Exploring Personality and Game Preferences in the Younger and

Older Population: A Pilot Study

A. F. A. de Vette

, M. Tabak

1,2

, M. G. H. Dekker-van Weering

and M. M. R. Vollenbroek-Hutten

1,2

University of Twente, Faculty of Electrical Engineering, Mathematics and Computer Science, Telemedicine Group,

P.O. Box 217, 7500 AE Enschede, The Netherlands

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

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

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

≥

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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