Psychiatry Journal

Psychiatry Journal / 2013 / Article

Research Article | Open Access

Volume 2013 |Article ID 301460 |

Geoffrey L. Ream, Luther C. Elliott, Eloise Dunlap, "Trends in Video Game Play through Childhood, Adolescence, and Emerging Adulthood", Psychiatry Journal, vol. 2013, Article ID 301460, 7 pages, 2013.

Trends in Video Game Play through Childhood, Adolescence, and Emerging Adulthood

Academic Editor: José Francisco Navarro
Received17 Jan 2013
Accepted18 Feb 2013
Published20 Mar 2013


This study explored the relationship between video gaming and age during childhood, adolescence, and emerging adulthood. It also examined whether “role incompatibility,” the theory that normative levels of substance use decrease through young adulthood as newly acquired adult roles create competing demands, generalizes to video gaming. Emerging adult video gamers ( ) recruited from video gaming contexts in New York City completed a computer-assisted personal interview and life-history calendar. All four video gaming indicators—days/week played, school/work day play, nonschool/work day play, and problem play—had significant curvilinear relationships with age. The “shape” of video gaming’s relationship with age is, therefore, similar to that of substance use, but video gaming appears to peak earlier in life than substance use, that is, in late adolescence rather than emerging adulthood. Of the four video gaming indicators, role incompatibility only significantly affected school/work day play, the dimension with the clearest potential to interfere with life obligations.

1. Introduction

1.1. Literature Review

Video games are an indelible part of the modern American early life course. A 2008 Pew Research Center survey found that 99% of males and 94% of females ages 12–17 play video games [1]. Video gaming begins in early childhood [2] and continues through adulthood [3]. Video games are a modality for instruction [4] and clinical intervention [5]. They facilitate cognitive development [6]. They provide experiences of freedom and competence [7], opportunities to socialize, a sense of mastery, a medium for identity development, and—not least—fun [8]. Video games focus attention in ways that are palliative for ADHD [9, 10] and mood disorders [11]. A “downside” is the development among between 4.9% and 9% of video gamers of problem video game play (PVGP), an addiction-like pattern of feeling out of control of time spent playing, neglecting normal responsibilities in order to play, and so forth [1218]. PVGP is distinct from merely liking video games or spending a lot of time playing them [1921]. The validity of PVGP is not only supported by survey and laboratory research. Players recognize elements of PVGP within their own experiences [22] and there are clinical screening instruments for it [23]. PVGP is among the “behavioral addictions” correlated with substance dependence [2426].

Because substance and behavioral addictions have similar biological mechanisms [2730], it may be possible to generalize theories about life trajectories of substance use and dependence to those of video game play and problem play. According to our current understanding [31], adolescence and emerging adulthood are critical periods for emergence of substance use and dependence. Adolescents, through typical adolescent experimentation with substances, discover substances’ usefulness for regulating negative emotions and then use them specifically for this purpose in emerging adulthood. This is a potential problem for public health reasons, as “self-medication” is connected with addiction. It is also a source of concern for developmental reasons, as growing through disequilibrating experiences is necessary for identity development [32], and self-medicating “the pain of growing up” instead of constructively confronting it may impede progress through developmental tasks [33, 34]. It is reasonable to suspect that video games have the potential to play a similar role to substances’, as youth discover video games’ potential for regulating negative emotions early on. A survey of middle school students found that 62% of boys and 44% of girls used video games to help them relax, 45% of boys and 29% of girls used them to cope with anger, and smaller numbers used them to forget problems and cope with loneliness [8]; some players intentionally use video games to escape real-life problems [20, 35].

For the average youth, substance use decreases through emerging and young adulthood as, according to role incompatibility theory, competing demands of school, work, and relationships make previous substance use levels untenable [3638]. However, some substance users, perhaps having developed addictive disorders, continue high levels of use and disengage from developmental tasks [31], with predictable consequences for both health and psychosocial development. There is already reason to believe that consistent video game play has long-term health consequences, with recent findings of lower mental and physical health among adult video gamers [39]. The present study explored the potential for role incompatibility effects on video gaming behavior.

1.2. Research Questions

If video gaming, or at least certain patterns of video gaming, fills a similar role in the life course to that of substance use, then average levels of duration/frequency of video gaming and PVGP should follow the same trends across the early life course as substance use, that is, rising through childhood and adolescence to an inflection point and then falling through emerging adulthood as competing demands of adult roles make previous levels of play untenable [31, 33, 40]. Analyses for the present study, therefore, tested the hypothesis that relationships between age and four different video gaming indicators—days/week of play, school/work day play, non-school/work day play, and problem play—were significantly curvilinear (i.e., with negative coefficients for quadratic terms indicating a downward inflection). Also consistent with the idea that video gaming responds similarly to life-course pressures as substance use, our analyses tested the hypothesis that the curvilinearity of the relationships between at least some video gaming indicators and age was statistically explained by role incompatibility [38, 40], operationalized using indicators of acquisition of adult roles of full-time work, higher education, and independent living. We expected the pattern of significant and nonsignificant coefficients for age and adult role indicators not to change even after controlling for known covariates of video gaming, including sugar and caffeine consumption [19, 4144], personality [45], gender [46, 47], and race [48, 49].

2. Methods

2.1. Participants

Participants were emerging/young adults ages 18–29 recruited in and around 52 different video game stores, arcades, internet/cyber cafes, game-themed convention booths, and retail stores with large video game departments in New York City. Time and day of data collection were varied to obtain a diverse sample. Quota sampling was used to obtain at least 10 participants for every “cell” that would be created by cross-tabulating gender, race, and illicit substance user status, with some participants screened out if they were not needed to meet a quota. Initial contact was made with 1090 potential participants. Of these, 150 were ineligible or screened out, and 238 declined to participate. If participants were interested but could not complete the interview at that time, interviewers set appointments. Ten cases were invalid because of incomplete responses. Our total valid n was 692, for a response rate of 692/940 = 74%.

2.2. Procedure

Field interviewers took participants to a mutually agreeable public location for the interview, often a park, fast-food restaurant, or coffee shop. Measures included a computer-assisted personal interview (CAPI) for time-invariant indicators, including demographics and personality variables. Time-varying variables including life-course indicators, caffeine and sugar consumption, and video game playing were measured via a quantitative life-history calendar (LHC; [5052]). The instrument itself was a computerized spreadsheet with a column for each year of life ages 6–29 and a row for each variable, so that participants answered each LHC question once for every year of life ages 6–present. This turned out to be not as tedious or fatiguing as we thought it would be before we pilot-tested it during measurement development; participants appeared to enjoy talking about their video gaming histories and watching the calendar grid fill up in front of them. Participants were compensated $30 plus any refreshments interviewers bought for them with a $5 per interviewee budget. The protocol was approved by the investigators’ institutional review boards.

2.3. Measures
2.3.1. Frequency/Duration Video Gaming and Problem Play

The LHC included three questions about duration/frequency of video gaming. Days/week of play was measured by “In a typical week, on how many days did you play games?” Separate questions were included for school/work day play: “On weekdays (or days that you had to go to school or work), how many hours per day did you play video games?” and nonschool/work day play: “On weekends (or days that you did not also have to go to school or work), how many hours per day did you play video games?” because the former seemed more likely to interfere with life obligations [53]. Responses were open-ended; the handful of responses that were not whole numbers were rounded to the nearest whole number for analyses. The LHC question for problem play (PVGP) was “Think about the problem video game playing criteria from the computerized interview—what was your degree of problem video game playing?” according to the scale of 0 = none, 1 = slight, 2 = moderate, 3 = high, and 4 = extreme. Although these single-item measures are probably more prone to random error than a multiple-item construct would have been, they were the only means by which all of the needed data could be collected in one sitting. Interviewers attempted to maximize the validity of these single-item responses in the LHC by referring respondents back to the standardized measures for present-day PVGP (not used in these longitudinal analyses) which they had already completed in the CAPI.

2.3.2. Sugar/Junk Food Consumption

This was the average of responses to one question about sugar drinks and another about high-sugar food, each on a 6-point Likert scale ranging from 0 = “never or less than once a week” to 5 = “exclusively—it was almost all I [ate/drank].”

2.3.3. Caffeine

Caffeinated drinks/day was the number of caffeinated drinks or caffeine pills the participant reported on days of caffeine use. Degree of problem use was a single-item measure with the same 5-point Likert scale as problem video game play. Zeroes were imputed for nonusers.

2.3.4. Adult Role Indicators

For each year, each of these factors was coded 1 if they applied to the participant for more than half of the year and 0 if not. They included whether the participant attended a 2-year or 4-year school, whether they held a full-time job, whether they lived in student housing, and whether they lived away from home (i.e., biological, adoptive, or foster family).

2.3.5. Personality

Personality measures were adapted from the National Longitudinal Survey of Adolescent Health (“Add Health” [54]). They included sensation-seeking, 13 items, Cronbach’s α = .64, shyness, seven items, α = .73, and sociability, three items, α = .69. These modest α’s may have made these variables less competitive for variance in multivariate models.

2.3.6. Demographics

These included race, gender, and age at the time of the interview.

2.4. Analysis Plan

Our analyses built upon an existing example of multilevel modeling (MLM) for LHC data [52], treating year-level observations as nested within individuals. Because few participants were older than 26 at the time of the interview, data for those ages were sparse and subject to apparent selection biases. Since this study was not concerned with trends beyond the end of emerging adulthood anyway, we used only data for ages ≤ 26. Each inferential model was a multivariate multilevel model conducted in Mplus 6.0 with days/week of play, school/work day play, and nonschool/work day play as count dependent variables and problem play as a continuous dependent variable. All continuous variables were grand-mean centered. Because model fit and percentage of variance explained statistics are not available in Mplus for count dependent variables, model fit was approximated using alternative models in which days/week of play, school/work day play, and nonschool/work day play were specified as continuous dependent variables. Standardized coefficients (STDYX in Mplus) are reported throughout. Although we ran a total of four models in order to distinguish the role incompatibility effect, we summarize relevant results from the first three in text and, in the interest of space, only describe the final model in a table.

3. Results

Participants were 22% white, 24% African-American/Black, 20% Latino, 20% Asian, and 14% other/mixed. About two-thirds (66%) were male. Mean age at the time of the interview was 21.2, SD = 3.1. Most (58%) had spent at least half a year as a 2-year or 4-year college student, and 42% reported at least half a year of full-time work. At least half a year of living in student housing was reported by 20%. Only 1% of participants had never lived away from home at the time of the interview; 5% were living away from home at age 17, 14% at 18, 49% at 19, 63% at 20, 76% at 21, 84% at 22, 88% at 23, and above 90% at later ages. Personality variables were roughly normally distributed and their means were close to their scales’ middle ranges (all 1–5): For shyness, M = 2.3, SD = 0.7; for sensation-seeking, M = 3.3, SD = 0.6; for sociability, M = 3.5, SD = 0.8.

Figure 1 describes trends in video gaming variables over childhood, adolescence, and emerging adulthood. Problem play scores were multiplied by four for display in Figure 1 so that they would fit into the same -axis range as the frequency/duration indicators. All four basic relationships depicted in Figure 1 were significantly curvilinear, according to results of multivariate multilevel models (table not shown; CFI and TLI > .999, RMSEA and SRMR < .001, within-level ’s between .02 and .09 and corresponding P’s ≤ .001) predicting all four video gaming indicators from linear and quadratic terms for age and controlling only for age at time of interview. In spite of the second inflection point around ages 21-22 that appears to emerge in Figure 1, no cubic terms were significant in these analyses and would have only been supported by data for less than half of participants if they had been significant. All four quadratic terms remained significant in a second analysis that included controls for caffeine and sugar consumption, race, gender, and personality, indicating that the curvilinear shape of the relationship between video gaming indicators and age was not affected by their entry into the model. In a third analysis in which these controls were removed and adult role indicators of higher education involvement, full-time work, living away from home, and living in student housing were introduced, the quadratic term for age as a predictor of school/work day play became nonsignificant, and all other quadratic terms remained significant, indicating that there was some shared variance in school/work day play only between the curvilinearity of its relationship with age and the life-course indicators. Additional findings from that model were that living away from home had a negative partial relationship with days/week of play, higher education involvement had a negative partial relationship with nonschool/work day play, and both variables had negative partial relationships with school/work day play.

Table 1 describes the fourth and final model, which includes adult role variables and controls. The quadratic term for age as a predictor of school/work day play remained nonsignificant and the only remaining significant partial relationships with adult role indicators were with school/work day play. Taken together with results from the reduced models, the general finding is that the life course indicators as a set explained variance in school/work day play that would otherwise have been attributable to the quadratic term for age. These life-course variables were (predictably) mostly constant throughout childhood and early adolescence; they could only have covaried with and explained variance in dependent variables through late adolescence and emerging adulthood. Therefore, it could reasonably be concluded that these indicators of transitions to adult roles explained the downward inflection in the relationship between school/work day play and age in late adolescence and emerging adulthood. This effect did not occur for any other video gaming indicator—although living away from home had a negative partial relationship with days/week of play, the quadratic term for age was still significant after adding life-course variables to the model.

Days/week playedSchool/work day hours/day playedNonschool/work day hours/day playedProblem play (PVGP)

Year-level variables:

Age (linear)1.86***1.51***1.37**0.42***
Age (quadratic)−1.37***−0.59a−0.99*−0.29*
Caffeinated drinks/day0.27**0.29**0.36**−0.06
Caffeine problem use−0.060.02−0.040.18***
Soda/junk food consumption0.65***0.32***0.68***0.18***
Attending 2yr or 4yr college0.02−0.14*−0.080.01
Holding full-time job0.04−0.15*−0.170.02
Living in student housing−0.080.003−0.050.01
Living away from home−0.20*−0.13−0.04−0.03

Participant-level variables:

Race: Black0.27* 0.29+0.41**−0.02
Race: Latino0.65***0.64***0.64***0.11*
Race: Asian0.110.01−0.020.11*
Race: Other/multiracial0.41***0.41**0.50***−0.03
Gender: Female−0.71***−0.59***−0.61***−0.17***
Personality: Sensation-seeking0.
Personality: Shyness0.*
Personality: Sociability0.
Age at time of interview−0.03−0.20−0.15−0.05

Model characteristics:

Intraclass correlation0.480.450.550.67
-squared within0.12*** 0.17*** 0.13*** 0.09***
-squared between0.14*** 0.08*** 0.08*** 0.07***

CFI and TLI > .999, RMSEA and SRMR < .001. Coefficients are standardized. Because model fit statistics, intraclass correlations, and -squared values are not available in MPlus for models including count dependent variables, these model fit statistics and the italicized -squared values are taken from an alternative model (also run using MPlus) in which days played and hours/day played were specified as continuous dependent variables. , , , and . aThis coefficient was significant in models that did not include life-course indicators.

Other findings in the final model were that caffeine use reliably predicted video game play, problem caffeine use predicted PVGP, and sugar/junk food consumption was associated with all indicators of video game play and PVGP. Black, Latino, and other/multiracial respondents played video games more often and longer than whites, and Latino and Asian respondents reported more PVGP than white respondents. Personality was generally unrelated to video gaming except for a significant association between shyness and PVGP; these tests may have been underpowered due to the marginal reliability of the measures.

4. Discussion

Results confirmed hypotheses that all four video gaming indicators studied—days/week of play, school/work day play, non-school/work day play, and problem play (PVGP)—had curvilinear relationships with age, rising through childhood and adolescence to a peak and then leveling off or decreasing in emerging adulthood. Other than that the inflection point was apparently earlier in the life course for video games than it is for substances (a common sense explanation for which would be that adolescents have easier access to video games than to substances) these trends were congruent with those which earlier research found between age and substance use [31, 40]. We also found the hypothesized “role incompatibility” [37, 38] effect, but only for school/work day play. Although the hypothesis which was only confirmed for one indicator would seem to weaken the case of role incompatibility with video gaming, school/work day play is the dimension with the clearest potential to interfere with life obligations, and interference with life obligations is an important distinguisher between benign and problematic patterns of video game play [53]. The findings could, therefore, be interpreted as indicating that, although video gaming generally levels off or decreases in emerging adulthood, this leveling off is only attributable to role incompatibility for video gaming that interferes with other life responsibilities. The evidence for this interpretation would, of course, be stronger if a similar finding had also emerged for problem play.

This study’s primary strength was its developmental/life-course perspective. Although video gaming studies usually focus on children and adolescents [55, 56], they do not often apply a developmental perspective. Another strength was its face-to-face interviewing methodology, which held participants to task so that they finished the survey and did not contrive responses. The LHC format was not only enjoyable for participants but allowed us to operationalize age as a continuous independent variable. Our study, like other LHC studies [50, 51], is limited in that data were retrospective, not truly longitudinal. Our participants often gave single responses for several-year increments, essentially imputing an average across several years in place of the randomly varying responses of a true longitudinal measure. Since one of MLM’s most important features is adjustment for inflated likelihood of type I error due to nonindependence of observations within level, our use of MLM [52] gives us the best chance of valid results in spite of this limitation, assuming these averages across years were not actually biased. However, the conceptual limitation remains that there were fewer distinct time points in the minds of participants than were represented in analyses. This study was also limited, as aforementioned, by the necessity of reliance on single-item indicators. Findings are also not necessarily generalizable beyond the population of emerging adults who frequent NYC video gaming contexts.

Although conclusions about development based on retrospective data are admittedly tentative, our findings may be used to support a case for gathering true longitudinal data, perhaps through including additional questions about video games and other media use in large-scale survey studies of adolescents like Monitoring the Future [57] or Add Health [54]. Video gaming is, after all, endemic to the early life course, and these and other findings suggest that it may have long-term influences on health and development.


This research was supported by Grant R01-DA027761, “Video Games’ Role in Developing Substance Use,” from the National Institute of Drug Abuse. The authors acknowledge the contributions of field interviewers Flutura Bardhi, Simon Wong, Elizabeth McGinsky, and Joel Mockovciak.


  1. A. Lenhart, J. Kahne, E. Middaugh, A. Macgill, C. Evans, and J. Vitak, “Teens, video games and civics,” Pew Research Center's Internet & American Life Project,, September 2008. View at: Google Scholar
  2. D. Riley, “The video game industry is adding 2–17 year-old gamers at a rate higher than that age group's population growth,” October 2011, NPD Group Press, View at: Google Scholar
  3. ESA, “2011 Sales, demographic, and usage data,” Essential Facts about the Computer and Video Game Industry,, 2011. View at: Google Scholar
  4. L. A. Annetta, “The “I's” have it: a framework for serious educational game design,” Review of General Psychology, vol. 14, no. 2, pp. 105–112, 2010. View at: Publisher Site | Google Scholar
  5. B. A. Primack, M. V. Carroll, M. McNamara et al., “Role of video games in improving health-related outcomes: a systematic review,” American Journal of Preventive Medicine, vol. 42, no. 6, pp. 630–638, 2012. View at: Google Scholar
  6. I. Spence and J. Feng, “Video games and spatial cognition,” Review of General Psychology, vol. 14, no. 2, pp. 92–104, 2010. View at: Publisher Site | Google Scholar
  7. J. McLeod and L. Lin, “A child’s power in game-play,” Computers & Education, vol. 54, no. 2, pp. 517–527, 2010. View at: Publisher Site | Google Scholar
  8. C. K. Olson, “Children's motivations for video game play in the context of normal development,” Review of General Psychology, vol. 14, no. 2, pp. 180–187, 2010. View at: Publisher Site | Google Scholar
  9. D. H. Han, Y. S. Lee, C. Na et al., “The effect of methylphenidate on Internet video game play in children with attention-deficit/hyperactivity disorder,” Comprehensive Psychiatry, vol. 50, no. 3, pp. 251–256, 2009. View at: Publisher Site | Google Scholar
  10. S. Houghton, N. Milner, J. West et al., “Motor control and sequencing of boys with Attention-Deficit/Hyperactivity Disorder (ADHD) during computer game play,” British Journal of Educational Technology, vol. 35, no. 1, pp. 21–34, 2004. View at: Google Scholar
  11. J. S. Lemmens, P. M. Valkenburg, and J. Peter, “Psychosocial causes and consequences of pathological gaming,” Computers in Human Behavior, vol. 27, no. 1, pp. 144–152, 2011. View at: Publisher Site | Google Scholar
  12. S. Bioulac, L. Arfi, and M. P. Bouvard, “Attention deficit/hyperactivity disorder and video games: a comparative study of hyperactive and control children,” European Psychiatry, vol. 23, no. 2, pp. 134–141, 2008. View at: Publisher Site | Google Scholar
  13. R. A. Desai, S. Krishnan-Sarin, D. Cavallo, and M. N. Potenza, “Video-gaming among high school students: health correlates, gender differences, and problematic gaming,” Pediatrics, vol. 126, no. 6, pp. e1414–e1424, 2010. View at: Publisher Site | Google Scholar
  14. D. Gentile, “Pathological video-game use among youth ages 8 to 18: a national study,” Psychological Science, vol. 20, no. 5, pp. 594–602, 2009. View at: Publisher Site | Google Scholar
  15. D. A. Gentile, H. Choo, A. Liau et al., “Pathological video game use among youths: a two-year longitudinal study,” Pediatrics, vol. 127, no. 2, pp. e319–e329, 2011. View at: Publisher Site | Google Scholar
  16. S. M. Grüsser, R. Thalemann, and M. D. Griffiths, “Excessive computer game playing: evidence for addiction and aggression?” Cyberpsychology and Behavior, vol. 10, no. 2, pp. 290–292, 2007. View at: Publisher Site | Google Scholar
  17. G. M. Hart, B. Johnson, B. Stamm et al., “Effects of video games on adolescents and adults,” Cyberpsychology and Behavior, vol. 12, no. 1, pp. 63–65, 2009. View at: Publisher Site | Google Scholar
  18. R. A. Tejeiro Salguero and R. M. Bersabé Morán, “Measuring problem video game playing in adolescents,” Addiction, vol. 97, no. 12, pp. 1601–1606, 2002. View at: Publisher Site | Google Scholar
  19. J. P. Charlton and I. D. W. Danforth, “Validating the distinction between computer addiction and engagement: online game playing and personality,” Behaviour and Information Technology, vol. 29, no. 6, pp. 601–613, 2010. View at: Publisher Site | Google Scholar
  20. R. T. A. Wood, “Problems with the concept of video game “addiction”: some case study examples,” International Journal of Mental Health and Addiction, vol. 6, no. 2, pp. 169–178, 2008. View at: Publisher Site | Google Scholar
  21. R. T. A. Wood and M. D. Griffiths, “Time loss whilst playing video games: is there a relationship to addictive behaviours?” International Journal of Mental Health and Addiction, vol. 5, no. 2, pp. 141–149, 2007. View at: Publisher Site | Google Scholar
  22. L. C. Elliott, G. L. Ream, and E. McGinsky, “Video game addiction: user perspectives,” in Critical Perspectives on Addiction, J. Netherland, Ed., pp. 225–243, Emerald Group, Bradford, UK, 2012. View at: Google Scholar
  23. S. Achab, M. Nicolier, F. Mauny et al., “Massively multiplayer online role-playing games: comparing characteristics of addict vs non-addict online recruited gamers in a French adult population,” BMC Psychiatry, vol. 11, article 144, 2011. View at: Publisher Site | Google Scholar
  24. G. L. Ream, L. C. Elliott, and E. Dunlap, “Patterns of and motivations for concurrent use of video games and substances,” International Journal of Environmental Research and Public Health, vol. 8, no. 10, pp. 3999–4012, 2011. View at: Publisher Site | Google Scholar
  25. G. L. Ream, L. C. Elliott, and E. Dunlap, “Playing video games while using or feeling the effects of substances: associations with substance use problems,” International Journal of Environmental Research and Public Health, vol. 8, no. 10, pp. 3979–3998, 2011. View at: Publisher Site | Google Scholar
  26. S. Sussman, N. Lisha, and M. Griffiths, “Prevalence of the addictions: a problem of the majority or the minority?” Evaluation and the Health Professions, vol. 34, no. 1, pp. 3–56, 2011. View at: Publisher Site | Google Scholar
  27. L. de Lecea, B. E. Jones, B. Boutrel et al., “Addiction and arousal: alternative roles of hypothalamic peptides,” Journal of Neuroscience, vol. 26, no. 41, pp. 10372–10375, 2006. View at: Publisher Site | Google Scholar
  28. D. H. Han, N. Bolo, M. A. Daniels, L. Arenella, I. K. Lyood, and P. F. Renshawe, “Brain activity and desire for Internet video game play,” Comprehensive Psychiatry, vol. 52, no. 1, pp. 88–95, 2011. View at: Publisher Site | Google Scholar
  29. J. A. López-Moreno, G. González-Cuevas, G. Moreno, and M. Navarro, “The pharmacology of the endocannabinoid system: functional and structural interactions with other neurotransmitter systems and their repercussions in behavioral addiction,” Addiction Biology, vol. 13, no. 2, pp. 160–187, 2008. View at: Publisher Site | Google Scholar
  30. A. M. Weinstein, “Computer and video game addiction—a comparison between game users and non-game users,” American Journal of Drug and Alcohol Abuse, vol. 36, no. 5, pp. 268–276, 2010. View at: Publisher Site | Google Scholar
  31. E. J. Arnett, “The developmental context of substance use in emerging adulthood,” Journal of Drug Issues, vol. 35, no. 2, pp. 235–254, 2005. View at: Google Scholar
  32. C. M. Ackerman, “The essential elements of Dabrowski's Theory of Positive Disintegration and how they are connected,” Roeper Review, vol. 31, no. 2, pp. 81–95, 2009. View at: Publisher Site | Google Scholar
  33. D. Baumrind and K. A. Moselle, “A developmental perspective on adolescent drug abuse,” Advances in Alcohol and Substance Abuse, vol. 4, no. 3-4, pp. 41–67, 1985. View at: Google Scholar
  34. R. J. Pandina, E. W. Labouvie, V. Johnson, and H. R. White, “The relationship between alcohol and marijuana use and competence in adolescence,” Journal of Health & Social Policy, vol. 1, no. 3, pp. 89–108, 1990. View at: Publisher Site | Google Scholar
  35. B. U. Stetina, O. D. Kothgassner, M. Lehenbauer, and I. Kryspin-Exner, “Beyond the fascination of online-games: probing addictive behavior and depression in the world of online-gaming,” Computers in Human Behavior, vol. 27, no. 1, pp. 473–479, 2011. View at: Publisher Site | Google Scholar
  36. L. A. Rohrbach, S. Sussman, C. W. Dent, and P. Sun, “Tobacco, alcohol, and other drug use among high-risk young people: a five-year longitudinal study from adolescence to emerging adulthood,” Journal of Drug Issues, vol. 35, no. 2, pp. 333–356, 2005. View at: Google Scholar
  37. K. Yamaguchi and D. B. Kandel, “Dynamic relationships between premarital cohabitation and illicit drug use: an event-history analysis of role selection and role socialization,” American Sociological Review, vol. 50, no. 4, pp. 530–546, 1985. View at: Publisher Site | Google Scholar
  38. K. Yamaguchi and D. B. Kandel, “On the resolution of role incompatibility: a life event history analysis of family roles and marijuana use,” American Journal of Sociology, vol. 90, no. 6, pp. 1284–1325, 1985. View at: Publisher Site | Google Scholar
  39. J. B. Weaver, D. Mays, S. Sargent Weaver et al., “Health-risk correlates of video-game playing among adults,” American Journal of Preventive Medicine, vol. 37, no. 4, pp. 299–305, 2009. View at: Publisher Site | Google Scholar
  40. K. A. Jochman and K. Fromme, “Maturing out of substance use: the other side of etiology,” in Handbook of Drug Use Etiology: Theory, Methods, and Empirical Findings, L. Scheier, Ed., pp. 565–578, American Psychological Association, Washington, DC, USA, 2010. View at: Google Scholar
  41. J. L. Fortuna, “Sweet preference, sugar addiction and the familial history of alcohol dependence: shared neural pathways and genes,” Journal of Psychoactive Drugs, vol. 42, no. 2, pp. 147–151, 2010. View at: Google Scholar
  42. J. L. Greenberg, S. E. Lewis, and D. K. Dodd, “Overlapping addictions and self-esteem among college men and women,” Addictive Behaviors, vol. 24, no. 4, pp. 565–571, 1999. View at: Publisher Site | Google Scholar
  43. G. Porter, V. Starcevic, D. Berle, and P. Fenech, “Recognizing problem video game use,” Australian and New Zealand Journal of Psychiatry, vol. 44, no. 2, pp. 120–128, 2010. View at: Publisher Site | Google Scholar
  44. P. Rozin and C. Stoess, “Is there a general tendency to become addicted?” Addictive Behaviors, vol. 18, no. 1, pp. 81–87, 1993. View at: Publisher Site | Google Scholar
  45. M. Mehroof and M. D. Griffiths, “Online gaming addiction: the role of sensation seeking, self-control, neuroticism, aggression, state anxiety, and trait anxiety,” Cyberpsychology, Behavior, and Social Networking, vol. 13, no. 3, pp. 313–316, 2010. View at: Publisher Site | Google Scholar
  46. L. M. Padilla-Walker, L. J. Nelson, J. S. Carroll, and A. C. Jensen, “More than a just a game: video game and internet use during emerging adulthood,” Journal of Youth and Adolescence, vol. 39, no. 2, pp. 103–113, 2010. View at: Publisher Site | Google Scholar
  47. C. A. Phillips, S. Rolls, A. Rouse, and M. D. Griffiths, “Home video game playing in schoolchildren: a study of incidence and patterns of play,” Journal of Adolescence, vol. 18, no. 6, pp. 687–691, 1995. View at: Publisher Site | Google Scholar
  48. P. Barrett, “White thumbs, black bodies: race, violence, and neoliberal fantasies in Grand Theft Auto: San Andreas,” Review of Education, Pedagogy, and Cultural Studies, vol. 28, no. 1, pp. 95–119, 2006. View at: Google Scholar
  49. B. DeVane and K. D. Squire, “The meaning of race and violence in Grand Theft Auto: San Andreas,” Games and Culture, vol. 3, no. 3-4, pp. 264–285, 2008. View at: Publisher Site | Google Scholar
  50. D. Freedman, A. Thornton, D. Camburn, D. Alwin, and L. Young-DeMarco, “The life history calendar: a technique for collecting retrospective data,” Sociological Methodology, vol. 18, pp. 37–68, 1988. View at: Google Scholar
  51. T. Glasner and W. van der Vaart, “Applications of calendar instruments in social surveys: a review,” Quality and Quantity, vol. 43, no. 3, pp. 333–349, 2009. View at: Publisher Site | Google Scholar
  52. M. Yoshihama and D. Bybee, “The life history calendar method and multilevel modeling: application to research on intimate partner violence,” Violence Against Women, vol. 17, no. 3, pp. 295–308, 2011. View at: Publisher Site | Google Scholar
  53. M. D. Griffiths, “The role of context in online gaming excess and addiction: some case study evidence,” International Journal of Mental Health and Addiction, vol. 8, no. 1, pp. 119–125, 2010. View at: Publisher Site | Google Scholar
  54. J. R. Udry and P. S. Bearman, “The national longitudinal study of adolescent health,” 1998, View at: Google Scholar
  55. D. Kuss and M. Griffiths, “Internet gaming addiction: a systematic review of empirical research,” International Journal of Mental Health and Addiction, vol. 10, no. 2, pp. 278–296, 2011. View at: Publisher Site | Google Scholar
  56. N. M. Petry, “Commentary on Van Rooij et al. (2011): “Gaming addiction”—a psychiatric disorder or not?” Addiction, vol. 106, no. 1, pp. 213–214, 2011. View at: Publisher Site | Google Scholar
  57. MTF, “Monitoring the future: a continuing study of American youth,” July 2011, View at: Google Scholar

Copyright © 2013 Geoffrey L. Ream et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

More related articles

 PDF Download Citation Citation
 Download other formatsMore
 Order printed copiesOrder

Related articles

We are experiencing issues with article search and journal table of contents. We are working on a fix as to remediate it and apologise for the inconvenience.

Article of the Year Award: Outstanding research contributions of 2020, as selected by our Chief Editors. Read the winning articles.