Designing Game-Based Cognitive Assessments for Elderly Adults Tiffany Tong University of Toronto 5 King’s College Road
[email protected] ABSTRACT
Gamification is the use of game-like properties in non-game scenarios, and is applied in the design of our application to stimulate cognition, and enjoyment, which often decreases due to age-related changes. The goal of the research reported here is to develop a game, targeted for elderly citizens, that has a user-friendly interface and the ability to predict cognitive ability. This paper will introduce and evaluate a game designed based on the classic carnival game called whack-a-mole, which has the objective of a user ‘hitting’ a 'mole'. Our version of the game underwent a usability study where we evaluated the game in an experiment, and investigated its cognitive predictive ability. This paper will discuss the results from the experiment, detail the limitations, and propose future research. Author Keywords
Calibration; digital game design; elderly; Fitts’ Law, game usability; gamification; human-computer interaction; interface design; serious games; speed-accuracy tradeoff; user experience; user-centered design.
Mark Chignell University of Toronto 5 King’s College Road
[email protected] cognitive impairments such as post-operative delirium [8]. There are many factors to consider when designing digital games for seniors, from choice of accessible device, to imagery, to detailed design parameters such as font sizes. Other special considerations include the question of calibration and how to determine a fair and appropriate scoring and rule system. In this paper, we introduce a digital game we designed based on the classic carnival game called ‘whack-a-mole’. In the traditional rendition of the game, a mole character appears and the objective is for the player to ‘whack’ the mole using a mallet. We present our game design process along with early results from our usability study. METHODOLOGY Requirement analysis
Human Factors; Design; Measurement.
To gain a better understanding of our target user group, their physical and cognitive abilities, as well as the type of games suitable for this population, we conducted a series of informal meetings with a team of healthcare experts consisting of physicians, psychologists, and occupational therapists. We learned that the ideal device for digital games for elderly adults is a medium that is touch-based, lightweight, portable, and easy to sanitize. This led us to develop our software for a 7-inch touch-based tablet which satisfies the criteria noted above.
INTRODUCTION
Game design
ACM Classification Keywords
H.5.2 [User Interfaces]: User-centered design, prototyping, evaluation/methodology; K.8 [Personal computing]: Games. General Terms
In Canada the fastest growing population group is senior citizens, ie. people over the age of 65 years [1]. The natural and inevitable process of aging can lead to a decline in cognitive and physical abilities. These aging-related changes can often make it difficult for elderly persons to participate in their daily living, and leisure activities. In this context, digital games may help to improve the cognitive well being of elderly adults [2]. Games that can measure cognitive abilities such as Central Executive Functions (EFs) may be particularly useful since EFs can predict
We began with paper-and-pencil prototypes to outline the architecture of the game. Using these low-fidelity prototypes, a set of digital and interactive wireframes were created. These medium-fidelity prototypes were used as a guiding framework in the implementation of the fully digital version of the game. The software was developed on the Android platform since it is open-source, and has backwards-compatibility, enabling it to be played on devices with earlier operating system versions. The tabletdevice used in this study was the Google Nexus 7, which measures 198.5 x 120 x 10.45 mm, and weighs 340 g [3].
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Gamification’13, October 2 – 4, 2013, Stratford, ON, Canada. Copyright 2013 ACM 978-1-XXXX-XXXX-X/XX/XX...$10.00.
In our digital version of the ‘whack-a-mole’ game, users should tap the mole (target) on a touch-based device with one of their fingers. In other alternatives of the game, to increase the difficulty level, a second (distractor) character (butterfly) appears, which the user is not supposed to hit. The objective of the game is to hit as many moles as
possible within a given time frame, and to avoid hitting butterflies. This game was selected as go/no go discrimination task, wherein users respond only to select targets (i.e. moles). The game can be modified in various ways to examine choice reaction time (CRT), and EFs (e.g. inhibition, shifting, and updating), which maintain and regulate cognitive processes [4]. Inhibition is the ability to suppress an action or behavior; updating is the ability to update and remove items from working memory, and shifting is the ability to switch between tasks [4]. Analysis of EF changes is significant as they have been used to predict cognitive conditions such as dementia, and delirium [5]. At the start of our game, the user can select their preferences by modifying five game parameters: target size, grid size, distractor style, feedback style, and game duration (see Figure 1). The target size option allows the user to select the size of the mole/butterfly target (150px, 175px, 200px). The grid size option describes how many rows and columns the game area will be (2x2, 3x3). The distractor style option allows the user to select whether they want to play a game with only moles or both moles and butterflies. The feedback style parameter allows users to select whether or not they want feedback when they hit a character. A ‘checkmark’ appears over the mole if it is hit, and an ‘x’ appears over the butterfly if it is hit (see Figure 2). Lastly, the length of the game duration can also be adjusted. Furthermore, the software has been programmed to record the x and y-coordinates of the user’s touch as well as the touch area size, and pressure.
between 21 and 51 years. The goal of the study was to determine how to calibrate the game based on the observed performance, and to examine the relationship between game performance and cognitive abilities. Thus our study was designed to answer the following research questions: •
Interface design: Is the game accessible to a healthy population?
•
Game design: How can the whack-a-mole game be calibrated? What are the rules for determining what a hit (point) is? What game parameters are most predictive of cognitive ability?
The experiment consisted of three parts: (i) a demographic and technology use survey, (ii) computer-based games, and (iii) tablet-based games and a tablet use questionnaire. The study began by asking participants to complete a questionnaire on their demographic information and use of touch-based technologies. Next, participants were asked to perform three tasks (a Stroop Test, a Colour Monitoring Task, and the Wisconsin-Card Sorting Test) on a computer to assess their EFs. Lastly, participants were asked to play the whack-a-mole game on the tablet. This was followed by an exit-questionnaire that evaluated experience with the tablet-based games, consisting of 9 items on a 5-point Likert scale. The tablet-based game portion of the study was implemented using a repeated-measures within-participant design with independent variables being target size (150, 175 and 200 pixels), grid size (2x2, and 3x3), distractor style (distractor absent, and distractor present), and feedback style (feedback absent, and feedback present). Each participant performed 8 blocks of trials. Within each block, the order of game parameters was randomized. The entire experiment took approximately 60 minutes to complete. The dependent variables in our study were the participant’s reaction time (RT), and selection response. The RT was measured as the time between the appearance of the target (mole/butterfly) and the user’s successful hit on the tablet. The user’s selection response is characterized as the user’s response to hitting a target. For instance, hitting a mole counts as a correct selection response, whereas hitting a butterfly counts as an incorrect selection response.
Figure 1. Screen capture of the game settings menu.
Participants were observed during the experiment to gain further insight into their interaction with tablet. The experiment was carried out in accordance with a protocol approved by a University of Toronto Research Ethics Board. Statistical analysis
Figure 2. Images of the mole (left), and butterfly (right) characters with feedback. Usability study
A usability study was conducted with 24 healthy participants, consisting of 7 females and 17 males, aged
Data was processed using MatLab and Microsoft Excel, and analyzed using SPSS.
RESULTS AND DISCUSSION
Equation 1. Fitts' Law equation used to model our results.
Computer-Based Cognitive Ability Tests
This section will describe the results from the Stroop Test (inhibition), Colour Monitoring Test (updating), and Wisconsin-Card Sorting Test (shifting). Correlation analysis was carried out on the performance measures of the three tests. There were significant relationships between the following EF scores: •
inhibition and shifting, r = .610, p < 0.001, and
•
inhibition and updating, r = .373, p < 0.05.
Tablet-Based Game Performance
The results were analyzed for statistical significance by performing one-way repeated-measures ANOVAs on the median CRT to compare the different game parameters. All statistics are reported significant at p < .05 The results show that RT was significantly affected by the target size, F(2, 46) = 6.51 , grid size, F(1, 23) = 25.27, , and distractor style, F(1, 23) = 91.68. The feedback style did not significantly affect RT, F(1, 23) = 0.733. This CRT data gives us insight into how to calibrate the game settings. For instance, users required more time to respond when playing the game with a grid size of 3x3 compared to 2x2, which suggests that we should allow the selection response time to be longer for a “point” with the larger grid size. Predicting Cognitive Ability through Game Performance
To assess the effectiveness of the game as a predictor of cognitive ability, regression analyses were performed. The strongest predictor of inhibition ability was games with a 3 x 3 grid and distractors present (R2 = .202. *p < .05.). No significant correlations were found between shifting ability and the game parameters. The strongest predictor of updating ability was games played with a 3 x 3 grid (R2 = .170. *p < .05.).
Equation 2. Index of difficulty equation.
The participants’ RT was plotted against the ID of the task. There was no significant linear fit (R2 was approximately zero) suggesting that Fitts’ Law did not apply to the game performance. While Fitts’ Law generally applies with pointing-devices it might not apply with a touch device. A second reason for the non-applicability of Fitts Law in our task may be that our calculation of A needs to be revised, as we assume that users are not moving their finger from their last touch position to the next target appearance. In actuality, users are most likely moving their finger to its natural resting position after hitting a target, thus making it impossible to estimate the starting point for the movement to be modeled by Fitts’ Law.
Modeling Game Performance With The Speed-Accuracy Tradeoff
Next the data was analyzed using a speed-accuracy tradeoff function, which describes the relationship between CRT and accuracy [7]. We plotted CRT on the x-axis and the distance from a user’s hit to the center of the target on the y-axis (see Figure 3). For data pooled across all participants we observed a linear fit (R2 = –0.323), showing a tendency for slower responses to be more accurate. This observed tradeoff provides further insight in to how to set the scoring and rule system for the game.
Modeling Game Performance with Fitts’ Law
The whack-a-mole game performance data was examined using Fitts’ Law, which looks at a user’s movement time (MT) to move to a target area based on the distance to the target and its size [4]. Fitts law is shown in Equation 1 where A is the movement distance from the user’s last hit to the center of the target (pixels). The W represents the width of the target (pixels). The constants a and b are empirically determined by fitting a linear relationship between MT and the index of difficulty (ID) of the task (see Equation 2). The ID is taken from the second half of the MT equation (see Equation 1). Figure 3. Data analyzed using the speed-accuracy tradeoff model. Tablet-Based Game Questionnaire
Participants generally preferred to play the game using two hands/multiple fingers/digits. Overall, the results from the questionnaire indicate high scores for ease of use of the tablet (M=4.10, SD=1.04). High scores were also reported
regarding the usability of the game with regards to the easyto-understand instructions (M=4.04, SD=1.00) and target sizes (M=4.71, SD=0.46). In summary, these findings suggest that our tablet-based game offers an enjoyable gaming experience that can be easily adjusted to a user’s preferences. Hardware
With regards to the hardware used for the whack-a-mole game, our evaluation indicated that the 7-inch tablet was acceptable and appropriate for our users. Further research is required to design a user-interface for elderly adults without prior gaming knowledge to enable users to play the game independently. Limitations
Limitations in our study include the use of university-based with unequal proportions of female to male participants. The age range of participants also tended towards adults in the 20–30 age range and thus further research is needed to determine the properties of the game with respect to elderly users. It would also be good to include a questionnaire surveying participants gaming experience in future studies. CONCLUSIONS
In this paper we focused on the use of gamification and a user-centered design approach in the development of a tablet-based game for the elderly that's user-friendly and cognitively stimulating. We have discussed the design of a tablet-based game evaluated on a healthy population, after which we plan to do further assessment with an elderly population. We also found the evaluation process of using observation The present findings provide a baseline against which the different effects of the game parameters on an elderly sample can be compared. As expected, we found that performance on our game is quite strongly related to inhibition ability. In the context of a healthcare setting, a suitable configured version of the game (e.g., for elderly users) may enable physicians to understand their patients’ cognitive status. The overall findings from this study suggest that our tablet-based game offers an enjoyable gaming experience that can be easily adjusted to a user’s preferences. Future work on the whack-a-mole game will include running a usability study with elderly adults with a larger sample size. We would also like to revisit the
application of Fitts’ Law on a tablet using a stylus-pen for input. ACKNOWLEDGMENTS
We thank all study participants for taking part in the usability study. We would also like to thank Dr. Jacques Lee and Dr. Mary Tierney of Sunnybrook Health Sciences Centre, and Dr. Tammy Sieminowski, and the occupational therapist team at Bridgepoint Active Healthcare for their advice concerning how to design for elderly adults in clinical settings. REFERENCES
1. Canadians in Context - Aging Population / Indicators of Well-being in Canada. http://www4.hrsdc.gc.ca/
[email protected]?iid=33. 2. Jung, Y., Li, K.J., Janissa, N.S., Gladys, W.L.C. and Lee, K.M. Games for a better life: effects of playing Wii games on the well-being of seniors in a long-term care facility. Proc. CHI 2009, ACM Press (2009), 0–5. 3. Nexus 7 Tech Specs. http://www.google.ca/nexus/7/specs/. 4. Miyake, A., and Friedman, N. The Nature and Organization of Individual Differences in Executive Functions. Current Directions in Psychological Science 21, (2012), 8–14. 5. Brandt, J., Aretouli, E., Neijstrom, E., Samek, J., Manning, K., Albert, M.S., and Bandeen-Roche, K. Selectivity of executive function deficits in mild cognitive impairment. Neuropsychology 23, (2009), 607–618. 6. Fitts, P.M. The information capacity of the human motor system in controlling the amplitude of movement. Journal of Experimental Psychology 47, 6 (1954), 381– 391. 7. Osman, A., Lianggang Lou, L., Muller-Gethmann H., Rinkenauer G., Mattes S., and Ulrich, R. Mechanisms of speed–accuracy tradeoff: evidence from covert motor processes. Biological Psychology 51, (2000), 173–199. 8. Smith, P., Attix, D., Weldon, B., Greene, N., and Monk T. Executive function and depression as independent risk factors for postoperative delirium. Anesthesiology 110, (2009), 781–787.
