Numerous games have been designed for the younger population. In addition, researchers have investigated younger players for cognitive and behavioural effects from playing video games. Many terminologies have been defined by researchers; player experience; player engagement and player enjoyment to mention a few. Do all of these terms mean one and the same construct, or are these different? The challenge is to identify key attributes to establish clear methods and measures to define player engagement.
Belchior, P., Marsiske, M., Sisco, S., Yam, A., & Mann, W. (2012). Older adults’ engagement with a video game training program. Activities, adaptation & aging, 36(4), 269–279. doi:10.1080/01924788.2012.702307
In comparison to studies on the effects of video games on younger adults, fewer studies have been focused on the effects of video games on the aging population. However, this area of research is growing in the area of using games for older population in the realm of cognitive deterioration and memory impairment, rehabilitation, social isolation, mental distress, emotional challenges. In this paper the authors argue that the use of video games to train the visual attention of the older population was more interesting and engaging than traditional brain training. The current intervention study focused on the engagement of older adults.
To measure engagement, the authors used the concept of flow  which is the state of heightened absorption in an activity and establishes a relation between the challenges of the game and the skills of the player.
The paper investigated the older adults engagement (or perceived flow) in the video game Medal of Honor or Tetris relative to the useful field of view (UFOV) training. They hypothesized that participants who received video game training (i.e., Medal of Honor or Tetris) would report higher flow scores as compared to participants who received UFOV training.
Participants were randomized into three groups (1) Medal of Honor training (n = 14); (2) Tetris training (n = 15); or (3) UFOV training (n = 16), whereby a between-subjects study protocol was used.
Based on the literature review, the authors indicated that Flow State Scale (FSS) conceptualizes flow in nine dimensions: Challenge (skill balance), action (awareness merging), clear goals, unambiguous feedback, concentration on task at hand, sense of control, loss of self-consciousness, transformation of time, and autotelic experience.
|Hypothesis||Participants who received video game training (i.e., Medal of Honor or Tetris) would report higher flow scores as compared to participants who received UFOV training. Intervention study|
|Pilot study||No Pilot Study|
|Participants||Forty five participants (F=24, M=25)||Mean Age =74.5|
|Method||Three Intervention groups(1) Medal of Honor training (n = 14); (2) Tetris training (n = 15); or (3) UFOV training (n = 16).3 training conditions (6 one-to-one 90 min sessions), training in pairs or individually|
|Time interval||2-3 weeks||Six sessions of 90 minutes eachEach participant had six FLOW scores|
|Materials||-Medal of Honor (FPS): on a Sony PlayStation 2, console model 97060, and a dual shock 2 analog controller, model 97026., 19” monitor-Tetris (Tetris World) : on a Sony PlayStation 2, console model 97060, and a dual shock 2 analog controller, model 97026., 19” monitor- UFOV training : UFOV training guide, computer, 21 inch screen||FPS , US Marine at Pearl Harbor.|
|Procedure||Six one-on-one, 90- minute sessions administered over 2–3 weeks. FSS after each session|
|Measures||FSS scale (Jackson and Marsh (1996)Factor Analysist testGame Score: greater score , indicates greater flow
Independent variable: video game training
Dependent variable: flow scores indicating engagement.
|Participants answered the Flow questionnaire after each training session|
|Findings||The authors infer that ‘there was growing enjoyment of and engagement with the games over the training period’|
The authors’ idea to use videogame to train older adults was due to the fact that video games were more interesting than standard UFOV training software. Each participant had 6 flow scores each from the 6 training sessions.
The authors used a bigger intervention study done earlier to leverage this study. I was curious to decipher the meaning of the term ‘interventions’ and the reason behind such usage by the authors in this paper. I could not find an explanation in the body of the article for the specific usage of the term ‘interventions’. Unless this is common knowledge, from a designer’s perspective I had to do some deep dive search on the web to understand this terminology. For the interest of the readers, I found that an intervention study, in the light of a training program, is where a sample population is subjected to repeated training of the same instrument or software or clinical study. The subjects are tested for improvements at each level of intervention. Interestingly I found a fantastic glossary of terms used in psychometric research; for those who are interested in getting a grasp of terminologies used in the theory and technique of psychological measurement, this site helps to make simple some of these terminologies.
|TOTAL||Medal of Honour||UFOV||Tretris|
|Training Session 1|
|Training Session 2|
|Training Session 3|
|Training Session 4|
|Training Session 5|
|Training Session 6|
Table 1: Mean Standardized Scores and Standard Deviations of Flow Scores (Reproduced)
The authors used the Flow State Scale (FSS) to measure flow leading to measuring engagement of the older population. This is a license to use scale. Flow scores were accessed from the three intervention groups after each of the six intervention sessions. Means and SD of the 45 participants belonging to specific intervention groups were determined and is indicated n Table 1 below. The authors indicate that a mixed between-within analysis of variance (ANOVA) was used. As explained earlier, since I was keen on knowing more about terminologies; a mixed between-within analysis of variance is also called a split-plot ANOVA. This combines a between-groups ANOVA and within-subjects ANOVA into one study. Hence in this experimental study, the between-subjects factor is the ‘intervention group’; and the within-subjects factor is ‘occasion’ (6 training sessions). Dependent variable was “flow score”. Because the study design had three groups for the same independent variable, the authors also used the post hoc t-test after doing a Bonferroni correction (a method used to counteract multiple comparisons). However, with 45 comparisons, the resulting p value was quite small (0.05/45) to indicate any significant interaction. Hence, the authors used the least squares difference (LSD) post hoc analysis where the six training sessions were compared to one another separately in each ‘intervention group’
The LSD results showed a significant increase in flow in the Medal of Honour group after the first sessions. Similar increase was seen in the Tetris group. However, participants in the UFOV group showed little change in flow. Based on this result, the authors inferred that the participants in the two video game installations experienced increased flow over time compared to the UFOV training.
Based on the results indicated above the authors infer that ‘there was growing enjoyment of and engagement with the games over the training period’. My concern is how did the authors infer that there was “enjoyment of the game’, when they were establishing ‘flow’ as a measure of engagement. Another point of discussion would be whether a within-subjects design study would have helped to understand the improvement in learning to play the game, and hence compare the same group in all the three intervention groups. A few questions that remain unanswered in my mind are: Is ‘flow’ the only dependent variable available to measure engagement? Are there other engagement elements that could be used to measure engagement in games and hence support the results from the ‘flow’ as a dependent variable?
I did not understand how the aggregate flow scores from each session were determined for each intervention group. This was not clearly explained in the paper, and perhaps these scores may have been results from the FSS data. Early in the paper, the authors establish that the FSS comprises of nine dimensions. Based on this, there was no explanation as to how these nine dimensions from the FSS resulted in a single flow score for each of the 45 participant, for each training session.
Personally, I liked the structure of the paper. The terminologies in the statistics section forced me on a path of discovery in trying to understand some of these terminologies. One of the main reasons for doing this is that the authors explained some of the reasoning behind the selection of some of the statistical analysis which provoked me to find literature and information to support my rudimentary understanding of these statistical terminologies. Overall, I learnt a few more statistical elements which make me infinitesimally more literate in my discovery of statistics.
1. Csikszentmihalyi, M. Play and Intrinsic Rewards. 15, 3 (1975), 41–63.
2. Image Credit Cheerful Aged Man Working On Computer