Gamification-based Behavioral Change in Children with Diabetes Mellitus

Abstract

Recently there has been a tremendous flowering of research in behavioural economics and in psychological and persuasive technology. This research helps designers to understand how children can change their behaviour and make the proper decisions in their lives. This paper aims to evaluate the behavioural change of children with diabetes mellitus in their daily lives after using a gamified health app designed and developed for this research. As proof of concept, the gamification was designed based on user experience by mapping the gamification mechanism to the user experience factors [1]. To perform it, the authors measured the changed behaviour based on performance, effectiveness, and efficiency that mapped to the gamification mechanism. A randomized controlled experiment was applied to measure the impact of gamification on children with diabetes. The researcher selected a random sample of 20 children with diabetes with the group between 6 to 12 years and then divided the sample randomly into two groups: a control group (A) consist of ten children and a treatment group (B) from another ten children. The results show that the treatment group of children who watched the video before playing the game have become more enthusiastic since they knew the idea of the game and its purpose than the control group. This has automatically influenced their eating behaviour, and they tend to eat healthy food. The children became more cautious in their eating habits than the control group, and the game had a significant impact in changing their eating behaviours. Our future direction is to investigate more metrics definition based on GM and test the theoretical and empirical on real case study.

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Procedia Computer Science 170 (2020) 442–449
1877-0509 © 2020 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the Conference Program Chairs.
10.1016/j.procs.2020.03.087
10.1016/j.procs.2020.03.087 1877-0509
© 2020 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the Conference Program Chairs.
Available online at www.sciencedirect.com
ScienceDirect
Procedia Computer Science 00 (2020) 000–000
www.elsevier.com/locate/pr
ocedia
1877-0509 © 2020 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the Conference Program Chairs.
The 11th International Conference on Ambient Systems, Networks and Technologies (ANT)
April 6-9, 2020, Warsaw, Poland
Gamification-based Behavioral Change in Children with Diabetes
Mellitus
Nahed Alsaleh and Reem Alnanih
*
Computer Science Department. Faculty of Computing and Information Technology.
King Abdulaziz University, Jeddah, Saudi Arabia
Abstract
Recently there has been a tremendous flowering of research in behavioural economics and in psychologi cal and persuasive
technology. This research helps designers to understand how children can change their behaviour and make the proper decisions
in their lives. This paper aims to evaluate the behavioural change of children with diabetes mellitus in their daily lives after using
a gamified health app designed and developed for this research. As proof of concept, the gamification was designed based on user
experience by mapping the gamification mechanism to the user experience factors [1]. To perform it, the author s measured the
changed behaviour based on performance, effectiveness, and efficiency that mapped to the gamification mechanism. A randomized
controlled experiment was applied to measure the impact of gamification on children with diabetes. The researcher selected a
random sample of 20 children with diabetes with the group between 6 to 12 years and then divided the sample randomly into two
groups: a control group (A) consist of ten children and a treatment group (B) from another ten children. The results show that the
treatment group of children who watched the video before playing the game have become more enthusiastic since they knew the
idea of the game and its purpose than the control group. This has automatically influenced their eating behaviour, and they tend to
eat healthy food. The children became more cautious in their eating habits than the control group, and the game had a significant
impact in changing their eating behaviours. Our future direction is to investigate more metrics definition based on GM and test the
theoretical and empirical on real case study.
© 2020 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the Conference Program Chairs.
Keywords:
Gamification; Behaviour change; Experiment test; Children with diabetes; Designing User Interface.
* Corresponding author. Tel.: +966-6952000 Ext. 27326.
E-mail address: ralnanih@kau.edu.sa
Available online at www.sciencedirect.com
ScienceDirect
Procedia Computer Science 00 (2020) 000–000
www.elsevier.com/locate/pr
ocedia
1877-0509 © 2020 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the Conference Program Chairs.
The 11th International Conference on Ambient Systems, Networks and Technologies (ANT)
April 6-9, 2020, Warsaw, Poland
Gamification-based Behavioral Change in Children with Diabetes
Mellitus
Nahed Alsaleh and Reem Alnanih*
Computer Science Department. Faculty of Computing and Information Technology.
King Abdulaziz University, Jeddah, Saudi Arabia
Abstract
Recently there has been a tremendous flowering of research in behavioural economics and in psychologi cal and persuasive
technology. This research helps designers to understand how children can change their behaviour and make the proper decisions
in their lives. This paper aims to evaluate the behavioural change of children with diabetes mellitus in their daily lives after using
a gamified health app designed and developed for this research. As proof of concept, the gamification was designed based on user
experience by mapping the gamification mechanism to the user experience factors [1]. To perform it, the author s measured the
changed behaviour based on performance, effectiveness, and efficiency that mapped to the gamification mechanism. A randomized
controlled experiment was applied to measure the impact of gamification on children with diabetes. The researcher selected a
random sample of 20 children with diabetes with the group between 6 to 12 years and then divided the sample randomly into two
groups: a control group (A) consist of ten children and a treatment group (B) from another ten children. The results show that the
treatment group of children who watched the video before playing the game have become more enthusiastic since they knew the
idea of the game and its purpose than the control group. This has automatically influenced their eating behaviour, and they tend to
eat healthy food. The children became more cautious in their eating habits than the control group, and the game had a significant
impact in changing their eating behaviours. Our future direction is to investigate more metrics definition based on GM and test the
theoretical and empirical on real case study.
© 2020 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the Conference Program Chairs.
Keywords:
Gamification; Behaviour change; Experiment test; Children with diabetes; Designing User Interface.
* Corresponding author. Tel.: +966-6952000 Ext. 27326.
E-mail address: ralnanih@kau.edu.sa
2 Author name / Procedia Computer Science 00 (2018) 000–000
1. Introduction
Using games and promoting health is a new research area. Existing publications confirm that games tool can be
beneficial to health. Scientific research and case studies have shown that games and stadiums have an impact on health
and should be considered as essential elements in the health domain [3]. Gamification is a design strategy that attempts
to reproduce the interactive powers of games and imitate key gameplay features in non-game contexts without
designing a complete game [4]. Gamification is not just points and badges; rather, organizations see it as an effective
tool for learning, improving play, and reaching business goals [5]. This has encouraged developers to think about
gamification and design more health apps for this category of user. Therefore, developers should pay attention to
health apps that not only track user health, but also motivate the user to correct unhealthy behaviours and help improve
their daily lives. Such apps aim to engage users by proposing competitive or collaborative challenges. However, more
studies should be undertaken that measure underlying psychological variables to arrive at more accurate links between
game mechanics, psychological effects, and behavioural outcomes. Applications and gamification are effective in
monitoring care for a wide range of chronic and persistent diseases, including diabetes mellitus (DM), that children
may experience, from mild to severe and from common to rare [6]. Chronic diseases are the leading cause of death
and disease worldwide and are largely spread by unhealthy behaviours. In fact, poor diet, frequent eating of sweets,
alcoholism, and smoking are common risk factors for cardiovascular disease, diabetes, and other chronic diseases,
which account for a significant amount of healthcare costs [7]. Consider behavioural informational interventions that
have the potential to assist individuals in developing behaviours to improve physical, mental, or behavioural health.
In particular, social media interventions have many advantages, including broad access through geographical barriers,
outreach, and cost-effectiveness. Now, it has become possible to take advantage of this technique to change health
behaviour for the better and promote significant improvements in the field of health behaviour change.
The aim of this paper is to evaluate the behavioural change of children with DM in their daily lives after using a
gamified health app designed and developed for this research. As proof of concept, the gamification was designed
based on user experience by mapping the gamification mechanism to the user experience factor [1]. The model in this
study is proposed with regard to the healthcare domain. The contribution of this work is to empirically apply the
theoretical approach that is designed and proposed by the authors [1]. The authors aim to consider the performance
evaluation by [2]. The paper investigates how to measure the performance of the gamification. We want to point out
that this designing approach of gamification is based on the Transtheoretical Model (TTM) [8] [9].
The rest of paper is organized as follows: Section 2 highlights related research work; Section 3 examines the subject
of designing gamification for behavioural change; Section 4 illustrates the experiment test and the result; Section 5
presents the discussion. Finally, in section 6, conclude the work.
2. Related Work
Recently there has been a tremendous flowering of research in behavioural economics and in psychological and
persuasive technology. This research helps designers understand how people can change their behaviour and make
the proper decisions in their lives. Many authors investigate the evaluation of changing behaviour. For example, in
[2], the authors have focused on the enjoyment of performance evaluation systems, which ensure that employees in
performance management are integrated with the performance evaluation system and encourage functional
enhancement of employee participation. The intention of working in this context is to suggest a new model of
gamification-based performance evaluation [2]. In [10], the authors have proposed a new model for the use of
gamification in improving the performance of sales personal. In [11], the research shows that less than one third of
the employees believe that their companies helped them to improve the performance management process and that
performance management is regularly among the lowest issues in employee satisfaction surveys. In [12], the authors

Nahed Alsaleh et al. / Procedia Computer Science 170 (2020) 442–449 443
Available online at www.sciencedirect.com
ScienceDirect
Procedia Computer Science 00 (2020) 000–000
www.elsevier.com/locate/pr
ocedia
1877-0509 © 2020 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the Conference Program Chairs.
The 11th International Conference on Ambient Systems, Networks and Technologies (ANT)
April 6-9, 2020, Warsaw, Poland
Gamification-based Behavioral Change in Children with Diabetes
Mellitus
Nahed Alsaleh and Reem Alnanih*
Computer Science Department. Faculty of Computing and Information Technology.
King Abdulaziz University, Jeddah, Saudi Arabia
Abstract
Recently there has been a tremendous flowering of research in behavioural economics and in psychologi cal and persuasive
technology. This research helps designers to understand how children can change their behaviour and make the proper decisions
in their lives. This paper aims to evaluate the behavioural change of children with diabetes mellitus in their daily lives after using
a gamified health app designed and developed for this research. As proof of concept, the gamification was designed based on user
experience by mapping the gamification mechanism to the user experience factors [1]. To perform it, the author s measured the
changed behaviour based on performance, effectiveness, and efficiency that mapped to the gamification mechanism. A randomized
controlled experiment was applied to measure the impact of gamification on children with diabetes. The researcher selected a
random sample of 20 children with diabetes with the group between 6 to 12 years and then divided the sample randomly into two
groups: a control group (A) consist of ten children and a treatment group (B) from another ten children. The results show that the
treatment group of children who watched the video before playing the game have become more enthusiastic since they knew the
idea of the game and its purpose than the control group. This has automatically influenced their eating behaviour, and they tend to
eat healthy food. The children became more cautious in their eating habits than the control group, and the game had a significant
impact in changing their eating behaviours. Our future direction is to investigate more metrics definition based on GM and test the
theoretical and empirical on real case study.
© 2020 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the Conference Program Chairs.
Keywords:
Gamification; Behaviour change; Experiment test; Children with diabetes; Designing User Interface.
* Corresponding author. Tel.: +966-6952000 Ext. 27326.
E-mail address: ralnanih@kau.edu.sa
Available online at www.sciencedirect.com
ScienceDirect
Procedia Computer Science 00 (2020) 000–000
www.elsevier.com/locate/pr
ocedia
1877-0509 © 2020 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the Conference Program Chairs.
The 11th International Conference on Ambient Systems, Networks and Technologies (ANT)
April 6-9, 2020, Warsaw, Poland
Gamification-based Behavioral Change in Children with Diabetes
Mellitus
Nahed Alsaleh and Reem Alnanih*
Computer Science Department. Faculty of Computing and Information Technology.
King Abdulaziz University, Jeddah, Saudi Arabia
Abstract
Recently there has been a tremendous flowering of research in behavioural economics and in psychologi cal and persuasive
technology. This research helps designers to understand how children can change their behaviour and make the proper decisions
in their lives. This paper aims to evaluate the behavioural change of children with diabetes mellitus in their daily lives after using
a gamified health app designed and developed for this research. As proof of concept, the gamification was designed based on user
experience by mapping the gamification mechanism to the user experience factors [1]. To perform it, the author s measured the
changed behaviour based on performance, effectiveness, and efficiency that mapped to the gamification mechanism. A randomized
controlled experiment was applied to measure the impact of gamification on children with diabetes. The researcher selected a
random sample of 20 children with diabetes with the group between 6 to 12 years and then divided the sample randomly into two
groups: a control group (A) consist of ten children and a treatment group (B) from another ten children. The results show that the
treatment group of children who watched the video before playing the game have become more enthusiastic since they knew the
idea of the game and its purpose than the control group. This has automatically influenced their eating behaviour, and they tend to
eat healthy food. The children became more cautious in their eating habits than the control group, and the game had a significant
impact in changing their eating behaviours. Our future direction is to investigate more metrics definition based on GM and test the
theoretical and empirical on real case study.
© 2020 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the Conference Program Chairs.
Keywords:
Gamification; Behaviour change; Experiment test; Children with diabetes; Designing User Interface.
* Corresponding author. Tel.: +966-6952000 Ext. 27326.
E-mail address: ralnanih@kau.edu.sa
2 Author name / Procedia Computer Science 00 (2018) 000–000
1. Introduction
Using games and promoting health is a new research area. Existing publications confirm that games tool can be
beneficial to health. Scientific research and case studies have shown that games and stadiums have an impact on health
and should be considered as essential elements in the health domain [3]. Gamification is a design strategy that attempts
to reproduce the interactive powers of games and imitate key gameplay features in non-game contexts without
designing a complete game [4]. Gamification is not just points and badges; rather, organizations see it as an effective
tool for learning, improving play, and reaching business goals [5]. This has encouraged developers to think about
gamification and design more health apps for this category of user. Therefore, developers should pay attention to
health apps that not only track user health, but also motivate the user to correct unhealthy behaviours and help improve
their daily lives. Such apps aim to engage users by proposing competitive or collaborative challenges. However, more
studies should be undertaken that measure underlying psychological variables to arrive at more accurate links between
game mechanics, psychological effects, and behavioural outcomes. Applications and gamification are effective in
monitoring care for a wide range of chronic and persistent diseases, including diabetes mellitus (DM), that children
may experience, from mild to severe and from common to rare [6]. Chronic diseases are the leading cause of death
and disease worldwide and are largely spread by unhealthy behaviours. In fact, poor diet, frequent eating of sweets,
alcoholism, and smoking are common risk factors for cardiovascular disease, diabetes, and other chronic diseases,
which account for a significant amount of healthcare costs [7]. Consider behavioural informational interventions that
have the potential to assist individuals in developing behaviours to improve physical, mental, or behavioural health.
In particular, social media interventions have many advantages, including broad access through geographical barriers,
outreach, and cost-effectiveness. Now, it has become possible to take advantage of this technique to change health
behaviour for the better and promote significant improvements in the field of health behaviour change.
The aim of this paper is to evaluate the behavioural change of children with DM in their daily lives after using a
gamified health app designed and developed for this research. As proof of concept, the gamification was designed
based on user experience by mapping the gamification mechanism to the user experience factor [1]. The model in this
study is proposed with regard to the healthcare domain. The contribution of this work is to empirically apply the
theoretical approach that is designed and proposed by the authors [1]. The authors aim to consider the performance
evaluation by [2]. The paper investigates how to measure the performance of the gamification. We want to point out
that this designing approach of gamification is based on the Transtheoretical Model (TTM) [8] [9].
The rest of paper is organized as follows: Section 2 highlights related research work; Section 3 examines the subject
of designing gamification for behavioural change; Section 4 illustrates the experiment test and the result; Section 5
presents the discussion. Finally, in section 6, conclude the work.
2. Related Work
Recently there has been a tremendous flowering of research in behavioural economics and in psychological and
persuasive technology. This research helps designers understand how people can change their behaviour and make
the proper decisions in their lives. Many authors investigate the evaluation of changing behaviour. For example, in
[2], the authors have focused on the enjoyment of performance evaluation systems, which ensure that employees in
performance management are integrated with the performance evaluation system and encourage functional
enhancement of employee participation. The intention of working in this context is to suggest a new model of
gamification-based performance evaluation [2]. In [10], the authors have proposed a new model for the use of
gamification in improving the performance of sales personal. In [11], the research shows that less than one third of
the employees believe that their companies helped them to improve the performance management process and that
performance management is regularly among the lowest issues in employee satisfaction surveys. In [12], the authors

444 Nahed Alsaleh et al. / Procedia Computer Science 170 (2020) 442–449
Author name / Procedia Computer Science 00 (2018) 000–000 3
have used the transtheoretical model extensively to try and encourage smoking cessation. This model is arguably the
dominant psychological model in this field. There is an evidence to suggest that the effectiveness of implementation
intention interventions might be enhanced if they were targeted. In [13], the authors highlighted different methods of
theory in terms of problems and solutions. They confirmed that the use of theory enhances the science of changing
behaviour by adding more details about the effect of the intervention. In [14], the authors have researched how to
improve glycaemic control among Type 2 diabetics using changing behaviour model, which called the Stage of
Change model. The authors conducted a study on two groups of patients with diabetes Type 2. The first group is the
control group that received routine care continued for 48 weeks. The second group is the intervention group that
treated with personalized care for Type2 using changing behaviour model for the same period time. The result of the
study assumed that the social condition at the time of the study played a negative role despite the adoption of the
changing behaviour model. In [15], the authors have examined seven self-behaviours identified by the American
Association of Diabetes Educators (AAED7). These seven essential self-care behaviours predict good outcomes in
people with diabetes. These are healthy eating, being physically active, monitoring blood sugar, being compliant with
medications, having good problem-solving skills, having healthy coping skills, and practicing risk-reduction
behaviours. It is critical that healthcare providers actively involve their patients in developing self-care regimens. This
regimen should be the best possible combination for every individual patient, and it should sound realistic to the
patient so that he or she can follow it. In [16], the authors proposed mobile apps that serve Asthma patients. Their
proposed apps supported with the features of risk identification and management. The authors have categorized their
apps according to three criteria: 1) providing health information; 2) serves as a disease diary; and 3) enabling users to
share that data with their care team. In [17], the authors have examined the design of the MedFit app, which is meant
to facilitate participation of people with cardiovascular disease (CVD) in an exercise-based rehabilitation program
remotely. Their paper details the development of the MedFit app. In [18], the authors have used a systematic review
of interventions designed to promote physical activity and/or healthy eating as a novel approach to classifying
intervention content according to change techniques and theoretically-derived technique combinations. This study
aims to assess the effectiveness of behaviour change BC interventions designed to promote physical activity and
healthy eating and investigate whether theoretically-specified BC techniques improve outcomes. Table 1 summarize
the above papers in terms of methodology, the evaluation methods, the target user, the behaviour change, the result
and the limitation.
Table 1. Summary of the Related Work
Paper
Changing
Behaviour
Method Used
Chronic Disease / Condition
[12]
ü
The transtheoretical model
Smoking
[13]
ü
Framework intervention mapping
Oral cancer
[14]
ü
Stage of Change (SOC) model
Type 2 diabetics
[15]
ü
Role of self-care in management of diabetes mellitus
Diabetics
[16]
ü
mAsthma app according to four criteria
Asthma
[17]
ü
MedFit App
Cardiovascular disease (CVD)
[18]
ü
Assess the effectiveness of active BC
Obesity
GameBetes
ü
Performance Measure of Gamification elements
Diabetes
3. Designing Gamification for behaviour change
In order to make behaviourally effective change, three objectives need to be achieved [19]:
1- Apply design expertise to build something that people like and accept.
In [1], the authors have linked the gamification mechanism to the TTM in a new relationship between them based on
the mechanisms used. The game mechanism contains challenges, points, manipulators, badges, rewards, leader boards,
4 Author name / Procedia Computer Science 00 (2018) 000–000
and levels [20]. The relationship consists of game strategies for learners to solve problems in a fun and engaging
atmosphere.
2- A clear understanding of user behaviour and how to influence it.
The authors achieved this point by studying the different principles that play an important role in behaviour change
based on the TTM. There are a number of studies working on the TTM theory of healthy behaviour change. These
include studies of how the TTM can help smokers and the obese. TTM is a model of intentional change that focuses
on the individual’s decision-making process. The theoretical framework of TTM has several stages. Each stage aims
to work on behaviour change. By achieving all the steps, user action is greatly improved.
3- A commitment to testing the output of the design and to considering the feedback.
In this context, the authors have proposed basic criteria to evaluate the performance of the behavioural change based
on [2], as outlined in Table 2:
Table 2: Performance Criteria
Attributes
Performance Criteria
Gamification
Mechanism
A
To collect the largest number of points
# Points
B
To provide sufficient rewards that players value
# rewards = badges
C
To pass all the levels
#levels
D
To spend the longest time in game
# times by min
E
To get on the leader board
# leader board
In this paper, we have considered design expertise by applying the principle of the user interface to design gamification
that meets the children’s requirements. The result of the survey distributed to the 171 Saudi families reveals that the
children surveyed prefer colours. Also, when parents asked about the main aspects of online games that draw the
attention of their children, 34% of them said multiple stages, 24.9% of them cited the presence of many colours and
shapes, and 23.7% answered motivation, while 12.6% identified the speed of the game. In order to be familiar with
the user behaviour of children with diabetes, an interview was conducted with five parents to clarify the important
factor that describes their children’s behaviour. The purpose was to determine what things a child needs to change his
or her health for the better and thereby to improve the behaviour of children with diabetes toward healthy sustainable
dietary habits. This also serves the second goal, which is to support the management of childhood diabetes by
educating children and helping them to modify their behaviour.
To measure the effectiveness of manipulation in changing the behaviour of children with diabetes DM, the researchers
focused on the effect of gamification on the behaviour of children. Because most children enjoy playing electronic
games, our goal is to instil a change in the behaviour of children with diabetes for the better health through GameBetes
application. This application is an educationally designed game t consist of three levels and three educational videos
explaining what the symptoms of diabetes are and how it affects diabetes on the body and what are the healthy eating
groups.
The authors aimed to define the metrics for action based on the seven self-behaviours identified by the American
Association of Diabetes Educators (AAED7). The action metric informed the researchers whether the player achieved
the desired results from the game. For example, if the desired outcome is a specific BMI level, and the action is
exercise, a sample metric would be: how much is the user exercising, and how often? A good metric must align with
the same tests as the outcome metrics reflect one of the know measures such as accurate, relabel, etc. [19]. Table3
illustrates the mapping of the gamification mechanism (GM) to the desired output and metrics.

Nahed Alsaleh et al. / Procedia Computer Science 170 (2020) 442–449 445
Author name / Procedia Computer Science 00 (2018) 000–000 3
have used the transtheoretical model extensively to try and encourage smoking cessation. This model is arguably the
dominant psychological model in this field. There is an evidence to suggest that the effectiveness of implementation
intention interventions might be enhanced if they were targeted. In [13], the authors highlighted different methods of
theory in terms of problems and solutions. They confirmed that the use of theory enhances the science of changing
behaviour by adding more details about the effect of the intervention. In [14], the authors have researched how to
improve glycaemic control among Type 2 diabetics using changing behaviour model, which called the Stage of
Change model. The authors conducted a study on two groups of patients with diabetes Type 2. The first group is the
control group that received routine care continued for 48 weeks. The second group is the intervention group that
treated with personalized care for Type2 using changing behaviour model for the same period time. The result of the
study assumed that the social condition at the time of the study played a negative role despite the adoption of the
changing behaviour model. In [15], the authors have examined seven self-behaviours identified by the American
Association of Diabetes Educators (AAED7). These seven essential self-care behaviours predict good outcomes in
people with diabetes. These are healthy eating, being physically active, monitoring blood sugar, being compliant with
medications, having good problem-solving skills, having healthy coping skills, and practicing risk-reduction
behaviours. It is critical that healthcare providers actively involve their patients in developing self-care regimens. This
regimen should be the best possible combination for every individual patient, and it should sound realistic to the
patient so that he or she can follow it. In [16], the authors proposed mobile apps that serve Asthma patients. Their
proposed apps supported with the features of risk identification and management. The authors have categorized their
apps according to three criteria: 1) providing health information; 2) serves as a disease diary; and 3) enabling users to
share that data with their care team. In [17], the authors have examined the design of the MedFit app, which is meant
to facilitate participation of people with cardiovascular disease (CVD) in an exercise-based rehabilitation program
remotely. Their paper details the development of the MedFit app. In [18], the authors have used a systematic review
of interventions designed to promote physical activity and/or healthy eating as a novel approach to classifying
intervention content according to change techniques and theoretically-derived technique combinations. This study
aims to assess the effectiveness of behaviour change BC interventions designed to promote physical activity and
healthy eating and investigate whether theoretically-specified BC techniques improve outcomes. Table 1 summarize
the above papers in terms of methodology, the evaluation methods, the target user, the behaviour change, the result
and the limitation.
Table 1. Summary of the Related Work
Paper
Changing
Behaviour
Method Used
Chronic Disease / Condition
[12]
ü
The transtheoretical model
Smoking
[13]
ü
Framework intervention mapping
Oral cancer
[14]
ü
Stage of Change (SOC) model
Type 2 diabetics
[15]
ü
Role of self-care in management of diabetes mellitus
Diabetics
[16]
ü
mAsthma app according to four criteria
Asthma
[17]
ü
MedFit App
Cardiovascular disease (CVD)
[18]
ü
Assess the effectiveness of active BC
Obesity
GameBetes
ü
Performance Measure of Gamification elements
Diabetes
3. Designing Gamification for behaviour change
In order to make behaviourally effective change, three objectives need to be achieved [19]:
1- Apply design expertise to build something that people like and accept.
In [1], the authors have linked the gamification mechanism to the TTM in a new relationship between them based on
the mechanisms used. The game mechanism contains challenges, points, manipulators, badges, rewards, leader boards,
4 Author name / Procedia Computer Science 00 (2018) 000–000
and levels [20]. The relationship consists of game strategies for learners to solve problems in a fun and engaging
atmosphere.
2- A clear understanding of user behaviour and how to influence it.
The authors achieved this point by studying the different principles that play an important role in behaviour change
based on the TTM. There are a number of studies working on the TTM theory of healthy behaviour change. These
include studies of how the TTM can help smokers and the obese. TTM is a model of intentional change that focuses
on the individual’s decision-making process. The theoretical framework of TTM has several stages. Each stage aims
to work on behaviour change. By achieving all the steps, user action is greatly improved.
3- A commitment to testing the output of the design and to considering the feedback.
In this context, the authors have proposed basic criteria to evaluate the performance of the behavioural change based
on [2], as outlined in Table 2:
Table 2: Performance Criteria
Attributes
Performance Criteria
Gamification
Mechanism
A
To collect the largest number of points
# Points
B
To provide sufficient rewards that players value
# rewards = badges
C
To pass all the levels
#levels
D
To spend the longest ti me in game
# times by min
E
To get on the leader board
# leader board
In this paper, we have considered design expertise by applying the principle of the user interface to design gamification
that meets the children’s requirements. The result of the survey distributed to the 171 Saudi families reveals that the
children surveyed prefer colours. Also, when parents asked about the main aspects of online games that draw the
attention of their children, 34% of them said multiple stages, 24.9% of them cited the presence of many colours and
shapes, and 23.7% answered motivation, while 12.6% identified the speed of the game. In order to be familiar with
the user behaviour of children with diabetes, an interview was conducted with five parents to clarify the important
factor that describes their children’s behaviour. The purpose was to determine what things a child needs to change his
or her health for the better and thereby to improve the behaviour of children with diabetes toward healthy sustainable
dietary habits. This also serves the second goal, which is to support the management of childhood diabetes by
educating children and helping them to modify their behaviour.
To measure the effectiveness of manipulation in changing the behaviour of children with diabetes DM, the researchers
focused on the effect of gamification on the behaviour of children. Because most children enjoy playing electronic
games, our goal is to instil a change in the behaviour of children with diabetes for the better health through GameBetes
application. This application is an educationally designed game t consist of three levels and three educational videos
explaining what the symptoms of diabetes are and how it affects diabetes on the body and what are the healthy eating
groups.
The authors aimed to define the metrics for action based on the seven self-behaviours identified by the American
Association of Diabetes Educators (AAED7). The action metric informed the researchers whether the player achieved
the desired results from the game. For example, if the desired outcome is a specific BMI level, and the action is
exercise, a sample metric would be: how much is the user exercising, and how often? A good metric must align with
the same tests as the outcome metrics reflect one of the know measures such as accurate, relabel, etc. [19]. Table3
illustrates the mapping of the gamification mechanism (GM) to the desired output and metrics.

446 Nahed Alsaleh et al. / Procedia Computer Science 170 (2020) 442–449
Author name / Procedia Computer Science 00 (2018) 000–000 5
Table 3. Mapping GM to Metrics
GM
TTM Stages
Desired Output
Justification ( Metrics)
Points
Contemplation
Performance
• Collecting the most points indicates the player's
performance
Badges
Preparation
Effectiveness
• Collecting badges indicates the player's effectiveness in the
game
Level Ups
Action
Advanced
• Levelling up demonstrates the advance of the player in
number of levels completed with minimal effort
Leader
boards
Maintenance
Completion
Fun to apply
• Maintenance motivates the player to continue learning
good behaviour in the game in a fun way by getting on the
leader boards in the game
Time
-
Efficiency
• The time spent in the game indicates the player's efficiency
in the game
We want to point out that there are trade-offs when creating output and action metrics. The most accurate metrics may
take too long to gather, and the cheapest metrics may be not reliable. Here, we have focused on the accuracy of results
and the credibility of diabetic children in the follow-up. This has enabled us to determine if the game is well peeffective
and usable and if it serves this category of patients. We sense a responsibility towards this group and the need for
technical intervention and proper guidance for healthy eating. We hope to achieve this through the game so that
children are more receptive than others. The authors are looking to action metrics that are sensitive enough to guide
them if there is a problem and accurate enough to generalize the results.
Defining the threshold to determine success and failure is considered. Here, we set a table and write questions based
on the seven behaviors. We want the results to be clear, and we want the results to reflect performance accurately. For
example, if the results consist of “sometimes” and above (“always”), these should be satisfactory and acceptable. If
they consist of “rarely” and “never,” they should indicate unsatisfactory performance. The following section represent
the experiment test, the list of tasks, and the result.
4. Experiment Test and Result
A randomized controlled experiment was applied to measure the impact of gamification on children with diabetes.
The researchers prepared a pre- and post-test questionnaire. For the pre-test questionnaire, the number of responses
was 59 members, who were in a particular group for parents of children with diabetes. The pre-test questionnaire was
to be filled in by the families before their children started the game.
The analysis of the pre-test questionnaire confirmed that 30.4% of children prefer to eat candy, while 28.6% prefer to
eat a cake made from sugar and sweets. Healthy eating followed by 17%. Then comes vegetables and fruits by 14%
and the juice by 6%. The rate considers high because children don’t realize the danger of eating these foods to their
health. The educational video that exists in the game aims to explain how the body suffers from eating sugar.
Then, the tester selected a random sample of 20 children with diabetes with the group between 6 to 12 years and
then divided the sample randomly into two groups: a control group (A) consist of ten children and a treatment group
(B) from another ten children. Group (A) played the game without watching the educational video. Group (B)
played the game after watching the video that presents the nutrients that the body needs, along with the right food to
be eaten. Both groups played for continuous two weeks, and both groups received the same set of tasks and applied
the same measures as mentioned in Table 4.
6 Author name / Procedia Computer Science 00 (2018) 000–000
Table 4. illustrates the list of tasks and the output metrics
List of Tasks
Metrics (Outcome)
How many points have been collected in the first level?
How many points have been collected in the second level?
Performance = the maximum number of collected points.
How many points have been collected in the third level?
How many badges have been achieved (yellow stars)?
Effectiveness = the highest number of badges
How many minutes the player played?
Efficiency = the maximum number of minutes consumed
in the game
After the children played for the continuous two weeks, the tester accessed the children’s account and collected all
the total of points, badges, and times that mentioned in Table 4. The following subsection interprets the result.
4.1 Performance Measurement
Figure 1 shows the average points that each group collected in all stages of the game. We consider that average
points reflect each player’s performance in the game. The red column indicates the control group A, and the blue
column represents the treatment group B. Based on the average, the result confirmed that the treatment group has a
better performance by 91% than the control group 63%.
Fig. 1. The Average for Points
4.2 Effectiveness Measurement
Figure 2 shows the badges average, which is the number of badges that each player collected in all stages of the game.
The average badges collected reflects each player’s effectiveness in the game. Based on the average, the result
confirms that the treatment group has better effectiveness by 8.5 than the control group 7.9.
4.3 Efficiency Measurement
Figure 3 shows the average time, which is the minutes that each player spent in all stages of the game. The average
time is reflecting the player's efficiency in the game. Based on the average, the result confirms that the treatment group
has a better efficiency of 17.9 than the control group 12.6.

Nahed Alsaleh et al. / Procedia Computer Science 170 (2020) 442–449 447
Author name / Procedia Computer Science 00 (2018) 000–000 5
Table 3. Mapping GM to Metrics
GM
TTM Stages
Desired Output
Justification ( Metrics)
Points
Contemplation
Performance
• Collecting the most points indicates the player's
performance
Badges
Preparation
Effectiveness
• Collecting badges indicates the player's effectiveness in the
game
Level Ups
Action
Advanced
• Levelling up demonstrates the advance of the player in
number of levels completed with minimal effort
Leader
boards
Maintenance
Completion
Fun to apply
• Maintenance motivates the player to continue learning
good behaviour in the game in a fun way by getting on the
leader boards in the game
Time
-
Efficiency
• The time spent in the game indicates the player's efficiency
in the game
We want to point out that there are trade-offs when creating output and action metrics. The most accurate metrics may
take too long to gather, and the cheapest metrics may be not reliable. Here, we have focused on the accuracy of results
and the credibility of diabetic children in the follow-up. This has enabled us to determine if the game is well peeffective
and usable and if it serves this category of patients. We sense a responsibility towards this group and the need for
technical intervention and proper guidance for healthy eating. We hope to achieve this through the game so that
children are more receptive than others. The authors are looking to action metrics that are sensitive enough to guide
them if there is a problem and accurate enough to generalize the results.
Defining the threshold to determine success and failure is considered. Here, we set a table and write questions based
on the seven behaviors. We want the results to be clear, and we want the results to reflect performance accurately. For
example, if the results consist of “sometimes” and above (“always”), these should be satisfactory and acceptable. If
they consist of “rarely” and “never,” they should indicate unsatisfactory performance. The following section represent
the experiment test, the list of tasks, and the result.
4. Experiment Test and Result
A randomized controlled experiment was applied to measure the impact of gamification on children with diabetes.
The researchers prepared a pre- and post-test questionnaire. For the pre-test questionnaire, the number of responses
was 59 members, who were in a particular group for parents of children with diabetes. The pre-test questionnaire was
to be filled in by the families before their children started the game.
The analysis of the pre-test questionnaire confirmed that 30.4% of children prefer to eat candy, while 28.6% prefer to
eat a cake made from sugar and sweets. Healthy eating followed by 17%. Then comes vegetables and fruits by 14%
and the juice by 6%. The rate considers high because children don’t realize the danger of eating these foods to their
health. The educational video that exists in the game aims to explain how the body suffers from eating sugar.
Then, the tester selected a random sample of 20 children with diabetes with the group between 6 to 12 years and
then divided the sample randomly into two groups: a control group (A) consist of ten children and a treatment group
(B) from another ten children. Group (A) played the game without watching the educational video. Group (B)
played the game after watching the video that presents the nutrients that the body needs, along with the right food to
be eaten. Both groups played for continuous two weeks, and both groups received the same set of tasks and applied
the same measures as mentioned in Table 4.
6 Author name / Procedia Computer Science 00 (2018) 000–000
Table 4. illustrates the list of tasks and the output metrics
List of Tasks
Metrics (Outcome)
How many points have been collected in the first level?
How many points have been collected in the second level?
Performance = the maximum number of collected points.
How many points have been collected in the third level?
How many badges have been achieved (yellow stars)?
Effectiveness = the highest number of badges
How many minutes the player played? Efficiency = the maximum number of minutes consumed
in the game
After the children played for the continuous two weeks, the tester accessed the children’s account and collected all
the total of points, badges, and times that mentioned in Table 4. The following subsection interprets the result.
4.1 Performance Measurement
Figure 1 shows the average points that each group collected in all stages of the game. We consider that average
points reflect each player’s performance in the game. The red column indicates the control group A, and the blue
column represents the treatment group B. Based on the average, the result confirmed that the treatment group has a
better performance by 91% than the control group 63%.
Fig. 1. The Average for Points
4.2 Effectiveness Measurement
Figure 2 shows the badges average, which is the number of badges that each player collected in all stages of the game.
The average badges collected reflects each player’s effectiveness in the game. Based on the average, the result
confirms that the treatment group has better effectiveness by 8.5 than the control group 7.9.
4.3 Efficiency Measurement
Figure 3 shows the average time, which is the minutes that each player spent in all stages of the game. The average
time is reflecting the player's efficiency in the game. Based on the average, the result confirms that the treatment group
has a better efficiency of 17.9 than the control group 12.6.

448 Nahed Alsaleh et al. / Procedia Computer Science 170 (2020) 442–449
Author name / Procedia Computer Science 00 (2018) 000–000 7
Fig. 2. Effectiveness Measurement
Fig. 3. Efficiency Measurement
After the children played the game, the tester distributed the post-test questionnaire to all the children in both groups.
The result summarizes as follow:
• In the control group, the result of the same set of questions reveals that their favourite food with the highest
percentage was cake and sweets eating by 30%. Then chocolate, fruits, and healthy eating are all in the same
proportion, 20%. Finally came the juice by 10%.
• In the treatment group, after the children watched the video, the ratio varies for the better. 50% of children
preferred healthy eating. Then they preferred to eat fruits and vegetables by 40%. There is a small group that
prefers to eat cake and sweets made of sugar by 10%.
5. Discussion
In this research, a comparison between two groups of diabetic children was conducted. The children in the first group
played the game without watching the educational video, while the children in the second group watched the video
before playing the game. The results were based on GM, such as the points, badges, and time. The results show that
the children who watched the video before playing the game have become more enthusiastic since they knew the idea
of the game and its purpose. This has automatically influenced their eating behaviour, and they tend to eat healthy
food, vegetables, and fruits rather than chocolate, cake, ice cream, and soda. The children became more cautious in
their eating habits than before, and this the game had a significant impact in changing their eating behaviours.
6. Conclusion and future work
In this research, we have designed and developed a game-based user experience factors to measure the behaviour
changes for children with diabetes to enhance the effectiveness of manipulation and minimize potential negative
effects. An experiment test was conducted considering the proposed guidelines for designing gamification to improve
the eating behaviours of children with diabetes. Also, the authors examined the extent to which gamification affects
children’s behaviour in a new way. The result confirmed that gamification plays a significant role in changing the
behaviour of children with diabetes by choosing healthy food such as fruit, vegetables, and non-health food such as
cake and chocolate. The result of the treatment group confirmed responding the children to the health food comparing
to the controlled group. Our future direction is to investigate more metrics definition based on GM and test the
theoretical and empirical on a more realistic case study.
8 Author name / Procedia Computer Science 00 (2018) 000–000
References
[1] Alsaleh, N. and R. Alnanih, Mapping Gamification Mechanisms to User Experience Factors for Designing User Interfaces. Journal of Computer
Science. Vol. 15. PP. 736-744. April 2019.
[2] Artara, A. and B. Huseynlib, Gamification Based Performance Evaluation System: A New Model Suggestion. ReaserchGate. Conference Paper
· November 2017.
[3] Klasnja, P., S. Consolvo, and W. Pratt. How to evaluate technologies for health behavior change in HCI research. in Proceedings of the SIGCHI
conference on human factors in computing systems. 2011. ACM.
[4] Filsecker, M. and D.T. Hickey, A multilevel analysis of the effects of external rewards on elementary students' motivation, engagement and
learning in an educational game. Computers & Education, 2014. 75: p. 136-148.
[5] Deterding, S., et al. Gamification. using game-design elements in non-gaming contexts. in CHI'11 extended abstracts on human factors in
computing systems. 2011. ACM.
[6] Sims, G., Google Play Store vs the Apple App Store: By the numbers. Retrieved on September, 2015. 24: p. 2015.
[7] Lister, C., et al., Just a fad? Gamification in health and fitness apps. JMIR serious games, 2014. 2(2): p. e9.
[8] Brick, N.E., T.E. MacIntyre, and M.J. Campbell, Thinking and action: a cognitive perspective on self-regulation during endurance performance.
Frontiers in physiology, 2016. 7: p. 159.
[9] Prochaska, J.O., Transtheoretical model of behavior change. Encyclopedia of behavioral medicine, 2013: p. 1997-2000.
[10] Vardarlõer, P. and K. İnan, Gamification model proposal for the improvement of sales personnel performance. Journal of Behavior at Work,
2017. 2(1): p. 8-19.
[11] Pulakos, E.D., Performance management: A new approach for driving business results. 2009: John Wiley & Sons. TALENT MANAG EMENT
ESSENTIALS. AJohn Wile y & Sons, Ltd., Publication. 2009.
[12] Prochaska, J.O. and C.C. DiClemente, Stages and processes of self-change of smoking: toward an integrative model of change. Journal of
consulting and clinical psychology, 1983. 51(3): p. 390.
[13] Bartholomew, L.K. and P.D. Mullen, Five roles for using theory and evidence in the design and testing of behavior change interventions.
Journal of Public Health Dentistry, 2011. 71: p. S20-S33.
[14] Partapsingh, V., R. Maharaj, and J. Rawlins, Applying the Stages of Change model to Type 2 diabetes care in Trinidad: A randomised tral.
Journal of negative results in biomedicine, 2011. 10(1): p. 13.
[15] Powers, M.A. and D. Marrero, Diabetes Self-management Education and Support in Type 2 Diabetes: A Joint Position Statement of the
American Diabetes Association, the American Association of Diabetes Educators, and the Academy of Nutrition and Dietetics. Diabetes Care
2015; 38: 1372-1382. Diabetes Care, 2016. 39(1): p. e17.
[16] Kenner, A., Asthma on the move: how mobile apps remediate risk for disease management. Health, Risk & Society, 2016. 17(7-8): p. 510-
529.
[17] Duff, O., et al., MedFit app, a behavior-changing, theoretically informed mobile app for patient self-management of cardiovascular disease:
user-centered development. JMIR formative research, 2018. 2(1): p. e8.
[18] Michie, S., et al., Effective techniques in healthy eating and physical activity interventions: a meta-regression. Health Psychology, 2009. 28(6):
p. 690.
[19] Wendel, S., Designing for behavior change: Applying psychology and behavioral economics. 2013: " O'Reilly Media, Inc.".
[20] Hamari, J., J. Koivisto, and H. Sarsa. Does Gamification Work?-A Literature Review of Empirical Studies on Gamification. in HICSS. 2014.

Nahed Alsaleh et al. / Procedia Computer Science 170 (2020) 442–449 449
Author name / Procedia Computer Science 00 (2018) 000–000 7
Fig. 2. Effectiveness Measurement
Fig. 3. Efficiency Measurement
After the children played the game, the tester distributed the post-test questionnaire to all the children in both groups.
The result summarizes as follow:
• In the control group, the result of the same set of questions reveals that their favourite food with the highest
percentage was cake and sweets eating by 30%. Then chocolate, fruits, and healthy eating are all in the same
proportion, 20%. Finally came the juice by 10%.
• In the treatment group, after the children watched the video, the ratio varies for the better. 50% of children
preferred healthy eating. Then they preferred to eat fruits and vegetables by 40%. There is a small group that
prefers to eat cake and sweets made of sugar by 10%.
5. Discussion
In this research, a comparison between two groups of diabetic children was conducted. The children in the first group
played the game without watching the educational video, while the children in the second group watched the video
before playing the game. The results were based on GM, such as the points, badges, and time. The results show that
the children who watched the video before playing the game have become more enthusiastic since they knew the idea
of the game and its purpose. This has automatically influenced their eating behaviour, and they tend to eat healthy
food, vegetables, and fruits rather than chocolate, cake, ice cream, and soda. The children became more cautious in
their eating habits than before, and this the game had a significant impact in changing their eating behaviours.
6. Conclusion and future work
In this research, we have designed and developed a game-based user experience factors to measure the behaviour
changes for children with diabetes to enhance the effectiveness of manipulation and minimize potential negative
effects. An experiment test was conducted considering the proposed guidelines for designing gamification to improve
the eating behaviours of children with diabetes. Also, the authors examined the extent to which gamification affects
children’s behaviour in a new way. The result confirmed that gamification plays a significant role in changing the
behaviour of children with diabetes by choosing healthy food such as fruit, vegetables, and non-health food such as
cake and chocolate. The result of the treatment group confirmed responding the children to the health food comparing
to the controlled group. Our future direction is to investigate more metrics definition based on GM and test the
theoretical and empirical on a more realistic case study.
8 Author name / Procedia Computer Science 00 (2018) 000–000
References
[1] Alsaleh, N. and R. Alnanih, Mapping Gamification Mechanisms to User Experience Factors for Designing User Interfaces. Journal of Computer
Science. Vol. 15. PP. 736-744. April 2019.
[2] Artara, A. and B. Huseynlib, Gamification Based Performance Evaluation System: A New Model Suggestion. ReaserchGate. Conference Paper
· November 2017.
[3] Klasnja, P., S. Consolvo, and W. Pratt. How to evaluate technologies for health behavior change in HCI research. in Proceedings of the SIGCHI
conference on human factors in computing systems. 2011. ACM.
[4] Filsecker, M. and D.T. Hickey, A multilevel analysis of the effects of external rewards on elementary students' motivation, engagement and
learning in an educational game. Computers & Education, 2014. 75: p. 136-148.
[5] Deterding, S., et al. Gamification. using game-design elements in non-gaming contexts. in CHI'11 extended abstracts on human factors in
computing systems. 2011. ACM.
[6] Sims, G., Google Play Store vs the Apple App Store: By the numbers. Retrieved on September, 2015. 24: p. 2015.
[7] Lister, C., et al., Just a fad? Gamification in health and fitness apps. JMIR serious games, 2014. 2(2): p. e9.
[8] Brick, N.E., T.E. MacIntyre, and M.J. Campbell, Thinking and action: a cognitive perspective on self-regulation during endurance performance.
Frontiers in physiology, 2016. 7: p. 159.
[9] Prochaska, J.O., Transtheoretical model of behavior change. Encyclopedia of behavioral medicine, 2013: p. 1997-2000.
[10] Vardarlõer, P. and K. İnan, Gamification model proposal for the improvement of sales personnel performance. Journal of Behavior at Work,
2017. 2(1): p. 8-19.
[11] Pulakos, E.D., Performance management: A new approach for driving business results. 2009: John Wiley & Sons. TALENT MANAG EMENT
ESSENTIALS. AJohn Wile y & Sons, Ltd., Publication. 2009.
[12] Prochaska, J.O. and C.C. DiClemente, Stages and processes of self-change of smoking: toward an integrative model of change. Journal of
consulting and clinical psychology, 1983. 51(3): p. 390.
[13] Bartholomew, L.K. and P.D. Mullen, Five roles for using theory and evidence in the design and testing of behavior change interventions.
Journal of Public Health Dentistry, 2011. 71: p. S20-S33.
[14] Partapsingh, V., R. Maharaj, and J. Rawlins, Applying the Stages of Change model to Type 2 diabetes care in Trinidad: A randomised tral.
Journal of negative results in biomedicine, 2011. 10(1): p. 13.
[15] Powers, M.A. and D. Marrero, Diabetes Self-management Education and Support in Type 2 Diabetes: A Joint Position Statement of the
American Diabetes Association, the American Association of Diabetes Educators, and the Academy of Nutrition and Dietetics. Diabetes Care
2015; 38: 1372-1382. Diabetes Care, 2016. 39(1): p. e17.
[16] Kenner, A., Asthma on the move: how mobile apps remediate risk for disease management. Health, Risk & Society, 2016. 17(7-8): p. 510-
529.
[17] Duff, O., et al., MedFit app, a behavior-changing, theoretically informed mobile app for patient self-management of cardiovascular disease:
user-centered development. JMIR formative research, 2018. 2(1): p. e8.
[18] Michie, S., et al., Effective techniques in healthy eating and physical activity interventions: a meta-regression. Health Psychology, 2009. 28(6):
p. 690.
[19] Wendel, S., Designing for behavior change: Applying psychology and behavioral economics. 2013: " O'Reilly Media, Inc.".
[20] Hamari, J., J. Koivisto, and H. Sarsa. Does Gamification Work?-A Literature Review of Empirical Studies on Gamification. in HICSS. 2014.