Designing Personalised Gamiﬁcation of mHealth Survey Applications

Paulina Adamczyk

1,2

, Sylwia Marek

1,2

, Ryszard Pr˛ecikowski

1,2

, Maciej Ku

1,2

Michał Grzeszczyk

1,3 a

, Maciej Malawski

1,2 b

and Aneta Lisowska

1,4 c

Sano Centre for Computational Medicine, Krakow, Poland

AGH University of Science and Technology, Krakow, Poland

Warsaw University of Technology, Warsaw, Poland

Poznan University of Technology, Poznan, Poland

Keywords:

User Interface Design, Gamiﬁcation, Personalisation, mHealth, Survey, Patient Reported Outcome (PRO).

Abstract:

To monitor patients’ well-being and evaluate the efﬁcacy of digital health intervention, patients are required to

regularly respond to standardised surveys. Responding to a large number of questionnaires is effortful and may

discourage mHealth app users from engaging with the intervention. Gamiﬁcation might reduce the burden of

self-reporting. However, researchers have adopted various approaches to the personalisation of gamiﬁcation

design: ranking of game elements by the user, Hexad Gamiﬁcation User Types classiﬁcation (G) and selection

of preferred design mockups (MU) . In this paper we report on a small population study involving 54 healthy

participants aged 17 to 60, and investigate if these alternative approaches lead to the same design choices. We

ﬁnd that different evaluation approaches lead to different choices of gamiﬁcation elements. We suggest to use

game element ranking in combination with mockup selection. Hexad player classiﬁcation might be less useful

in the context of mHealth applications design.

1 INTRODUCTION

Digital health interventions (DHI) rely on patient-

reported outcomes (PRO) for evaluation of the efﬁ-

cacy of the intervention and monitoring of patients’

physical and mental well-being. The PROs (Cella

et al., 2015) are collected through standardised sur-

veys targeting different dimensions of well-being.

Unfortunately, surveys which contain numerous ques-

tions might be laborious to complete and discouraging

for DHI study participants. Gamiﬁcation of surveys

might potentially reduce the burden of self-reporting.

It has been shown to improve user experience (Harms

et al., 2015) and participation (Cechanowicz et al.,

2013) in online market research questionnaires. In

the context of mobile health (mHealth) applications,

game elements such as points or leaderboards has

been leveraged to improved nutrition (Chow et al.,

2020), increase physical activity (Xu et al., 2022)

and support medication adherence (Tran et al., 2022).

However, little research has been done in mHealth on

designing gamiﬁcation of self-reporting.

The design of gamiﬁcation depends both on the

https://orcid.org/0000-0002-5304-1020

https://orcid.org/0000-0001-6005-0243

https://orcid.org/0000-0002-4489-5956

application context and the target users (Hamari et al.,

2014). Different users might prefer different game el-

ements. Jia et al. suggested that personalisation is

an important step in gamiﬁcation design which could

help avoid demotivating users by inadequate game el-

ement selection (Jia et al., 2016). There are various

approaches to designing the personalisation of gami-

ﬁed health applications. For behaviour change appli-

cations researchers have utilised the best-worst scal-

ing approach to rank game elements by user pref-

erence (Schmidt-Kraeplin et al., 2019; Berger and

Jung, 2021). Carlier et al. , on the other hand, used

the Hexad Gamiﬁcation User Types (Tondello et al.,

2016) questionnaire to group users according to their

gameplay preferences, and designed survey applica-

tion screens to suit each type of gamer (Carlier et al.,

2021).

In this study we explore whether the alternative

approaches to evaluating user preferences lead to

comparable gamiﬁcation design choices. We also in-

vestigate which game elements are the most prefer-

able by users in the context of health and well-being

surveys delivered through a mobile application. To

address these questions we conduct a small study wh-

cih utilizes game element ranking, the Hexad Gamiﬁ-

cation User Types questionnaire and design mockups.

224

Adamczyk, P., Marek, S., Precikowski, R., Ku

s, M., Grzeszczyk, M., Malawski, M. and Lisowska, A.

Designing Personalised Gamiﬁcation of mHealth Survey Applications.

DOI: 10.5220/0011603800003414

In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 5: HEALTHINF, pages 224-231

ISBN: 978-989-758-631-6; ISSN: 2184-4305

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

2 RELATED WORK

Gamiﬁcation refers to the use of game elements in

a non-game setting (Deterding et al., 2011). In the

context of mHealth applications, gamiﬁcation might

directly promote engaging in a target behavior (e.g.

physical exercise, ﬁlling a health survey) or facilitate

attitude change and learning (e.g. health education)

(Johnson et al., 2016).

Game elements frequently utilised in health ap-

plications include: goal setting, progress track-

ing, leaderboards, points, badges, social integration

(connecting/sharing), levels and narratives (Schmidt-

Kraeplin et al., 2019; Berger and Jung, 2021). This

list overlaps with the survey of gamiﬁcation elements

(Carlier et al., 2021) in DHI, with the exception of

goal setting and narratives, which might be less ade-

quate for responding to survey questions and for self-

reporting.

To decide which game elements to include in the

app researchers utilise various methods at the de-

sign stage, such as game element rankings (Schmidt-

Kraeplin et al., 2019; Berger and Jung, 2021) and tar-

get app user surveys (Tondello et al., 2016; Carlier

et al., 2021).

2.1 Game Elements Ranking

One way to obtain a ranking of user preferences is to

use the best-worst scaling (BWS) approach, in which

users are presented with a set of elements and asked

to select the elements they consider the best and the

worst. BWS is preferable to direct ranking mecha-

nisms or rating scales, because it enables investiga-

tion of decision rules at different ranking depths, en-

forces discrimination between the available options,

prevents scoring scale-related biases and ensures that

the importance of each option is not rated equally

(Louviere et al., 2015).

Schmidt-Kraeplie et al. utilised the BWS method

for the design of a behavior change support system

facilitating physical activity (Schmidt-Kraeplin et al.,

2019), and found that in this context users tend to pre-

fer progress, goals, points and levels. In turn, Berger

and Jung (Berger and Jung, 2021) investigated user

preferences in the context of nutrition apps and found

that similar gamiﬁcation features – namely, goal set-

ting, performance graph, progress, rewards and levels

– receive top ranks.

We carried out a study to determine the ranking

of game elements in the context of a health PROs ap-

plication and compare it with ranks obtained in other

health application contexts.

2.2 The Gamiﬁcation User Types Hexad

Scale

Another approach to personalisation of gamiﬁcation

design is to use the Hexad Scale developed by Mal-

czewski (Diamond et al., 2015) and later reﬁned by

Tondello et al. (Tondello et al., 2016) in which users

are assigned to the following gamer types: Socializer,

Free Spirit, Achiever, Philanthropist, Player and Dis-

ruptor. This classiﬁcation is based on user responses

to 24 statements, such as "I like sharing my knowl-

edge" or "Rewards are a great way to motivate me".

The user indicates his/her agreement with each state-

ment on a 7-point Likert scale.

Carlier et al. designed four different versions of

surveys using game elements corresponding to four

Hexad gamer types: Free Spirit, Achiever, Philan-

thropist, Player (Carlier et al., 2021). The authors

carried out a small comparative study involving 28

users, where 16 participants ﬁlled out a gamiﬁed sur-

vey matched to their gamer type while the remaining

participants ﬁlled out a non-gamiﬁed survey. They

found that in gamiﬁed conditions, users perceived the

time taken to respond to questions as shorter, and that

gamiﬁcation did not impact the quality of results.

We ﬁnd these results encouraging and we extend

the work of Carlier et al. with a study of person-

alised gamiﬁcation of the full mHealth application

with regularly repeated PROs, rather than a one-off

survey (Carlier et al., 2021) . We investigate whether

the classiﬁcation of gamiﬁcation user types using the

hexad scale can guide personalisation of health PROs

application design. Our goal is to encourage regular

interaction with the PROs.

3 METHOD

In our study we consider two main approaches to

gamiﬁcation: 1) ranking of gamiﬁcation features and

2) Hexad-based approach, which includes a choice of

mockups (MU) designed to theoretically cater to dif-

ferent Hexad player types (G) .

Unlike Carier et al. who presented questions of

gamiﬁcatied PRO to study participants, we focus our

gamiﬁed mockup design on the “Thank you" screen

(See Fig. 2). The ﬁnal moments of each experience

disproportionately affect its retrospective evaluation

(Kahneman et al., 1993). The “Thank you" screen is

the the last screen of the PRO incorporating gamiﬁ-

cation elements, and may strongly impact the user’s

overall evaluation of the application, as well as their

future engagement.

The study consists of four steps: 1) Introduction

Designing Personalised Gamiﬁcation of mHealth Survey Applications

225

Figure 1: Overview of the Survey Process.

and collection of demographic information 2) Rank-

ing of gamiﬁcation elements 3) Hexad Player Type

Questionnaire and 4) Paired Hexad Mockup compari-

son (See Figure 1). Note that the player type obtained

from the Hexad questionnaire serves as a grouping

factor. Therefore the experiment has two independent

variables (gamiﬁcation elements and Hexad Mockups

(MU) ) along with one covariate (Hexad Player Type

(G) ).

The study was conducted as an online survey and

the participants were recruited via social media (Face-

book) as well as via Sano’s general internal Slack

channel. Participation was voluntary and no ﬁnan-

cial compensation was provided. We included par-

ticipants who own smartphones and were, in general,

open to using mobile health applications.

3.1 Study Introduction

Participants are presented with study instructions,

along with an explanation of the context of the study.

In particular, the introduction highlights that the study

focuses on personalisation and engagement with mo-

bile health applications in order to reduce the bur-

den of self-reporting. Afterwards, participants pro-

vide their demographic information, which includes

gender and age.

3.2 Gamiﬁcation Elements

The gamiﬁcation features that we use in our “Thank

you screen” designs are chosen from sets suggested

by Malczewski (Diamond et al., 2015) and later

reﬁned by Tondello et al. (Tondello et al., 2016)

(Table 1). Game elements which have been shown

to increase user engagement when ﬁlling the survey

but are not appropriate for the "Thank You" screen

(such as the progress bar), were excluded from this

analysis.

There are six gamiﬁcation elements that we anal-

yse. Sharing is a feature for distributing updates about

user’s progress to their friends and colleagues. In

the context of the mHealth survey application, ’shar-

ing’ does not mean sharing replies to the PRO sur-

vey (these are intended to be shared only with the

clinician who runs the study), but rather sharing an

announcement that one has managed to complete the

self-reporting task with their friends or family mem-

bers. Feedback allows submission of remarks about

the application to the developers, while points are a

simple feature for keeping score through the whole

process of interaction with the app – for example,

every time the patient ﬁlls in the PRO questionnaire

they receive a reward in the form of points. Leveling

up involves gaining experience points after success-

fully completing some tasks within the application,

and reaching new levels. For example, in an applica-

tion where the patient is expected to complete mul-

tiple PROs, completion of each PRO may increase

the user’s level. Some people ﬁnd Voting and point-

ing drawbacks engaging and this gamiﬁcation feature

might be used for assessing the application. Leader-

boards/ranking enable users to compare their results

with others, for example by visualising the number

of tasks completed by the given user vs. the number

of tasks completed by other users. Note that in the

context of the mHealth app the user might prefer to

choose a nickname so that they are not identiﬁable on

the leaderboard.

In our study, participants choose one favoured

game element from the pair presented to them. For

example, one participant is presented with the follow-

ing game elements: points and sharing, and they are

asked "Which game element do you prefer?". Par-

ticipants reviewed all possible gamiﬁcation element

pairs, and had to select their preferred game element

from each pair.

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226

3.3 Hexad Player Types Questionnaire

Personalisation of gamiﬁcation elements can be sup-

ported by the Hexad Player Types survey, which con-

sist of 24 personality questions such as "I like defeat-

ing obstacles" or "It is important to me to feel like I

am part of a community" (Tondello et al., 2016). Par-

ticipants were asked to rate their agreement with each

statement on the 7-point Likert scale, where 1 means

strong disagreement, 4 means no opinion, while 7

means strong agreement. Then, based on participants’

responses, their corresponding gamer types were de-

termined from among the following: Socializer (G)

, Achiever (G) , Disruptor (G) , Player (G) , Philan-

thropist (G) and Free spirit.

3.4 Design of Mockups

We design mockups of the “Thank you screen” so that

they contain gamiﬁcation elements for ﬁve out of six

types - Achiever (MU) , Philanthropist (MU) , Disrup-

tor (MU) , Player (MU) and Socializer (MU) . The

Free spirit (MU) user type was omitted, based on the

observation that game design elements matching the

Free spirit (MU) (e.g. exploratory tasks, easter eggs

or customization) concern other screens of the appli-

cation rather than the one displayed after completing

the survey in the app. Applying it to the research

questionnaire would make it inconsistent with other

mockups containing the “Thank you screen”. The

mockups created for the questionnaire are shown in

Fig. 2 and the gamiﬁcation elements matching each

user type are presented in Table 1. Similarly as in the

case of game element selection, participants are asked

to choose their favourite mockup from each presented

pair. All combinations of mockup pairs are presented

to each participant.

3.5 Data Analysis

Finally, we compare the results collectively and indi-

vidually. We analyse game elements, mockups and

player types with the highest response rate and check

whether they correspond to each other. We perform

quantitative and qualitative comparison of the outputs

from the questionnaire in order to determine whether

a speciﬁc user type selects features related to that

type, as suggested by Tondello et al. (Tondello et al.,

2016).

(a) Achiever (MU) . (b) Player (MU) .

(e) Disruptor (MU) .

Figure 2: Survey completion mockup screens for ﬁve Gam-

iﬁcation User Types considered in the study.

4 RESULTS

We surveyed 54 users aged 17-60, (38.9% female and

61.1% male). There was no signiﬁcant difference in

responses between genders, and thus the division was

ignored during further analysis. The majority of re-

sponses (74%) come from younger participants ( ≤ 30

yrs).

4.1 Hexad Gamiﬁcation User Type

Table 1 shows the number of respondents for each

gamiﬁcation user type as classiﬁed using the Hexad

scale. Philanthropist (G) was the most prominent

gamer type in our study population and Disruptor (G)

the least.

Note that some users had more than one domi-

nant gamer type and were counted in all categories

where they had top scores; therefore, the total number

of users does not add up to the number of surveyed

participants. Figure 3a demonstrates gamer type clas-

siﬁcation overlap. The thickness of the line between

Designing Personalised Gamiﬁcation of mHealth Survey Applications

227

Table 1: From left: Categorisation of gamiﬁcation user

types, number of study participants falling into each gamer

type category, game elements associated with the gamer

type in our mockups (See Fig. 2), top game elements se-

lected by participants in each gamer category and top se-

lected mockup.

Hexad

Gamer Types

No. of users

Game elements

associated with

hexad type

in mock-ups

Top 2 game elements

Top 2 selected

mock ups

Philanthropist 22

sharing

feedback

leveling up,

points

player,

socializer

Free Spirit 19 NA

leveling up,

points/leaderboards

player,

socializer

Player 13 leadboards

leveling up,

leaderboards

player,

socializer

Achiever 10 leveling up

leveling up,

points

player,

socializer

Socialiser 9

points

sharing

leveling up,

points

socializer,

player

Disruptor 1

voting and

pointing drawbacks

feedback

leaderboards,

points

socializer,

player

each pair of gamer types corresponds to the frequency

of participants being classiﬁed as both types. When

there is no line between two types it means that not

a single participant was classiﬁed as both – e.g. no-

body was a strong match for the socializer and player

types at the same time. Free spirit (G) was most fre-

quently connected with other gamiﬁcation user types,

followed by Philanthropist (G) (see Fig.3a).

4.2 Game Element Ranking

Table 1 also shows the top game element and mockup

chosen per user type. The top game element selected

were: levels, points and leaderboards. The least pre-

ferred elements were feedback and sharing.

Figure 3b shows the frequency which which each

combination of two game elements ranked in the top

two. Note that almost all combinations of game ele-

ments achieved this ranking at least once, except for

sharing and voting/pointing drawbacks. Users fre-

quently preferred pairs of game elements which nat-

urally match each other, e.g. points and leveling up

or leveling up and leaderboards. Interestingly, the

top-two selection of points and leaderboards was less

common (See Figure 3b).

The ranking of game elements did not vary

strongly between the gamiﬁcation user types. The

feature most frequently selected as the ﬁrst preference

was leveling up (see Fig 3c) for all player types except

Disruptor (G) , who preferred leaderboards.

4.3 Mockup Selection

The Player (MU) ranked ﬁrst, and the Socialiser

(MU) second. The selection of Player (MU) corre-

sponds to selection of the game element leaderboards

which is associated with the Player (G) and points

associated with the Achiever (G) . The top mockup

selection is partly consistent with the ranking of top

game elements. It includes game elements which

ranked 2nd (points) and 3rd (leaderboards), but not

1st (leveling up).

Interestingly, the second favourite mockup, So-

cialiser (MU) , also does not include the favourite

game element leveling up and is associated with one

of the least represented gamer types in our study pop-

ulation. It also includes the least favourable feature of

sharing. This result suggests that mockup selection

might lead to different gamiﬁcation design choices

than game elements ranking.

4.4 Hexad Gamer Type vs Gamiﬁcation

Design Approaches

The co-occurrence heatmap in Fig. 4 shows that clas-

siﬁcation of the gamer type (G) does not strongly cor-

respond to selection of game elements. The gamer

type (G) also does not perfectly match the design

mockup selection (See Fig. 5). Each gamer type is

analysed in more detail below:

Socializer (G) - Respondents classiﬁed as Socializ-

ers (G) were responsive to leveling up or points and

preferred Socializers (MU) . The Hexad gamer clas-

siﬁcation matched the top mockup selection but their

top preferred game elements did not include sharing,

which was expected to be associated with Socializer

(G) .

Achiever (G) - Similarly to Socializer (G) , these

users were mostly interested in points and leveling up.

In terms of mockups they chose mainly Player (MU)

or Socializer (MU) design, and not Achiver (MU) ,

which included their preferred game element and was

designed to match their gamer type.

Disruptor (G) - This user actively chose leader-

boards or points game elements and Socializer (MU)

or Player (MU) . There was only one Disruptor re-

spondent and therefore they could have signiﬁcantly

distorted the results. After detailed investigation it

turned out that they were probably straight-lining

(continuously selecting the same answer).

Philanthropist (G) - The analysis reveals that Phi-

lanthropists prefer leveling up or points. This is un-

expected for this gamer type but might be explained

considering that users were frequently classiﬁed as

Philanthropist (G) in combination with other gamer

types: Socialiser (G) and Players (G) (See 3a).

Player (G) - Player (G) shows the highest co-

occurrence with leaderboards, which were designed

precisely for this gamer type. The same observa-

tion is noticeable in relation to the mockups where

Player(G) shows the strongest preference for the

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228

(a) Gamer Type.

(b) Selected Top 2 game elements.

selection.

Figure 3: Co-occurrence graphs.

Player (MU) mockup.

Free spirit (G) - Results for this gamer type were

very close to those obtained for the Philanthropist (G)

. This is another case where the most frequently cho-

sen game elements do not match the association of

game elements and gamer type suggested by Tondello

et al. (Tondello et al., 2016).

Regardless of gamer type classiﬁcation, a surpris-

Figure 4: Co-occurrence of gamer types (G) and gamiﬁca-

tion elements.

Figure 5: Co-occurrence of gamer types (G) and chosen

mockups (MU) .

ingly large number of respondents chose points and

leveling up as their preferred gamiﬁcation elements,

and Socializer (MU) or Achiever (MU) as their fa-

vorite. The less frequently selected mockups (Disrup-

tor mockup, Philanthropist mockup) are potentially

less eye-catching, no matter what type of gamer the

study participant is.

5 DISCUSSION

We conducted a small scale study investigating two

different approaches to personalised gamiﬁcation de-

sign: ranking of game elements by the user and se-

lection of preferred design mockups containing game

features related to different Hexad Gamer Types. We

found that these approaches lead to a choice of dif-

ferent game elements. leveling up, the game element

which ranked the highest, was not present in the fa-

vorite mockup Player (MU) . This suggests that a sim-

Designing Personalised Gamiﬁcation of mHealth Survey Applications

229

ple ranking of game elements in isolation from full-

screen design might be insufﬁcient for guiding deci-

sions on personalisation of gamiﬁcation design.

Interestingly, the top game elements, selected re-

gardless of gamer classiﬁcation, were: levels, points

and leaderboards. The least preferred elements

were feedback and sharing. This ranking is in line

with rankings obtained in other mHealth applications

(Schmidt-Kraeplin et al., 2019; Berger and Jung,

2021). Both in applications focused on physical ac-

tivity(Schmidt-Kraeplin et al., 2019) and nutrition

(Berger and Jung, 2021), the levels game element

ranked highly, while game elements relying on social

connections, such as sharing or user forums, were the

least favorable. This ﬁnding suggest that preference

for game elements might be consistent across some

mHealth applications, including even seemingly dif-

ferent settings for PROs apps. In the mHealth context,

users might not wish to share their progress since they

regard such information as too sensitive. Further re-

search is required across different digital health in-

terventions to validate these ﬁndings, establish a gen-

eral understanding of what is preferred and what is

not, and uncover the rationale behind ranking certain

game elements as preferred and others as undesirable

in various mHealth app gamiﬁcation contexts.

In our study the Hexad Gamer Type classiﬁcation

was not helpful in uncovering the gamiﬁcation design

that the user might prefer. The links discovered in a

larger population by Tondell et al. (Tondello et al.,

2016) between gamer types and game element selec-

tion were not fully replicated in our small-scale study.

The main limitation of our study is potential partici-

pant selection bias. The majority of our participants

fell into the Philanthropist game user type, which is

in line with Carlier et al. , who also found that users

volunteering to evaluate their design were mostly Phi-

lanthropist (G) (Carlier et al., 2021). This gamer type

distribution, however, might not necessarily be repre-

sentative of all future users of our mHealth app.

The gamer type classiﬁcation resulting from the

Hexad questionnaire also did not correspond to se-

lection of the mockups. This might be due to the

fact that a large proportion of our respondents had

two dominant gamer types, e.g. Philantropists (G)

were frequently also Socializers (G) and Freespirts

(G) were also Players (G) (See Fig 3a). The Hexad

Gamer classiﬁcation might be more appropriate for

more sophisticated gameplay than typically present

in mHealth applications. Therefore, to drive gami-

ﬁcation design in the health application context we

suggest using a combination of game element ranking

and mockups rather than the Hexad questionnaire.

The results show that game element ranking

should not be done in the isolation. It would have

been advantageous to also obtain a ranking of paired

game elements to better match the mockup, such as

Socialiser, including two game elements: points and

sharing. The Achiever mockup, even while includ-

ing elements favored by users, i.e. leveling up, might

have been disadvantaged due to the lack of combina-

tion with other game elements.

6 CONCLUSION

We conducted a study with 54 participants investigat-

ing different approaches to personalised gamiﬁcation

design: ranking of game elements by the user, Hexad

Gamiﬁcation User Types classiﬁcation and selection

of preferred design mockups. We found that these

approaches lead to the choice of different game ele-

ments. A simple ranking of game elements obtained

in isolation from full-screen design might be insufﬁ-

cient for guiding decisions regarding gamiﬁcation de-

sign. Therefore, we suggest to obtain user rankings of

paired game elements alongside mockup selection. In

the context of mHealth survey applications, the Hexad

player classiﬁcation has been shown to be less help-

ful in guiding gamiﬁcation design. We found that in

the context of PROs applications, the range of pre-

ferred gamiﬁcation options could be narrowed down

to mockups tailored to Socialiser and Player gamer

types.

In future work we intend to follow Carlier et al.

(Carlier et al., 2021) and perform an experimental

evaluation of the preferred gamiﬁcation designs (So-

cialiser and Player (MU) ) vs. a non-gamiﬁed sur-

vey, in terms of user experience, data quality and cog-

nitive load measured by wearable devices (Lisowska

et al., 2021). The ultimate goal is to encourage users

to retake the surveys regularly; therefore, we aim to

reduce the burden of self-reporting through gamiﬁca-

tion. The survey must remain reﬂective of users’ well-

being, hence we need to be mindful of the impact of

gamiﬁcation on the quality of data captured.

ACKNOWLEDGEMENTS

This publication is supported by the European

Union’s Horizon 2020 research and innovation pro-

gramme under grant agreement Sano No. 857533 and

the International Research Agendas programme of the

Foundation for Polish Science, co-ﬁnanced by the Eu-

ropean Union under the European Regional Develop-

ment Fund.

HEALTHINF 2023 - 16th International Conference on Health Informatics

230

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