Exploring Personality and Game Preferences in the Younger and
Older Population: A Pilot Study
A. F. A. de Vette
1
, M. Tabak
1,2
, M. G. H. Dekker-van Weering
2
and M. M. R. Vollenbroek-Hutten
1,2
1
University of Twente, Faculty of Electrical Engineering, Mathematics and Computer Science, Telemedicine Group,
P.O. Box 217, 7500 AE Enschede, The Netherlands
2
Roessingh Research and Development, Telemedicine Group, P.O. Box 310, 7500 AH Enschede, The Netherlands
Keywords: Gaming, Game Preference, Gamification, Game-based, Telemedicine, Personality, Player Type, Tailoring,
Adherence, Older Adult, Elderly.
Abstract: Aim: Engagement in gamified applications can be increased by effectively meeting end-user preferences for
game content. To design this tailored content insight in user preferences is necessary, obtained from user
classification models. This pilot study aims to explore the hypothesised relation between personality traits
and preference for game characteristics that is the basis for a new user classification model, deduced from the
Five Domains of Play theory.
Methods: An online questionnaire consisted of the 10-item Big Five Inventory to determine personality, and
five questions on the preference for game examples to determine game preference.
Results: 216 participants completed the questionnaire (M=39 years, SD=17). For the group of participants
younger than 60, four out of five personality traits correlate significantly but weakly with their corresponding
game preference domains (r=0,13-0,30, p<0,05). For the participants older than 60, no significant correlations
were found.
Conclusion: Personality and game preferences are weakly related in persons younger than sixty years old,
while no relation was found for the older participants. For the latter, this may be due to a lack of gaming
experience. We therefore propose to extend research towards a field study by providing actual games to play
on beforehand.
1 INTRODUCTION
Demographic changes, such as population growth and
longer life expectancy, increase the burden on the
healthcare system. Telemedicine can alleviate this
burden by enabling professionals to provide care at
distance in patients’ daily environment (Jansen-
Kosterink, 2014). A group that can particularly
benefit from telemedicine is the older adult (Pavel et
al, 2009). Telemedicine supports them in maintaining
a healthier lifestyle that maintains autonomy,
independence and quality of life. Adherence to
telemedicine interventions is related to improved
health outcomes (Huis in ‘t Veld et al., 2010), but is
low and decreasing over time (Tabak et al., 2014,
Evering et al., 2013). A possible explanation for the
drop in adherence is the lack of motivation given by
a professional in face-to-face contact, or plain
boredom. Gamification is identified as a possible
strategy to increase adherence by adding the
motivational pull that (video) games inherently have,
although it is unknown how to apply gamification to
produce the needed long-term engagement in
telemedicine. This is particularly the case for the
older adult, since their preferences are not known
from being an unlikely consumer of modern video
games. Therefore, there is a need for methods to
create gamification for telemedicine solutions, which
are tailored to the end-user preferences for optimal
engagement.
User engagement, which is key to adherence, is
known to significantly increase when preferences of
the user are effectively met (Petty et al., 1979, Bakkes
et al., 2012). Our previous research showed several
classifications, to categorise both younger and older
users (De Vette et al, 2015), based on for example in-
game behaviour or gaming motivation. Examples
include Bartle (Bartle, 1996), Yee (Yee, 2005) and
Vette, A., Tabak, M., Weering, M. and Vollenbroek-Hutten, M.
Exploring Personality and Game Preferences in the Younger and Older Population: A Pilot Study.
In Proceedings of the International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AWE 2016), pages 99-106
ISBN: 978-989-758-180-9
Copyright
c
2016 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
99
LeBlanc (Hunicke et al., 2004). These taxonomies are
designed for use in a specific application, such as
enterprise gamification or massive multiplayer online
roleplay games (MMORPGs, for example World of
Warcraft). It is unknown if these are suitable for
application in gamification design for telemedicine
solutions. Furthermore, we foresee that older adults
demand a different approach, as we also know is the
case with many exergames (Gerling et al., 2010), but
none of the classifications presented target the older
user specifically.
The Five Domains of Play theory (5D)
(Vandenberghe, 2012) could potentially be suitable
for use in telemedicine context. It stands out from the
other classifications mentioned as it creates a profile
of user preferences, rather than a singular label, based
on personality which is an understanding applicable
to all ages. According to this theory, people strive to
express or satisfy their personality traits in games,
whether these already are expressed in daily life or
not. The 5D theory translates the five factors of the
Five Factor Model of personality (FFM) into five
aspects of gaming motivation i.e. game content
characteristics (table 1). According to the theory, the
score of the personality trait should correlate
positively with the score of the translated game
domain: a high score on Openness to Experience
would mean a preference for a game with high
Novelty game content characteristics.
A negative correlation however may be expected
between Neuroticism and Threat. As far as we know
these relations have not been investigated.
So far, studies present inconsistent results on
predicting the effectiveness of the application of
personality in gaming (Orji et al., 2014, Zammitto,
2010, Teng, 2009). In one study, personality traits
have been related to preference for game genres and
low predictive capability was found (Zammitto,
2009), while another study shows a significant
correlation between personality and game genres
(Peever et al., 2012). Game genres however do not
describe explicit game features and may not give an
accurate indication of game preferences (Apperley,
2006).
If a classification based on the 5D theory indeed
appears to be viable, it can give us insight in the user
that is essential to design tailored content. Moreover,
it provide us with a method to dissect games into
distinctive elements. There is a need for a reliable
classification of telemedicine end-users according to
their preferences, to aid the development of the
tailored game content that is essential for creating
engaging applications through gamification. As a first
step toward creating tailored game content, we
investigate the hypothesised existence of a relation
between personality and game preference based on
the 5D theory, for the younger as well as for the older
adult.
2 METHODS
2.1 Participants, Materials and
Measures
Participants were recruited through Facebook and via
e-mail to colleagues and other acquaintances, who
forwarded the request to others as well. Furthermore,
Alifa, a local social community service centre in
Enschede, the Netherlands, sent requests to its older
volunteers.
Table 1: The Five Factor Model of personality and the Five Domains of Play model, including personality/game preferences
for both extremes (deduced by authors from Vandenberghe, 2012).
FFM factor
low score 5D domain high score
Cautious, predictable Openness to Experience Inventive, curious
Conventional experiences Novelty Open, imaginative experiences
Careless, impulsive Conscientiousness Efficient, organised
Low difficulty, contentment Challenge High difficulty, achievement
Reserved, solitary Extraversion Energetic, outgoing
Single-player, slow pace Stimulation Multi-player, excitement
Analytical, detached Agreeableness Friendly, compassionate
Violence, competition Harmony Cooperation, altruism
Confident, secure Neuroticism Nervous, sensitive
Cheerful, calm Threat Stressful, hostile
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Measures were taken by means of an online
questionnaire, created in SurveyMonkey and
accessible through a URL. The questionnaire was
available both in English and in Dutch. Age and
gender were asked first. Play frequency was
determined by the question ‘How often do you
(approximately) play games?’ (answer possibilities
Daily, Weekly, Monthly, Once every 6 months, Once
a year or less). Play frequency was split into two
groups: 1) frequent (weekly or more, answers: daily,
weekly) and 2) non-frequent (montly or less, answers:
montly, once every six months, once a year or less).
Game preference was determined through five
questions, one for each of the five domains described
by the 5D theory (table 1). In each question, the game
content is described through an explanation and
depicted examples of a domain’s extremes. The
participant indicated his or her perceived level of
satisfaction (nine-point scale) from the proposed
game content (fig. 1). Personality was measured
subsequently, by means of the 10-item Big Five
Inventory (percentage from 5-point scale). This short
version reaches adequate levels in terms of
convergence with an established instrument, test
repeatability and reliability (Gosling et al., 2003,
Rammstedt et al., 2007). The FFM model suggests a
normal distribution of scores (ranging from 0 to 100
with an average score of 50 on each factor)
2.2 Statistical Analysis
Data analysis was performed using Statistical
Package for Social Sciences (SPSS v.22). Age was
partitioned in 1) younger than 60 (< 60) and 2) 60 or
older ( 60). We define an age threshold assuming
that people aged 60 and up have less affinity with
technology. First, the data for each person was sorted
for the five factors of the FFM model (abbreviated P,
from personality) and the five domains of the 5D
model (abbreviated G, from game preference),
together with their gender, age and play frequency.
Then, data distributions of the five factors and the five
domains were analysed using the Shapiro-Wilk test,
descriptives of skewness and kurtosis, boxplots and
histograms. There were no outliers found in analysis
of the boxplots. All factors and domains were found
non-normally distributed, even after data
transformation was applied (log2, log10, sqrt, x
2
).
Hence, correlations were explored with Spearman’s
Rho (rank correlation coefficient, appropriate for
ordinal variables), for all groups, using a significance
level of α = 0,05. Correlations between the factors and
related domains as intended by the models were
studied (Openness to Experience with Novelty,
Conscientiousness with Challenge, and so on). For
the subgroups < 60 and 60, correlations not
intended by the models, between all factors and
domains, were studied as well. We consider
correlation strength as the predictive value of the
personality characteristic for the preference on the
matching game preference domain. Correlation
strength is interpreted as follows: r < ± 0,1 is little or
no correlation, ± 0,1 r < ± 0,3 is a weak relation, ±
0,3 r < ± 0,5 is a moderate relation and r ± 0,5 a
strong relation.
3 RESULTS
3.1 Participant Characteristics
In total 243 persons filled in the questionnaire, of
which 216 fully completed. The Dutch version was
used in 67% of the cases. The age range was 16 to 81
years old, 66% of participants were male (n=143),
34% female (n=73). In fig. 2, an overview of
participant characteristics is given.
Mean values of scores per age group on
personality and game preference can be seen in table
2. Considering an expected average of 50 out of 100,
the scores on Openness (72 out of 100) and
Conscientiousness (66 out of 100) are relatively high.
The mean values for personality scores of the
different age groups are close together. Minor
differences are also found between the groups of
frequent and non-frequent players.
The mean values for game preference show more
variety, particularly the scores of the 60 group
(range 24-62). While it seems that personality has not
changed much for the older adult, game preferences
show differences compared to the younger group. For
example the score on the Novelty domain, which
would imply that the older player prefers games that
resemble the real world or are otherwise familiar.
Although not statistically convincing, the older adult
seems to prefer a somewhat lower amount of
Challenge than the younger group, while being more
Conscientious. Also, a lower level of Stimulation and
Threat are preferred by the older adult, as well as a
slightly higher level of Harmony.
3.2 Correlations between Personality
and Game Preference
When exploring the intended correlations between
personality (P) and game preference (G), we find the
following correlations, presented in table 3. No strong
Exploring Personality and Game Preferences in the Younger and Older Population: A Pilot Study
101
Figure 1: Question 1 on game preferences (excerpt: Novelty).
Figure 2: Overview of participants.
All
(n = 216)
Play frequency Age
Weekly (1, 2)
(n = 119)
Monthly (3, 4, 5)
(n = 97)
< 60
(n = 177)
60
(n = 39)
Weekly (1, 2)
(n = 98)
Monthly (3, 4, 5)
(n = 79)
Weekly (1, 2)
(n = 21)
Monthly (3, 4, 5)
(n = 18)
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Table 2: Mean values of personality and game preference scores (M = mean value, SE = standard error), all n = 216, < 60 n
= 177, 60 n = 39.
correlations were found (r ± .5), correlations range
from little or none at all to moderate. For all
participants as well as in both subgroups, a significant
correlation between Agreeableness and Harmony is
absent.
From the overview of correlations between
personality and game preference (table 3), we notice
weak significant correlations between three out of
five personality factors and their antagonist in the
game preference model for all participants, and four
for the group < 60 years old. In these two groups, we
find a negative significant correlation for
Conscientiousness with Challenge, which is
contradictory to the theory. For frequent players, the
preference for Novelty is significantly correlated to
Openness but not for non-frequent players. For non-
frequent players, Extraversion and Stimulation are
significantly correlated, which is not the case for
frequent players.
Exploring all possible correlations between
personality and game preferences for the group of age
< 60 (table 4), we notice multiple significant
correlations outside the factors as intended (shaded).
A moderate, significant correlation exists for
Openness to Experience and Challenge.
Agreeableness was not found to be related to
preference within the Harmony domain and also does
not show any (significant) correlations with other
game preference aspects. Other factors, Openness to
Experience, Conscientiousness and Extraversion, do
seem to influence the preference for content within
this domain. Stimulation correlates only with
Extraversion. Lastly, there is a significant positive
correlation for Neuroticism with Threat.
Exploring all correlations for the group of 60
years old (table 5), we only find a moderate,
significant correlation for Agreeableness and
Stimulation.
4 DISCUSSION
In this study, the relations between personality factors
and the preference for game content based on the Five
Domains of Play model were examined, in search for
methods to use in the tailoring of game content to the
user for the effective use of gamification. Our most
important findings are the presence of weak
significant correlations between personality and
game preference for people younger than 60 years
old, and the absence of significant correlations for
people aged 60 and up as intended by the model. Two
moderate significant correlations were found outside
the supposedly related traits (< 60 Challenge –
Openness and 60 Stimulation – Agreeableness), but
we are uncertain how to interpret these.
Personality seems to give an indication of game
preferences according to the theory for participants
younger than sixty years old, but the numbers are not
convincing. Four out of five relations as they were
intended by the original models correlate
significantly, although weakly for this group. This
could mean that the model can be used to determine
game content for certain user groups, but a refinement
of the game preference domains should be made in
order to deal with ambiguity and increase internal
consistency. For example, Agreeableness was not
found to be predictive for preference within the
Harmony domain as the model suggested which is
possibly due to the ambiguity of the Harmony
domain, seemingly showing contradictory traits for
scores on both sides of the domain spectrum. Also,
both competition and violence are supposed to
correspond with a low score on Agreeableness.
Among the findings is a negative relation between
Conscientiousness and Challenge. This is
inconsistent to the model and may imply that a highly
Exploring Personality and Game Preferences in the Younger and Older Population: A Pilot Study
103
Table 3: Correlation coefficients and significance between personality and game preferences.
Table 4: Overview of all correlations between personality (P) and game preference (G) for participants < 60.
Table 5: Overview of all correlations between personality (P) and game preference (G) for participants 60.
conscientious person would prefer an unchallenging
game rather than the opposite. Our findings suggest
that a more elaborate questionnaire is needed in future
studies to deeper examine the underlying facets that
both personality traits and game preference domains
consist of, so that these details are not covered by an
overall domain score.
No relations between personality and game
preference were found for participants of sixty years
and older. Although the smaller sample size may be
of influence, we expect that the older participants
were not able to relate to the game content in the
questionnaire to the same extent as the younger
group. Older adults, although increasingly interested
in video games, have a much different frame of
reference than people of later generations (Nap et al.,
2014) from being inexperienced or unaware of the
variety of games currently existing. There is a
possibility that preferences of the older group are
somewhat more uniform because of this awareness.
P_openness P_conscientiousness P_extraversion P_agreeableness P_neuroticism
Correlation Coefficient
,299
**
-,156
*
-,132
*
-,015
,217
**
Sig. (1-tailed)
,000 ,019 ,040 ,422 ,002
Correlation Coefficient
,318
**
-,191
**
-,093 ,026
,253
**
Sig. (1-tailed)
,000 ,006 ,110 ,366 ,000
Correlation Coefficient
,092 ,003
,129
*
-,033 -,003
Sig. (1-tailed)
,113 ,485 ,043 ,332 ,484
Correlation Coefficient
-,150
*
,157
*
,129
*
,059 ,013
Sig. (1-tailed)
,023 ,019 ,044 ,217 ,430
Correlation Coefficient
,276
**
-,255
**
-,095 ,015
,187
**
Sig. (1-tailed)
,000 ,000 ,104 ,421 ,006
* = Correlation is significant at the 0.05 level (1-tailed)
** = Correlation is significant at the 0.01 level (1-tailed)
Intended relation G and P
Correlations
< 60
Spearman's
rho
G_novelty
G_challenge
G_stimulation
G_harmony
G_threat
P_openness P_conscientiousness P_extraversion P_agreeableness P_neuroticism
Correlation Coefficient
-,059 -,152 -,020 ,049 -,147
Sig. (1-tailed)
,361 ,179 ,453 ,384 ,185
Correlation Coefficient
,055 -,085 ,135 -,001 -,146
Sig. (1-tailed)
,371 ,303 ,207 ,497 ,187
Correlation Coefficient
,091 ,180 -,038
,370
*
-,089
Sig. (1-tailed)
,291 ,137 ,409 ,010 ,295
Correlation Coefficient
-,064 -,016 -,098 ,072 ,167
Sig. (1-tailed)
,350 ,462 ,277 ,332 ,155
Correlation Coefficient
-,016 ,154 ,030 ,009 -,153
Sig. (1-tailed)
,462 ,175 ,428 ,479 ,176
* = Correlation is significant at the 0.05 level (1-tailed)
** = Correlation is significant at the 0.01 level (1-tailed)
Intended relation G and P
G_stimulation
G_harmony
G_threat
Correlations
60
Spearman's
rho
G_novelty
G_challenge
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The mean on the Novelty domain for this group is
much lower than expected when compared to the
Openness to Experience score. This would indicate
that the older player prefers games that resemble the
real world or possibly more traditional games as these
are familiar for this target group, such as card and
board games. The older participants score lower on
Openness and higher on Conscientiousness than the
overall average, which is according to expected
change in personality from ageing (Lucas, 2009,
Mroczek et al., 2006).
This study provided a first exploration of using
personality as a method to tailor game content for the
effective use of gamification. In comparison to
previous studies (as mentioned in the introduction),
our study can neither offer solid statements on the
usefulness of applying personality in gaming. The
correlations we have found between personality traits
and game preferences, expressed in five domains, are
not convincing enough to do so. A limitation of this
study is that both models were assessed in a
simplified form; the used BFI-10 provides less in-
depth information on psychometric measures than a
full FFM inventory.
For future studies we would therefore recommend
to investigate personality and game preference using
a more detailed questionnaire, looking deeper into the
six facets for each FFM factor and possibly
corresponding factors for each 5D domain to gain a
better understanding of both. We see a particular need
to investigate and refine the model in the domain
Harmony, in which both violence and competition are
captured. Furthermore, it is unknown if the
correlating factors also determine the choice for
certain games, and which factors play the most
important role in effectively tailoring game content to
the user. We would recommend a different study
setup to evaluate the older user, in which the frame of
reference is presented to the user by means of actual
games previous to analysing their game preferences.
5 CONCLUSIONS
This study investigated the relations between
personality and game preference by means of an
online questionnaire, in order to tailor game content
to the needs of individuals or user groups. Results
show that several weak significant correlations exist
for persons younger than sixty years old. For
participants of sixty years and older, no significant
relations have been found. We cautiously conclude
that personality may give an indication of game
preferences but, working towards practical use of this
knowledge, research should be extended to 1) deeper
examine the models used to determine personality
and game preference, 2) gain insight in how to assign
content corresponding to preference of the user for
effective use in gamification and 3) further explore
and refine the models used in a field study towards
focused on the older adult user.
ACKNOWLEDGEMENTS
This work is part of the PERSSILAA project
(www.perssilaa.eu) and the MAGGY project
(www.maggygame.nl). PERSSILAA (PERsonalised
ICT Supported Service for Independent Living and
Active Ageing) is sponsored by the EU (FP7-ICT-
2013-10). MAGGY (Mobile Activity Game for
Elderly) is sponsored by the Netherlands
Organization for Scientific Research (NWO),
Creative Industry Program (314-99-002).
Author Disclosure Statement
The author declares that no competing financial
interests exist.
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