The study sample comprised N = 1,424 adolescents, aged between 14 and 18 years (mean age = 16.2 years, SD = 1.2), recruited from high schools across the Lazio region in Italy. Schools were selected to provide a representative cross-section of socioeconomic and demographic backgrounds, ensuring diverse adolescent subgroups were included. A stratified random sampling approach was used to achieve an even distribution of gender, age, and socioeconomic status, factors that may impact gambling behaviors and related outcomes (31).

Data collection was conducted in classroom settings during school hours across multiple high schools in the Lazio region, maintaining a controlled and standardized environment for all participants. Access to schools was facilitated through collaboration with local educational authorities, which allowed for scheduling data collection sessions in a way that minimized disruption to regular academic activities.

Means, standard deviations, and frequency distributions were calculated for all main variables, including gambling frequency, gambling severity, temporal perspectives, sensation-seeking, and cognitive distortions. Pearson correlation analyses were conducted to examine relationships between temporal perspectives, sensation-seeking, cognitive distortions, and gambling behaviors (frequency and severity). This provides preliminary insights into the strength and direction of associations.

Participants were grouped by gambling risk level (e.g., non-problem, at-risk, problem gamblers) based on PGSI scores. ANOVA tests were conducted to assess whether temporal perspectives, sensation-seeking, and cognitive distortions differed across these groups. Interaction terms between temporal perspectives (present and future orientations) and sensation-seeking/cognitive distortions were tested to examine potential moderation effects, using the PROCESS macro for SPSS (41). All variables were screened for normality prior to parametric analysis. Gambling severity showed moderate positive skew; thus, bootstrapped confidence intervals (1,000 samples) were computed for regression analyses involving this variable. Nonparametric tests (Spearman’s rho) were used when appropriate. Demographic covariates (age, sex, SES) were included in multivariate models as control variables. Zero-order correlations between these covariates and the dependent variable are reported in Supplementary Table S2 . The distribution of the dependent variable is illustrated in Supplementary Figure S1 . Hierarchical multiple regression was used to evaluate the incremental contribution of temporal perspectives to gambling behavior. In Step 1, sensation-seeking and cognitive distortions were entered as established dispositional and cognitive predictors. In Step 2, the five temporal perspective subscales were added to assess their unique explanatory power above these known factors. This approach allowed us to isolate the contribution of temporal orientation in the prediction of gambling severity and frequency, while controlling for established correlates. Moderation analyses were then conducted to test whether the association between temporal perspectives and gambling outcomes varied as a function of sensation-seeking or cognitive distortions. These interactions were grounded in prior literature suggesting that the impact of time perspective on behavior may be amplified in adolescents with heightened impulsivity or distorted beliefs about gambling. Continuous predictors were mean-centered, and interaction terms were computed accordingly. Data were screened for missing values, outliers, and normality violations. Cases with more than 10% missing responses were excluded. Assumptions for regression analyses, including linearity, homoscedasticity, and multicollinearity, were tested, with necessary corrections applied. All regression models report standardized beta coefficients, and effect sizes for ANCOVAs and moderation effects are reported using partial eta squared (η²p), interpreted according to standard conventions (small ≥.01, moderate ≥.06, large ≥.14). All analyses were conducted using SPSS version 26 and PROCESS macro version 3.4 (41) for moderation analyses.

The final sample consisted of N = 1,424 adolescents (52% male, 48% female), aged between 14 and 18 years (mean age = 16.2 years, SD = 1.2). Table 1 presents a summary of demographic characteristics, including gender, age group, and socioeconomic status.

Socioeconomic status (SES) was assessed using two indicators: (1) Parental Education Level: Participants provided information about the highest level of education attained by their parents or guardians. Responses were categorized into three levels: low (less than high school diploma), medium (high school diploma), and high (university degree or higher). (2) Self-Reported Family Income: Adolescents were asked to estimate their family’s economic condition based on a scale ranging from below average to above average compared to the general population. Responses were also grouped into three categories: low, medium, and high. These two indicators were combined to create an SES classification for each participant, with low SES representing both lower education and lower income, medium SES reflecting average levels, and high SES representing both higher education and income levels.

Descriptive statistics were calculated for all key variables, including gambling frequency, gambling severity, sensation-seeking, cognitive distortions, and temporal perspectives. Regarding the types of gambling, the most frequently reported activities included online sports betting (41%), scratch cards (38%), and gambling via smartphone apps or social media platforms (27%). Table 2 provides a summary of means, standard deviations, and observed ranges for each variable.

Figure 1 displays the distribution of PGSI scores across the sample. As expected, the distribution is positively skewed, with most participants reporting low-risk gambling and a smaller subset falling in the moderate-to-high risk range. This distribution supports the decision to supplement linear models with logistic regression.

The correlations in Table 3 provide valuable insights into the relationships between gambling behaviors (frequency and severity) and key predictors, including sensation-seeking, cognitive distortions, and temporal perspectives (past-negative, past-positive, present-hedonistic, and future).

Correlational analyses ( Table 2 ) revealed that PGSI was negatively correlated with future orientation (r = –.28, p <.001), and positively correlated with sensation seeking (r = .23, p <.001), present orientation (r = .19, p <.001), and cognitive distortions (r = .31, p <.001).

Figure 2 illustrates pairwise relationships between PGSI scores and psychological variables. Notably, PGSI appears inversely related to future orientation, while associations with sensation seeking and present orientation are more modest. The linear patterns support the regression approach used.

To further investigate the role of temporal perspectives, sensation-seeking, and cognitive distortions across different gambling risk levels, participants were categorized based on their PGSI (Problem Gambling Severity Index) scores. Three groups emerged based on these scores:

ANOVA tests were conducted to examine whether there were significant differences in temporal perspectives (present-hedonistic and future orientation), sensation-seeking, and cognitive distortions (overall and specific distortions) across these groups. Table 4 provides a summary of the results, including mean scores, standard deviations, F-values, p-values, and effect sizes (η²) for each variable across groups.

The ANOVA tests revealed significant differences across gambling risk groups in each of the assessed variables. Problem gamblers scored significantly higher on present-hedonistic orientation than both non-problem and at-risk gamblers (F(2, N) = 15.34, p <.001, η² = 0.12). This supports the hypothesis that a present-focused orientation, associated with seeking immediate gratification, is strongly linked to problem gambling behaviors. Non-problem gamblers had significantly higher future orientation scores than both at-risk and problem gamblers (F(2, N) = 10.89, p <.001, η² = 0.09), indicating that an emphasis on future planning may serve as a protective factor against risky behaviors like gambling. Significant differences were also found in sensation-seeking across groups, with problem gamblers scoring the highest (F(2, N) = 18.56, p <.001, η² = 0.13). This finding highlights sensation-seeking as a prominent risk factor, suggesting that individuals drawn to high-risk, stimulating activities may be more vulnerable to problematic gambling. Cognitive distortions were also significantly higher among problem gamblers (F(2, N) = 14.67, p <.001, η² = 0.11). This effect was particularly pronounced in subscales measuring Luck/Perseverance Bias (F(2, N) = 12.34, p <.001, η² = 0.10) and Illusion of Control (F(2, N) = 16.23, p <.001, η² = 0.12). Problem gamblers were more likely to believe that gambling outcomes could be influenced by luck or personal skill, reinforcing gambling persistence despite losses.

Post-hoc tests (Tukey HSD) showed that problem gamblers scored significantly higher than both non-problem and at-risk gamblers on present-hedonistic orientation, sensation-seeking, and cognitive distortions. Conversely, non-problem gamblers scored significantly higher on future orientation compared to the other groups.

To further investigate differences in psychological traits between adolescents with low and high levels of gambling involvement, a series of ANCOVAs were conducted comparing high-risk (PGSI ≥ 4) and low-risk groups (see Table 5 ). Each model was adjusted for age, gender, and socioeconomic status (SES). The results showed a statistically significant difference in future orientation, with the high-risk group reporting lower scores (F(1, 94) = 5.94, p = .017, η²p = .07), indicating a moderate effect according to standard conventions (η²p ≥.06). A trend toward significance was observed for cognitive distortions (F(1, 94) = 3.87, p = .052, η²p = .04), reflecting a small-to-moderate effect size. No significant group differences were found for sensation-seeking (F = 2.10, p = .150, η²p = .02) or present orientation (F = 1.56, p = .215, η²p = .01), though both effects fall within the small range of partial eta squared.

As shown in Table 5 , adolescents classified as high-risk gamblers (PGSI ≥ 4) reported significantly lower levels of future orientation compared to their low-risk counterparts, even after controlling for age, gender, and SES. This effect was moderate in size (η²p = .07), indicating that a reduced capacity to project into the future may be a relevant factor in distinguishing more problematic gambling behaviors. Cognitive distortions also showed a trend toward significance (p = .052), suggesting a potential role in reinforcing maladaptive beliefs around gambling outcomes. No statistically significant differences emerged for sensation seeking or present orientation, although descriptive trends aligned with theoretical expectations.

Figure 3 compares mean scores for future orientation and cognitive distortions between high-risk and low-risk gambling groups. Adolescents classified as high-risk exhibited lower future orientation and slightly higher cognitive distortions, in line with the ANCOVA findings.

To further investigate the unique contributions of temporal perspectives, sensation-seeking, and cognitive distortions to gambling behaviors, hierarchical regression analyses were conducted separately for gambling frequency and gambling severity. Prior to conducting the regression models, multicollinearity diagnostics were performed. Variance Inflation Factors (VIF) for all predictors ranged from 1.2 to 2.4, well below the commonly used threshold of 5, indicating no significant multicollinearity issues. In these analyses, sensation-seeking and cognitive distortions were entered in the first step to account for their established influence on gambling behavior. Temporal perspectives were then added in the second step to determine their incremental predictive value beyond these established predictors. This approach allows us to assess whether temporal orientations (e.g., past-negative, past-positive, present-hedonistic, present-fatalistic, and future) provide additional explanatory power over sensation-seeking and cognitive biases.

To explore whether the relationships between temporal perspectives and gambling behaviors (frequency and severity) were moderated by sensation-seeking and cognitive distortions, moderation analyses were conducted using interaction terms. Specifically, we examined if sensation-seeking and cognitive distortions intensified or weakened the impact of temporal perspectives (particularly present-hedonistic and future orientations) on gambling outcomes. The analyses utilized the PROCESS macro for SPSS (41), with interaction terms tested separately for each temporal perspective.

A significant interaction was found between present-hedonistic orientation and sensation-seeking on gambling severity (β = .15, p <.01), indicating that the effect of present-hedonistic orientation on gambling severity increases as sensation-seeking levels rise.

This finding suggests that adolescents with high sensation-seeking tendencies who also exhibit a strong present-hedonistic orientation are at the highest risk for severe gambling problems. The combination of a desire for immediate pleasure and a propensity for thrill-seeking may drive these adolescents toward more frequent and intense gambling. Table 8 below shows the regression coefficients for this interaction, highlighting the significant contribution of sensation-seeking as a moderator in the relationship between present-hedonistic orientation and gambling severity.

A significant interaction was observed between future orientation and cognitive distortions on gambling frequency (β = -.12, p <.05). This interaction indicates that the protective effect of future orientation on gambling frequency is weaker for adolescents with high levels of cognitive distortions. While a future orientation typically discourages gambling by emphasizing long-term consequences, cognitive distortions related to gambling (such as illusions of control) may counteract this effect. Adolescents with a future orientation but high cognitive distortions may still engage in gambling due to biased beliefs about winning or control, even if they are generally goal-oriented and future-focused. Table 9 presents the interaction between future orientation and cognitive distortions, showing how cognitive distortions modify the influence of future orientation on gambling frequency.

An interaction effect was also found between present-fatalistic orientation and sensation-seeking on gambling frequency (β = .10, p <.05), suggesting that adolescents who perceive life as predetermined and simultaneously seek thrill are more likely to gamble frequently.

This interaction indicates that a fatalistic view of the present, when combined with high sensation-seeking, can drive adolescents toward frequent gambling. This profile, characterized by a sense of fatalism and a drive for immediate stimulation, may contribute to gambling as a form of escapism or thrill-seeking. Table 10 below details the interaction between present-fatalistic orientation and sensation-seeking, illustrating how sensation-seeking moderates the influence of a fatalistic present orientation on gambling frequency.

These tables provide a clear overview of how sensation-seeking and cognitive distortions influence the relationship between temporal perspectives and gambling behaviors. Sensation-seeking amplifies the impact of both present-hedonistic and present-fatalistic orientations on gambling behaviors, highlighting a compounded risk for adolescents who are both thrill-seeking and present-focused. Cognitive distortions reduce the protective influence of future orientation on gambling frequency, suggesting that adolescents with strong cognitive biases may continue to gamble despite a generally goal-oriented and future-focused mindset. In the moderation models, significant interaction effects were followed up with simple slopes analyses. For example, the interaction between sensation-seeking and present-hedonistic orientation revealed that gambling severity increased more sharply among adolescents scoring high on both dimensions (β = .24, p <.001; simple slope high = .38 vs. low = .14).

Overall, effect sizes ranged from small to moderate, underscoring the multifactorial nature of adolescent gambling and suggesting that both temporal orientations and personality traits contribute meaningfully, though not overwhelmingly, to gambling risk profiles. These findings suggest that interventions aimed at reducing adolescent gambling should consider both temporal perspectives and individual personality traits, such as sensation-seeking and cognitive biases. Addressing these factors in combination may enhance the effectiveness of preventive programs.

The findings suggest several implications for developing interventions to reduce adolescent gambling. Programs aimed at promoting a future-oriented perspective may help adolescents recognize the long-term consequences of gambling, potentially reducing their likelihood of engaging in such behaviors. Educational initiatives that encourage goal-setting and long-term planning could foster a protective future orientation, aligning with previous studies that emphasize the importance of future time perspective in mitigating risky behaviors (29, 47). Interventions should address the role of sensation-seeking by providing safer outlets for thrill-seeking and impulsivity, such as structured sports or adventure activities (15).

Cognitive restructuring interventions could also play a crucial role in correcting gambling-related cognitive distortions. By challenging beliefs about skill and control in gambling, such interventions could weaken the persistence of gambling behaviors among adolescents (9, 42). Targeting these cognitive biases has shown success in cognitive-behavioral therapies for problem gambling, where individuals learn to recognize the randomness of gambling outcomes and avoid the illusion of control (48–50).

Adolescents' engagement in risk behaviors is also associated with altered regulatory processes, where emotion regulation, temporal discounting, and impulsivity play crucial roles (51–55). In this context, the integration of psychosomatic and neurobiological models has become increasingly relevant in understanding how early experiences and cognitive-emotional frameworks shape decision-making and risk sensitivity. Evidence from mind-body psychotherapy and psychosocial genomics suggests that experiential interventions may promote cognitive restructuring and emotional regulation through bottom-up pathways involving somatic awareness and neuroplastic change (56, 57). The present findings underline the need for prevention efforts specifically tailored to adolescents with high sensation-seeking tendencies. These individuals may benefit from targeted interventions that redirect their need for stimulation toward healthier and structured outlets such as sports, music, or experiential learning activities. School-based programs should incorporate self-regulation training aimed at improving decision-making, emotional control, and delay of gratification. By offering adolescents practical tools to manage impulsivity while maintaining stimulation, such interventions may reduce vulnerability to risky gambling behaviors (58).

These findings carry several implications for prevention efforts aimed at adolescents. First, interventions that strengthen future-oriented thinking, such as goal-setting training or episodic future thinking, may help reduce susceptibility to gambling by fostering long-term planning and self-regulation. Second, programs that challenge gambling-related cognitive distortions (e.g., illusions of control or predictive bias) through psychoeducation and cognitive restructuring could reduce risky beliefs that sustain gambling behavior. Finally, recognizing that adolescents high in sensation-seeking are more vulnerable to gambling, interventions may benefit from incorporating components focused on healthy risk-taking and emotional regulation strategies.

Preventive actions can be effectively implemented in schools or via online platforms, using serious games and interactive activities to engage youth (6, 17). Recent reviews also emphasize the importance of tailoring content to impulsivity profiles and cognitive vulnerabilities in order to improve intervention uptake and efficacy (8, 59). Taken together, our findings underscore the need for multi-component interventions that address both dispositional traits and temporal-cognitive styles.

While this study provides valuable insights into the interplay of temporal perspectives, sensation-seeking, and cognitive distortions in adolescent gambling, several limitations should be acknowledged. First, the cross-sectional design limits causal interpretations of the relationships among these variables. Longitudinal studies could clarify the directionality of these associations, particularly whether present-focused perspectives and sensation-seeking predispose adolescents to gambling or whether gambling itself reinforces these traits (34).

The sample, while diverse, was region-specific to Lazio, Italy, which may limit the generalizability of findings to other cultural contexts. Research in other regions and with culturally diverse populations could validate and extend these results. Lastly, self-report measures, though widely used, are susceptible to social desirability bias. Future studies might incorporate behavioral or observational measures to assess gambling tendencies and sensation-seeking more objectively (42).

While no direct measure of temporal discounting was included in the present study, the use of time perspective scales offers an indirect yet meaningful proxy for adolescents’ orientation toward delayed versus immediate outcomes. Future research may benefit from integrating behavioral discounting tasks to more precisely capture impulsive decision-making mechanisms underlying gambling risk.

Future research may benefit from testing mediation models or full structural equation models to explore indirect pathways between dispositional traits, cognitive factors, and gambling behavior. Such models could clarify whether variables like cognitive distortions mediate the effect of sensation seeking on gambling severity, offering a more nuanced understanding of vulnerability structures.

Although the current study did not test interactions between sensation seeking and cognitive distortions, this combination may represent a particularly potent risk profile for adolescent gambling. Sensation seeking may increase the propensity to engage in novel or risky behaviors, while cognitive distortions may reinforce erroneous beliefs about control or probability in gambling. Future research should examine whether the joint presence of high sensation seeking and strong cognitive distortions amplifies vulnerability to gambling problems, potentially in a synergistic rather than additive fashion.

One key limitation of this study is its cross-sectional design, which precludes causal inferences. Although we found significant associations between temporal perspectives, cognitive distortions, and gambling severity, these findings reflect correlational relationships. It is possible, for instance, that engaging in gambling behaviors reinforces present-oriented thinking or cognitive biases, rather than the reverse. Future research using longitudinal designs is necessary to examine the directionality of these effects and clarify whether temporal perspectives and cognitive distortions serve as precursors to gambling behavior or emerge as consequences of repeated gambling experiences. Prospective studies could help identify critical developmental periods during which these psychological traits exert the greatest influence on gambling onset or escalation.

We acknowledge also the limitations related to the skewed distribution of gambling severity and the modest role of demographic covariates. To mitigate these issues, we employed robust analytic techniques and clearly distinguished exploratory subgroup observations from confirmatory findings. While some discrepancies emerged between correlational and subgroup analyses, we refrain from implying selectivity without formal statistical comparison and caution against overinterpreting isolated effects given reduced statistical power in subgroups. Although some divergences in correlational patterns were observed across subgroups, no formal r-to-z comparisons were conducted (60); therefore, we refrain from inferring statistical selectivity between coefficients.

It is also important to consider the limitations inherent in self-report data, particularly in adolescents. While anonymity may reduce impression management, the absence of a dedicated social desirability scale limits our ability to quantify potential response bias. Future research could benefit from including such measures or adopting implicit or behavioral tools to triangulate self-reported gambling behaviors and cognitive tendencies.