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Telemedicine can alleviate the increasing demand for elderly care caused by the rapidly aging population. However, user adherence to technology in telemedicine interventions is low and decreases over time. Therefore, there is a need for methods to increase adherence, specifically of the elderly user. A strategy that has recently emerged to address this problem is gamification. It is the application of game elements to nongame fields to motivate and increase user activity and retention.
This research aims to (1) provide an overview of existing theoretical frameworks for gamification and explore methods that specifically target the elderly user and (2) explore user classification theories for tailoring game content to the elderly user. This knowledge will provide a foundation for creating a new framework for applying gamification in telemedicine applications to effectively engage the elderly user by increasing and maintaining adherence.
We performed a broad Internet search using scientific and nonscientific search engines and included information that described either of the following subjects: the conceptualization of gamification, methods to engage elderly users through gamification, or user classification theories for tailored game content.
Our search showed two main approaches concerning frameworks for gamification: from business practices, which mostly aim for more revenue, emerge an applied approach, while academia frameworks are developed incorporating theories on motivation while often aiming for lasting engagement. The search provided limited information regarding the application of gamification to engage elderly users, and a significant gap in knowledge on the effectiveness of a gamified application in practice. Several approaches for classifying users in general were found, based on archetypes and reasons to play, and we present them along with their corresponding taxonomies. The overview we created indicates great connectivity between these taxonomies.
Gamification frameworks have been developed from different backgrounds—business and academia—but rarely target the elderly user. The effectiveness of user classifications for tailored game content in this context is not yet known. As a next step, we propose the development of a framework based on the hypothesized existence of a relation between preference for game content and personality.
It is expected that 25% of the European population will be older than 65 years in 2050 because of global population aging [
Gamification, the application of game elements to nongame fields, may be such a strategy [
Choice and personalization of content [
For this purpose, the aim of the paper is to (1) provide an understanding of the theoretical background of gamification, including existing frameworks for developing gamification both in general and specifically for the elderly population, and (2) explore existing user classification theories that may serve for the tailoring of game content to the target user. Because of the newness of this field of research, we opt for a broad view on activities in gamification that occur not only within but also outside of scientific research. In future research, we will work toward a user classification of the elderly population that can be used to develop evidence-based gamification strategies and tangible design guidelines for gamification in health care.
In a succession of 3 Internet searches, a broad approach to the subject of gamification was taken to gain insight into the many developments in gamification that occur both inside and outside of the scientific world. We performed a search in the scientific search engines PubMed, Scopus, and Google Scholar and in diverse nonscientific sources: from game designer blogs and conference videos to MOOCs (massive open online courses) and YouTube videos. In this paper, gamification is defined as the use of elements from games in nongame contexts to improve user experience and engagement without making that system a full game as is the case with serious games including exergames (combination of exercise and gaming) [
First, we have researched the conceptualization of gamification from a theoretical perspective (see
Included in the results were articles and other works that present a theoretical basis for the development of gamification, defined as the presence of a framework that is either theoretical and/or based on established scientific foundations or proven effective through evaluation in practice. Therefore, beyond the scope of our paper are numerous works on gamified applications with a black box design.
This section demonstrates the current state of gamification, starting with the concept of gamification in a broader sense and then focusing on gamification for elderly people. We provide an overview of existing frameworks for gamification along with their contexts and backgrounds. With this, we aim to define the status quo in research and provide a deeper understanding of the concept and its use and misuse.
Gamification has gained popularity in diverse fields such as (interactive) marketing and scientific applications, generating different definitions of gamification. Currently, there is no consensus about a definition, mainly due to the underlying perception of what the game elements are exactly in terms of level of abstraction and whether the gamified application is game-like or not. Gamification is often roughly defined as the use of elements from games in nongame contexts; a more refined definition regards gamification as the identification of that which makes games captivating and engaging followed by the transfer of this knowledge to nongame contexts, increasing user enjoyment [
We found a couple of approaches toward the conceptualization of gamification. One emerges from business practices, such as marketing, customer loyalty, and employee engagement; the other from academia and not sales driven, often specifically aiming to incorporate theories on motivation, engagement, and behavior change.
Frameworks for gamification in business and academia.
Business | Academia |
Cunningham and Zichermann (2011) | Aparicio et al (2012) |
Werbach and Hunter (2012) | Nicholson (2012) |
Duggan (Badgeville, 2012) | Sakamoto et al (2012) |
In business-oriented, or corporate, gamification, the number of successful initiatives, in terms of increased user engagement or revenue, that use gamification has been rapidly increasing in the past few years [
There are several authors within this business orientation, such as Cunningham and Zichermann [
However, the way gamification is applied in business context receives a lot of criticism as analysts estimate that the bigger part of current gamified applications will not meet their business objectives, mainly due to poor design [
Scientific research from within academia, the second approach we distinguish, includes few frameworks on the theoretical foundations of gamification. Aparicio et al [
Several differences between the frameworks from business and academia (
The contrast between business and academic frameworks.
Business | Academia |
Applied | Conceptual |
Simplicity | Complexity |
Practical guidelines | Methods inexplicit |
Proven worthy in practice | Earlier stage of development, less empirical support |
Lacking depth, oversimplified | Solid scientific foundation |
Short-term engagement suffices | Aiming for durable motivation |
Immensely popular | Mostly unknown |
While gamification is gaining popularity in telemedicine [
By contrast, Minge et al [
IJsselsteijn et al [
Overview of papers described.
Source | Topic |
IJsselsteijn et al (2007) [ |
Design opportunities for engaging games for elderly |
Gerling et al (2011) [ |
Potential of gamification for engaging (frail) elderly |
Minge et al (2011) [ |
Attitude of elderly toward gamification |
Link et al (2014) [ |
Effect of game elements on motivation of elderly |
User classification holds a key role in the development of tailored game content, as it gives thorough insight into the preferences that individuals or subgroups within a target group may have [
Approaches to classify the user.
The earliest and most cited player taxonomy in a gaming context is the Bartle player type theory. It was developed for the first virtual multiuser environment, text-based dungeons (multiuser dungeons, or MUDs), by observing and analyzing player patterns. Bartle proposes 4 player types (
Similarly, but in the context of enterprise gamification, Marczewski [
Another approach to create player archetypes is through personality. Personality traits have been extensively studied and researched since the 1880s [
In the context of games and gaming, several attempts on predicting the effectiveness of the application of FFM showed inconsistent results [
Five-factor model traits and corresponding gaming motivation traits (deduced from Vandenberghe [
Low score | Trait | High score |
Cautious, predictable | Openness to experience | Inventive, curious |
|
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Careless, impulsive | Conscientiousness | Efficient, organized |
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Reserved, solitary | Extraversion | Energetic, outgoing |
|
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Analytical, detached | Agreeableness | Friendly, compassionate |
|
|
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Confident, secure | Neuroticism | Nervous, sensitive |
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Two examples illustrate specific gaming elements derived from motivation facets. First, the imagination of the user correlates with a preference for either fantasy or realism: someone who scores high on imagination will tend to prefer games that take place in exotic worlds, whereas someone with a low score will prefer games that take place in a world much like ours. Second, scoring high on adventurousness correlates with a preference for exploration and a desire for encountering new things, much like the Bartle type explorer, whereas a low score indicates a preference for local play styles such as building or farming that do not involve leaving the boundary of the known [
Bartle’s player type model.
Marczewski’s player type model.
Yee [
A taxonomy of game aesthetics, or what makes a game fun, can be found in the mechanics, dynamics, and aesthetics framework by LeBlanc et al [
Yee’s model motivations of play in MMORPGs: the components and subcomponents.
Although the taxonomies aforementioned appear very different concerning the types of classes, many parallels can be found between the characteristics of each class. We present the results in an overview chart (
In the models of Marczewski and Yee, which both have Bartle as point of reference, we see a clear analogy between the achievers and socializers and also in the attributes of the free spirit (interacting with the system, autonomy), the explorer (interacting with the world), and immersion (discovery, exploration). Although Yee does not have a separate type for the killer or disruptor, provocation and domination are present in achievement. Linking to Lazzaro and LeBlanc, achievement is similar to the concept of hard fun and challenge; easy fun (which includes the motive of immersion) and discovery are similar to exploring; and the people factor and fellowship and expression relate to the social aspect. The model of Vandenberghe not only seems all-embracing, but it also adds a dimension to each personality trait. The killer can be linked to a very low score on harmony, the achiever to a high score on challenge, the explorer to a high score on novelty, the socializer to a high score on stimulation. The trait threat is quite unique and only linked to submission. According to Vandenberghe, this trait may not be pointing out what keeps a player playing but what makes the player decide to stop playing.
None of the taxonomies presented target the elderly user specifically. Furthermore, we do not know of any methods regarding the mapping of this target group on the existing taxonomies, mainly because the gaming industry does not focus on this group as a consumer for video games. Moreover, the taxonomies are in most cases designed for use in a specific application, such as enterprise gamification or MMORPGs, and it is not known how suitable they are for application in telemedicine interventions. We can identify many parallels between the models, and we consider that the 5 domains of play stand out from the rest. Unlike the other models, an individual is not given a singular class label or a combination of those. Instead, a complete character description can be created based on preference for certain aspects or elements of games. What makes this theory even more attractive is that it describes the user based on personality, a universal understanding regardless of age.
Chart of connections between taxonomies (arrow: direct derivative of, line: high similarity in concept, dots: closely related concepts).
The first objective of this study was to provide an overview of theoretical frameworks for the application of gamification and of methods for gamification that specifically target the elderly user. Second, we have explored user classification theories, which are needed to gain insight into the user and serve as a tool to effectively tailor content. We have found that current frameworks for gamification rarely target the elderly user. The effectiveness of the use of user classifications for tailored game content is not yet known, neither are there indications for classifying the elderly user with these theories. How can we use these results to systematically design effective gamified telemedicine applications for elderly?
Frameworks for gamification emerge from two main approaches. First, there is a business-oriented approach, with examples of success in practice, using an easy-to-apply framework to gamify applications. However, the frameworks from this approach may also be oversimplified, which suffices for marketing purposes but possibly not for long-term engagement needed in telemedicine. Second, frameworks created within academia target for higher causes, such as better education and health outcomes. These frameworks often make use of established theories but are complex, and, at the time of writing, not used in practice. In both approaches, no appropriate framework was found to design gamification for elderly users and application in telemedicine. Therefore, a new framework should be created that is of sufficient depth but applicable in practice and supported by empirical data on its effectiveness. To do so, we would position our future research in academia and take example of the studies presented within this approach. Just like the authors discussed [
Our study showed two approaches for user classification theories: archetypes, where classes are user types with associated preferences, and reasons to play, where classes are based on attributes that describe the user preference. None of the found taxonomies seem to be applicable in telemedicine for elderly users due to the very different context and audience for which they have been developed and the fact that we are not familiar with the use of these taxonomies in practice. However, a high level of understanding of the target group will greatly contribute to designing effectively engaging content. This can be achieved by a taxonomy for game design specifically for elderly users. Creating such a taxonomy and corresponding game content can be difficult, because older adults may relate to video games differently than younger users as they might not be able to draw from earlier experience with video games. To create such a classification, it would be most desirable to observe the behavior of intended users in games, but the scarcity of elderly gamers (and limited availability of games for elderly people) does not provide sufficiently representative subjects for the whole target group.
Although from the taxonomies found none seem directly suitable for creating our future framework, the 5 domains of the play model [
Advantages of creating a framework within the academic approach are the possibility of using solid scientifically established theories and incorporating existing motivational theories and instruments that relate to the objective of gamification to motivate and engage. Serious games and exergames for elderly users [
We suggest developing a framework for gamification that is based on solid scientific foundations and includes a user classification that specifically assesses the elderly user. We base this classification on the 5 domains of the play model that predicts the existence of a relation between preference for game content and personality. In a study, we need to explore this relation as well as opportunities for use for the intended target group and context. When we know more of these aspects, a gamification framework can be developed by which the classification of the elderly user is used to effectively create tailored, engaging game content. Subsequently, the framework needs to be put to practice and evaluated for empirical support of its effectiveness.
Keywords first search.
Keywords second search.
five-factor model
information communication technology
massive multiplayer online role-play games
massive open online course
multiuser dungeon
This work is part of the PERSSILAA project [
None declared.