Problematic internet use and its relationship with motor proficiency among adolescents participating in extracurricular sports (a comparative study of Indonesia and Latvia)

Introduction: Motor proficiency and problematic internet use are increasingly viewed as intertwined facets of adolescent development, strongly influenced by physical activity and digital engagement; however, cross-cultural evidence distinguishing adolescents with and without extracurricular sports participation is still limited. This study aimed to examine and compare the relationship between motor proficiency (MP) and problematic internet use (PIU) in adolescents from in Indonesia and Latvia. Additionally, this study explored the association between healthy lifestyle behaviors, MP, and PIU among adolescents who participate extracurricular sports (ES) and non-sports (NS). This study utilized a cross-sectional research design.

Methods: A total of 257 adolescents from Indonesia (n = 200) and Latvia (n = 57) completed the Problematic and Risky Internet Use Screening Scale (PRIUSS), four subtests from the Bruininks-Oseretsky Test of Motor Proficiency II (BOT-2), and self-reports on weekly PA and internet use. Group differences between NS and ES participants were analyzed using Mann–Whitney U tests. Associations between PIU and MP were examined with Spearman’s rho correlations and winsorized linear regression to limit the influence of extreme values (5th–95th percentiles).

Results: A significant negative correlation was found between PIU and MP in ES participants from Indonesia (r_s = −0.367, p < 0.01). Meanwhile, regression analysis revealed a favorable correlation between physical activity and MP in Indonesia and Latvia, particularly among adolescents engaged in ES. In both countries, study-related and non-study-related internet usage were significant predictors for PIU, particularly among NS participants.

Discussion: The study found that participation in structured activities, such as ES, appears to support motor proficiency and reduce problematic internet use. Allocating time to regular physical activity and moderating leisure Internet use may protect adolescent health. These findings highlight the importance of context-specific strategies to promote active lifestyles and reduce risky digital behaviors.

1 Introduction

Modern digital life shapes how people connect, work, learn, and spend their free time–all of which are heavily influenced by internet use (Browne et al., 2021; Odgers and Jensen, 2020). Digital technology provides young people significant access to information and connectivity. For example, a parent survey by the Latvian Children’s Aid Fund (LBAF) showed that 18% of children under 18 spend five or more hours daily on smart devices, while 43% spend three to 5 h daily (Khoshtaria et al., 2021; Nîmante and Repina, 2019). Latvia, a digitally mature European country, almost everyone has access to the internet. In 2023 95% of households with children in Latvia had access to a computer and the internet, and 91% of students aged 16–24 used the internet daily for educational purposes (Eurostat, 2025). Meanwhile, in Indonesia, by 2024, internet use reached 79.5%, with over 87% of young people aged 12–27 active online (Rahman et al., 2024). A survey in Indonesia by previous researchers in 2020 across 34 provinces in Indonesia revealed that over 19% of Indonesian adolescents struggled with internet addiction (Hanifah et al., 2023).

Researchers have linked excessive and uncontrolled internet use to adverse consequences such as sleep disturbances, anxiety, depression, and impaired social functioning (Mihajlović et al., 2023; Veliks et al., 2023). The term “Problematic Internet Use” (PIU) was introduced by Beard and Wolf to identify excessive internet use that causes psychological, social, educational, and occupational problems in a person’s life (Nogueira-López et al., 2023; Wartberg et al., 2021). Adolescents, due to their high dependence on digital technology, are at particular risk of experiencing these problems. This life stage is crucial for physical, psychological, and social development, as key behaviors such as screen time habits, physical activity levels, and daily routines like sleep and time management begin to form and stabilize (Abdelghaffar et al., 2019; Badenes-Ribera et al., 2019). Furthermore, PIU has the potential to hinder physical development by hindering the acquisition and execution of fine and gross motor skills. These motor skills, typically acquired through play, physical education (PE), and extracurricular sports, are crucial for the development of self-confidence and the maintenance of an active lifestyle later in life, in addition to their impact on physical health (Coimbra et al., 2021; O’Brien et al., 2023b).

Adolescence is a critical period for motor maturation, characterized by significant increases in fine and gross motor skills due to physical growth, hormonal changes, and brain development (Coutelle et al., 2024). During this time, the brain’s prefrontal cortex undergoes synaptic pruning, increasing efficiency and allowing for better motor planning and control (Drenowatz and Greier, 2019; Madsen et al., 2020). Motor skills in adolescence refer to the development and mastery of gross motor skills (such as coordination, balance, and agility) and fine motor skills (such as handwriting, cutting, and playing musical instruments), (Malina, 2014). Motor proficiency, on the other hand, refers to an individual’s overall level of competence in performing a variety of motor tasks, reflecting both the quality and efficiency of movement performance across different activities (Barnett et al., 2022; Utley, 2018). Time spent in specific sports activities during this adolescent stage contributes positively to strength and motor skills. This cycle of low competence and inactivity can create a negative feedback loop in which poor motor skills reduce participation in activities, thereby limiting opportunities for skill acquisition and reinforcing inactivity, with long-term implications for health (Duncan and Stodden, 2024; Kolunsarka et al., 2023). Consistent participation in sports and physical activity is crucial for further refining these skills and promoting overall development (Malina et al., 2025). Agility, motor coordination, strength, and speed skills continue to improve and develop during middle adolescence (Malina et al., 2016). Girls generally perform better than boys on balance tasks but typically do not show improvements in motor performance after age 14, while boys continue to improve throughout adolescence. In adolescent boys, peak maximal speed occurs before PHV (peak high velocity), and peak strength and power occur after PHV (Malina, 2014; Malina et al., 2016).

Extracurricular sports foster self-discipline, goal setting, time management, and resilience–skills that contribute to academic success and overall well-being. Youth sports programs and specific sports clubs share similar goals, including enjoyment of exercise, development of general and sport-specific motor skills, promotion of regular physical activity, increased social interaction, and teaching values associated with sportsmanship (Berger et al., 2020; Gilman et al., 2004). Regular involvement in sports is also associated with improved mood, reduced symptoms of anxiety and depression, and the development of strong peer relationships. These benefits support cognitive, emotional, and social functioning while enhancing self-control and executive functions, which counteract the impulsivity and poor time management often associated with PIU (Haegele et al., 2020; Klavina et al., 2022; Urbanova, 2020).

In Indonesia, the legal framework governing sports activities, including extracurricular sports, is regulated by the Sports Law of the Ministry of Youth and Sports and the Regulation of the Minister of Education and Culture (Dimyati, 2006). Extracurricular activities in Indonesia are supplementary pursuits conducted outside regular school hours, designed to cultivate students’ potential, talents, and interests, encompassing popular sports like football and basketball, as well as artistic endeavors such as music, dance, and theater (Mustafa and Dwiyogo, 2020). Despite the known benefits to students, extracurricular organizers, namely schools, still face many obstacles, including issues related to time, facilities, human resources, and environmental support (Gilman, 2001; Isma and Yusuf, 2025). Not all schools can accommodate the diversity of student interests; consequently, students who are able can join paid or free sports clubs outside of school (Kementerian Pemberdayaan Perempuan dan Perlindungan Anak dan Badan Pusat Statistik, 2019). Unfortunately, there is no data available on the number of extracurricular participants nationally in Indonesia.

In Latvia, extracurricular sports in primary schools are primarily organized through after-school clubs and municipal sports schools (Dirba, 2013). These institutions offer children opportunities to engage in activities outside of physical education lessons. Latvia has a network of 94 sports schools, comprising 64 municipal and 30 private schools, collectively serving 46,069 students who train in 39 different sports disciplines. The system is supervised by 1,945 coaches, highlighting a strong commitment to sports education. The state provides financial support for coaches’ salaries and social security (Centrālās Statistikas Pārvaldes [CSP], 2025; Kārkliņa et al., 2021). This widespread involvement in structured and voluntary activities reflects Latvia’s emphasis on fostering physical and creative development among young people. By integrating sports and cultural programs, the country promotes a balanced approach to education that supports health, discipline, and social cohesion.

The objectives of this study were to determine and compare the relationship between problematic internet use and motor proficiency in adolescents from Latvia and Indonesia. In addition, this study explored the association between PIU, MP and healthy lifestyle behaviors among adolescents in Indonesia and Latvia participating in extracurricular sports (ES) and non-sports (NS) activities.

2 Materials and methods

This study utilized a cross-sectional research design. This research was conducted in two junior high schools in Latvia and two junior high schools in Indonesia. The results in each country were analyzed and compared across the two groups: (1) adolescents engaging in extracurricular sports at least once a week (ES group) and (2) Adolescents who do not engage in extracurricular sport (NS group). This study has complied with the ethical license as per the decision letter from the Ethics Committee of the LSPA (No S/51813, 20.01.2024). The signed consents forms were obtained from the parents of all study participants (see Figure 1).

FIGURE 1

Figure 1. Participant enrollment process flow chart.

2.1 Participants

In total 257 adolescents (12–16 years of age) from Latvia and Indonesia were involved in this study (mean age of 13.57, SD = 0.86). Of all, 57 students were from Latvia (mean age of 13.42, SD = 0.63), and 200 from Indonesia (mean age of 13.61 SD = 0.91). Among the participants 108 were involved in ES (see Table 1). Sample size estimation was conducted using G*Power 3.1 software. With the significance level set at α = 0.05, statistical power at 0.80, and a medium effect size (f = 0.15), the recommended minimum sample size was 51 participants per group.

TABLE 1

Table 1. Descriptive statistics of participants’ characteristics [Means (SD)].

2.2 Instruments

2.2.1 Motor proficiency

Motor proficiency was assessed with four subtests of the Bruininks-Oseretsky Test of Motor Proficiency II (BOT-2): (1) bilateral coordination (7 items, score range = 0-24 points), (2) balance (9 items, score range = 0-37 points), (3) running and speed (5 items, score range = 0-52 points), and (4) strength (5 items, score range = 0-42 points). Each raw score on a sub- test was converted to a point score to allow performance to be measured on a graded scale. The individual item points were summed to derive a subtest point score. The total score of the four subtests was then calculated and converted into scale scores, standard scores, percentiles, age equivalents, and descriptive categories (Klavina et al., 2022). The time required to complete the test is 15 min (Bruininks and Bruininks, 2005; Yoon et al., 2024). This measurement is conducted in the school gym during physical education sessions. Prior to data collection, the teachers provided students with practice during one learning session. Researchers were assisted by physical education teachers and volunteer teams in Latvia and Indonesia, all of whom had received 2 weeks of training on the testing and assessment procedures. Additionally, the researchers created a video tutorial that encompassed detailed test instructions and assessment criteria.

2.2.2 Problematic internet use

This study used the Problematic Risky Internet Use Screening Scale (PRIUSS) to assess the risk of PIU (Jelenchick and Christakis, 2014). The PRIUSS consists of 18 items organized into three subscales: social impairment (6 items), emotional impairment (5 items), and risky/impulsive internet use (7 items). Responses are given on a 5-point Likert scale, ranging from 0 (“never”) to 4 (“very often”) (Klavina et al., 2023; Moreno and Jelenchick, 2014). Based on their total scores, adolescents who scored 26 points and more were classified as being at high risk, while those who scored between 15 and 25 - moderate risk for PIU. Data were collected using both an online application and paper questionnaires.

2.2.3 Demographic and healthy lifestyle behavioral

The questions were adapted from the Health Behavior in School aged Children (HBSC) included: age, Body Mass Index (BMI), gender, screen time, and, physical activity hours per week (PA_hours/week), internet usage hours for study on weekdays, and internet usage hours for non-study on weekends. To assess daily physical activity, adolescents were asked to indicate the number of days and hours during the past week that they engaged in moderate-to-vigorous physical activity in school or out of school (free time) (Maniaci et al., 2023). To assess daily screen time, adolescents were asked to report how many hours per day they spent using the internet (for both study and non-study purposes) during weekdays and weekends (Klavina et al., 2021a). The average response was used as a measure of screen time. A cutoff of 3 h per day was used to account for time spent on the internet and to keep the results comparable with other international comparative studies (Hoying et al., 2016). Data was collected used an online application and paper printouts. The questionnaire was backtranslated from English into Latvian and Indonesian (Darling et al., 2020).

2.3 Statistical analysis

All analyses were conducted using SPSS version 27. The Shapiro-Wilk test indicated that variables such as MP and problematic internet use (PIU) did not meet the assumptions of normality. As a result, these variables were analyzed using nonparametric methods. Correlations using Spearman’s rho were calculated to evaluate the relationships between PIU and MP for Indonesian and Latvian adolescents independently. Group differences between NS and ES participants were examined using the Mann–Whitney U test. Linear regression analyses to investigate predictors of problematic internet use, motor proficiency, and healthy lifestyle behaviors, to reduce the influence of extreme values, continuous variables were winsorized at the 5th and 95th percentiles (Jadhav and Kashid, 2014). Additionally for the further analysis we employed quantile regression (QR) at the 10%, 25%, 50%, 75%, and 90% quantiles, The QR results revealed substantial distributional heterogeneity.

3 Results

Table 1 presents the descriptive statistics for the study variables. Demographic results of mean age and BMI did not present significant differences between the ES and NS groups in both countries. In Indonesia, ES participants demonstrated significantly higher MP with the mean score of 126.54 (SD = 16.12; p = 0.045) and lower mean score of 15.95 (SD = 7.60; p = 0.004) for PIU compared to NS participants. However, in Latvia, the MP and PIU mean scores did not present the significant differences between the ES and NS groups. Significant differences were observed between the groups, with students involved in ES reporting higher levels of physical activity than those in NS groups in Latvia for both daily (p = 0.009) and weekly (p = 0.001) physical activity hours, as well as lower daily and weekly non-study internet use (p = 0.048). Regarding PIU outcomes presenting proportion of participants scoring at moderate and high risk, the 32.6 % of Indonesia NS group students scored at high risk while in ES group only 13 % presented high risk of PIU. In Latvia ES and NS groups exhibited a smaller difference (31.3% vs. 44.0%).

As shown in Table 2, Spearman’s correlation indicated a significant negative relationship between MP and PIU in the Indonesia ES group (r_s = −0.367, p < 0.001), demonstrating that improved MP was linked to lower PIU. No significant correlations between other groups were found in the Indonesia ES (r_s = −0.174, p = 0.096), Latvia NS (r_s = −0.033, p = 0.874), or Latvia ES (r_s = −0.256, p = 0.155) groups.

TABLE 2

Table 2. Correlations between PIU and MP scores.

Table 3 linear regression for predicting PIU and MP across country and activity groups, to reduce the influence of extreme values, continuous variables were winsorized at the 5th and 95th percentiles. For Indonesian participants in NS group, internet use for non-study purposes (B = 6.825, p = 0.001) and for study purposes (B = 0.714, p = 0.017) were significant predictors of PIU. For MP, the Indonesia ES groups showed positive results from the predictors, daily (B = 6.430, p < 0.001) and weekly physical activity (B = 2.966, p = 0.007). Comparably, in Latvia MP was positively influenced by WPA in both NS (B = 10.096, p = 0.008) and ES (B = 2.962, p = 0.044) groups. Among Latvia participants in NS, both IUNS (B = 4.274, p < 0.001) and IUS (B = 3.138, p = 0.005) were associated with higher PIU, while daily internet use (IUD, B = −2.339, p = 0.013) was negatively related. For Latvia participants in ES, IUNS (B = 4.492, p < 0.001) and IUS (B = 2.891, p = 0.012) predicted higher PIU, whereas IUD (B = −2.581, p = 0.014) showed a negative association.

TABLE 3

Table 3. Linear regression winsorized for predicting problematic internet use (PIU) and motor proficiency (MP).

Additionally, for the further analysis we employed quantile regression (QR) at the 10%, 25%, 50%, 75%, and 90% quantiles (see Figure 2). The QR results revealed substantial distributional heterogeneity. For PIU, daily PA was associated with lower scores in Indonesia ES (β = 7.207–2.789, τ = 0.10–0.90), with WPA benefiting higher quantiles and non-study internet use showing protective effects. Indonesia NS showed stronger daily PA effects at τ = 0.25 (β = 15.679) and negative associations for non-study internet use at τ = 0.10 (β = −4.111). In Latvia, ES participants exhibited modest positive effects of physical activity, while NS participants showed negative daily physical activity associations (β = −23.659 to −12.000, τ = 0.10–0.90) and strong positive weekly PA effects (β = 16.386, τ = 0.10). For MP, daily physical activity (PAD) positively predicted performance across multiple quantiles in both Indonesian cohorts (β = 2.789–15.679, p < 0.05), with stronger effects in NS participants; weekly PA was beneficial at lower quantiles in Latvia NS (τ = 0.10–0.25), while Latvia ES showed a borderline positive daily PA effect at τ = 0.90.

FIGURE 2

Figure 2. Scatterplots of statistically significant quantile regression coefficients (β) for predictors of problematic internet use and motor proficiency across τ = (τ = 0.10, 0.25, 0.50, 0.75, 0.90). Colors indicate groups; marker shapes denote predictors. Non-significant estimates (p ≥ 0.05) were omitted.

4 Discussion

This study aimed to examine and compare the motor proficiency (MP) and problematic internet use (PIU) in adolescents from Latvia and Indonesia. Additionally, this study explored the association between healthy lifestyle behaviors, MP and PIU among adolescents who participating in extracurricular sports (ES) and non-sports (NS).

4.1 Problematic internet use

The findings revealed group- and country-specific outcomes that have important implications for adolescent health and physical development. Indonesia, adolescents participating in extracurricular sports (ES) exhibited significantly lower PIU scores compared to non-sport (NS) group, and a markedly smaller proportion were at risk of PIU. These findings align with prior research showing that engagement in structured physical activity can serve as a protective factor against excessive internet use by promoting self-discipline, social interaction, and time management (Babic et al., 2014; Kircaburun et al., 2020; Kuss and Gainsbury, 2021). Adolescents involved in organized sports typically have less discretionary screen time and greater exposure to real-world social and physical experiences, which may mitigate tendencies toward problematic online behaviors (Alonso and Romero, 2021; Chao et al., 2020; Leménager et al., 2018).

In contrast, Latvian results showed no significant difference in overall PIU between sports and non-sports participants. However, there were meaningful behavioral distinctions between the two groups, for example, students engaged in extracurricular sports reported significantly higher levels of both daily and weekly physical activity, as well as lower daily and weekly non-study internet use. These results indicate that, although PIU scores themselves are not significantly different, sports participation in Latvia still contributes to healthier lifestyle patterns and reduced recreational screen time (Montag et al., 2021). The results were almost similar to previous research on adolescents in Latvia, which revealed that 31% of adolescents there were at risk of PIU and a significant negative correlation between the total PRIUSS score and physical activity (Klavina et al., 2021b).

The Latvian findings can also be interpreted within a sociocultural framework. Latvia’s high digital integration and widespread internet accessibility may normalize frequent online use, blurring the line between functional and problematic engagement. Thus, while sports participation positively affects activity balance, its influence may not be strong enough to alter standardized PIU scores (Chao et al., 2020). Interventions in such contexts should therefore combine physical activity promotion with education on healthy digital habits, focusing on managing non-study screen time and fostering digital self-regulation. Latvia, like many European countries, has widespread digital access and education-related internet use, which may normalize higher online engagement across adolescents (Peterka-Bonetta et al., 2019; Wartberg et al., 2016). This suggests that lifestyle patterns or sample size limitations may influence the observable protective effect of sport participation.

4.2 Motor proficiency

In Indonesia, ES participants demonstrated significantly higher MP scores compared to their NS group, a finding consistent with prior evidence that structured physical activity, and organized sports play a critical role in enhancing fundamental motor skills, coordination, and overall motor competence (Kolunsarka et al., 2023; Logan et al., 2018; Wilson and Barnett, 2020). The majority of Indonesian ES adolescents achieved average or above-average MP, suggesting that consistent exposure to structured movement patterns and skill-based practice fosters neuromuscular control and movement confidence (Cattuzzo et al., 2016; Garciá-Hermoso et al., 2021). Participation in sports also provides social and motivational contexts that encourage persistence, mastery, and regular engagement, which are essential for motor learning and sustained physical activity (Lubans et al., 2017; Utesch et al., 2019; Vandorpe et al., 2012).

In contrast, Latvian adolescents exhibited no significant difference in motor proficiency between students participating in extracurricular sports and those who did not, though sports participants reported higher daily and weekly physical activity allocation time. This suggests that, within the Latvian context, both groups may already share comparable levels of motor competence, likely due to widespread access to physical education and active environments. The total scores of MP subtests presented similar results as in earlier research done in Latvia (Klavina et al., 2022). Latvia’s educational system places strong emphasis on regular physical education, school sports days, and outdoor movement activities, which may help maintain a generally adequate level of MP across adolescents (Dirba, 2013; Nîmante and Repina, 2019). Furthermore, research from Northern and Eastern Europe has shown that countries with robust school-based physical activity programs and supportive environments for play tend to exhibit smaller disparities in motor development between youth with different activity backgrounds (Gísladóttir et al., 2014; Radièuks et al., 2024; Sigmundsson et al., 2021). Previous studies in European contexts have shown that when school curricula emphasize daily physical education and unstructured outdoor play, differences in motor skills between ES and NS participants tend to diminish (Dirba, 2013; Huotari et al., 2018; Luz et al., 2017). Moreover, variations in sports culture, training frequency, and socioeconomic access may moderate the relationship between organized activity and motor development (O’Brien et al., 2023a; Philpott et al., 2021).

These findings imply that in highly active cultures or education systems emphasizing universal movement participation, ES may enhance overall activity levels rather than create large gaps in MP. Thus, while the Latvian data did not reveal significant group differences in MP, the behavioral and environmental contexts indicate a generally favorable setting for adolescent motor development.

4.3 Relationship between motor proficiency and problematic internet use

The present study identified a significant negative association between MP and PIU among ES adolescents in Indonesia, indicating that higher levels of motor competence were associated with lower PIU risk. This relationship suggests that better-developed motor skills may reflect broader psychosocial and self-regulatory capacities that protect against excessive or maladaptive digital engagement (Robinson et al., 2016). Adolescents with higher MP often display greater self-efficacy, motivation, and engagement in physical and social activities, which can reduce the appeal of prolonged screen-based behaviors (dos Santos et al., 2021; Utesch et al., 2019). Conversely, lower motor competence has been linked to reduced physical activity participation, social withdrawal, and sedentary tendencies all factors that may contribute to increased vulnerability to PIU (Babic et al., 2014; Kwan et al., 2016).

Interestingly, this association was not significant among ES participant in Latvian group, suggesting that contextual and behavioral factors may moderate this relationship. Participation in organized sports may already provide structured routines, social connectedness, and time management demands that buffer against problematic internet use (Chen and Fan, 2024; Kuss and Gainsbury, 2021; Şalvarlı and Griffiths, 2021). In Latvia, greater digital integration in education, or more balanced screen habits may have attenuated this association (Dick et al., 2021; Khoshtaria et al., 2021; Peterka-Bonetta et al., 2019). These findings align with prior research indicating that physical competence and self-regulation capacities are interlinked, influencing adolescents’ capacity to manage both offline and online behaviors (Klavina et al., 2023; Lai et al., 2023).

4.4 Healthy lifestyle behaviors: physical activity and internet use

The regression analysis revealed that predictors of PIU and MP among adolescents differed by country and group, reflecting distinct behavioral and cultural patterns. In Indonesia, NS participants showed that both study-related and non-study Internet use significantly predicted PIU, with non-study or leisure use being the strongest predictor, suggesting that unstructured online activity poses a higher risk for problematic behaviors compared to academic use. This finding aligns with previous studies indicating that leisure-oriented Internet use is more strongly linked to addictive or problematic use than functional or study-related engagement (Gao, 2015; Zhang et al., 2023). In contrast, in Latvia, both ES and NS groups exhibited significant effects for study and non-study Internet use as positive predictors of PIU, whereas total Internet use per day showed a negative relationship, we assumed that structured or balanced online routines mitigated problematic tendencies. Regarding MP, PA hours per day and per week were consistent and significant predictors across both Indonesian and Latvian groups, demonstrating that higher frequency and duration of PA contribute meaningfully to better motor competence (Klavina et al., 2022). These findings are consistent with prior evidence highlighting the reciprocal relationship between MP and PA engagement (Malina et al., 2016; Rogers et al., 2018). The strong influence of PA on MP across both contexts underscores the universal importance of promoting active lifestyles, while the associations between specific Internet use patterns and PIU emphasize the need to regulate non-study online activities. Overall, the regression outcomes suggest that structured behaviors whether through organized sports or purposeful Internet use play a protective role in adolescent development by fostering physical competence and reducing the risk of problematic digital habits.

The regression analyses indicated that predictors of problematic internet use (PIU) and motor proficiency (MP) differed by country and activity group, highlighting the role of adolescents’ habitual behaviors. To reduce the influence of extreme values, continuous variables were winsorized at the 5th and 95th percentiles. Linear regression analyses were then conducted using the winsorized variables to examine predictors of problematic internet use and motor proficiency across sport participation and country. Specifically, in Indonesian NS adolescents, both study-related and leisure Internet use predicted higher PIU, with leisure use showing the strongest effect. This finding aligns with prior research showing that unstructured Internet engagement increases the risk of problematic behaviors, whereas functional use poses lower risk (Kuss and Gainsbury, 2021). These results may reflect limited structured habits outside school, emphasizing the protective potential of organized sports and purposeful routines.

In contrast, Latvian adolescents showed a slightly different pattern: both study- and non-study internet use purposes positively predicted PIU, but total daily internet use was negatively associated. This suggests that balanced and structured online habits can mitigate the risk of problematic behaviors, consistent with prior studies on self-regulation and moderated Internet use as protective factors (Colder Carras and Kardefelt-Winther, 2018; Özer and Odacı, 2020). Collectively, these findings indicate that habitual behaviors, including both online and offline routines, are critical determinants of adolescent Internet engagement across different contexts.

Regarding MP, daily and weekly physical activity consistently predicted higher motor proficiency across both countries and activity groups. This supports well-established evidence that habitual engagement in physical activity enhances motor competence, which in turn encourages further participation in active behaviors (Duncan et al., 2017; Stodden et al., 2009). Therefore, structured physical activity appears to be universally beneficial for motor development. Importantly, quantile regression analyses complemented these findings by revealing heterogeneity in the effects of habitual behaviors. In Indonesia, physical activity had the strongest impact on MP among adolescents with lower or median motor competence, while leisure Internet use most strongly affected PIU among higher-risk adolescents. Meanwhile, in Latvia, the effects of physical activity and Internet use were more quantile-specific, highlighting that individual habits interact with baseline skill levels and risk profiles. These results reinforce prior research advocating distribution-sensitive analyses for a nuanced understanding of adolescent health behaviors (Furuya and Fletcher, 2022).

5 Limitations and future directions

Despite its contributions, this study has several limitations. First, the sample sizes differed substantially between the two countries, which may limit the robustness of cross-country comparisons. Language barriers were also one of the reasons why researchers were unable to reach enough participants, especially in Latvia. Additionally, the cross-sectional design prevents causal inference, and reliance on self-reported data introduces potential recall and social desirability bias. Contextual factors such as school health program structure, parental involvement, and socioeconomic status were not directly assessed but may have influenced the outcomes. Future research should employ longitudinal and mixed method designs to unpack causal pathways and explore mediating factors such as executive functioning, digital literacy, and peer norms. Additional strategies to achieve more equitable participant numbers are advised. Furthermore, potential constraints in data collection across various countries should be acknowledged, as disparities in environmental conditions and cultural contexts may affect the consistency and comparability of the data collection process. Moreover, qualitative studies involving educators and adolescents may illuminate how inclusion practices affect PA experiences in real-world settings.

6 Conclusion

This study demonstrates that participation in extracurricular sports (ES) and engagement in structured physical activity was linked to higher motor proficiency (MP) and lower problematic internet use (PIU) among adolescents from the sampled populations in Indonesia and Latvia. In Indonesia, ES participation was associated with higher MP, greater allocation of time to physical activity, and lower PIU, with higher MP independently predicting lower PIU. In Latvia, differences in MP and PIU between ES and non-sports participants were minimal, though ES participants still showed higher activity levels and lower non-study Internet use. Overall, these findings highlight that promoting healthy lifestyle behaviors including regular physical activity and balanced Internet use can support adolescents’ motor development and reduce PIU, emphasizing the need for context-specific strategies to support adolescent health and development.

Data availability statement

The datasets presented in this article are not readily available and will not be shared with any party without the permission of the corresponding author. Requests to access the datasets should be directed to cml6a3ludXJ1bGZhMzFAZ21haWwuY29t.

Ethics statement

This study has complied with the ethical license as per the decision letter from the Ethics Committee of the LSPA (Latvijas Sporta pedagoǵijas akadēmija) (No S/51813, 20.01.2024). The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation in this study was provided by the participants’ legal guardians/next of kin. Written informed consent was obtained from the individual(s), and minor(s)’ legal guardian/next of kin, for the publication of any potentially identifiable images or data included in this article.

Author contributions

RN: Conceptualization, Funding acquisition, Project administration, Software, Writing – original draft, Writing – review & editing, Methodology, Visualization. AK: Conceptualization, Supervision, Validation, Writing – original draft, Writing – review & editing, Data curation, Investigation, Methodology, Resources.

Funding

The author(s) declared that financial support was received for this work and/or its publication. This research was funded by Riga Stradins University Doctoral Grant Number 6-DN-20/2/2025 dated February 3, 2025.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

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