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In addition, other studies [ 19 , 27 ] showed that gamification worsened data quality but had positive effects on engagement. The gamification applied by Birk et al [ 23 ] not only did not have a positive impact on data quality and engagement but also worsened them [ 19 ]. These mixed findings are potentially due to 4 main reasons: 1 Most gamified cognitive tasks have been developed by cognitive psychologists, not professional gamification designers, and for scientists, the clinical effectiveness of a gamified task is important, with less focus on employing effective and creative gamification designs [ 41 ]; 2 a variety of gamification techniques have been applied to cognitive tasks [ 9 , 24 , 38 ]; 3 gamification techniques have been applied to different cognitive tasks [ 24 ]; and 4 the results obtained from gamified cognitive tasks are often preliminary and limited by small sample sizes.
Also, the considered duration for evaluating the efficacy of gamified tasks is relatively short [ 9 , 23 – 25 , 42 , 43 ]. There are recommendations and design guidelines to integrate game elements into cognitive tasks eg, [ 9 , 30 ]. However, to our knowledge, they did not propose a detailed and step-by-step framework that clearly shows what factors are essential to designing gamification in these tasks from early stages eg, planning and preparation phases to develop, evaluate, and disseminate gamified tasks, followed by monitoring the efficacy of such tasks in the long term.
In general, several gamification frameworks have been developed by experts, not designed for cognitive tasks eg, [ 44 – 53 ]. However, they suffer from 3 main limitations: 1 Most of them have been designed for enterprise and business contexts, with less focus on health contexts [ 54 ]; 2 cognitive tasks need to engage participants for the long term, but existing frameworks have not been designed for this purpose [ 54 , 55 ]; and 3 they have not specified how game elements should be added to a particular context.
Incorporating game elements in cognitive tasks may jeopardize data quality by imposing an additional cognitive load to these tasks [ 56 ]. For example, Katz et al [ 25 ] gamified the N-Back task by including a real-time scoring system while completing the task.
The gamified task, in comparison to the actual task, negatively impacted data quality. Despite the papers that have shown mixed findings of using game elements in cognitive tasks, we assume that gamification can positively influence data quality and user engagement. Therefore, we are proposing a framework to guide the process of incorporating game elements in cognitive tasks by synthesizing 1 existing gamification design frameworks, 2 gamification efforts in cognitive assessment and training, and 3 field experiences.
We approached the research problem through the design science research DSR methodology [ 57 ]. Design science is an accepted research methodology in information systems. It emphasizes that research should be firmly grounded in existing knowledge and target the context in which the developed artifact must be used, to create scientifically sound artifacts eg, theories, models, and methods [ 58 ]. The 2 main steps for conducting DSR include 1 developing artifacts and 2 evaluation of the developed artifacts [ 53 ].
In our DSR approach, similar to Morschheuser et al [ 53 ], we employed an assembly-based situational method engineering methodology proposed by Brinkkemper [ 59 ]. Method engineering is an approach in information systems to develop new methods from recognized fragments of existing methods knowledge to propose a situational method tuned to the situation of the project at hand.
According to method engineering, 3 phases are needed to develop a new method [ 59 ]: 1 building a method database, which includes all the resources required for the development of a new situational method; 2 constructing the situational method through assembling of the methods fragments from the method database; and 3 evaluation of the developed method.
Figure 3 provides an overview of our assembly-based situational method engineering. Situational method engineering approach followed for developing the framework adapted from [ 53 , 59 ]. We selected the following 3 resources to gather the knowledge required for proposing the intended framework: 1 studies that proposed a gamification design framework, since by synthesizing them, we could extract the general framework for the design of gamification; 2 projects that used gamification in cognitive assessment or training to extract key factors and considerations for gamifying these contexts; 3 the experiences of relevant experts to integrate the evidence from live environments.
We used systematic literature review strategies in the Google search engine, Google Scholar, PubMed, and Research Gate to use a wide variety of relevant papers. The search was started in May and lasted until the submission date. Furthermore, search strategies such as checking the reference lists of included studies and cited reference searching were applied.
To select the most highly regarded gamification frameworks, the following 4 metrics were used: 1 framework was not focused on the parts or steps of the gamification design process, but covering the maximum number of steps; 2 framework was determined to be worthy in terms of efficacy by calculating the number of its citations; 3 framework was developed by gamification experts we considered an individual an expert based on whether she or he published at least 10 scientific articles concerning gamification issues ; 4 framework was developed using a robust methodology.
First, we included projects that published reports about the impacts of game elements on data quality and user engagement in the form of a scientific paper, to obtain sufficient author-presented analytical expressions on how game elements should be incorporated into cognitive tasks. However, it used other game-inspired designs such as serious games, video games, games with a purpose, and game-like interventions. We erred on the side of caution to minimize the potential loss of relevant sources.
From the early stages, the framework was screened and judged by a homogeneous group of 17 experts from relevant disciplines, including information technology, game, gamification, and cognitive psychology.
The average years of experience are presented in Table 1. After extracting each piece of evidence and then applying it to the under-development framework, the whole of the framework was visualized for expert evaluation. Then, the framework was refined based on the feedback collected.
After comparing the selected frameworks in terms of main characteristics, merits, and demerits, 2 general gamification design frameworks were elaborated.
The first framework was based on analyses presented in Multimedia Appendix 1. The phases and activities of these frameworks will be described in detail in the subsequent sections while describing the proposed framework. As abstracted in Figure 3 , most articles did not use a specific or formal framework for gamifying cognitive tasks. Therefore, either we could not extract a process, or the process obtained consisted of only one chunk. Finally, the isolated processes, fragments, and general gamification frameworks were converged based on their commonalities and unique features to assemble the intended framework.
We identified a total of 35 gamification design frameworks these frameworks are listed in Multimedia Appendix 1. Of these, 11 frameworks were selected for more in-depth analysis these frameworks are highlighted in bold in Multimedia Appendix 1. A total of 63 gamification projects in cognitive assessment or training was selected. For more details and raw data about the works included, such as their targeted cognitive functions or impairments, please see both tables in Multimedia Appendix 2.
To address the need for a framework that more effectively integrates game elements into cognitive assessment and training, we introduce a framework consisting of 7 phases: 1 preparation; 2 knowing users; 3 exploring existing tools for assessing or training the targeted cognitive context and determining the suitability of game-up and mapping techniques; 4 ideation; 5 prototyping using OMDE; 6 development; and 7 disseminating and monitoring. These phases are grouped into 3 overarching categories: predevelopment, development, and postdevelopment Figure 4.
Although the framework phases are presented sequentially, they are not necessarily to be conducted linearly since different ideas and directions may be explored while integrating gamification into targeted cognitive tasks.
Therefore, projects are encouraged to loop back through the phases continuously [ 61 , 62 ]. The framework aims to facilitate creating more effective gamified cognitive tasks by using an interdisciplinary team of gamification designers, cognitive experts, and target users.
Only a truly multidisciplinary team has the knowledge and expertise to address the complex factors involved in the design of gamification into cognitive tasks [ 61 , 63 , 64 ]. Gamification designers are not familiar enough with the process and execution of targeted cognitive tasks. As a result, they may incorporate game elements inappropriately [ 19 , 23 – 25 , 33 , 65 ].
Therefore, gamification designer and cognitive expert involvement is needed throughout the design process, and target users should be involved throughout phases 2, 4, 5, and 6. The involvement of target users in these phases places their needs and motivations at the center of attention. The first 3 framework phases are primarily about information gathering to develop a more well-accepted and scientific gamified task in later stages.
Steps 4, 5, and 6 ideation, prototyping using OMDE, and development, respectively follow 2 main objectives: 1 generating gamification design ideas around targeted cognitive tasks stages 4 and 5 and 2 developing actual gamified cognitive tasks through frequent consultation with gamification and cognitive experts and regular testing with target users stage 6.
Finally, once the efficacy of a gamified cognitive task has been demonstrated in phase 6, the task is disseminated to its target audience and then monitored periodically to maintain its effectiveness over the long term stage 7. Defining objectives has been recommended in most reviewed gamification frameworks 9 of 11 and will support a later stage to figure out if the desired goals have been achieved [ 66 ].
The interdisciplinary team should list all potential objectives and then rank and justify the list in terms of importance since trade-offs of less important goals for more important ones might be needed [ 49 , 53 ].
Finally, as the team goes through gamification design and development, it can go back to the list to focus on what is really important [ 49 ].
Therefore, the defined objectives should be achievable, specific, relevant, measurable, and time-bound [ 66 ]. It can be carried out by detecting the problem that gamification should solve by gathering and analyzing quantitative and qualitative information.
After determining the problem, the root reason that caused the problem must be motivational. Otherwise, gamification is not suitable [ 66 ]. The segmentation helps the team choose a more acceptable design to gamify targeted cognitive tasks. Typically, gamification through motivational affordances enriches information systems [ 53 ]. Therefore, it is essential to conduct this phase 9 of 11 frameworks have had one step for understanding users.
People are motivated by different motivational affordances based on characteristics such as their age, gender, and culture. The Octalysis gamification framework is widely used to segment users based on their motivations [ 68 ]. Octalysis was developed by Chou [ 68 ] as an octagon with 8 core drivers of individuals on each side: 1 epic meaning and calling, 2 development and accomplishment, 3 empowerment of creativity and feedback, 4 ownership and possession, 5 social influence and relatedness, 6 scarcity and impatience, 7 unpredictability and curiosity and, 8 loss and avoidance.
The game strategies or elements that are associated with each driver have been grouped next to it. The girls have a scarf Figure 5 since these children only identify women and girls who wear a scarf in Iranian culture, girls and women wear a scarf. For more information about designing a user-friendly interface for people facing cognitive and physical disabilities, please see [ 2 , 69 – 73 ].
At this stage, the interdisciplinary team should thoroughly acquaint itself with existing tools for assessing or training the targeted cognitive functions or impairments through methods such as consulting with cognitive experts and gathering quantitative and qualitative information.
This work helps the team incorporate game elements into these tools without changing their process and execution and find integration points for adding game elements [ 74 ]. Describing the tools at a granular level is required. Otherwise, it is not guaranteed that the next framework steps will lead to the desired outcomes [ 53 , 74 ]. Three main tools that can be explored for cognitive assessment and training purposes are standard computerized or not computerized cognitive tasks, existing cognitive games, and existing video games.
Selecting appropriate cognitive tasks is very important. Some tasks may have better performance than others [ 24 , 75 ]. Computer versions have now been made for many standard cognitive tasks, which are cheaper, more repeatable, and easier to administer and distribute [ 76 ].
Many of these tasks can be found in [ 77 – 79 ]. Regarding existing cognitive games, many games have been developed based on standard cognitive tasks [ 80 ]. It is beneficial to find these games since they can be reused for current cognitive assessment and training purposes, or the initial inspiration for gamifying current tasks can be obtained by reviewing the style of these games for integrating game elements into a cognitive task [ 25 , 33 , 43 , 75 ]. Regarding existing video games, it has also been demonstrated that classical video games of different genres that have not been inherently designed to assess or train cognitive functions can be reused as a standard cognitive task.
Video game challenges come in various forms, and players have to use their underlying neural systems and cognitive abilities to win these games [ 81 – 90 ]. Each cognitive function is typically characterized by a set of parameters estimated from a gameplay to reuse for assessing and training. In other words, the team must identify which cognitive skills are central to each gameplay [ 91 ].
For winning games like Tetris and Candy Crush, mental rotation and spatial reasoning skills are required [ 91 ]. Card games like Solitaire and FreeCell have a reasonable correlation with classical measurements of executive functions and planning abilities [ 92 , 93 ].
The team can explore existing games from platforms such as the App Store and Google Play. According to Green and Bavelier [ 83 ], Doherty et al [ 91 ], and expert experiences, it is unnecessary to find appropriate games through earlier methods for categorizing games such as genre-based methods since they are no longer effective. Games that have never overlapped in terms of content and mechanics now have many points of overlap [ 83 ].
After collecting the tools, the interdisciplinary team should determine whether game-up and mapping techniques can be used instead of designing a new gamified cognitive task from scratch. Game-up refers to adding game elements such as colors, animations, sound effects, and a backstory into standard cognitive tasks without changing their fundamental properties such as stimuli, design, and procedure [ 5 , 26 , 94 ] 21 of 63 studies used the game-up technique.
The developed gamified cognitive tasks based on game-up are often presented in the form of a battery of mini-games. Each mini-game focuses on a specific cognitive function eg, [ 64 , 76 , 95 ]. For example, Zeng et al [ 76 ] gamified a computerized cognitive test battery to detect impairments in 5 cognitive functions involved in developing a major neurocognitive disorder.
For each test, some mini-games were designed in the context of ADLs such as cooking, cleaning, and shopping. The main feature of game-up is simplicity in terms of its mechanics and design [ 7 , 65 , 96 , 97 ]. This feature is useful for individuals who suffer from cognitive impairments like children with learning disabilities and ADHD who have weak working memory capacity [ 65 , 96 ].
Therefore, gamified cognitive tasks for these children should be broken into short and discrete tasks [ 65 , 96 ]. Mapping refers to reusing an existing game cognitive and classical games as a cognitive task and can save considerable time and effort that have been applied in the design of explored games [ 98 , 99 ].
The mapping technique was used by 21 of the 63 studies eg, [ 72 , 92 , 93 , – ]. On the other hand, the used game elements and storylines may not align with participants’ preferences [ , , ]. Moreover, they may impose an additional cognitive load. Therefore, the exact cognitive demands of selected games should be identified by analyzing their structural characteristics [ 83 ].
Each game’s structural characteristics should be examined individually since different games, even those that fall into one category such as action or first-shooting person, may require greatly different cognitive demands [ 81 , 85 , , ]. In mapping, it is also possible to mash up various games for cognitive assessment and training purposes [ 81 ]. For example, dyslexia is associated with a variety of underlying deficits in phonological, auditory, motor, memory, and visual attentional processes.
According to previous findings showing the core deficit in dyslexia is related to attentional problems, Franceschini et al [ 81 ] explored 10 action games to train dyslexic children. Action games can enhance a wide variety of visual attentional abilities, such as segmenting items both in time and across space.
The interdisciplinary team is involved in a highly iterative design process through the next 3 phases ideation, prototyping using OMDE, and development. Iterative processes enable the team to obtain more creative and effective gamification designs. Of the 11 frameworks, 5 have one or more steps that should be iterated until the desired designs and outcomes are reached ie, [ 46 , 48 – 50 , 53 ].
The steps that are often performed iteratively are ideation, prototyping, and development [ 53 ]. At this stage, the team combines the analyses and materials obtained in new ways to produce apt and innovative ideas to engage target users.
It is necessary to involve a cross-functional group of people from cognitive experts, gamification designers, and target users to start this stage [ 61 , 62 ]. This work helps the interdisciplinary team to collect a greater number of more varied and creative ideas [ 61 , 62 ].
The participants should be encouraged to use different ways to be creative [ 61 , 62 ]. Brainstorming, co-creation workshops, and mind mapping are some methods [ 53 , 61 , 62 ]. The important question at this stage is how to help participants find the ideas. One solution is to explore existing games, gamification designs, and examples that may be a perfect fit for the current project [ 62 ]. Various types of moles exist in different game versions, such as ninja, pirate, samurai, and batman moles [ ].
Exploring many games and gamification examples and then mashing them up to fit the current problem is another right approach for generating ideas to gamify the current task [ 62 ]. After preparing ideas, similar ideas should be clustered using affinity diagrams; then, the clusters should be prioritized using methods such as dot voting.
This work helps the team to focus on important ideas in the next 2 phases [ 50 , 53 ]. After collecting the right ideas, the interdisciplinary team needs to start prototyping. Prototyping is the stage in which the team implements the ideas into tangible forms to see how they actually work. Prototyping saves time and resources by helping the team to identify refinements required before solidifying a design [ 61 , 62 ].
During each iteration of the prototyping, the team can use the OMDE design guideline to 1 check the motivational characteristics of prototypes such as fun, flow, engagement, positive emotions and 2 validate prototypes from cognitive psychology aspects. OMDE divides the components of a gamified cognitive task into 4 categories: objects, mechanics, dynamics, and emotions. Objects are a gamified cognitive task’s assets, such as visual assets, images, audios, videos, and animations [ 60 ].
Mechanics refer to a gamified task’s components at the level of game rules, algorithms, and data representation [ 60 ]. Emotions refer to whatever emotions users experience while interacting with the gamified task [ 44 ].
Participants may experience different emotions such as fear, happiness, anger, sadness, and pride while interacting with the gamified task [ 44 ]. Dynamics and emotions emerge from the selected objects and mechanics [ 44 , 52 , 60 ]. For instance, a leaderboard mechanic leads to dynamics such as competition and comparison and emotions such as fear and happiness. Some participants may be afraid of being judged by others, and the use of the leaderboard may demotivate them from continuing the gamified task.
Or, many participants may enjoy these dynamics, and the leaderboard can motivate them. Therefore, displaying participants’ statuses in the leaderboard must be an optional feature in a gamified task. Good dynamics and emotions are vital to ensuring a strong user commitment to participation [ 44 , 52 , 60 ].
Then, in each iteration, the team must list what dynamics and emotions emerge from the gamified task in practice and then compare the responses with the desired ones to determine if the desired responses have been reached. The team cannot accurately predict what dynamics and emotions will emerge from a gamified task. Therefore, it is necessary to use OMDE iteratively [ 44 , 52 ].
In gamified cognitive tasks, it is also essential to validate the components of OMDE from cognitive aspects because they may impose an additional cognitive load. In this circumstance, participant errors increase, and the reliability of the data obtained decreases. In the standard task, a sequence of stimuli is presented for ms. Participants should respond to circles but not to squares. In the standard task, a circle is very different from a square.
If yes, how can we do so? If yes, how should the degree of separation between cognitive and gamified sections be adjusted? During this stage, actual gamified cognitive tasks are developed through frequent consultation with relevant experts gamification and cognitive experts and regular testing with target users Figure 6.
Based on the examined gamification efforts in cognitive tasks, to test the efficacy of gamified tasks, rigorous evaluations are required in terms of user engagement and data quality eg, [ 9 , 19 – 21 , 23 – 27 , 34 , 38 – 40 ].
To assess how gamified cognitive tasks impact the quality of data and to indicate the maturity of these tasks to be used as a valid clinical tool, they must be evaluated for 2 essential properties [ 43 ]: 1 reliability, which refers to the extent to which a task’s results are consistent and repeatable, and there are 4 types of reliability test-retest reliability, parallel forms reliability, internal consistency reliability, and interrater reliability and 2 validity, which refers to how well a task measures what it claims and includes criterion validity, content validity, construct validity, face validity, external validity, and ecological validity.
In cognitive training, it may also be necessary to measure to what extent gamified tasks can impact transfer effects [ 38 , 41 , 83 ]. New tasks and situations are included to measure transfer effects. Transfer effects are divided into near and far transfer effects.
Cognitive training has near transfer effects if it improves cognitive skills that are highly similar to trained cognitive skills. Far transfer effects refer to improvements in cognitive skills that are less similar to trained skills. There are 2 other essential factors for conducting rigorous evolutions: 1 selecting sufficient sample sizes and 2 selecting an appropriate duration for evaluation.
Most gamification efforts in cognitive contexts have used small sample sizes to evaluate the efficacy of gamified cognitive tasks [ 9 ]. Also, little consideration has been given to using statistical analyses such as power analysis for a sample size calculation [ 9 , ]. For more information about how to calculate sufficient sample sizes, please see [ , ]. Insufficient sample sizes limit the reliability and generalizability of the results [ 9 , , ].
Moreover, only a few studies have used randomized controlled trials RCTs to evaluate gamified cognitive tasks [ 30 ].
In clinical research, RCTs are considered the most robust study design for evaluating the effectiveness of a new tool due to the ability of RCTs to minimize several forms of bias [ 61 ]. RCTs randomly assign participants to an experimental group and a control group.
The use of an RCT design comparing gamified experimental group and nongamified control group versions of the same cognitive task has been highly recommended to evaluate the potential efficacy of gamified tasks [ 9 , 30 ]. Regarding selecting the appropriate duration for evaluation, participants are not involved in the gamified task over the long term but instead participate for a short time.
In turn, it remains unclear after how long participants feel boredom and how the quality of data will alter in these circumstances [ 19 , 25 ]. Moreover, a short duration can cause errors due to participants’ unfamiliarity with the gamified task. In this regard, using short tutorials and warm-up sessions before actual evaluation sessions has been recommended [ 65 , 76 ]. Once the gamified cognitive task’s efficacy has been demonstrated in the previous step, the task is finally disseminated to its target audience.
There have long been calls for disseminating and sharing well-evaluated digital health interventions due to the abundance of low-quality interventions currently available to the public [ 17 , 61 ]. Disseminating gamified tasks provides access to the broader population that may benefit the most from these tasks and helps the industry invest in these interventions more quickly [ 61 , 73 ]. Disseminating can occur via the App Store or Google Play.
Also, industry partnerships can support a more effective and sustainable dissemination of gamified cognitive tasks [ 61 , ]. It is also highly recommended that projects disseminate their findings, experiences, and methods for developing gamified cognitive tasks to scientific journals, conferences, researchers, and digital mental health intervention developers.
It can advance future gamified tasks and improve their effectiveness [ 61 , ]. Disseminating can also include open sharing of gamified cognitive task codes via GitHub [ ] or allowing free access to a mobile health platform such as Mobile Sensor Data-to-Knowledge MD2K [ ]. For disseminating gamified tasks, 2 other important factors should be considered by the team. First, according to expert experiences and [ 91 ] in collaboration with cognitive experts, appropriate guidelines and prescriptions should be prepared for using gamified cognitive tasks by clinics and target users eg, determining the minimum effort and time that target users should spend to improve their cognitive skills.
These instructions help mental experts and target users to use gamified tasks effectively. Therefore, gathering target users’ information should be undertaken with their explicit consent [ 23 , 47 , 50 , 53 , 73 ]. For instance, different levels of cognitive impairments may exist among users, and after a cognitive training program through the gamified task, users’ cognitive status may improve. The initial effects of game elements can diminish over time [ 47 , 53 ].
This paper proposes a 7-step framework to guide the design, development, and evaluation of gamified cognitive tasks designed to assess or train cognition.
Within these steps, there are a series of key recommendations on how each step should be operationalized. Along with the framework, the article presents the OMDE guideline at stage 5 of the framework prototyping using OMDE , which contains vital recommendations for advancing the understanding of design complexities when applying gamification in cognitive tasks.
The prototypes of the framework were designed and evaluated extensively by evidence from 3 sources: 1 existing gamification design frameworks, 2 project reports of applying game elements into cognitive assessment and training, and 3 expert experiences.
To our knowledge, this is the first study of its kind that has converged these sources to propose a unified model to design gamification in cognitive tasks. The significant point about gamification efforts in cognitive tasks is that they do not use a specific design process to incorporate game elements.
We used an alternative solution, such that by observing each work, we tried to extract clues or pieces of information to propose an abstract process for each of them, if possible. This work helped us to identify critical factors and considerations for gamifying cognitive tasks. One main finding of this study that gamification designers need to be aware of is that an intermediate design is required for gamifying cognitive tasks, meaning that game elements cannot be selected without considering the targeted cognitive context characteristics.
Otherwise, an irrelevant cognitive load may jeopardize data quality. Therefore, collaboration between both gamification and mental experts is required to examine the interplay of game elements and cognitive processes [ 9 ].
The intermediate design is a crucial feature that differentiates the gamification design framework required for cognitive assessment and training from other contexts.
Based on examined gamification efforts in cognitive tasks, we concluded that 2 techniques have been widely used to gamify cognitive tasks besides designing a new gamified task from scratch: 1 gaming-up an existing cognitive task and 2 mapping an existing game cognitive and classical games to a cognitive function or impairment to assess or train it. The required details to use game-up and mapping techniques were provided in step 3 of the framework as far as possible. The main limitation was that only a few studies have discussed how game elements impact participants when interacting with the gamified task and how they should be utilized in cognitive tasks to positively influence data quality and user engagement.
Gathering evidence from the mentioned sources only gave an initial evaluation for the proposed framework because the number of experts and relevant studies was limited. Hence, a more robust evaluation is necessary.
For this purpose, we listed most of the recognized experts in the area to evaluate and refine the framework in subsequent studies. By further developing the framework exploited in this work and utilizing machine learning and deep learning algorithms, it is possible to create a recommender system that can suggest the most appropriate game elements according to characteristics of the targeted cognitive context and users’ preferences or requirements. In our work, only existing games that have been developed or examined in scientific papers were investigated.
It is also possible to analyze a great number of current games, from brain games to classical games, for further development of the framework. Due to the time constraints, establishing the feasibility of proposing different gamification design frameworks for cognitive functions that inherently share similar cognitive processes like processing speed Gs and working memory [ 20 ] was not provided. While more work is needed to further refine and evaluate the framework, we believe our framework has great potential to be used as a foundation for developing effective gamified cognitive tasks.
Furthermore, ideas presented in the paper can be further developed and researched by many other researchers and practitioners. Conflicts of Interest: None declared. Published online May Author information Article notes Copyright and License information Disclaimer. Corresponding author. Corresponding Author: Ali Khaleghi moc. Multimedia Appendix 2.
Abstract Background Cognitive tasks designed to measure or train cognition are often repetitive and presented in a monotonous manner, features that lead to participant boredom and disengagement. Objective This study aimed to propose a framework to guide the design of gamification in cognitive tasks.
Methods We employed a design science research DSR approach to provide a framework for gamifying cognitive assessments and training by synthesizing current gamification design frameworks and gamification works in cognitive assessment and training, as well as incorporating field experiences. Results We proposed a framework consisting of 7 phases: 1 preparation; 2 knowing users; 3 exploring existing tools for assessing or training a targeted cognitive context and determining the suitability of game-up and mapping techniques; 4 ideation; 5 prototyping using the Objects, Mechanics, Dynamics, Emotions OMDE design guideline; 6 development; and 7 disseminating and monitoring.
Conclusions We found that 1 an intermediate design framework is needed to gamify cognitive tasks, which means that game elements should be selected by considering current cognitive assessment or training context characteristics since game elements may impose an irrelevant cognitive load that, in turn, can jeopardize data quality; 2 in addition to developing a new gamified cognitive task from scratch, 2 gamification techniques are widely used first, adding game elements to an existing cognitive task and second, mapping an existing game to a cognitive function or impairment to assess or train it ; and 3 further research is required to investigate the interplay of cognitive processes and game mechanics.
Keywords: cognitive tasks, boredom, motivation, gamification, game elements, framework, process, gamification design, cognitive training, cognitive assessment.
Introduction Background Statistics show that the cognitive assessment and training market will achieve a growth rate of Open in a separate window. Figure 1.
Figure 2. Challenges With Designing Gamification for Cognitive Assessment or Training Despite the growing trend towards using gamification in cognitive tasks, its impacts on participant engagement and data quality are not stable. Objectives Despite the papers that have shown mixed findings of using game elements in cognitive tasks, we assume that gamification can positively influence data quality and user engagement.
Methods Overview We approached the research problem through the design science research DSR methodology [ 57 ]. Figure 3. Knowledge Base Defining Sources of Evidence We selected the following 3 resources to gather the knowledge required for proposing the intended framework: 1 studies that proposed a gamification design framework, since by synthesizing them, we could extract the general framework for the design of gamification; 2 projects that used gamification in cognitive assessment or training to extract key factors and considerations for gamifying these contexts; 3 the experiences of relevant experts to integrate the evidence from live environments.
Exploring Relevant Papers We used systematic literature review strategies in the Google search engine, Google Scholar, PubMed, and Research Gate to use a wide variety of relevant papers. Inclusion Criteria for Selecting Gamification Frameworks To select the most highly regarded gamification frameworks, the following 4 metrics were used: 1 framework was not focused on the parts or steps of the gamification design process, but covering the maximum number of steps; 2 framework was determined to be worthy in terms of efficacy by calculating the number of its citations; 3 framework was developed by gamification experts we considered an individual an expert based on whether she or he published at least 10 scientific articles concerning gamification issues ; 4 framework was developed using a robust methodology.
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