The Effects of Gaming 20+ Hours Per Week: A Data Analysis

On June 18th 2018, the final draft of the ICD-11 diagnostic manual was published. This publication was watched carefully by the gaming community due to the addition of Gaming Disorder. Gaming Disorder would allow people to be diagnosed with a video game addiction, a decision that was hotly debated by academics.

In the period following June 18th, misinformation and misreporting on Gaming Disorder was at an all-time high. The misreporting that drew the most negative attention from both academics and the gaming community was the claim that 20 hours of gaming per week equated to a video game addiction.

This figure is not stated anywhere in the ICD’s diagnostic criteria and is a gross misrepresentation of said diagnostic criteria. However, this misinformation reached a wide audience, with nearly 1.3 million views on Facebook alone. So why should this be a cause for concern?

In 2018, a study was published investigating the validity of video game addiction diagnostic guidelines. In this study, a population of young people was identified that did not meet the criteria for a video game addiction, but still felt guilty about spending time playing video games. An explanation offered by the researchers was that external influences such as controlling parents made them feel guilty about spending their free time playing video games. This guilt and pressure may have been enough to damage young peoples’ enjoyment of video games.

Considering the mental health benefits that video games can bring, it is important to allow young people to play video games in an amount that does not detract from their quality of life. Due to this reporting, parents may now believe that this figure is under 20 hours per week. But is this really the case?

I decided to introduce data to this dispute and conduct my own statistical analyses on the 20+ hours of gaming per week figure. For this task, I relied on a combination of the Gaming Disorder diagnostic criteria (e.g. ‘gaming takes precedence over other life interests and daily activities’), and what I believed parents would be most concerned about to develop four key hypotheses:

• Addictive Behaviours: If gaming is an addictive behaviour, could it be a gateway for other addictions such as alcohol and substance misuse? Gaming for 20+ hours could be symptomatic of an ‘addictive personality’ (Noble, 2000), and addictions have been found to co-occur with one another (Bergh & Kühlhorn, 1994).
• Social Life: Are young people less likely to spend time with their friends if they are stuck at home playing video games? Solitary addictions such as gambling are associated with increased loneliness and social isolation (Trevorrow & Moore, 1998).
• Mental Health: Are young people more likely to experience low mood if they just sit and stare at a screen? There is an established relationship between addictions and poor mental health (Volkow, 2004; Quigley et al., 2015; Anda et al., 2002).
• Academic Achievement: Do young people suffer academically due to playing video games? Addictive behaviours have been found to impact achievement in young people (Partnership for Drug-Free Kids, 2013).

As usual, there will be a summary at the bottom if you do not wish to read everything. Thank you and please enjoy!

Contents

• • 1. The Data
    2. Data Analysis Strategy and Checking
    3. Results
    4. Discussion
    5. Critique
    6. Summary
    7. References

The Data

This analysis will be conducted using the 2010 wave of the Health Behaviour in School-Aged Children (HBSC) study. The HBSC is a large-scale endeavour to improve young peoples’ wellbeing by researching their health behaviours and social context. Data is gathered for this study via an anonymous questionnaire. Please note that while I have previously analysed data from the 2014 wave, it was necessary to use data from the 2010 wave as the 2014 wave did not ask about hours spent consuming media. The 2010 wave of the study includes a total of 213,595 11-, 13- and 15-year olds from 40 countries.

This dataset is open-access and is open for all researchers to analyse, providing that they abide by the License Agreement. In accordance with the License Agreement, I must copy and paste the acknowledgement text below. Feel free to move on to the next section.

HBSC is an international study carried out in collaboration with WHO/EURO. The International Coordinator of the 2009/10 survey was Prof. Candace Currie and the Data Bank Manager was Prof. Oddrun Samdal. The 2009/10 survey was conducted by Principal Investigator Candace Currie in 40 countries. For details, see http://www.hbsc.org.

Data Analysis Strategy and Checking

Please note that this section is optional, but provides transparency on the data and how it will be analysed.

To gain rich, holistic detail on the lives of young people, data will be analysed using what is known as a regression analysis; I will be conducting six in total.

Simply put, regression analyses are used to explain why something happens (known as ‘variance explained’). For example, when trying to explain number of ice creams sold on a day, significant predictors may include variables such as temperature, time of year etc.

As regression analyses produce a lot of statistics, I have decided to report on the variance explained by each model, and β values, t values and significance values for significantly contributing variables. While β values can be thought of as how much the variable contributes to explaining a behaviour, t values demonstrate the strength of the relationship.

To help readers understand the variables that have been entered into the regression analysis, I have grouped variables together into easy-to-understand categories. For researchers or those who are curious, the coding structure for these variables can be viewed here.

Individual factors: This category includes the age and gender of the participant.

Addictive factors: This category involves the frequency with which young people have smoked, been drunk, or engaged in cannabis use in the past 30 days.

Bullying and fighting: This category includes three variables: whether someone has been bullied in the past two months, whether someone has bullied another in the past two months, and the number of times a young person has been involved in a physical fight.

Family life: This category involves the ease with which young people can discuss problems in their life with their mother, father, brother or sister.

Social life: This category measures the quality of a young person’s social life. Participants were asked how easy they find it to discuss problems with their best friend and friends of the same/opposite gender. Participants were also asked how many close male/female friends they have and how often they spend time with friends after school.

Health: This category takes both physical and mental wellbeing into consideration. Participants were asked their frequency of experiencing: back aches, bouts of nervousness, difficulties sleeping, feeling dizzy, stomach-aches, headaches, bad temper and feeling low. Participants were also asked to rate the quality of their physical health and their own life satisfaction. BMI was worked out by the HBSC research team using a combination of height and weight.

School life: All variables pertaining to school life and academic performance were included in this category. These include: whether the young person likes school, how they rate their academic performance, whether students like to be together, whether students are helpful, whether students accept the young person, and whether the young person feels pressured by schoolwork.

Socioeconomic status (SES): This category includes measures of wealth and social status for young people and their family. These variables include: number of cars in the household, how often the family go on holiday together, number of computers, whether the young person has their own bedroom, whether the young person frequently goes to bed hungry, and whether they consider their family to be wealthy.

Media consumption: In the 2010 wave of the HBSC, young people were asked how long they spent playing video games, watching TV, and using a computer during both the weekday and weekend. Weekday and weekend scores were used to calculate total amounts of each respective media consumption per week. This led to the creation of three new variables: whether a young person played 20+ hours of games per week, whether a young person watched 20+ hours of TV per week, and whether a young person spent 20+ hours per week on the computer.

Before conducting the regression analyses, I wanted to ensure that the regression results would be accurate by checking a number of data assumptions. Please feel free to ignore anything written in red, this is all statistical talk.

The multicollinearity of variables was assessed using both Tolerance and Variance Inflation Factor (VIF) measures. No VIF value exceeded 5 and each Tolerance value exceeded 0.2, suggesting that variables are sufficiently independent for the analysis. The influence of undue cases was assessed using Mahalanobis Distance, Cook’s Distance and Centered Leverage Values. A total of 20 participants had a Centered Leverage Value that exceeded three times the mean; these participants were removed from the analysis. Multiple participants had a Mahalanobis Distance value which exceeded 15. However, Stevens (2002) recommends that the Cook’s Distance value should be consulted before removing participants with a Mahalanobis Distance value exceeding 15. Due to the very small size of Cook’s Distance values in the analysis (Maximum: 0.003) which is to be expected from having a very large sample size, no further participants were removed as part of this check.

Results

After data cleaning, the participant pool for data analysis was 213,575. This pool included 108,490 (50.8%) females and 105,085 (49.2%) males.

195,873 (91.7%) participants gave enough information about their video game time to work out whether they played 20 hours or more per week.  From this pool, 41,937 young people (21.4%) played video games for 20 hours or more per week. Playing for 20 hours or more was more common for male gamers (r = .221, p < .001) and older gamers (r = .049, p < .001).

I will now discuss the finding of each regression analysis under their respective subheading.

Getting Drunk

26 variables explained 32% of underage drinking until drunk. Gaming for 20 hours plus was not a significant predictor of getting drunk (β = .004, t = 1.226, p = .220).

The top five predictors of frequently getting drunk are: smoking cigarettes (β = .364, t = 101.701, p < .001), cannabis use (β = .213, t = 63.177, p < .001), being older (β = .075, t = 20.620, p < .001), getting involved in physical fights (β = .057, t = 16.040, p < .001), and frequently going to bed hungry (β = .040, t = 12.395, p < .001). A full table of all 26 predictors and model statistics can be viewed here.

Smoking

34 variables explained 35% of underage smoking frequency. Gaming for 20 hours plus was the 17th strongest predictor, but had protective effects: young people were less likely to smoke if they played 20 hours or more per week (β = .019, t = 5.432, p < .001).

The top five predictors of frequent underage smoking are: frequently getting drunk (β = .348, t = 101.701, p < .001), cannabis use (β = .164, t = 49.280, p < .001), being older (β = .138, t = 38.780, p < .001), spending more time with friends after school (β = .082, t = 25.248, p < .001), and lower self-ratings of academic achievement (β = .067, t = 20.112, p < .001). A full table of all 34 predictors and model statistics can be viewed here.

Cannabis Use

26 variables explained 20.8% of frequent underage cannabis use. Gaming for 20 hours plus was not a significant predictor of this (β = .005, t = 1.272, p = .203).

The top five predictors of frequent underage cannabis use are: frequently getting drunk (β = .248, t = 63.177, p < .001), smoking cigarettes (β = .200, t = 49.280, p < .001), getting involved in physical fights (β = .080, t = 20.898, p < .001), frequently going to bed hungry (β = .056, t = 16.019, p < .001), and being a bully (β = .043, t = 11.626, p < .001). A full table of all 26 predictors and model statistics can be viewed here.

Social Life

33 variables explained 13.8% of spending time with friends after school. Gaming for 20 hours plus was the 16th strongest predictor: young people were more likely to spend time with friends if they played games for 20 hours or more per week (β = .035, t = 9.006, p < .001).

The top five predictors of spending time after school with friends are: having more close male friends (β = .121, t = 30.562, p < .001), smoking cigarettes (β = .108, t = 25.225, p < .001), having more close female friends (β = .095, t = 24.245, p < .001), finding it easier to confide in your best friend (β = .078, t = 17.308, p < .001), and having fewer or no family cars (β = .067, t = 17.241, p < .001). A full table of all 33 predictors and model statistics can be viewed here.

Mental Health

28 variables explained 42.6% of the frequency with which young people feel low. Gaming for 20 hours plus was not a significant predictor of low mood (β = .001, t = 0.320, p = .749).

The top five predictors of low mood are: having anger problems (β = .281, t = 78.715, p < .001), frequently feeling nervous (β = .160, t = 45.216, p < .001), lower life satisfaction (β = .140, t = 41.009, p < .001), suffering bouts of dizziness (β = .074, t = 21.871, p < .001), and being female (β = .063, t = 17.318, p < .001). A full table of all 28 predictors and model statistics can be viewed here.

Academic Achievement

28 variables explained 17.3% of young people’s self-reports of their academic achievement. Gaming for 20 hours plus was the 22nd strongest predictor: young people were more likely to rate their academic achievement more poorly if they played games for 20 hours or more (β = .015, t = 3.931, p < .001).

The top five predictors of self-reporting better grades are: liking school (β = .172, t = 45.552, p < .001), having better health (β = .111, t = 28.941, p < .001), having higher life satisfaction (β = .096, t = 23.111, p < .001), smoking fewer or no cigarettes (β = .085, t = 20.083, p < .001), and believing your family to be wealthy (β = .065, t = 17.533, p < .001). Conversely, variables such as feeling pressured by schoolwork (β = .055, t = 15.074, p < .001) and spending more time with friends after school (β = .039, t = 10.546, p < .001) were found to lead to worse reports of academic achievement. A full table of all 28 predictors and model statistics can be viewed here.

Discussion

After reports that equated 20 hours of gaming per week to a video game addiction, I set out to explore the impact of 20+ hours of gaming on young peoples’ lives. If 20+ hours of gaming per week were equal to an addiction, it could have a disruptive effect on young people’s lives in four ways: an increased likelihood of further addictive behaviours, a poorer social life, poorer mental health, and reduced academic achievement. The data analysis demonstrated the following:

1. 20+ hours of gaming per week was not related to other addictive behaviours. In fact, playing games for this amount per week reduced the likelihood of underage smoking.
2. Young people who play 20+ hours of video games per week were more likely to spend time with friends after school.
3. Playing games for 20+ hours per week was not related to low mood in young people.
4. There was a small but statistically significant relationship between playing games for 20+ hours per week and reporting your academic achievement to be lower.

Playing games for 20+ hours per week either does not relate to or protects against addictive behaviours, encourages socialisation, and does not relate to mental wellbeing, but may detract from academic performance. However, analysing data via regression analyses grants me a rich and holistic view into the lives of young people. Using data from the analysis, I have three potential explanations for the relationship between gaming and academic achievement:

1. The second largest predictor of academic performance is health, meaning that young people with poor health believe they do poorer in school. If young people are frequently ill and cannot attend school, they may spend the time they would have spent in school playing video games.
2. The sixth largest predictor of academic performance is feeling pressured by schoolwork: young people who feel pressured feel that their achievement is poorer. As video games can be a powerful source of anxiety relief, young people may be engaging in distractive behaviours instead of confronting their academic difficulties.
3. 21 variables predict more of academic achievement than 20+ hours of gaming, and there are larger extra-curricular based predictors such as spending time with friends after school. It is possible that some young people are indulging in too many fun after school activities and not leaving enough time for studies.

As I always aim to be as helpful as possible in my research, I have recommendations for tackling each issue:

1. Schools should make a greater effort to digitise their content. When I taught high school in 2016, this was still far from the norm. Students were delighted that I uploaded all of my material online and they could do some work while they were sick, albeit at a reduced pace. This should hopefully help students who are unable to attend school.
2. Parents could encourage their child to have transparent conversations about their academic progress. If there is a subject or two that their child is struggling with, parents could work with the child and/or the school to help improve their grades and figure out where and how they are struggling.
3. If a young person neglects their studies for an abundance of other activities, parents could have open and calm discussions with their child about how best to manage their time. This would be more productive than automatically stripping their child of something that makes them happy.

The findings of this study reinforce the need to report responsibly on mental health. While an enormous population (nearly 1.3 million from Facebook alone) have viewed the claim that 20 hours of gaming per week equates to an addiction, the data does not support this argument. These young people do not suffer from low mood, do not engage in other addictive behaviours, and enjoy a pleasant social life. While there is an association between gaming and academic achievement, this association pales in comparison to factors such as health and feeling stressed by schoolwork. As a result of media reports, parents may engage in unnecessarily restrictive parenting that is not supported by data. Such restrictive parenting has been argued to reduce enjoyment of video games and leisure time in young people (Carras & Kardefelt-Winther, 2018). To ensure that young people do not feel guilty about spending time relaxing, irresponsible reporting of this nature needs to be avoided in the future.

Critique

All good research provides self-critique so that other researchers can improve upon the methodology. Before concluding, I would like to offer a few self-critiques of this study:

• Due to later versions of the HBSC study not asking young people how long they spend consuming media, it was necessary to use data from the 2010 wave of the study. This means that the data predates large gaming booms such as Fortnite, but it could be argued that parents have been concerned about the negative impacts of video game consumption for around 35 years.
• This point is a double-edged sword, but young people were only asked how long they played video games ‘on a computer or games console’ – excluding mobile games. If you’re someone who doesn’t like mobile-playing Candy Crushers in their gaming data then this study does not include Candy Crush et al., but the downside is that these findings may not be applicable to mobile gamers.
• A number of variables in this analysis come from self-report data. While self-report data is great for gathering details from peoples’ lives, they can be subject to inaccuracies and subjectivity. Perhaps the variable that suffers most from this is the academic achievement variable. Instead of having an objective measure of achievement such as grades, young people were asked to report their academic achievement from ‘very good’ to ‘below average’. ‘Very good’ was the third least popular option for young people, with ‘good’ and ‘average’ being the most popular responses. With this in mind, it would be interesting for this analysis to be repeated with objective measures of education rather than subjective measures.

Summary

• In June 2018, media outlets equated playing video games for 20 hours per week to a video game addiction. This claim was made with no reference to data and no consideration for the currently proposed diagnostic criteria. As this widely-seen article may spark restrictive parenting that is argued to be harmful, I set out to understand the impact of 20+ hours of gaming per week through data analysis.
• In this analysis, I examined the relationship between 20+ hours of gaming per week and four key factors: engaging in addictive behaviours, socialising, mental health, and academic achievement. This analysis was conducted using the 2010 wave of the Health Behaviour in School-Aged Children (HBSC) study. This study was conducted in 40 countries and examined the health and social factors of 213,595 11-15 year olds. The final participant sample following data checking and cleaning was 213,575.
• 41,937 young people reported playing video games for 20+ hours per week. This figure was not related to underage drinking, cannabis use or poor mental health. Gaming for 20+ hours per week was found to reduce the likelihood of underage smoking and increased the likelihood of young people spending time with friends after school. There was a small but statistically significant relationship between 20+ hours of gaming per week and self-reporting lower academic achievement, but this relationship is overshadowed by factors such as poor health, school stress and life satisfaction. Data-driven recommendations for improving academic achievement include encouraging schools to digitise their content to benefit sick students, and having calm and patient conversations with young people regarding their academic progress and leisure time.
• The findings of this study stress the need to report responsibly on mental health. While an enormous population (nearly 1.3 million from Facebook alone) have viewed the claim that 20 hours of gaming per week equates to an addiction, the data does not support this argument. As irresponsible reporting may result in overly restrictive parenting and young people feeling guilty about spending their leisure time relaxing, reporting of this nature needs to be avoided in the future.

Thank you all very much for reading! This hard work would not be possible without the support of my wonderful Patrons. I would particularly like to thank my Platinum Patrons: Matt Demers, Albert S Calderon, Kyle T, Andrew Shirvis, redKheld, DigitalPsyche, Brent Halen, Dimelo ‘Derp’ Waterson, Hagbard Celine, Aprou, Nathan, Austin Enright, Dr. Shane Tilton, SK120, Teodoro Elizondo, NotGac and ——–. Thank you!

 

References

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Bergh, C., & Kühlhorn, E. (1994). Social, psychological and physical consequences of pathological gambling in Sweden. Journal of Gambling Studies, 10(3), 275-285.

Carras, M. C., & Kardefelt-Winther, D. (2018). When addiction symptoms and life problems diverge: A latent class analysis of problematic gaming in a representative multinational sample of European adolescents. European Child & Adolescent Psychiatry, 27(4), 513-525.

Quigley, L., Yakovenko, I., Hodgins, D. C., Dobson, K. S., el-Guebaly, N., Casey, D. M., Currie, S. R., Smith, G. J., Williams, R. J., & Schopflocher, D. P. (2015). Comorbid problem gambling and major depression in a community sample. Journal of Gambling Studies, 31(4), 1135-1152.

Noble, E. P. (2000). Addiction and its reward process through polymorphisms of the D2 dopamine receptor gene: a review. European Psychiatry, 15(2), 79-89.

Partnership for Drug-Free Kids. (2013). Substance Use in 12th Grade Dropouts Greater Than in Teens Who Stay in School. Retrieved 14th January, 2019 from https://drugfree.org/learn/drug-and-alcohol-news/substance-use-in-12th-grade-dropouts-greater-than-in-teens-who-stay-in-school/.

Stevens, J. P. (2002). Applied multivariate statistics for the social sciences (4th ed.). Hillsdale, NJ: Erlbaum

Trevorrow, K., & Moore, S. (1998). The association between loneliness, social isolation and women’s electronic gaming machine gambling. Journal of Gambling Studies, 14(3), 263-284.

Volkow, N. D. (2004). The reality of comorbidity: Depression and drug abuse. Biological Psychiatry, 56(10), 714-717.