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Journal of Behavioral Addictions logoLink to Journal of Behavioral Addictions
. 2025 Aug 28;14(3):1315–1325. doi: 10.1556/2006.2025.00068

Temperament, brain structure and problematic video gaming in adolescence – A six-year longitudinal study

Ines Mürner-Lavanchy 1,2, Silvano Sele 1, Julian Simmons 3, Dan I Lubman 4,5, Sarah Whittle 6,7, Nicholas B Allen 8, Michael Kaess 1,9,*
PMCID: PMC12486287  PMID: 40875492

Abstract

Background

Growing concerns regarding problematic gaming highlight the need for prospective longitudinal research to explore potential targets for prevention. Markers that can be observed during early adolescence, prior to the establishment of problematic behaviors, may be particularly informative. Two potential predictors of interest that have been shown to reflect important developmental and psychopathological processes are temperament and brain structure, which respectively provide self-reported and objective markers of individual differences.

Methods

Temperament (n = 245) and brain volume (n = 154) were assessed at 11–13 years, and problematic video gaming (dimensional gaming addiction score; n = 130) at 17–19 years, in adolescents selected from a community sample to maximize variation in temperament. Associations between temperament and problematic video gaming were tested. Further models explored whether brain volume, and interactions between brain volume and temperament explained additional variance in predicting problematic video gaming.

Results

Negative affectivity (b = 2.94 [95% CI 0.32, 5.57]), as well as male gender (b = −6.61 [−10.64, −2.59]), were associated with later problematic video gaming. Also, lower effortful control in male participants was associated with higher odds for problematic video gaming in later adolescence (b = 4.32 [CI 0.24, 8.39]). Exploratory analyses showed modest evidence for an interaction between effortful control and amygdala volume in predicting problematic video gaming.

Conclusions

This six-year prospective longitudinal study, confirms associations between negative affectivity and effortful control and later problematic video gaming. Further, higher effortful control might have a protective role in individuals with larger amygdalae, who are vulnerable to mental health disorders, such as video gaming addiction.

Keywords: temperament, video gaming addiction, problematic video gaming, internet gaming, adolescent, brain structure, longitudinal

Introduction

Concerns over problematic video gaming have increased over the last decade due to enhanced access to computers, the internet, mobile devices and technological advancements allowing for more frequent, intense and immersive gaming experiences. While for many, video gaming is a popular leisure activity that they engage in for excitement, fun, and relaxation, a small subgroup of players reports problematic and excessive play (Gentile et al., 2017). The terminology surrounding problematic video gaming has undergone significant evolution. Initially, terms like 'Internet-related disorders' were used to describe a range of behaviors linked to excessive internet use (Young, 1996). However, this term faced criticism, as it suggested the internet itself was inherently addictive, rather than specific content accessed through it, such as social media, online gambling, or video gaming – all of which can have addictive components (van Rooij & Prause, 2014). Among these, video gaming has emerged as a central focus, with 'Internet Gaming Disorder' included as a condition for further study in the DSM-5 (American Psychiatric Association, 2013) and ‘Gaming Disorder (online/offline)’ as a formal condition in the ICD-11 (World Health Organization, 2018) used to describe problematic engagement with gaming content. In our article, we use the term 'problematic video gaming' to reflect a more nuanced, dimensional approach that captures the full spectrum of behaviors, ranging from mild (potentially normative) to severe forms of problematic video gaming. Understanding this spectrum is essential for the identification of early risk factors, particularly during adolescence – a critical developmental period marked by significant changes and challenges (Crone, Van Duijvenvoorde, & Peper, 2016; McGorry et al., 2024). By identifying these early predictors, we can better inform prevention strategies aimed at reducing the risk of developing gaming disorder.

A multitude of factors have been associated with problematic video gaming in adolescence, including socio-demographics (e.g. gender, income) (Jackson, von Eye, Witt, Zhao, & Fitzgerald, 2011; Lemmens, Valkenburg, & Peter, 2009), family dynamics (e.g., lack of perceived support) (Benarous et al., 2019; Torres-Rodríguez, Griffiths, Carbonell, & Oberst, 2018; Wernicke & Montag, 2022) and individual characteristics (Kökönyei et al., 2019; Mößle & Rehbein, 2013; Sánchez-Llorens et al., 2023). Among individual characteristics, temperament is a particularly important factor to consider. Cross-sectional studies have found several temperamental traits to be associated with problematic video gaming in adolescence, with high negative affectivity and neuroticism as well as impulsivity (or low effortful control), and low conscientiousness most consistently related to high scores on measures of gaming and gaming addiction (E. J. Kim, Namkoong, Ku, & Kim, 2008; Kuss & Griffiths, 2012; Nordin & Nylander, 2007; Ozturk, Ekinci, Ozturk, & Canan, 2013; Rehbein, Kleimann, & Mössle, 2010). However, few studies have examined temperament as a predictor of problematic video gaming within prospective, longitudinal studies.

One early longitudinal study investigated the role of temperament among 3,034 Singaporean pupils aged 8–13 years, and found that impulsivity, lower social competence, and poorer emotion regulation abilities were associated with video gaming addiction two years later (Gentile et al., 2011). Similarly, a more recent study in a Norwegian cohort of 740 children found that better social competence and emotion regulation (but not negative affect, surgency or effortful control) at age 8 predicted fewer symptoms of internet gaming disorder (IGD) at age 10 (Wichstrøm, Stenseng, Belsky, von Soest, & Hygen, 2019). The paucity of longitudinal studies across different age ranges and extended time periods highlights the need for further investigation into temperament as a predictor of problematic video gaming from late childhood to late adolescence, using a dimensional approach that encompasses a spectrum of gaming-related symptoms.

Amidst the scarcity of longitudinal research in this area, there is value in exploring brain structure as an objective marker of risk for problematic video gaming. Brain regions involved in reward, motivation, executive control and memory (namely, the striatum, orbitofrontal cortex [OFC], anterior cingulate cortex [ACC], dorsolateral prefrontal cortex, hippocampus and amygdala) – previously implicated in substance addiction (Lüscher & Janak, 2021; Ray Li & Sinha, 2008; Volkow et al., 2010) and the behavioral addiction of gambling disorder (Potenza, 2013) – have also been found to be altered in individuals with internet gaming disorder (Ko et al., 2015; Kuss & Griffiths, 2012; Lin, Dong, Wang, & Du, 2015; Weinstein & Lejoyeux, 2020). However, it remains unclear whether brain volume predicts later problematic video gaming independently of self-reported temperament. Given that temperament can be assessed by means of self-report questionnaires, semi-structured interviews or clinical observations, the potential value of brain structure as a predictor lies in its ability to provide incremental predictive power beyond temperament, particularly in the context of prevention efforts, where cost-effective, accessible measures are crucial (Ekhtiari et al., 2024; Li, He, Elhai, Montag, & Yang, 2025; Volkow & Boyle, 2018). While temperament and brain structure may share some variance, they could also function as independent predictors of problematic video gaming. It is possible that certain temperamental traits may only predispose individuals to problematic gaming behaviors in the context of specific vulnerabilities in brain structure, highlighting the potential interaction between these factors.

The first aim of this longitudinal study was to investigate whether temperament measured in late childhood/early adolescence (at 11–13 years), was associated with video gaming addiction behaviors at the age of 17–19 years in a community sample. Based on previous findings, we hypothesized that high negative affectivity and low effortful control would predict later problematic video gaming (Gentile et al., 2011; Wichstrøm et al., 2019). Due to the mixed previous findings, the second aim was to explore whether brain volume in regions that have been found to be associated with addictive behaviors had an incremental value in predicting video gaming addiction during late adolescence. Finally, we explored interactions between temperament and brain volume in the prediction of video gaming addiction.

Methods

Participants and procedure

Data for the present study stem from a cohort of individuals recruited from schools throughout Melbourne, Australia, as part of the Orygen Adolescent Development Study (OADS), a prospective, longitudinal study following school children from ages 11–13 to 17–19 years of age. School children were recruited from 100 representative schools across metropolitan Melbourne (N = 2,280). This initial cohort was screened using the revised Early Adolescent Temperament Questionnaire (EATQ-R) (L. K. Ellis, 2002; L. Ellis & Rothbart, 2001). Equal numbers of adolescents were recruited across the following ranges of scores on each of the higher order factors of the EATQ-R: 0 to 1 SDs above and below the mean, 1 to 2 SDs above and below the mean, 2 to 2.5 SDs above and below the mean, and >2.5 SDs above and below the mean. This resulted in selection of 425 (16%) adolescents showing even variation across each of the higher order traits of interest, with some emphasis in the distribution at the tails. Of the selected adolescents, 245 agreed to participate in at least the first wave of research. This selection process resulted in a sample whose distribution of temperamental risk retained the original variance associated with the larger screening sample but which also represented a broad spectrum of risk for later onset of emotional and behavioral problems (Whittle et al., 2008).

At four waves over a period of approximately eight years (10/2004–08/2012), participants completed various psychometric tests and clinical interviews as well as neurobiological, genetic and endocrinological assessments. A battery of self-report assessments was administered within a classroom context or at participants' home using pen-and-paper methods. EATQ-R data and MRI data were collected during the first wave of the study from 2004 to 2005 when participants were aged 11–13 years old (this data was also collected at further waves, but only data from the first wave was used in the present study). Problematic video gaming was assessed during the fourth wave of the study from 2010 to 2012 when participants were aged between 17 and 19 years old.

Measures

Sociodemographic information

Demographic data including age and gender, was obtained using the Demographic and Medical History Form of the Schedule for Affective Disorders and Schizophrenia for School-Age Children, Present and Lifetime version (Kaufman et al., 1997).

Temperament

Temperament traits were assessed using the EATQ-R (L. K. Ellis, 2002; L. Ellis & Rothbart, 2001), a 65-item self-report questionnaire designed for children and adolescents aged between 9 and 15 years. Items are rated on a five-point Likert scale from 1 (almost always untrue), 2 (usually untrue), 3 (sometimes true, sometimes untrue), 4 (usually true) to 5 (almost always true). In previous research, items have been used to derive four higher-order factors (negative affectivity, effortful control, surgency and affiliativeness). Analysis of the EATQ-R on 177 adolescents revealed moderate to good test-retest reliability over an 8-week period with alpha levels ranging from 0.69 to 0.85 (L. K. Ellis, 2002). In the OADS data, the four temperament factors have shown good internal consistencies in two separate administrations (Cronbach α values for negative affectivity: 0.82 and 0.78, effortful control: 0.82 and 0.83, surgency: 0.82 and 0.81, and affiliativeness: 0.77 and 0.76, respectively) (Whittle et al., 2008). An EATQ-R total score was not computed for the present study.

Problematic video gaming

Problematic video gaming was measured using the Addiction-Engagement Questionnaire (AEQ) (Charlton & Danforth, 2007), a dimensional measure which recognizes a distinction between milder, engagement-related behaviors and stronger, addiction-related behaviors towards video games. It contains twenty-four positively and negatively phrased items, twelve of which measure the level of addiction towards video games while the other twelve measure the level of engagement (Blinka, Ťápal, & Škařupová, 2021). While the original version of the AEQ referred to a game named Asheron's Call, the present study referred to the use of any kind of video games (“This questionnaire is about your use of video games (Xbox, Playstation, Wii, PC games, etc.) over the past three months”). Responses are given on a seven-point Likert scale ranging from 1 (completely disagree) to 7 (completely agree). Scores for the twelve items comprising the addiction component of the AEQ were summed after inversely formulated items were recoded so that each participant received a total score ranging from 12–84. In this dimensional measure, a higher score implies an increasing tendency toward addictive usage of video games. Respondents were asked at the beginning of the questionnaire whether they played video games including Xbox, PlayStation, Wii and PC games, also including online games, over the past three months. Those who answered “no” were exempt from completing the rest of the questionnaire and received a total score of 12 on the AEQ. The twelve addiction items showed good internal consistency for the addiction component of the AEQ (Cronbach's alpha = 0.79) (Charlton & Danforth, 2007, 2010). A threshold for video gaming addiction based on the AEQ was defined by applying Brown's (1993)behavioral addiction criteria (salience, mood modification, tolerance, withdrawal, conflict, relapse, and problems). In line with previous studies (e.g., Rehbein, Kliem, Baier, Mößle, & Petry, 2015), the threshold was reached when more than five of the Brown's criteria were responded to with “agree somewhat”, “agree”, or “completely agree”, i.e. values 5 to 7 on a seven-point Likert scale. In the present study, this categorical score was used for sample description, but not in further, dimensional analyses.

Brain volume

Magnetic resonance images were obtained using a 3-Tesla scanner at the Brain Research Institute, Austin and Repatriation Medical Centre, Melbourne, Australia. The gradient echo volumetric acquisition sequence was performed with the following settings: TR = 36 ms; TE = 9 ms; flip angle = 35, field of view = 20 × 20 cm, pixel matrix = 410 × 410) to obtain 124 T1-weighted contiguous 1.5-mm thick slices (voxel dimensions = 0.4883 × 0.4883 × 1.5 mm).

Images were preprocessed using the Functional Magnetic Resonance Imaging of the Brain (FMRIB) software library. Each scan was stripped of all non-brain tissue, resampled to 1 mm, and aligned to the MNI 152 average template using FLIRT. This registration served to align each image axially along the anterior commissure-posterior commissure plane and sagittally along the interhemispheric fissure without any deformation. Regions of interest (ROIs) were defined and quantified based on previous techniques developed and published by the Melbourne Neuropsychiatry Centre (Whittle et al., 2008). All of the ROIs were traced using the software package ANALYZE (Mayo Clinic, Rochester, MN). Brain tissue was segmented into gray and white matter, and CSF using an automated algorithm, as implemented in FAST (FMRIB's automated segmentation tool) (Zhang, Brady, & Smith, 2001). An estimate of whole brain volume was obtained by summing gray and white matter pixel counts (i.e., whole brain volume included cerebral gray and white matter, the cerebellum, and brainstem, but not the ventricles, cisterns, or CSF). ACC and OFC estimates were based on gray matter pixel counts contained within defined ROIs. Amygdala and hippocampal estimates were based on total voxels within the defined ROIs. ROI boundaries were defined per previously published protocols (ACC: Fornito et al., 2006; OFC: Riffkin et al., 2005; Amygdala and Hippocampus: adapted from Velakoulis et al., 2006. For all ROIs, a higher score indicates greater volume. See Whittle et al. (2008) for a detailed description of the manual tracing approach. Intra- and interrater reliabilities were calculated for each raw ROI volume. Intraclass correlation coefficients (most > 0.9 and none < 0.8) were deemed acceptable for all of the ROIs.

Statistical analyses

Analyses were conducted using Stata, version 17. Sample characteristics were described as proportions for categorical data and as mean ± SD for continuous data. Four temperament factors were composed, by averaging scores on the corresponding subscales (attention, inhibition and activation control for effortful control; frustration, depression and aggression for negative affectivity; affiliation, perceptual sensitivity, pleasure sensitivity for affiliativeness; and high intensity pleasure, fear (reverse coded) and shyness (reverse coded) for surgency) (L. K. Ellis, 2002). To enhance interpretability of the AEQ gaming addiction score, we repositioned the lower bound of the variable to zero by subtracting 12 from each data point. All temperament factors and ROI variables were z-standardized. For all models, we used full information maximum likelihood (FIML) to handle missing data, as this method includes all available data points for model estimation (Enders & Bandalos, 2001). All models were controlled for gender.

To investigate whether temperament at the first assessment was associated with problematic video gaming at the follow-up, two structural equation models were fit: 1) a null model containing all variables, with covariances between the temperament factors and problematic video gaming set to zero and 2) a model containing all temperament factors predicting problematic video gaming. Models were compared using a likelihood ratio (LR) test. This model comparison was repeated using the AEQ gaming engagement score to test for the specificity of associations for video gaming addiction behaviors vs. normative engaged gaming. As the male preponderance in problematic video gaming was confirmed in our data, we further used generalized structural equation models to analyze in an exploratory manner, whether there were interactions between gender and temperament factors in predicting AEQ gaming addiction score.

To examine whether brain volumes explained additional variance in predicting problematic video gaming, two models were estimated: 1) a null model containing temperament factors and brain volume in all ROIs, with temperament factors predicting problematic video gaming, and covariance between brain volume and problematic video gaming set to zero 2) a model with both the four temperament factors and brain volume in all ROIs predicting problematic video gaming. In other words, models were compared using LR test to examine whether model fit improved by adding a regression path (covariance) between brain volumes in all ROIs and AEQ gaming addiction score.

We further explored interactions between temperament and brain volume in predicting problematic video gaming, using a similar model comparison approach. First, we calculated a model containing all temperament factors, all ROIs and sixteen interactions (4 temperament factors by 4 ROIs). We then investigated interaction effects separately by ROI (models containing all four temperament factors, one ROI and four interaction terms between temperament and the respective ROI). Finally, to further isolate interaction effects, we calculated sixteen models containing only one temperament factor and one ROI, as well as their interaction. All models controlled for intracranial volume and gender. All analyses were repeated used bootstrapping with 5,000 repetitions as a non-parametric method for SEMs. These analyses yielded similar results to the original analyses.

Interpretation of the findings was based on overall patterns and magnitude of differences rather than individual p values alone (Sterne & Davey Smith, 2001).

Ethics

The study procedures were carried out in accordance with the Declaration of Helsinki. The OADS was approved by the Human Research Ethics Committee of the University of Melbourne, Australia. In accordance with ethics guidelines, a complete description of the study was provided to participants and their families and, as a prerequisite for participation in the study, both written and informed consent had to be obtained from participants and either their parent or guardian.

Results

Descriptives

245 adolescents took part in the baseline assessment, while MRI data was available from 154 adolescents. AEQ gaming addiction scores were obtained from 130 individuals at the follow-up assessment. Sample characteristics are displayed in Table 1. 75 (57.7%) participants did report engaging in gaming (47 males and 28 females). One out of ten adolescents met the threshold for video gaming addiction according to the AEQ (≥ 5 of the Brown's criteria endorsed). There were no differences between participants who completed the follow-up assessments and participants who did not on measures of temperament (all p-values > 0.39) or brain volume (all p-values > 0.17) (Cheetham et al., 2014).

Table 1.

Participant characteristics

Variable N Mean SD range
Age at baseline 245 12.44 0.43 11.34–13.60
Female gender (n, %) 245 125 51.02 –
Temperament factors
Effortful control 237 3.53 0.61 1.87–4.81
Negative affectivity 228 2.59 0.67 1.11–4.79
Affiliativeness 238 3.40 0.55 1.78–4.92
Surgency 228 2.74 0.64 1.08–4.17
AEQ gaming addiction score 130 20.74 12.40 12–74
Brain volume ROIs
Left anterior cingulate cortex 153 10379.31 2097.327 4,308–16,132
Right anterior cingulate cortex 153 10546.12 2083.05 3,680–16,018
Left orbitofrontal cortex 154 19809.04 4340.76 7,831–30,247
Right orbitofrontal cortex 154 20330.60 4051.04 7,532–29,445
Left hippocampus 153 2764.28 328.78 2,046–3,830
Right hippocampus 153 2934.58 346.84 2,175–4,072
Left amygdala 153 1897.56 266.34 1,322–2,863
Right amygdala 153 1839.17 278.16 1,157–2,685
Intracranial volume 154 1,526,565 129,809 1,211,010–1,958,200
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Note. ROI = region of interest.

Pairwise correlations between all included variables can be found in the Supplementary materials (Table S1). To summarize the significant correlations, age at baseline correlated with affiliativeness (r = −0.17, p = 0.007). Gender correlated with effortful control (rpoint biserial = 0.25, p = 0.001), negative affectivity (rpb = −0.15, p = 0.022) and affiliativeness (rpb = 0.20, p = 0.002). Effortful control correlated with all other temperament factors (negative affectivity: r = −0.65, p < 0.001, affiliativeness: r = 0.18, p = 0.007, surgency: r = 0.30, p < 0.001). Negative affectivity additionally correlated with surgency (r = −0.42, p < 0.001). Problematic video gaming correlated with gender (rpb = −0.37, p < 0.001), effortful control (r = −0.36, p < 0.001), and negative affectivity (r = 0.38, p < 0.001).

Among the brain ROIs, hippocampal volume correlated with age at baseline (r = 0.22, p = 0.006), gender (r = −0.16, p = 0.04) and affiliativeness (r = −0.19, p = 0.02); amygdala volume correlated with gender (r = −0.25, p = 0.002) and affiliativeness (r = −0.19, p = 0.02); and intracranial volume correlated with gender (r = −0.47, p < 0.001), affiliativeness (r = −0.22, p = 0.007) and problematic video gaming (r = 0.25, p = 0.02). Brain volumes in all ROIs correlated with each other (all p-values < 0.007).

Model comparisons

The LR test showed a better model fit for the model including temperament factors predicting problematic video gaming, in comparison to the null model (χ2(4) = 23.86, p < 0.001, coefficient of determination = 0.24). Model results are shown in Table 2.

Table 2.

Associations between temperament and problematic video gaming

AEQ Gaming addiction score Coeff. 95% CI p
Effortful control −1.56 −4.11, 0.98 0.229
Negative affectivity 2.94 0.32, 5.57 0.028
Affiliativeness −0.87 −2.85, 1.11 0.388
Surgency 0.02 −2.14, 2.18 0.983
Gender −6.61 −10.64, −2.59 0.001
constant 19.32 12.77, 25.87 <0.001
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Note. Results from structural equation model (number of observations = 245). Gender: 1 = male, 2 = female. Bold values denote statistical significance (p < 0.05).

While the effects of the four temperament factors were difficult to separate due to high correlations among them, negative affectivity and effortful control seemed to be most strongly associated with problematic video gaming (while being strongly correlated with each other, r = −0.65). Note, however, that in the full model, only negative affectivity was significant at the conventional p < 0.05 level.

We additionally tested for interactions between gender and temperament factors (separately) in exploratory analyses. The additional analyses yielded evidence for an interaction between gender and effortful control, results which are depicted in Table 3 and Fig. 1. The lower effortful control in late childhood, the higher was problematic video gaming in later adolescence in male participants, an association which was not found for females. There was no evidence for interactions between gender and any of the other three temperament factors.

Table 3.

Interaction between temperament and gender in predicting problematic video gaming

AEQ gaming addiction score Coeff. 95% CI p
Effortful control −3.85 −7.11, 0.58 0.021
Gender female −7.04 −11.09, −2.99 0.001
Gender by effortful control 4.32 0.24, 8.39 0.038
Negative affectivity 3.04 0.41, 5.68 0.024
Affiliativeness −0.61 −2.63, 1.41 0.556
Surgency 0.03 −2.14, 2.21 0.976
constant 12.40 9.40, 15.41 <0.001
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Note. Results from generalized structural equation model including the main effect of effortful control, gender, the interaction between effortful control and gender, as well as negative affectivity, affiliativeness and surgency as control variables. (number of observations = 124). Gender: 1 = male, 2 = female. Bold values denote statistical significance (p < 0.05).

Fig. 1.

Fig. 1.

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Predicted problematic video gaming in male and female adolescent participants

There was no evidence that temperament factors predicted the AEQ engagement score (χ2(4) = 2.99, p = 0.560, coefficient of determination = 0.085).

According to the LR test, comparing a model containing only temperament factors predicting problematic video gaming with a model additionally containing brain volume in examined ROIs (two separate models for left and right hemispheric ROIs), there was no evidence that including brain volume in the model increased model fit (left hemisphere: χ2(4) = 0.64, p = 0.96, right hemisphere: χ2(4) = 0.04, p = 0.99). The full model results can be found in the Supplementary materials, Tables S2 and S3.

Comparing a model including all interaction effects between temperament and brain volume, to a model without interactions, yielded weak evidence for increased model fit in the left hemisphere (χ2(16) = 23.43, p = 0.10) and the right hemisphere (χ2(16) = 23.75, p = 0.10; full model results in the Supplementary materials, Tables S4 and S5), which prompted us to further explore interaction effects in models for each ROI separately. There was some evidence, that the model including interaction terms was better than a model without interactions for the right amygdala (χ2(4) = 10.12, p = 0.04; see Supplementary Table S6), and the right ACC (χ2(4) = 9.75, p = 0.04) however, no such evidence was found for the left amygdala, left ACC, left and right OFC, or left and right hippocampus models. Finally, among the models calculated separately for each ‘ROI by temperament factor’ combination, there was some evidence for interactions between effortful control and brain volume in predicting problematic gaming: models including an interaction between effortful control and left amygdala, right amygdala, and right ACC showed better fit than models without the interaction (left amygdala: χ2(1) = 6.27, p = 0.01; right amygdala: χ2(1) = 7.74, p = 0.005; right ACC: χ2(1) = 5.47, p = 0.02). These effects would not survive a conventional correction for multiple comparisons (0.05/32 = 0.001). The interactions are illustrated in Fig. 2 and all model results can be found in Table S7 of the Supplementary materials. A further model testing a three-way interaction with gender did not provide evidence for such an interaction.

Fig. 2.

Fig. 2.

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Interactions between effortful control and brain volume in predicting problematic video gaming

Discussion

The two main objectives of this study were to explore the relationship between temperament at age 11–13 and problematic video gaming at age 17–19 and to investigate whether brain volume had incremental value in predicting these behaviors in a community sample of adolescents. Consistent with previous evidence of a male predominance of video gaming and addictive video gaming, our results confirm male gender as a predictive factor. The reasons for this gender difference are much debated, but difficult to pinpoint (Chiu, Lee, Huang, & Huang, 2004; Jackson et al., 2011). One contributing factor could be the societal acceptance of video gaming as a more normative activity for male than for female adolescents, reinforcing gender-based disparities in gaming behaviors. Further, video game design has historically targeted males by featuring combat and action themes and emphasizing competition, which are stereotypically associated with male interest. However this trend might have changed in contemporary game design, and recent studies have started addressing the specificities of female gaming (Lopez-Fernandez, Williams, & Kuss, 2019).

We found that temperament in late childhood was prospectively associated with problematic video gaming in later adolescence. This is consistent with previous cross-sectional research on temperament and video gaming addiction (E. J. Kim et al., 2008; Kuss & Griffiths, 2012; Nordin & Nylander, 2007; Ozturk et al., 2013; Rehbein et al., 2010). In particular, higher negative affectivity was related to higher gaming addiction scores. One hypothesized explanation for this might be that children and adolescents engage in gaming to cope with frustration, negative feelings or loneliness (Brand, Young, Laier, Wölfling, & Potenza, 2016; Wan & Chiou, 2006; Wölfling, Thalemann, & Grüsser-Sinopoli, 2008). Moreover, a somewhat weaker effect was found for effortful control, with lower scores predicting higher levels of video gaming addiction behaviors. This effect was driven by male participants, as we found that gender interacted with temperament in predicting video gaming addiction behaviors: Higher effortful control in males was related with lower video gaming addiction behaviors in late adolescence, an association not found in females. While previous research has not reported a gender interaction, the association between higher effortful control and lower gaming is not surprising. Individuals with lower effortful control have more difficulties regulating their impulses, which is likely to result in a higher risk for addiction in general (Jentsch et al., 2014). A number of previous studies have focused on measures of the related construct of personality, rather than temperament per se. These studies found lower levels of conscientiousness, openness and agreeableness (Esteve et al., 2022; Sánchez-Llorens et al., 2023) and higher levels of neuroticism (Rodríguez-Ruiz et al., 2023; Wernicke & Montag, 2022) to be related with problematic video gaming (González-Bueso et al., 2018; N. Kim, Hughes, Park, Quinn, & Kong, 2016; Ryu et al., 2019; Torres-Rodríguez et al., 2018; Wang, Ho, Chan, & Tse, 2015), which is generally consistent with our findings. These findings highlight the potential for selective prevention of problematic video gaming during adolescence based on individual temperamental profiles rather than biological markers. Adolescents with high negative affectivity or low effortful control, particularly males, could benefit from tailored programs that enhance self-regulation and emotional coping skills. Based on established prevention frameworks, such interventions might include regular mental health screenings, education programs for adolescents and caregivers on healthy gaming habits, and structured strategies like contingency management and goal-setting (King & Delfabbro, 2018). Additionally, parental involvement through family media agreements and facilitating alternative activities could further support at-risk youth, aligning with targeted prevention approaches (King et al., 2017).

The second aim of this study was to test the incremental value of brain volume in selected ROIs for the prediction of problematic video gaming above temperament alone. Interestingly, while we did not find main effects of brain volume on later problematic video gaming, additional exploratory analyses showed preliminary evidence for an interaction between temperament and brain volume. While in participants with high effortful control, amygdala volume of the left and right hemisphere was negatively correlated with problematic gaming (higher volume associated with lower problematic gaming), in those with lower effortful control, amygdala volume was positively correlated with problematic gaming. In their model, Whittle et al. (2006) established the neurobiological basis of the three core temperamental factors of negative affectivity, positive affectivity and constraint, with constraint as a non-affective dimension interacting with positive and negative affectivity by influencing links between stimuli and response. The amygdala, prominent for its function in detecting threat and processing fear has been found to possess a broader role of responding to novelty and processing arousal (Schwartz, Wright, Shin, Kagan, & Rauch, 2003). As such, increased amygdala activity and size has consistently been found to be related with negative affectivity-related disorders such as depression and anxiety (Drevets, 1999) and interestingly, with craving following exposure to drugs in drug-dependent individuals (Bonson et al., 2002). It has previously been argued that variation in amygdala responses to novelty might underlie temperamental constructs on the ends of a behavioral continuum of approach and withdrawal (Kagan, Reznick, & Snidman, 1987, 1988). A very tentative interpretation of the weak evidence that we found for an interaction between amygdala volume and effortful control in predicting problematic video gaming might therefore be that in individuals with large amygdalae, who may be vulnerable to affective disorders, effortful control might serve as a protective factor in reducing the risk for problematic video gaming.

Our study faces several limitations. Problematic video gaming was only assessed at the fourth wave of the study, making it impossible to control for initial levels of video gaming or track the development of gaming behavior. Initial assessments of problematic gaming might enhance predictive capability in future studies. Moreover, we focused on a dimensional measure of problematic gaming rather than formal diagnoses. Further, having examined a community sample, our study results are somewhat representative of the general population of adolescents. Additionally, while our research encompassed all video gaming genres, a more focused examination of specific genres, such as Massively Multiplayer Online Role-Playing Games (MMORPGs), which are thought to be more addictive than other genres, might provide more nuanced insights (Kuss, van Rooij, Shorter, Griffiths, & van de Mheen, 2013; Rehbein et al., 2010; Wang et al., 2014). Finally, an analytical limitation is given by the high intercorrelations between ROI volumes, potentially biasing analyses.

Conclusion

Extending previous work, our findings demonstrate the role of gender and temperament in late childhood as prospective predictors of problematic video gaming in later adolescence. High negative affectivity as well as low effortful control in males were associated with an increased risk of later problematic video gaming. Determining early predictors of problematic video gaming might facilitate timely prevention. This prevention might be aimed at and specifically tailored for children with high negative affectivity as well as boys with low effortful control.

Supplementary material

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Acknowledgments

We thank all participants and their parents for taking part in this study. We further extend our thanks to all collaborators and contributors of the OADS study. Authors acknowledge Aboriginal and Torres Strait Islander people of the unceded land on which Australian authors work, learn and live. We pay respect to Elders past, present and future, and acknowledge the importance of Indigenous knowledge in the Academy.

Funding Statement

Funding sources: The Orygen Adolescent Development Study (OADS) was funded by the Colonial Foundation, the National Health and Medical Research Council, and the Australian Research Council.

Footnotes

Authors' contribution: IML: conceptualization, formal analysis, methodology, visualization, writing – original draft, writing – review & editing; SS: formal analysis, methodology, writing – review & editing; JS: data curation, investigation, project administration, writing – review & editing; DIL: investigation, writing – review & editing; SW: funding acquisition, data curation, investigation, project administration, writing – review & editing; NBA: funding acquisition, data curation, investigation, project administration, writing – review & editing; MK: conceptualization, methodology, writing – review & editing, supervision.

Conflict of interest: Nicholas B. Allen has an equity interest in, and receives salary from, Ksana Health Inc. No Ksana Health products or services were used in the current study. The remaining authors have no conflicts to interest to disclose.

Contributor Information

Ines Mürner-Lavanchy, Email: ines.muerner-lavanchy@unibas.ch.

Silvano Sele, Email: silvano.sele@unibe.ch.

Julian Simmons, Email: jgs@unimelb.edu.au.

Dan I. Lubman, Email: dan.lubman@monash.edu.

Sarah Whittle, Email: swhittle@unimelb.edu-au.

Nicholas B. Allen, Email: nallen3@uoregon.edu.

Michael Kaess, Email: michael.kaess@upd.ch.

Data availability

All derived data and code are publicly available on the Dryad (https://doi.org/10.5061/dryad.zpc866th0).

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

All derived data and code are publicly available on the Dryad (https://doi.org/10.5061/dryad.zpc866th0).

Articles from Journal of Behavioral Addictions are provided here courtesy of Akadémiai Kiadó

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