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Article

Drinking Motives, Mental Health, and Adolescent Alcohol Use Among Croatian Adolescents

by
Roberta Matković
1,* and
Josipa Glavaš
2
1
Mental Health Department, Public Health Institute of Split-Dalmatia County, 21000 Split, Croatia
2
Department of School and Adolescent Medicine, Public Health Institute of Split-Dalmatia County, 21000 Split, Croatia
*
Author to whom correspondence should be addressed.
Psychol. Int. 2025, 7(4), 102; https://doi.org/10.3390/psycholint7040102
Submission received: 27 October 2025 / Revised: 9 December 2025 / Accepted: 13 December 2025 / Published: 18 December 2025
Versions Notes

Abstract

Adolescence is a critical period during which alcohol is the most commonly used substance worldwide, and such use has detrimental effects on neurobiological, psychosocial, and physiological development. Despite substantial international evidence, little is known about the concurrent influence of drinking motives and internalizing symptoms on adolescent alcohol use, particularly in the Croatian context, where adolescent drinking rates remain high. A cross-sectional study using a survey questionnaire was conducted in 2024 in Split-Dalmatia County, Croatia. The final stratified cluster sample comprised 925 students (58.8% of the planned sample), with a mean age of 15.41 years. Using hierarchical regression analysis, the results showed that the final model, which included both drinking motives and mental health indicators, explained 39.6% of the variance in alcohol use, 37.2% of the variance in binge drinking, and 31.8% of the variance in alcohol intoxication. Male sex was consistently associated with all three outcomes, whereas age was positively associated with alcohol use and binge drinking. Drinking motives contributed the largest proportion of the explained variance. Furthermore, lower levels of loneliness and higher levels of anxiety were associated with more frequent alcohol use, while lower stress and higher anxiety were associated with more frequent intoxication. Drinking motives are stronger predictors of adolescent alcohol use and risky drinking patterns than internalizing symptoms whose predictive strength was generally small. Prevention programs should address aspects of drinking motives in addition to promoting mental health.

1. Introduction

Alcohol is the most widely used substance among adolescents (according to WHO 10–19 age) worldwide (Gray & Squeglia, 2018; World Health Organization, 2023, 2025). Typically beginning in adolescence, alcohol use often progresses from experimentation to high-risk patterns, with early initiation increasing the risk of later substance use disorders (Charrier et al., 2024; Sjödin et al., 2021; Aiken et al., 2018; H. Kim, 2024; Skogen et al., 2014; Hingson et al., 2006). Alcohol use among adolescents represents a significant public health concern due to its numerous detrimental effects on neurobiological, psychosocial, physiological development (Spear, 2018; Castelpietra et al., 2022; Erskine et al., 2015; Patton et al., 2016; Azzopardi et al., 2019) and the economic burden associated with alcohol-related harm. International evidence shows substantial cross-country variation in the societal costs of alcohol use in the general population. When all cost components are considered, the global economic burden may exceed 2.6% of GDP (Manthey et al., 2021). In the United States, underage drinking alone accounts for nearly 10% of the total economic cost of alcohol use (Sacks et al., 2015), while data from Korea indicate that harmful drinking among adolescents corresponds to approximately 0.05% of the national GDP (J. Kim et al., 2010). Furthermore, alcohol use among adolescents is strongly associated with high-risk behaviors and is often a major contributing factor to accidents, violence, and risky sexual behaviors (Donovan, 2004; Patton et al., 2016). Finally, there is a well-established link between alcohol use and psychopathology, with mental health difficulties such as anxiety increasing the likelihood of subsequent alcohol dependence (Swendsen et al., 2010). In Europe, nearly 17 million young people had a mental or substance use disorder in 2019, with strong correlations between the two (Castelpietra et al., 2022).
Recent data highlight significant alcohol use among Croatian adolescents (Charrier et al., 2024; ESPAD Group, 2025a). HBSC surveys (2014–2022) showed higher alcohol use among 11-year-old boys, but by age 15, girls reported slightly higher consumption (71% vs. 67%), while boys consistently reported more intoxication (Charrier et al., 2024). Girls also reported poorer mental health, including greater loneliness and lower well-being, which may increase vulnerability to alcohol use (Cosma et al., 2023). ESPAD (2024) findings confirmed above-average drinking: 55.6% past-month use, 17.3% intoxication, and 42.1% binge drinking, with minor sex differences in consumption and intoxication and similar rates of intoxication (ESPAD Group, 2025b).
Despite extensive research on the links between adolescent substance use and mental health, causality remains unclear, as most studies rely on observational data (Halladay et al., 2025). Alcohol use is positively associated with various mental health issues, including loneliness, depression, anxiety, and ADHD (H. Kim, 2024; Skogen et al., 2014; Johannessen et al., 2017; Pedrelli et al., 2016; Conway et al., 2016). Loneliness may increase vulnerability to risk behaviors (Christiansen et al., 2021; H. Kim, 2024; Stickley et al., 2014; Yang et al., 2023) though findings are mixed. For example, during the COVID-19 pandemic, adolescents who maintained social interactions reported fewer depressive symptoms but were more likely to engage in episodic heavy drinking, suggesting that social connections can both protect mental health and facilitate risky behaviors (Temple et al., 2022). These findings illustrate the dual role of social connectedness, highlighting that factors which protect mental health may simultaneously facilitate opportunities for alcohol use, which is directly relevant to the mechanisms explored in the present study.
Research consistently identifies drinking motives as key predictors of adolescent alcohol use (Cooper, 1994). The four-factor model distinguishes enhancement (internal–positive, e.g., drinking for pleasure), social (external–positive, e.g., drinking to be sociable), coping (internal–negative, e.g., drinking to manage stress), and conformity motives (external–negative, e.g., drinking to fit in). Social motives are the most common, followed by enhancement, coping, and conformity (Kuntsche et al., 2005; Kuntsche et al., 2014; Sjödin et al., 2021; Freichel et al., 2023; Mackinnon et al., 2017). Different motives predict distinct drinking patterns: enhancement motives strongly predict heavy and binge drinking (Bresin & Mekawi, 2020; Pigeaud et al., 2025; Kuntsche et al., 2005; Lannoy et al., 2019), social motives relate to moderate drinking (Kuntsche et al., 2005), and coping motives are linked to higher consumption, often triggered by stress (Bartel et al., 2022; Windle & Windle, 2018), though coping-with-depression motives show weaker associations with binge drinking (Bartel et al., 2022). Overall, while social enjoyment drives much adolescent drinking, enhancement and coping motives are most closely tied to problematic use.
Among several theories linking drinking motives and mental health to adolescent alcohol use (Khantzian, 1997; Hussong et al., 2011; Donovan, 2004), the biopsychosocial model (Donovan, 2004) is particularly appropriate for this study, as it integrates biological, psychological, and social factors, connecting mental health issues with coping motives and social influences with conformity motives, and thereby capturing the complex interplay of internal and external drivers of adolescent drinking.
There remains limited evidence on the concurrent influence of drinking motives and internalizing symptoms on adolescent alcohol use, binge drinking, and intoxication. This study seeks to address this gap in the Croatian setting by examining past-month alcohol use, binge drinking, and intoxication among adolescents, with a focus on the predictive roles of sociodemographic factors (age, gender), drinking motives (social, enhancement, conformity, coping), and internalizing symptoms (stress, anxiety, depression, loneliness).
Based on theory and empirical findings, we hypothesize that: (H1) higher levels of mental health difficulties (anxiety, stress, depression, loneliness) will be positively associated with alcohol use, binge drinking, and intoxication; (H2) alcohol use will be positively associated with social motives, while binge drinking and intoxication will be positively associated with enhancement motives.

2. Materials and Methods

2.1. Methods

The study received approval from the Ministry of Science, Education and Youth (Zagreb, Croatia), as well as the Ethics Committee of the Public Health Institute of Split-Dalmatia County (Split, Croatia). Throughout its preparation and implementation, the research adhered to the Code of Ethics in Research with Children (Ajduković & Keresteš, 2020).
A stratified sampling strategy was applied to obtain approximately 6% of the total student population of the seventh and eighth grades of primary schools, as well as all four grades of secondary schools in Split-Dalmatia County, the largest county in Croatia. Sampling included regular public schools only, while private schools (attended by 2.2% of the county’s student population) were excluded due to low representation. In accordance with the ESPAD methodology, art-oriented secondary schools (0.8% of the population) were also excluded from the sampling frame. Stratification for primary schools was based on geographical location (mainland vs. islands), while for secondary schools it additionally included school type (grammar school, four-year vocational programs, three-year vocational programs). Schools within each stratum were randomly selected to meet proportional sampling requirements. The composition of the realized sample closely matched population-level distributions. Following school selection, twenty-three primary and secondary schools were randomly selected and invited to participate in the study. After all school principals provided consent, a coordinator was appointed at each school. Coordinators received training to familiarize themselves with the research content and implementation procedures. With support from classroom teachers, coordinators distributed written notifications to parents, detailing the study’s aims and objectives, its organizers, and the intended use of collected data. Parents then signed consent forms for their children’s participation. Additionally, students retained the right to decline participation at any time, either before or during the research process.
A cross-sectional study was conducted at a single measurement point using a survey questionnaire created through an online form (Google Forms). Data collection took place between October and December 2024. Through cluster sampling and initial consent from school principals, we obtained the full set of students from the selected classes, representing the maximum number of potential participants (N = 1572) who received invitations to take part in the study. Following the collection of parental consent and students’ assent, as well as accounting for routine student absences on data collection days, the final achieved sample comprised 925 students (58.8%). Participants included 57.9% male students (N = 536) and 42.1% female students (N = 389). The mean age was 15.4 years (range = 13–19).
The research aimed to investigate behavioral problems among adolescents, such as the use of tobacco, alcohol, and marijuana, along with indicators of mental health, sleep quality, and socio-emotional competencies. This paper presents a subset of these findings, focusing on alcohol use in the past 30 days (consumption, intoxication, and binge drinking), mental health and loneliness indicators, and motives for drinking. Sociodemographic variables assessed include age, gender, grade, parental education level, and perceived household economic status.

2.2. Measures

Alcohol consumption was examined using three questions: “How many times (if any) have you drunk alcoholic beverages?”, “How many times (if any) have you drunk five or more drinks in a row within a short period of time?” and “How many times (if any) have you gotten drunk or been heavily under the influence of alcohol to the point that you walked unsteadily, were unable to speak properly, vomited, or could not remember what happened?” All three questions referred to the time period of the last 30 days. Respondents answered regarding consumption and drunkenness on a scale from 1 = never to 7 = 40 or more times. For the question on binge drinking, responses were given on a scale from 1 = never to 6 = 10 or more occasions. In this study, the responses to these three questions are the criterion variables. Tests for normality of distribution revealed that the distributions deviated from normal, which is expected in epidemiology of consumption and behavioral research (Foley et al., 2004; Hallgren, 2012).
The Depression, Anxiety, and Stress Scale—21 items (DASS-21; Lovibond & Lovibond, 1995) was used in its Croatian version (Ivezić et al., 2012) validated among Croatian adolescents to measure levels of depression, anxiety, and stress. The questionnaire consists of 21 statements rated on a 5-point scale reflecting how often each experience occurred during the past week (0 = did not apply to me at all, 4 = applied to me most of the time). Each of the three subscales—depression, anxiety, and stress—includes seven items. Subscale scores are calculated by summing item responses and multiplying the result by two, with higher scores reflecting more severe symptoms. The DASS-21 showed high internal consistency in this study, with Cronbach’s alpha (α) values of 0.853 for anxiety, 0.863 for depression, and 0.885 for stress, and with McDonald’s omega (ω) values of 0.855 for anxiety, 0.865 for depression, and 0.887 for stress.
UCLA Loneliness Scale—Short Form. Loneliness was measured using the short version of the UCLA Loneliness Scale (Allen & Oshagan, 1995), which has been translated, validated, and adapted for use in Croatian adolescents (Lacković-Grgin et al., 2002). The instrument includes seven items assessing a single dimension of loneliness. The items do not specify a particular time frame to which the feelings refer. Participants rated each item on a 5-point scale (1 = does not apply to me at all, 5 = completely applies to me), with higher scores indicating greater loneliness. The scale showed strong internal reliability with a Cronbach’s alpha (α) of 0.858 and a McDonald’s omega (ω) of 0.859.
Drinking motives were assessed using 12 items, consisting of 11 items adapted from the DMQ-R-SF (Kuntsche & Kuntsche, 2009) and one additional item. The items reflect various reasons for alcohol use in the last twelve months (e.g., I drink because it helps me enjoy parties; because it helps when I feel depressed or nervous; to cheer myself up when I’m in a bad mood), rated on a 5-point scale (1 = never, 5 = almost always). A modified, shortened version of DMQ-based motive items has been applied in Croatian adult samples (Kaliterna Lipovčan et al., 2013; Glavak Tkalić et al., 2013), although no formal psychometric validation of the DMQ-R-SF structure has been available for Croatian adolescents. In the present study, we therefore conducted factor analysis to verify the structure and internal consistency of the adapted item set within our sample. To identify the key motives underlying different drinking behaviors (alcohol consumption, binge drinking, intoxication), a factor analysis was conducted to determine the factor structure of the items. The first factor analysis, conducted using Principal Axis Factoring (PAF) with Promax rotation, 83.66% of the total variance, demonstrated a cleaner solution showing excellent data suitability for structural determination. The Kaiser-Meyer-Olkin measure verified high sampling adequacy (KMO = 0.945), and Bartlett’s Test of Sphericity (χ2(66) = 12,899.847, p < 0.001) indicated that correlations between items were sufficiently large for factor analysis (Tabachnick & Fidell, 2013). The analysis extracted four factors that together accounted for 83.37% of the total variance. Examination of the Pattern Matrix, which shows the unique contributions of items to the factors, revealed a clear structure: Factor 1 consisted of items related to social enhancement (e.g., “because it improves parties and celebrations”, “because it makes social gatherings more fun”); Factor 2 encompassed conformity motives (e.g., “to fit in with a group I like”, “so I won’t feel left out”); Factor 3 represented coping motives (e.g., “because it helps when I feel depressed or nervous”, “to forget about my problems”); while Factor 4 comprised items related to internal enhancement and intoxication (e.g., “just to get drunk”, “because I like myself the way I am when I’m under the influence”). Upon reviewing the item content and classifying the motives into social, enhancement, conformity, and coping drinking motives according to Cooper (1994), it was determined that one item (“because it helps me enjoy a party”) was semantically inconsistent with the dimension to which it contributed and was therefore excluded from further analysis (DeVellis, 2017). A repeated factor analysis with 11 items confirmed a robust factor structure. A KMO value of 0.935 and Bartlett’s Test of Sphericity (χ2(55) = 11,510.080, p < 0.001) confirmed the data’s suitability (Tabachnick & Fidell, 2013). The four-factor solution, which accounted for and more theoretically coherent interpretation of the factors compared to the initial solution. This final structure was used to form composite variables for drinking motives for the purpose of subsequent analyses. Social, enhancement, and coping motives were each comprised of three items, while conformity motives comprised two items (Table 1). For the purposes of this study, dimension scores were calculated as the arithmetic mean of the responses to the items constituting each dimension (e.g., Widaman & Revelle, 2023), where higher scores indicate stronger motives. All dimensions demonstrated strong internal reliability: Social motives: Cronbach’s alpha (α) = 0.952, McDonald’s omega (ω) = 0.953; Enhancement motives: Cronbach’s alpha (α) = 0.886, McDonald’s omega (ω) = 0.888; Coping motives: Cronbach’s alpha (α) = 0.930, McDonald’s omega (ω) = 0.931; Conformity motives: Cronbach’s alpha (α) = 0.895, McDonald’s omega (ω) = 0.895.
Data analysis was carried out using IBM SPSS Statistics 20 (IBM Corp, 2011; Armonk, NY, USA). Hierarchical regression analyses were performed in SPSS to examine the predictive contribution of sociodemographic variables, drinking motives, and mental health indicators.

3. Results

Table 2 shows Spearman’s correlations between demographic characteristics, drinking motives, mental health, and different forms of alcohol consumption, using Cohen’s (1988) guidelines for interpretation. Age shows consistent, positive moderate relationships with alcohol behaviors, with older adolescents reporting higher levels of consumption, binge drinking episodes, and intoxication. Gender shows significant but weak negative correlations with these behaviors, indicating that males report slightly higher levels. All three measures of alcohol behavior are highly correlated with each other (0.542 to 0.696). The strongest associations with alcohol consumption are observed for drinking motives (0.451 to 0.639), with social, enhancement, and coping motives showing moderate to high correlations with consumption, binge drinking, and intoxication. These motives are also highly intercorrelated, which is expected given Cooper’s theoretical framework (1994). Mental health indicators (stress, depression, anxiety, and loneliness) show very weak and mostly non-significant links with alcohol consumption, although they are strongly interconnected.
To explore the associations between alcohol consumption, binge drinking, intoxication, drinking motives, and mental health, three hierarchical regression analyses were performed.
In the first analysis, the criterion was alcohol consumption during the past 30 days, with predictors entered in three sequential steps. The first block included sociodemographic factors (age and gender), the second block included mental health indicators (stress, anxiety, depression, and loneliness), and the third block included drinking motives (social, enhancement, coping, conformity) (Table 3).
It was found that the entire set of predictors statistically significantly explains alcohol consumption (F(10, 914) = 61.60; p < 0.001). The total percentage of explained variance of alcohol consumption in the last 30 days in adolescents is 39.6%. All three predictor blocks individually made statistically significant contributions to explaining alcohol consumption (p < 0.01). Drinking motives accounted for the largest proportion of explained variance (26.2%), followed by sociodemographic factors (10.5%), while mental health indicators explained a small but significant additional part (3.5%).
At the individual predictor level, statistically significant associations with higher alcohol consumption were found for male gender, older age, higher anxiety, lower loneliness, and higher social, enhancement, and coping drinking motives. the strength of the beta coefficients differed substantially. Drinking motives and demographic factors had moderate β coefficients (β ranging from 0.129 to 0.203), whereas anxiety and loneliness had small β coefficients (β < 0.10) according to Cohen’s (1988) and Tabachnick and Fidell (2013) guidelines. Consequently, although the relationships with anxiety and loneliness are statistically significant, their β coefficients were small, indicating a limited statistical contribution compared to demographic factors and drinking motives.
Next, a hierarchical regression analysis was performed with alcohol intoxication in the past 30 days as the criterion. Predictors were entered in three successive steps: the first block included sociodemographic factors (age and gender), the second block included mental health indicators (stress, anxiety, depression, and loneliness), and the third block included drinking motives (social, enhancement, coping, and conformity) (Table 4).
The analysis revealed that the entire set of predictors statistically significantly (F(10, 914) = 44.03, p < 0.001) explained alcohol intoxication in the past 30 days among adolescents and explained 31.8% of the variance, with all three blocks contributing significantly (p < 0.001). The block of drinking motives was again the most substantial, adding 25.6% to the explained variance, with mental health (4.3%) and sociodemographic factors (2.6%) explaining considerably less variance. An analysis of the unique predictors showed that enhancement motives (β = 0.395) had the largest β coefficient for intoxication. Conformity (β = 0.142) and coping (β = 0.108) motives were also significant, showing small β coefficients. In contrast, the significant mental health predictors, anxiety (β = 0.138) and stress (β = −0.130), exhibited small β coefficients (Cohen, 1988; Tabachnick & Fidell, 2013). Social motives were not a significant predictor. Regarding other predictors, male gender was a significant but weak predictor, while age was no longer significant in the final step.
Finally, a hierarchical regression analysis was conducted to predict binge drinking (consuming five or more drinks in a row) in the past 30 days among adolescents. Predictors were entered in three steps: the first block included sociodemographic factors (age and gender), the second block included mental health indicators (stress, anxiety, depression, and loneliness), and the third block included drinking motives (social, enhancement, coping, conformity) (Table 5).
The results indicated that the entire set of predictors statistically significantly accounted for binge drinking in the past 30 days among adolescents (F(10, 913) = 55.74, p < 0.001), explaining 37.2% of the variance. All three blocks of predictors made statistically significant contributions (p < 0.001). Drinking motives were again the strongest predictor, accounting for an additional 27.1% of the variance, followed by sociodemographic factors (8.3%), while mental health explained the smallest amount of variance (2.5%). In the final step, the β coefficients showed that enhancement motives (β = 0.325) had the largest β coefficients, followed by coping motives (β = 0.135). The β coefficients of social motives was significant but weaker (β = 0.108). Among demographics, male gender and older age remained significant, though with smaller β coefficients (β = −0.116 and β = 0.099, respectively) (Cohen, 1988; Tabachnick & Fidell, 2013). None of the mental health indicators were significant predictors of binge drinking.

4. Discussion

The results of this study, conducted in Croatia’s largest region (out of 21 in total), reveal interesting patterns of association between alcohol consumption, intoxication, and binge drinking, and both drinking motives and mental health.
The findings indicate that the hypotheses were partially confirmed. The first hypothesis, which proposed that higher levels of internalizing difficulties would be positively associated with more frequent alcohol use and risky drinking patterns, was only partially supported. Specifically, anxiety was significantly and positively associated with alcohol consumption and intoxication; stress was significantly and negatively associated with intoxication; and loneliness was significantly and negatively associated with alcohol use. However, all of these associations were very weak predictors. The second hypothesis was fully supported: social, enhancement, and coping motives were associated with alcohol use, while enhancement, coping, and conformity motives consistently predicted binge drinking and intoxication. Overall, drinking motives emerged as stronger predictors than mental health indicators, which aligns with theoretical perspectives suggesting that motives represent more proximal drivers of drinking behavior than emotional states (Cooper, 1994; Kuntsche et al., 2005).
These results are in concordance with previous findings that confirm that motives, especially enhancement motives, are the key predictors of risky alcohol use patterns in adolescence (Bresin & Mekawi, 2020; Pigeaud et al., 2025; Lannoy et al., 2019). In our study, enhancement motives were the strongest individual predictor of alcohol use, intoxication, and binge drinking, confirming the notion that adolescent drinking comes from the pursuit of pleasure, sensation-seeking, and positive effect, especially in a context where alcohol is a social norm and easily attainable (Brezovec, 2022). Coping motives emerged as the weakest, but persistent predictor, which aligns with prior research indicating that in adolescence, permanent emotional regulation is still developing, and that alcohol is rarely used as a way to regulate negative emotions in this period of life (Bartel et al., 2022; Windle & Windle, 2018). However, the complete absence of association between social motives and intoxication is unexpected, given that previous findings suggested otherwise (Freichel et al., 2023; Sjödin et al., 2021). One possible explanation is that alcohol intoxication is broadly accepted and normalized within the Croatian sociocultural context, as suggested by prior research (e.g., Brezovec, 2022). In such contexts, social reasons for drinking may be less differentiating because they reflect a wider social norm rather than individual-specific motives. Consequently, although the descriptive results of our sample do not indicate high levels of social motives, their limited variability may have contributed to their weaker predictive role compared with other drinking motives.
It is important to note that the total explained variance in the models is fairly high, with R2 values ranging from 31.8% to 39.6%. That is in the upper range of what is usually considered acceptable in psychosocial research of adolescent risk behaviors (Cohen, 1988), indicating that the combination of included predictors offers a significant insight into individual differences in alcohol use. Across all models, drinking motives accounted for the greatest variance (approximately 25–27%), while demographic and mental health indicators contributed relatively little. This pattern additionally confirms the central role of drinking motives as direct predictors of alcohol use, in contrast to a more diffuse and indirect effect of internalized symptoms.
The connection between mental health and modalities of alcohol use is not fully consistent with available research and considered theory. While anxiety is associated with alcohol use and intoxication, and this aligns with previous findings (Johannessen et al., 2017), this association is weak. However, in contrast to expectations, this study found that lower stress levels were linked to intoxication and lower loneliness levels were linked to alcohol use. Other research has shown contrasting results, specifically, that loneliness is linked to higher alcohol consumption (Jalil & Mahfoud, 2025). Our unexpected findings suggest that internalized symptoms do not predict alcohol use uniquely and stably, and their effects may depend on developmental phase, specific drinking patterns, and especially for the Croatian environment primarily on social contexts. Socially isolated or shy adolescents have fewer opportunities to drink (Varga & Piko, 2015; Yang et al., 2023) which supports the view that alcohol consumption in adolescents is conditioned by social influence, rather than emotional vulnerability.
Although an association between modalities of drinking and depression was expected (Johannessen et al., 2017; Pedrelli et al., 2016), it is possible that levels of depression and alcohol use are independent characteristics of adolescence or they are dependent on some other factors (Danzo et al., 2017). Also, the possible explanation of the lack of association could reflect the severity of the problem, which differs from addictive patterns of alcohol use associated with depression (Hammerton et al., 2023). Gavurova et al. (2020) found that the link between stress and alcohol use was mediated by depression: higher stress increased depression, which then led to higher alcohol use among students. Since our results suggested otherwise, we may attribute these differences to some other unexamined markers such as personality traits, family and school dynamics, or peer influences. This indicates that mental health variables indirectly affect alcohol use, possibly explaining their weak association. These factors can shape both perceived stress and drinking patterns, further highlighting the complexity of the relationship between stress and intoxication during adolescence.
Regardless of the unexpected directions or the lack of association between mental health and alcohol use, examined variables of mental health generally explain little all three modalities of drinking, which can be attributed to the fact that alcohol use happens in social context, and is driven by peer norms, social identity and fun seeking (Frederiksen et al., 2012; Kuntsche et al., 2005; Brezovec, 2022). Overall, these patterns indicate that drinking motives, particularly enhancement and coping motives are more direct, specific predictors of adolescent alcohol use, whereas mental health difficulties show a weaker, diffuse, and more indirect effect (Graziano et al., 2012; Freichel et al., 2023; Kuntsche et al., 2005; Lannoy et al., 2019; Bresin & Mekawi, 2020).
Finally, these results were obtained while controlling for gender and age, and it was consistently observed that boys more frequently consumed alcohol, became intoxicated, and engaged in binge drinking, whereas older students reported higher frequencies of alcohol use and binge drinking. Although our findings are in line with previous studies (Charrier et al., 2024; Patton et al., 2016; Windle & Windle, 2018), it is important to consider recent statistical reports (Charrier et al., 2024; ESPAD Group, 2025a) suggesting that alcohol consumption is no longer limited to boys.
Our results have important practical implications, particularly in contexts where adolescent drinking is socially accepted. Prevention programs should target drinking motives and the regulation of drinking behavior while promoting social and emotional competencies, strengthening self-confidence, and encouraging healthy, alcohol-free activities. School-based mental health programs can support these goals by including training in emotional regulation, strategies for structured and meaningful use of free time, and activities that foster a sense of belonging and improve the quality of peer relationships. Such programs help young people develop self-control, communication, decision-making, and coping skills, thereby reducing peer pressure and the likelihood of risky drinking (Griffin & Botvin, 2010; Sánchez-Puertas et al., 2022). Programs that incorporate parental involvement, peer education, and social norm correction are particularly effective in reducing alcohol consumption and delaying the onset of drinking, highlighting the importance of engaging both peers and parents, as the social environment plays a crucial role in shaping adolescent behavior and alcohol-related decisions (Griffin & Botvin, 2010; Sánchez-Puertas et al., 2022).

Limitations

Despite its valuable findings, this study has several limitations. The cross-sectional design prevents causal inference, making it impossible to determine whether drinking motives precede alcohol use or emerge as a consequence of consumption. The reliance on self-reported data represents an additional limitation, as such measures may be subject to social desirability bias (Razavi, 2001). Moreover, inconsistencies in the time frames of the measures (alcohol-related behaviors referring to the past month, drinking motives to the past year, and mental health indicators to the past week) may have affected the strength of the observed associations. An additional methodological limitation that should be kept in mind when interpreting the results concerns the strong intercorrelations between drinking motives, particularly enhancement motives, which approach commonly noted multicollinearity thresholds, although they remain within acceptable limits (Field, 2017). Such high associations likely resulted in the suppression of regression coefficients, meaning that the obtained beta coefficients primarily reflect net (relative) rather than total effects of individual motives, which also helps explain their modest beta values despite the substantial increase in explained variance.
Future research should therefore employ longitudinal designs to better capture temporal relationships and consider additional psychological variables, such as self-esteem, impulsivity, and perceived social support, to clarify the mechanisms linking mental health and alcohol use. Further work is also needed to examine potential moderation effects (e.g., whether social motives are stronger among adolescents experiencing greater loneliness, or whether coping motives are more pronounced among those with higher anxiety) as well as mediation processes that may explain how mental health influences drinking behaviors. Finally, because the study was conducted in a single country, the generalizability of the findings to the national level may be limited.

5. Conclusions

In conclusion, this study offers important insights into adolescent alcohol use, showing that drinking motives, particularly social, enhancement, and coping motives, play a central role in shaping different patterns of consumption, while mental health indicators exhibit weaker and more context-dependent associations. These findings underscore the need for prevention efforts that account for the diverse functions that alcohol serves in adolescents’ lives and that directly target drinking motives. Interventions that strengthen emotional regulation and promote healthy social interactions may be especially effective in reducing risky drinking during adolescence.

Author Contributions

Conceptualization, R.M. and J.G.; methodology, R.M.; formal analysis, R.M.; investigation, R.M.; data curation, R.M.; writing—original draft preparation, R.M. and J.G.; writing—review and editing, R.M. and J.G.; visualization, J.G.; supervision, R.M. and J.G.; project administration, R.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with Declaration of Helsinki, and the approval from the Ministry of Science, Education and Youth (602-01/24-01/00236; 27 November 2024), as well as the Ethics Committee of the Public Health Teaching Institute of Split-Dalmatia County (007-05/24-02/001; 15 October 2024).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study. Throughout its preparation and implementation, the research adhered to the Code of Ethics in Research with Children (Ajduković & Keresteš, 2020).

Data Availability Statement

Data are available upon request.

Acknowledgments

We would like to thank the schools principals and school coordinators, as well as the parents and children, who agreed to participate and share their responses with us.

Conflicts of Interest

The authors declare no conflicts of interest.

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Table 1. Factor Structure, Descriptive Statistics, and Reliability Indices for Drinking Motive Scales.
Table 1. Factor Structure, Descriptive Statistics, and Reliability Indices for Drinking Motive Scales.
Factor 1Factor 2Factor 3Factor 4Msdαω
Social motives 1.891.290.9520.953
… because it improves parties and celebrations0.997 1.911.37
… because it makes social gatherings more fun0.988 1.921.37
… because it’s fun0.660 1.821.32
Conformity motives 1.380.890.8950.895
… so, I don’t feel left out 0.844 1.370.93
… to fit in with a group I like 0.792 1.390.94
Coping motives 1.551.040.9300.931
… because drinking helps me when I feel depressed or nervous 0.909 1.901.33
… to cheer me up when I’m in a bad mood 0.869 1.591.13
… because it helps me forget about my problems 0.587 1.581.14
Enhancement motive 1.601.040.8860.888
… because I like myself the way I am when I’m drunk 0.6521.511.08
… just to get high 0.4651.481.05
… because I like the feeling 0.3581.811.31
Factor 1–4 = factor loadings, M = mean, SD = standard deviation, α = Cronbach’s alpha, ω = McDonald’s omega.
Table 2. Spearman correlation.
Table 2. Spearman correlation.
MSD12345678910111213
1gender1.420.491
2age15.411.67−0.130 **1
3alcohol consumption2.091.60−0.159 **0.347 **1
4binge drinking1.921.51−0.160 **0.307 **0.696 **1
5intoxication1.340.96−0.141 **0.179 **0.542 **0.591 **1
6stress9.639.520.351 **−0.0630.0560.0140.0371
7depression7.248.460.248 **−0.0360.092 **0.0520.068 *0.766 **1
8anxiety7.728.750.323 **−0.109 **0.0540.0170.077 *0.800 **0.730 **1
9loneliness13.346.090.076 *−0.044−0.0330.0010.0190.449 **0.507 **0.456 **1
10social motives1.891.29−0.0380.354 **0.639 **0.560 **0.483 **0.154 **0.182 **0.122 **0.0431
11enhancement motive1.601.04−0.0590.301 **0.616 **0.580 **0.543 **0.145 **0.176 **0.147 **0.079 *0.847 **1
12coping motives1.551.04−0.0310.285 **0.571 **0.554 **0.507 **0.153 **0.163 **0.164 **0.087 **0.769 **0.826 **1
13conformity motives1.380.89−0.094 **0.193 **0.451 **0.468 **0.477 **0.159 **0.202 **0.197 **0.105 **0.601 **0.681 **0.690 **1
** p < 0.01; * p < 0.05; M = mean, SD = standard deviation.
Table 3. Hierarchical regression analyses predicting alcohol consumption over the previous 30 days.
Table 3. Hierarchical regression analyses predicting alcohol consumption over the previous 30 days.
Predictors1st Step2nd Step3rd Step
βtβtβt
Step 1: Demographics
Gender−0.134−4.28 **−0.191−5.76 **−0.140−4.99 **
Age0.2798.87 **0.2859.18 **0.1294.73 **
Step 2: Mental Health
Stress −0.033−0.56−0.050−0.99
Depression 0.0971.820.0300.66
Anxiety 0.1723.07 **0.0952.01 *
Loneliness −0.099−2.73 **−0.063−2.09 *
Step 3: Drinking Motives
Social motives 0.1853.72 **
Enhancement motive 0.2033.35 **
Coping motives 0.1913.83 **
Conformity motives 0.0120.30
Total Model
R20.1050.1400.403
Adjusted R20.1030.1350.396
ΔR20.105 **0.035 **0.262 **
* p < 0.05; ** p < 0.01; β—standardized regression coefficient; R2—coefficient of multiple determination; Adjusted R2—adjusted coefficient of multiple determination; ΔR2—change in coefficient of multiple determination.
Table 4. Hierarchical regression analysis predicting intoxication in the last 30 days.
Table 4. Hierarchical regression analysis predicting intoxication in the last 30 days.
Predictors1st Step2nd Step3rd Step
βtβtβt
Step 1: Demographics
Gender−0.077−2.36 *−0.126−3.63 **−0.062−2.08 *
Age0.1334.06 **0.1434.45 **0.0210.71
Step 2: Mental Health
Stress −0.141−2.27 *−0.130−2.44 *
Depression 0.1041.880.0380.80
Anxiety 0.2474.24 **0.1382.74 **
Loneliness −0.032−0.860.0010.02
Step 3: Drinking Motives
Social motives −0.080−1.51
Enhancement motives 0.3956.13 **
Coping motives 0.1082.03 *
Conformity motives 0.1423.32 **
Total Model
R20.0260.0690.325
Adjusted R20.0240.0630.318
ΔR20.026 **0.043 **0.256 **
* p < 0.05; ** p < 0.01; β—standardized regression coefficient; R2—coefficient of multiple determination; Adjusted R2—adjusted coefficient of multiple determination; ΔR2—change in coefficient of multiple determination.
Table 5. Hierarchical regression analysis predicting binge drinking in the last 30 days.
Table 5. Hierarchical regression analysis predicting binge drinking in the last 30 days.
Predictors1st Step2nd Step3rd Step
βtβtβt
Step 1: Demographics
Gender−0.124−3.88 **−0.172−5.08 **−0.116−4.05 **
Age0.2447.68 **0.2517.96 **0.0993.57 **
Step 2: Mental Health
Stress −0.041−0.67−0.052−1.01
Depression 0.0500.92−0.017−0.37
Anxiety 0.1793.14 **0.0931.93
Loneliness −0.056−1.52−0.020−0.64
Step 3: Drinking Motives
Social motives 0.1082.12 *
Enhancement motive 0.3255.26 **
Coping motives 0.1352.65 **
Conformity motives 0.0270.66
Total Model
R20.0830.1080.379
Adjusted R20.0810.1020.372
ΔR20.083 **0.025 **0.271 **
* p < 0.05; ** p < 0.01; β—standardized regression coefficient; R2—coefficient of multiple determination; Adjusted R2—adjusted coefficient of multiple determination; ΔR2—change in coefficient of multiple determination.
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Matković, R.; Glavaš, J. Drinking Motives, Mental Health, and Adolescent Alcohol Use Among Croatian Adolescents. Psychol. Int. 2025, 7, 102. https://doi.org/10.3390/psycholint7040102

AMA Style

Matković R, Glavaš J. Drinking Motives, Mental Health, and Adolescent Alcohol Use Among Croatian Adolescents. Psychology International. 2025; 7(4):102. https://doi.org/10.3390/psycholint7040102

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Matković, Roberta, and Josipa Glavaš. 2025. "Drinking Motives, Mental Health, and Adolescent Alcohol Use Among Croatian Adolescents" Psychology International 7, no. 4: 102. https://doi.org/10.3390/psycholint7040102

APA Style

Matković, R., & Glavaš, J. (2025). Drinking Motives, Mental Health, and Adolescent Alcohol Use Among Croatian Adolescents. Psychology International, 7(4), 102. https://doi.org/10.3390/psycholint7040102

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