Remember me
Gaming represents one of the most widespread forms of virtual leisure worldwide. Its relevance is reflected in a global gamer population exceeding 3 billion gamers []. In Spain, 77% of young people aged 6 to 14 years and 85.1% of adolescents aged 14 to 18 years engage in gaming regularly [,]. Although gaming has transcended age barriers and become a pastime across generations [], it is during childhood that both traditional (nondigital) and digital gaming habits are formed. Both digital and nondigital gaming share a core feature: their capacity to engage players through intrinsic motivation []. However, the key distinction between both categories lies in the potential maladaptive use of digital gaming, which can develop into an addictive disorder called gaming disorder (GD). GD is recognized as a persistent gaming behavior that significantly impairs psychosocial functioning in both the DSM-5-TR (Diagnostic and Statistical Manual of Mental Disorders [Fifth Edition, Text Revision]) and the ICD-11 (International Classification of Diseases, 11th Revision) psychiatric classifications. However, while the DSM-5-TR classifies GD as a condition warranting further research, the ICD-11 recognizes it as a formal disorder [,]. Despite these nosological differences, this study adopts the American Psychiatric Association’s DSM-5 (Diagnostic and Statistical Manual of Mental Disorders [Fifth Edition]) based framework for GD, which remains widely used in research and clinical settings [].
GD represents a growing public health concern, affecting approximately 3% of gamers worldwide [] and 4.6% of young male gamers []. In Spain, its prevalence among adolescents aged 14-18 years is 7.1%, rising to 11.3% among male gamers compared to 2.7% among female gamers []. This higher prevalence among adolescent male gamers is consistent with established risk factors for GD, such as male sex and adolescence [], along with gaming-related characteristics like online gaming []. Moreover, a growing body of research highlights gaming motivation as a significant factor in the development of GD []. Specifically, the self-determination theory (SDT) [] provides a comprehensive framework for understanding the etiology of GD in youths [].
SDT posits that gaming facilitates the fulfillment of the 3 fundamental psychological needs—autonomy (volition and personal agency), competence (sense of efficacy), and relatedness (social connection)—both in-game and in real-life [,]. Building on SDT, the need density hypothesis [] proposes that GD may develop when in-game need satisfaction (NS) significantly exceeds real-life need satisfaction [].
Although widely accepted [,], empirical support for this framework is inconsistent. Some studies could not replicate the expected effects of the imbalance between NS and real-life need satisfaction [], while others found no direct association between NS and GD [,]. According to the need density hypothesis, recent research suggests that real-life need frustration may be a stronger predictor of GD than low real-life need satisfaction [,], especially when combined with high NS [,,]. However, gaming environments can also frustrate these needs—defined as the active thwarting rather than merely the absence of satisfaction [,]—an aspect not yet fully addressed by SDT [,].
Originally, SDT posits that NS is linked to positive gaming outcomes, such as enjoyment, intrinsic motivation, vitality, engagement, and well-being [,,-], while in-game need frustration (NF) is associated with dysfunctional and obsessive use patterns that contribute to addictive behaviors, such as GD [,]. Notably, both real-life need frustration and NF have been linked to GD [,,,]. However, research on NF has received considerably less attention compared to real-life need frustration [,,].
This gap is not unexpected, as SDT’s motivational model of gaming has historically centered on the study of NS [,], with more limited theoretical development concerning NF []. This emphasis on NS is also reflected in the availability of validated instruments designed to measure NS—such as the Player Experience of Need Satisfaction (PENS) [], the Game Experience Questionnaire [], and the Ubisoft Perceived Experience Questionnaire []—but not in the assessment of NF. To date, the prevailing approach has assessed NF in conjunction with NS, typically through adapted versions of the Basic Psychological Need Satisfaction and Frustration Scale (BPNSFS) [] for gaming contexts [,]. However, most studies have used ad hoc adaptations without evaluating their psychometric properties []. This is particularly noteworthy, given that a validated version of the Basic Psychological Need Satisfaction and Frustration Scale for Gaming (BPNSFS-G) specifically designed to assess NS and NF is already available [].
The current state of the literature reveals several critical gaps that warrant further investigation. First, research on NF and its relationship with GD remains both limited and methodologically inconsistent. Second, most studies have focused on university students or adult samples [,,,], while only a few studies have examined NS or NF using adolescent samples [,]. Finally, the available instruments to assess these constructs have been developed exclusively in English, restricting the generalizability of findings.
In response, this study aimed to translate the BPNSFS-G into Spanish, adapt it for the adolescent population, and validate its psychometric properties in a sample of adolescent gamers. Specifically, the study assessed the scale’s structural validity, internal consistency, as well as construct and criterion validity. Once the psychometric adequacy was established, we tested two hypotheses based on the current state of literature (1) that NF would show a stronger association with GD than NS and (2) that NS would be more closely related to gaming time and frequency than NF. Additionally, we conducted measurement invariance analyses across subgroups defined by known GD risk factors such as the developmental stage (early vs middle adolescence), sex (girls vs boys), and the gaming mode (online vs offline). This approach enabled us to explore the functioning of NS and NF across different adolescent gamer subpopulations. To our knowledge, this is the first study to examine these questions.
Participants were recruited through convenience sampling from 27 schools in Valencia, where the “Gamer” GD prevention program [] was conducted from October 2022 to May 2023. The program lasts 3 weeks with 1 session per week. A pre- and postevaluation was conducted, but this study focuses exclusively on the baseline assessment, using a cross-sectional design. The questionnaires were paper-based and self-administered in groups under the supervision of prevention specialists.
The program targeted students aged 10 to 15 years (fifth year in primary school to second year in secondary school). Participants were classified into early adolescence (10-12 years old) and middle adolescence (13-15 years old) []. The inclusion criteria were no missing responses on any questionnaire item, sufficient Spanish proficiency, and identification as gamers on predefined criteria (see Demographic and Gaming Patterns Measures section for details).
Of the 1747 students evaluated, 1174 met the inclusion criteria (mean age 12.07, SD 1.23 years; early adolescents: 359/1174, 30.6% and middle adolescents: 815/1174, 69.4%), with 1037 participants reporting their sex (female: 400/1037, 38.6% and male: 637/1037, 60.6%) and 511 reporting their gaming modality (online gamers: 388/511, 75.9% and offline gamers: 123/511, 24.1%).
Ethical ConsiderationsThe study was approved by the University of Valencia Ethics Committee (approval H1482079199937), in accordance with institutional and national ethical standards, including the Declaration of Helsinki and Spanish regulations on biomedical research and personal data protection. Participation was voluntary and anonymous, and institutional agreement was obtained from the participating schools prior to data collection. Questionnaires used unique anonymized ID codes, with no personally identifiable information collected, and data were stored securely and accessed only by authorized research personnel. No financial compensation was provided.
ProcedureThe BPNSFS-G [] was adapted into Spanish () using the back-translation method [], following a 6-phase structure []. First, 2 independent translations of the original scale were produced in adolescent psychology and gaming motivation research, all experienced in psychometric validation, linguistic adaptation, and proficient in English. Second, these versions were reviewed and merged into a single refined Spanish version. In the third phase, a bilingual English linguist back-translated this version, which was then assessed by the expert panel for its semantic, conceptual, and cultural equivalence with the original scale.
The fourth phase involved adapting the questionnaire to the adolescent population at both linguistic and methodological levels. First, the panel made linguistic modifications to simplify terminology and improve item clarity for adolescents while maintaining the original meaning. Furthermore, the response scale was reduced from 7 to 5 points to enhance reliability within this population []. This modification was deemed necessary because simpler formats are more appropriate for the cognitive development–level characteristic of early adolescence []. In addition, item wording was adjusted to ensure applicability to both online and offline gaming experiences, referencing gamers’ experiences with the games they usually play [], rather than restricting responses to a specific gaming modality [].
The fifth phase consisted of a pilot study conducted in 4 schools (mean age 12.26, SD 0.91 years; girls: 92/199, 46.2%; boys: 93/199, 46.7%; nonbinary sex: 6/199, 3%; early adolescents: 49/199, 24.6%; middle adolescents: 150/199, 75.4%). Based on feedback from students, teachers, and prevention specialists, minor linguistic adjustments were made. A second pilot study was conducted in 2 additional schools (mean age 11.79, SD 1.07 years; girls: 25/56, 44.7%; boys: 31/56, 53.4%; early adolescents: 32/56, 55.2%; middle adolescents: 24/56, 41.4%), confirming the scale’s comprehensibility. The expert panel approved the final version of the instrument, confirming its content validity for use in the main study.
MeasuresSatisfaction and Frustration of Basic Psychological Needs During GameplayThe BPNSFS-G [] is a self-administered questionnaire designed to assess NS and NF, each conceptualized as a second-order construct composed of 3 first-order dimensions: autonomy, competence, and relatedness in-game needs.
The original version comprises 15 items rated on a 7-point Likert scale. For this study, the scale was adapted to a 5-point Likert format (0=strongly disagree to 4=strongly agree). In the original questionnaire, each item ended with the phrase “in my current favorite online game.” In the adapted version, this was changed to “the games you normally play” as an introductory statement to broaden its applicability beyond online gaming.
The satisfaction subscale consists of 7 items, selected from the PENS []. Higher scores indicate that video games are perceived as a source of need fulfillment within the gaming context. The subscale includes 3 items for autonomy (eg, “I feel that the decisions I make are the ones I really want to make”), 2 for competence (eg, “I feel skilled at what I do”), and 2 for relatedness (eg, “I feel close to the people who are important to me in the game”).
The frustration subscale consists of 8 items, adapted from the BPNSFS []. Higher scores indicate that video games are perceived as a context where psychological needs are thwarted. The subscale includes 3 items for autonomy (eg, “I feel forced to do many things that I would not do if I had a choice”), 3 for competence (eg, “I feel insecure about my abilities in the game”), and 2 for relatedness (eg, “I feel excluded from the group I want to belong to in the video game”).
Gaming DisorderGD was assessed using an abbreviated version of the Video Game Dependency Test [], adapted to the DSM-5 and ICD-11 diagnostic criteria for GD []. This self-administered, paper-based questionnaire consists of 10 items assessing gaming-related behaviors (eg, “I spend less time doing other activities because video games take up a significant part of my time”) and their impact over the past 12 months, rated on a 5-point Likert scale (0=strongly disagree to 4=strongly agree). The total score ranges from 0 to 40, with higher scores indicating greater symptom severity.
This version of the Video Game Dependency Test has been adapted for Spanish-speaking adolescents, demonstrating adequate internal consistency in this study (Cronbach α=0.85) comparable to the original validation (Cronbach α=0.874) [].
Demographic and Gaming Pattern MeasuresThis set of ad hoc items was designed to assess participants’ sociodemographic characteristics and gaming patterns. Sociodemographic data comprised age, sex, and school grade. Gaming behavior was measured through daily gaming time and weekly gaming frequency, using 3 self-reported items on a 5-point ordinal scale. Daily gaming time was measured separately for weekdays and weekends, with response options ranging from 1=I do not play, 2=less than 1 hour, 3=1-2 hours, 4=2-3 hours, to 5=3 or more hours. Weekly gaming frequency was evaluated with response options ranging from 1=I do not usually play, 2=1-2 days, 3=3-4 days, 4=5-6 days, to 5=every day. Higher scores on time and frequency measures indicate greater gaming engagement. These 3 items were used as inclusion criteria when participants scored ≥3 on both daily gaming time and weekly frequency. Additionally, participants reported their preferred gaming modality, indicating whether they primarily played offline or online.
Statistical AnalysesData analyses were conducted using SPSS Statistics (version 28; IBM Corp) and RStudio (Posit Software, PBC). Given the study’s multivariate design, structural equation modeling was used to assess factorial structure, measurement invariance, and construct validity. The diagonal weighted least squares estimation method was applied due to the ordinal scale and nonnormal distribution of the data [].
To analyze structural validity, exploratory factor analysis (EFA) was first conducted using principal component analysis with Varimax rotation. Data adequacy was confirmed using the Kaiser-Meyer-Olkin measure (≥0.80) and Bartlett test (P<.001), and factors were then retained based on eigenvalues (>1) and communalities (h2≥0.30). Second, confirmatory factor analysis (CFA) tested the factor structure derived from the EFA against competing theoretical models, with model fit evaluated using chi-square (P>.001), comparative fit index (CFI; >0.95), root-mean-square error of approximation (RMSEA; <0.06), and standardized root-mean-square residual (SRMR; <0.08) []. Third, measurement invariance was assessed across sex (boys vs girls), the developmental stage (early vs middle adolescents), and the gaming modality (online vs offline) using a multilevel approach. This included testing configural, metric, and scalar invariance, with changes in ΔCFI (≤0.01) and ΔRMSEA (≤0.015) as criteria. For ΔSRMR, thresholds were set at ≤0.03 for metric and ≤0.01 for scalar invariance [].
Following the selection of the final factorial model, descriptive statistics including mean, SD, skewness, and kurtosis were computed to analyze the distribution of scores. Group differences were examined using independent samples 2-tailed t tests (bilateral) with 95% CIs and Hedges g effect sizes (small≥0.2, medium≥0.5, or large≥0.8). Finally, internal consistency was assessed using Cronbach α and composite reliability (CR; ≥0.7). Item reliability was assessed using Cronbach α if an item was deleted (α–item) and the corrected item-total correlation (CITC) was 0.3 []. Construct and criterion validity were tested via CFA and Fisher bilateral correlations with gaming behavior indicators (daily gaming time and weekly frequency) and GD. Other indicators of internal validity were assessed using standardized factor loadings (λₑ>0.4), squared multiple correlations (R2≥0.5), and average variance extracted (AVE) to evaluate convergent validity (AVE≥0.5; CR≥0.7; and λₑ>0.5), as well as discriminant validity (AVE>r2 between factors) [,]. AVE and CR were manually calculated using standard formulas []:
λei represents the standardized factor loading of item i, n is the number of items in the construct, and θi refers to the standardized measurement error associated with each item.
The objective of this section was to examine the internal structure and measurement invariance of the scale. The scale was originally designed as a unified instrument to assess both NS and NF. The results of exploratory, confirmatory, and multigroup factor analyses supported a differentiated structure, leading to the validation of 2 independent measures: a 3-factor second-order model for NS and a unidimensional model for NF.
First, the internal structure of the questionnaire was subjected to an EFA. The results of the EFA indicated a 2D structure with 2 first-order factors composed of NS and NF (Kaiser-Meyer-Olkin measure=0.812; χ2105=3799.0; P<.001). The first factor (NS) obtained an eigenvalue of 2.26 and explained 22.66% of the variance, while the second factor (NF) obtained an eigenvalue of 3.4 and explained 17.73% of the variance. The principal component analysis excluded items with low values in their communalities, including: “I am not clear if I can do things well in the video game” (competence frustration=0.17) and “I feel that what I choose in video games really expresses how I am” (autonomy satisfaction=0.27).
Second, a CFA was performed across the predefined models to identify the best fit for the data. The structure obtained from the EFA was rejected, despite its consistency with the theoretical model (χ264=863.9; P>.001; CFI=0.82; RMSEA=0.11; 90% CI 0.097-0.12; SRMR=0.09). We then tested 2 factorial structures defined in the literature through competitive CFA. Both models included 6 factors for in-game needs, but model 1 was defined by a second-order structure [] and model 2 by a first-order structure [].
Model 2 (χ275=617.4; P>.001; CFI=0.92; RMSEA=0.072; 90% CI=0.064-0.079; SRMR=0.067) performed better than model 1 (χ283=853.1; P>.001; CFI=0.89; RMSEA=0.088; 90% CI 0.076-0.09; SRMR=0.084), which improved after eliminating the problematic items identified in EFA (model 1: χ283=853.1; P>.001; CFI=0.88; RMSEA=0.088; 90% CI 0.076-0.09; SRMR=0.084; and model 2: χ275=617.4; P>.001; CFI=0.92; RMSEA=0.072; 90% CI 0.064-0.079; SRMR=0.067). However, both models were rejected because of the RMSEA values.
Since the original questionnaire measured NS independently (PENS) [,], a second-order model with 3 first-order dimensions for both NS and NF (model 3) was tested. This decision followed the unsatisfactory results of the CFA, where an attempt to declare NS and NF as independent second-order factors resulted in a poor fit (χ284=766.1; P>.001; CFI=0.88; RMSEA=0.081; 90% CI 0.077-0.088; SRMR=0.09), even after excluding the problematic items (χ259=505.3; P>.001; CFI=0.91; RMSEA=0.081; 90% CI 0.073-0.09; SRMR=0.084).
The CFA results improved notably when the fit of model 3 was analyzed separately for both scales. For the NS scale, the fit approached ideal values (χ211=52.3; P>.001; CFI=0.98; RMSEA=0.058; 90% CI 0.041-0.077; SRMR=0.029), which improved further after eliminating the autonomy satisfaction item, as it did not meet the cutoff for factor saturation (λe=0.456). Additionally, the 3-factor structure without this item (χ26=12.4; P=.05; CFI=0.99; RMSEA=0.052; 90% CI 0.028-0.079; SRMR=0.015) was superior to the unidimensional structure (χ214=425.7; P>.001; CFI=0.81; RMSEA=0.16; 90% CI 0.15-0.17; SRMR 0.077). shows the final structure of the NS scale.
Figure 1. 3D model of in-game need satisfaction. Conversely, the results of model 3 on the NF scale were quite satisfactory but not enough because of RMSEA (χ217=87.4; P>.001; CFI=0.957; RMSEA=0.076; 90% CI 0.061-0.092; SRMR=0.033) and a problematic item from the competence frustration factor (λe=0.389). Analyses were replicated, removing this item, although the results were lower (χ211=67.8; P>.001; CFI=0.962; RMSEA=0.087; 90% CI 0.069-0.107; SRMR=0.033). Finally, a unidimensional model was tested (χ220=99.1; P>.001; CFI=0.952; RMSEA=0.075; 90% CI 0.061-0.089; SRMR=0.036), which did improve by removing this item (χ214=82.3; P>.001; CFI=0.96; RMSEA=0.08; 90% CI 0.064-0.098; SRMR=0.036), reaching very satisfactory values when relating the error covariances of items 1, 4, 6, and 7 of the scale (χ211=32.8; P>.001; CFI=0.986; RMSEA=0.053; 90% CI 0.032-0.074; SRMR=0.023). shows the final structure of the NF scale.
Figure 2. Unidimensional model of in-game need frustration. Finally, CFA supported a 3D second-order structure for NS and a unidimensional structure for NF. After confirming different structures for NS and NF, its measurement invariance was tested. The analyses showed that both scales met the invariance criteria for developmental stage () and sex () groups, but not for the modality of play (). Specifically, the NF scale met both levels of invariance for the measurement of girls and boys as well as early and middle adolescents, although the NS scale only reached weak invariance for the developmental stage.
Table 1. Invariance of in-game need satisfaction (NS) and in-game need frustration (NF) based on developmental stage.GroupsDevelopmental stageP valueCFIa (Δ)RMSEAb (Δ)SRMRc (Δ)aCFI: comparative fit index.
bRMSEA: root-mean-square error of approximation.
cSRMR: standardized root-mean-square residual.
dNot applicable.
Table 2. Invariance of in-game need satisfaction (NS) and in-game need frustration (NF) based on sex.GroupsSexP valueCFIa (Δ)RMSEAb (Δ)SRMRc (Δ)aCFI: comparative fit index.
bRMSEA: root-mean-square error of approximation.
cSRMR: standardized root-mean-square residual.
dNot applicable.
Table 3. Invariance of in-game need satisfaction (NS) and in-game need frustration (NF) based on the gaming modality.GroupsGaming modalityP valueCFIa (Δ)RMSEAb (Δ)SRMRc (Δ)aCFI: comparative fit index.
bRMSEA: root mean-square-error of approximation.
cSRMR: standardized root-mean-square residual.
dNot applicable.
Descriptive Statistics and Differences Between GroupsOverall, NS was higher than NF, which had notably low values, although NS scores showed greater variability (). Specifically, autonomy (followed by competence) showed the highest satisfaction, while relatedness had the lowest.
Table 4. Description of the scores of the scales of basic need satisfaction and frustration in the gaming contexta.Scales and itemsMean (SD)SkewnessKurtosis“While playing the video games I normally play ...”aOriginally translated from Spanish ().
bNS: in-game need satisfaction.
cNF: in-game need frustration.
The scoring trend of the different groups was similar to the overall sample scores (-). The highest NS scores were obtained by the online gamers’ subgroup, followed by the male participants, and finally by the early adolescents. These same populations also obtained higher scores on the NF, although in this case, the subgroup that presented greater frustration was the early adolescents, followed by the online gamers, and finally the male participants.
Effect sizes indicated that the population variables used to define gamer groups based on needs were relevant for sex and the game modality but not for the developmental stage. However, while the effect size was medium-high in the case of the NS factors, NF had a low or no effect. Therefore, it seems that these personal variables had a greater impact on the experience of NS than on NF.
Table 5. Differences between adolescent groups on the scores of in-game need satisfaction (NS) and in-game need frustration (NF) scales.aThe P value reflects the 2-tailed hypothesis test.
bAS: autonomy satisfaction.
cCS: competence satisfaction.
dRS: relatedness satisfaction.
Table 6. Differences between sex groups on the scores of in-game need satisfaction (NS) and in-game need frustration (NF) scales.aThe P value reflects the 2-tailed hypothesis test.
bAS: autonomy satisfaction.
cCS: competence satisfaction.
dRS: relatedness satisfaction.
Table 7. Differences between gaming modality groups on the scores of in-game need satisfaction (NS) and in-game need frustration (NF) scales.aThe P value reflects the 2-tailed hypothesis test.
bAS: autonomy satisfaction.
cCS: competence satisfaction.
dRS: relatedness satisfaction.
Psychometric Evaluation of the ConstructsTo ensure the psychometric properties of the scale’s factors, we analyzed the main reliability and validity indicators () and their relationship with gaming patterns and GD (). Item correlation analyses revealed higher correlations between items within the same factor than between items of different factors for the NS scale, but not for the NF scale. This supports the multifactorial and single-factor structures, respectively. Both scales showed adequate discriminant validity through the squared correlational values (r2) between factors (autonomy-competence=0.179; autonomy-relatedness=0.084; competence-relatedness=0.169 [NS]; NS-NF=0.012).
Table 8. Psychometric study of in-game psychological needs factors.aNS: in-game need satisfaction.
bNF: in-game need frustration.
cAS: autonomy satisfaction.
dCronbach α=0.58; composite reliability=0.58; average variance extracted=0.49; R2=0.62.
eCS: competence satisfaction.
fCronbach α=0.66; composite reliability=0.73; average variance extracted=0.58; R2=0.89.
gRS: relatedness satisfaction.
hCronbach α=0.75; composite reliability=0.68; average variance extracted=0.64; R2=0.4.
iTS: total score.
jCronbach α=0.75; composite reliability=0.82; average variance extracted=0.57.
kCronbach α=0.77; composite reliability=0.83; average variance extracted=0.41.
lr: Pearson bilateral correlations.
Table 9. Associations between in-game psychological needs and patterns of gaming use and problematic behavior.
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