A data-driven approach for examining the demand for relaxation games on Steam during the COVID-19 pandemic.

The COVID-19 pandemic has been a major source of stress for a majority of people that might have negative long-term effects on mental health and well-being. In recent years, video games and their potential positive effects on stress relief have been researched and "relaxation" has been an important keyword in marketing a certain kind of video game. In a quasi-experimental design, this study investigated the increase of average daily player peak (ADPPs) for the COVID period compared to the pre-COVID period and if this increase was significantly larger for relaxing games in contrast to non-relaxing games. Results showed a medium-sized increase of ADPPs over all types of games but no difference between relaxing games and non-relaxing games. These results are discussed in regards to their potential of presenting gaps between the current theoretical models of the influence of video games on mental health and actual observed player behaviour.

Introduction

With an infection rate of over 60 million cases to date [1], the COVID-19 pandemic has reached the dimensions of a global health crisis, affecting almost every nation in the world. Besides the direct physical health consequences, which most commonly involve fever, coughs, and breathlessness, the pandemic negatively impacts mental health and psychological wellbeing to a not negligible extent. This does not necessarily include a direct infection with the virus but may rather be due to overall social and personal difficulties arising from the crisis, such as financial losses, uncertainty about the future, and public health measures like social distancing and contact restrictions. Common mental health responses to the COVID-19 pandemic are symptoms of anxiety, depression, and stress [2-4], with the latter being amongst the most frequently reported adverse consequences in the global population. For instance, Salari et al. [3] identified a stress prevalence of 29.6% in the general population as a reaction to the pandemic, based on a meta-analysis of 5 studies with a total sample size of over 9,000 individuals from various countries. High rates of post-traumatic stress disorder (PTSD), psychological distress, and stress during the COVID-19 pandemic have also been identified in a systematic review by Xiong et al. [4], who incorporated data from several regions in Asia, Europe, and America in their analyses.

Stress, mental health, and wellbeing

Stress has long been known to have a wider negative impact on mental health. Since the 1970’s, there have been extensive reviews about the consequences of stress-inducing life events on psychological health, describing various risks such as developing depressive symptoms, anxiety disorders, or even schizophrenia [5-9]. Stress has not only been shown to have a negative impact on mental health, but also on behavioural variables, such as drinking, smoking, and violent behaviour [10-13]—variables that in turn adversely influence health and well-being. Greenberg et al. [14] further collected literature describing the emotional and cognitive changes that resulted of stress, giving more context to the dangers of stressful life events for mental health. Besides that, plenty of studies have reported an inverse relationship between stress and subjective life satisfaction, providing evidence of the unfavourable influences that stress has on psychological wellbeing [15-18].

Video games as stress relief

These various and severe adverse health consequences demonstrate the importance of ways to reduce psychological distress. One promising method that gained popularity in recent years are video games. More and more studies provide arguments that link the role of video games to mental health improvements. As an example, casual video games (e.g., pinball, card games) have been shown to reduce physical stress responses, and with that psychological consequences of stress [19]. Similarly, Holmes et al. [20, 21] and Iyadurai et al. [22] noted the potential of Tetris to reduce flashbacks after trauma exposure in PTSD patients, showing that even pathological stress responses can be mitigated by playing video games. Systematic reviews on the topic further underpin the potential of video games to improve general mental health outcomes, including stress relief [23-26].

Indeed, there is an increasing interest for video games that go beyond the mere purpose of being entertaining—commonly referred to as serious games. More and more health practitioners rely on the health and educational benefits of such games, validating their therapeutic incremental value [27]. More importantly, there is evidence that people systematically play video games for recovery purposes or after straining situations [28], with middle-aged adults even citing stress relief as a main reason to play [29]. These findings can be extended to a wider group of participants: Ferguson and Olson [30] for instance identified stress relief as a common game play motive among children, while Mazurek et al. [31] reported similar results in adults with autism spectrum disorder. Reinecke et al. [32] further highlighted the special role of actively playing video games as compared to passive media exposure in eliciting the experience of recovery. This growing interest in playing video games for recreational purposes also becomes apparent in search trends on popular video game distribution services. On the Steam platform for example, users can tag games with specific attributes they believe are appropriate for this game. Frequently applied tags become featured categories in the Steam Store, which can be used by players to discover relevant games. The tag “relaxing” as such a category yields now over 2,800 results in the Steam Store, providing further evidence of relaxation as a common player experience.

Possible influences of the COVID-19 pandemic on gaming activity

As the COVID-19 pandemic represents a major cause of stress to many people, it is reasonable to believe that there has emerged a growing popularity in playing video games for recreational purposes during this time. Indeed, reports of changes in leisure activity associated with the pandemic are vast and hint towards a higher interest in gaming. For example, in a sample of 3,052 US adults, Meyer et al. [33] found reduced physical activity and increased screen time. Increased sedentary behaviour was also reported for university students [34]. Naturally, these findings extend to countries all over the world: a decrease of physical activity and increased activities in front of a screen have been shown for adults in India [35], adults and children in China [36, 37], and students in Italy [38].

The present research

Building upon the above-mentioned empirical findings, the current study seeks to investigate the demand for relaxing video games during the COVID-19 pandemic by analysing player data obtained from the Steam database. Based on reports describing a growing interest in digital activities during the pandemic, it was hypothesized that the peak number of daily players averaged over the time period March to November 2020 increased compared to the average daily player peak (ADPP) from March to November 2019 (i.e., before the outbreak of the pandemic). The daily peak number of players was selected for averaging in order to represent a game’s popularity, thereby operationalising demand. As there is evidence that recreation is a desired player experience, particularly in stressful times, a higher rise in ADPPs for relaxation games was expected as compared to games not associated with relaxation. While there is no consent on an exact beginning of the pandemic and countries showing different progressions regarding reported cases and institutional consequences, March 2020 was reported as the beginning of stress-related effects in many studies [2-4] and official public consideration of stress-related concerns made by the World Health Organization (WHO; [39]).

Materials and methods

Dataset and preprocessing

The current study hypotheses and the analysis plan were preregistered on OSF. Details on the preregistration can be found under [40]. The dataset used in the current study maps player data from Steam, the largest online game distribution platform for PC games, with over 90 million monthly active users in 2018 and over 20 billion hours of gameplay in 2019 according to Steam’s Year in Review reports [41, 42]. The data set was obtained from SteamDB, an independent information service providing data about the applications available on the Steam platform. The information accessible on the website includes for instance statistics about number of followers, number of positive and negative reviews, playtime, and concurrent players for every single day since the release of the game.

The harvested dataset initially contained records from 4,648 of the most played games on Steam. The information included for each game in the initial dataset were name, developer, release date, and the peak number of average daily players (ADPP) for each month since release of the game. For these games, ADPPs were calculated for the time period March to November 2019 (pre-COVID-19 period), and for March to November 2020 (COVID-19 period). Games for which there were missing values in ADPPs for any of the months in the observed time period were eliminated beforehand, resulting in 4,147 remaining applications. In addition, games were deleted from the dataset when released after February 15, 2019, leaving 2,929 games in the dataset. This was done in order to allow for a minimum 2-week warm-up period during which player numbers might not yet be representative of the actual popularity of the game. Furthermore, games with an average peak of less than 3 players per day for the 2019 time period were excluded from the dataset, resulting in 2,379 remaining games.

Games were then selected for the test and control groups. To this end, the existing dataset was augmented with columns containing popular tags and genre for each game. The selection of games followed pre-defined coding criteria and was conducted by both researchers, regularly checking for inter-coder agreement. As an overall criterion, the application had to be a game; demos and other software (e.g., developer tools or utilities) were excluded. For the test group, additional selection criteria were as follows: (1) the game must be tagged as ‘relaxing’, which included 208 games; (2) a detailed analysis of trailer, game description, and further tags must not lead to the conclusion that the game is not primarily relaxing. To be included in the test group, a game therefore must not show any of the following: (a) Other mood tags, such as ‘funny’ or ‘emotional’ are tagged with a higher priority than ‘relaxing’. While relaxing games might also include elements that evoke other moods, this was a important step to ensure the experimental group only included games that are prioritise their role in relaxing players over evoking other moods, such as sadness, melancholy, or excitement and humor. After this step, 178 games remained in the test group. (b) Gameplay appears to be action-heavy and intense. After analysing trailer, descriptions, and other promotional material on the Steam Page, games with a high action focus that might be considered relaxing for some, but are generally not designed as a “relaxing game” were excluded. After this step, 169 games remained in the test group. (c) Main elements of the game contain horror, sexual or mature content, or similar characteristics not considered compatible with the main aim of providing a relaxing experience for the player, but rather other emotional aspects that might for some be considered relaxing. Through this procedure, 143 games were identified for the test group (relaxing games). For the control group, 2,124 possible games remained in the dataset. Extreme outliers with more than 7 standard deviations from the mean were further removed from the dataset, leading to 2,216 games in total, with 138 relaxing games, and 2,078 non-relaxing games. It should be noted that test and control group were exclusive, meaning that all games identified as relaxing games were eliminated from the control group pool. This procedure was chosen to have replicable and controlled methods to categorize game characteristics based on how they are presented and described in Steam. Forming groups made it possible to compare a unique subset of games associated with relaxation with a control group to examine effects.

Finally, matching procedures were applied for further statistical analysis. This was done in order to reduce covariate imbalances between test and control group, thereby avoiding a confounded assignment of games to the respective group. Data pre-processed with matching generally produce more robust inferences that are tied to fewer assumptions as compared to non-matched data [43]. The current data set was thus matched based on ADPP in the pre-COVID-19 period (March to November 2019) as the covariate. Matching was achieved using a greedy nearest neighbour algorithm. With this method, each test unit is assigned one control unit, based on the smallest distance in propensity scores between both units. Greedy matching was chosen as it takes the closest match for each value in the test group, which has been shown to perform very efficiently with datasets containing a large pool of control units to chose from [44, 45]. The matching procedure led to a final dataset of 138 games in each the control group and test group. A detailed overview over the selection process can be seen in Fig 1. A preliminary power analysis uncovered the need of 210 participants to reveal a medium-sized effect with a power of 1—β = 0.95. Because non-experimental data needs generally more conservative analyses, the final sample size of 276 was deemed appropriate to reveal a medium-sized effect.

Fig 1

Flow diagram of the number of games screened and included in the study.

Analysis

Data analysis was performed with the statistical computing software R. Both parametric and non-parametric analyses of variance were considered for the current dataset. Due to the violation of assumptions of parametric tests (e.g., ANCOVA, or change value ANOVA), non-parametric alternatives such as Quade’s test and Wilcoxon signed-rank test were used.

Ethics statement

Written consent was granted after reviewing the methods of our study by the Physical Sciences Ethics Committee of the University of York in a fast-track procedure. The full statement states: The researchers have taken all reasonable steps to ensure ethical practice in this study and I can identify no significant ethical implications requiring a full ethics application submission to the Physical Sciences Ethics Committee. I have checked and approved all supporting documents required for this application. I understand that completion of this form indicates that from the ethical point of view I am willing to share responsibility for the work being conducted.

Results

138 games in the relaxing group were matched by the covariate (average daily players for 2019) with 2,077 games in the control group using the MatchIt package [46] in R. The final sample consisted of 276 games with 138 games in each group. Descriptive data regarding release years and game genres can be found in Tables 1 and 2. Mean propensity scores for relaxing (M = 0.06) and control (M = 0.06) groups showed a standardized average mean difference of 0 and a maximal distance of 0.01. Fig 2 shows a visualization of the propensity scores for the matched and unmatched units. Mean covariate value was M = 226.19 for the relaxing group, and M = 226.19 for the matched control group. A complete overview of descriptive statistics can be viewed in Table 3. Overall, nearest neighbor matching [47] resulted in a well-balanced dataset in regards to covariate similarity (see Fig 3 for the empirical quantile-quantile [eQQ] plot). However, due to the nature of the data acquisition, the distribution of the covariate was skewed for both groups.

Table 1

Release years of games in the relaxing group and control group.

Group	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019
RelaxingGroup	1	0	2	1	2	9	14	18	19	29	36	7
ControlGroup	1	1	5	8	1	8	15	14	17	36	30	2
Total	2	1	7	9	3	17	29	32	36	65	66	9

Table 2

Genres of games for the relaxing group and the matched control group.

Genre	Relaxing	Control
Action	1	91
Adventure	31	11
Casual	35	9
Horror	0	5
Puzzle	10	0
Racing	1	1
RolePlayingGame	5	8
Simulation	46	3
Sports	2	3
Strategy	7	7
Total	138	138

Fig 2

Propensity scores of matched and unmatched samples.

Relaxing games are the treated units and control games are the control units.

Table 3

Descriptive data of ADPPs by group.

		2019		2020
Groups	N	M	SD	M	SD
Relaxing	138	226.19	612.16	246.54	652.76
Control	138	226.19	657.38	240.43	657.39
Total	276	226.19	611.26	243.49	653.89

Sample size (N), mean (M) and standard deviation (SD) for the ADPPs in 2019 and 2020 by groups.

Fig 3

Empirical quantile-quantile plot of the covariate.

Depicted are the quantiles of the covariate (ADPPs 2019) for the relaxing and control group before and after matching. Small distances between sample points and the diagonal indicate close similarity.

To test whether relaxing games and control games showed a similar expected ADPP progression based on time preceding the pandemic (2015–2019), a preliminary linear regression analysis was conducted, predicting ADPP by time and group. There was no significant main effect for time (t[752] = 1.67, p = .10) and no interaction effect between time and group (t[752] = 0.55, p = .58). A graph of the progression of ADPPs over time by group can be viewed in S1 Fig.

Preliminary tests of ANCOVA assumptions using scatterplots showed a sufficient linear relationship between the independent variable (average daily players for 2020) and the covariate (see Fig 4). Regression slopes for both groups were parallel, pointing towards homogeneity, which was tested using a two-way ANOVA for the interaction between covariate and group (F[1, 272] = 0.05, p = .82).

Fig 4

Scatterplot of the relation between ADPPs in 2019 and ADPPs in 2020 by groups.

Scales have been log-transformed for clearer data presentation.

Results showed no indication for an interaction, so homogeneity of regression slopes was assumed. Just like for the covariate, distributions of the dependents variable and its residuals were highly skewed, which violates not only basic assumptions for ANCOVA, but for a potential change value ANOVA, which is commonly used as an alternative to present average treatment effects on treated (ATTs), i.e. in this case the gain of average daily players of the relaxing games group specifically between 2019 and 2020.

It was therefore decided to explore the overall time effect and the ATT using more robust non-parametric methods that are not distribution dependent. First, the overall time effect (the increase in ADPPs for both groups combined between 2019 and 2020) was calculated using a Wilcoxon signed-rank test [48]. The test showed a significant difference between 2019 and 2020 (Z = 4.70, p <.01). So, between March 2020 and October 2020, a significantly higher daily average peak per game was observed than for the same period in 2019. Mean ADPPs increased from M = 226.19 in 2019 to M = 243.49 in 2020 (see Table 3 for more information). An effect size was calculated to quantify the interpretation of the effect after Rosenthal [49]. Using Cohen’s criteria [50], the calculated effect size (r = 0.20) can be considered a medium-sized effect.

To test if this increase was even larger for the relaxing games compared to the control games, the ATT was calculated using Quades’s rank analysis of covariance [51]. This method utilises the residuals of the regression of ranked dependent variables and ranked covariates, rather than the non-normal distributed variables themselves. Results showed no significant group effect (F[1, 67] = 0.05, p = .88), meaning the ATT was not significantly different from the average treatment effects on the control group. In other words: No significant difference of average daily player increase was observed between the relaxing game group and the control group. A boxplot visualising the group differences can be seen in Fig 5.

Fig 5

Boxplots of the differences in ADPPs in 2020 by groups.

Scales have been log-transformed for clearer data presentation.

Discussion

To examine the increase of demand for video games associated with relaxation during the COVID-19 pandemic, relaxing games and matched non-relaxing games have been compared in regards to their average daily player peaks for the periods of March to October in 2019 and 2020 respectively. Results revealed a medium-sized increase of ADPPs over both groups for the COVID-19 period compared to the pre-COVID-19 period, but no significant differences between both groups regarding this increase.

While the hypothesized overall increase in ADPPs was found, there was no evidence to support the hypothesized special role of relaxation related games during the pandemic. There are various implications for these findings that possibly uncover common misunderstandings regarding relaxation-seeking in very stressful times and individual reasons to play video games of certain styles.

The role of relaxing games in increased daily player peaks

The COVID-19 pandemic has vast influences on people’s everyday behaviour and the presented increase of ADPPs in the current study mirrors the current findings in the literature, including the findings of the increase of screen time all over the world (e. g. [33–36, 38]). During the months of lockdown, availability for outdoor activities was very limited and digital alternatives, including video games, have experienced a rise in popularity. In fact, there is no reason to doubt an overall increase in digital activities, but there is disagreement regarding the reasons and consequences of these behaviours, specifically for video games.

Just as there is not much doubt about the behavioural changes that resulted from the pandemic, the literature provides a huge amount of convincing evidence regarding the changes relating to mental health, specifically psychological stress [2-4]. Multiple studies associated the COVID-19 related increased screen time and decreased physical activity with mental health problems (e.g. [52, 53]). The pandemic itself and consequences of the lockdowns for people’s lifestyles have been identified as risk factors for psychological problems. However, more and more recent studies provide arguments that link the role of video games in specific not to a mental health danger, but to mental health improvement and protections against such risk factors (e.g. [19, 20, 25]). It further seems that people are more and more drawn to video games as a way to actively recuperate from stressful events [28, 29]. While the present findings do not hold any information about why people play more during the pandemic, the increased numbers of daily players across all games do not contradict this particular view. It is however important to note that this overall increase might not be related to COVID-19 but rather represent a naturally expected increase of ADPPs independent from the pandemic, but possibly related to factors such as reduced cost or increased accessibility of technology. As S1 Fig shows, ADPP peaks for most game seem to lie in 2018 with a big decrease in ADPPs for all games between 2018 and 2019. It is very much possible that the pandemic had an effect on this trajectory from 2019 onwards that seems to invert a decreasing trend, its significance does however appear insignificant considering the the overall variation in ADPPs over time and the unexplained decrease of ADPPs after 2018, which might represent multiple reasons, such as a natural decrease in interest after a few years post-release. In any case, the data does not allow for conclusive attribution of observed trends. Not only is it questionable to assume the pandemic had a large effect on player increase, the presented data shows no support for the assumption that that people would play even more games associated with relaxation to cope with the stress caused by the pandemic.

Therefore, the overall effect might just be a product of natural interest variation, although more available free time during the pandemic might also explain some of the variance reversing the declining trend on ADPPs over time. Due to lockdown restrictions more time is spent at home, so it is quite natural to expect more time spent on common indoor activities, such as video games. While it makes sense to think of video games as ways to counter the mental strain caused by the pandemic, it is important to control for variables like increased boredom due to the restrictions. It might even be argued that natural interest decline can be seen as a simpler explanation, especially considering that no difference between relaxing games and control games was found. Future research investigating the overall rise in video game player peaks should therefore make sure to control for variables like boredom and natural interest decline to better explore how much video games are actually played for the potential mental health benefits. It might be that the vast majority of players do not play video games as a way to relieve stress, but rather because there is not much else to do. If this is the case, an important follow-up question would be: Why are people not playing more as a way to decrease stress? This is a question that has many potential implications for the game industry and players alike. As the view of video games as a way to reduce stress is quite a new direction, it could be that people are just not aware of the potential positive effects or that these effects are not convincing enough to change actual behaviour. Another explanation would be the perception of the current game catalogue that is considered “relaxing”. There might be limited appeal of this type of game that includes mostly simulation, adventure, and casual games (see Table 2), even if these games are seen as emotionally profitable. In summary, there is definitely room to research these assumptions to get a more complete picture of how games are viewed and why games are played, which also might hold further information about potential strategies in marketing and designing games specifically for relaxation (i.e. serious games). It is however important to note that the present findings do not hold strong evidence for or against any theory on why people are playing games, so further research is clearly necessary to explore this.

Means of relaxation

It might therefore still be true that stress relief is an important selling point for video games, especially in very stressful times. If this is the case, another perspective to explain the data might emerge if relaxation from video games is not seen as specifically seeking out relaxing video game elements and styles, but relaxing from playing any video game, regardless of style or genre. A wide variety of individual differences regarding physiological stress-responses have been reported [54] and emotion regulation technique effectiveness is also known to be highly dependent on individual differences [55, 56]. Game elements that consistently fall under the definition of “relaxing” might therefore not mirror what individual people find relaxing and seek out. One possible counter-argument could be the amount of mostly violent action games in the control group (see Table 2), which may not help to reduce stress, but rather cause it (e.g. [57, 58]). Recently, more research has been conducted that demonstrates the importance of individual differences in response to violent games (e.g. [59, 60]) and even shed light on important elements with a positive relation to mental health. For example, Collins and Cox [61] found that action games and shooters have the largest effect on after work recovery than any other genre. Moreover, social interactions and support have been identified as important factors moderating positive effects of video games on mental health [60, 61]. In fact, social interactions have consistently been shown to be an important buffer against the negative impacts of stress (e.g. [62]), which also relates to social components within otherwise violent video games. Maroney et al. [63] for example argue that all types of games provide means to reduce negative states, depending on individual stress-coping mechanisms, and provide again more evidence for the importance of social interactions as a mediator for these effects. As COVID-19 does not only lead to more stress, it also leads to less social interactions, so it makes sense that there is an increased demand for games with social components, which also might lead to a more regulated stress-response. In other words, we might not just relax with games that are specifically made for relaxation purposes.

This might be a viewpoint that should be considered when reviewing the role of playing video games during the COVID-19 pandemic. In current research, a high emphasis is put on specific games with relaxing elements, and how these elements could be associated with well-being. Specifically games like Animal Crossing: New Horizons have been used as an example of the possible effects of video games during the pandemic [64, 65]. While this study does not invalidate this approach, it might be worthwhile to expand the research to include all kinds of games, game elements, styles, and genres to better understand how video games as a whole can be used to fight stress and increase mental health, especially during a highly straining period like the COVID-19 pandemic.

Limitations and future directions

While this study does provide many indications of the role of video games during the COVID-19 pandemic, it is important to note that it does not allow for definite conclusions. It is possible that none of the provided explanations are sufficient to understand the precise nature of why there was no difference between the groups. However, it can be argued that the possibilities provided are relevant research topics that are worthwhile to investigate. In fact, one of the biggest findings is the lack of clarity regarding the results. On the one hand, it is still perfectly reasonable to believe that video games have an important role in relieving COVID-19-related stress, while on the other hand there seems to be a gap between theoretical assumptions and actual player behaviour that needs to be filled by experimental studies. So, while no major conclusions can be drawn, this study can be seen as an indication of the importance of further research that includes actual player behaviour.

Additionally, some limitations have to be considered that emerged as a consequence of the study design. As no randomization was possible and a quasi-experimental design was chosen, it might be the case that a naturally lower statistical power could have influenced the reported results. Measures like the reported effect size of the overall ADPP increase must be interpreted with caution, as these could behave differently (and often more conservative) than the respective measures for parametric tests. The limited sample size also limits the probability of revealing very small effect sizes that still could be relevant for data containing video game players all over the world. To have a more precise picture of the discussed effect, a large-scale experimental study would be appropriate, although the informational gain might not be worth the effort. Another characteristic of this study is the restriction to Steam and to a certain player base. Steam is only a part of the whole video game industry and does not reflect players on consoles or mobile devices. Because relaxing games were limited in ADPPs and controls were matched, most of the most popular games with hundreds of thousands of daily players had to be excluded. As a result, this study provides insights about a specific type of game and may not be generalizable for other platforms. It also does not reflect types of players, which could also be an interesting target for further examination. Rather, this study explores the effect of game characteristics and has limited implications for overall player behaviour, as observations were games and not individual players. Methodological limitations are however a consequence of this data-driven approach, which is essential to make sense of the theoretical assumptions that are more and more emerging. These issues can only be solved by conducting even more empirical studies, in the best case experimental studies, to really inform about how well certain theories translate to the real world. Because the games were categorized into fixed groups with considerable in-group variance, it becomes also apparent that game research is in need of more work towards game aspect classification. For example, relaxation could be seen as a continuous dimension, representing players’ perceived value of having a relaxing experience with a game, which would open the door to new analysis tools, such as linear regression models. Before this is possible, obtainable and valid measures need to be established to turn game characteristics into valid constructs for experimental data analysis. The present study provides an example of how to form controlled and distinct groups that can be compared, which gives more insight into differences and similarities, but is restricted to the nature of the groups.

Overall, it is important to view this study as part of the evidence exploring the current link between theoretical frameworks and real-life data in the research about the mental health benefits of video games. There are limits with respect to generalization and it would be misguided to conclude that the demand of relaxing games is currently equal to the demand of non-relaxing games. Instead, the results should be interpreted in regards to their informational value when it comes to the gaps that are currently present within this field. More data-driven approaches should be applied to address these gaps and connect the various theories with actual gaming practices.

Mean ADPP by time for relaxing games vs control games.

(TIF)

Click here for additional data file.

11 Oct 2021

PONE-D-21-07746A data-driven approach for examining the demand for relaxation games on Steam during the COVID-19 pandemicPLOS ONE

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Review of “A data-driven approach for examining the demand for relaxation games on Steam during the COVID-19 pandemic”

Manuscript Overview: The authors strive to test two hypotheses: I) that gaming became more prevalent during the pandemic (as measured by average daily player peak) and II) that this was particularly true for “relaxing” video games. Using data from Steam (the largest online video game distribution platform) the authors created a matched set of 138 relaxing games and 138 non-relaxing games. Mean average daily player peak increased from 2019 to 2020 for both types of games (supporting the first hypothesis), but did not do so in a differential manner (failing to support the second hypothesis).

Comments/questions:

1) My biggest issue/question is that the authors’ questions are all with regard to a change in time (i.e., changes from 2019 to 2020) with the specific thought that the pandemic (2020) might be been “different.” However, they don’t have an appropriate baseline to examine what type of change would have been expected in the absence of a global pandemic.

As an example, the percentage of self-identified Democrats who believe that racial discrimination in the United States significantly hinders equality rose from 64% in 2019 to 68% in 2020. While it’s theoretically possible that the increase was related to something that happened in 2020, that possibility becomes much less likely when you look at the changes from the previous years (i.e., 62% in 2018, 57% in 2017, 52% in 2016, etc.). When the historical data is available, it’s clear that 2020 fits pretty nicely in a long-term monotonic trend. There’s no disjunction as would be predicted if something that occurred at the start of 2020 (noting that all of the data above was collected prior to the murder of George Floyd, which may well create a disjunction in 2021).

This is obviously important in interpreting the current data. Was the increase in ADPP “unexpected” given long-term trends? What about the increase per game type? For instance, it’s certainly possible that the “expected” long-term trend for relaxation games would be to decrease from year to year, while the expectation for the control games would be an increase. If that were true, then the current findings (where both increased by a similar amount), would have a *very* different interpretation.

So, in all, in order for the results to be interpretable/meaningful, the authors really need to move toward an analysis strategy where they examine potential deviations from an expected trajectory from 2019 to 2020 (and obviously by going back through time, it would be possible to model whether any deviations, if found, are “unusual” deviations).

2) Asking the particular question the authors are interested in is obviously super strongly predicated on the ability to appropriately categorize games.

As such, I think:

a. The relevant section in the methods would benefit from additional detail.

i. For instance, it would be typical in coding this sort of dataset to indicate how many games failed each of the various criteria (i.e., how many games had a “relaxing” tag, but then visual inspection suggested that they were “action-heavy”).

ii. Similarly, explanation for the criteria would also be useful (i.e., it’s unclear to me why it’s a problem if a relaxing game is also funny?).

b. It would also be worth walking through why the appropriate contrast is between relaxing games and “non-relaxing” games. It seems like the stronger test of (what at least I view as the core hypothesis) would pit relaxing games against stressful games (i.e., testing relaxing versus everything else is a weaker test than pitting relaxing versus the opposite of relaxing on the dimension of interest).

3) The authors will want to justify starting with March 2020 since (globally speaking) that is a bit “in-between” with respect to the start of the pandemic (e.g., where I am in the United States, nothing really changed at all until the third week of March 2020 – and so using March in the data set is weakening the data set).

Reviewer #2: Thank you so much for this research. Your paper adds, as you say, real-life data to explore theories on the use/impact of serious games. I can't speak to the statistical analysis part.

What went well: Your discussion section answered all the critics I had. For instance; you didn't assume that relaxation games were the solution. People play video games to relax regardless of style or genre; and stress- reducing activities are different for everyone.

Questions I still have:

Your findings point out that more people are playing more games and this increased prior to COVID. Now does this coincide with anything else happening in the game environment? Cost? Improved technology? Improved internet access?

You ask: Why are people not playing more as a way to decrease stress? Consider that the broader messages are telling us to get up and move, go out for a walk etc. Gaming is part of our 'feel good' activities, but one needs a break from staring at a screen regardless of how stress-reducing it is.

Your paper is well written, well argued. My two questions fit into how does gaming fit into the broader context. They stem from curiosity more than criticism so don't feel pressured to change anything in the paper.

**********

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Reviewer #1: No

Reviewer #2: No

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1 Nov 2021

Thank you so much for your consideration of our study “A data-driven approach for examining the demand for relaxation games on Steam during the COVID-19 pandemic” and the opportunity to revise the manuscript based on constructive feedback.

We are very thankful for both reviewers and their thoughtful comments. We’re very happy that both reviewers agree that this study potentially provides a worthwhile contribution and found that the presentation, data availability, and statistical analyses all meet the necessary requirements for publication. Even more happy are we for the very well argued and illustrated suggestions that were presented to improve the manuscript. We have included a detailed response to every comment that goes into more detail how we approached the changes and the nature of the changes that we have made and that can be viewed in the new manuscript, including track changes. We especially hope that we addressed the main issue of reviewer 1 regarding the methodological approach of the study. We included additional analyses to provide further justification of our approach that include the proposed historical data to show that our two groups can be meaningfully compared to draw conclusions.

We also apologize for not sufficiently having addressed the additional journal requirements in the original submission. We hope that the revision reflects all style requirements. We also included the full ethics statements in the methods section of the manuscript. If there are still any concerns or questions, please do not hesitate to approach us. Thank you very much.

Response to the reviewer comments on manuscript PONE-D-21-07746

“A data-driven approach for examining the demand for relaxation games on Steam during the COVID-19 pandemic”

Reviewer #1

The authors strive to test two hypotheses: I) that gaming became more prevalent during the pandemic (as measured by average daily player peak) and II) that this was particularly true for “relaxing” video games. Using data from Steam (the largest online video game distribution platform) the authors created a matched set of 138 relaxing games and 138 non-relaxing games. Mean average daily player peak increased from 2019 to 2020 for both types of games (supporting the first hypothesis), but did not do so in a differential manner (failing to support the second hypothesis).

First of all, we are very thankful for this thoughtful and insightful review that offers some perspectives we missed. We hope to address all concerns in the revised manuscript.

1) My biggest issue/question is that the authors’ questions are all with regard to a change in time (i.e., changes from 2019 to 2020) with the specific thought that the pandemic (2020) might be been “different.” However, they don’t have an appropriate baseline to examine what type of change would have been expected in the absence of a global pandemic.

As an example, the percentage of self-identified Democrats who believe that racial discrimination in the United States significantly hinders equality rose from 64% in 2019 to 68% in 2020. While it’s theoretically possible that the increase was related to something that happened in 2020, that possibility becomes much less likely when you look at the changes from the previous years (i.e., 62% in 2018, 57% in 2017, 52% in 2016, etc.). When the historical data is available, it’s clear that 2020 fits pretty nicely in a long-term monotonic trend. There’s no disjunction as would be predicted if something that occurred at the start of 2020 (noting that all of the data above was collected prior to the murder of George Floyd, which may well create a disjunction in 2021).

This is obviously important in interpreting the current data. Was the increase in ADPP “unexpected” given long-term trends? What about the increase per game type? For instance, it’s certainly possible that the “expected” long-term trend for relaxation games would be to decrease from year to year, while the expectation for the control games would be an increase. If that were true, then the current findings (where both increased by a similar amount), would have a *very* different interpretation.

So, in all, in order for the results to be interpretable/meaningful, the authors really need to move toward an analysis strategy where they examine potential deviations from an expected trajectory from 2019 to 2020 (and obviously by going back through time, it would be possible to model whether any deviations, if found, are “unusual” deviations).

Thank you for this comment. This is an important point that we don’t want people to be confused about. Our main response here lies in how we define our research question. We now realize that it was a bit misrepresented in the original manuscript. Our main research question is not concerned with testing how much influence the pandemic had on player increase. You are right in saying that this cannot be sufficiently answered without examining what increase we would normally expect over time. Looking at a long-term trend would be a worthwhile contribution, but this was not the main hypothesis we wanted to explore. We are concerned with how ADPPs development differ between an experimental group (relaxing games) vs. a control group, i.e., is there a special increase in attention for relaxing games vs. other games during the pandemic?

If all games get naturally more playtime between 2019 and 2020, our main question if relaxing games are (for whatever reason) special in this increase can still be answered, even if we assume the pandemic had no influence on overall player base increase. In this context, it becomes less important to know how the pandemic influences the number of players: Whatever the influence is, it is not uniquely different for relaxing games.

This is also why we chose this specific control group and no other group that relates to how relaxing or not-relaxing a game is. This control provides us with the expectation we need to statistically test differences. For your example, this would be if a specific subgroup of Democrats had a different increase in this beliefve between 2019 and 2020 compared to any other Democrat - regardless of the reasons or time trajectory. Theories provide explanations of why relaxing games are more played during the pandemic, but we found in fact no evidence for that. You are right in saying that we can’t even say if games in general had a pandemic-related player increase, which in our opinion does strengthen the notion further that relaxing games do not play a special role in times of the pandemic.

This explanation of course only holds true if we can assume that relaxing games have a similar “trajectory” as all other games. Our assumption was that this would be a natural consequence of our methodology: All games are part of the same population (specifically part of games available on the Steam online store) and to have a baseline for expected trajectory, we tested our hypothesis 1: Over all games (which is relaxing and all other games), there is an increase, so this increase was our baseline. Our strategy was to compare relaxing games to a control, that are both created from the same population - a population that provided our baseline in hypothesis 1, so we knew what to expect for ADPP change in 2019 vs. 2020 for games in general.

We now realize that this might not hold true in every case, so we added historical data for the games to our analyses to test if relaxing games and control games ADPPs developed similarly over time. We added the following paragraph in our Results section:

“To test whether relaxing games and control games showed a similar expected ADPP progression based on time preceding the pandemic (2015-2019), a preliminary linear regression analysis was conducted, predicting ADPP by time and group. There was no significant main effect for time (t[752] = 1.67,p= .10) and no interaction effect between time and group (t[752] = 0.55,p= .58). A graph of the progression of ADPPs over time by group can be viewed in S1 Fig.” (lLines 192-197)

We hope that this would provide enough evidence to assume that relaxing games and control games stem indeed from the same population, which is necessary for our other analyses. We matched control games based on their 2019 performance - so all games start at the same point. The difference between trajectories for relaxing games and control games can therefore be reflected in our ANCOVA, so we can make conclusions about if this subgroup behaves differently than our expected outcome.

This explanation is very relevant for our second (and main) hypothesis. Our first hypothesis was tested not to show that COVID increased ADPPs over all games, but to create a baseline - to create the expected trajectory. It is very true that our first hypothesis cannot be meaningfully related to the pandemic. This would be an interesting question to ask, but we fear that it would distract from our main point and further complicate analyses and therefore possible interpretations. We hope that the main message taken from our study would show the gap between theoretical work and supporting data.

To address all your concerns appropriately, we’ve made further changes to the manuscript. We included an illustration of the expected historic trajectory (2015-2020) for our dataset and added this in the supplementary material (see S1 Fig). Most importantly, we found no difference in group trajectory. We also did not find an overarching time effect, this has to be however taken cautiously as our regression does simplify all possible contributions to ADPP change. Our illustration shows that there could be a complex association between time and other related factors (such as technology development) that might be intriguing to research further, but we did not want to shift the focus and overcomplicate our findings. Rather, we used the data and the figure to validate our assumption about expected trajectory and further explain how our study fits into the research of COVID-19 related effects on player base change, so we added the following section to the discussion:

“While the present findings do not hold any information about why people play more during the pandemic, the increased numbers of daily players across all games do not contradict this particular view. It is however important to note that this overall increase might not be related to COVID-19 but rather represent a naturally expected increase of ADPPs independent from the pandemic, but possibly related to factors such as reduced cost or increased accessibility of technology. As Fig S1 shows, ADPP peaks for most games seem to lie in 2018 with a big decrease in ADPP for all games between 2018 and 2019. It is very much possible that the pandemic had an effect on this trajectory from 2019 onwards that seems to invert a decreasing trend, its significance does appear however insignificant considering the the overall variation in ADPPs over time and the unexplained decrease of ADPPs after 2018, which might represent multiple reasons, such as a natural decrease in interest after a few years post-release. In any case, the data does not allow for conclusive attribution of observed trends. Not only is it questionable to assume the pandemic had a large effect on player increase, the presented data shows no support for the assumption that that people would play even more games associated with relaxation to cope with the stress caused by the pandemic.“ (lLines 264-280)

We also changed some of the reasoning in the literature and discussion sections, further eliminating confusion about how the main effect could point to pandemic-related influences vs. random observation within a time frame, as the data does not allow for such inferences. We hope these changes are sufficient to underline the main message our study provides.

2) Asking the particular question the authors are interested in is obviously super strongly predicated on the ability to appropriately categorize games.

As such, I think:

a. The relevant section in the methods would benefit from additional detail.

i. For instance, it would be typical in coding this sort of dataset to indicate how many games failed each of the various criteria (i.e., how many games had a “relaxing” tag, but then visual inspection suggested that they were “action-heavy”).

Thank you for this suggestion. We tried to make our selection process as easily understandable and replicable as possible. In line with your suggestion, we included the exact number of games that were excluded through every step of our selection process. This includes the following sections:

“(1) the game must be tagged as ‘relaxing’, which included 208 games;” (line 121)

“(a) Other mood tags, such as ‘funny’ or ‘emotional’ are tagged with a higher priority than ‘relaxing’ […]. After this step, 178 games remained in the test group.” (line 124)

“(b) Gameplay appears to be action-heavy and intense. […]. After this step, 169 games remained in the test group.” (line 129)

“(c) Main elements of the game contain horror, sexual or mature content, or similar characteristics […]. Through this procedure, 143 games were identified for the test group.” (line 133)

For more details see lines 121-138 in the manuscript.

ii. Similarly, explanation for the criteria would also be useful (i.e., it’s unclear to me why it’s a problem if a relaxing game is also funny?).

This was also reworked in the manuscript. We added further information about our inclusion criteria and detailed reasoning. The main aim of the study was to compare relaxing games as a type of game and not just a game that is by a certain number of people considered a relaxing experience. Because of that, it was important to us to separate it from other target experiences and moods, if these other moods had a higher priority over “relaxation”. Further explanation of these criteria includes the following additions:

“(a) Other mood tags, such as ‘funny’ or ‘emotional’ are tagged with a higher priority than ‘relaxing’. While relaxing games might also include elements that evoke other moods, this was an important step to ensure the experimental group only included games that are prioritize their role in relaxing players over evoking other moods, such as sadness, melancholy, or excitement and humor.” (lines 124-129)

“(b) Gameplay appears to be action-heavy and intense. After analyzing trailer, descriptions, and other promotional material on the Steam Page, games with a high action focus that might be considered relaxing for some, but are generally not designed as a "relaxing game" were excluded.” (lines 129-134)

“(c) Main elements of the game contain horror, sexual or mature content, or similar characteristics not considered compatible with the main aim of providing a relaxing experience for the player, but rather other emotional aspects that might for some be considered relaxing.” (lines 133-137)

b. It would also be worth walking through why the appropriate contrast is between relaxing games and “non-relaxing” games. It seems like the stronger test of (what at least I view as the core hypothesis) would pit relaxing games against stressful games (i.e., testing relaxing versus everything else is a weaker test than pitting relaxing versus the opposite of relaxing on the dimension of interest).

This is a very valid point that was part of many discussions of the initial design of the experiment. There are however multiple problems that would limit the validity further in our opinion. While the experience of relaxation could arguably be seen as a dimension of continuous data, there is no real way of mapping all Steam available games onto this dimension. Rather, tags and available game aspects were used to categorize games into “genre”-style categories to form groups. This provided the ability to statistically compare the group of relaxing games with a sound control group of other games. As we described in our response to your first concern, this control group represents our expected outcome for all games, as shown for the first hypothesis. We agree that the comparison of relaxing vs. specifically stressful games would be an interesting addition, but creating a “stressful” category proved very difficult with our approach and would need more elaborate coding criteria. This in turn would open many more questions regarding sufficient control groups for both newly formed groups and in our opinion would have ultimately distracted from the main take-away, which is: Are games that can be identified as being part of the “relaxing games” category any different than other games? We see this question as an important and non-trivial contribution to many questions, including how game characteristics research could be conducted and valid (while also achievable with accessible data). To do this, group formation was necessary. Having multiple control groups would have strengthened the approach, but there are no Steam tags that represent the exact opposite of “relaxation”, so we opted for a traditional control condition. Forming more groups or finding ways to operationalize characteristics on a continuous dimension would be a next logical step. To better address possible concerns and justify our approach, we added two new paragraphs to the document:

“This procedure was chosen to have replicable and controlled methods to categorize game characteristics based on how they are presented and described in Steam. Forming groups made it possible to compare a unique subset of games associated with relaxation with a control group to examine effects.” (lLines 143-146)

“These issues can only be solved by conducting even more empirical studies, in the best case experimental studies, to really inform about how well certain theories translate to the real world. Because the games were categorized into fixed groups considerable in-group variance, it becomes also apparent that game research is in need of more work towards game aspect classification. For example, relaxation could be seen as a continuous dimension, representing players' perceived value of having a relaxing experience with a game, which would open the door to new analysis tools, such as linear regression models. Before this is possible, obtainable and valid measures need to be established to turn game characteristics into valid constructs for experimental data analysis. The present study provides an example of how to form controlled and distinct groups that can be compared, which gives more insight into differences and similarities, but is restricted to the nature of the groups.” (lLines 382-393)

3) The authors will want to justify starting with March 2020 since (globally speaking) that is a bit “in-between” with respect to the start of the pandemic (e.g., where I am in the United States, nothing really changed at all until the third week of March 2020 – and so using March in the data set is weakening the data set).

Thank you for this comment. This is an important point that needs to be better highlighted. The COVID pandemic was (and still is) not a continuous event that had not the same consequences for all countries, while Steam represents a big player base of almost the entire world. A stronger approach would be a time-series analysis for each country (or maybe even each part of a country that has unique and significant rules or media coverages that might impact the behaviour of players). Either way, it is important to better communicate this. We chose March 2020 as the beginning not to represent the beginning of most country restrictions, but as the beginning of COVID as a major pandemic in in public awareness (which arguably began much earlier for China and around the beginning of March for most of Europe). It was chosen to not mistakenly have March 2020 influence the pre-covid era, when most studies about increased stress began showing effect beginning in March. It’s important to understand that reports and public understanding and consequences are very heterogenic, but March was chosen based on these other studies and the official report of the WHO regarding mental health consideration during the COVID-19 pandemic that was made public (Reference: World Health Organization. (2020). Mental health and psychosocial considerations during the COVID-19 outbreak, 18 March 2020 (No. WHO/2019-nCoV/MentalHealth/2020.1). World Health Organization.) To make this more clearclearer, we added the following paragraph to the manuscript:

“While there is no consent on an exact beginning of the pandemic and countries showing different progressions regarding reported cases and institutional consequences, March 2020 was reported as the 87beginning of stress-related effects in many studies [2-4] and official public consideration of stress-related concerns made by the World Health Organization (WHO; [39]).” (lLines 86-89)

Reviewer #2

Thank you so much for this research. Your paper adds, as you say, real-life data to explore theories on the use/impact of serious games. I can't speak to the statistical analysis part.

What went well: Your discussion section answered all the critics I had. For instance; you didn't assume that relaxation games were the solution. People play video games to relax regardless of style or genre; and stress- reducing activities are different for everyone.

Thank you for these kind words. It was important to us to showcase how research and methodological approaches could be used to explain behaviour in various circumstances and why this is a good thing. Data-driven analyses can and should provide new insights about how people act - and always test prevalent theories. We hope we achieved this with this revision.

Questions I still have:

Your findings point out that more people are playing more games and this increased prior to COVID. Now does this coincide with anything else happening in the game environment? Cost? Improved technology? Improved internet access?

This is an interesting point. As a quasi-experimental setup it is almost impossible to evaluate all factors that impact such a comparison and it would be false to assume that March-November 2019 vs. March-November 2020 only had COVID-related differences. In fact, in the revised analysis, we found that an increase in gameplay was expected even without COVID, which is probably related to all factors you’ve mentioned and even more - as interest in gaming seems to increase continuously. Still, we believe that there is meaning in our results, as even with this expected growth, there was a increase in playtime (which could have many possible reasons), but this did not differentiate relaxing from non-relaxing games. We included some of your questions in our revised discussion in the hopes of inspiring more questions around ADPP-influencing factors (see line 264-280).

You ask: Why are people not playing more as a way to decrease stress? Consider that the broader messages are telling us to get up and move, go out for a walk etc. Gaming is part of our 'feel good' activities, but one needs a break from staring at a screen regardless of how stress-reducing it is.

Your paper is well written, well argued. My two questions fit into how does gaming fit into the broader context. They stem from curiosity more than criticism so don't feel pressured to change anything in the paper.

This is true and an interesting explanation that might need further examination. Our theories suggest that people seek stress-reducing behaviours, especially in times of stress. Games offer these - but there might be a limit. This is a field that is not well understood yet: How well can stress be regulated using video games? How well does a combined effect of outdoor activity and gameplay explain stress reduction, compared to just playing games? And are people aware of this and behave accordingly? This research provides opportunities to ask many more questions, which is exactly what one would hope (even though it lacks universal answers, such is the nature of human behaviour research).

Submitted filename: Response_To_Reviewers.docx

Click here for additional data file.

1 Dec 2021

A data-driven approach for examining the demand for relaxation games on Steam during the COVID-19 pandemic

PONE-D-21-07746R1

Dear Dr. Croissant,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

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The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

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Reviewer #2: Yes

**********

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Reviewer #1: (No Response)

Reviewer #2: Yes

**********

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Reviewer #1: (No Response)

Reviewer #2: Yes

**********

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Reviewer #1: (No Response)

Reviewer #2: Yes

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Reviewer #2: The authors responded to the reviewers comments with thoughtful reflection. They made changes to the paper based upon these recommendations.

**********

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7 Dec 2021

PONE-D-21-07746R1

A data-driven approach for examining the demand for relaxation games on Steam during the COVID-19 pandemic

Dear Dr. Croissant:

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