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Gaming Activity and Possible Changes in Gaming Behavior Among Young People During the COVID-19 Pandemic: Cross-sectional Online Survey Study

Gaming Activity and Possible Changes in Gaming Behavior Among Young People During the COVID-19... Background: Young people’s daily lives and social interactions changed remarkably during the COVID-19 pandemic as schools and cinemas closed, leisure activities were cancelled, and gatherings were regulated. Questions have been raised by the media, schools, policy makers, and research communities about the effect on young people’s online behaviors. Objective: This cross-sectional study aimed to study self-reported changes in gaming, focusing on a younger section of the population during the COVID-19 pandemic in Sweden. We also wanted to look at potential risk factors behind problematic gaming during the pandemic, including gaming patterns, gambling behavior, psychological distress, certain sociodemographic characteristics, health factors, and school situation. Methods: This was an anonymous online survey study of web panel participants in Sweden (n=1501) to study changes in gaming behaviors during the COVID-19 pandemic. Self-reported increases in gaming were analyzed in logistic regression analyses against sociodemographic and health factors. Results: Within the study population that reported changes in gaming activity, we found significant differences in age, employment status, disposable income, whether they ever played on loot boxes, time spent at home, school attendance, psychological distress, and gambling and gaming problems, as well as significant differences in changes in alcohol consumption and exercise habits. When examining the 16–24-year-old age group who reported changes in gaming activity, we found significant differences within the group in disposable income, time at home, and school attendance. When examining the 25–39-year-old age group who reported changes in gaming activity, we found significant differences within the group in employment status, disposable income, time spent at home, whether the respondents were studying, school attendance level, psychological distress, and gaming problems, as well as significant differences in changes in alcohol consumption and exercise habits. Psychological distress (all age groups analyzed together; 25–39-year-old age group), drinking less alcohol (all age groups analyzed together), spending more time at home (all age groups analyzed together), gaming problems, and exercising less (25–39-year-old age group) were positively correlated with a self-reported increase in gaming activity. Being employed (25–39-year-old age group) and being over 40 years of age (all age groups analyzed together) were negatively correlated with increased gaming. We found no significant correlations in the 16–24-year-old age group. Conclusions: Those who reported increased gaming during the COVID-19 pandemic were more likely to be 16 years to 39 years old. In the age group of 25 years to 39 years old, the increase was associated with psychological distress, reporting less exercise, and being unemployed. COVID-19 may present as a risk factor of increased online gaming in a small but vulnerable group. More research and preferably longitudinal studies are needed in the field of gaming and effects of the COVID-19 pandemic. (JMIR Serious Games 2022;10(1):e33059) doi: 10.2196/33059 https://games.jmir.org/2022/1/e33059 JMIR Serious Games 2022 | vol. 10 | iss. 1 | e33059 | p. 1 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Claesdotter-Knutsson et al KEYWORDS COVID-19 pandemic; gaming; screen time; psychological distress interventions [35]. Sweden’s approach to prevent virus spread Introduction has not included lockdown or stay-at-home orders but rather recommendations. Most workplaces encouraged their workers The index case of the now widespread COVID-19 pandemic to work from home, and regulations in opening hours have been caused by the SARS-CoV-2 virus originated in Wuhan, the in effect for places such as restaurants and shopping malls, capital of Hubei Province, China, on December 8, 2019 [1]. On leading people in general to spend a lot more time inside with January 31, 2020, the first confirmed case of SARS-CoV-2 viral possible access to screens. infection was recorded in Sweden. In subsequent months, COVID-19 reached most countries in Europe [2], and on March Young people’s daily lives and social interactions have changed 11, 2020, the World Health Organization (WHO) declared the remarkably in the COVID-19 pandemic because of school and outbreak a pandemic. cinema closures, the cancellation of leisure activities, and restrictions on gatherings. Without the daily routine of going As well as physiological harm, the COVID-19 pandemic has to school, young people had unlimited opportunities to play also had an enormous effect on people’s mental health [3,4]. video games, making them vulnerable to developing problems Research has confirmed that rates of depression, addiction, related to excessive gaming. anxiety, and other psychiatric disorders rose during the pandemic [5-10]. Research has shown an increase in screen time and the Internet gaming disorder (IGD), it should be noted, is included consumption of digital entertainment during the pandemic, in the 11th re vision of the International Classification of particularly online gaming and related activities such as video Diseases (ICD 11), defined as a gaming behavior of sufficient game streaming [4,9,11-13]. This phenomenon has been seen severity to result in significant impairment in areas of function worldwide. In India, for example, WinZO Games reported 300% [36]. However, the Diagnostic and Statistical Manual of Mental greater user engagement, 30% higher traffic in online mobile Disorders (DSM-5) has described IGD as “necessitating further gaming, and 35% higher usage in multiplayer modes [14], and clinical experience and research before inclusion as a formal another Indian mobile-based online gaming platform reported disorder” [37]. an almost 200% increase in the user base during the pandemic, With regard to gambling habits, there has also been considerable with 75,000 new users [15]. A 70% increase in Fortnite gaming research on the impact of the COVID-19 pandemic [38]. Our has been seen in Italy [16]. In the United States, one of the research in this area has shown that the increase in gambling in largest telecom providers reported a 75% rise in online activity the general population in Sweden is limited, but the group who [17]. In 2020, the world’s largest video game digital distribution does report increased gambling activity also reports psychosocial service, Steam, reported 20 million active users, an all-time problems [38-41]. Researchers have raised the concern that high [18]. The WHO has supported gaming as an activity that more time spent at home could increase gaming and its possible promotes social distancing and reduces the loneliness that might negative side effects, but there is very little research on the follow, including the gaming industry’s media campaign actual situation [18]. We thus set out to see if gaming habits #PlayApartTogether, which mixes guidelines to prevent the changed during the COVID-19 pandemic in the same way as spread of COVID-19 with messages encouraging online gaming gambling habits. [19-22]. It has been suggested that online gaming could be psychosocially beneficial to young people—cognitively, The aim of this cross-sectional study was thus to study motivationally, emotionally, and socially [23,24]—and self-reported changes in gaming during the COVID-19 especially so in the COVID-19 pandemic, when pandemic, focusing on a younger section of the Swedish recommendations and regulations to stop the spread of the virus population. We also considered the potential risk factors for have affected everyone’s lives. Gaming has been shown to problematic gaming during the pandemic, including gaming reduce loneliness [25,26] and, even though concerns have been patterns, gambling behavior, psychological distress, certain raised about its addictive potential, research has shown that sociodemographic characteristics, health factors, and school frequent gaming does not have to be problematic [27] and most situation. gamers’ gaming habits are changeable and not fixed [28,29]. It has also been suggested that gaming could work as a coping Methods mechanism against stress [30,31]. However, excessive screen time has been shown to be associated with a range of negative Setting mental health outcomes, including anxiety and depression, in The first wave of the COVID-19 pandemic in Sweden was in adolescents considered particularly vulnerable to a problematic the spring of 2020. In the summer of 2020, virus transmission use of online media [32-34]. Softening its excessively positive decreased, and the second wave started in the autumn of 2020. initial message about gaming, the WHO’s mental health After only a partial decrease in virus transmission in the winter information (#HealthyAtHome—Mental Health) recommended of 2020-2021, early 2021 saw a third wave of virus transmission a balance between time spent on screens—and gaming in and an increased hospital-admitted disease burden in Sweden particular—and offline activities [22]. [42]. This study was cross-sectional and based on a self-report online survey study carried out in Sweden in March 2021 during Governmental virus protection strategies have differed around the third wave of the ongoing COVID-19 pandemic in the the world from recommendations to more mandatory https://games.jmir.org/2022/1/e33059 JMIR Serious Games 2022 | vol. 10 | iss. 1 | e33059 | p. 2 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Claesdotter-Knutsson et al country. At the time of the study, secondary schools in Sweden attendance had been affected by the remote learning situation had started to open up for on-site teaching, but the national during the COVID-19 pandemic (less absence, more absence, COVID-19 strategies regarding leisure activities and restrictions or not affected). The final questions were about gaming, on gatherings of more than 8 people were still in effect. specifically whether their personal gaming habits had changed, excluding games for money (more, no change, less, I do not Participants and Procedures engage in gaming). We also asked about loot boxes: whether We used the market survey company Ipsos and their online web in the last 12 months they had engaged in gaming involving survey panel. Ipsos is a market survey company that has broad currency inside a video game, with the purpose to either gain experience in conducting survey studies in the area of addictive money or advantages in the video game (yes, no). disorders. Ipsos abides by the International Chamber of The Game Addiction Scale for Adolescents (GASA) is one of Commerce (ICC)/ESOMAR Code. The Ipsos web panel has the most frequently used questionnaires for gaming addiction previously been used for online surveys in the course of our [44-47]. The scale was theoretically based on the DSM-5 criteria research [43]. In this study, we invited respondents from the for pathological gambling [47]. Each question covers 1 criterion, general population, aged 16 years and older. Participants for answered on a 5-point continuum scale: 1 (never), 2 (rarely), 3 the Ipsos web panel were invited with the information that the (sometimes), 4 (often), 5 (very often). It should, according to survey would address “computer gaming, gambling for money the developer, be accounted as endorsed when rated 3 or higher. and other behavioral patterns in Sweden during The DSM-5 requires half (or more) of their criteria to be met, COVID-19—association with mental health, social situation while scholars within the field of gaming suggest a ranking of and attitudes to the pandemic.” The survey was made accessible the criteria: the “core approach.” The criteria tolerance, mood only when the respondent provided electronic informed consent. modification, and cognitive salience are said to be associated Participants on the Ipsos web panel enroll voluntarily to take with engagement rather than addiction, while the contrary market surveys, political opinion polls, and similar surveys, for applies for the criteria withdrawal, relapse, conflict, and which they earn points they can redeem as goods or services. problems [47-49]. In order to distinguish between levels of Most surveys are worth a point and each point is worth severity within the group of gamers, the core approach was approximately €1 (US $1.14). The survey is sent out to different applied, whereby the individuals who met all the core criteria age groups of web panel participants until a gender and age (relapse, withdrawal, conflict, and problems) constituted the distribution close to that of the general population is achieved. group addicted gamers. The respondents who endorsed 2 or 3 In this study, invitations were sent until some 1500 complete of the core criteria but none of the peripheral criteria (salience, answers were obtained. In addition, in this study, the final tolerance, mood modification) were grouped as problem gamers, distribution of age groups, gender, and geographical location and those that endorsed all 3 of the peripheral criteria but not (regions) was compared by Ipsos with those of the general more than one of the core criteria were grouped as engaged population, such that the data set was weighted according to a gamers [46,50]. The remaining respondents comprised the group summarized weighting score derived from these 3 variables. “noproblem” gamers, as individuals below the cut-off for When the survey was halted, the final sample consisted of 1501 engaged gaming. individuals. The study was carried out from March 19, 2021 through March 29, 2021. The study was reviewed and approved Since both the problem gamers and the addicted gamers were by the Swedish Ethical Review Board (File: 2021/00369). assumed to be associated with more severe gaming behavior as well as more negative outcomes [46,51], these 2 groups also Measures constituted one combined group (2-4 endorsed core criteria): Basic sociodemographic variables comprised gender (female addicted/problem gamers. or male), age (divided into 2 age groups: 16-24 years; ≥25 Psychological distress was measured using the Kessler-6 scale years), monthly income (divided into 3 groups: SEK [52]. This scale measures symptoms of depression and anxiety 10,000-20,000 [US $1107.48-$2214.97]; SEK 20,000–40,000 perceived during the past 6 months. The Kessler scores (0-4 for [US $2214.97-$4429.94]; SEK ≥40,000 [US ≥$4429.94]), level each question) were summed, and a total score of 5 or more was of education (university, secondary school [age 16-19 years], classified as psychological distress [52]. primary school [age 6-16 years], other), employment status (studying, employed, unemployed, retired, other). The The level of potential gambling problems was measured with questionnaire began with questions about changes in the the 9-item Problem Gambling Severity Index (PGS-I) [53], respondent’s personal behavior during the COVID-19 pandemic where each of the statements addresses the preceding 12 months. (“since these changes in Sweden started”); whether they, during For the PGS-I scale, the scores of 0-3 for each question were this period, had spent more or less time at home (much more, summed: A score of 0 indicated no problem with gambling; a slightly more, unchanged, or less time at home); and whether score of 1-2 indicated a low risk of gambling problems; 3-7 they had consumed more or less alcohol (more, less, unchanged, indicated a moderate risk of gambling problems; and 8 or more or don’t drink at all). Thereafter, questions were asked regarding indicated gambling problems. schooling situation—whether the respondents attended school Statistical Analysis of any kind including university (yes or no) and whether their level of achievement had been affected by the remote learning Weighting adjustments were applied to compensate for situation during the pandemic (for the better, for the worse, or nonresponse and noncoverage and to make the sample estimates not affected). Finally, the participants were asked if their school conform to external values. The reporting of prevalence https://games.jmir.org/2022/1/e33059 JMIR Serious Games 2022 | vol. 10 | iss. 1 | e33059 | p. 3 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Claesdotter-Knutsson et al measures and group-wise comparisons related to the weighted 37.9% (569/1501) answered they had never played video games, data and statistical tests were applied using the Chi-square test, neither now nor before the pandemic, and were therefore while binary logistic regression analyses were carried out using removed from further analysis. The remaining study population the unweighted data. Binary logistic regression analyses were (932/1501) was used for further analysis. We chose to study carried out with increased changes in gaming behavior (yes/no) the younger section of the population. Of the population who as the dependent variable in order to study potential independent did game, 16.4% (153/932) were in the 16–24-year-old age variables associated with the outcome. For all models, odds group, and 30.8% (287/932) were in the 25–39-year-old age ratios (ORs) with 95% CIs are presented. For all statistical group. Gender was equally distributed in both age groups. We analyses, SPSS version 25.0 (IBM Corp, Armonk, NY) was then looked at the various demographic and socioeconomic used [54]. factors described in the previous sections and their relationships with self-reported changes in gaming activity (increased, Results decreased, unchanged). Descriptive Data on Changes in Gaming Behavior by Demographic and Socioeconomic Factors (Weighted Data) Overview The descriptive data for the sample are shown in Table 1. The questionnaire was answered by 1501 participants, of whom Table 1. Survey respondents and the final study populations by age group. Characteristics Total sample (n=1501), n (%) Age groups 16-24 years (n=190), n (%) 25-39 years (n=392), n (%) Never gamed 569 (37.9) 37 (19.5) 105 (26.8) Study population 932 (62.1) 153 (80.5) 287 (73.2) Gender Female 452 (48.5) 68 (44.4) 152 (53.0) Male 480 (51.5) 85 (55.6) 135 (47.0) (P=.002), time spent at home (P<.001), changes in alcohol All Age Groups consumption (P<.001), changes in exercise habits (P<.001), We found significant differences according to age group whether the respondents were studying (P<.001), school (P<.001), employment status (P<.001), disposable income attendance (P<.001), Kessler score (P<.001), PGS-I score (P<.001), whether respondents ever played on loot boxes (P<.001), and GASA score (P<.001; Table 2). https://games.jmir.org/2022/1/e33059 JMIR Serious Games 2022 | vol. 10 | iss. 1 | e33059 | p. 4 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Claesdotter-Knutsson et al Table 2. Gaming behavior by demographic characteristics among all participants. Characteristics Gaming behavior, n (%) P value Participants, n (%) Increased Unchanged Decreased   Total sample 357 (38.3) 541 (58.0) 34 (3.6) 932 (100) N/A Gender Female 179 (39.6) 259 (57.3) 14 (3.1) .55 452 (48.5) Male 178 (37.1) 282 (58.8) 20 (4.2) 480 (51.5) Age group (years) 16-24 87 (56.9) 59 (38.6) 7 (4.6) <.001 153 (16.4) 25-39 146 (50.9) 126 (43.9) 15 (5.2) 287 (30.8) 40-59 77 (25.8) 213 (71.2) 9 (3.0) 299 (32.1) ≥60 47 (24.4) 143 (74.1) 3 (1.6) 193 (20.7) Employment status Studying 93 (61.2) 52 (34.2) 7 (4.6) <.001 152 (16.3) Employed 174 (33.4) 325 (62.4) 22 (4.2) 521 (55.9) Unemployed 34 (54.0) 28 (44.4) 1 (1.6) 63 (6.8) Retired 48 (27.7) 122 (70.5) 3 (1.7) 173 (18.6) Other 8 (34.8) 14 (60.9) 1 (4.3) 23 (2.5) Level of education Primary school 37 (43.5) 47 (55.3) 1 (1.2) .60 85 (9.1) Secondary school 135 (36.9) 214 (58.5) 17 (4.6) 366 (39.3) University 173 (38.4) 261 (58.0) 16 (3.6) 450 (48.3) Other 12 (38.7) 19 (61.3) 0 (0.0) 31 (3.3) Disposable income (SEK ) <20,000 150 (48.7) 146 (47.4) 12 (3.9) <.001 308 (33.0) 20,000-40,000 157 (34.5) 284 (62.4) 14 (3.1) 455 (48.8) >40,000 50 (29.6) 111 (65.7) 8 (4.7) 169 (18.1) Loot box Yes 54 (51.9) 45 (43.3) 5 (4.8) .002 104 (11.2) No 208 (35.3) 363 (61.6) 18 (3.1) 589 (63.2) Missing 95 (39.7) 133 (55.6) 11 (4.6) 239 (25.6) Time at home Much more 269 (47.4) 284 (50.0) 15 (2.6) <.001 568 (60.9) Slightly more 71 (28.2) 168 (66.7) 13 (5.2) 252 (27.0) Unchanged 15 (14.0) 86 (80.4) 6 (5.6) 107 (11.5) Less time 2 (40.0) 3 (60.0) 0 (0.0) 5 (0.5) Change in alcohol habits More alcohol 49 (50.5) 43 (44.3) 5 (5.2) <.001 97 (10.4) Unchanged 133 (27.7) 339 (70.6) 8 (1.7) 480 (51.5) Less alcohol 117 (51.1) 98 (42.8) 14 (6.1) 229 (24.6) Does not drink 58 (46.0) 61 (48.4) 7 (5.6) 126 (13.5) Change in exercise habits https://games.jmir.org/2022/1/e33059 JMIR Serious Games 2022 | vol. 10 | iss. 1 | e33059 | p. 5 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Claesdotter-Knutsson et al Characteristics Gaming behavior, n (%) P value Participants, n (%) Increased Unchanged Decreased   More exercise 87 (35.5) 147 (60.0) 11 (4.5) <.001 245 (26.3) Unchanged 89 (29.1) 208 (68.0) 9 (2.9) 306 (32.8) Less exercise 168 (49.3) 161 (47.2) 12 (3.5) 341 (36.6) Never 13 (32.5) 25 (62.5) 2 (5.0) 40 (4.3) In school Yes 107 (59.4) 66 (36.7) 7 (3.9) <.001 180 (19.3) No 250 (33.2) 475 (63.2) 27 (3.6) 752 (80.7) School performance Unchanged 26 (47.3) 28 (50.9) 1 (1.8) .08 55 (5.9) Better 26 (65.0) 11 (27.5) 3 (7.5) 40 (4.3) Worse 55 (64.7) 27 (31.8) 3 (3.5) 85 (9.1) Missing 250 (33.2) 475 (63.2) 27 (3.6) 752 (80.7) School attendance Unchanged 61 (57.0) 44 (41.1) 2 (1.9) <.001 107 (11.5) Less 25 (69.4) 11 (30.6) 0 (0.0) 36 (3.9) More 21 (56.8) 11 (29.7) 5 (13.5) 37 (4.0) Missing 250 (33.2) 475 (63.2) 27 (3.6) 752 (80.7) PGS-I No problem with gambling 157 (32.6) 310 (64.3) 15 (3.1) <.001 482 (51.7) Low risk of gambling problems 40 (43.0) 48 (51.6) 5 (5.4) 93 (10.0) Moderate risk of gambling 39 (52.7) 34 (45.9) 1 (1.4) 74 (7.9) problems Gambling problems 18 (56.3) 14 (43.8) 0 (0.0) 32 (3.4) Missing 103 (41.0) 135 (53.8) 13 (5.2) 251 (26.9) Kessler-6 Score 0-4: no psychological 79 (20.8) 286 (75.3) 15 (3.9) <.001 380 (40.8) distress Score 5-24: moderate psycho- 273 (50.5) 251 (46.4) 17 (3.1) 541 (58.0) logical distress Missing 5 (45.5) 4 (36.4) 2 (18.2) 11 (1.2) GASA Addicted/problem gamer 40 (66.7) 17 (28.3) 3 (5.0) <.001 60 (6.4) Engaged gamer 36 (66.7) 17 (31.5) 1 (1.9) 54 (5.8) No problem 281 (34.4) 507 (62.0) 30 (3.7) 818 (87.8) N/A: not applicable. A currency exchange rate of SEK 1=US $0.11 is applicable. PGS-I: Problem Gambling Severity Index. GASA: Game Addiction Scale for Adolescents. Ages of 16 Years to 24 Years In this age group, we found significant differences in disposable income (P=.04), time spent at home (P=.002), and school attendance (P=.02; Table 3). https://games.jmir.org/2022/1/e33059 JMIR Serious Games 2022 | vol. 10 | iss. 1 | e33059 | p. 6 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Claesdotter-Knutsson et al Table 3. Gaming behavior by demographic characteristics among participants 16 years to 24 years of age. Characteristics Gaming behavior, n (%) P value Participants, n (%) Increased Unchanged Decreased   Total Total sample 87 (56.9) 59 (38.6) 7 (4.6) 153 (100) N/A Gender Female 34 (50.0) 31 (45.6) 3 (4.4) .27 68 (44.4) Male 53 (62.4) 28 (32.9) 4 (4.7) 85 (55.6) Employment status Studying 59 (57.3) 38 (36.9) 6 (5.8) .89 103 (67.3) Employed 22 (56.4) 16 (41.0) 1 (2.6) 39 (25.5) Unemployed 5 (62.5) 3 (37.5) 0 (0.0) 8 (5.2) Retired — — — Other 1 (33.3) 2 (66.7) 0 (0.0) 3 (2.0) Level of education Primary school 27 (71.1) 10 (26.3) 1 (2.6) .25 38 (24.8) Secondary school 43 (53.8) 32 (40.0) 5 (6.3) 80 (52.3) University 17 (48.6) 17 (48.6) 1 (2.9) 35 (22.9) Other — — — — Disposable income (SEK ) <20,000 64 (53.8) 48 (40.3) 7 (5.9) .04 119 (77.8) 20,000-40,000 23 (74.2) 8 (25.8) 0 (0.0) 31 (20.3) >40,000 0 (0.0) 3 (100) 0 (0.0) 3 (2.0) Loot box Yes 16 (69.6) 6 (26.1) 1 (4.3) .19  23 (15.0) No 33 (49.3) 32 (47.8) 2 (3.0) 67 (43.8) Missing 38 (60.3) 21 (33.3) 4 (6.3) 63 (41.2) Time at home Much more 68 (64.8) 35 (33.3) 2 (1.9) .002 105 (68.6) Slightly more 18 (47.4) 17 (44.7) 3 (7.9) 38 (24.8) Unchanged 0 (0.0) 7 (77.8) 2 (22.2) 9 (5.9) Less time 1 (100) 0 (0.0) 0 (0.0) 1 (0.7) Change in alcohol habits More alcohol 8 (42.1) 10 (52.6) 1 (5.3) .14 19 (12.4) Unchanged 24 (54.5) 20 (45.5) 0 (0.0) 44 (28.8) Less alcohol 35 (57.4) 23 (37.7) 3 (4.9) 61 (39.9) Does not drink 20 (69.0) 6 (20.7) 3 (10.3) 29 (19.0) Change in exercise habits More exercise 30 (53.6) 25 (44.6) 1 (1.8) .22 56 (36.6) Unchanged 13 (43.3) 15 (50.0) 2 (6.7) 30 (19.6) Less exercise 40 (67.8) 16 (27.1) 3 (5.1) 59 (38.6) Never 4 (50.0) 3 (37.5) 1 (12.5) 8 (5.2) In school https://games.jmir.org/2022/1/e33059 JMIR Serious Games 2022 | vol. 10 | iss. 1 | e33059 | p. 7 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Claesdotter-Knutsson et al Characteristics Gaming behavior, n (%) P value Participants, n (%) Increased Unchanged Decreased   Total Yes 58 (56.9) 39 (38.2) 5 (4.9) .96  102 (66.7) No 29 (56.9) 20 (39.2) 2 (3.9) 51 (33.3) School performance Unchanged 9 (36.0) 15 (60.0) 1 (4.0) .15 25 (16.3) Better 13 (61.9) 7 (33.3) 1 (4.8) 21 (13.7) Worse 36 (64.3) 17 (30.4) 3 (5.4) 56 (36.6) Missing 29 (56.9) 20 (39.2) 2 (3.9) 51 (33.3) School attendance Unchanged 30 (53.6) 25 (44.6) 1 (1.8) .02 56 (36.6) Less 16 (66.7) 8 (33.3) 0 (0.0) 24 (15.7) More 12 (54.5) 6 (27.3) 4 (18.2) 22 (14.4) Missing 29 (56.9) 20 (39.2) 2 (3.9) 51 (33.3) PGS-I No problem with gambling 24 (53.3) 20 (44.4) 1 (2.2) .76 45 (29.4) Low risk of gambling problems 7 (43.8) 8 (50.0) 1 (6.3) 16 (10.5) Moderate risk of gambling 13 (65.0) 7 (35.0) 0 (0.0) 20 (13.1) problems Gambling problems 2 (40.0) 3 (60.0) 0 (0.0) 5 (3.3) Missing 41 (61.2) 21 (31.3) 5 (7.5) 67 (43.8) Kessler-6 Score 0-4: no psychological 10 (40.0) 14 (56.0) 1 (4.0) .16 25 (16.3) distress Score 5-24: moderate psycho- 74 (60.7) 44 (36.1) 4 (3.3) 122 (79.7) logical distress Missing 3 (50.0) 1 (16.7) 2 (33.3) 6 (3.9) GASA Addicted/problem gamer 13 (76.5) 3 (17.6) 1 (5.9) .20 17 (11.1) Engaged gamer 9 (75.0) 3 (25.0) 0 (0.0) 12 (7.8) No problem 65 (52.4) 53 (42.7) 6 (4.8) 124 (81.0) N/A: not applicable. No responses to this category. A currency exchange rate of SEK 1=US $0.11 is applicable. PGS-I: Problem Gambling Severity Index. GASA: Game Addiction Scale for Adolescents. spent at home (P=.005), changes in alcohol consumption Ages of 25 Years to 39 Years (P=.003), changes in exercise habits (P=.001), whether the In this age group, we found significant differences in respondents were studying (P=.007), and Kessler score (P<.001; employment status (P=.004), disposable income (P=.01), time Table 4). https://games.jmir.org/2022/1/e33059 JMIR Serious Games 2022 | vol. 10 | iss. 1 | e33059 | p. 8 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Claesdotter-Knutsson et al Table 4. Gaming behavior by demographic characteristics among participants 25 years to 39 years of age.  Characteristics Gaming behavior, n (%) P value Participants, n (%) Increased Unchanged Decreased Total sample 146 (50.9) 126 (43.9) 15 (5.2) 287 (100) N/A Gender Female 81 (53.3) 65 (42.8) 6 (3.9) .48 152 (53.0) Male 65 (48.1) 61 (45.2) 9 (6.7) 135 (47.0) Employment status Studying 33 (76.7) 9 (20.9) 1 (2.3) .004 43 (15.0) Employed 93 (44.3) 104 (49.5) 13 (6.2) 210 (73.2) Unemployed 15 (68.2) 7 (31.8) 0 (0.0) 22 (7.7) Retired — — — Other 5 (41.7) 6 (50.0) 1 (8.3) 12 (4.2) Level of education Primary school 5 (50.0) 5 (50.0) 0 (0.0) .74 10 (3.5) Secondary school 53 (46.1) 54 (47.0) 8 (7.0) 115 (40.0) University 80 (54.1) 61 (41.2) 7 (4.7) 148 (51.6) Other 8 (57.1) 6 (42.9) 0 (0.0) 14 (4.9) Disposable income (SEK ) <20,000 52 (66.7) 21 (26.9) 5 (6.4) .01 78 (27.2) 20,000-40,000 82 (45.3) 91 (50.3) 8 (4.4) 181 (63.1) >40,000 12 (42.9) 14 (50.0) 2 (7.1) 28 (9.8) Loot box Yes 28 (58.3) 17 (35.4) 3 (6.3) .45 48 (16.7) No 84 (49.7) 77 (45.6) 8 (4.7) 169 (58.9) Missing 34 (48.6) 32 (45.7) 4 (5.7) 70 (24.4) Time at home Much more 105 (59.3) 65 (36.7) 7 (4.0) .005 177 (61.7) Slightly more 33 (38.8) 45 (52.9) 7 (8.2) 85 (29.6) Unchanged 8 (32.0) 16 (64.0) 1 (4.0) 25 (8.7) Less time — — —   — Change in alcohol habits More alcohol 20 (62.5) 9 (28.1) 3 (9.4) .003 32 (11.1) Unchanged 53 (41.4) 73 (57.0) 2 (1.6) 128 (44.6) Less alcohol 49 (58.3) 28 (33.3) 7 (8.3) 84 (29.2) Does not drink 24 (55.8) 16 (37.2) 3 (7.0) 43 (15.0) Change in exercise habits More exercise 32 (47.8) 29 (43.3) 6 (9.0) .001 67 (23.3) Unchanged 34 (37.4) 53 (58.2) 4 (4.4) 91 (31.7) Less exercise 77 (65.3) 37 (31.4) 4 (3.4) 118 (41.1) Never 3 (27.3) 7 (63.6) 1 (9.1) 11 (3.8) In school https://games.jmir.org/2022/1/e33059 JMIR Serious Games 2022 | vol. 10 | iss. 1 | e33059 | p. 9 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Claesdotter-Knutsson et al  Characteristics Gaming behavior, n (%) P value Participants, n (%) Increased Unchanged Decreased Yes 43 (68.3) 18 (28.6) 2 (3.2) .007 63 (22.0) No 103 (46.0) 108 (48.2) 13 (5.8) 224 (78.0) School performance Unchanged 15 (65.2) 8 (34.8) 0 (0.0) .13 23 (8.0) Better 10 (66.7) 3 (20.0) 2 (13.3) 15 (5.2) Worse 18 (72.0) 7 (28.0) 0 (0.0) 25 (8.7) Missing 103 (46.0) 108 (48.2) 13 (5.8) 224 (78.0) School attendance Unchanged 26 (66.7) 12 (30.8) 1 (2.6) .80 39 (13.6) Less 8 (80.0) 2 (20.0) 0 (0.0) 10 (3.5) More 9 (64.3) 4 (28.6) 1 (7.1) 14 (4.9) Missing 103 (46.0) 108 (48.2) 13 (5.8) 224 (78.0) PGS-I No problem with gambling 55 (45.8) 59 (49.2) 6 (5.0) .48 120 (41.8) Low risk of gambling problems 23 (56.1) 15 (36.6) 3 (7.3) 41 (14.3) Moderate risk of gambling 18 (58.1) 12 (38.7) 1 (3.2) 31 (10.8) problems Gambling problems 12 (66.7) 6 (33.3) 0 (0.0) 18 (6.3) Missing 38 (49.4) 34 (44.2) 5 (6.5) 77 (26.8) Kessler-6 Score 0-4: no psychological 21 (29.6) 43 (60.6) 7 (9.9) <.001 71 (24.7) distress Score 5-24: moderate psycho- 124 (57.7) 83 (38.6) 8 (3.7) 215 (74.9) logical distress Missing 1 (100) 0 (0.0) 0 (0.0) 1 (0.3) GASA Addicted/problem gamer 21 (65.6) 9 (28.1) 2 (6.3) .14 32 (11.1) Engaged gamer 16 (66.7) 7 (29.2) 1 (4.2) 24 (8.4) No problem 109 (47.2) 110 (47.6) 12 (5.2) 231 (80.5) N/A: not applicable. No responses to this category. A currency exchange rate of SEK 1=US $0.11 is applicable. PGS-I: Problem Gambling Severity Index. GASA: Game Addiction Scale for Adolescents. 0.27-0.68) and ≥60 years (OR 0.57, 95% CI 0.33-0.97) as well Comparison of Increased Gaming in Different as with much more time spent at home (OR 3.96, 95% CI Outcomes (Unweighted Data) 2.15-7.28). We also found a significant correlation with drinking The multivariable analysis using binary logistic regression less alcohol (OR 1.93, 95% CI 1.34-7.28) and self-reported not models of the potential predictors of increased changes (yes drinking alcohol (OR 1.66, 95% CI 1.05-2.61). Increased gaming versus no) are presented in the following sections. was also significantly correlated with a Kessler score greater than 5 (OR 2.44, 95% CI 1.73-3.44) and with the GASA All Age Groups categories of engaged gamer (OR 2.27, 95% CI 1.23-4.20) and Increased gaming was significantly negatively correlated with addicted/problem gamer (OR 2.37, 95% CI 1.26-4.47; Table the age group of 40 years to 59 years (OR 0.43, 95% CI 5). https://games.jmir.org/2022/1/e33059 JMIR Serious Games 2022 | vol. 10 | iss. 1 | e33059 | p. 10 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Claesdotter-Knutsson et al Table 5. The likelihood of increased gaming behavior (increased vs unchanged/decreased) among all participants (n=932). Characteristics OR (95% CI) Age groups (years) 16-24 Reference 25-39 0.97 (0.63-1.50) 40-59 0.43 (0.27-0.68) ≥60 0.57 (0.33-0.97) Time at home Unchanged Reference Much more 3.96 (2.15-7.28) Slightly more 1.72 (0.89-3.31) Less time 3.54 (0.44-28.28) Change in alcohol habits Unchanged Reference More alcohol 1.62 (0.99-2.67) Less alcohol 1.93 (1.34-2.78) Does not drink 1.66 (1.05-2.61) Kessler-6 Score 0-4: no psychological distress Reference Score 5-24: moderate psychological distress 2.44 (1.73-3.44) GASA No problem Reference Engaged gamer 2.27 (1.23-4.20) Addicted/problem gamer 2.37 (1.26-4.47) OR: odds ratio. GASA: Game Addiction Scale for Adolescents. Ages of 16 Years to 24 Years In this age group, no correlations were found (Table 6). https://games.jmir.org/2022/1/e33059 JMIR Serious Games 2022 | vol. 10 | iss. 1 | e33059 | p. 11 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Claesdotter-Knutsson et al Table 6. The likelihood of increased gaming behavior (increased vs unchanged/decreased) among participants 16 years to 24 years of age  Characteristics OR (95% CI) Disposable income (SEK ) <20,000 Reference 20,000-40,000 5.69 (0.5-65.0) >40,000 Loot box Yes Reference No 0.66 (0.15-2.89) Time at home Unchanged Reference Much more — Slightly more — Less time — School attendance Unchanged Reference Less 2.5 (0.61-10.3) More 1.39 (0.33-5.85) OR: odds ratio. A currency exchange rate of SEK 1=US $0.11 is applicable. Could not be estimated. increased gaming (OR 2.27, 95% CI 1.20-4.27), and Kessler Ages of 25 Years to 39 Years scores greater than 5 were positively correlated with a In this age group, employment was negatively correlated with self-reported increase in gaming activity (OR 2.36, 95% CI a self-reported increase in gaming (OR 0.41, 95% CI 0.18-0.92). 1.27-4.41; Table 7). Self-reporting less exercise was positively correlated with https://games.jmir.org/2022/1/e33059 JMIR Serious Games 2022 | vol. 10 | iss. 1 | e33059 | p. 12 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Claesdotter-Knutsson et al Table 7. The likelihood of increased gaming behavior (increased vs unchanged/decreased) among participants 25 years to 39 years of age. Characteristics OR (95% CI) Employment status Studying Reference Employed 0.41 (0.18-0.92) Unemployed 0.82 (0.24-2.74) Retired Other 0.30 (0.07-1.30) Change in alcohol habits Unchanged Reference More alcohol 1.49 (0.62-3.58) Less alcohol 1.61 (0.88-2.97) Does not drink 1.65 (0.77-3.55) Change in exercise habits Unchanged Reference More 1.29 (0.64-2.59) Less 2.27 (1.20-4.27) Never 0.60 (0.14-2.60) Time at home Unchanged Reference Much more 1.60 (0.59-4.32) Slightly more 0.94 (0.34-2.64) Less time — Kessler-6 Score 0-4: no psychological distress Reference Score 5-24: moderate psychological distress 2.36 (1.27-4.41) OR: odds ratio. Could not be estimated. correlated with a self-reported increase in gaming activity. Being Discussion employed (25–39-year-old age group) and being over 40 years of age (all age groups analyzed together) were negatively This cross-sectional study aimed to look at self-reported changes correlated with increased gaming. We did not find any in gaming behavior during the third wave of the COVID-19 significant correlations in the 16–24-year-old age group. pandemic in Sweden. We also wanted to look at potential risk factors for problematic gaming during the pandemic, including Comparison With Prior Work gaming patterns, gambling behavior, psychological distress, a Research from the early phases of the COVID-19 pandemic number of sociodemographic characteristics, health factors, and showed worrying figures for increased screen time among young school situation during the pandemic. We used data from a web people, raising questions about whether this would continue panel of 1501 respondents who answered questions on gaming and how it would affect the younger population [55-57]. Gaming and gambling. The results on gambling are presented elsewhere. disorder has been recognized as a public health problem of Principal Findings importance, but the majority of people who engage in gaming do not fulfil the criteria for gaming disorder [58]. In most We found several factors associated with changed gaming studies, the overall prevalence of gaming disorder is around 3% behavior, but on further analysis, only psychological distress [48]. In our study, 62% of respondents self-reported that they (all age groups analyzed together and the 25–39-year-old age sometimes played video games. We found 38% self-reported group), drinking less alcohol (all age groups analyzed together), an increase in gaming since the start of the COVID-19 spending more time at home (all age groups analyzed together), pandemic. When looking at gaming and possible changes in the gaming problems (all age groups analyzed together), and younger population, in the 16–24-year-old age group, 57% exercising less (25–39-year-old age group) were positively self-reported an increase in gaming, and in the 25–39-year-old https://games.jmir.org/2022/1/e33059 JMIR Serious Games 2022 | vol. 10 | iss. 1 | e33059 | p. 13 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Claesdotter-Knutsson et al age group, 51% reported an increase in gaming. Other whole school day, the children in the other countries did their researchers have seen similar results. Frequencies and screen schoolwork under their parents’ supervision [56,57]. time had increased during the COVID-19 pandemic when In India, which has seen intermittent total lockdowns throughout Paschke et al [59] looked at those aged 10 years to 17 years and the pandemic, meaning people have had to stay at home, an compared their usage frequency and screen time from before increase in gaming has been seen in those aged 25 years to 35 the pandemic with their behavior in lockdown. Lemenager et years [14]. There have also been public health efforts by the al [60] found the same tendency: 71.4% of participants estimated WHO to encourage people to engage in gaming a general increase in their online media consumption during the (#PlayApartTogether) to promote social distancing and prevent lockdown, and some 10% self-reported a rise in gaming activity the virus spread [21]. In our study, looking at all age groups during the COVID-19 pandemic, of whom men aged between together, we saw a correlation between staying home much 18 years and 24 years showed the highest increase in gaming. more and self-reporting an increase in gaming. The relationship Studies from before the COVID-19 pandemic had shown the was not found in the 2 age groups under 40 years of age. It is same tendency, with young men reported to play computer important to bear in mind that our sample was rather limited in games more frequently and for longer duration [61,62], making size, and possibly a larger sample size would show additional them vulnerable to developing addictive gaming behaviors correlations. [63-65]. Balhara et al [58] looked at university students’ gaming behavior during the COVID-19 pandemic and found those who Being 40 years old or more seemed to reduce the reporting of used gaming as a tool to reduce stress showed an increase in increased gaming, not surprisingly since gaming is more gaming activity during the pandemic. Before the COVID-19 common in younger people [61,62]. We found a positive pandemic, it was known that gamers who gave escapism (a correlation between less exercise and increased gaming in the coping technique to handle negative emotions) as a reason for age group of 25 years to 39 years. Sedentary lifestyles and gaming were more commonly problem gamers [66,67]. increased gaming have been known to co-occur during the COVID-19 pandemic, making researchers call for parents, When analyzing the whole sample who reported increased schools, and decision makers to mandate physical activity and gaming, we found positive associations with engaged gaming keep outdoor facilities open as long as possible, even in but also with addicted/problem gaming; this relationship was lockdowns [55,75]. Psychological distress was positively not seen in the 2 younger groups. Increased time spent gaming associated with increased gaming in the whole sample as well is a risk factor for developing gaming problems/addiction as in those aged 25 years to 39 years. The association of [63-65]. Previous studies have shown a huge male predominance excessive gaming with comorbidities such as sleeping disorders, in problematic gaming behavior [27,68,69]. Men have been obesity, depression, and anxiety is well known [68,76-78], reported to play computer games more frequently and for longer marking out the group who self-reported increased gaming and duration [61,62], whereas there is a female predominance in psychological distress as vulnerable and a focus of concern. smartphone use [70]. Massively multiplayer online role-playing The same was observed by King et al [18]: Some individuals games (MMORPGs) and multiplayer online battle arenas may develop an increasingly unhealthy pattern of gaming due (MOBAs), both with a predominance of male players, have to pandemic-related psychological distress, because they find been found to have an addictive potential due to their specific gaming relieves stress. We found respondents aged 25 years to structural characteristics of advancement and social interactions 39 years who reported increased gaming during the pandemic [28,29,71-74]. Researchers have also looked at the cortical were less likely to be in work. Unemployment might facilitate region and found that gender differences in IGD might be their gaming activities, enabling them to spend more time at associated with different cortical thickness in and around the home, possibly under greater stress. posterior cingulate cortex, the region thought to be involved in cognitive control and reward/loss processing and hence thought When examining those aged 16 years to 24 years who reported to play a role in addiction [68]. These are plausible explanations increased gaming, we did not find any correlations with for the gender differences. In this study, we did not see that associated factors. One possible reason why those in this age gender was associated with self-reported increased gaming, group who reported increased gaming did not seem to suffer possibly because the numbers were too small. from psychological distress could be that gaming involves social aspects and thus increased their ability to cope with social During the COVID-19 pandemic, the media, schools, and isolation in a functional way. This would seem to be confirmed parents all wondered whether remote learning would see by a report by Bora [14] showing higher usage in multiplayer schoolchildren spending more time using digital media, not modes. Playing video games together helped people reduce including homework. This was to some extent true for April feelings of loneliness and stress and to stay in touch with friends 2020 to June 2020, when a 69%-76% rise in online media use [58]. was reported for children in the United Kingdom, Spain, and Belgium compared with a 36% rise in the Swedish population Gaming was already viewed by some in a positive light for [56,57]. Although not immediately comparable, the difference helping to develop cognitive skills such as reasoning, spatial can to some extent be explained by the fact that, in the Swedish awareness, and problem-solving [74], and it is now also material, the majority of pupils were aged 13 years to 16 years, considered a way of maintaining social contact during lockdown. compared with an age range of 5 years to 12 years in the other It would be desirable for authorities to issue recommendations countries, and while the Swedish children were online for the for preferred types of video games that enhance social activity https://games.jmir.org/2022/1/e33059 JMIR Serious Games 2022 | vol. 10 | iss. 1 | e33059 | p. 14 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Claesdotter-Knutsson et al and physical health while avoiding the pitfalls of unhealthy Strengths gaming. Despite the extreme interest in the possible increase in gaming during the COVID-19 pandemic, whether in popular science or Limitations more clinical and scientific contexts, there is still a significant This study has several limitations. The study was based on a lack of studies focusing on the younger population. This study self-assessment questionnaire, rather than a standardized, contributes important information about possible changes in structured clinical interview that would allow a more accurate gaming behavior during the COVID-19 pandemic. assessment. Against that, questionnaires are widely used in epidemiological studies, and in other studies, they have been Conclusion considered to give a satisfactory picture of the situation [39,41]. Those who reported increased gaming during the COVID-19 Our data were based on a web panel survey, and although the pandemic were more likely to be 16 years to 39 years old. In study sample was designed and weighted to represent the general those aged 16 years to 24 years, increased gaming was not population, it is hard to know whether the respondents’ original associated with any risk factors. In the 25–39-year-old age choice to enroll in a web panel is associated with other group, the increase was associated with psychological distress, characteristics and, in this case, with gaming habits that differ reporting less exercise, and being unemployed. COVID-19 may from those of their peers in the general population. Our sample present a risk factor for increased online gaming in a small but size is too small to draw generalized conclusions. vulnerable group. More research and preferably longitudinal studies are needed in the field of gaming and the effects of the COVID-19 pandemic. Conflicts of Interest AH has a researcher position at Lund University, which is sponsored by the Swedish state-owned gambling operator, AB Svenska Spel. 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BMC Psychiatry 2017 Jul 19;17(1):260 [FREE Full text] [doi: 10.1186/s12888-017-1408-x] [Medline: 28724403] Abbreviations DSM-5: Diagnostic and Statistical Manual of Mental Disorders GASA: game addiction scale for adolescents ICC: International Chamber of Commerce ICD: International Classification of Diseases IGD: internet gaming disorder https://games.jmir.org/2022/1/e33059 JMIR Serious Games 2022 | vol. 10 | iss. 1 | e33059 | p. 18 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Claesdotter-Knutsson et al MMORPG: massive multiplayer online role-playing game MOBA: multiplayer online battle arena OR: odds ratio PGS-I: Problem Gambling Severity Index WHO: World Health Organization Edited by N Zary; submitted 23.08.21; peer-reviewed by Z Yan; comments to author 18.09.21; revised version received 07.10.21; accepted 13.11.21; published 25.01.22 Please cite as: Claesdotter-Knutsson E, André F, Håkansson A Gaming Activity and Possible Changes in Gaming Behavior Among Young People During the COVID-19 Pandemic: Cross-sectional Online Survey Study JMIR Serious Games 2022;10(1):e33059 URL: https://games.jmir.org/2022/1/e33059 doi: 10.2196/33059 PMID: 34817386 ©Emma Claesdotter-Knutsson, Frida André, Anders Håkansson. Originally published in JMIR Serious Games (https://games.jmir.org), 25.01.2022. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Serious Games, is properly cited. The complete bibliographic information, a link to the original publication on https://games.jmir.org, as well as this copyright and license information must be included. https://games.jmir.org/2022/1/e33059 JMIR Serious Games 2022 | vol. 10 | iss. 1 | e33059 | p. 19 (page number not for citation purposes) XSL FO RenderX http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png JMIR Serious Games JMIR Publications

Gaming Activity and Possible Changes in Gaming Behavior Among Young People During the COVID-19 Pandemic: Cross-sectional Online Survey Study

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JMIR Publications
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2291-9279
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10.2196/33059
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Abstract

Background: Young people’s daily lives and social interactions changed remarkably during the COVID-19 pandemic as schools and cinemas closed, leisure activities were cancelled, and gatherings were regulated. Questions have been raised by the media, schools, policy makers, and research communities about the effect on young people’s online behaviors. Objective: This cross-sectional study aimed to study self-reported changes in gaming, focusing on a younger section of the population during the COVID-19 pandemic in Sweden. We also wanted to look at potential risk factors behind problematic gaming during the pandemic, including gaming patterns, gambling behavior, psychological distress, certain sociodemographic characteristics, health factors, and school situation. Methods: This was an anonymous online survey study of web panel participants in Sweden (n=1501) to study changes in gaming behaviors during the COVID-19 pandemic. Self-reported increases in gaming were analyzed in logistic regression analyses against sociodemographic and health factors. Results: Within the study population that reported changes in gaming activity, we found significant differences in age, employment status, disposable income, whether they ever played on loot boxes, time spent at home, school attendance, psychological distress, and gambling and gaming problems, as well as significant differences in changes in alcohol consumption and exercise habits. When examining the 16–24-year-old age group who reported changes in gaming activity, we found significant differences within the group in disposable income, time at home, and school attendance. When examining the 25–39-year-old age group who reported changes in gaming activity, we found significant differences within the group in employment status, disposable income, time spent at home, whether the respondents were studying, school attendance level, psychological distress, and gaming problems, as well as significant differences in changes in alcohol consumption and exercise habits. Psychological distress (all age groups analyzed together; 25–39-year-old age group), drinking less alcohol (all age groups analyzed together), spending more time at home (all age groups analyzed together), gaming problems, and exercising less (25–39-year-old age group) were positively correlated with a self-reported increase in gaming activity. Being employed (25–39-year-old age group) and being over 40 years of age (all age groups analyzed together) were negatively correlated with increased gaming. We found no significant correlations in the 16–24-year-old age group. Conclusions: Those who reported increased gaming during the COVID-19 pandemic were more likely to be 16 years to 39 years old. In the age group of 25 years to 39 years old, the increase was associated with psychological distress, reporting less exercise, and being unemployed. COVID-19 may present as a risk factor of increased online gaming in a small but vulnerable group. More research and preferably longitudinal studies are needed in the field of gaming and effects of the COVID-19 pandemic. (JMIR Serious Games 2022;10(1):e33059) doi: 10.2196/33059 https://games.jmir.org/2022/1/e33059 JMIR Serious Games 2022 | vol. 10 | iss. 1 | e33059 | p. 1 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Claesdotter-Knutsson et al KEYWORDS COVID-19 pandemic; gaming; screen time; psychological distress interventions [35]. Sweden’s approach to prevent virus spread Introduction has not included lockdown or stay-at-home orders but rather recommendations. Most workplaces encouraged their workers The index case of the now widespread COVID-19 pandemic to work from home, and regulations in opening hours have been caused by the SARS-CoV-2 virus originated in Wuhan, the in effect for places such as restaurants and shopping malls, capital of Hubei Province, China, on December 8, 2019 [1]. On leading people in general to spend a lot more time inside with January 31, 2020, the first confirmed case of SARS-CoV-2 viral possible access to screens. infection was recorded in Sweden. In subsequent months, COVID-19 reached most countries in Europe [2], and on March Young people’s daily lives and social interactions have changed 11, 2020, the World Health Organization (WHO) declared the remarkably in the COVID-19 pandemic because of school and outbreak a pandemic. cinema closures, the cancellation of leisure activities, and restrictions on gatherings. Without the daily routine of going As well as physiological harm, the COVID-19 pandemic has to school, young people had unlimited opportunities to play also had an enormous effect on people’s mental health [3,4]. video games, making them vulnerable to developing problems Research has confirmed that rates of depression, addiction, related to excessive gaming. anxiety, and other psychiatric disorders rose during the pandemic [5-10]. Research has shown an increase in screen time and the Internet gaming disorder (IGD), it should be noted, is included consumption of digital entertainment during the pandemic, in the 11th re vision of the International Classification of particularly online gaming and related activities such as video Diseases (ICD 11), defined as a gaming behavior of sufficient game streaming [4,9,11-13]. This phenomenon has been seen severity to result in significant impairment in areas of function worldwide. In India, for example, WinZO Games reported 300% [36]. However, the Diagnostic and Statistical Manual of Mental greater user engagement, 30% higher traffic in online mobile Disorders (DSM-5) has described IGD as “necessitating further gaming, and 35% higher usage in multiplayer modes [14], and clinical experience and research before inclusion as a formal another Indian mobile-based online gaming platform reported disorder” [37]. an almost 200% increase in the user base during the pandemic, With regard to gambling habits, there has also been considerable with 75,000 new users [15]. A 70% increase in Fortnite gaming research on the impact of the COVID-19 pandemic [38]. Our has been seen in Italy [16]. In the United States, one of the research in this area has shown that the increase in gambling in largest telecom providers reported a 75% rise in online activity the general population in Sweden is limited, but the group who [17]. In 2020, the world’s largest video game digital distribution does report increased gambling activity also reports psychosocial service, Steam, reported 20 million active users, an all-time problems [38-41]. Researchers have raised the concern that high [18]. The WHO has supported gaming as an activity that more time spent at home could increase gaming and its possible promotes social distancing and reduces the loneliness that might negative side effects, but there is very little research on the follow, including the gaming industry’s media campaign actual situation [18]. We thus set out to see if gaming habits #PlayApartTogether, which mixes guidelines to prevent the changed during the COVID-19 pandemic in the same way as spread of COVID-19 with messages encouraging online gaming gambling habits. [19-22]. It has been suggested that online gaming could be psychosocially beneficial to young people—cognitively, The aim of this cross-sectional study was thus to study motivationally, emotionally, and socially [23,24]—and self-reported changes in gaming during the COVID-19 especially so in the COVID-19 pandemic, when pandemic, focusing on a younger section of the Swedish recommendations and regulations to stop the spread of the virus population. We also considered the potential risk factors for have affected everyone’s lives. Gaming has been shown to problematic gaming during the pandemic, including gaming reduce loneliness [25,26] and, even though concerns have been patterns, gambling behavior, psychological distress, certain raised about its addictive potential, research has shown that sociodemographic characteristics, health factors, and school frequent gaming does not have to be problematic [27] and most situation. gamers’ gaming habits are changeable and not fixed [28,29]. It has also been suggested that gaming could work as a coping Methods mechanism against stress [30,31]. However, excessive screen time has been shown to be associated with a range of negative Setting mental health outcomes, including anxiety and depression, in The first wave of the COVID-19 pandemic in Sweden was in adolescents considered particularly vulnerable to a problematic the spring of 2020. In the summer of 2020, virus transmission use of online media [32-34]. Softening its excessively positive decreased, and the second wave started in the autumn of 2020. initial message about gaming, the WHO’s mental health After only a partial decrease in virus transmission in the winter information (#HealthyAtHome—Mental Health) recommended of 2020-2021, early 2021 saw a third wave of virus transmission a balance between time spent on screens—and gaming in and an increased hospital-admitted disease burden in Sweden particular—and offline activities [22]. [42]. This study was cross-sectional and based on a self-report online survey study carried out in Sweden in March 2021 during Governmental virus protection strategies have differed around the third wave of the ongoing COVID-19 pandemic in the the world from recommendations to more mandatory https://games.jmir.org/2022/1/e33059 JMIR Serious Games 2022 | vol. 10 | iss. 1 | e33059 | p. 2 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Claesdotter-Knutsson et al country. At the time of the study, secondary schools in Sweden attendance had been affected by the remote learning situation had started to open up for on-site teaching, but the national during the COVID-19 pandemic (less absence, more absence, COVID-19 strategies regarding leisure activities and restrictions or not affected). The final questions were about gaming, on gatherings of more than 8 people were still in effect. specifically whether their personal gaming habits had changed, excluding games for money (more, no change, less, I do not Participants and Procedures engage in gaming). We also asked about loot boxes: whether We used the market survey company Ipsos and their online web in the last 12 months they had engaged in gaming involving survey panel. Ipsos is a market survey company that has broad currency inside a video game, with the purpose to either gain experience in conducting survey studies in the area of addictive money or advantages in the video game (yes, no). disorders. Ipsos abides by the International Chamber of The Game Addiction Scale for Adolescents (GASA) is one of Commerce (ICC)/ESOMAR Code. The Ipsos web panel has the most frequently used questionnaires for gaming addiction previously been used for online surveys in the course of our [44-47]. The scale was theoretically based on the DSM-5 criteria research [43]. In this study, we invited respondents from the for pathological gambling [47]. Each question covers 1 criterion, general population, aged 16 years and older. Participants for answered on a 5-point continuum scale: 1 (never), 2 (rarely), 3 the Ipsos web panel were invited with the information that the (sometimes), 4 (often), 5 (very often). It should, according to survey would address “computer gaming, gambling for money the developer, be accounted as endorsed when rated 3 or higher. and other behavioral patterns in Sweden during The DSM-5 requires half (or more) of their criteria to be met, COVID-19—association with mental health, social situation while scholars within the field of gaming suggest a ranking of and attitudes to the pandemic.” The survey was made accessible the criteria: the “core approach.” The criteria tolerance, mood only when the respondent provided electronic informed consent. modification, and cognitive salience are said to be associated Participants on the Ipsos web panel enroll voluntarily to take with engagement rather than addiction, while the contrary market surveys, political opinion polls, and similar surveys, for applies for the criteria withdrawal, relapse, conflict, and which they earn points they can redeem as goods or services. problems [47-49]. In order to distinguish between levels of Most surveys are worth a point and each point is worth severity within the group of gamers, the core approach was approximately €1 (US $1.14). The survey is sent out to different applied, whereby the individuals who met all the core criteria age groups of web panel participants until a gender and age (relapse, withdrawal, conflict, and problems) constituted the distribution close to that of the general population is achieved. group addicted gamers. The respondents who endorsed 2 or 3 In this study, invitations were sent until some 1500 complete of the core criteria but none of the peripheral criteria (salience, answers were obtained. In addition, in this study, the final tolerance, mood modification) were grouped as problem gamers, distribution of age groups, gender, and geographical location and those that endorsed all 3 of the peripheral criteria but not (regions) was compared by Ipsos with those of the general more than one of the core criteria were grouped as engaged population, such that the data set was weighted according to a gamers [46,50]. The remaining respondents comprised the group summarized weighting score derived from these 3 variables. “noproblem” gamers, as individuals below the cut-off for When the survey was halted, the final sample consisted of 1501 engaged gaming. individuals. The study was carried out from March 19, 2021 through March 29, 2021. The study was reviewed and approved Since both the problem gamers and the addicted gamers were by the Swedish Ethical Review Board (File: 2021/00369). assumed to be associated with more severe gaming behavior as well as more negative outcomes [46,51], these 2 groups also Measures constituted one combined group (2-4 endorsed core criteria): Basic sociodemographic variables comprised gender (female addicted/problem gamers. or male), age (divided into 2 age groups: 16-24 years; ≥25 Psychological distress was measured using the Kessler-6 scale years), monthly income (divided into 3 groups: SEK [52]. This scale measures symptoms of depression and anxiety 10,000-20,000 [US $1107.48-$2214.97]; SEK 20,000–40,000 perceived during the past 6 months. The Kessler scores (0-4 for [US $2214.97-$4429.94]; SEK ≥40,000 [US ≥$4429.94]), level each question) were summed, and a total score of 5 or more was of education (university, secondary school [age 16-19 years], classified as psychological distress [52]. primary school [age 6-16 years], other), employment status (studying, employed, unemployed, retired, other). The The level of potential gambling problems was measured with questionnaire began with questions about changes in the the 9-item Problem Gambling Severity Index (PGS-I) [53], respondent’s personal behavior during the COVID-19 pandemic where each of the statements addresses the preceding 12 months. (“since these changes in Sweden started”); whether they, during For the PGS-I scale, the scores of 0-3 for each question were this period, had spent more or less time at home (much more, summed: A score of 0 indicated no problem with gambling; a slightly more, unchanged, or less time at home); and whether score of 1-2 indicated a low risk of gambling problems; 3-7 they had consumed more or less alcohol (more, less, unchanged, indicated a moderate risk of gambling problems; and 8 or more or don’t drink at all). Thereafter, questions were asked regarding indicated gambling problems. schooling situation—whether the respondents attended school Statistical Analysis of any kind including university (yes or no) and whether their level of achievement had been affected by the remote learning Weighting adjustments were applied to compensate for situation during the pandemic (for the better, for the worse, or nonresponse and noncoverage and to make the sample estimates not affected). Finally, the participants were asked if their school conform to external values. The reporting of prevalence https://games.jmir.org/2022/1/e33059 JMIR Serious Games 2022 | vol. 10 | iss. 1 | e33059 | p. 3 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Claesdotter-Knutsson et al measures and group-wise comparisons related to the weighted 37.9% (569/1501) answered they had never played video games, data and statistical tests were applied using the Chi-square test, neither now nor before the pandemic, and were therefore while binary logistic regression analyses were carried out using removed from further analysis. The remaining study population the unweighted data. Binary logistic regression analyses were (932/1501) was used for further analysis. We chose to study carried out with increased changes in gaming behavior (yes/no) the younger section of the population. Of the population who as the dependent variable in order to study potential independent did game, 16.4% (153/932) were in the 16–24-year-old age variables associated with the outcome. For all models, odds group, and 30.8% (287/932) were in the 25–39-year-old age ratios (ORs) with 95% CIs are presented. For all statistical group. Gender was equally distributed in both age groups. We analyses, SPSS version 25.0 (IBM Corp, Armonk, NY) was then looked at the various demographic and socioeconomic used [54]. factors described in the previous sections and their relationships with self-reported changes in gaming activity (increased, Results decreased, unchanged). Descriptive Data on Changes in Gaming Behavior by Demographic and Socioeconomic Factors (Weighted Data) Overview The descriptive data for the sample are shown in Table 1. The questionnaire was answered by 1501 participants, of whom Table 1. Survey respondents and the final study populations by age group. Characteristics Total sample (n=1501), n (%) Age groups 16-24 years (n=190), n (%) 25-39 years (n=392), n (%) Never gamed 569 (37.9) 37 (19.5) 105 (26.8) Study population 932 (62.1) 153 (80.5) 287 (73.2) Gender Female 452 (48.5) 68 (44.4) 152 (53.0) Male 480 (51.5) 85 (55.6) 135 (47.0) (P=.002), time spent at home (P<.001), changes in alcohol All Age Groups consumption (P<.001), changes in exercise habits (P<.001), We found significant differences according to age group whether the respondents were studying (P<.001), school (P<.001), employment status (P<.001), disposable income attendance (P<.001), Kessler score (P<.001), PGS-I score (P<.001), whether respondents ever played on loot boxes (P<.001), and GASA score (P<.001; Table 2). https://games.jmir.org/2022/1/e33059 JMIR Serious Games 2022 | vol. 10 | iss. 1 | e33059 | p. 4 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Claesdotter-Knutsson et al Table 2. Gaming behavior by demographic characteristics among all participants. Characteristics Gaming behavior, n (%) P value Participants, n (%) Increased Unchanged Decreased   Total sample 357 (38.3) 541 (58.0) 34 (3.6) 932 (100) N/A Gender Female 179 (39.6) 259 (57.3) 14 (3.1) .55 452 (48.5) Male 178 (37.1) 282 (58.8) 20 (4.2) 480 (51.5) Age group (years) 16-24 87 (56.9) 59 (38.6) 7 (4.6) <.001 153 (16.4) 25-39 146 (50.9) 126 (43.9) 15 (5.2) 287 (30.8) 40-59 77 (25.8) 213 (71.2) 9 (3.0) 299 (32.1) ≥60 47 (24.4) 143 (74.1) 3 (1.6) 193 (20.7) Employment status Studying 93 (61.2) 52 (34.2) 7 (4.6) <.001 152 (16.3) Employed 174 (33.4) 325 (62.4) 22 (4.2) 521 (55.9) Unemployed 34 (54.0) 28 (44.4) 1 (1.6) 63 (6.8) Retired 48 (27.7) 122 (70.5) 3 (1.7) 173 (18.6) Other 8 (34.8) 14 (60.9) 1 (4.3) 23 (2.5) Level of education Primary school 37 (43.5) 47 (55.3) 1 (1.2) .60 85 (9.1) Secondary school 135 (36.9) 214 (58.5) 17 (4.6) 366 (39.3) University 173 (38.4) 261 (58.0) 16 (3.6) 450 (48.3) Other 12 (38.7) 19 (61.3) 0 (0.0) 31 (3.3) Disposable income (SEK ) <20,000 150 (48.7) 146 (47.4) 12 (3.9) <.001 308 (33.0) 20,000-40,000 157 (34.5) 284 (62.4) 14 (3.1) 455 (48.8) >40,000 50 (29.6) 111 (65.7) 8 (4.7) 169 (18.1) Loot box Yes 54 (51.9) 45 (43.3) 5 (4.8) .002 104 (11.2) No 208 (35.3) 363 (61.6) 18 (3.1) 589 (63.2) Missing 95 (39.7) 133 (55.6) 11 (4.6) 239 (25.6) Time at home Much more 269 (47.4) 284 (50.0) 15 (2.6) <.001 568 (60.9) Slightly more 71 (28.2) 168 (66.7) 13 (5.2) 252 (27.0) Unchanged 15 (14.0) 86 (80.4) 6 (5.6) 107 (11.5) Less time 2 (40.0) 3 (60.0) 0 (0.0) 5 (0.5) Change in alcohol habits More alcohol 49 (50.5) 43 (44.3) 5 (5.2) <.001 97 (10.4) Unchanged 133 (27.7) 339 (70.6) 8 (1.7) 480 (51.5) Less alcohol 117 (51.1) 98 (42.8) 14 (6.1) 229 (24.6) Does not drink 58 (46.0) 61 (48.4) 7 (5.6) 126 (13.5) Change in exercise habits https://games.jmir.org/2022/1/e33059 JMIR Serious Games 2022 | vol. 10 | iss. 1 | e33059 | p. 5 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Claesdotter-Knutsson et al Characteristics Gaming behavior, n (%) P value Participants, n (%) Increased Unchanged Decreased   More exercise 87 (35.5) 147 (60.0) 11 (4.5) <.001 245 (26.3) Unchanged 89 (29.1) 208 (68.0) 9 (2.9) 306 (32.8) Less exercise 168 (49.3) 161 (47.2) 12 (3.5) 341 (36.6) Never 13 (32.5) 25 (62.5) 2 (5.0) 40 (4.3) In school Yes 107 (59.4) 66 (36.7) 7 (3.9) <.001 180 (19.3) No 250 (33.2) 475 (63.2) 27 (3.6) 752 (80.7) School performance Unchanged 26 (47.3) 28 (50.9) 1 (1.8) .08 55 (5.9) Better 26 (65.0) 11 (27.5) 3 (7.5) 40 (4.3) Worse 55 (64.7) 27 (31.8) 3 (3.5) 85 (9.1) Missing 250 (33.2) 475 (63.2) 27 (3.6) 752 (80.7) School attendance Unchanged 61 (57.0) 44 (41.1) 2 (1.9) <.001 107 (11.5) Less 25 (69.4) 11 (30.6) 0 (0.0) 36 (3.9) More 21 (56.8) 11 (29.7) 5 (13.5) 37 (4.0) Missing 250 (33.2) 475 (63.2) 27 (3.6) 752 (80.7) PGS-I No problem with gambling 157 (32.6) 310 (64.3) 15 (3.1) <.001 482 (51.7) Low risk of gambling problems 40 (43.0) 48 (51.6) 5 (5.4) 93 (10.0) Moderate risk of gambling 39 (52.7) 34 (45.9) 1 (1.4) 74 (7.9) problems Gambling problems 18 (56.3) 14 (43.8) 0 (0.0) 32 (3.4) Missing 103 (41.0) 135 (53.8) 13 (5.2) 251 (26.9) Kessler-6 Score 0-4: no psychological 79 (20.8) 286 (75.3) 15 (3.9) <.001 380 (40.8) distress Score 5-24: moderate psycho- 273 (50.5) 251 (46.4) 17 (3.1) 541 (58.0) logical distress Missing 5 (45.5) 4 (36.4) 2 (18.2) 11 (1.2) GASA Addicted/problem gamer 40 (66.7) 17 (28.3) 3 (5.0) <.001 60 (6.4) Engaged gamer 36 (66.7) 17 (31.5) 1 (1.9) 54 (5.8) No problem 281 (34.4) 507 (62.0) 30 (3.7) 818 (87.8) N/A: not applicable. A currency exchange rate of SEK 1=US $0.11 is applicable. PGS-I: Problem Gambling Severity Index. GASA: Game Addiction Scale for Adolescents. Ages of 16 Years to 24 Years In this age group, we found significant differences in disposable income (P=.04), time spent at home (P=.002), and school attendance (P=.02; Table 3). https://games.jmir.org/2022/1/e33059 JMIR Serious Games 2022 | vol. 10 | iss. 1 | e33059 | p. 6 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Claesdotter-Knutsson et al Table 3. Gaming behavior by demographic characteristics among participants 16 years to 24 years of age. Characteristics Gaming behavior, n (%) P value Participants, n (%) Increased Unchanged Decreased   Total Total sample 87 (56.9) 59 (38.6) 7 (4.6) 153 (100) N/A Gender Female 34 (50.0) 31 (45.6) 3 (4.4) .27 68 (44.4) Male 53 (62.4) 28 (32.9) 4 (4.7) 85 (55.6) Employment status Studying 59 (57.3) 38 (36.9) 6 (5.8) .89 103 (67.3) Employed 22 (56.4) 16 (41.0) 1 (2.6) 39 (25.5) Unemployed 5 (62.5) 3 (37.5) 0 (0.0) 8 (5.2) Retired — — — Other 1 (33.3) 2 (66.7) 0 (0.0) 3 (2.0) Level of education Primary school 27 (71.1) 10 (26.3) 1 (2.6) .25 38 (24.8) Secondary school 43 (53.8) 32 (40.0) 5 (6.3) 80 (52.3) University 17 (48.6) 17 (48.6) 1 (2.9) 35 (22.9) Other — — — — Disposable income (SEK ) <20,000 64 (53.8) 48 (40.3) 7 (5.9) .04 119 (77.8) 20,000-40,000 23 (74.2) 8 (25.8) 0 (0.0) 31 (20.3) >40,000 0 (0.0) 3 (100) 0 (0.0) 3 (2.0) Loot box Yes 16 (69.6) 6 (26.1) 1 (4.3) .19  23 (15.0) No 33 (49.3) 32 (47.8) 2 (3.0) 67 (43.8) Missing 38 (60.3) 21 (33.3) 4 (6.3) 63 (41.2) Time at home Much more 68 (64.8) 35 (33.3) 2 (1.9) .002 105 (68.6) Slightly more 18 (47.4) 17 (44.7) 3 (7.9) 38 (24.8) Unchanged 0 (0.0) 7 (77.8) 2 (22.2) 9 (5.9) Less time 1 (100) 0 (0.0) 0 (0.0) 1 (0.7) Change in alcohol habits More alcohol 8 (42.1) 10 (52.6) 1 (5.3) .14 19 (12.4) Unchanged 24 (54.5) 20 (45.5) 0 (0.0) 44 (28.8) Less alcohol 35 (57.4) 23 (37.7) 3 (4.9) 61 (39.9) Does not drink 20 (69.0) 6 (20.7) 3 (10.3) 29 (19.0) Change in exercise habits More exercise 30 (53.6) 25 (44.6) 1 (1.8) .22 56 (36.6) Unchanged 13 (43.3) 15 (50.0) 2 (6.7) 30 (19.6) Less exercise 40 (67.8) 16 (27.1) 3 (5.1) 59 (38.6) Never 4 (50.0) 3 (37.5) 1 (12.5) 8 (5.2) In school https://games.jmir.org/2022/1/e33059 JMIR Serious Games 2022 | vol. 10 | iss. 1 | e33059 | p. 7 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Claesdotter-Knutsson et al Characteristics Gaming behavior, n (%) P value Participants, n (%) Increased Unchanged Decreased   Total Yes 58 (56.9) 39 (38.2) 5 (4.9) .96  102 (66.7) No 29 (56.9) 20 (39.2) 2 (3.9) 51 (33.3) School performance Unchanged 9 (36.0) 15 (60.0) 1 (4.0) .15 25 (16.3) Better 13 (61.9) 7 (33.3) 1 (4.8) 21 (13.7) Worse 36 (64.3) 17 (30.4) 3 (5.4) 56 (36.6) Missing 29 (56.9) 20 (39.2) 2 (3.9) 51 (33.3) School attendance Unchanged 30 (53.6) 25 (44.6) 1 (1.8) .02 56 (36.6) Less 16 (66.7) 8 (33.3) 0 (0.0) 24 (15.7) More 12 (54.5) 6 (27.3) 4 (18.2) 22 (14.4) Missing 29 (56.9) 20 (39.2) 2 (3.9) 51 (33.3) PGS-I No problem with gambling 24 (53.3) 20 (44.4) 1 (2.2) .76 45 (29.4) Low risk of gambling problems 7 (43.8) 8 (50.0) 1 (6.3) 16 (10.5) Moderate risk of gambling 13 (65.0) 7 (35.0) 0 (0.0) 20 (13.1) problems Gambling problems 2 (40.0) 3 (60.0) 0 (0.0) 5 (3.3) Missing 41 (61.2) 21 (31.3) 5 (7.5) 67 (43.8) Kessler-6 Score 0-4: no psychological 10 (40.0) 14 (56.0) 1 (4.0) .16 25 (16.3) distress Score 5-24: moderate psycho- 74 (60.7) 44 (36.1) 4 (3.3) 122 (79.7) logical distress Missing 3 (50.0) 1 (16.7) 2 (33.3) 6 (3.9) GASA Addicted/problem gamer 13 (76.5) 3 (17.6) 1 (5.9) .20 17 (11.1) Engaged gamer 9 (75.0) 3 (25.0) 0 (0.0) 12 (7.8) No problem 65 (52.4) 53 (42.7) 6 (4.8) 124 (81.0) N/A: not applicable. No responses to this category. A currency exchange rate of SEK 1=US $0.11 is applicable. PGS-I: Problem Gambling Severity Index. GASA: Game Addiction Scale for Adolescents. spent at home (P=.005), changes in alcohol consumption Ages of 25 Years to 39 Years (P=.003), changes in exercise habits (P=.001), whether the In this age group, we found significant differences in respondents were studying (P=.007), and Kessler score (P<.001; employment status (P=.004), disposable income (P=.01), time Table 4). https://games.jmir.org/2022/1/e33059 JMIR Serious Games 2022 | vol. 10 | iss. 1 | e33059 | p. 8 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Claesdotter-Knutsson et al Table 4. Gaming behavior by demographic characteristics among participants 25 years to 39 years of age.  Characteristics Gaming behavior, n (%) P value Participants, n (%) Increased Unchanged Decreased Total sample 146 (50.9) 126 (43.9) 15 (5.2) 287 (100) N/A Gender Female 81 (53.3) 65 (42.8) 6 (3.9) .48 152 (53.0) Male 65 (48.1) 61 (45.2) 9 (6.7) 135 (47.0) Employment status Studying 33 (76.7) 9 (20.9) 1 (2.3) .004 43 (15.0) Employed 93 (44.3) 104 (49.5) 13 (6.2) 210 (73.2) Unemployed 15 (68.2) 7 (31.8) 0 (0.0) 22 (7.7) Retired — — — Other 5 (41.7) 6 (50.0) 1 (8.3) 12 (4.2) Level of education Primary school 5 (50.0) 5 (50.0) 0 (0.0) .74 10 (3.5) Secondary school 53 (46.1) 54 (47.0) 8 (7.0) 115 (40.0) University 80 (54.1) 61 (41.2) 7 (4.7) 148 (51.6) Other 8 (57.1) 6 (42.9) 0 (0.0) 14 (4.9) Disposable income (SEK ) <20,000 52 (66.7) 21 (26.9) 5 (6.4) .01 78 (27.2) 20,000-40,000 82 (45.3) 91 (50.3) 8 (4.4) 181 (63.1) >40,000 12 (42.9) 14 (50.0) 2 (7.1) 28 (9.8) Loot box Yes 28 (58.3) 17 (35.4) 3 (6.3) .45 48 (16.7) No 84 (49.7) 77 (45.6) 8 (4.7) 169 (58.9) Missing 34 (48.6) 32 (45.7) 4 (5.7) 70 (24.4) Time at home Much more 105 (59.3) 65 (36.7) 7 (4.0) .005 177 (61.7) Slightly more 33 (38.8) 45 (52.9) 7 (8.2) 85 (29.6) Unchanged 8 (32.0) 16 (64.0) 1 (4.0) 25 (8.7) Less time — — —   — Change in alcohol habits More alcohol 20 (62.5) 9 (28.1) 3 (9.4) .003 32 (11.1) Unchanged 53 (41.4) 73 (57.0) 2 (1.6) 128 (44.6) Less alcohol 49 (58.3) 28 (33.3) 7 (8.3) 84 (29.2) Does not drink 24 (55.8) 16 (37.2) 3 (7.0) 43 (15.0) Change in exercise habits More exercise 32 (47.8) 29 (43.3) 6 (9.0) .001 67 (23.3) Unchanged 34 (37.4) 53 (58.2) 4 (4.4) 91 (31.7) Less exercise 77 (65.3) 37 (31.4) 4 (3.4) 118 (41.1) Never 3 (27.3) 7 (63.6) 1 (9.1) 11 (3.8) In school https://games.jmir.org/2022/1/e33059 JMIR Serious Games 2022 | vol. 10 | iss. 1 | e33059 | p. 9 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Claesdotter-Knutsson et al  Characteristics Gaming behavior, n (%) P value Participants, n (%) Increased Unchanged Decreased Yes 43 (68.3) 18 (28.6) 2 (3.2) .007 63 (22.0) No 103 (46.0) 108 (48.2) 13 (5.8) 224 (78.0) School performance Unchanged 15 (65.2) 8 (34.8) 0 (0.0) .13 23 (8.0) Better 10 (66.7) 3 (20.0) 2 (13.3) 15 (5.2) Worse 18 (72.0) 7 (28.0) 0 (0.0) 25 (8.7) Missing 103 (46.0) 108 (48.2) 13 (5.8) 224 (78.0) School attendance Unchanged 26 (66.7) 12 (30.8) 1 (2.6) .80 39 (13.6) Less 8 (80.0) 2 (20.0) 0 (0.0) 10 (3.5) More 9 (64.3) 4 (28.6) 1 (7.1) 14 (4.9) Missing 103 (46.0) 108 (48.2) 13 (5.8) 224 (78.0) PGS-I No problem with gambling 55 (45.8) 59 (49.2) 6 (5.0) .48 120 (41.8) Low risk of gambling problems 23 (56.1) 15 (36.6) 3 (7.3) 41 (14.3) Moderate risk of gambling 18 (58.1) 12 (38.7) 1 (3.2) 31 (10.8) problems Gambling problems 12 (66.7) 6 (33.3) 0 (0.0) 18 (6.3) Missing 38 (49.4) 34 (44.2) 5 (6.5) 77 (26.8) Kessler-6 Score 0-4: no psychological 21 (29.6) 43 (60.6) 7 (9.9) <.001 71 (24.7) distress Score 5-24: moderate psycho- 124 (57.7) 83 (38.6) 8 (3.7) 215 (74.9) logical distress Missing 1 (100) 0 (0.0) 0 (0.0) 1 (0.3) GASA Addicted/problem gamer 21 (65.6) 9 (28.1) 2 (6.3) .14 32 (11.1) Engaged gamer 16 (66.7) 7 (29.2) 1 (4.2) 24 (8.4) No problem 109 (47.2) 110 (47.6) 12 (5.2) 231 (80.5) N/A: not applicable. No responses to this category. A currency exchange rate of SEK 1=US $0.11 is applicable. PGS-I: Problem Gambling Severity Index. GASA: Game Addiction Scale for Adolescents. 0.27-0.68) and ≥60 years (OR 0.57, 95% CI 0.33-0.97) as well Comparison of Increased Gaming in Different as with much more time spent at home (OR 3.96, 95% CI Outcomes (Unweighted Data) 2.15-7.28). We also found a significant correlation with drinking The multivariable analysis using binary logistic regression less alcohol (OR 1.93, 95% CI 1.34-7.28) and self-reported not models of the potential predictors of increased changes (yes drinking alcohol (OR 1.66, 95% CI 1.05-2.61). Increased gaming versus no) are presented in the following sections. was also significantly correlated with a Kessler score greater than 5 (OR 2.44, 95% CI 1.73-3.44) and with the GASA All Age Groups categories of engaged gamer (OR 2.27, 95% CI 1.23-4.20) and Increased gaming was significantly negatively correlated with addicted/problem gamer (OR 2.37, 95% CI 1.26-4.47; Table the age group of 40 years to 59 years (OR 0.43, 95% CI 5). https://games.jmir.org/2022/1/e33059 JMIR Serious Games 2022 | vol. 10 | iss. 1 | e33059 | p. 10 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Claesdotter-Knutsson et al Table 5. The likelihood of increased gaming behavior (increased vs unchanged/decreased) among all participants (n=932). Characteristics OR (95% CI) Age groups (years) 16-24 Reference 25-39 0.97 (0.63-1.50) 40-59 0.43 (0.27-0.68) ≥60 0.57 (0.33-0.97) Time at home Unchanged Reference Much more 3.96 (2.15-7.28) Slightly more 1.72 (0.89-3.31) Less time 3.54 (0.44-28.28) Change in alcohol habits Unchanged Reference More alcohol 1.62 (0.99-2.67) Less alcohol 1.93 (1.34-2.78) Does not drink 1.66 (1.05-2.61) Kessler-6 Score 0-4: no psychological distress Reference Score 5-24: moderate psychological distress 2.44 (1.73-3.44) GASA No problem Reference Engaged gamer 2.27 (1.23-4.20) Addicted/problem gamer 2.37 (1.26-4.47) OR: odds ratio. GASA: Game Addiction Scale for Adolescents. Ages of 16 Years to 24 Years In this age group, no correlations were found (Table 6). https://games.jmir.org/2022/1/e33059 JMIR Serious Games 2022 | vol. 10 | iss. 1 | e33059 | p. 11 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Claesdotter-Knutsson et al Table 6. The likelihood of increased gaming behavior (increased vs unchanged/decreased) among participants 16 years to 24 years of age  Characteristics OR (95% CI) Disposable income (SEK ) <20,000 Reference 20,000-40,000 5.69 (0.5-65.0) >40,000 Loot box Yes Reference No 0.66 (0.15-2.89) Time at home Unchanged Reference Much more — Slightly more — Less time — School attendance Unchanged Reference Less 2.5 (0.61-10.3) More 1.39 (0.33-5.85) OR: odds ratio. A currency exchange rate of SEK 1=US $0.11 is applicable. Could not be estimated. increased gaming (OR 2.27, 95% CI 1.20-4.27), and Kessler Ages of 25 Years to 39 Years scores greater than 5 were positively correlated with a In this age group, employment was negatively correlated with self-reported increase in gaming activity (OR 2.36, 95% CI a self-reported increase in gaming (OR 0.41, 95% CI 0.18-0.92). 1.27-4.41; Table 7). Self-reporting less exercise was positively correlated with https://games.jmir.org/2022/1/e33059 JMIR Serious Games 2022 | vol. 10 | iss. 1 | e33059 | p. 12 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Claesdotter-Knutsson et al Table 7. The likelihood of increased gaming behavior (increased vs unchanged/decreased) among participants 25 years to 39 years of age. Characteristics OR (95% CI) Employment status Studying Reference Employed 0.41 (0.18-0.92) Unemployed 0.82 (0.24-2.74) Retired Other 0.30 (0.07-1.30) Change in alcohol habits Unchanged Reference More alcohol 1.49 (0.62-3.58) Less alcohol 1.61 (0.88-2.97) Does not drink 1.65 (0.77-3.55) Change in exercise habits Unchanged Reference More 1.29 (0.64-2.59) Less 2.27 (1.20-4.27) Never 0.60 (0.14-2.60) Time at home Unchanged Reference Much more 1.60 (0.59-4.32) Slightly more 0.94 (0.34-2.64) Less time — Kessler-6 Score 0-4: no psychological distress Reference Score 5-24: moderate psychological distress 2.36 (1.27-4.41) OR: odds ratio. Could not be estimated. correlated with a self-reported increase in gaming activity. Being Discussion employed (25–39-year-old age group) and being over 40 years of age (all age groups analyzed together) were negatively This cross-sectional study aimed to look at self-reported changes correlated with increased gaming. We did not find any in gaming behavior during the third wave of the COVID-19 significant correlations in the 16–24-year-old age group. pandemic in Sweden. We also wanted to look at potential risk factors for problematic gaming during the pandemic, including Comparison With Prior Work gaming patterns, gambling behavior, psychological distress, a Research from the early phases of the COVID-19 pandemic number of sociodemographic characteristics, health factors, and showed worrying figures for increased screen time among young school situation during the pandemic. We used data from a web people, raising questions about whether this would continue panel of 1501 respondents who answered questions on gaming and how it would affect the younger population [55-57]. Gaming and gambling. The results on gambling are presented elsewhere. disorder has been recognized as a public health problem of Principal Findings importance, but the majority of people who engage in gaming do not fulfil the criteria for gaming disorder [58]. In most We found several factors associated with changed gaming studies, the overall prevalence of gaming disorder is around 3% behavior, but on further analysis, only psychological distress [48]. In our study, 62% of respondents self-reported that they (all age groups analyzed together and the 25–39-year-old age sometimes played video games. We found 38% self-reported group), drinking less alcohol (all age groups analyzed together), an increase in gaming since the start of the COVID-19 spending more time at home (all age groups analyzed together), pandemic. When looking at gaming and possible changes in the gaming problems (all age groups analyzed together), and younger population, in the 16–24-year-old age group, 57% exercising less (25–39-year-old age group) were positively self-reported an increase in gaming, and in the 25–39-year-old https://games.jmir.org/2022/1/e33059 JMIR Serious Games 2022 | vol. 10 | iss. 1 | e33059 | p. 13 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Claesdotter-Knutsson et al age group, 51% reported an increase in gaming. Other whole school day, the children in the other countries did their researchers have seen similar results. Frequencies and screen schoolwork under their parents’ supervision [56,57]. time had increased during the COVID-19 pandemic when In India, which has seen intermittent total lockdowns throughout Paschke et al [59] looked at those aged 10 years to 17 years and the pandemic, meaning people have had to stay at home, an compared their usage frequency and screen time from before increase in gaming has been seen in those aged 25 years to 35 the pandemic with their behavior in lockdown. Lemenager et years [14]. There have also been public health efforts by the al [60] found the same tendency: 71.4% of participants estimated WHO to encourage people to engage in gaming a general increase in their online media consumption during the (#PlayApartTogether) to promote social distancing and prevent lockdown, and some 10% self-reported a rise in gaming activity the virus spread [21]. In our study, looking at all age groups during the COVID-19 pandemic, of whom men aged between together, we saw a correlation between staying home much 18 years and 24 years showed the highest increase in gaming. more and self-reporting an increase in gaming. The relationship Studies from before the COVID-19 pandemic had shown the was not found in the 2 age groups under 40 years of age. It is same tendency, with young men reported to play computer important to bear in mind that our sample was rather limited in games more frequently and for longer duration [61,62], making size, and possibly a larger sample size would show additional them vulnerable to developing addictive gaming behaviors correlations. [63-65]. Balhara et al [58] looked at university students’ gaming behavior during the COVID-19 pandemic and found those who Being 40 years old or more seemed to reduce the reporting of used gaming as a tool to reduce stress showed an increase in increased gaming, not surprisingly since gaming is more gaming activity during the pandemic. Before the COVID-19 common in younger people [61,62]. We found a positive pandemic, it was known that gamers who gave escapism (a correlation between less exercise and increased gaming in the coping technique to handle negative emotions) as a reason for age group of 25 years to 39 years. Sedentary lifestyles and gaming were more commonly problem gamers [66,67]. increased gaming have been known to co-occur during the COVID-19 pandemic, making researchers call for parents, When analyzing the whole sample who reported increased schools, and decision makers to mandate physical activity and gaming, we found positive associations with engaged gaming keep outdoor facilities open as long as possible, even in but also with addicted/problem gaming; this relationship was lockdowns [55,75]. Psychological distress was positively not seen in the 2 younger groups. Increased time spent gaming associated with increased gaming in the whole sample as well is a risk factor for developing gaming problems/addiction as in those aged 25 years to 39 years. The association of [63-65]. Previous studies have shown a huge male predominance excessive gaming with comorbidities such as sleeping disorders, in problematic gaming behavior [27,68,69]. Men have been obesity, depression, and anxiety is well known [68,76-78], reported to play computer games more frequently and for longer marking out the group who self-reported increased gaming and duration [61,62], whereas there is a female predominance in psychological distress as vulnerable and a focus of concern. smartphone use [70]. Massively multiplayer online role-playing The same was observed by King et al [18]: Some individuals games (MMORPGs) and multiplayer online battle arenas may develop an increasingly unhealthy pattern of gaming due (MOBAs), both with a predominance of male players, have to pandemic-related psychological distress, because they find been found to have an addictive potential due to their specific gaming relieves stress. We found respondents aged 25 years to structural characteristics of advancement and social interactions 39 years who reported increased gaming during the pandemic [28,29,71-74]. Researchers have also looked at the cortical were less likely to be in work. Unemployment might facilitate region and found that gender differences in IGD might be their gaming activities, enabling them to spend more time at associated with different cortical thickness in and around the home, possibly under greater stress. posterior cingulate cortex, the region thought to be involved in cognitive control and reward/loss processing and hence thought When examining those aged 16 years to 24 years who reported to play a role in addiction [68]. These are plausible explanations increased gaming, we did not find any correlations with for the gender differences. In this study, we did not see that associated factors. One possible reason why those in this age gender was associated with self-reported increased gaming, group who reported increased gaming did not seem to suffer possibly because the numbers were too small. from psychological distress could be that gaming involves social aspects and thus increased their ability to cope with social During the COVID-19 pandemic, the media, schools, and isolation in a functional way. This would seem to be confirmed parents all wondered whether remote learning would see by a report by Bora [14] showing higher usage in multiplayer schoolchildren spending more time using digital media, not modes. Playing video games together helped people reduce including homework. This was to some extent true for April feelings of loneliness and stress and to stay in touch with friends 2020 to June 2020, when a 69%-76% rise in online media use [58]. was reported for children in the United Kingdom, Spain, and Belgium compared with a 36% rise in the Swedish population Gaming was already viewed by some in a positive light for [56,57]. Although not immediately comparable, the difference helping to develop cognitive skills such as reasoning, spatial can to some extent be explained by the fact that, in the Swedish awareness, and problem-solving [74], and it is now also material, the majority of pupils were aged 13 years to 16 years, considered a way of maintaining social contact during lockdown. compared with an age range of 5 years to 12 years in the other It would be desirable for authorities to issue recommendations countries, and while the Swedish children were online for the for preferred types of video games that enhance social activity https://games.jmir.org/2022/1/e33059 JMIR Serious Games 2022 | vol. 10 | iss. 1 | e33059 | p. 14 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Claesdotter-Knutsson et al and physical health while avoiding the pitfalls of unhealthy Strengths gaming. Despite the extreme interest in the possible increase in gaming during the COVID-19 pandemic, whether in popular science or Limitations more clinical and scientific contexts, there is still a significant This study has several limitations. The study was based on a lack of studies focusing on the younger population. This study self-assessment questionnaire, rather than a standardized, contributes important information about possible changes in structured clinical interview that would allow a more accurate gaming behavior during the COVID-19 pandemic. assessment. Against that, questionnaires are widely used in epidemiological studies, and in other studies, they have been Conclusion considered to give a satisfactory picture of the situation [39,41]. Those who reported increased gaming during the COVID-19 Our data were based on a web panel survey, and although the pandemic were more likely to be 16 years to 39 years old. In study sample was designed and weighted to represent the general those aged 16 years to 24 years, increased gaming was not population, it is hard to know whether the respondents’ original associated with any risk factors. In the 25–39-year-old age choice to enroll in a web panel is associated with other group, the increase was associated with psychological distress, characteristics and, in this case, with gaming habits that differ reporting less exercise, and being unemployed. COVID-19 may from those of their peers in the general population. Our sample present a risk factor for increased online gaming in a small but size is too small to draw generalized conclusions. vulnerable group. More research and preferably longitudinal studies are needed in the field of gaming and the effects of the COVID-19 pandemic. Conflicts of Interest AH has a researcher position at Lund University, which is sponsored by the Swedish state-owned gambling operator, AB Svenska Spel. 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BMC Psychiatry 2017 Jul 19;17(1):260 [FREE Full text] [doi: 10.1186/s12888-017-1408-x] [Medline: 28724403] Abbreviations DSM-5: Diagnostic and Statistical Manual of Mental Disorders GASA: game addiction scale for adolescents ICC: International Chamber of Commerce ICD: International Classification of Diseases IGD: internet gaming disorder https://games.jmir.org/2022/1/e33059 JMIR Serious Games 2022 | vol. 10 | iss. 1 | e33059 | p. 18 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Claesdotter-Knutsson et al MMORPG: massive multiplayer online role-playing game MOBA: multiplayer online battle arena OR: odds ratio PGS-I: Problem Gambling Severity Index WHO: World Health Organization Edited by N Zary; submitted 23.08.21; peer-reviewed by Z Yan; comments to author 18.09.21; revised version received 07.10.21; accepted 13.11.21; published 25.01.22 Please cite as: Claesdotter-Knutsson E, André F, Håkansson A Gaming Activity and Possible Changes in Gaming Behavior Among Young People During the COVID-19 Pandemic: Cross-sectional Online Survey Study JMIR Serious Games 2022;10(1):e33059 URL: https://games.jmir.org/2022/1/e33059 doi: 10.2196/33059 PMID: 34817386 ©Emma Claesdotter-Knutsson, Frida André, Anders Håkansson. Originally published in JMIR Serious Games (https://games.jmir.org), 25.01.2022. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Serious Games, is properly cited. The complete bibliographic information, a link to the original publication on https://games.jmir.org, as well as this copyright and license information must be included. https://games.jmir.org/2022/1/e33059 JMIR Serious Games 2022 | vol. 10 | iss. 1 | e33059 | p. 19 (page number not for citation purposes) XSL FO RenderX

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Published: Jan 25, 2022

Keywords: COVID-19 pandemic; gaming; screen time; psychological distress

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