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Physical Wellness Among Gaming Adults: Cross-Sectional Study

Physical Wellness Among Gaming Adults: Cross-Sectional Study Background: Video and hobby gaming are immensely popular among adults; however, associations between gaming and health have primarily been investigated in children and adolescents. Furthermore, most research has focused on electronic gaming, despite traditional hobby gaming gaining prominence. Objective: To determine whether the number of platforms used, platform preference, and gaming time are associated with obesity, physical activity, sedentary behavior, and cardiovascular risk factors in an adult gaming population. Methods: We conducted a cross-sectional analysis using data obtained from 292 participants who attended a large Midwestern gaming convention. We collected data using a computer-based questionnaire that comprised questions on gaming behavior, demographics, physical activity (using the International Physical Activity Questionnaire), and health characteristics. In addition, we used multivariable-adjusted linear and logistic regression to model health outcomes as a function of the number of platforms used, platform preference, and weekday and weekend gaming time quartile. Results: After adjusting for covariates, we observed a significant linear trend for increasing odds of being obese and higher weekend sitting time by the number of platforms used (P=.03 for both). The platform preference and weekend gaming time quartile exhibited significant associations with odds of meeting physical activity recommendations (P=.047 and P=.03, respectively). In addition, we observed higher odds of being obese among those reporting that they sat most or all of the time while gaming [odds ratio (OR) 2.69 (95% CI 1.14-6.31) and OR 2.71 (95% CI 1.06-6.93), respectively]. Conclusions: In adult gamers, the number of platforms used, which platforms they prefer to play on, and the amount of time spent gaming on weekends could have significant implications for their odds of being obese and meeting physical activity recommendations. (JMIR Serious Games 2018;6(2):e12) doi: 10.2196/games.9571 KEYWORDS video games; electronic gaming; traditional gaming; obesity; physical activity; sedentary behavior Prior studies have established an association between video Introduction game use and obesity in children, independent of the time spent watching television and physical activity [2,3]. Additionally, Although electronic gaming is a popular leisure activity among health-oriented gaming interventions have been primarily young adults, little is known about the impact of gaming conducted in children [4,5]. Although research on gaming has participation on health in this age group [1]. Most research focused on children, a systematic review of health gaming investigating the relationship between gaming and healthy research by Kharrazi et al [6] reported that 65 of 108 studies lifestyle behaviors or health outcomes has focused on children. http://games.jmir.org/2018/2/e12/ JMIR Serious Games 2018 | vol. 6 | iss. 2 | e12 | p. 1 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Arnaez et al enrolled participants no older than 20 years. Furthermore, after questionnaire. Of these, we excluded 8 participants who reported adjusting for the study sample size, the mean age of participants they did not play games on any platform, 14 who reported >35 in studies on gaming was 13 years, which is not reflective of hours of recreational physical activity per week, and 18 who the current gaming population. Furthermore, some studies on had missing data on physical activity, platform preference, adults have found that gaming is typically associated with a sitting time, or BMI. Hence, we enrolled 292 participants in higher body mass index (BMI), particularly among males [7-9]. this study. In 2017, the Electronic Software Association reported that 67% Measurement of Video and Hobby Game Playing of American households own at least one gaming console and The first section of the questionnaire gathered information on 65% of Americans play games at least 3 hours/week, resulting gaming platform preferences, time spent gaming, and amount in U.S. $ 30.4 billion spent on games and game-related of sitting while gaming. We asked participants to identify the equipment [10]. In addition, hobby games (eg, tabletop board two gaming platforms that they used most often among the games, collectible card games, and role-playing games) have following: computer, console, handheld, tabletop, live action also witnessed an upsurge in sales in recent years [11]. While role-play (LARP), phone, and tablet platforms. In addition, electronic and hobby gaming are typically viewed as sedentary participants identified other platforms that they used besides activities, energy expenditure could differ between different the two most used platforms. Furthermore, we asked participants types of gaming (eg, live action role-play vs tabletop role-play), about the proportion of time they spent sitting while gaming, especially with the rise of “exergames” that incorporate physical whether they took breaks during gaming and how frequent, and movement and activity as a part of the core game mechanics whether they felt that they had worked out after a gaming session [12-14]. was over. Sedentary behavior, defined as “any waking behavior Measurement of Physical Activity and Sedentary characterized by an energy expenditure ≤1.5 metabolic Behaviors equivalents of task, while in a sitting, reclining, or lying We used the International Physical Activity Questionnaire posture,” has been associated with several adverse health (IPAQ) to determine the usual level of physical activity [20]. outcomes, including obesity, metabolic syndrome, type 2 Specifically, we used the IPAQ to gather information on leisure diabetes, and cardiovascular disease [15-19]. Studies on time walking, moderate-to-vigorous physical activity (MVPA), sedentary behavior have primarily focused on television and typical methods of transportation in the 7 days before watching, total screen time, or overall sitting time, while few arriving at the convention. In addition, we used the Sedentary have attempted to investigate differential associations for Behavior Questionnaire to evaluate time spent in the following specific types of sedentary behavior, including video or hobby 8 different sedentary activities on an average weekday or gaming. weekend: gaming, watching television, talking on the phone, This study aims to add to the current literature on gaming and doing office work, listening to music, reading, playing an health by addressing two consistent issues. First, most research instrument, or doing artwork or crafts [21]. Furthermore, 9 concerning the relationship between gaming and health has categories were provided for response, ranging from no time focused on children and adolescents. Although adult gamers on an activity to ≥6 hours/day. constitute the largest portion of the gaming audience, limited Measurement of Health and Demographic research has been conducted on this population. Second, the Characteristics assessment of gaming in several studies has been limited in scope. In past research, gaming is often examined as either time In addition to collecting data on physical activity and sedentary spent gaming or whether participants gamed at all. Most studies time, we assessed other health characteristics, including height, focus solely on electronic gaming; however, when tabletop weight, smoking status, and number of cigarettes smoked per gaming is considered, both types are considered one gaming day. In addition, participants completed questions on their group. Thus, the relationship between health and the type of medical history, including the diagnosis of high blood pressure, gaming people prefer as well as the different forms of gaming nongestational diabetes, elevated cholesterol, myocardial people use remains unclear. Hence, this study aims to address infarction, stroke, and cancer, as well as whether they had a these limitations by focusing on an adult gaming population disability that limited their physical activity. We also asked and using multiple measures of gaming behavior to elucidate participants how many servings of fruit and vegetables they ate how gaming correlates with health in this population. per day. Finally, we asked participants to provide demographic information, including ethnicity, race, gender, marital status, Methods age, income, country of residence, education level, and employment status. Participants Statistical Analysis In this study, participants were recruited among attendees of a In this study, all statistical analyses were performed using SAS large Midwestern tabletop gaming convention held in summer statistical software, version 9.4 (SAS Institute Inc, Cary, North 2014. They completed an online questionnaire that assessed Carolina, United States). The exposures of interest were the gaming habits, physical activity, sedentary time, and other health preferred gaming platform, number of platforms used, and time characteristics, either at the convention center or off-site at a spent gaming. The outcomes of interest were BMI, obesity status later time. A total of 332 individuals completed the online http://games.jmir.org/2018/2/e12/ JMIR Serious Games 2018 | vol. 6 | iss. 2 | e12 | p. 2 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Arnaez et al (BMI<30 vs BMI≥30), physical activity, sedentary time, and Results presence of a cardiovascular risk factor (diagnosis of ≥1 of diabetes, hypertension, and high cholesterol). Characteristics of Study Population We assessed the baseline characteristics of the study population The study population was predominantly white (259 of 290, by calculating means (SD) for continuous variables and 89.3%) and male (197 of 187, 68.6%), with a mean age of 34.2 frequencies (%) for categorical variables. In addition, we (SD 10.6) years (Multimedia Appendix 1). Most participants assessed means (SD) and frequencies (%) of health were either overweight (77 of 292, 26.4%) or obese (154 of characteristics based on the number of platforms used and 2 292, 47.3%), with a mean BMI of 31.2 (SD 8.8) kg/m . The platform preference. In this and all analyses for platform mean hours per week of MVPA was 5.2 (SD 5.9), with 166 of preference, gaming on a handheld console, phone, or tablet was 292 (56.9%) participants reporting that they fulfilled the physical grouped into the “other electronic” category due to small sample activity guidelines of at least 2.5 hours of MVPA per week. sizes in each of these categories. Furthermore, we used the Nearly one-quarter (66 of 290, 22.8%) of the participants Kruskal–Wallis test and Fisher’s tests with Monte Carlo responded that they had a disability or health condition that approximation to test for the significance of the association restricted their ability to be physically active. In fact, 67 of 290 between platform preference and each variable. (23.1%) participants reported that they had a cardiovascular risk factor. The most preferred gaming platforms were tabletop We then assessed the association between the number of gaming gaming (116 of 292, 39.7%) and computer gaming (103 of 292, platforms used and BMI, obesity, physical activity, sedentary 35.3%). Most participants (196 of 292, 67.1%) used at least time, and cardiovascular risk factors. As these were not normally three different platforms. distributed, BMI was log-transformed, and the sitting time was square root-transformed. For physical activity, we categorized Characteristics of Study Population by Platform participants depending on whether they fulfilled the physical Preference activity 2008 Physical Activity Guidelines for Americans of We assessed the baseline characteristics according to the type 2.5 hours of MVPA per week [22]. While linear regression was of platform that was most preferred (Table 1). We observed used for analyzing continuous outcomes of BMI and sitting significant differences by the platform preference in age, time, logistic regression was used to analyze the categorical weekend sitting time, weekday and weekend gaming time, outcomes of obesity, meeting physical activity guidelines, and proportion of time spent sitting, and servings of vegetables per the presence of cardiovascular risk factors. Furthermore, we day (P=.002, P=.002, P<.001, P<.001, P=.001, and P=.02, conducted a test for linear trend by treating the number of respectively). While participants who preferred tabletop games platforms used as a continuous variable. were the oldest, with a mean age of 36.4 (SD 9.8) years, those Similar analyses were performed to examine the association who preferred console games were the youngest, with a mean between platform preference, as well as time spent gaming, and age of 30.6 (SD 10.9) years. Weekend sitting time was the each of the health outcomes. For platform preference, highest among participants who preferred LARP [9.9 (SD 3.5) participants who preferred tabletop gaming were assigned to hours/day] and the lowest among those who preferred tabletop the reference group, and analysis of covariance was used for games [7.0 (SD 3.6) hours/day]. Although participants who the continuous outcomes (BMI and sedentary time) to test the preferred computer games reported the highest time spent significance of platform preference overall. In addition, we used gaming on both weekdays and weekends [2.5 (SD 1.7) and 3.0 fractional logistic regression to test the significance of platform (SD 1.8) hours/day, respectively], they also constituted the preference for dichotomous outcomes. Then, weekday and highest proportion of participants who fulfilled physical activity weekend gaming time was examined in quartiles to investigate recommendations (65 of 103, 63.1%). associations between gaming time and each outcome. Besides, Characteristics of Study Population by Number of the median of the gaming time quartiles was modeled as a continuous variable to test for linear trend. Furthermore, we Platforms Played used logistic regression to investigate the association between Next, we assessed the baseline characteristics by the number of the proportion of time spent sitting while gaming and the odds platforms used (Multimedia Appendix 2). We observed of being obese. significant differences by the number of platforms used in age and time spent gaming on weekdays and weekends (P=.01, For all analyses of gaming and health outcomes, we used P=.02, and P=.002, respectively). In addition, participants who age-adjusted and multivariable-adjusted models. All reported playing ≥4 platforms were the youngest, with a mean multivariable-adjusted models included age, race, gender, age of 31.3 (SD 9.1) years, whereas those who only played 1-2 employment, income (income >75,000/year vs income platforms were older, with mean ages of 37.4 (SD 13.4) and ≤75,0000/year), servings of fruit per day, and servings of 37.1 (SD 11.6) years, respectively. Not surprisingly, the mean vegetables per day. Based on the exposure and outcome, other time spent gaming on weekdays and weekends increased as the covariates included in multivariable-adjusted models were number of platforms used increased. Furthermore, those who fulfilling physical activity guidelines, the presence of a played ≥4 platforms reported 2.0 (SD 1.6) hours/day of gaming disability, the number of platforms used, and weekday and on a typical weekday and 2.6 (SD 1.7) hours/day of gaming on weekend gaming time. The specific variables included in each a typical weekend day. model are listed in the footnotes of each table in this study. http://games.jmir.org/2018/2/e12/ JMIR Serious Games 2018 | vol. 6 | iss. 2 | e12 | p. 3 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Arnaez et al Table 1. Means (SD) and frequency (%) of characteristics by gaming platforms most preferred. Characteristic Tabletop (n=116) Computer (n=103) Console (n=41) Other Electronic (n=25) LARP (n=7) P value Age, years, mean (SD) 36.4 (9.8) 33.0 (11.4) 30.6 (10.9) 35.0 (9.3) 31.9 (8.0) .002 b 2 BMI , kg/m , mean (SD) 32.1 (8.5) 30.4 (8.4) 30.6 (8.6) 30.2 (10.6) 34.6 (13.3) .41 Obese, n (%) 58 (50) 45 (43.7) 23 (56.1) 8 (32) 4 (57.1) .32 MVPA , hours/week, mean (SD) 4.5 (4.6) 5.2 (5.7) 6.7 (8.0) 5.0 (7.5) 7.3 (8.8) .94 ≥2.5 MVPA, hours/week, n (%) 63 (54.3) 65 (63.1) 23 (56.1) 11 (44) 4 (57.1) .47 Sitting time, hours/day, mean (SD) Weekday 8.7 (4.9) 9.4 (4.1) 9.0 (4.0) 9.6 (5.4) 11.3 (4.8) .35 Weekend 7.0 (3.6) 8.6 (3.6) 8.6 (4.4) 8.0 (5.5) 9.9 (3.5) .002 Time spent gaming, hours/day, mean (SD) Weekday 1.0 (1.2) 2.5 (1.7) 2.0 (1.7) 1.1 (0.9) 0.9 (1.0) <.001 Weekend 1.3 (1.4) 3.0 (1.8) 2.5 (1.7) 1.4 (1.2) 1.4 (1.3) <.001 Cardiovascular risk factors, n (%) Yes 24 (20.9) 25 (24.5) 8 (19.5) 9 (36.0) 1 (14.3) .51 No 91 (79.1) 77 (75.5) 33 (80.5) 16 (64.0) 6 (85.7) Proportion of time spent sitting while gaming, n (%) Half or less 17 (14.9) 12 (11.9) 4 (9.8) 7 (28) 5 (71.4) .001 Most or All 97 (85.1) 89 (88.1) 37 (90.2) 18 (72) 2 (28.6) Take breaks while gaming, n (%) Yes 96 (85.7) 78 (80.4) 30 (76.9) 17 (80.9) 6 (100) .53 No 16 (14.3) 19 (19.6) 9 (23.1) 4 (19.1) 0 Frequency of breaks, n (%) Every ≤55 minute 63 (65.6) 48 (61.5) 21 (70.0) 6 (35.3) 4 (66.7) .76 1 hour + 33 (34.4) 30 (38.5) 9 (30.0) 11 (64.7) 2 (33.3) Servings of fruit per day, mean (SD) 1.0 (0.9) 1.0 (0.9) 1.2 (1.1) 1.1 (1.2) 0.5 (0.7) .38 Servings of vegetables per day, mean 2.0 (1.2) 1.9 (1.2) 1.6 (1.2) 1.5 (1.1) 0.9 (0.8) .02 (SD) LARP: live action role-play. BMI: body mass index. MVPA: moderate to vigorous physical activity. weekend sitting time and number of platforms used (P =.03; trend Association of Number of Platforms Played and beta=.11); however, no significant associations existed between Physical Wellness the number of platforms used and physical activity or the Table 2 presents the associations of obesity, physical activity, presence of cardiovascular risk factors. sedentary time, and cardiovascular risk factors with the number Association of Platform Preference With Physical of platforms used. We observed that a higher number of Wellness platforms used was significantly associated with a higher BMI in the age-adjusted model but was only marginally significant Table 3 presents the results of the regression analysis for the in the multivariable-adjusted model (P=.045 and P=.07, platform preference with the same outcomes as the previous respectively). Compared with participants who reported using analysis. In multivariable-adjusted models, a significant 3 platforms, those who reported using ≥4 platforms exhibited association was observed between the preferred gaming platform a multivariable-adjusted odds ratio (OR) of 1.55 (95% CI and fulfilling physical activity guidelines (P=.04). Compared 0.78-3.09) for obesity, whereas those who reported using 1 with participants who preferred tabletop games, those who platform exhibited an OR of 0.48 (95% CI 0.14-1.58; P =.03). preferred computer games had higher odds of performing 2.5 trend In addition, we observed a significant positive linear trend for hours of MVPA per week (OR 2.70, 95% CI 1.28-5.69). http://games.jmir.org/2018/2/e12/ JMIR Serious Games 2018 | vol. 6 | iss. 2 | e12 | p. 4 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Arnaez et al Table 2. Associations between the number of gaming platforms used and health outcomes. Characteristic Gaming platforms P value 1 2 3 4+ a,b BMI Age-adjusted −0.11 −0.02 Reference 0.03 .045 Multivariable-adjusted −0.07 −0.02 Reference 0.04 .07 Obese, OR (95% CI) Age-adjusted 0.58 (0.22-1.50) 0.76 (0.42-1.38) 1.00 1.41 (0.78-2.54) .03 Multivariable-adjusted 0.48 (0.14-1.58) 0.78 (0.39-1.54) 1.00 1.55 (0.78-3.09) .03 ≥2.5 MVPA , hours/week, OR (95% CI) Age-adjusted 1.18 (0.46-3.05) 0.77 (0.43-1.39) 1.00 0.68 (0.38-1.23) .46 Multivariable-adjusted 1.06 (0.34-3.30) 0.73 (0.38-1.42) 1.00 0.61 (0.32-1.18) .50 Weekday Total Sitting, hours/day Age-adjusted 0.004 −0.01 Reference 0.03 .79 Multivariable-adjusted −0.10 −0.02 Reference 0.11 .26 Weekend Total Sitting, hours/day Age-adjusted −0.16 −0.18 Reference 0.08 .03 Multivariable-adjusted −0.22 −0.19 Reference 0.05 .03 Cardiovascular Risk Factors, OR (95% CI) Age-adjusted 0.46 (0.12-1.78) 1.02 (0.48-2.17) 1.00 1.49 (0.69-3.21) .14 Multivariable-adjusted 0.45 (0.10-2.03) 1.12 (0.49-2.56) 1.00 1.61 (0.68-3.79) .19 BMI: body mass index. Log-transformed variable; beta estimate. Adjusted for age, race, gender, education, employment, income >75,000/year, meeting physical activity recommendation, disability, servings of fruit per day, and servings of vegetables per day. OR: odds ratio. MVPA: moderate to vigorous physical activity. Adjusted for age, race, gender, education, employment, income >75,000/year, disability, servings of fruit per day, and servings of vegetables per day. Square root-transformed variable; beta estimate. Adjusted for age, race, gender, education, employment, income >75,000/year, meeting physical activity recommendation, servings of fruit per day, and servings of vegetables per day. In addition, in the age-adjusted model, a significant association risk factors. Multimedia Appendix 3 presents the regression existed between the preferred gaming platform and weekend results for the weekday gaming time quartile. Participants who sitting time (P=.02). Participants who preferred tabletop games gamed 1-3 hours on weekdays tended to report cardiovascular reported spending the least amount of time sitting on weekends, risk factors with an OR of 3.23 (95% CI 1.10-9.53). However, whereas participants who preferred LARP reported spending we observed no significant linear trends for the weekday gaming the most time sitting on weekends. However, the association time overall. We observed a significant association between between the gaming platform and weekend sitting was weekend gaming time and fulfilling physical activity guidelines attenuated in multivariable-adjusted models (P=.11). after adjusting for covariates (P=.03 and P=.02, respectively; Furthermore, we observed no significant associations between Table 4). Furthermore, participants who gamed >3 hours/day the preferred gaming platform and BMI or the presence of on a typical weekend had 0.40 (95% CI 0.19-0.85) times the cardiovascular risk factors. odds of performing 2.5 hours/week of MVPA. Association of Weekday and Weekend Gaming Time Association of Time Spent Sitting While Gaming and With Physical Wellness Obesity Next, we assessed the association of weekday and weekend Furthermore, we examined the odds of being obese by the gaming time with obesity, physical activity, and cardiovascular proportion of time spent sitting while gaming (Figure 1). http://games.jmir.org/2018/2/e12/ JMIR Serious Games 2018 | vol. 6 | iss. 2 | e12 | p. 5 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Arnaez et al Table 3. Associations among gaming platform most preferred, body mass index, physical activity, and sitting time. Characteristic Tabletop Computer Console Other Electronic LARP P value b,c BMI Age-adjusted Reference −0.04 −0.03 −0.07 0.06 .60 Reference −0.02 −0.004 −0.02 0.05 .96 Multivariable-adjusted Obese, OR (95% CI) Age-adjusted 1.00 0.84 (0.49-1.44) 1.47 (0.70-3.06) 0.48 (0.19-1.21) 1.48 (0.32-6.98) .28 1.00 0.91 (0.43-1.93) 1.99 (0.73-5.39) 0.35 (0.11-1.13) 0.84 (0.10-7.13) .20 Multivariable-adjusted ≥2.5 MVPA , hours/week, OR (95% CI) Age-adjusted 1.00 1.43 (0.83-2.47) 1.06 (0.51-2.20) 0.66 (0.28-1.57) 1.11 (0.24-5.20) .48 1.00 2.70 (1.28-5.69) 1.07 (0.43-2.65) 0.65 (0.24-1.78) 0.66 (0.10-4.47) .04 Multivariable-adjusted Weekday Total Sitting, hours/day Age-adjusted Reference 0.17 0.08 0.14 0.44 .38 Reference 0.15 −0.04 0.07 0.80 .19 Multivariable-adjusted Weekend Total Sitting, hours/day Age-adjusted Reference 0.31 0.25 0.16 0.53 .02 Reference 0.20 0.001 0.06 0.59 .11 Multivariable-adjusted Cardiovascular Risk Factors, OR (95% CI) Age-adjusted 1.00 1.71 (0.84-3.48) 1.55 (0.56-4.28) 2.83 (1.02-7.86) 1.04 (0.11-9.72) .31 1.00 1.98 (0.77-5.08) 1.46 (0.41-5.20) 2.40 (0.72-7.96) 0.85 (0.07-10.59) .51 Multivariable-adjusted LARP: live action role-play. BMI: body mass index. Log-transformed variable; beta estimate. Adjusted for age, race, gender, education, employment, income >75,000/year, meeting physical activity recommendation, weekday gaming quartile, weekend gaming quartile, number of platforms used, disability, servings of fruit per day, and servings of vegetables per day. OR: odds ratio. MVPA: moderate to vigorous physical activity. Adjusted for age, race, gender, education, employment, income >75,000/year, weekday gaming quartile, weekend gaming quartile, number of platforms used, disability, servings of fruit per day, and servings of vegetables per day. Square root-transformed variable; beta estimate. Adjusted for age, race, gender, education, employment, income >75,000/year, meeting physical activity recommendation, number of platforms used, disability, servings of fruit per day, and servings of vegetables per day. Adjusted for age, race, gender, education, employment, income >75,000/year, meeting physical activity recommendation, weekday gaming quartile, weekend gaming quartile, number of platforms used, servings of fruit per day, and servings of vegetables per day. http://games.jmir.org/2018/2/e12/ JMIR Serious Games 2018 | vol. 6 | iss. 2 | e12 | p. 6 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Arnaez et al Table 4. Associations among weekend gaming time, body mass index, and physical activity (N=292). Characteristics Weekend gaming time, hours/day P value Quartile 1 (≤0.5) Quartile 2 (>0.5-<2) Quartile 3 (2-3) Quartile 4 (>3) n (%) 78 (26.6) 51 (17.5) 103 (35.3) 60 (20.1) a,b BMI Age-adjusted Reference −0.01 0.001 0.04 .33 Multivariable-adjusted Reference 0.03 −0.002 0.06 .21 Obese, OR (95% CI) Age-adjusted 1.00 0.77 (0.37-1.57) 1.14 (0.63-2.07) 1.09 (0.55-2.18) .63 Multivariable-adjusted 1.00 0.87 (0.41-1.88) 1.11 (0.58-2.12) 1.06 (0.49-2.26) .81 ≥2.5 MVPA , hours/week, OR (95% CI) Age-adjusted 1.00 0.60 (0.29-1.23) 0.76 (0.41-1.40) 0.38 (0.19-0.77) .01 Multivariable-adjusted 1.00 0.59 (0.28-1.25) 0.84 (0.44-1.61) 0.40 (0.19-0.85) .03 Cardiovascular Risk Factors, OR (95% CI) Age-adjusted 1.00 0.82 (0.33-2.02) 1.03 (0.48-2.21) 1.33 (0.53-3.30) .45 Multivariable-adjusted 1.00 0.84 (0.33-2.13) 1.00 (0.45-2.13) 1.03(0.39-2.72) .88 BMI: body mass index. Log-transformed variable; beta estimate. Adjusted for age, race, gender, education, income >75,000/year, employment, meeting physical activity recommendations, disability, servings of fruit per day, and servings of vegetables per day. OR: odds ratio. MVPA: moderate to vigorous physical activity. Adjusted for age, race, gender, education, income >75,000/year, employment, disability, servings of fruit per day, and servings of vegetables per day. Adjusted for age, race, gender, education, income >75,000/year, employment, meeting physical activity recommendations, servings of fruit per day, and servings of vegetables per day. Figure 1. Multivariable-adjusted odd ratios for being obese according to proportion of time spent sitting while gaming. Adjusted for age, race, gender, education, income over 75k/yr, employment, meeting PA recommendations, disability, weekday and weekend gaming time quartile, number of platforms played, servings of fruit per day, and servings of vegetables per day. Error bar denotes 95% confidence interval. http://games.jmir.org/2018/2/e12/ JMIR Serious Games 2018 | vol. 6 | iss. 2 | e12 | p. 7 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Arnaez et al Compared with those who spent half of the time or less sitting gender; however, none of the interactions were statistically while they gamed, gamers who sat most of the time or all of the significant. Thus, we did not stratify by gender in our final time while they gamed had 2.62 (95% CI 1.11-6.19) and 2.67 models. In the Dunton et al [8] study, the association between (95% CI 1.04-6.86) times the odds of being obese, respectively. any gaming and higher BMI was only noted in individuals who had <60 minutes of physical activity a day; results among active Discussion people were similar to the findings of our study. Gaming and Physical Activity Principal Findings Nonetheless, findings from this study were similar to those from This cross-sectional study among attendees of a large gaming the study by Ballard et al [7] with regard to the association convention highlighted some critical associations between between gaming time and physical activity. We found that as gaming and physical wellness. The total number of gaming weekend gaming time increased, the odds of fulfilling physical platforms used exhibited a substantial, positive association with activity recommendations decreased. In the Ballard et al [7] both obesity and weekend sitting time. In addition, the specific study, the frequency of game play was significantly negatively platform preferred for gaming was significantly associated with correlated with the duration of exercising (r=−.21; P<.05). fulfilling physical activity guidelines, where individuals who Moreover, the duration of video game play was significantly preferred computer games were more likely to fulfill physical negatively correlated with the frequency of exercising (r=−.21; activity guidelines than those who preferred tabletop games. P<.05) and days of walking (r=−.22; P<.05). Not surprisingly, more time spent gaming on weekends was associated with decreased odds of fulfilling physical activity The most surprising finding was that individuals who preferred recommendations. Furthermore, this study established that the computer gaming were more likely to report engaging in 2.5 proportion of time spent sitting while gaming was associated hours of MVPA per week compared with other groups, despite with higher odds of being obese. reporting the highest amount of time spent gaming on weekends; this association could partially be due to the age distribution. Gaming and Obesity A higher proportion of individuals were in the 18-25 age range Previously, three cross-sectional studies established an who preferred computer gaming, and this age range comprised association between game playing and BMI. Ballard et al [7] a higher proportion of individuals who fulfilled physical activity reported that the length of video gaming sessions positively recommendations. Additionally, the proportion of individuals associated with BMI (r=.27; P<.01) in a study comprising 116 who were obese was also lower for participants who preferred male participants. In a study including 562 participants, Weaver computer gaming (45/103, 43.7%) than it was for those who et al [9] reported that persons who gamed exhibited a preferred tabletop gaming (58/116, 50%). We reanalyzed after significantly higher mean BMI than nongamers among males, additionally adjusting for obesity. Those who preferred computer but not females. Dunton et al [8] reported a significant gaming continued to exhibit significantly higher odds of interaction between gaming and physical activity that impacted fulfilling physical activity recommendations than those who the relationship between gaming and BMI in a study comprising preferred tabletop gaming. 10,984 adults. For those with <60 minutes of MVPA per day, Time Spent Sitting While Gaming and Obesity the predicted marginal mean BMI was significantly higher (P<.001) in those who gamed at all than in those who did not This study reported that participants who sat for most or all of game. No significant differences were observed in the predicted their time spent gaming had higher odds of being obese. On marginal mean BMI between gamers and nongamers among average, participants spent 1.69 and 2.07 hours on gaming those who had at least 60 minutes of MVPA. In contrast, this during the week and weekends, respectively, rendering gaming study did not establish a significant association between a substantial source of sedentary activity. Recent research has weekday or weekend gaming time and BMI or the odds of being indicated that breaking up sedentary time can exert health obese. benefits associated with obesity. Using isotemporal substitution, Healy et al [23] established that decreasing the mean prolonged The differences in findings can be explained by several potential sedentary time (sedentary bout ≥30 minutes) by 30 minutes and reasons. First, differences exist among studies in the increasing the nonprolonged sedentary time (sedentary bout classification of gaming. The Ballard et al [7] study included <30 minutes) by 30 minutes is associated with a 0.35 kg/m individuals with a substantial variation in gaming habits, ranging reduction in BMI. Using isotemporal substitution, Gupta et al from individuals who gamed very infrequently (never to a few [24] reported that replacing long sedentary bouts (sedentary times per month) to individuals who gamed almost every day bout >30 minutes) with brief bouts of sedentary behavior of the week. The Dunton et al [8] and Weaver et al [9] studies only compared gamers with nongamers. In contrast, this study (sedentary bout ≤5 minutes) is associated with a 0.87 kg/m was conducted among attendees of a large tabletop gaming reduction in BMI. These findings could be a major factor convention; therefore, it comprised few nongamers in the explaining the association between proportions of time spent analysis and participants were mostly gamers, averaging 12.6 sitting while gaming and obesity. In this study, most participants hours/week of gaming time. In addition, the Ballard et al [7] (64.8%) took breaks from gaming every ≤55 minutes; however, study only enrolled males; however, we enrolled both males a much smaller portion (17.6%) took breaks every ≤25 minutes. and females. The Weaver et al [9] study was stratified by gender As a result, those gaming for longer bouts are unlikely to break and found significant associations in males only. In this study, we examined interactions between our exposures of interest and http://games.jmir.org/2018/2/e12/ JMIR Serious Games 2018 | vol. 6 | iss. 2 | e12 | p. 8 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Arnaez et al them up into smaller bouts of time that are more beneficial to gaming had limitations as we did not obtain information on body composition. whether participants used exergames and only asked about specific games rather than game types. Third, the small sample Strengths & Limitations size hindered the statistical power of the analysis. Reviews on This study has several strengths. First, we assessed and gaming health literature by LeBlanc et al [25] and Kharrazi et examined gaming in multiple ways: the number of platforms al [6] have identified low sample size as a consistent issue that used, platform preference, and weekday and weekend gaming arises in this field of research. Comparable studies by Ballard time quartile. Past research has not distinguished gaming by et al [7] and Weaver et al [9] also had a limited sample size, either the type of platform that was preferred or the number of with 116 and 562 participants, respectively. Fourth, we used a platforms an individual used but has typically categorized self-reported measure of physical activity, and such measures individuals as gamers or nongamers [8,9]. In fact, previous have had issues with overestimation of physical activity in prior research has also focused primarily on electronic gaming, studies [26,27]. As with all observational studies, we cannot whereas this study considered hobby gaming as well. Second, eliminate the possibility of residual or unmeasured confounding. prior studies have focused on total gaming time without Finally, as this was a cross-sectional study, we cannot make attempting to parse out associations for weekday and weekend any inference as to the direction of the relationships observed. gaming time separately. It is imperative to examine weekday Conclusion and weekend gaming separately as the amount of leisure time is much more limited on weekdays compared with weekends. In summary, we found that the number of gaming platforms Finally, this study focused on adults who game, an used associates with higher odds of being obese, while platform underrepresented demographic in gaming research, despite being preference and weekend gaming time associates with the odds the largest demographic of gamers [6,10]. of fulfilling physical activity recommendations. Further research on gaming and health in adults would benefit from extensive, This study also has several limitations. First, our study cohort longitudinal studies to facilitate the examination of prospective comprised gamers who were adequately enthusiastic about the associations between gaming characteristics and clinical hobby and healthy to attend a gaming convention and might outcomes, as well as using objective measurements of physical not be representative of adult gamers in general. Second, we activity using accelerometers. Given the popularity of gaming were only able to enroll a very small number of nongamers among both adults and children, there is a need to better (n=8). Thus, our analysis only included game-playing adults, understand the relationship between gaming and health outcomes and we could not assess how these associations compare with so as to determine strategies to potentially use gaming to help a nongaming adult population. In addition, the assessment of improve physical wellness. Acknowledgments We would like to thank the Indiana University School of Public Health - Bloomington for providing funding for this project. Conflicts of Interest None declared. Multimedia Appendix 1 Baseline characteristics of the study population (n=292). [PDF File (Adobe PDF File), 79KB-Multimedia Appendix 1] Multimedia Appendix 2 Means (SD) and N (%) of characteristics by the number of platforms played. [PDF File (Adobe PDF File), 66KB-Multimedia Appendix 2] Multimedia Appendix 3 Associations of weekday gaming time with obesity, physical activity, and cardiovascular risk factors. [PDF File (Adobe PDF File), 38KB-Multimedia Appendix 3] References 1. U.S. Bureau of Labor Statistics. 2017. Average hours per day spent in selected leisure and sports activities by age URL: https://www.bls.gov/charts/american-time-use/activity-leisure.htm[WebCite Cache ID 6vHwwTPty] 2. Tremblay MS, Willms JD. Is the Canadian childhood obesity epidemic related to physical inactivity? Int J Obes Relat Metab Disord 2003 Sep;27(9):1100-1105. [doi: 10.1038/sj.ijo.0802376] [Medline: 12917717] http://games.jmir.org/2018/2/e12/ JMIR Serious Games 2018 | vol. 6 | iss. 2 | e12 | p. 9 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Arnaez et al 3. Stettler N, Signer TM, Suter PM. Electronic games and environmental factors associated with childhood obesity in Switzerland. 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An Eigth Year of Growth in Hobby Games URL: https://icv2.com/articles/news/view/36975/ an-eighth-year-growth-hobby-games [accessed 2017-11-25] [WebCite Cache ID 6vGi3NDtq] 12. Bailey BW. Energy Cost of Exergaming. Arch Pediatr Adolesc Med 2011 Jul 04;165(7):597-602. [doi: 10.1001/archpediatrics.2011.15] 13. Graves L, Stratton G, Ridgers ND, Cable NT. Comparison of energy expenditure in adolescents when playing new generation and sedentary computer games: cross sectional study. BMJ 2007 Dec 22;335(7633):1282-1284 [FREE Full text] [doi: 10.1136/bmj.39415.632951.80] [Medline: 18156227] 14. Graves LE, Ridgers ND, Williams K, Stratton G, Atkinson G, Cable NT. The Physiological Cost and Enjoyment of Wii Fit in Adolescents, Young Adults, and Older Adults. Journal of Physical Activity and Health 2010 May;7(3):393-401. [doi: 10.1123/jpah.7.3.393] 15. Chomistek AK, Manson JE, Stefanick ML, Lu B, Sands-Lincoln M, Going SB, et al. 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Tremblay MS, Aubert S, Barnes JD, Saunders TJ, Carson V, Latimer-Cheung AE, et al. Sedentary Behavior Research Network (SBRN) – Terminology Consensus Project process and outcome. Int J Behav Nutr Phys Act 2017 Jun 10;14(1). [doi: 10.1186/s12966-017-0525-8] 20. Hagströmer M, Oja P, Sjöström M. The International Physical Activity Questionnaire (IPAQ): a study of concurrent and construct validity. Public Health Nutr 2006 Sep;9(6):755-762. [Medline: 16925881] 21. Rosenberg DE, Norman GJ, Wagner N, Patrick K, Calfas KJ, Sallis JF. Reliability and Validity of the Sedentary Behavior Questionnaire (SBQ) for Adults. Journal of Physical Activity and Health 2010 Nov;7(6):697-705. [doi: 10.1123/jpah.7.6.697] 22. U.S. Department of Health and Human Services. 2008. Physical Activity Guidelines for Americans URL: https://health. gov/paguidelines/guidelines/adults.aspx [accessed 2017-11-27] [WebCite Cache ID 6vHyJSH4s] 23. Healy GN, Winkler EA, Brakenridge CL, Reeves MM, Eakin EG. Accelerometer-derived sedentary and physical activity time in overweight/obese adults with type 2 diabetes: cross-sectional associations with cardiometabolic biomarkers. PLoS One 2015;10(3):e0119140 [FREE Full text] [doi: 10.1371/journal.pone.0119140] [Medline: 25775249] 24. Gupta N, Heiden M, Aadahl M, Korshøj M, Jørgensen MB, Holtermann A. What Is the Effect on Obesity Indicators from Replacing Prolonged Sedentary Time with Brief Sedentary Bouts, Standing and Different Types of Physical Activity during Working Days? A Cross-Sectional Accelerometer-Based Study among Blue-Collar Workers. PLoS One 2016 May 17;11(5):e0154935 [FREE Full text] [doi: 10.1371/journal.pone.0154935] [Medline: 27187777] 25. LeBlanc AG, Chaput J, McFarlane A, Colley RC, Thivel D, Biddle SJ, et al. Active video games and health indicators in children and youth: a systematic review. PLoS One 2013;8(6):e65351 [FREE Full text] [doi: 10.1371/journal.pone.0065351] [Medline: 23799008] http://games.jmir.org/2018/2/e12/ JMIR Serious Games 2018 | vol. 6 | iss. 2 | e12 | p. 10 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Arnaez et al 26. Sebastião E, Gobbi S, Chodzko-Zajko W, Schwingel A, Papini C, Nakamura P, et al. The International Physical Activity Questionnaire-long form overestimates self-reported physical activity of Brazilian adults. Public Health 2012 Nov;126(11):967-975. [doi: 10.1016/j.puhe.2012.07.004] [Medline: 22944387] 27. Johnson-Kozlow M, Sallis JF, Gilpin EA, Rock CL, Pierce JP. Comparative validation of the IPAQ and the 7-Day PAR among women diagnosed with breast cancer. Int J Behav Nutr Phys Act 2006 Mar 31;3:7 [FREE Full text] [doi: 10.1186/1479-5868-3-7] [Medline: 16579852] Abbreviations BMI: body mass index IPAQ: International Physical Activity Questionnaire LARP: live action role-play MVPA: moderate-to-vigorous physical activity OR: odds ratio Edited by G Eysenbach; submitted 04.12.17; peer-reviewed by J Hwang, J Bervoets, C Carrion, R Kretschmann; comments to author 25.01.18; revised version received 20.03.18; accepted 03.04.18; published 12.06.18 Please cite as: Arnaez J, Frey G, Cothran D, Lion M, Chomistek A JMIR Serious Games 2018;6(2):e12 URL: http://games.jmir.org/2018/2/e12/ doi: 10.2196/games.9571 PMID: 29895516 ©James Arnaez, Georgia Frey, Donetta Cothran, Margaret Lion, Andrea Chomistek. Originally published in JMIR Serious Games (http://games.jmir.org), 12.06.2018. 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 http://games.jmir.org, as well as this copyright and license information must be included. http://games.jmir.org/2018/2/e12/ JMIR Serious Games 2018 | vol. 6 | iss. 2 | e12 | p. 11 (page number not for citation purposes) XSL FO RenderX http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png JMIR Serious Games JMIR Publications

Physical Wellness Among Gaming Adults: Cross-Sectional Study

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Abstract

Background: Video and hobby gaming are immensely popular among adults; however, associations between gaming and health have primarily been investigated in children and adolescents. Furthermore, most research has focused on electronic gaming, despite traditional hobby gaming gaining prominence. Objective: To determine whether the number of platforms used, platform preference, and gaming time are associated with obesity, physical activity, sedentary behavior, and cardiovascular risk factors in an adult gaming population. Methods: We conducted a cross-sectional analysis using data obtained from 292 participants who attended a large Midwestern gaming convention. We collected data using a computer-based questionnaire that comprised questions on gaming behavior, demographics, physical activity (using the International Physical Activity Questionnaire), and health characteristics. In addition, we used multivariable-adjusted linear and logistic regression to model health outcomes as a function of the number of platforms used, platform preference, and weekday and weekend gaming time quartile. Results: After adjusting for covariates, we observed a significant linear trend for increasing odds of being obese and higher weekend sitting time by the number of platforms used (P=.03 for both). The platform preference and weekend gaming time quartile exhibited significant associations with odds of meeting physical activity recommendations (P=.047 and P=.03, respectively). In addition, we observed higher odds of being obese among those reporting that they sat most or all of the time while gaming [odds ratio (OR) 2.69 (95% CI 1.14-6.31) and OR 2.71 (95% CI 1.06-6.93), respectively]. Conclusions: In adult gamers, the number of platforms used, which platforms they prefer to play on, and the amount of time spent gaming on weekends could have significant implications for their odds of being obese and meeting physical activity recommendations. (JMIR Serious Games 2018;6(2):e12) doi: 10.2196/games.9571 KEYWORDS video games; electronic gaming; traditional gaming; obesity; physical activity; sedentary behavior Prior studies have established an association between video Introduction game use and obesity in children, independent of the time spent watching television and physical activity [2,3]. Additionally, Although electronic gaming is a popular leisure activity among health-oriented gaming interventions have been primarily young adults, little is known about the impact of gaming conducted in children [4,5]. Although research on gaming has participation on health in this age group [1]. Most research focused on children, a systematic review of health gaming investigating the relationship between gaming and healthy research by Kharrazi et al [6] reported that 65 of 108 studies lifestyle behaviors or health outcomes has focused on children. http://games.jmir.org/2018/2/e12/ JMIR Serious Games 2018 | vol. 6 | iss. 2 | e12 | p. 1 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Arnaez et al enrolled participants no older than 20 years. Furthermore, after questionnaire. Of these, we excluded 8 participants who reported adjusting for the study sample size, the mean age of participants they did not play games on any platform, 14 who reported >35 in studies on gaming was 13 years, which is not reflective of hours of recreational physical activity per week, and 18 who the current gaming population. Furthermore, some studies on had missing data on physical activity, platform preference, adults have found that gaming is typically associated with a sitting time, or BMI. Hence, we enrolled 292 participants in higher body mass index (BMI), particularly among males [7-9]. this study. In 2017, the Electronic Software Association reported that 67% Measurement of Video and Hobby Game Playing of American households own at least one gaming console and The first section of the questionnaire gathered information on 65% of Americans play games at least 3 hours/week, resulting gaming platform preferences, time spent gaming, and amount in U.S. $ 30.4 billion spent on games and game-related of sitting while gaming. We asked participants to identify the equipment [10]. In addition, hobby games (eg, tabletop board two gaming platforms that they used most often among the games, collectible card games, and role-playing games) have following: computer, console, handheld, tabletop, live action also witnessed an upsurge in sales in recent years [11]. While role-play (LARP), phone, and tablet platforms. In addition, electronic and hobby gaming are typically viewed as sedentary participants identified other platforms that they used besides activities, energy expenditure could differ between different the two most used platforms. Furthermore, we asked participants types of gaming (eg, live action role-play vs tabletop role-play), about the proportion of time they spent sitting while gaming, especially with the rise of “exergames” that incorporate physical whether they took breaks during gaming and how frequent, and movement and activity as a part of the core game mechanics whether they felt that they had worked out after a gaming session [12-14]. was over. Sedentary behavior, defined as “any waking behavior Measurement of Physical Activity and Sedentary characterized by an energy expenditure ≤1.5 metabolic Behaviors equivalents of task, while in a sitting, reclining, or lying We used the International Physical Activity Questionnaire posture,” has been associated with several adverse health (IPAQ) to determine the usual level of physical activity [20]. outcomes, including obesity, metabolic syndrome, type 2 Specifically, we used the IPAQ to gather information on leisure diabetes, and cardiovascular disease [15-19]. Studies on time walking, moderate-to-vigorous physical activity (MVPA), sedentary behavior have primarily focused on television and typical methods of transportation in the 7 days before watching, total screen time, or overall sitting time, while few arriving at the convention. In addition, we used the Sedentary have attempted to investigate differential associations for Behavior Questionnaire to evaluate time spent in the following specific types of sedentary behavior, including video or hobby 8 different sedentary activities on an average weekday or gaming. weekend: gaming, watching television, talking on the phone, This study aims to add to the current literature on gaming and doing office work, listening to music, reading, playing an health by addressing two consistent issues. First, most research instrument, or doing artwork or crafts [21]. Furthermore, 9 concerning the relationship between gaming and health has categories were provided for response, ranging from no time focused on children and adolescents. Although adult gamers on an activity to ≥6 hours/day. constitute the largest portion of the gaming audience, limited Measurement of Health and Demographic research has been conducted on this population. Second, the Characteristics assessment of gaming in several studies has been limited in scope. In past research, gaming is often examined as either time In addition to collecting data on physical activity and sedentary spent gaming or whether participants gamed at all. Most studies time, we assessed other health characteristics, including height, focus solely on electronic gaming; however, when tabletop weight, smoking status, and number of cigarettes smoked per gaming is considered, both types are considered one gaming day. In addition, participants completed questions on their group. Thus, the relationship between health and the type of medical history, including the diagnosis of high blood pressure, gaming people prefer as well as the different forms of gaming nongestational diabetes, elevated cholesterol, myocardial people use remains unclear. Hence, this study aims to address infarction, stroke, and cancer, as well as whether they had a these limitations by focusing on an adult gaming population disability that limited their physical activity. We also asked and using multiple measures of gaming behavior to elucidate participants how many servings of fruit and vegetables they ate how gaming correlates with health in this population. per day. Finally, we asked participants to provide demographic information, including ethnicity, race, gender, marital status, Methods age, income, country of residence, education level, and employment status. Participants Statistical Analysis In this study, participants were recruited among attendees of a In this study, all statistical analyses were performed using SAS large Midwestern tabletop gaming convention held in summer statistical software, version 9.4 (SAS Institute Inc, Cary, North 2014. They completed an online questionnaire that assessed Carolina, United States). The exposures of interest were the gaming habits, physical activity, sedentary time, and other health preferred gaming platform, number of platforms used, and time characteristics, either at the convention center or off-site at a spent gaming. The outcomes of interest were BMI, obesity status later time. A total of 332 individuals completed the online http://games.jmir.org/2018/2/e12/ JMIR Serious Games 2018 | vol. 6 | iss. 2 | e12 | p. 2 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Arnaez et al (BMI<30 vs BMI≥30), physical activity, sedentary time, and Results presence of a cardiovascular risk factor (diagnosis of ≥1 of diabetes, hypertension, and high cholesterol). Characteristics of Study Population We assessed the baseline characteristics of the study population The study population was predominantly white (259 of 290, by calculating means (SD) for continuous variables and 89.3%) and male (197 of 187, 68.6%), with a mean age of 34.2 frequencies (%) for categorical variables. In addition, we (SD 10.6) years (Multimedia Appendix 1). Most participants assessed means (SD) and frequencies (%) of health were either overweight (77 of 292, 26.4%) or obese (154 of characteristics based on the number of platforms used and 2 292, 47.3%), with a mean BMI of 31.2 (SD 8.8) kg/m . The platform preference. In this and all analyses for platform mean hours per week of MVPA was 5.2 (SD 5.9), with 166 of preference, gaming on a handheld console, phone, or tablet was 292 (56.9%) participants reporting that they fulfilled the physical grouped into the “other electronic” category due to small sample activity guidelines of at least 2.5 hours of MVPA per week. sizes in each of these categories. Furthermore, we used the Nearly one-quarter (66 of 290, 22.8%) of the participants Kruskal–Wallis test and Fisher’s tests with Monte Carlo responded that they had a disability or health condition that approximation to test for the significance of the association restricted their ability to be physically active. In fact, 67 of 290 between platform preference and each variable. (23.1%) participants reported that they had a cardiovascular risk factor. The most preferred gaming platforms were tabletop We then assessed the association between the number of gaming gaming (116 of 292, 39.7%) and computer gaming (103 of 292, platforms used and BMI, obesity, physical activity, sedentary 35.3%). Most participants (196 of 292, 67.1%) used at least time, and cardiovascular risk factors. As these were not normally three different platforms. distributed, BMI was log-transformed, and the sitting time was square root-transformed. For physical activity, we categorized Characteristics of Study Population by Platform participants depending on whether they fulfilled the physical Preference activity 2008 Physical Activity Guidelines for Americans of We assessed the baseline characteristics according to the type 2.5 hours of MVPA per week [22]. While linear regression was of platform that was most preferred (Table 1). We observed used for analyzing continuous outcomes of BMI and sitting significant differences by the platform preference in age, time, logistic regression was used to analyze the categorical weekend sitting time, weekday and weekend gaming time, outcomes of obesity, meeting physical activity guidelines, and proportion of time spent sitting, and servings of vegetables per the presence of cardiovascular risk factors. Furthermore, we day (P=.002, P=.002, P<.001, P<.001, P=.001, and P=.02, conducted a test for linear trend by treating the number of respectively). While participants who preferred tabletop games platforms used as a continuous variable. were the oldest, with a mean age of 36.4 (SD 9.8) years, those Similar analyses were performed to examine the association who preferred console games were the youngest, with a mean between platform preference, as well as time spent gaming, and age of 30.6 (SD 10.9) years. Weekend sitting time was the each of the health outcomes. For platform preference, highest among participants who preferred LARP [9.9 (SD 3.5) participants who preferred tabletop gaming were assigned to hours/day] and the lowest among those who preferred tabletop the reference group, and analysis of covariance was used for games [7.0 (SD 3.6) hours/day]. Although participants who the continuous outcomes (BMI and sedentary time) to test the preferred computer games reported the highest time spent significance of platform preference overall. In addition, we used gaming on both weekdays and weekends [2.5 (SD 1.7) and 3.0 fractional logistic regression to test the significance of platform (SD 1.8) hours/day, respectively], they also constituted the preference for dichotomous outcomes. Then, weekday and highest proportion of participants who fulfilled physical activity weekend gaming time was examined in quartiles to investigate recommendations (65 of 103, 63.1%). associations between gaming time and each outcome. Besides, Characteristics of Study Population by Number of the median of the gaming time quartiles was modeled as a continuous variable to test for linear trend. Furthermore, we Platforms Played used logistic regression to investigate the association between Next, we assessed the baseline characteristics by the number of the proportion of time spent sitting while gaming and the odds platforms used (Multimedia Appendix 2). We observed of being obese. significant differences by the number of platforms used in age and time spent gaming on weekdays and weekends (P=.01, For all analyses of gaming and health outcomes, we used P=.02, and P=.002, respectively). In addition, participants who age-adjusted and multivariable-adjusted models. All reported playing ≥4 platforms were the youngest, with a mean multivariable-adjusted models included age, race, gender, age of 31.3 (SD 9.1) years, whereas those who only played 1-2 employment, income (income >75,000/year vs income platforms were older, with mean ages of 37.4 (SD 13.4) and ≤75,0000/year), servings of fruit per day, and servings of 37.1 (SD 11.6) years, respectively. Not surprisingly, the mean vegetables per day. Based on the exposure and outcome, other time spent gaming on weekdays and weekends increased as the covariates included in multivariable-adjusted models were number of platforms used increased. Furthermore, those who fulfilling physical activity guidelines, the presence of a played ≥4 platforms reported 2.0 (SD 1.6) hours/day of gaming disability, the number of platforms used, and weekday and on a typical weekday and 2.6 (SD 1.7) hours/day of gaming on weekend gaming time. The specific variables included in each a typical weekend day. model are listed in the footnotes of each table in this study. http://games.jmir.org/2018/2/e12/ JMIR Serious Games 2018 | vol. 6 | iss. 2 | e12 | p. 3 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Arnaez et al Table 1. Means (SD) and frequency (%) of characteristics by gaming platforms most preferred. Characteristic Tabletop (n=116) Computer (n=103) Console (n=41) Other Electronic (n=25) LARP (n=7) P value Age, years, mean (SD) 36.4 (9.8) 33.0 (11.4) 30.6 (10.9) 35.0 (9.3) 31.9 (8.0) .002 b 2 BMI , kg/m , mean (SD) 32.1 (8.5) 30.4 (8.4) 30.6 (8.6) 30.2 (10.6) 34.6 (13.3) .41 Obese, n (%) 58 (50) 45 (43.7) 23 (56.1) 8 (32) 4 (57.1) .32 MVPA , hours/week, mean (SD) 4.5 (4.6) 5.2 (5.7) 6.7 (8.0) 5.0 (7.5) 7.3 (8.8) .94 ≥2.5 MVPA, hours/week, n (%) 63 (54.3) 65 (63.1) 23 (56.1) 11 (44) 4 (57.1) .47 Sitting time, hours/day, mean (SD) Weekday 8.7 (4.9) 9.4 (4.1) 9.0 (4.0) 9.6 (5.4) 11.3 (4.8) .35 Weekend 7.0 (3.6) 8.6 (3.6) 8.6 (4.4) 8.0 (5.5) 9.9 (3.5) .002 Time spent gaming, hours/day, mean (SD) Weekday 1.0 (1.2) 2.5 (1.7) 2.0 (1.7) 1.1 (0.9) 0.9 (1.0) <.001 Weekend 1.3 (1.4) 3.0 (1.8) 2.5 (1.7) 1.4 (1.2) 1.4 (1.3) <.001 Cardiovascular risk factors, n (%) Yes 24 (20.9) 25 (24.5) 8 (19.5) 9 (36.0) 1 (14.3) .51 No 91 (79.1) 77 (75.5) 33 (80.5) 16 (64.0) 6 (85.7) Proportion of time spent sitting while gaming, n (%) Half or less 17 (14.9) 12 (11.9) 4 (9.8) 7 (28) 5 (71.4) .001 Most or All 97 (85.1) 89 (88.1) 37 (90.2) 18 (72) 2 (28.6) Take breaks while gaming, n (%) Yes 96 (85.7) 78 (80.4) 30 (76.9) 17 (80.9) 6 (100) .53 No 16 (14.3) 19 (19.6) 9 (23.1) 4 (19.1) 0 Frequency of breaks, n (%) Every ≤55 minute 63 (65.6) 48 (61.5) 21 (70.0) 6 (35.3) 4 (66.7) .76 1 hour + 33 (34.4) 30 (38.5) 9 (30.0) 11 (64.7) 2 (33.3) Servings of fruit per day, mean (SD) 1.0 (0.9) 1.0 (0.9) 1.2 (1.1) 1.1 (1.2) 0.5 (0.7) .38 Servings of vegetables per day, mean 2.0 (1.2) 1.9 (1.2) 1.6 (1.2) 1.5 (1.1) 0.9 (0.8) .02 (SD) LARP: live action role-play. BMI: body mass index. MVPA: moderate to vigorous physical activity. weekend sitting time and number of platforms used (P =.03; trend Association of Number of Platforms Played and beta=.11); however, no significant associations existed between Physical Wellness the number of platforms used and physical activity or the Table 2 presents the associations of obesity, physical activity, presence of cardiovascular risk factors. sedentary time, and cardiovascular risk factors with the number Association of Platform Preference With Physical of platforms used. We observed that a higher number of Wellness platforms used was significantly associated with a higher BMI in the age-adjusted model but was only marginally significant Table 3 presents the results of the regression analysis for the in the multivariable-adjusted model (P=.045 and P=.07, platform preference with the same outcomes as the previous respectively). Compared with participants who reported using analysis. In multivariable-adjusted models, a significant 3 platforms, those who reported using ≥4 platforms exhibited association was observed between the preferred gaming platform a multivariable-adjusted odds ratio (OR) of 1.55 (95% CI and fulfilling physical activity guidelines (P=.04). Compared 0.78-3.09) for obesity, whereas those who reported using 1 with participants who preferred tabletop games, those who platform exhibited an OR of 0.48 (95% CI 0.14-1.58; P =.03). preferred computer games had higher odds of performing 2.5 trend In addition, we observed a significant positive linear trend for hours of MVPA per week (OR 2.70, 95% CI 1.28-5.69). http://games.jmir.org/2018/2/e12/ JMIR Serious Games 2018 | vol. 6 | iss. 2 | e12 | p. 4 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Arnaez et al Table 2. Associations between the number of gaming platforms used and health outcomes. Characteristic Gaming platforms P value 1 2 3 4+ a,b BMI Age-adjusted −0.11 −0.02 Reference 0.03 .045 Multivariable-adjusted −0.07 −0.02 Reference 0.04 .07 Obese, OR (95% CI) Age-adjusted 0.58 (0.22-1.50) 0.76 (0.42-1.38) 1.00 1.41 (0.78-2.54) .03 Multivariable-adjusted 0.48 (0.14-1.58) 0.78 (0.39-1.54) 1.00 1.55 (0.78-3.09) .03 ≥2.5 MVPA , hours/week, OR (95% CI) Age-adjusted 1.18 (0.46-3.05) 0.77 (0.43-1.39) 1.00 0.68 (0.38-1.23) .46 Multivariable-adjusted 1.06 (0.34-3.30) 0.73 (0.38-1.42) 1.00 0.61 (0.32-1.18) .50 Weekday Total Sitting, hours/day Age-adjusted 0.004 −0.01 Reference 0.03 .79 Multivariable-adjusted −0.10 −0.02 Reference 0.11 .26 Weekend Total Sitting, hours/day Age-adjusted −0.16 −0.18 Reference 0.08 .03 Multivariable-adjusted −0.22 −0.19 Reference 0.05 .03 Cardiovascular Risk Factors, OR (95% CI) Age-adjusted 0.46 (0.12-1.78) 1.02 (0.48-2.17) 1.00 1.49 (0.69-3.21) .14 Multivariable-adjusted 0.45 (0.10-2.03) 1.12 (0.49-2.56) 1.00 1.61 (0.68-3.79) .19 BMI: body mass index. Log-transformed variable; beta estimate. Adjusted for age, race, gender, education, employment, income >75,000/year, meeting physical activity recommendation, disability, servings of fruit per day, and servings of vegetables per day. OR: odds ratio. MVPA: moderate to vigorous physical activity. Adjusted for age, race, gender, education, employment, income >75,000/year, disability, servings of fruit per day, and servings of vegetables per day. Square root-transformed variable; beta estimate. Adjusted for age, race, gender, education, employment, income >75,000/year, meeting physical activity recommendation, servings of fruit per day, and servings of vegetables per day. In addition, in the age-adjusted model, a significant association risk factors. Multimedia Appendix 3 presents the regression existed between the preferred gaming platform and weekend results for the weekday gaming time quartile. Participants who sitting time (P=.02). Participants who preferred tabletop games gamed 1-3 hours on weekdays tended to report cardiovascular reported spending the least amount of time sitting on weekends, risk factors with an OR of 3.23 (95% CI 1.10-9.53). However, whereas participants who preferred LARP reported spending we observed no significant linear trends for the weekday gaming the most time sitting on weekends. However, the association time overall. We observed a significant association between between the gaming platform and weekend sitting was weekend gaming time and fulfilling physical activity guidelines attenuated in multivariable-adjusted models (P=.11). after adjusting for covariates (P=.03 and P=.02, respectively; Furthermore, we observed no significant associations between Table 4). Furthermore, participants who gamed >3 hours/day the preferred gaming platform and BMI or the presence of on a typical weekend had 0.40 (95% CI 0.19-0.85) times the cardiovascular risk factors. odds of performing 2.5 hours/week of MVPA. Association of Weekday and Weekend Gaming Time Association of Time Spent Sitting While Gaming and With Physical Wellness Obesity Next, we assessed the association of weekday and weekend Furthermore, we examined the odds of being obese by the gaming time with obesity, physical activity, and cardiovascular proportion of time spent sitting while gaming (Figure 1). http://games.jmir.org/2018/2/e12/ JMIR Serious Games 2018 | vol. 6 | iss. 2 | e12 | p. 5 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Arnaez et al Table 3. Associations among gaming platform most preferred, body mass index, physical activity, and sitting time. Characteristic Tabletop Computer Console Other Electronic LARP P value b,c BMI Age-adjusted Reference −0.04 −0.03 −0.07 0.06 .60 Reference −0.02 −0.004 −0.02 0.05 .96 Multivariable-adjusted Obese, OR (95% CI) Age-adjusted 1.00 0.84 (0.49-1.44) 1.47 (0.70-3.06) 0.48 (0.19-1.21) 1.48 (0.32-6.98) .28 1.00 0.91 (0.43-1.93) 1.99 (0.73-5.39) 0.35 (0.11-1.13) 0.84 (0.10-7.13) .20 Multivariable-adjusted ≥2.5 MVPA , hours/week, OR (95% CI) Age-adjusted 1.00 1.43 (0.83-2.47) 1.06 (0.51-2.20) 0.66 (0.28-1.57) 1.11 (0.24-5.20) .48 1.00 2.70 (1.28-5.69) 1.07 (0.43-2.65) 0.65 (0.24-1.78) 0.66 (0.10-4.47) .04 Multivariable-adjusted Weekday Total Sitting, hours/day Age-adjusted Reference 0.17 0.08 0.14 0.44 .38 Reference 0.15 −0.04 0.07 0.80 .19 Multivariable-adjusted Weekend Total Sitting, hours/day Age-adjusted Reference 0.31 0.25 0.16 0.53 .02 Reference 0.20 0.001 0.06 0.59 .11 Multivariable-adjusted Cardiovascular Risk Factors, OR (95% CI) Age-adjusted 1.00 1.71 (0.84-3.48) 1.55 (0.56-4.28) 2.83 (1.02-7.86) 1.04 (0.11-9.72) .31 1.00 1.98 (0.77-5.08) 1.46 (0.41-5.20) 2.40 (0.72-7.96) 0.85 (0.07-10.59) .51 Multivariable-adjusted LARP: live action role-play. BMI: body mass index. Log-transformed variable; beta estimate. Adjusted for age, race, gender, education, employment, income >75,000/year, meeting physical activity recommendation, weekday gaming quartile, weekend gaming quartile, number of platforms used, disability, servings of fruit per day, and servings of vegetables per day. OR: odds ratio. MVPA: moderate to vigorous physical activity. Adjusted for age, race, gender, education, employment, income >75,000/year, weekday gaming quartile, weekend gaming quartile, number of platforms used, disability, servings of fruit per day, and servings of vegetables per day. Square root-transformed variable; beta estimate. Adjusted for age, race, gender, education, employment, income >75,000/year, meeting physical activity recommendation, number of platforms used, disability, servings of fruit per day, and servings of vegetables per day. Adjusted for age, race, gender, education, employment, income >75,000/year, meeting physical activity recommendation, weekday gaming quartile, weekend gaming quartile, number of platforms used, servings of fruit per day, and servings of vegetables per day. http://games.jmir.org/2018/2/e12/ JMIR Serious Games 2018 | vol. 6 | iss. 2 | e12 | p. 6 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Arnaez et al Table 4. Associations among weekend gaming time, body mass index, and physical activity (N=292). Characteristics Weekend gaming time, hours/day P value Quartile 1 (≤0.5) Quartile 2 (>0.5-<2) Quartile 3 (2-3) Quartile 4 (>3) n (%) 78 (26.6) 51 (17.5) 103 (35.3) 60 (20.1) a,b BMI Age-adjusted Reference −0.01 0.001 0.04 .33 Multivariable-adjusted Reference 0.03 −0.002 0.06 .21 Obese, OR (95% CI) Age-adjusted 1.00 0.77 (0.37-1.57) 1.14 (0.63-2.07) 1.09 (0.55-2.18) .63 Multivariable-adjusted 1.00 0.87 (0.41-1.88) 1.11 (0.58-2.12) 1.06 (0.49-2.26) .81 ≥2.5 MVPA , hours/week, OR (95% CI) Age-adjusted 1.00 0.60 (0.29-1.23) 0.76 (0.41-1.40) 0.38 (0.19-0.77) .01 Multivariable-adjusted 1.00 0.59 (0.28-1.25) 0.84 (0.44-1.61) 0.40 (0.19-0.85) .03 Cardiovascular Risk Factors, OR (95% CI) Age-adjusted 1.00 0.82 (0.33-2.02) 1.03 (0.48-2.21) 1.33 (0.53-3.30) .45 Multivariable-adjusted 1.00 0.84 (0.33-2.13) 1.00 (0.45-2.13) 1.03(0.39-2.72) .88 BMI: body mass index. Log-transformed variable; beta estimate. Adjusted for age, race, gender, education, income >75,000/year, employment, meeting physical activity recommendations, disability, servings of fruit per day, and servings of vegetables per day. OR: odds ratio. MVPA: moderate to vigorous physical activity. Adjusted for age, race, gender, education, income >75,000/year, employment, disability, servings of fruit per day, and servings of vegetables per day. Adjusted for age, race, gender, education, income >75,000/year, employment, meeting physical activity recommendations, servings of fruit per day, and servings of vegetables per day. Figure 1. Multivariable-adjusted odd ratios for being obese according to proportion of time spent sitting while gaming. Adjusted for age, race, gender, education, income over 75k/yr, employment, meeting PA recommendations, disability, weekday and weekend gaming time quartile, number of platforms played, servings of fruit per day, and servings of vegetables per day. Error bar denotes 95% confidence interval. http://games.jmir.org/2018/2/e12/ JMIR Serious Games 2018 | vol. 6 | iss. 2 | e12 | p. 7 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Arnaez et al Compared with those who spent half of the time or less sitting gender; however, none of the interactions were statistically while they gamed, gamers who sat most of the time or all of the significant. Thus, we did not stratify by gender in our final time while they gamed had 2.62 (95% CI 1.11-6.19) and 2.67 models. In the Dunton et al [8] study, the association between (95% CI 1.04-6.86) times the odds of being obese, respectively. any gaming and higher BMI was only noted in individuals who had <60 minutes of physical activity a day; results among active Discussion people were similar to the findings of our study. Gaming and Physical Activity Principal Findings Nonetheless, findings from this study were similar to those from This cross-sectional study among attendees of a large gaming the study by Ballard et al [7] with regard to the association convention highlighted some critical associations between between gaming time and physical activity. We found that as gaming and physical wellness. The total number of gaming weekend gaming time increased, the odds of fulfilling physical platforms used exhibited a substantial, positive association with activity recommendations decreased. In the Ballard et al [7] both obesity and weekend sitting time. In addition, the specific study, the frequency of game play was significantly negatively platform preferred for gaming was significantly associated with correlated with the duration of exercising (r=−.21; P<.05). fulfilling physical activity guidelines, where individuals who Moreover, the duration of video game play was significantly preferred computer games were more likely to fulfill physical negatively correlated with the frequency of exercising (r=−.21; activity guidelines than those who preferred tabletop games. P<.05) and days of walking (r=−.22; P<.05). Not surprisingly, more time spent gaming on weekends was associated with decreased odds of fulfilling physical activity The most surprising finding was that individuals who preferred recommendations. Furthermore, this study established that the computer gaming were more likely to report engaging in 2.5 proportion of time spent sitting while gaming was associated hours of MVPA per week compared with other groups, despite with higher odds of being obese. reporting the highest amount of time spent gaming on weekends; this association could partially be due to the age distribution. Gaming and Obesity A higher proportion of individuals were in the 18-25 age range Previously, three cross-sectional studies established an who preferred computer gaming, and this age range comprised association between game playing and BMI. Ballard et al [7] a higher proportion of individuals who fulfilled physical activity reported that the length of video gaming sessions positively recommendations. Additionally, the proportion of individuals associated with BMI (r=.27; P<.01) in a study comprising 116 who were obese was also lower for participants who preferred male participants. In a study including 562 participants, Weaver computer gaming (45/103, 43.7%) than it was for those who et al [9] reported that persons who gamed exhibited a preferred tabletop gaming (58/116, 50%). We reanalyzed after significantly higher mean BMI than nongamers among males, additionally adjusting for obesity. Those who preferred computer but not females. Dunton et al [8] reported a significant gaming continued to exhibit significantly higher odds of interaction between gaming and physical activity that impacted fulfilling physical activity recommendations than those who the relationship between gaming and BMI in a study comprising preferred tabletop gaming. 10,984 adults. For those with <60 minutes of MVPA per day, Time Spent Sitting While Gaming and Obesity the predicted marginal mean BMI was significantly higher (P<.001) in those who gamed at all than in those who did not This study reported that participants who sat for most or all of game. No significant differences were observed in the predicted their time spent gaming had higher odds of being obese. On marginal mean BMI between gamers and nongamers among average, participants spent 1.69 and 2.07 hours on gaming those who had at least 60 minutes of MVPA. In contrast, this during the week and weekends, respectively, rendering gaming study did not establish a significant association between a substantial source of sedentary activity. Recent research has weekday or weekend gaming time and BMI or the odds of being indicated that breaking up sedentary time can exert health obese. benefits associated with obesity. Using isotemporal substitution, Healy et al [23] established that decreasing the mean prolonged The differences in findings can be explained by several potential sedentary time (sedentary bout ≥30 minutes) by 30 minutes and reasons. First, differences exist among studies in the increasing the nonprolonged sedentary time (sedentary bout classification of gaming. The Ballard et al [7] study included <30 minutes) by 30 minutes is associated with a 0.35 kg/m individuals with a substantial variation in gaming habits, ranging reduction in BMI. Using isotemporal substitution, Gupta et al from individuals who gamed very infrequently (never to a few [24] reported that replacing long sedentary bouts (sedentary times per month) to individuals who gamed almost every day bout >30 minutes) with brief bouts of sedentary behavior of the week. The Dunton et al [8] and Weaver et al [9] studies only compared gamers with nongamers. In contrast, this study (sedentary bout ≤5 minutes) is associated with a 0.87 kg/m was conducted among attendees of a large tabletop gaming reduction in BMI. These findings could be a major factor convention; therefore, it comprised few nongamers in the explaining the association between proportions of time spent analysis and participants were mostly gamers, averaging 12.6 sitting while gaming and obesity. In this study, most participants hours/week of gaming time. In addition, the Ballard et al [7] (64.8%) took breaks from gaming every ≤55 minutes; however, study only enrolled males; however, we enrolled both males a much smaller portion (17.6%) took breaks every ≤25 minutes. and females. The Weaver et al [9] study was stratified by gender As a result, those gaming for longer bouts are unlikely to break and found significant associations in males only. In this study, we examined interactions between our exposures of interest and http://games.jmir.org/2018/2/e12/ JMIR Serious Games 2018 | vol. 6 | iss. 2 | e12 | p. 8 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Arnaez et al them up into smaller bouts of time that are more beneficial to gaming had limitations as we did not obtain information on body composition. whether participants used exergames and only asked about specific games rather than game types. Third, the small sample Strengths & Limitations size hindered the statistical power of the analysis. Reviews on This study has several strengths. First, we assessed and gaming health literature by LeBlanc et al [25] and Kharrazi et examined gaming in multiple ways: the number of platforms al [6] have identified low sample size as a consistent issue that used, platform preference, and weekday and weekend gaming arises in this field of research. Comparable studies by Ballard time quartile. Past research has not distinguished gaming by et al [7] and Weaver et al [9] also had a limited sample size, either the type of platform that was preferred or the number of with 116 and 562 participants, respectively. Fourth, we used a platforms an individual used but has typically categorized self-reported measure of physical activity, and such measures individuals as gamers or nongamers [8,9]. In fact, previous have had issues with overestimation of physical activity in prior research has also focused primarily on electronic gaming, studies [26,27]. As with all observational studies, we cannot whereas this study considered hobby gaming as well. Second, eliminate the possibility of residual or unmeasured confounding. prior studies have focused on total gaming time without Finally, as this was a cross-sectional study, we cannot make attempting to parse out associations for weekday and weekend any inference as to the direction of the relationships observed. gaming time separately. It is imperative to examine weekday Conclusion and weekend gaming separately as the amount of leisure time is much more limited on weekdays compared with weekends. In summary, we found that the number of gaming platforms Finally, this study focused on adults who game, an used associates with higher odds of being obese, while platform underrepresented demographic in gaming research, despite being preference and weekend gaming time associates with the odds the largest demographic of gamers [6,10]. of fulfilling physical activity recommendations. Further research on gaming and health in adults would benefit from extensive, This study also has several limitations. First, our study cohort longitudinal studies to facilitate the examination of prospective comprised gamers who were adequately enthusiastic about the associations between gaming characteristics and clinical hobby and healthy to attend a gaming convention and might outcomes, as well as using objective measurements of physical not be representative of adult gamers in general. Second, we activity using accelerometers. Given the popularity of gaming were only able to enroll a very small number of nongamers among both adults and children, there is a need to better (n=8). Thus, our analysis only included game-playing adults, understand the relationship between gaming and health outcomes and we could not assess how these associations compare with so as to determine strategies to potentially use gaming to help a nongaming adult population. In addition, the assessment of improve physical wellness. Acknowledgments We would like to thank the Indiana University School of Public Health - Bloomington for providing funding for this project. Conflicts of Interest None declared. Multimedia Appendix 1 Baseline characteristics of the study population (n=292). [PDF File (Adobe PDF File), 79KB-Multimedia Appendix 1] Multimedia Appendix 2 Means (SD) and N (%) of characteristics by the number of platforms played. 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PLoS One 2013;8(6):e65351 [FREE Full text] [doi: 10.1371/journal.pone.0065351] [Medline: 23799008] http://games.jmir.org/2018/2/e12/ JMIR Serious Games 2018 | vol. 6 | iss. 2 | e12 | p. 10 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Arnaez et al 26. Sebastião E, Gobbi S, Chodzko-Zajko W, Schwingel A, Papini C, Nakamura P, et al. The International Physical Activity Questionnaire-long form overestimates self-reported physical activity of Brazilian adults. Public Health 2012 Nov;126(11):967-975. [doi: 10.1016/j.puhe.2012.07.004] [Medline: 22944387] 27. Johnson-Kozlow M, Sallis JF, Gilpin EA, Rock CL, Pierce JP. Comparative validation of the IPAQ and the 7-Day PAR among women diagnosed with breast cancer. Int J Behav Nutr Phys Act 2006 Mar 31;3:7 [FREE Full text] [doi: 10.1186/1479-5868-3-7] [Medline: 16579852] Abbreviations BMI: body mass index IPAQ: International Physical Activity Questionnaire LARP: live action role-play MVPA: moderate-to-vigorous physical activity OR: odds ratio Edited by G Eysenbach; submitted 04.12.17; peer-reviewed by J Hwang, J Bervoets, C Carrion, R Kretschmann; comments to author 25.01.18; revised version received 20.03.18; accepted 03.04.18; published 12.06.18 Please cite as: Arnaez J, Frey G, Cothran D, Lion M, Chomistek A JMIR Serious Games 2018;6(2):e12 URL: http://games.jmir.org/2018/2/e12/ doi: 10.2196/games.9571 PMID: 29895516 ©James Arnaez, Georgia Frey, Donetta Cothran, Margaret Lion, Andrea Chomistek. Originally published in JMIR Serious Games (http://games.jmir.org), 12.06.2018. 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 http://games.jmir.org, as well as this copyright and license information must be included. http://games.jmir.org/2018/2/e12/ JMIR Serious Games 2018 | vol. 6 | iss. 2 | e12 | p. 11 (page number not for citation purposes) XSL FO RenderX

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JMIR Serious Games – JMIR Publications

Published: Jun 12, 2018

Keywords: video games; electronic gaming; traditional gaming; obesity; physical activity; sedentary behavior

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