operationalized by using a validated scale assessing the stage of
change (“SoC”) within the context of physical activity(Marcus etal. 2008). To ana-
lyze the effect of behavior change intentions on the perceived persuasiveness of the
2 https ://erb.cs.uni-saarl and.de/, last accessed 2021/01/10 20:20:33.
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gameful design elements, participants were split into two groups: “Low-SoC” (par-
ticipants who did not take action so far, having a SoC ≤ 3) and “High-SoC” (par-
ticipants who did take action, having a SoC ≥ 4). This follows the same procedure
as was done by Xiao etal. (2004), who split participants in preaction (not achieving
their goal) and action (at their goal) groups. Next, participants’ Hexad user type was
determined using the Hexad user type scale(Tondello etal. 2016).
Afterward, the main part of the online survey started. Here, participants were
shown the 12 storyboards in a randomized order. To measure the persuasiveness
of each gameful design element depicted in the storyboards, we adapted the per-
ceived persuasiveness scale by Drozd etal. (2012) in the same way as was done by
Orji etal. (2017). The scale consists of four items being measured on 7-point Likert
scales. In line with previous research using this scale(Orji etal. 2014, 2017), the
internal consistency is excellent, with Cronbach’s alpha = .97. Since a Shapiro–Wilk
test revealed that the perceived persuasiveness responses were not normally dis-
tributed, we used nonparametric tests for the analysis. Consequently, the effect of
behavior change intentions on the perceived persuasiveness of the gameful design
elements was assessed by using Mann-Whitney U tests. For correlation analyses,
Kendall’s
𝜏
was used, since it is well-suited for nonparametric data(Howell 2002).
For the interpretation of the correlations, it should be considered that Kendall’s
𝜏
is
usually lower than Pearsons r for the same effect sizes. Therefore, we transformed
interpretation thresholds for Pearson’s r to Kendall’s
𝜏
, according to Kendall’s for-
mula(Walker 2003) (small effect:
𝜏
= 0.2; medium effect:
𝜏
= 0.3 ; large effect:
𝜏
=
0.5).
5.2 Results
After excluding nine participants who were either unable to exercise or answered
all gaming-related questions with “Strongly disagree,” we considered 179 valid
responses. Of those participants, 55.3% self-reported their gender as female, 44.1%
as male and 0.6% as non-binary. Most participants (38%) were aged 18–24 years,
followed by 25–31 (34.1%), 32–38 (17.3%), 39–45 (6.7%) and younger than 18
(1.7%). The remaining participants were aged 45 and older (1.7%). Participants
stated that they had a passion for video games (M = 3.70, SD = 1.11, Mdn = 4.00)
and that they frequently play video games (M = 3.58, SD = 1.24, Mdn = 4.00). Sev-
enty-two participants were in the Low-SoC and 107 participants in the High-SoC
group. Regarding the average scores of the Hexad user types questionnaire, Free
Spirits (M=22.75, SD=3.43) and Achievers (M=22.09, SD=3.40) showed the high-
est and second-highest average scores, followed by Players (M=21.56, SD=4.14)
and Socialisers (M=19.27, SD = 4.77). Philanthropists (M = 17.50, SD = 2.49) and
Disruptors (M = 16.30, SD = 4.76) followed with lower average scores.
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5.2.1 SoC andgameful design elements
To investigate whether behavior change intentions have a moderating effect on the
perceived persuasiveness of the gameful design elements, we performed a two-sided
Mann–Whitney U test to analyze potential differences in the two groups (Low-
SoC and High-SoC) for each gameful design element. Following the suggestions
provided by Armstrong (2014), we did not adjust probability values for these tests,
because we interpreted these tests independently (as unrelated group means) and
because the results were used to inform the hypotheses in the laboratory experiment
presented in Sect.7.
The overview of the results can be found in Table2. It can be seen that the per-
ceived persuasiveness of the gameful design elements is different between the two
groups. We found significant differences between the two groups for four gameful
Table 2 Perceived persuasiveness of gameful design elements in the Low- and High-SoC groups and
results of Mann–Whitney U tests (“Diff. sig.”)
These results were published in our previous paper(Altmeyer etal. 2019)
Low-SoC High-SoC Diff. sig.
Virtual character M = 4.05, SD = 1.77, M = 3.94, SD = 1.81,
Mdn = 4.50 Mdn = 4.25
Custom goal M = 4.34, SD = 1.49, M = 4.70, SD = 1.55,
Mdn = 4.63 Mdn = 5.25
Personalized goal M = 4.88, SD = 1.44, M = 4.93, SD = 1.38,
Mdn = 5.00 Mdn = 5.25
Challenge M = 4.32, SD = 1.65, M = 4.88, SD = 1.27, p = 0.045
Mdn = 4.75 Mdn = 5.00 U = 3173.50
Badges M = 3.95, SD = 1.57, M = 4.46, SD = 1.40, p = 0.028
Mdn = 4.00 Mdn = 4.75 U = 3108.50
Points M = 4.39, SD = 1.46, M = 4.52, SD = 1.43,
Mdn = 5.00 Mdn = 4.50
Rewards M = 5.16, SD = 1.48, M = 5.50, SD = 1.39,
Mdn = 5.25 Mdn = 5.75
Knowledge sharing M = 4.06, SD = 1.52, M = 4.26, SD = 1.51,
Mdn = 4.25 Mdn = 4.50
Unlockable content M = 4.70, SD = 1.49, M = 4.84, SD = 1.53,
Mdn = 5.00 Mdn = 5.00
Cheating M = 2.12, SD = 1.16, M = 2.35, SD = 1.44,
Mdn = 2.00 Mdn = 2.00
Social collaboration M = 4.23, SD = 1.56, M = 4.81, SD = 1.61, p = 0.009
Mdn = 4.88 Mdn = 5.25 U = 2963.50
Social competition M = 4.09, SD = 1.74, M = 4.61, SD = 1.76, p = 0.048
Mdn = 4.50 Mdn = 4.75 U = 3180.50
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design elements. For instance, Badges and Challenges were perceived as signifi-
cantly more persuasive in the High-SoC than in the Low-Soc group. Also, Social
Competition and Social Collaboration were perceived as significantly more per-
suasive in the High-SoC group. In sum, we establish result R1: Behavior change
intentions have a moderating effect on the perceived persuasiveness of gameful
design elements in the physical activity context.
This main result is potentially explainable by goal-setting theory, stating that
goals are most effective when users are committed to them(Tondello etal. 2018a;
Locke and Latham 2002). This is unlikely for users in the Low-SoC group, since
their motivation to increase their physical activity levels is not yet internalized and
thus commitment is lower. Specifically regarding Badges and Challenges, partici-
pants in the Low-SoC group might have considered themselves as not to be able to
reach the established goals(Fogg 2002). A potential reason for the significant dif-
ference between the groups regarding social gameful design elements (Social Com-
petition and Social Collaboration) might be related to the fear to not be able to keep
up with other users(Fogg 2002). This might have detrimentally affected users’ per-
ceived persuasiveness in the Low-SoC group. In sum, these findings show that the
SoC is a relevant factor that should be considered in personalizing gameful systems
in the physical activity context.
5.2.2 Hexad user types andgameful design elements
To analyze the impact of Hexad user types on the perceived persuasiveness of game-
ful design elements, we followed the approach of previous research using the Hexad
model(Tondello etal. 2016; Orji etal. 2018; Kotsopoulos etal. 2018) and analyzed
Table 3 Kendall’s
𝜏
and significance between the Hexad user types and the gameful design elements
Bold entries represent expected correlations (as stated in Table1). *p < .05, **p < .01. These results
were published in our previous paper(Altmeyer etal. 2019)
AC DI FS PH PL SO
Virtual character .237** .114*
Custom goal .205** .132* .119* – .106*
Personalized goal .211** – – .145** – –
Challenge .200** .145** – .177** –
Badges .122* –––.223**
Points .201** .110* .192** .169** .105*
Rewards .114* .152** .250** .109*
Knowledge sharing .123* .234** – .175**
Unlockable content .140** .143** .163** –
Cheating .157** ––––
Social collaboration .147** .153** .145** .216** .314**
Social competition .105* .370** .204**
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correlations between Hexad user-type scores and the perceived persuasiveness of
each gameful design element.
The overview of these findings is shown in Table3. It can be seen that 16 posi-
tive correlations between Hexad user types and gameful design elements out of
17 expected correlations (see Table1) were found. This replicates previous find-
ings(Tondello etal. 2016; Orji et al. 2018; Kotsopoulos etal. 2018) and supports
the usefulness of the Hexad user-type model in the physical activity context. The
positive correlation between the gameful design element “Virtual Character” and
the “Achiever” user type is the only correlation that was expected, but could not
be found, given our data. Based on this, we establish R2: The Hexad user type
has a moderating effect on the perceived persuasiveness of gameful design ele-
ments in the physical activity context. In addition to expected correlations, some
unexpected correlations were found. This could be a result of considering a different
context and using storyboards instead of textual descriptions, compared to Tondello
etal. (2016). It is also in line with previous research(Orji etal. 2018; Tondello etal.
2016). A more detailed discussion of the online study can be found in Sect.8.
6 Endless universe: design andimplementation ofapersonalized
gameful application toencourage physical activity
The actual effects of personalizing gameful applications based on behavior change
intentions and Hexad user types on task performance and user experience cannot be
investigated without allowing users to interact and experience the gameful design
elements in a real system. Therefore, we implemented Endless Universe, a gameful
application that builds upon the results of the online study to investigate the effects
of personalization on these aspects.
6.1 Design andconcept
Endless Universe ties the distance covered on a treadmill to the progress within sev-
eral gameful design elements. To investigate which effects personalization has on
measures related to the users’ performance and experience, we decided to use the
findings from the storyboards-based online study presented before to tailor Endless
Universe to a specific user group.
6.1.1 Theme
We decided to use outer space as the main theme of the gameful application. This
decision is based on previous research using gameful applications encouraging
physical activity, which demonstrated that this theme is well perceived within the
physical activity context(Saksono et al. 2015; Doyle et al. 2011a, b; Finkelstein
etal. 2010; Buttussi et al. 2007; Cuzzort and Starner 2008). The core mechanic in
the gameful application is a spaceship exploring an endless universe. Hereby, the
real-time distance covered by the user on the treadmill has a direct influence on the
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speed of the spaceship moving forward in the space exploration. The spaceship is
shown prominently in the middle of the screen, and a moving illusion is created by
animating the background of the scene (i.e., stars and particles are moving faster or
slower). The distance covered by the user is shown permanently in the application.
When starting the application for the first time, an introduction is given to the users,
explaining that they belong to an alien species which is competing to explore the
universe with their spaceships. Figure3 shows a screenshot of the application.
6.1.2 Goal setting
Endless Universe establishes a target distance to cover, which is shown next to the
distance covered in the main screen of the application. This target distance is per-
sonalized to the user, i.e., based on a users’ fitness level. This was done to make sure
that the target distance is reachable to all users and thus comparable. This is in line
with previous research within this context(Lin etal. 2006; Miller and Mynatt 2014).
More specifically, this target distance was 10% higher than the previously covered
distance. The gameful design elements, which are described next, operate on this
target distance.
6.1.3 Gameful design elements
The findings of the storyboards-based online study presented above show that
behavior change intentions and Hexad user types are relevant factors for personal-
izing gameful applications encouraging physical activity. Based on these findings,
we derived a set of gameful design elements to investigate the effects of person-
alization. As such, we decided to use the gameful design elements Badges, Chal-
lenges and Social Competition. These gameful design elements were shown to be
Fig. 3 Screenshot of the Endless Universe application. The distance covered, target distance, number
of badges unlocked and current position on the leaderboard are shown on the top. The leaderboard and
badges are shown on the right side of the screen 
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perceived as significantly more persuasive in the High-SoC than in the Low-SoC
group (R1, see Table2) and to be positively correlated with the Achiever, Player
and Socialiser Hexad user types (R2, see Table3). Therefore, we expected that
Endless Universe should be suitable for users belonging to the High-SoC group
or scoring particularly high on Achiever, Socialiser or Player. The realization of
the gameful design elements is described in the following:
Badges There are three different badges in the gameful application. To
account for interpersonal performance differences, the thresh-
olds to unlock badges were established relatively to the target
distance. The first badge is unlocked when reaching 20% of
the target distance and is visualized through a bronze trophy.
The second badge, a silver trophy, is unlocked when reach-
ing 50% of the target distance. Finally, the golden badge is
unlocked when reaching 100% of the target distance. This
progression concept follows the recommendations related to
progression stairs in games by Werbach and Hunter (2012).
The badges were shown on the right side of the screen and
darkened until they were unlocked. The remaining distance
until unlocking the next badge was shown permanently below
the badges. Based on R1 and R2, this gameful design element
should be perceived particularly well by users belonging to
the High-SoC group and users scoring high on the Achiever or
Player factors of the Hexad.
Challenges The ultimate challenge of Endless Universe is to reach the
target distance. This is explained to the user as part of the
onboarding procedure before starting the gameful applica-
tion. When reaching the target distance and thus mastering the
main challenge of the application, a so-called explorer of the
day trophy is unlocked and shown to the user. This gameful
design element should be perceived particularly well by users
belonging to the High-SoC group (R1) and users having a
high Achiever score (R2).
Social Competition We used a leaderboard to introduce social competition to the
gameful application, positioned on the right-hand side of the
screen. In this leaderboard, fictitious users were shown, simi-
lar to previous gamification studies(Mekler etal. 2017). This
was done to ensure the comparability across participants, i.e.,
that all participants had the same chance to rise in ranks, and
to avoid introducing a confounding variable (Von Ahn and
Dabbish 2008). Similar to Badges, there were three other ficti-
tious users who covered distances that were calculated in rela-
tion to the target distance described above. The fictitious user
on the first rank covered the target distance, the fictitious user
on the second rank covered 5% less than the target distance
and the fictitious user on the third rank covered 8% less than
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the target distance. This follows the same progression scheme
as was used for Badges and thus follows recommendations
established by Werbach and Hunter (2012).
6.2 Implementation
The user interface part of Endless Universe was implemented as a web applica-
tion and capturing the distance covered on the treadmill was realized by using an
Arduino Uno board and a QRE1113 infrared reflectance sensor is comprised of
an infrared emitting LED and an infrared sensitive phototransistor. The hardware
and user interface are explained in the following.
6.2.1 Hardware tocapture thecovered distance onthetreadmill
Since the covered distance is a direct input to the gameful application, we imple-
mented a system to track the distance covered on the treadmill. We placed reflect-
ing light tape on the belt of the treadmill in equal, pre-defined distances and used
an infrared reflectance sensor to detect the tape. We used an Arduino Uno, which
was connected to a PC via USB to send an event to the main application running
on the PC whenever a tape was detected.
6.2.2 User interface
The number of events that were triggered when the reflecting tape on the belt was
detected by the Arduino was sent via USB to a NodeJS Express webserver run-
ning on a PC in a real time. The webserver calculated the distance covered based
on the number of detections, i.e., the total distance could be derived with a maxi-
mum discrepancy of 3.1 meters (the tape was placed every 3.1 meters). Besides
calculating the covered distance, the webserver is responsible for the game logic,
i.e., deriving the current rank of the user on the leaderboard, checking whether a
badge should be unlocked and whether the main challenge was completed. This
information is populated to the frontend using bidirectional websockets. The
frontend itself was realized using HTML, CSS and JavaScript. Three.js was used
for the visualization of the space, the rocket and to create the moving illusion
with various speeds. Moreover, Bootstrap was used to make sure that the applica-
tion adapts to various screen sizes, and jQuery was used to manipulate the DOM
of the web application whenever updated data from the webserver have been sent.
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7 Laboratory study: eects ofpersonalization
To investigate whether the findings from the online study (R1, R2), which were
based on the perception of storyboards, lead to effects on a user’s performance or
experience when actually interacting with a gameful application, we conducted a
laboratory study. In this laboratory study, participants were running on a tread-
mill and thus interacted with Endless Universe. In the following, the procedure,
method and the results of this study are presented.
7.1 Procedure andmethod
The study followed a within-subjects design with two conditions. When recruit-
ing participants, we used the same validated questionnaire as in the online study to
assess the SoC within the context of physical activity(Marcus etal. 2008), to make
sure that an equal number of Low- and High-SoC participants was recruited. In the
baseline condition, participants were running on a treadmill without getting any
kind of feedback. (The display of the treadmill was covered using black foil.) In the
intervention condition, Endless Universe was deployed on a 10-inch tablet device,
which was placed where the display of the treadmill is located, to ensure that par-
ticipants can easily see the gameful application. The study started with the baseline
phase to avoid detrimental effects when removing gameful design elements(Hamari
and Sarsa 2014) and to establish the target distance in the intervention phase (to
make sure that the target distance is reachable to all users(Lin et al. 2006; Miller
and Mynatt 2014). After giving informed consent, participants were asked to fill out
a survey. In this survey, demographic data were collected. Next, the Hexad user type
was assessed using the validated questionnaire by Tondello etal. (2018), followed
by a validated questionnaire to assess the SoC within the context of physical activ-
ity(Marcus etal. 2008).
After completing this survey, participants were asked to run on the treadmill for
10 minutes in a speed that they felt comfortable with. They were told to stop running
when feeling uncomfortable. Drinks were provided.
After running for 10 minutes, participants were asked to complete a second sur-
vey. In this survey, the validated version of the Positive and Negative Affect Sched-
ule (“PANAS”)(Watson etal. 1988) was administered in order to assess affective
experiences while running. Next, participants were asked to fill out the 22-item task
evaluation questionnaire of the Intrinsic Motivation Inventory (“IMI”) (McAuley
et al. 1989; Ryan 1982) to assess intrinsic motivation and enjoyment of the run-
ning activity. Finally, Borg’s Rating of Perceived Exertion (“RPE”)(Borg 1970) was
administered to assess how exhausting participants perceived the activity. In this
scale, users choose a number between 6 (“no exertion”) and 20 (“maximum exertion
possible”) to describe their perceived exertion. Finally, a date for the intervention
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Potential andeffects ofpersonalizing gameful fitness…
phase was scheduled. We made sure that there is a break of at least one full day
between the baseline and intervention phase.
The intervention phase followed exactly the same procedure. The only differ-
ence was that Endless Universe was in place while running. The task was exactly
the same, i.e., participants were asked to run on the treadmill for 10 minutes in a
speed that they felt comfortable with. The target distance was established based on
the covered distance in the baseline phase, as described in Sect.6.1.2. After running
for 10 minutes, the same questionnaires as in the baseline (PANAS, IMI, RPE) were
administered.
Participants were compensated by a 10 Euro amazon gift card. The study has
been reviewed and received ethics clearance through an institutional Research Eth-
ics Committee (#19-12-3).3
7.2 Hypotheses
Based on the findings of the storyboards-based pre-study and previous research, we
expected to find evidence for the following hypotheses:
H1 One-size-fits-all gamification affects performance and experience
H1a The covered distance is higher when using Endless Universe
H1b Users perceive running as more enjoyable using Endless Universe
H1c Users have stronger affective experiences with Endless Universe
H2 SoC affects performance and experience with Endless Universe
H2a The improvement in distance is higher for High-SoC users
H2b High-SoC users perceive Endless Universe as more enjoyable
H2c High-SoC users have stronger affective experiences
H3 Hexad types affect performance and experience with Endless Universe
H3a The improvement in distance is higher for AC, PL, SO
H3b AC, PL, SO perceive Endless Universe as more enjoyable
H3c AC, PL, SO have stronger affective experiences
H1 is motivated by previous work, showing that gameful applications can
increase physical activity and can have positive effects on the user experience when
doing sports(Aldenaini etal. 2020; Koivisto and Hamari 2019). Consequently, H1
can be seen as a replication of previous work and is important to demonstrate the
overall effectiveness and validity of Endless Universe. H1 is analyzed by conduct-
ing paired samples t-tests or Wilcoxon signed-rank tests (when the assumptions of
the t-test were not met). H2 stems from findings of the storyboards-based online
study presented in Sect. 5. In this study, we found that the perceived persuasive-
ness of Social Competition, Badges and Challenges is significantly higher among
High-SoC users. Since we are using these gameful design elements in Endless Uni-
verse, we expect that the increased perceived persuasiveness should be reflected in
3 https ://erb.cs.uni-saarl and.de/, last accessed 2021/01/10 20:20:33.
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M.Altmeyer et al.
1 3
an increased actual performance and experience. H2 is analyzed by splitting partici-
pants in Low- and High-SoC groups and conducting independent-samples t tests or
Mann–Whitney U tests (when assumptions of the t test were not met). Similarly, H3
bases on our findings from the online study, which revealed significant correlations
between the Socialiser, Achiever, Player and the aforementioned gameful design ele-
ments. Also, previous research has demonstrated similar correlations for these game-
ful design elements in different contexts(Tondello etal. 2016; Orji etal. 2018; Kot-
sopoulos etal. 2018; Hallifax etal. 2019). To analyze H3, we calculated bivariate
correlation coefficients. Similar to the online study, we used Kendall’s
𝜏
, since it is
well suited for nonparametric data(Howell 2002). Also, research has recommended
using Kendall’s
𝜏
when the sample size is rather low(Bishara and Hittner 2012).
Since we established one-directional hypotheses beforehand (H3a, H3b, H3c) and
to further increase the power of the correlation analysis, we used one-sided tests.
Again, when interpreting the correlation coefficients, it should be considered that
Kendall’s
𝜏
is lower than Pearson’s r for the same effect sizes (see Sect.5.1).
7.3 Results
We recruited 20 participants. Of those participants, 11 self-reported their gender
as male and 9 as female. Most participants (50%) were aged 25–31 years, followed
by 18–24 (45%) and 32–38 (5%). The number of participants across the Low- and
Table 4 Dependent variables of the laboratory study for the baseline and intervention condition and
results of paired samples t-tests/Wilcoxon signed-rank tests (“Diff. sig.”) comparing them
Baseline Intervention Diff. sig.
N=20
N=20
Distance covered M = 0.96, SD = 0.32, M = 1.13, SD = 0.36, p = 0.003
[km] Mdn = 0.97 Mdn = 1.12 Z = 24.00
RPE M = 9.35, SD = 2.11, M = 11.10, SD = 2.95, p = 0.027
[scale from 6 to 20] Mdn = 9.00 Mdn = 11.50 t = -2.40
IMI enjoyment M = 4.88, SD = 1.97, M = 5.43, SD = 1.42, -
[scale from 1 to 7] Mdn = 5.50 Mdn = 5.67
IMI competence M = 4.42, SD = 1.89, M = 5.38, SD = 1.35, p = 0.008
[scale from 1 to 7] Mdn = 4.84 Mdn = 5.84 t = -2.97
IMI pressure M = 1.73, SD = 1.04, M = 6.07, SD = 1.09, p<0.001
[scale from 1 to 7] Mdn = 1.33 Mdn = 6.50 t = -10.40
IMI choice M = 6.03, SD = 1.27, M = 2.40, SD = 1.56, p<0.001
[scale from 1 to 7] Mdn = 6.67 Mdn = 2.00 t = 7.42
PANAS pos. M = 3.03, SD = 0.56, M = 3.40, SD = 0.83, -
[scale from 1 to 5] Mdn = 2.90 Mdn = 3.45
PANAS neg. M = 2.91, SD = 0.14, M = 2.90, SD = 0.0.17, -
[scale from 1-5] Mdn = 3.00 Mdn = 2.90
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High-SoC groups was equal (10 participants in each group). Regarding the aver-
age scores of the Hexad user types questionnaire, Achievers (
M
=
24.80, SD
=
2.35
)
and Philanthropists (
=
=
) showed the highest and second-high-
est average scores, followed by Players (
M
=
24.10, SD
=
2.92
) and Free-Spir-
its (
M
=
23.40, SD
=
3.12
). Socialisers (
M
=
22.90, SD
=
3.80
) and Disruptors
(
M
=
17.40, SD
=
3.95
) followed with lower average scores.
7.3.1 Effects of“One‑Size‑Fits‑All” gamification
First, we investigated whether Endless Universe has an effect on the performance
of users, i.e., whether it motivated participants to cover more distance than in the
baseline. This is important to replicate previous research, which showed the effec-
tiveness of one-size-fits-all gamification in this domain(Altmeyer etal. 2018b; Chen
and Pu 2014; Koivisto and Hamari 2019).
Table 4 shows the means, standard deviations, medians and significant dif-
ferences for all dependent variables of the study for the baseline and intervention
phase. We found a significant difference in the covered distance between the base-
line and intervention condition (
Z
=
24.00, p
=
0.003
). Based on this, we estab-
lish result R3: Participants covered a significantly higher distance when using
Endless Universe. Next, we analyzed whether RPE differs across the conditions.
Again, we found a significant difference between the intervention and baseline
phase in perceived exertion (
t
=−
2.40, p
=
0.027
). Thus, R4: Perceived Exertion
is higher when using Endless Universe confirms that the increased distance (R3)
is also reflected in the subjectively higher feeling of exertion. Regarding enjoyment
and user experience, we compared the factors of the IMI and PANAS. Here, we
found a significant difference for the competence (
t
=−
2.97, p
=
0.008
), pressure
(
t=−10.40
,
p
<
.001
), and choice (
t=7.42
,
p
<
.001
) factors of the IMI. No sig-
nificant effects were found for the enjoyment factor (
p
=
0.20
). Thus, we establish
R5: Perceived competence and pressure is higher when using Endless Universe
and R6: Perceived choice is lower when using Endless Universe. Regarding affec-
tive experience, no significant effects were found for the positive (
p
=
0.08
) nor the
negative affect factor (
p
=
0.62
).
7.3.2 Effects ofSoC‑personalization
Similar to the online study, we split participants in Low- and High-SoC groups and
compared these two groups to check for significant effects. To ensure the compa-
rability of the improvement of performance, we did not consider the absolute dis-
tance but calculated the relative improvement (i.e., we divided the distance covered
in the intervention phase by the distance covered in the baseline phase). Table 5
provides an overview of descriptive data and significant differences. It can be seen
that we could not find a significant effect in distance improvement between the
Low- and High-SoC groups (
p
=
0.07
), and no significant effect was found for the
perceived exertion between the groups (
p
=
0.24
). In addition, none of the factors
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M.Altmeyer et al.
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Table 5 Dependent variables of the laboratory study in the Low- and High-SoC groups and results of
independent t tests/Mann–Whitney U tests (“Diff. sig.”) comparing them
Low-SoC High-SoC Diff. sig.
N=10
N=10
Distance improvement M = 1.37, SD = 0.31, M = 1.11, SD = 0.31,
[intervention/baseline] Mdn = 1.23 Mdn = 1.15
RPE M = 11.90, SD = 2.89, M = 10.30, SD = 2.95,
[scale from 6–20] Mdn = 12.00 Mdn = 11.00
IMI enjoyment M = 5.13, SD = 1.77, M = 5.73, SD = 0.97,
[scale from 1–7] Mdn = 5.33 Mdn = 5.83
IMI competence M = 5.07, SD = 1.62, M = 5.70, SD = 1.01,
[scale from 1–7] Mdn = 5.17 Mdn = 6.00
IMI pressure M = 5.80, SD = 1.25, M = 6.33, SD = 0.87,
[scale from 1 to 7] Mdn = 6.00 Mdn = 6.67
IMI choice M = 2.80, SD = 1.63, M = 2.00, SD = 1.23,
[scale from 1 to 7] Mdn = 3.33 Mdn = 1.67
PANAS pos. M = 3.02, SD = 0.55, M = 3.77, SD = 0.92, p = 0.040
[scale from 1 to 5] Mdn = 2.75 Mdn = 4.10 t = 2.21
PANAS neg. M = 2.80, SD = 0.13, M = 3.00, SD = 0.15, p = 0.005
[scale from 1 to 5] Mdn = 2.80 Mdn = 3.00 t = 3.16
Table 6 Kendall’s
𝜏
and
significance between the Hexad
user types and the dependent
variables in the laboratory study
*p < .05, **p < .01
AC DI FS PH PL SO
Distance improvement - .387** – – –
[intervention/baseline]
RPE – – – – –
[scale from 6 to 20]
IMI Enjoyment
[scale from 1 to 7]
IMI competence .304*
[scale from 1 to 7]
IMI pressure .467** –––
[scale from 1 to 7]
IMI choice
[scale from 1–7]
PANAS pos.
[scale from 1 to 5]
PANAS neg.
[scale from 1 to 5]
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Potential andeffects ofpersonalizing gameful fitness…
of the IMI revealed a significant difference (enjoyment:
p
=
0.36
; competence:
p
=
0.31
; pressure:
p
=
0.28
; choice:
p
=
0.23
). However, we found a significant
effect for affective experiences, i.e., a significant effect was found for both positive
(
t
=
2.21, p
=
0.040
) and negative affect (
t
=
3.16, p
=
0.005
). Both positive and
negative affects were significantly higher in the High-SoC group. Consequently,
we establish R7: Participants in the High-SoC group had stronger affective
experiences.
7.3.3 Effects ofhexad personalization
The results of the correlation analysis can be seen in Table6. When analyzing the sig-
nificant correlations between the dependent variables of the laboratory study and the
AC, PL, SO Hexad user types, we found that the score in the Socialiser factor of the
Hexad is positively correlated to the perceived competence of the IMI when interact-
ing with Endless Universe, having a medium effect size. This suggests that Socialis-
ers perceived the feedback of the gameful design elements as particularly confirming
and leads to R8: Endless Universe positively affected the perceived competence of
Socialisers. We also found correlations for Hexad user types besides AC, PL and SO.
For these remaining Hexad user types, we expected to find either no conclusive cor-
relations or expected that negative effects on the user experience or performance would
be found. Since we did not have specific a priori formulated assumptions for these user
types, we used two-tailed tests for them. We found a negative, medium-sized correla-
tion between the distance improvement and the disruptor. This suggests that disruptors
were not encouraged to increase their performance by Endless Universe and leads to
R9: The performance of Disruptors was negatively affected by Endless Universe.
Also, we found a medium-to-strong positive correlation between the perceived pressure
and free spirits. This means that R10: Perceived pressure was particularly high for
Free Spirits when using Endless Universe.
8 Discussion andlimitations
In the course of the two main studies of this paper, we investigated Hexad user types
and behavioral intentions as factors to personalize gameful applications in the con-
text of physical activity. First, we investigated the potential of these factors by creat-
ing storyboards illustrating twelve commonly used gameful design elements in the fit-
ness context. After ensuring that the storyboards are comprehensive and explain the
intended gameful design elements in a qualitative pre-study (
N=8
), we conducted an
online study assessing the perceived persuasiveness of each gameful design element.
Our findings support the potential of these personalization factors. Next, we imple-
mented a gameful application aiming to motivate users to cover a higher distance on a
treadmill to investigate whether the theoretical findings of the storyboards-based study
lead to effects on performance, enjoyment or affective experiences when allowing users
to interact with a real implementation of gameful design elements. In this section, we
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discuss the findings of the online study using storyboards, the laboratory study using
the gameful application and the contributions of our paper.
8.1 Storyboards‑based online study
In the online study, we investigated the effect of behavior change intentions and
Hexad user types on the perceived persuasiveness of twelve commonly used game-
ful design elements in the physical activity context using storyboards. We contrib-
ute two main findings: First, we found multiple significant differences between both
groups in the perceived persuasiveness of gameful design elements, supporting
the potential and relevance of behavior change intentions as a factor to personalize
gameful applications in the physical activity domain (R1). The second contribution
of the online study lies in supporting the validity of the Hexad model in the physi-
cal activity context. Confirming previous findings(Tondello etal. 2016; Orji etal.
2018), we found 16 out of 17 expected correlations between gameful design ele-
ments and Hexad user types. Thus, our findings validate previous results(Tondello
et al. 2016; Marczewski 2015) in another context and illustrate the usefulness of
Hexad user types as a static factor to explain user preferences in this domain (R2).
On a more abstract level, these findings show that considering contextual motiva-
tion (operationalized through SoC; increasing SoC reflecting more intrinsic moti-
vation (Mullan and Markland 1997) might complement static factors such as the
Hexad model and should be investigated further in future work. As a limitation, it
should be noted that the storyboards, although evaluated for their suitability, are a
matter of interpretation. This is particularly relevant for how the gameful design
elements were illustrated and described. Related to this, the main limitation of the
online study is the utilization of storyboards and assessing perceived persuasion.
While this approach is common in personalization research targeting gameful sys-
tems(Orji etal. 2013, 2018; Halko and Kientz 2010; Hallifax etal. 2019; Altmeyer
etal. 2018a; Orji et al. 2014), it does not allow to assess actual effects when giv-
ing participants the chance to experience a gameful application and interact with
its gameful design elements. To bridge this limitation, we implemented a game-
ful application encouraging physical activity and investigated its effects on perfor-
mance, affective experiences and enjoyment.
8.2 Laboratory study
In the laboratory study, we used the aforementioned gameful application and inves-
tigated its effectiveness and the effects of behavioral intentions and Hexad user
types. We used the findings from the online study to decide which gameful design
elements to use. Consequently, we ended up using Badges, Challenges and Social
Competition. These elements were shown to be perceived as significantly more per-
suasive for user in the High-SoC group in the online study. Also, expected correla-
tions were found between the perceived persuasiveness of these three elements and
the Hexad user types such as Socialiser, Achiever and Player. Thus, by using these
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Potential andeffects ofpersonalizing gameful fitness…
gameful design elements, we expected to see positive effects on the aforementioned
dependent variables for High-SoC users and users scoring particularly high on the
Socialiser-, Achiever-, or Player- factors of the Hexad.
As a first step of our analysis, we investigated whether the gameful elements
used in ”Endless Universe” are effective (H1). We found that ”Endless Universe”
led to a significant increase in covered distance on the treadmill (R3) and also to a
subjectively higher exertion (R4), thus supporting H1a: The covered distance is
higher when using “Endless Universe” . This finding is important as it replicates
previous research(Aldenaini etal. 2020; Koivisto and Hamari 2019) and thus dem-
onstrates the validity of the gameful application itself. We also analyzed whether
there is a difference in factors of the IMI. We found that perceived competence and
perceived pressure are significantly higher when using ”Endless Universe” (R5)
and that perceived choice is significantly lower (R6). The increased perceived com-
petence is considered as a positive predictor of intrinsic motivation and thus con-
tributes positively to the enjoyment of ”Endless Universe”(Ryan etal. 2006). On
the other hand, perceived pressure is considered as a negative predictor of intrinsic
motivation(Wilde et al. 2009). However, the increase in perceived pressure might
also be related to a higher immersion, an enhanced focus on the task and thus a
higher sense of flow(Harms etal. 2015; Csikszentmihalyi 1997). Therefore, the sig-
nificant increase in perceived pressure might be perceived both negatively and posi-
tively and should be studied in future work. The fact that perceived choice is sig-
nificantly lower when using ”Endless Universe” might be related to the introduction
of gameful design elements, which establish certain goals and norms which might
establish more guidance and thus lead to less choice. Taking R5 and R6 together,
we consider H1b: Users perceive running as more enjoyable using “Endless
Universe” as partially supported. Since no significant effects were found regarding
positive or negative affect, H1c: Users have stronger affective experiences with ”
Endless Universe is not supported. These mixed results regarding user experience
(H1b, H1c) might be related to interpersonal differences in the perception of game-
ful design elements, which have been shown by previous research (Tondello etal.
2016; Orji etal. 2018) and as part of the online study (R1,R2).
Therefore, as a next step, we analyzed whether such interpersonal differences
could be explained by considering the behavioral intention and Hexad user type of
participants. Regarding behavioral intentions (H2), we did not find any significant
effects between Low- and High-SoC users regarding distance improvement or per-
ceived exertion. Thus, H2a: The improvement in distance is higher for High-
SoC users is not supported, given our data. A potential reason could be related to
observer effects, i.e., the effect that participants act more ethically, more conscien-
tiously or more efficiently when being observed(Monahan and Fisher 2010). During
the experiment, one researcher was in the same room as the participant. This might
have affected Low-SoC users more than High-SoC users to improve their perfor-
mance, since Low-SoC users might have wanted to avoid drawing attention to the
fact that they were performing worse than others. Consequently, they might have
powered more in the baseline, but could not improve in the intervention. Regarding
H2b: High-SoC users perceive “Endless Universe” as more enjoyable, we found
no significant differences on the respective IMI factors (enjoyment, competence).
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Thus, this hypothesis cannot be supported. However, it should be noted that the
sample size to compare the Low- and High-SoC users was rather small (10 partici-
pants per group), which means that the chance of not finding small- to medium-sized
effects is relatively high. Therefore, we acknowledge that the absence of significant
effects (H2a, H2b) should not be seen as supporting evidence for the respective null
hypotheses. Descriptively, both factors were considerably higher in the High-SoC
group, which might suggest that a significant difference could have been found with
more participants in each group and that the size of the actual effect was too small to
be detected with a total N of 20. Finally, we found a significant increase in both pos-
itive and negative affects among High-SoC users (R7). This supports H2c: High-
SoC users have stronger affective experiences.
The fact that positive affect was significantly higher when using ”Endless Uni-
verse” supports that tailoring a gameful application to the SoC of users positively
affects the user experience. Given that also negative affect was significantly higher
when using ”Endless Universe,” these results need to be interpreted more carefully.
There is a lot of criticism of considering positive and negative affect as polar oppo-
sites (Russell and Carroll 1999). Research has found strong positive correlations
between the latent factors of positive and negative affect. Also, the instrument that
we used, PANAS, actually does not measure opposite affective experiences (as the
names of the latent variables might suggest)(Russell and Carroll 1999). In fact, the
items of positive affect were chosen to represent a latent variable (named positive
affect), which is defined as activation plus pleasantness. The negative items were
chosen to represent a latent construct (named negative affect) defined as activation
plus unpleasantness(Watson etal. 1988; Russell and Carroll 1999). This shows that
these two latent constructs are not opposite on activation, which ultimately means
that they are not opposite. We also found supporting evidence of this effect in our
data. When analyzing a potential correlation between positive and negative affects,
which should be strongly negative, assuming a bipolarity of both latent variables, we
found that there exists an insignificant positive correlation between positive and neg-
ative affects (Kendall’s
𝜏
= 0.25, p=0.17). This supports the assumption that activa-
tion was the deciding cause for the increase in negative affect, instead of unpleasant-
ness. This assumption is further supported by research showing that negative affect
can lead to a positive user experience, especially within gameful systems(Bopp
et al. 2016). Thus, we conclude that the increase in negative affect seems to be
related to higher arousal and activation. Considering a significant increase in pos-
itive affect, this allows to interpret the results related to affective experience in a
way that supports the assumption of a better user experience when using ”Endless
Universe.
Regarding Hexad user types, we found no evidence for H3a: The improvement
in distance is higher for AC, PL, SO. Considering that correlations between game-
ful design elements and Hexad user types using self-reported measures were rather
weak(Tondello etal. 2016; Orji etal. 2018), the absence of significant correlations
between the improvement in distance and these Hexad user types might be related
to the low sample size and the resulting low test power. Future work should con-
sider a higher number of participants in order to be able to detect small- to medium-
sized correlations. However, it should be noted that we found a negative correlation
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Potential andeffects ofpersonalizing gameful fitness…
between the Disruptor and distance improvement (R9), suggesting that Hexad user
types seem to have an actual effect on performance. Furthermore, we found that per-
ceived competence was positively correlated to the Socialiser user type (R8) and
that perceived pressure was negatively correlated to the Free Spirit user type (R10).
R8 can be interpreted as partially supporting evidence for H3b: AC, PL, SO per-
ceive “Endless Universe” as more enjoyable. For Players and Achievers, no signif-
icant correlations were found, meaning that H3b is only supported for the Socialiser.
However, taking also R10 into account, the importance of Hexad user types as a fac-
tor moderating the user experience in a gameful fitness application is strengthened
and should be investigated further in upcoming interventions. Lastly, we did not find
significant correlations regarding affective experiences; thus, H3c: AC, PL, SO
have stronger affective experiences is not supported, given our data. Potentially,
this could indicate that tailoring for Hexad user types affects measures related to
motivation and the perception of gameful design elements more than the measures
related to emotional responses evoked by those gameful design elements. However,
this needs to be investigated in future work. Also, it should be noted that we used
concrete implementations of gameful design elements, implying that certain design
decisions needed to be made, which in turn might have affected the perception of the
gameful design elements.
Finally, regarding the question of whether gameful fitness systems should be per-
sonalized using behavior change intentions or Hexad user types, the short answer
based on our findings is ”most probably yes.” No evidence was found for person-
alization affecting immediate performance-related measures (H2a, H3a). However,
we found significant positive effects on the user experience of participants (H2c,
H3b). This indicates that personalization using behavior change intentions or Hexad
user types might affect the performance or behavior of users in the long run, i.e., the
improved user experience might lead to improved retention rates and participants
might be more motivated to keep increasing their physical activity. Consequently,
beneficial effects on the performance and behavior of users are expected when con-
ducting studies over a longer time span. This is an important direction that should be
followed in future work.
9 Conclusion andfuture work
We investigated behavior change intentions and Hexad user types as factors to
personalize gameful fitness systems. First, we created storyboards explaining
twelve commonly used gameful design elements in the context of encouraging
walking. These storyboards were made publicly available for replication purposes
and to allow their usage for future studies. After showing that these storyboards
explain the intended gameful design elements in a qualitative pre-study, we used
them in an online study to explore whether behavioral intentions and Hexad user
types moderate the perceived persuasiveness of them. The findings of this study
supported the importance of both factors for personalization, since we found sig-
nificant differences between Low- and High-SoC users for several gameful design
elements as well as replicated previously found correlations between Hexad user
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types and gameful design elements in the physical activity context. Next, we used
these findings to conceptualize and implement a personalized, gameful system
encouraging physical activity on a treadmill. This system was used in a labora-
tory study to analyze whether personalization based on the findings of the story-
boards-based online study has any effect on the users’ performance, enjoyment
and affective experiences while running. This is important, as it tackles the prob-
lem that storyboards-based studies (which have been mostly used in past research)
do not allow users to interact and experience gameful design elements. Our labo-
ratory study showed that personalization based on behavior change intentions and
Hexad user types does not seem to affect the immediate performance. However,
significant effects were found on the user experience, i.e., on motivational aspects
and on affective experiences. This improved user experience suggests that the
behavior and performance of users might be positively affected in the long run,
when personalizing gameful fitness applications.
Therefore, future work should investigate effects of personalization in user
studies over a longer time span. This would allow to investigate whether the
increased user experience leads to an increased performance over time. As an
alternative, giving users the chance to decide whether they would want to use
the system regularly would have provided more insights on potential effects on
the behavior of users and could be studied in future work, too. Also, in-the-wild
studies should be conducted to alleviate potential observer effects and study the
impact of personalization in a more natural setting. This would shed light on
whether the absence of effects when tailoring for the SoC is related to an observer
bias in the laboratory setting. It is also important to investigate the impact of per-
sonalization on the user experience further to better understand the reasons for
the effects that were found in this article, e.g., if the increase in the pressure factor
of the IMI when personalizing the gameful application is related to an increased
immersion. Also, more participants should be recruited, to be able to find effects
with smaller effect sizes (which were reported in previous research). This would
decrease the chance of type II errors, i.e., stating that there is no effect when
a true effect is to be found, especially regarding potential correlations between
Hexad user types and performance-related measures. Additionally, future work
should investigate whether our findings can be replicated in different health-
related contexts or using different gameful applications, to analyze the external
validity of our results. We also recommend to investigate different ways of opera-
tionalizing context-related motivation, besides behavior change intentions, as this
factor is neglected in personalization research for gameful applications. Finally,
it seems worthwhile to study different measures such as flow and immersion to
better understand the effects of personalization on the user experience. Related
to this, more objective variables such as psychophysiological measures could be
taken into account to better understand the various user experience-related find-
ings of our studies.
Acknowledgements This work is partially supported by the German Federal Ministry of Education and
Research (16SV8364).
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707
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Potential andeffects ofpersonalizing gameful fitness…
Funding Open Access funding enabled and organized by Projekt DEAL.
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mons licence, and indicate if changes were made. The images or other third party material in this article
are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the
material. If material is not included in the article’s Creative Commons licence and your intended use is
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directly from the copyright holder. To view a copy of this licence, visit http://creat iveco mmons .org/licen
ses/by/4.0/.
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M.Altmeyer et al.
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Maximilian Altmeyer is a researcher at the German Research Center for Artificial Intelligence and a PhD
student at Saarland University. In his research, he investigates the effects of gamification on the moti-
vation of users in behavior change contexts, whether motivation can be enhanced through personalized
gameful design, which factors are relevant to consider for personalization and which effects personaliza-
tion has on users in gameful applications. Max published several papers at top conferences such as CHI
and CHI Play.
Pascal Lessel is a postdoctoral researcher at the German Research Center for Artificial Intelligence. His
research interests are situated in gaming-related contexts such as gamification and game live-streams.
In these contexts, he investigates specifically whether offering more autonomy for users is reasonable
and which benefits can be achieved through this. Understanding ways to customize and personalize these
experiences is another important research direction for him, for example, here, he does research on “bot-
tom-up” gamification, i.e., enabling users to decide on their gamification at the system’s runtime. Pascal
published several papers at top conferences such as CHI and CHI Play.
Subhashini Jantwal is a former graduate student at Saarland University, now working as a user experi-
ence designer. She received her Bachelor’s degree in Computer Science Engineering from Uttarakhand
Technical University. After developing a keen interest in human–computer interaction, she pursued her
Master’s in Media Informatics from Saarland University with a focus on design and implementation of
gameful software solutions. As a part of her Master’s thesis, she worked under the Media Informatics
chair at the German Research Center for AI, at Saarbrücken. Her interests include behavioral sciences,
computer science and media design pertaining to designing innovative human–machine interactions.
Linda Muller is a former undergraduate student at Saarland University, where she received her Bach-
elor’s degree in Media Informatics. Her thesis focused on personalized gameful design in health-related
contexts and the role of Hexad user types and behavior change intentions. Her research interests include
human–computer interaction, games and gameful design.
Florian Daiber is a postdoctoral researcher at the Ubiquitous Media Technology Lab (UMTL) at the
German Research Center for Artificial Intelligence (DFKI) in Saarbrücken, Germany. His main research
is in the field of human–computer interaction, 3D user interfaces and ubiquitous sports technologies. Cur-
rently, Florian is mainly involved in projects on 3D interaction in mixed realities and wearable technolo-
gies for sports and health. Florian is co-organizing the UbiComp Workshop on Ubiquitous Computing in
the Mountains, the Workshop on Understanding human–computer interaction in Outdoor Recreation, the
workshop on everyday proxy objects for virtual reality and the workshop on cross-reality (XR) interac-
tion. He served on various program committees in the field of human–computer interaction, e.g., ACM
ETRA, IEEE VR, and ACM Symposium on Computer–Human Interaction in Play (CHI PLAY).
Antonio Krüger is a CEO and scientific director of the German Research Center for Artificial Intelli-
gence GmbH (DFKI) and head of the department “Cognitive Assistants” at DFKI. He is a full professor
for Computer Science at Saarland University (since 2009), Head of the Ubiquitous Media Technology
Lab and scientific director of the Innovative Retail Laboratory (IRL) at DFKI. From 2004 to 2009, he was
a professor of computer science and geoinformatics at the University of Münster and acted as the manag-
ing director of the institute for geoinformatics. He studied computer science and economics at Saarland
University and finished his PhD in 1999 as a member of the Saarbrücken graduate school of Cognitive
Science. Antonio has published more than 200 scientific articles and papers in internationally recognized
journals and conferences and is member of several steering committees, editorial boards and scientific
advisory committees.
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... One of the most popular applications of gamification is using game elements in fitness applications [3,4]. However, mixed outcomes have let gamification research both in general [5] and in the fitness context [6,7] question the applicability of universal design and increasingly focus on individual differences in how gamification is perceived and used to guide personalized gamification design [5]. Instead of executing a one-size-fits-all design, the prospect of personalisation and adaptivity affords a design that can be automatically informed, rearranged, and redeployed based on users' actions and reactions [5]. ...
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... All of these typologies share common concepts that are reflected in certain types, such as achievement, exploration or sociability [9], and several researchers have attempted to relate the different user types to each other [8,9,13] (see Table 1 for an overview). For example, the concept of achievement is prevalent in each of the four typologies, while the HEXAD model adds the Player as a type that is extrinsically rather than intrinsically driven [6]. In contrast, the BrainHex model describes Survivors and Daredevils motivated by intense gaming experiences absent in other typologies [13]. ...
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... In the last decade, gamification (i.e., the design approach of products, activities, services and systems to create similar motivational experiences as games usually create [1]), has increasingly become an important research topic in different contexts [2]- [5]. The application of gamification in contexts such as education [6], health [7], and government services [8], in general, seeks to affect the user behavior, engaging them during the use of gamified environments [9]. ...
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... This observation indicates that tailored and personalized interventions perform better with respect to their effectiveness in promoting disease awareness and adoption of preventive measures. Previous research has also found that personalizing persuasive game interventions is essential to handle interpersonal differences (Altmeyer et al., 2021). Therefore, we recommend designers to develop users' models and personalize the persuasive game accordingly. ...
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ABSTRACT Persuasive games are widely implemented in the healthcare domain to promote behaviour change among individuals. Previous research shows that using persuasive games increases motivation and awareness, leading to a positive change in behaviour. However, there is little knowledge on which persuasive strategies will motivate people at different Stages of Behaviour Change and whether tailoring persuasive games to match users’ stages of change will increase their effectiveness with respect to their motivational appeal towards promoting disease awareness and prevention using the ARCS motivation scales and their intention to adopt the precautionary measures. To address this gap, using COVID-19 as a case study, we designed two different versions of a persuasive game, called COVID Pacman, using different persuasive strategies. The two versions of the game target the same goal of motivating the adoption of precautionary measures. We conducted a quantitative study (N=127) followed by semistructured interviews of 18 participants. The results of conducting an ANOVA on the quantitative data and thematic analysis on the qualitative study show that tailoring the persuasive games to individual’s stages of change by using appropriate persuasive strategies increased their effectiveness with respect to their ability to motivate people to adopt the precautionary measures towards disease prevention compared to the non-tailored version.
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This study aims to determine the effect of perceived usefulness, perceived ease of use, attitude toward using, and behavioral intention to use the service of e-commerce-based accounting information systems on Millennial MSME performance. The population in this research were all Millennial MSME actors in Medan City. The research sample comprised 46 respondents from this population using the purposive sampling method. According to the findings of this research, perceived usefulness has a positive and significant effect on MSMEs' performance, perceived ease of use has no significant effect on MSMEs' performance, attitude toward using has no significant effect on MSMEs' performance, and behavioral intention to use has no significant effect on MSMEs' performance. However, perceived usefulness, perceived ease of use, attitude toward using, and behavioral intention to use all significantly impact MSMEs' performance. This research highlights the effectiveness of MSME technology acceptance in adopting e-commerce and provides an overview of the condition of MSMEs amid technological advances, as well as empirical evidence to carry out further guidance for MSME actors.
Thesis
Gamification, the use of game elements in non-game contexts, has been shown to help people reaching their goals, affect people's behavior and enhance the users' experience within interactive systems. However, past research has shown that gamification is not always successful. In fact, literature reviews revealed that almost half of the interventions were only partially successful or even unsuccessful. Therefore, understanding the factors that have an influence on psychological measures and behavioral outcomes of gamified systems is much in need. In this thesis, we contribute to this by considering the context in which gamified systems are applied and by understanding personal factors of users interacting with the system. Guided by Self-Determination Theory, a major theory on human motivation, we investigate gamification and its effects on motivation and behavior in behavior change contexts, provide insights on contextual factors, contribute knowledge on the effect of personal factors on both the perception and effectiveness of gamification elements and lay out ways of utilizing this knowledge to implement personalized gamified systems. Our contribution is manifold: We show that gamification affects motivation through need satisfaction and by evoking positive affective experiences, ultimately leading to changes in people's behavior. Moreover, we show that age, the intention to change behavior, and Hexad user types play an important role in explaining interpersonal differences in the perception of gamification elements and that tailoring gamified systems based on these personal factors has beneficial effects on both psychological and behavioral outcomes. Lastly, we show that Hexad user types can be partially predicted by smartphone data and interaction behavior in gamified systems and that they can be assessed in a gameful way, allowing to utilize our findings in gamification practice. Finally, we propose a conceptual framework to increase motivation in gamified systems, which builds upon our findings and outlines the importance of considering both contextual and personal factors. Based on these contributions, this thesis advances the field of gamification by contributing knowledge to the open questions of how and why gamification works and which factors play a role in this regard.
Conference Paper
Full-text available
A pandemia da Covid-19 mudou a vida social das pessoas. A educação, em especial, não passou ilesa a esse processo, enfrentando uma situação que trouxe, entre outros aspectos, o choque de trocar a sala de aula por atividades do ensino remoto. Para tanto, a gamificação social é uma alternativa que pode impactar a experiência dos estudantes no ensino remoto. Diante disso, realizamos um estudo com 17 estudantes e uma professora para entender como a gamificação social influencia o processo de ensino e aprendizagem no ensino remoto. Os resultados demostram que a gamificação social pode impactar a experiência dos estudantes, mas chama atenção para a necessidade de abordar outros aspectos da personalização da gamificação.
Article
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Persuasive technology (PT) is increasingly being used in the health and wellness domain to motivate and assist users with different lifestyles and behavioral health issues to change their attitudes and/or behaviors. There is growing evidence that PT can be effective at promoting behaviors in many health and wellness domains, including promoting physical activity (PA), healthy eating, and reducing sedentary behavior (SB). SB has been shown to pose a risk to overall health. Thus, reducing SB and increasing PA have been the focus of much PT work. This paper aims to provide a systematic review of PTs for promoting PA and reducing SB. Specifically, we answer some fundamental questions regarding its design and effectiveness based on an empirical review of the literature on PTs for promoting PA and discouraging SB, from 2003 to 2019 (170 papers). There are three main objectives: (1) to evaluate the effectiveness of PT in promoting PA and reducing SB; (2) to summarize and highlight trends in the outcomes such as system design, research methods, persuasive strategies employed and their implementaions, behavioral theories, and employed technological platforms; (3) to reveal the pitfalls and gaps in the present literature that can be leveraged and used to inform future research on designing PT for PA and SB.
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Persuasive technologies have been identified as a potential motivational tool to tackle the rising problem of physical inactivity worldwide, with research showing they are more likely to be successful if tailored to the target audience. However, in the physical activity domain, there is limited research on how culture moderates users’ susceptibility to the various persuasive features employed in mobile health applications aimed to motivate behavior change. To bridge this gap, we conducted an empirical study among 256 participants from collectivist (n = 67) and individualist (n = 189) cultures to determine their culture-specific persuasion profiles with respect to six persuasive features commonly employed in fitness applications on the market. The persuasive features include two personal features (goal-setting/self-monitoring and reward) and four social features (competition, cooperation, social learning and social comparison). We based our study on the rating of storyboards (on which each of the six persuasive features is illustrated) and the ranking of the six persuasive features in terms of perceived persuasiveness. The results of our analysis showed that users from individualist and collectivist cultures significantly differ in their persuasion profiles. Based on our rating measure, collectivist users are more likely to be susceptible to all six persuasive features (personal and social) than individualist users, who are only likely to be susceptible to personal features. However, based on our ranking measure, individualist users are more likely to be susceptible to personal features (goal-setting/self-monitoring and reward) than collectivist users. In contrast, collectivist users are more likely to be susceptible to social features (cooperation and social learning) than individualist users. Based on these findings, we provide culture-specific persuasive technology design guidelines. Our study is the first to uncover the moderating effect of culture on users’ susceptibility to commonly employed persuasive features in fitness applications.
Conference Paper
Full-text available
Gamification is widely used to foster user motivation. Recent studies show that users can be more or less receptive to different game elements, based on their personality or player profile. Consequently, recent work on tailored gamification tries to identify links between user types and motivating game elements. However findings are very heterogeneous due to different contexts, different typologies to characterize users, and different implementations of game elements. Our work seeks to obtain more generalizable findings in order to identify the main factors that will support design choices when tailoring gamification to users' profiles and provide designers with concrete recommendations for designing tailored gamification systems. For this purpose, we ran a crowdsourced study with 300 participants to identify the motivational impact of game elements. Our study differs from previous work in three ways: first, it is independent from a specific user activity and domain; second, it considers three user typologies; and third, it clearly distinguishes motivational strategies and their implementation using multiple different game elements. Our results reveal that (1) different implementations of a same motivational strategy have different impacts on motivation, (2) dominant user type is not sufficient to differentiate users according to their preferences for game elements, (3) Hexad is the most appropriate user typology for tailored gamification and (4) the motivational impact of certain game elements varies with the user activity or the domain of gamified systems.
Thesis
Full-text available
Gameful design, the process of creating a system with affordances for gameful experiences, can be used to increase user engagement and enjoyment of digital interactive systems. It can also be used to create applications for behaviour change in areas such as health, wellness, education, customer loyalty, and employee management. However, existing research suggests that the qualities of users, such as their personality traits, preferences, or identification with a task, can influence gamification outcomes. It is important to understand how to personalize gameful systems, given how user qualities shape the gameful experience. Current evidence suggests that personalized gameful systems can lead to increased user engagement and be more effective in helping users achieve their goals than generic ones. However, to create these kinds of systems, designers need a specific method to guide them in personalizing the gameful experience to their target audience. To address this need, this thesis proposes a novel method for personalized gameful design divided into three steps: (1) classification of user preferences, (2) classification and selection of gameful design elements, and (3) heuristic evaluation of the design. Regarding the classification of user preferences, this thesis evaluates and validates the Hexad Gamification User Types Scale, which scores a person in six user types: philanthropist, socialiser, free spirit, achiever, player, and disruptor. Results show that the scale’s structural validity is acceptable for gamification studies through reliability analysis and factor analysis. For classification and selection of gameful design elements, this thesis presents a conceptual framework based on participants’ self-reported preferences, which classifies elements in eight groups organized into three categories: individual motivations (immersion and progression), external motivations (risk/reward, customization, and incentives), and social motivations (socialization, altruism, and assistance). And to evaluate the design of gameful applications, this thesis introduces a set of 28 gameful design heuristics, which are based on motivational theories and gameful design methods and enable user experience professionals to conduct a heuristic evaluation of a gameful application. Furthermore, this thesis describes the design, implementation, and pilot evaluation of a software platform for the study of personalized gameful design. It integrates nine gameful design elements built around a main instrumental task, enabling researchers to observe and study the gameful experience of participants. The platform is flexible so the instrumental task can be changed, game elements can be added or removed, and the level and type of personalization or customization can be controlled. This allows researchers to generate different experimental conditions to study a broad range of research questions. Our personalized gameful design method provides practical tools and clear guidelines to help designers effectively build personalized gameful systems.
Chapter
Full-text available
The concept of goals is prominent in information systems and also artificial intelligence literature such as goal-oriented requirements engineering and self-adaptive systems. Digital motivation systems, e.g. gamification and persuasive technology, utilise the concept of behavioural goals which require a different mind-set on how to elicit and set them up, how to monitor deviation from such goals and how to ensure their completion. Behavioural goals are characterised by a range of factors which are not the main focus in classic information systems and AI literature such as self-efficacy, perceived usefulness. To engineer software supporting goal setting, a concretised taxonomy of goals would help a better-managed analysis and design process. In this paper, we provide a detailed classification of behavioural goals and their associated properties and elements (types, sources, monitoring, feedback, deviation and countermeasures). As a method, we review the literature on goal setting theory and its application in different disciplines. We subsequently develop five reference checklists which would act as a reference point for researchers and practitioners in persuasive and motivational systems.
Conference Paper
Self-Determination Theory (SDT), a major psychological theory of human motivation, has become increasingly popular in Human-Computer Interaction (HCI) research on games and play. However, it remains unclear how SDT has advanced HCI games research, or how HCI games scholars engage with the theory. We reviewed 110 CHI and CHI PLAY papers that cited SDT to gain a better understanding of the ways the theory has contributed to HCI games research. We find that SDT, and in particular, the concepts of need satisfaction and intrinsic motivation, have been widely applied to analyse the player experience and inform game design. Despite the popularity of SDT-based measures, however, prominent core concepts and mini-theories are rarely considered explicitly, and few papers engage with SDT beyond descriptive accounts. We highlight conceptual gaps at the intersection of SDT and HCI games research, and identify opportunities for SDT propositions , concepts, and measures to more productively inform future work.
Conference Paper
Gamified systems may help older adults to remain physically, cognitively and socially active, which has positive effects on well-being. However, social aspects of psychological well-being change during life course, i.e., the importance of positive social relationships for well-being increases between young or middle aged persons and seniors. In this paper, we explore whether these changes are reflected in the game preferences of seniors aged 75 and older. We report findings of a semi-structured interview and a preliminary player classification survey (N=18, mean age=84.61). We found indications that there are differences in game preferences and the perception of game elements that are related to the increased importance of social relationships for well-being in older ages.
Conference Paper
The motivational impact of gamification elements differs substantially across users. To account for these differences, we investigate Hexad user types and behavior change intentions as factors to personalize gamifed, persuasive fitness systems. We conducted an online study (N = 179), measuring the perceived persuasiveness of twelve gamification elements using storyboards. Results show the applicability of the Hexad user type in the Physical Activity domain. Besides replicating correlations between gamification elements and user types, we also found correlations which were hypothesized in literature, but not yet shown. Our main contribution is to show that behavior change intentions influence the perception of gamification elements in general and affect the set of relevant elements for each user type. Since a static set of elements has been suggested for each user type so far, this is an important finding, leading to potentially more effective personalization approaches.
Conference Paper
Sedentary behavior is a determining cause for numerous illnesses, such as cardiovascular diseases or diabetes. To counter this, the World Health Organization recommends at least 30 minutes of activity, such as walking, every day. We present a system making use of fitness trackers to gather step counts and showing them using a gamified mobile app and a gamified public display. We performed a four-week in-the-wild study (N=12) to evaluate the system and the effects of introducing a public display on walking behavior. We found that adding the public display significantly increased step counts and users' motivation to walk, which seems to be attributable to the significant increase in social relatedness. In semi-structured interviews, we learned that besides encouraging socialization, the public display made participants aware that their step data is visible for outsiders and that they could be confronted with their performance, providing additional motivation to increase their step counts.