{"paper_id":"30448c2e-91c2-414f-97fe-fabc1be53e1b","body_text":"The associations between sports team participation and adolescent health risk behaviors in the United States | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The associations between sports team participation and adolescent health risk behaviors in the United States Hao Sun, Senna A, Guojian Li, Jinbang Zhu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8586391/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background Sports team participation is widely regarded as a protective factor for adolescent development. However, current evidence remains inconsistent regarding its association with specific health risk behaviors. This study aimed to characterize the divergent patterns of substance use, risky driving, and sexual behaviors among U.S. adolescents participating in sports teams over a 24-year period. Methods Data from the Youth Risk Behavior Surveillance System (YRBSS) Combined Datasets (1999–2023) were analyzed, comprising a nationally representative sample of high school students (Grades 9–12). The primary exposure was the frequency of sports team participation. Multivariable logistic regression models were utilized to estimate for four domains of risk behaviors (driving, substance use, illicit drug use, and sexual activity), adjusting for covariates such as gender, age, race/ethnicity, and BMI percentile. Results were expressed as predictions with 95% confidence intervals (95% CIs). Results Sports team participation was significantly negatively associated with cigarette smoking and most illicit drug use behaviors. In contrast, it was significantly associated with higher rates of texting while driving and alcohol consumption. With respect to sexual behavior, students who participated in sports teams were more likely to report recent sexual activity; however, they also demonstrated significantly higher compliance with condom use. Conclusions Sports team participation shows distinct associations with adolescent risk behaviors, highlighting the potential importance of interventions. Greater participation was linked to higher engagement in risky driving, alcohol use, and sexual activity, while showing protective associations with cigarette smoking and the majority of illicit drug use, and exhibiting higher compliance with condom use. These findings underscore the necessity of integrating targeted, behavior-specific prevention strategies into sports settings to maximize health benefits while mitigating unintended harms. Adolescents Sports team participation Health risk behaviors YRBSS Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Adolescent risk behaviours encompass behaviours and psychological states that increase susceptibility to adverse health and developmental outcomes. These include health-compromising behaviours, such as alcohol and drug use, risky driving, violence, and unsafe sexual practices [1]. Recent evidence indicates a shift in the profile of adolescent risk behaviours. While several traditional risk behaviours, most notably cigarette smoking, alcohol consumption, and sexual activity, have declined over time [2]. For example, cigarette use among U.S. adolescents decreased markedly between 1991 and 2021, with prevalence falling from 70.1% to 17.8% [4]. Similarly, the prevalence of past-month binge drinking (≥ 5 drinks on one occasion) more than halved, declining from 26% in 2002 to 12% in 2016 [5]. From a life course perspective, engagement in risk-taking behaviours during adolescence is associated with adverse outcomes across multiple domains, including well-being, physical and mental health, and academic functioning [8]. Such changes may have enduring effects that extend across the life course. Accordingly, early prevention of engagement in risk behaviours is critical to reducing long-term adverse consequences. Sports team participation is defined as the engagement of adolescents in organized, rule-based, and typically competitive sports activities as members of a team [9]. Current research widely recognizes sports team participation as a critical protective factor for positive youth development. Its core value lies in providing a structured social environment that confers benefits across psychological (e.g., reduced depressive symptoms, enhanced self-esteem), social (e.g., improved social skills, teamwork acquisition), and physical health domains (e.g., increased physical activity levels, reduced body fat) [8]. Existing research suggests that participation in organised sports is generally associated with enhanced psychosocial functioning among adolescents and may reduce antisocial behaviour [10] and aggression [11], thereby contributing to lower levels of risk behaviours such as violence and delinquency and fostering individual resilience. However, these protective effects are neither uniform nor unconditional. Evidence from systematic reviews and national-level studies indicates that, under certain conditions—such as high training intensity, competitive pressure, and specific team cultural norms—sports and team participation may also be associated with increased engagement in risk behaviours, including alcohol misuse and other substance use[12][13]. These findings suggest that adolescents involved in sports teams may experience a distinct risk profile and, in some contexts, a potentially elevated prevalence of certain risk behaviours compared with their peers. Despite this, current research has not comprehensively characterised the prevalence of risk behaviours among adolescents participating in sports teams, nor has it clearly established whether their levels of risk behaviours differ systematically from those of non-participants. To address this research gap, this study aims to examine the association between the frequency of sports team participation and a range of adolescent risk behaviours, including dangerous driving, substance use (alcohol, tobacco, and illicit drugs), and sexual activity. The findings seek to characterise patterns of risk behaviour among adolescents. Methods Study design and participants Data for this study were obtained from the Youth Risk Behavior Surveillance System (YRBSS), a national public health surveillance system established in 1991 by the Centers for Disease Control and Prevention (CDC). The YRBSS employs standardised data collection and analytic procedures to systematically monitor a broad range of health risk behaviours among adolescents. These data provide an empirical foundation for the development, implementation, and evaluation of strategies aimed at promoting youth health and preventing disease [14]. The survey is conducted biennially, typically in odd-numbered years, during the spring semester (February to May). Questionnaire development and preparatory activities commence in the autumn of the preceding even-numbered year, with final datasets usually released in June of the subsequent year. Designed and administered by the Centers for Disease Control and Prevention, and approved by its Institutional Review Board, the survey employs a three-stage cluster sampling design to obtain a nationally representative sample of high school students from public, Catholic, and other non-public schools across the United States. As the YRBSS data used in this study are publicly available secondary data, the present analysis was exempt from additional ethical review in accordance with established standards for research ethics. Youth Risk Behavior Survey (YRBS) Combined Datasets spanning the period from 1991 to 2023. As sports team participation was consistently assessed only from the 1999 survey cycle onward, the analytical sample utilised in this study was restricted to data collected between 1999 and 2023. The study population comprised students in grades 9 to 12, typically aged 14 to 18 years. as the measurement of non-binary gender identity was introduced in the 2021 survey cycle, these data were excluded to ensure comparability of trends across survey years. Measures Predictors (Independent variable) The independent variable was derived from Question 78 of the YRBS Combined Datasets. This item assessed the number of sports teams on which students participated. Participants were asked: \"During the past 12 months, on how many sports teams did you play? (Count any teams run by your school or community groups.)\" Response options included \"0 teams\", \"1 team\", \"2 teams\", and \"3 or more teams\". Outcome (Dependent variable) Four domains of adolescent risk behaviours were included as outcome measures in this study. First, risky driving behaviours were assessed using two items: driving after drinking alcohol (Q10) and texting while driving (Q11). Second, smoking and alcohol behaviours were measured using three items: cigarette use (Q33), electronic vapor product use (Q36), and alcohol use (Q42). Third, drug use behaviours comprised seven indicators: marijuana use (Q46), cocaine use (Q50), inhalant use (Q51), heroin use (Q52), methamphetamine use (Q53), ecstasy use (Q54), and illicit injection drug use (Q55). Finally, sexual risk behaviours were assessed using two items: history of sexual activity (Q59) and condom use at last sexual intercourse (Q61). Detailed descriptions of the measurement items and the full questionnaire related to these risk behaviours are provided in Additional file 1: Table S1 . The specific survey years and data characteristics used for each risk behaviour domain are summarised in Additional file 1: Table S2. Covariates Sociodemographic and anthropometric variables were included as covariates. Gender, age, race/ethnicity, and body mass index (BMI) were obtained via self-report using standard YRBSS items. Sex was operationalised as a binary variable (male and female) to ensure consistency across survey cycles and comparability over time. Age was categorised into seven response options reflecting single-year age groups: ≤12 years, 13 years, 14 years, 15 years, 16 years, 17 years, and ≥ 18 years. Race/ethnicity was classified into four mutually exclusive categories: White; Black or African American; Hispanic/Latino; and All Other Races, the latter comprising respondents identifying with racial/ethnic groups not represented in the first three categories. Anthropometric status was assessed using the BMI percentile (BMIPCT), which was calculated by the CDC based on self-reported height and weight and standardised for age and sex according to U.S. growth reference charts. BMIPCT was treated as a continuous variable in the analyses to capture variation in relative body weight status across adolescence. Statistical analysis Participants were included if they possessed complete data for at least one risk behavior, provided that they also had complete demographic data. The specific data characteristics and years utilized for each risk behavior are presented in Additional file 1: Tables S3 and S4. All risk behaviors were analyzed as binary variables, and descriptions of the processed variables are displayed in Additional file 1: Table S5. Descriptive statistics were reported for participant characteristics, measures of risk behaviors, and sports team participation. Categorical variables were presented as frequencies and percentages [n (%)], while continuous variables were reported as means and standard deviations [mean (SD)]. In order to explore the associations between the sports team participation behavior and risk behaviours, an ordinal logistic model were built, with sports team participation as the predictors, and the risk behaviours (Driving,smoking and drinking, drug use, sexual activity) as the response variables, adjusted for Gender, Age, Race, BMI. Beta and 95%CI were reported for each model. The predictions were plotted for visualized comparison. Statistical significance level was set as 0.05. All the analysis were conducted with R language[15], and plotted with ggplot [16]. Results Descriptive analysis Table 1 presents the distribution of adolescent risk behaviours in the study sample. Overall, the prevalence of risky driving behaviours was relatively low for alcohol-impaired driving (3.8%), whereas texting while driving was more common, reported by nearly one-quarter of respondents (23.8%). With respect to substance use, most adolescents reported never having used cigarettes (83.7%) or electronic vapor products (78.4%); however, over one-third reported having consumed alcohol (37.8%). Marijuana use was reported by 40.3% of participants, while the prevalence of other illicit drug use remained comparatively low, with lifetime use ranging from 1.4% for injected drug use to 9.7% for inhalant use. Regarding sexual behaviours, approximately one-third of adolescents (34.1%) reported having engaged in sexual activity in the past three months. Among those who were sexually active, condom use at last sexual intercourse was reported by fewer than one-third of respondents (28.9%), indicating a substantial proportion of adolescents engaging in unprotected sex. Table 1 Distribution of Adolescent Risk Behaviors in the Study Sample Name Levels Stats Drinking-and-driving No driving or 0 times 55295 (96.2%) Alcohol-impaired driving 2195 (3.8%) Texting-while-driving No driving or 0 times 43784 (76.2%) Texting while driving 13706 (23.8%) Cigarette use Never 118013 (83.7%) Ever 22920 (16.3%) Electronic vapor use Never used 37765 (78.4%) Ever used 10431 (21.6%) Alcohol use Never 87656 (62.2%) Ever 53277 (37.8%) Marijuana use Never used 90919 (59.7%) Ever used 61274 (40.3%) Cocaine use Never used 141674 (93.7%) Ever used 9468 (6.3%) Inhalant use Never used 136265 (90.3%) Ever used 14687 (9.7%) Heroin use Never used 151014 (98.2%) Ever used 2819 (1.8%) Methamphetamine use Never used 147490 (95.9%) Ever used 6343 (4.1%) Ecstasy use Never used 129741 (94.1%) Ever used 8078 (5.9%) Illegal injected drug use Never used 148764 (98.6%) Ever used 2188 (1.4%) Sexual activity (past 3 months) No partnered sex 95009 (65.9%) Had sex 49226 (34.1%) Condom use at last sexual intercourse No condom use 102610 (71.1%) Used condom 41625 (28.9%) Associations between driving risk behaviours and sports team participation Figure 1 illustrates the associations between sports team participation frequency and driving-related risk behaviours. Corresponding odds ratios (ORs) for these behaviours are presented in Additional file 1: Table S6. Overall, the likelihood of engaging in driving risk behaviours increased with a greater number of sports teams participated in. Among the driving-related outcomes, texting while driving demonstrated the most pronounced increase across levels of sports team participation. Approximately 12% of adolescents who did not participate in any sports teams reported texting while driving, compared with 26% among those who participated in three or more sports teams (Additional file 1: Table S6). Associations between smoking and alcohol behaviour and sports team participation Figure 2 illustrates the associations between sports team participation frequency and substance use behaviours, including cigarette use, electronic vapor product use, and alcohol consumption. The corresponding odds ratios (ORs) for these outcomes are presented in Additional file 1: Table S6. Overall, alcohol consumption and electronic vapor use increased with a higher number of sports teams participated in, whereas cigarette use decreased as sports team participation increased. Among these behaviours, alcohol consumption exhibited the largest increase. Adolescents who never participated in sports teams had a substantially lower prevalence of alcohol use (32.7%, 95% CI: 32.3%–33.1%) compared with those who participated in three or more sports teams (36.6%, 95% CI: 35.8%–37.3%; Additional file 1: Table S6). In contrast, cigarette use showed the most pronounced decrease across levels of sports team participation. 13.8% of adolescents who did not participate in any sports teams reported cigarette use, whereas this proportion declined to 8.9% among those who participated in three or more sports teams (Additional file 1: Table S6). Associations between drug use behaviors and sports team participation Figure 3 illustrates the associations between sports team participation frequency and drug use behaviours. The corresponding odds ratios (ORs) are presented in Additional file 1: Table S6. With the exception of heroin use and illicit injection drug use, most drug use behaviours demonstrated a decreasing trend as the number of sports teams participated in increased. Marijuana use was the most prevalent drug-related risk behaviour across all participation levels and exhibited the largest absolute decline. Approximately 40.6% of adolescents who did not participate in any sports teams reported marijuana use, compared with 37.4% among those who participated in three or more sports teams (Additional file 1: Table S6). In contrast, illicit injection drug use showed the most pronounced increase with greater sports team participation. The prevalence of illicit injection drug use was lower among adolescents who never participated in sports teams (1.3%, 95% CI: 1.2%–1.4%) than among those who participated in three or more sports teams (2.0%, 95% CI: 1.8%–2.3%; Additional file 1: Table S6). Associations between sexual risk behavior and sports team participation Figure 4 illustrates the associations between sports team participation frequency and sexual risk behaviours. The corresponding odds ratios (ORs) are presented in Additional file 1: Table S6. Overall, sexual risk behaviours increased with a higher number of sports teams participated in. Among the sexual risk outcomes examined, recent sexual activity demonstrated the most pronounced increase. Approximately 31.2% of adolescents who did not participate in any sports teams reported having sexual intercourse in the past three months, compared with 36.8% among those who participated in three or more sports teams However, regarding protective behaviors, high-frequency participants demonstrated higher compliance with condom use at last sexual intercourse, with the prevalence increasing from 25.2% (95% CI: 24.8%–25.6%) in non-participants to 32.4% (95% CI: 31.6%–33.1%) in those participating in three or more sports teams Additional file 1: Table S6). Discussion Overview of finding Our study examined the association between the frequency of sports team participation and engagement in health risk behaviours among adolescents. The findings indicate that higher levels of sports team participation were positively associated with risky driving behaviours (texting while driving and drinking and driving), alcohol use, and sexual risk behaviours. In contrast, sports team participation were negatively associated with marijuana use and cigarette smoking. Risky Driving Behaviors This study identified a significant positive association between the frequency of sports team participation and risky driving behaviours among adolescents. One plausible explanation for the observed increase in texting while driving involves social and psychological mechanisms associated with sports team participation. First, the strong social cohesion and intensive peer networks characteristic of sports teams may heighten adolescents’ perceived need for constant communication, potentially extending into contexts where such behaviour is unsafe, including while driving. Second, psychological factors may also contribute. Previous research has documented that athletes often demonstrate comparative optimism , a cognitive bias characterised by an inflated perception of one’s own skills and invulnerability to risk. As described by Martha, Laurendeau, and Griffet [17], this heightened confidence in personal reflexes and driving ability may lead adolescent athletes to underestimate the dangers of distracted driving, such as texting while driving, and to overestimate their capacity to manage multiple tasks simultaneously. Consistent with prior research ur findings indicate that sports participation is associated with a higher prevalence of drinking and driving behaviours among adolescents. Previous studies have similarly reported that features of the team environment, including social norms and alcohol-centred bonding practices, may contribute to elevated alcohol-related risk behaviours among sports participants [20]. This increased risk does not appear to be inevitable or unmodifiable. Evidence suggests that structured, institution-level interventions can substantially mitigate drink-driving risk within sports settings. For example, Rowland, Toumbourou, and Allen [21] demonstrated that the implementation of the Good Sports program was associated with an 8% reduction per season in the odds of drink-driving among club members. This program employs a rigorous tiered accreditation framework designed to reshape the alcohol environment within sports clubs. Key components include mandatory Responsible Service of Alcohol (RSA) practices to prevent sales to minors and intoxicated individuals, restrictions on alcohol promotions that encourage high-risk consumption, and the provision of safe transport strategies such as key-deposit schemes, taxi vouchers, and incentives for designated drivers. In addition, participating clubs are required to develop formal written policies that embed these practices into club governance structures, thereby establishing sustained institutional constraints on risky drinking behaviours. Furthermore, the observed increase in risky driving behaviours may also be partly attributable to greater driving exposure associated with sports team participation. For example, qualitative research involving parents in rural communities has highlighted that adolescents often travel considerable distances to attend away games and training sessions. This increased driving frequency and cumulative mileage, necessitated by sports participation, may objectively elevate the risk of traffic incidents [22]. In addition, the peer passenger effect may represent another important contributing mechanism. Adolescents involved in sports teams frequently transport teammates or participate in carpooling arrangements for training and competitions. A substantial body of evidence indicates that the presence of peer passengers is associated with increased engagement in risky driving behaviours among young drivers [23][24]. In such contexts, interactions with peers—such as discussions about training or upcoming matches—may distract drivers and impair attention, thereby increasing crash risk [25]. Cigarette Smoking and Alcohol Use Our study identified a significant positive association between the frequency of sports team participation and the prevalence of alcohol use and e-cigarette use among adolescents. Conversely, a significant inverse association was found with the prevalence of cigarette use.[26]. For instance, a systematic review indicated that sports participants consistently exhibit higher levels of alcohol consumption compared to non-participants during adolescence and early adulthood; notably, this elevated consumption pattern may persist throughout the lifespan[12].A longitudinal study revealed a significant longitudinal association between team sport participation during adolescence and increased binge drinking behaviors in late adolescence and early adulthood[13].This underscores the critical importance of timely identification and intervention regarding the potential risky drinking culture within sports settings. One explanation for these elevated consumption levels lies in the \"celebratory drinking\" norms prevalent in team sports, which position alcohol as a form of social currency, thereby fostering a high level of acceptance among athletes [12]. Another possible explanation is that high alcohol consumption is frequently intertwined with aggressive behaviors and masculinity norms inherent in sports culture [27],These cultural factors may collectively reinforce the normalization and legitimization of Alcohol use within sports teams. Second, regarding traditional cigarette use, our study identified a significant protective role of sports participation. This finding aligns with the classic research of Wichstrøm and Wichstrøm[28], who observed that sports participation during adolescence serves as an effective deterrent against future tobacco use. This is largely because the immediate detrimental impact of smoking on cardiorespiratory function is fundamentally in conflict with athletes' goals of maintaining physical fitness and performance. Furthermore, coaches play a pivotal role in the physical, psychological, and social health of sports participants [29]. For instance, a study demonstrated that participation in team sports with a coach significantly reduces the risk of smoking initiation among adolescents[30]. As core managers of the sports environment, coaches not only regulate behavior by establishing strict team norms but, more crucially, the anti-smoking messages they convey during training effectively enhance adolescents' self-efficacy to refuse smoking, thereby reinforcing their smoking refusal attitudes[31]. However, it is worth noting that e-cigarette use exhibits a trend directly opposite to that of traditional cigarettes; namely, it is positively associated with sports participation. The findings of Veliz et al. [32] corroborate this, confirming that while sports participation protects adolescents from traditional cigarette use, it conversely increases the risk of e-cigarette use. Recent qualitative research provides insight into the mechanisms underlying this paradox. Interviews with adolescent athletes reveal that many harbor a \"health illusion,\" believing that e-cigarettes, unlike traditional cigarettes, do not impair sports performance or physical fitness[33].Consequently, within sports club culture, e-cigarettes have successfully circumvented the defense mechanisms associated with the notion that \"smoking damages health,\" thereby establishing themselves as a widely accepted mode of nicotine delivery. Future educational efforts must be strengthened to explicitly highlight the adverse effects of nicotine on specific health metrics, such as cardiorespiratory endurance and muscle recovery, in order to effectively curb e-cigarette use. Drug Use Behaviors Regarding marijuana use, the declining trend confirmed by this study aligns closely with the conclusions of previous systematic reviews. A systematic review of longitudinal studies confirmed that while sports participation may increase alcohol use, it is generally associated with a reduction in illicit drug use, particularly marijuana systematic review reached the same conclusion, noting a significant negative association between sports participation and illicit drug use[34]. This protective effect has been corroborated by longitudinal research focusing on adolescents. These studies found that participation in organized sports significantly reduced the rate of increase in marijuana and tobacco use during late adolescence[28]. One potential explanation for this phenomenon is that the detrimental impact of marijuana on reaction time and cardiorespiratory function conflicts with athletes' goals of optimizing performance. Another contributing factor involves strict coach supervision and institutionalized drug testing policies within sports teams, which substantially increase the social costs and the risk of detection associated with marijuana use. Previous research examining the relationship between adolescent sports and illicit drug use has predominantly suggested that sports participation serves as a protective factor. For instance, Veliz et al. [35] noted that while sports participation generally functions as a protective factor against heroin use, they explicitly identified a significant increase in the risk of heroin use among adolescents involved in high-contact sports. This stands in contrast to our findings. This discrepancy may be attributed to differences in sample size and the selection of indicators. To explain the elevated risk identified in the present study, we propose two potential explanatory mechanisms. First, pain management pathways following sports injuries may constitute a \"gateway\" to heroin use. Research by Veliz, Boyd, and McCabe indicated that adolescents involved in sports face a significantly increased risk of non-medical use of prescription opioids (NMUPO) due to frequent injuries [36].Given that heroin is pharmacologically classified as an opioid and is lower in cost, the early abuse of prescription analgesics often serves as a gateway to heroin use. A systematic review by Ekhtiari et al. further underscores the severity of this issue, reporting that the lifetime prevalence of opioid use among high school athletes is extremely high[37].Secondly, the rise in drug use may be linked to the extreme measures taken by adolescent athletes in their quest for enhanced sports performance and an ideal muscular physique.Research by Ganson et al. [38] indicates that adolescents engaging in \"muscle-enhancing behaviors\" face a higher risk of polysubstance use, a phenomenon often rooted in the excessive internalization of masculinity norms.A study examining adolescent anabolic-androgenic steroid (AAS) use revealed that team sports participants are at a higher risk of using and injecting steroids in their pursuit of muscle strength. Crucially, the study identified a significant association between steroid use and injection drug use (IDU) [39]. This implies that the extreme physical demands of competitive sports may lower adolescents' psychological barriers to injection, thereby rendering them more susceptible to high-risk behaviors associated with injection drug use. Sexual Risk Behaviors The observed rise in sexual activity aligns with previous studies. It is worth noting that the prevalence of sexual activity increased sharply among adolescents involved in three or more sports teams. Research by Habel et al. [40]found that students participating in daily sports activities had a significantly higher likelihood of engaging in sexual intercourse compared to non-participants. Miller et al.[41] provided a deeper explanation for this phenomenon through the lens of \"Jock Identity\" theory. Their study indicated that sports team participation enhances adolescents' social activity; mediated by \"dating frequency,\" this participation increases opportunities for potential sexual contact, thereby indirectly leading to a higher prevalence of sexual behavior. Regarding condom use, sports participation demonstrated a significant protective effect, highlighting athletes' advantages in health decision-making. Habel et al.[40] found that athletes had a higher likelihood of using a condom during their last sexual intercourse. The study by Savage and Holcomb, primarily focused on the female population, further corroborates this trend. Their research indicates that adolescent female athletes exhibit heightened protective awareness in sexual decision-making, with significantly higher rates of condom use during their most recent sexual intercourse compared to the general U.S. female adolescent population[42]. The underlying mechanism for this protective behavior may lie in the significant enhancement of adolescents' empowerment and self-efficacy through sports participation[43]. This self-confidence and sense of bodily control, stemming from sports training, equip adolescents with the capacity to negotiate with partners and insist on condom use during sexual decision-making, thereby resulting in a lower risk of \"unprotected sexual behavior.\" This protective effect may also be attributed to the health education function inherent in the sports environment. Existing research has confirmed that sports participation effectively enhances adolescents' HIV/AIDS prevention knowledge and reduces related stigma [44][45]. Integrating sports with sexual health education has also demonstrated immense potential in increasing condom use and reducing unintended pregnancies[46].This implies that organized sports environments serve not only as venues for socialization but also as effective vehicles for disseminating norms regarding safe sexual behavior. Implication Our findings reveal that adolescent sports team participants are subject to distinct patterns of risk exposure. Educators must prioritize addressing the significantly elevated rates of dangerous driving, alcohol abuse, and sexual risk behaviors within this population, as well as the emerging risk of illicit injection drug use among high-intensity participants. Furthermore, this study identifies a significant shift in nicotine consumption patterns: while sports participation exerts a protective effect against traditional cigarette smoking, it is associated with a contrasting upward trend in e-cigarette use. These findings underscore the need for future interventions to implement more targeted health education strategies. Currently, distracted driving has emerged as a critical public health crisis in the United States. Despite various promotional and educational initiatives launched by government agencies to heighten public awareness, data indicate that this issue remains acute among adolescents. Future intervention strategies should leverage models such as the NHTSA's \"Distraction-Free Pledge.\" This initiative, when spearheaded by coaches, integrates safe driving education into daily training routines, effectively establishing it as an internal team norm. Current evidence demonstrates that this approach significantly enhances adolescents' willingness to adhere to safe driving behaviors and reduces the prevalence of self-reported distracted driving[47]. Standing in contrast to the national decline, our findings substantiate the protective role of sports participation in promoting condom use. Notably, even among multi-team participants associated with heightened sexual activity, there was a concurrent increase in the rate of condom use. This finding suggests that policymakers should leverage the \"protective advantage\" of the sports environment, transforming it into a vehicle for sexual health education. We suggest training coaches to recognize risk indicators and to seamlessly integrate sexual health discussions into informal contexts to effectively strengthen adolescents' consciousness regarding sexual safety. Given that the athlete population has demonstrated higher compliance with condom use, schools should not view them solely as a risk group but rather as potential \"Peer Educators.\" Leveraging athletes' high social status within the adolescent subculture to position them as opinion leaders for promoting safe sexual behaviors could help reverse the current downward trend in condom use rates among adolescents. Strength and weakness This study represents one of the largest investigations to date examining the association between adolescent sports team participation and risk behaviors. A key strength lies in the utilization of a large-scale, nationally representative dataset derived from the YRBSS, spanning a 24-year period from 1999 to 2023. Furthermore, unlike previous studies that have predominantly focused on isolated risk domains (e.g., restricting analysis to substance use alone), this study adopts a holistic perspective by simultaneously examining four distinct dimensions: dangerous driving, tobacco and alcohol consumption, illicit drug use, and sexual behaviors. This approach facilitates a more nuanced understanding of the differential impacts of sports participation across various adolescent health behaviors. Notwithstanding its significance, this study is subject to several limitations. First, the cross-sectional design precludes the establishment of causal relationships. Second, the absence of subgroup analyses based on gender, socioeconomic status (SES), and sport type (e.g., contact vs. non-contact) limits our ability to explore heterogeneity within specific populations. Third, the study relies on self-reported data. Despite the implementation of anonymity, the findings remain susceptible to recall bias and social desirability bias; specifically, students may underreport sensitive risk behaviors due to privacy concerns, potentially leading to an underestimation of the results. Future direction Based on these findings, future research should advance along four key trajectories. First, to transcend mere correlational associations, researchers should employ causal inference methodologies to rigorously examine the specific impact of sports participation on risk behaviors. Second, more granular subgroup analyses are critical. Future studies should stratify data by sport type (e.g., contact vs. non-contact; individual vs. team), competitive level, gender, and socioeconomic status to uncover heterogeneous effects within the athlete population. Third, longitudinal research designs should be implemented to elucidate temporality, thereby disentangling whether sports participation confers a causal protective benefit or if observed patterns are driven by self-selection bias. Fourth, to mitigate the limitations of self-reporting, future scholarship should adopt a multi-informant approach, collecting data through diverse channels (e.g., peer, parental, or coach assessments) to triangulate findings and ensure data reliability. Conclusion Drawing on nationally representative YRBSS data from 1999 to 2023, this study demonstrates that adolescent sports team participation is associated with a mixed pattern of health risk behaviours rather than a uniformly protective effect. Greater participation was linked to higher engagement in risky driving, alcohol use, and sexual activity, while showing protective associations with cigarette smoking and marijuana use, alongside increased use of electronic cigarettes and certain high-risk drug behaviours among highly involved youth. These findings highlight the dual role of organised sport as both a developmental asset and a context of elevated risk exposure, underscoring the need for targeted, behaviour-specific prevention strategies embedded within sports settings to maximise health benefits and mitigate unintended harms. Abbreviations AAS : Anabolic-androgenic steroid AIDS : Acquired Immunodeficiency Syndrome AOR : Adjusted Odds Ratio BMI : Body Mass Index BMIPCT : Body Mass Index Percentile CDC : Centers for Disease Control and Prevention CI : Confidence Interval HIV : Human Immunodeficiency Virus IDU : Injection drug use NHTSA : National Highway Traffic Safety Administration NMUPO : Non-medical use of prescription opioids OR : Odds Ratio RSA : Responsible Service of Alcohol SD : Standard Deviation SES : Socioeconomic status YRBS : Youth Risk Behavior Survey YRBSS : Youth Risk Behavior Surveillance System Declarations Ethics approval and consent to participate This study involves the secondary analysis of publicly available data from the Youth Risk Behavior Surveillance System (YRBSS). Since the dataset is completely de-identified and contains no personally identifiable information, ethical approval or Institutional Review Board (IRB) review was not required for this study in accordance with relevant laws and regulations. The original data collection protocol was approved by the Institutional Review Board of the Centers for Disease Control and Prevention (CDC). Informed consent was obtained from all participants during the primary data collection, comprising parental permission and student assent. Consent for publication Not applicable. Availability of data and materials The datasets generated and/or analyzed during the current study are publicly available in the CDC YRBSS repository at [https://www.cdc.gov/yrbs]. Competing interests The authors declare that they have no competing interests. Funding The authors received no specific funding for this work. Authors' contributions HS: Conceptualization, Methodology, Formal analysis, Software, Writing-original draft. A: Data curation, Investigation. GL: Data curation, Investigation. JZ (corresponding author): Conceptualization, Methodology, Supervision, Project administration, Writing-review and editing. All authors contributed to the drafting of the manuscript. The authors have reviewed and endorsed the final submission. Acknowledgements The authors would like to thank the Centers for Disease Control and Prevention (CDC) for the design and management of the Youth Risk Behavior Surveillance System (YRBSS) and for making the data publicly available for research. We also extend our gratitude to all the students, teachers, and school administrators who participated in the survey and data collection process. References Bozzini AB, Bauer A, Maruyama J, Simões R, Matijasevich A: Factors associated with risk behaviors in adolescence: a systematic review. Brazilian Journal of Psychiatry 2020, 43:210–221. 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Campbell Systematic Reviews 2024, 20(1):e1381. van der Sluys M-E, Zijlmans J, Ket J, Marhe R, Popma A, Scherder EJ, van der Laan PH: The efficacy of physical activity interventions in reducing antisocial behavior: a meta-analytic review. Journal of Experimental Criminology 2024, 20(2):347–373. Ouyang N, Liu J: Effect of physical activity interventions on aggressive behaviors for children and adolescents: A systematic review and meta-analysis. Aggression and violent behavior 2023, 69:101821. Kwan M, Bobko S, Faulkner G, Donnelly P, Cairney J: Sport participation and alcohol and illicit drug use in adolescents and young adults: A systematic review of longitudinal studies. Addictive behaviors 2014, 39(3):497–506. Murray RM, Sabiston CM, Doré I, Bélanger M, O'Loughlin JL: Longitudinal associations between team sport participation and substance use in adolescents and young adults. Addictive behaviors 2021, 116:106798. Brener ND, Mpofu JJ, Krause KH, Everett Jones S, Thornton JE, Myles Z, Harris WA, Chyen D, Lim C, Arrey L et al: Overview and Methods for the Youth Risk Behavior Surveillance System - United States, 2023. MMWR Suppl 2024, 73(4):1–12. R Core Team: R: A Language and Environment for Statistical Computing. In.: R Foundation for Statistical Computing; 2025. Wickham H: ggplot2: Elegant Graphics for Data Analysis: Springer International Publishing; 2016. Martha C, Laurendeau J, Griffet J: Comparative optimism and risky road traffic behaviors among high‐risk sports practitioners. Journal of Risk Research 2010, 13(4):429–444. Mays D, Thompson NJ: Alcohol-related risk behaviors and sports participation among adolescents: An analysis of 2005 youth risk behavior survey data. Journal of Adolescent Health 2009, 44(1):87–89. Hartmann D, Massoglia M: Reassessing the Relationship Between High School Sports Participation and Deviance: Evidence of Enduring, Bifurcated Effects. The Sociological Quarterly 2007, 48(3):485–505. Mays D, Thompson N, Kushner HI, Mays II DF, Farmer D, Windle M: Sports-specific factors, perceived peer drinking, and alcohol-related behaviors among adolescents participating in school-based sports in Southwest Georgia. Addictive behaviors 2010, 35(3):235–241. Rowland B, Toumbourou J, Allen F: Drink-driving in community sports clubs: Adopting the Good Sports alcohol management program. Accident Analysis & Prevention 2012, 48:264–270. Gill SK, Shults RA, Cope JR, Cunningham TJ, Freelon B: Teen driving in rural North Dakota: A qualitative look at parental perceptions. Accident Analysis & Prevention 2013, 54:114–121. Spence H, Buckley L, Truelove V: A systematic review of the role of peer passengers on young driver behaviour. Transportation Research Part F: Traffic Psychology and Behaviour 2026, 116:103406. Ouimet MC, Pradhan AK, Brooks-Russell A, Ehsani JP, Berbiche D, Simons-Morton BG: Young drivers and their passengers: a systematic review of epidemiological studies on crash risk. Journal of Adolescent Health 2015, 57(1):S24–S35. e26. Curry AE, Mirman JH, Kallan MJ, Winston FK, Durbin DR: Peer passengers: how do they affect teen crashes? J Adolesc Health 2012, 50(6):588–594. Walczak B, Walczak A, Tricas-Sauras S, Kołodziejczyk J: Does sport participation protect adolescents from alcohol consumption? A scoping review. International journal of environmental research and public health 2023, 20(7):5417. Wichstrøm T, Wichstrøm L: Does sports participation during adolescence prevent later alcohol, tobacco and cannabis use? Addiction 2009, 104(1):138–149. Tézier B, Barros K, Geidne S, Bardid F, Grieco S, Johnson S, Kokko S, Lambe B, Lefebvre A, Lane A et al: The health promoting sports coach: theoretical background and practical guidance. BMC Sports Science, Medicine and Rehabilitation 2025, 17(1):17. Adachi-Mejia AM, Gibson Chambers JJ, Li Z, Sargent JD: The Relative Roles of Types of Extracurricular Activity on Smoking and Drinking Initiation Among Tweens. Academic Pediatrics 2014, 14(3):271–278. McGee CE, Trigwell J, Fairclough SJ, Murphy RC, Porcellato L, Ussher M, Foweather L: Effect of a sport-for-health intervention (SmokeFree Sports) on smoking-related intentions and cognitions among 9-10 year old primary school children: a controlled trial. BMC Public Health 2016, 16(1):445. Veliz P, McCabe SE, McCabe VV, Boyd CJ: Adolescent sports participation, e-cigarette use, and cigarette smoking. American journal of preventive medicine 2017, 53(5):e175–e183. Yazidjoglou A, Watts C, Joshy G, Banks E, Freeman B: The relationship between sports performance, physical activity and e-cigarette use among Australian adolescents: A qualitative study. Tobacco Induced Diseases 2025, 23:10.18332/tid/199474. Lisha NE, Sussman S: Relationship of high school and college sports participation with alcohol, tobacco, and illicit drug use: A review. Addictive Behaviors 2010, 35(5):399–407. Veliz P, Boyd CJ, McCabe SE: Nonmedical Use of Prescription Opioids and Heroin Use Among Adolescents Involved in Competitive Sports. Journal of Adolescent Health 2017, 60(3):346–349. Veliz PT, Boyd C, McCabe SE: Playing Through Pain: Sports Participation and Nonmedical Use of Opioid Medications Among Adolescents. American Journal of Public Health 2013, 103(5):e28–e30. Ekhtiari S, Yusuf I, AlMakadma Y, MacDonald A, Leroux T, Khan M: Opioid use in athletes: a systematic review. Sports health 2020, 12(6):534–539. Ganson KT, Rodgers RF, Murray SB, Nagata JM: Associations between muscle-building exercise and concurrent e-cigarette, cigarette, and cannabis use among US adolescents. PLoS one 2022, 17(12):e0278903. Schneider KE, Webb L, Boon D, Johnson RM: Adolescent Anabolic-Androgenic Steroid Use in Association with Other Drug Use, Injection Drug Use, and Team Sport Participation. J Child Adolesc Subst Abuse 2020, 29(4-6):246–251. Habel MA, Dittus PJ, De Rosa CJ, Chung EQ, Kerndt PR: Daily participation in sports and students' sexual activity. Perspectives on sexual and reproductive health 2010, 42(4):244–250. Miller KE, Farrell MP, Barnes GM, Melnick MJ, Sabo D: Gender/racial differences in jock identity, dating, and adolescent sexual risk. Journal of Youth and Adolescence 2005, 34(2):123–136. Savage MP, Holcomb DR: Adolescent female athletes' sexual risk-taking behaviors. Journal of Youth and Adolescence 1999, 28(5):595–602. Lehman SJ, Koerner SS: Adolescent women's sports involvement and sexual behavior/health: A process-level investigation. Journal of Youth and Adolescence 2004, 33(5):443–455. Goyal AK, Saini J: Sports activities as an integrative approach to overcome stigma associated with HIV/AIDS. Journal of Integrative Medicine and Research 2024, 2(3):137–142. Kaufman Z, Spencer T, Ross D: Effectiveness of sport-based HIV prevention interventions: a systematic review of the evidence. AIDS and Behavior 2013, 17(3):987–1001. Saphir MN, Salem MK, Tahir P, Devanaboyina VL, Decker M: Integrated sports and sexual and reproductive health education for young people: A global scoping review. 2023. Nicolls M, Truelove V, Stefanidis KB: Examining the impact of interventions in reducing self-reported engagement in distracted driving: A systematic review. Accident Analysis & Prevention 2024, 202:107608. Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.docx Supplementary Information Additional file 1: .docx. Table S1. Survey questions and original response options from the YRBSS. Table S2. Data availability and starting survey years for risk behavior domains. Table S3. Demographic characteristics of the study population by driving safety, tobacco and alcohol use, and sexual health behaviors (1999–2023). Table S4. Demographic characteristics of the study population by illicit drug use behaviors (1999–2023). Table S5. Operational definitions and categorization of binary outcome variables. Table S6. Adjusted predicted probabilities of risk behaviors by sports team participation frequency. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 08 Feb, 2026 Editor invited by journal 16 Jan, 2026 Editor assigned by journal 14 Jan, 2026 Submission checks completed at journal 14 Jan, 2026 First submitted to journal 12 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-8586391\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":589419456,\"identity\":\"efc34a3e-d0a5-4d78-8d1f-8eb3cc4b704a\",\"order_by\":0,\"name\":\"Hao Sun\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Wuhan Sports University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Hao\",\"middleName\":\"\",\"lastName\":\"Sun\",\"suffix\":\"\"},{\"id\":589419457,\"identity\":\"12ed07d3-f1b3-4121-a237-843b460a64e4\",\"order_by\":1,\"name\":\"Senna 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08:57:09\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1207868,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8586391/v1/85826a7f-fc07-4a03-af6b-b0f9c3fa9ab7.pdf\"},{\"id\":102538889,\"identity\":\"6f22c27a-8ea9-4593-947d-6db5cbcf792b\",\"added_by\":\"auto\",\"created_at\":\"2026-02-12 18:17:01\",\"extension\":\"docx\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":60959,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eSupplementary Information\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAdditional file 1: .docx.\\u003c/p\\u003e\\n\\u003cp\\u003eTable S1. Survey questions and original response options from the YRBSS.\\u003c/p\\u003e\\n\\u003cp\\u003eTable S2. Data availability and starting survey years for risk behavior domains.\\u003c/p\\u003e\\n\\u003cp\\u003eTable S3. Demographic characteristics of the study population by driving safety, tobacco and alcohol use, and sexual health behaviors (1999–2023).\\u003c/p\\u003e\\n\\u003cp\\u003eTable S4. Demographic characteristics of the study population by illicit drug use behaviors (1999–2023).\\u003c/p\\u003e\\n\\u003cp\\u003eTable S5. Operational definitions and categorization of binary outcome variables. Table S6. Adjusted predicted probabilities of risk behaviors by sports team participation frequency.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Additionalfile1.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8586391/v1/9788d1b4b20427c6a2f7b0a2.docx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"The associations between sports team participation and adolescent health risk behaviors in the United States\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eAdolescent risk behaviours encompass behaviours and psychological states that increase susceptibility to adverse health and developmental outcomes. These include health-compromising behaviours, such as alcohol and drug use, risky driving, violence, and unsafe sexual practices [1]. Recent evidence indicates a shift in the profile of adolescent risk behaviours. While several traditional risk behaviours, most notably cigarette smoking, alcohol consumption, and sexual activity, have declined over time [2]. For example, cigarette use among U.S. adolescents decreased markedly between 1991 and 2021, with prevalence falling from 70.1% to 17.8% [4]. Similarly, the prevalence of past-month binge drinking (\\u0026ge;\\u0026thinsp;5 drinks on one occasion) more than halved, declining from 26% in 2002 to 12% in 2016 [5].\\u003c/p\\u003e \\u003cp\\u003eFrom a life course perspective, engagement in risk-taking behaviours during adolescence is associated with adverse outcomes across multiple domains, including well-being, physical and mental health, and academic functioning [8]. Such changes may have enduring effects that extend across the life course. Accordingly, early prevention of engagement in risk behaviours is critical to reducing long-term adverse consequences.\\u003c/p\\u003e \\u003cp\\u003eSports team participation is defined as the engagement of adolescents in organized, rule-based, and typically competitive sports activities as members of a team [9]. Current research widely recognizes sports team participation as a critical protective factor for positive youth development. Its core value lies in providing a structured social environment that confers benefits across psychological (e.g., reduced depressive symptoms, enhanced self-esteem), social (e.g., improved social skills, teamwork acquisition), and physical health domains (e.g., increased physical activity levels, reduced body fat) [8]. Existing research suggests that participation in organised sports is generally associated with enhanced psychosocial functioning among adolescents and may reduce antisocial behaviour [10] and aggression [11], thereby contributing to lower levels of risk behaviours such as violence and delinquency and fostering individual resilience. However, these protective effects are neither uniform nor unconditional. Evidence from systematic reviews and national-level studies indicates that, under certain conditions\\u0026mdash;such as high training intensity, competitive pressure, and specific team cultural norms\\u0026mdash;sports and team participation may also be associated with increased engagement in risk behaviours, including alcohol misuse and other substance use[12][13]. These findings suggest that adolescents involved in sports teams may experience a distinct risk profile and, in some contexts, a potentially elevated prevalence of certain risk behaviours compared with their peers. Despite this, current research has not comprehensively characterised the prevalence of risk behaviours among adolescents participating in sports teams, nor has it clearly established whether their levels of risk behaviours differ systematically from those of non-participants.\\u003c/p\\u003e \\u003cp\\u003eTo address this research gap, this study aims to examine the association between the frequency of sports team participation and a range of adolescent risk behaviours, including dangerous driving, substance use (alcohol, tobacco, and illicit drugs), and sexual activity. The findings seek to characterise patterns of risk behaviour among adolescents.\\u003c/p\\u003e\"},{\"header\":\"Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStudy design and participants\\u003c/h2\\u003e \\u003cp\\u003e Data for this study were obtained from the Youth Risk Behavior Surveillance System (YRBSS), a national public health surveillance system established in 1991 by the Centers for Disease Control and Prevention (CDC). The YRBSS employs standardised data collection and analytic procedures to systematically monitor a broad range of health risk behaviours among adolescents. These data provide an empirical foundation for the development, implementation, and evaluation of strategies aimed at promoting youth health and preventing disease [14].\\u003c/p\\u003e \\u003cp\\u003eThe survey is conducted biennially, typically in odd-numbered years, during the spring semester (February to May). Questionnaire development and preparatory activities commence in the autumn of the preceding even-numbered year, with final datasets usually released in June of the subsequent year. Designed and administered by the Centers for Disease Control and Prevention, and approved by its Institutional Review Board, the survey employs a three-stage cluster sampling design to obtain a nationally representative sample of high school students from public, Catholic, and other non-public schools across the United States. As the YRBSS data used in this study are publicly available secondary data, the present analysis was exempt from additional ethical review in accordance with established standards for research ethics.\\u003c/p\\u003e \\u003cp\\u003eYouth Risk Behavior Survey (YRBS) Combined Datasets spanning the period from 1991 to 2023. As sports team participation was consistently assessed only from the 1999 survey cycle onward, the analytical sample utilised in this study was restricted to data collected between 1999 and 2023. The study population comprised students in grades 9 to 12, typically aged 14 to 18 years. as the measurement of non-binary gender identity was introduced in the 2021 survey cycle, these data were excluded to ensure comparability of trends across survey years.\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eMeasures\\u003c/h3\\u003e\\n\\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003ePredictors (Independent variable)\\u003c/h2\\u003e \\u003cp\\u003eThe independent variable was derived from Question 78 of the YRBS Combined Datasets. This item assessed the number of sports teams on which students participated. Participants were asked: \\\"During the past 12 months, on how many sports teams did you play? (Count any teams run by your school or community groups.)\\\" Response options included \\\"0 teams\\\", \\\"1 team\\\", \\\"2 teams\\\", and \\\"3 or more teams\\\".\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eOutcome (Dependent variable)\\u003c/h3\\u003e\\n\\u003cp\\u003eFour domains of adolescent risk behaviours were included as outcome measures in this study. First, risky driving behaviours were assessed using two items: driving after drinking alcohol (Q10) and texting while driving (Q11). Second, smoking and alcohol behaviours were measured using three items: cigarette use (Q33), electronic vapor product use (Q36), and alcohol use (Q42). Third, drug use behaviours comprised seven indicators: marijuana use (Q46), cocaine use (Q50), inhalant use (Q51), heroin use (Q52), methamphetamine use (Q53), ecstasy use (Q54), and illicit injection drug use (Q55). Finally, sexual risk behaviours were assessed using two items: history of sexual activity (Q59) and condom use at last sexual intercourse (Q61).\\u003c/p\\u003e \\u003cp\\u003eDetailed descriptions of the measurement items and the full questionnaire related to these risk behaviours are provided in Additional file 1: Table \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e. The specific survey years and data characteristics used for each risk behaviour domain are summarised in Additional file 1: Table S2.\\u003c/p\\u003e\\n\\u003ch3\\u003eCovariates\\u003c/h3\\u003e\\n\\u003cp\\u003eSociodemographic and anthropometric variables were included as covariates. Gender, age, race/ethnicity, and body mass index (BMI) were obtained via self-report using standard YRBSS items. Sex was operationalised as a binary variable (male and female) to ensure consistency across survey cycles and comparability over time. Age was categorised into seven response options reflecting single-year age groups: \\u0026le;12 years, 13 years, 14 years, 15 years, 16 years, 17 years, and \\u0026ge;\\u0026thinsp;18 years. Race/ethnicity was classified into four mutually exclusive categories: White; Black or African American; Hispanic/Latino; and All Other Races, the latter comprising respondents identifying with racial/ethnic groups not represented in the first three categories. Anthropometric status was assessed using the BMI percentile (BMIPCT), which was calculated by the CDC based on self-reported height and weight and standardised for age and sex according to U.S. growth reference charts. BMIPCT was treated as a continuous variable in the analyses to capture variation in relative body weight status across adolescence.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStatistical analysis\\u003c/h2\\u003e \\u003cp\\u003eParticipants were included if they possessed complete data for at least one risk behavior, provided that they also had complete demographic data. The specific data characteristics and years utilized for each risk behavior are presented in Additional file 1: Tables S3 and S4. All risk behaviors were analyzed as binary variables, and descriptions of the processed variables are displayed in Additional file 1: Table S5.\\u003c/p\\u003e \\u003cp\\u003eDescriptive statistics were reported for participant characteristics, measures of risk behaviors, and sports team participation. Categorical variables were presented as frequencies and percentages [n (%)], while continuous variables were reported as means and standard deviations [mean (SD)].\\u003c/p\\u003e \\u003cp\\u003eIn order to explore the associations between the sports team participation behavior and risk behaviours, an ordinal logistic model were built, with sports team participation as the predictors, and the risk behaviours (Driving,smoking and drinking, drug use, sexual activity) as the response variables, adjusted for Gender, Age, Race, BMI. Beta and 95%CI were reported for each model. The predictions were plotted for visualized comparison. Statistical significance level was set as 0.05. All the analysis were conducted with R language[15], and plotted with ggplot [16].\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eDescriptive analysis\\u003c/h2\\u003e \\u003cp\\u003eTable\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e presents the distribution of adolescent risk behaviours in the study sample. Overall, the prevalence of risky driving behaviours was relatively low for alcohol-impaired driving (3.8%), whereas texting while driving was more common, reported by nearly one-quarter of respondents (23.8%). With respect to substance use, most adolescents reported never having used cigarettes (83.7%) or electronic vapor products (78.4%); however, over one-third reported having consumed alcohol (37.8%). Marijuana use was reported by 40.3% of participants, while the prevalence of other illicit drug use remained comparatively low, with lifetime use ranging from 1.4% for injected drug use to 9.7% for inhalant use.\\u003c/p\\u003e \\u003cp\\u003eRegarding sexual behaviours, approximately one-third of adolescents (34.1%) reported having engaged in sexual activity in the past three months. Among those who were sexually active, condom use at last sexual intercourse was reported by fewer than one-third of respondents (28.9%), indicating a substantial proportion of adolescents engaging in unprotected sex.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eDistribution of Adolescent Risk Behaviors in the Study Sample\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"3\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eName\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLevels\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eStats\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eDrinking-and-driving\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNo driving or 0 times\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e55295 (96.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAlcohol-impaired driving\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2195 (3.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eTexting-while-driving\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNo driving or 0 times\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e43784 (76.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTexting while driving\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e13706 (23.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eCigarette use\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNever\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e118013 (83.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eEver\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e22920 (16.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eElectronic vapor use\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNever used\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e37765 (78.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eEver used\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e10431 (21.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eAlcohol use\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNever\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e87656 (62.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eEver\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e53277 (37.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eMarijuana use\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNever used\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e90919 (59.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eEver used\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e61274 (40.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eCocaine use\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNever used\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e141674 (93.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eEver used\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e9468 (6.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eInhalant use\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNever used\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e136265 (90.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eEver used\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e14687 (9.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eHeroin use\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNever used\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e151014 (98.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eEver used\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2819 (1.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eMethamphetamine use\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNever used\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e147490 (95.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eEver used\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6343 (4.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eEcstasy use\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNever used\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e129741 (94.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eEver used\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e8078 (5.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eIllegal injected drug use\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNever used\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e148764 (98.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eEver used\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2188 (1.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eSexual activity (past 3 months)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNo partnered sex\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e95009 (65.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHad sex\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e49226 (34.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eCondom use at last sexual intercourse\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNo condom use\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e102610 (71.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eUsed condom\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e41625 (28.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eAssociations between driving risk behaviours and sports team participation\\u003c/h2\\u003e \\u003cp\\u003eFigure \\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e illustrates the associations between sports team participation frequency and driving-related risk behaviours. Corresponding odds ratios (ORs) for these behaviours are presented in Additional file 1: Table S6. Overall, the likelihood of engaging in driving risk behaviours increased with a greater number of sports teams participated in.\\u003c/p\\u003e \\u003cp\\u003eAmong the driving-related outcomes, texting while driving demonstrated the most pronounced increase across levels of sports team participation. Approximately 12% of adolescents who did not participate in any sports teams reported texting while driving, compared with 26% among those who participated in three or more sports teams (Additional file 1: Table S6).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eAssociations between smoking and alcohol behaviour and sports team participation\\u003c/h2\\u003e \\u003cp\\u003eFigure \\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e illustrates the associations between sports team participation frequency and substance use behaviours, including cigarette use, electronic vapor product use, and alcohol consumption. The corresponding odds ratios (ORs) for these outcomes are presented in Additional file 1: Table S6.\\u003c/p\\u003e \\u003cp\\u003eOverall, alcohol consumption and electronic vapor use increased with a higher number of sports teams participated in, whereas cigarette use decreased as sports team participation increased. Among these behaviours, alcohol consumption exhibited the largest increase. Adolescents who never participated in sports teams had a substantially lower prevalence of alcohol use (32.7%, 95% CI: 32.3%\\u0026ndash;33.1%) compared with those who participated in three or more sports teams (36.6%, 95% CI: 35.8%\\u0026ndash;37.3%; Additional file 1: Table S6).\\u003c/p\\u003e \\u003cp\\u003eIn contrast, cigarette use showed the most pronounced decrease across levels of sports team participation. 13.8% of adolescents who did not participate in any sports teams reported cigarette use, whereas this proportion declined to 8.9% among those who participated in three or more sports teams (Additional file 1: Table S6).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eAssociations between drug use behaviors and sports team participation\\u003c/h2\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eFigure \\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e illustrates the associations between sports team participation frequency and drug use behaviours. The corresponding odds ratios (ORs) are presented in Additional file 1: Table S6. With the exception of heroin use and illicit injection drug use, most drug use behaviours demonstrated a decreasing trend as the number of sports teams participated in increased.\\u003c/p\\u003e \\u003cp\\u003eMarijuana use was the most prevalent drug-related risk behaviour across all participation levels and exhibited the largest absolute decline. Approximately 40.6% of adolescents who did not participate in any sports teams reported marijuana use, compared with 37.4% among those who participated in three or more sports teams (Additional file 1: Table S6).\\u003c/p\\u003e \\u003cp\\u003eIn contrast, illicit injection drug use showed the most pronounced increase with greater sports team participation. The prevalence of illicit injection drug use was lower among adolescents who never participated in sports teams (1.3%, 95% CI: 1.2%\\u0026ndash;1.4%) than among those who participated in three or more sports teams (2.0%, 95% CI: 1.8%\\u0026ndash;2.3%; Additional file 1: Table S6).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eAssociations between sexual risk behavior and sports team participation\\u003c/h2\\u003e \\u003cp\\u003eFigure \\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e illustrates the associations between sports team participation frequency and sexual risk behaviours. The corresponding odds ratios (ORs) are presented in Additional file 1: Table S6. Overall, sexual risk behaviours increased with a higher number of sports teams participated in.\\u003c/p\\u003e \\u003cp\\u003eAmong the sexual risk outcomes examined, recent sexual activity demonstrated the most pronounced increase. Approximately 31.2% of adolescents who did not participate in any sports teams reported having sexual intercourse in the past three months, compared with 36.8% among those who participated in three or more sports teams However, regarding protective behaviors, high-frequency participants demonstrated higher compliance with condom use at last sexual intercourse, with the prevalence increasing from 25.2% (95% CI: 24.8%\\u0026ndash;25.6%) in non-participants to 32.4% (95% CI: 31.6%\\u0026ndash;33.1%) in those participating in three or more sports teams Additional file 1: Table S6).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eOverview of finding\\u003c/h2\\u003e \\u003cp\\u003eOur study examined the association between the frequency of sports team participation and engagement in health risk behaviours among adolescents. The findings indicate that higher levels of sports team participation were positively associated with risky driving behaviours (texting while driving and drinking and driving), alcohol use, and sexual risk behaviours. In contrast, sports team participation were negatively associated with marijuana use and cigarette smoking.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec17\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eRisky Driving Behaviors\\u003c/h2\\u003e \\u003cp\\u003eThis study identified a significant positive association between the frequency of sports team participation and risky driving behaviours among adolescents. One plausible explanation for the observed increase in texting while driving involves social and psychological mechanisms associated with sports team participation.\\u003c/p\\u003e \\u003cp\\u003eFirst, the strong social cohesion and intensive peer networks characteristic of sports teams may heighten adolescents\\u0026rsquo; perceived need for constant communication, potentially extending into contexts where such behaviour is unsafe, including while driving. Second, psychological factors may also contribute. Previous research has documented that athletes often demonstrate \\u003cem\\u003ecomparative optimism\\u003c/em\\u003e, a cognitive bias characterised by an inflated perception of one\\u0026rsquo;s own skills and invulnerability to risk. As described by Martha, Laurendeau, and Griffet [17], this heightened confidence in personal reflexes and driving ability may lead adolescent athletes to underestimate the dangers of distracted driving, such as texting while driving, and to overestimate their capacity to manage multiple tasks simultaneously.\\u003c/p\\u003e \\u003cp\\u003eConsistent with prior research ur findings indicate that sports participation is associated with a higher prevalence of drinking and driving behaviours among adolescents. Previous studies have similarly reported that features of the team environment, including social norms and alcohol-centred bonding practices, may contribute to elevated alcohol-related risk behaviours among sports participants [20]. This increased risk does not appear to be inevitable or unmodifiable.\\u003c/p\\u003e \\u003cp\\u003eEvidence suggests that structured, institution-level interventions can substantially mitigate drink-driving risk within sports settings. For example, Rowland, Toumbourou, and Allen [21]\\u003c/p\\u003e \\u003cp\\u003edemonstrated that the implementation of the Good Sports program was associated with an 8% reduction per season in the odds of drink-driving among club members. This program employs a rigorous tiered accreditation framework designed to reshape the alcohol environment within sports clubs. Key components include mandatory Responsible Service of Alcohol (RSA) practices to prevent sales to minors and intoxicated individuals, restrictions on alcohol promotions that encourage high-risk consumption, and the provision of safe transport strategies such as key-deposit schemes, taxi vouchers, and incentives for designated drivers. In addition, participating clubs are required to develop formal written policies that embed these practices into club governance structures, thereby establishing sustained institutional constraints on risky drinking behaviours.\\u003c/p\\u003e \\u003cp\\u003eFurthermore, the observed increase in risky driving behaviours may also be partly attributable to greater driving exposure associated with sports team participation. For example, qualitative research involving parents in rural communities has highlighted that adolescents often travel considerable distances to attend away games and training sessions. This increased driving frequency and cumulative mileage, necessitated by sports participation, may objectively elevate the risk of traffic incidents [22].\\u003c/p\\u003e \\u003cp\\u003eIn addition, the \\u003cem\\u003epeer passenger effect\\u003c/em\\u003e may represent another important contributing mechanism. Adolescents involved in sports teams frequently transport teammates or participate in carpooling arrangements for training and competitions. A substantial body of evidence indicates that the presence of peer passengers is associated with increased engagement in risky driving behaviours among young drivers [23][24]. In such contexts, interactions with peers\\u0026mdash;such as discussions about training or upcoming matches\\u0026mdash;may distract drivers and impair attention, thereby increasing crash risk [25].\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec18\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eCigarette Smoking and Alcohol Use\\u003c/h2\\u003e \\u003cp\\u003eOur study identified a significant positive association between the frequency of sports team participation and the prevalence of alcohol use and e-cigarette use among adolescents. Conversely, a significant inverse association was found with the prevalence of cigarette use.[26]. For instance, a systematic review indicated that sports participants consistently exhibit higher levels of alcohol consumption compared to non-participants during adolescence and early adulthood; notably, this elevated consumption pattern may persist throughout the lifespan[12].A longitudinal study revealed a significant longitudinal association between team sport participation during adolescence and increased binge drinking behaviors in late adolescence and early adulthood[13].This underscores the critical importance of timely identification and intervention regarding the potential risky drinking culture within sports settings. One explanation for these elevated consumption levels lies in the \\\"celebratory drinking\\\" norms prevalent in team sports, which position alcohol as a form of social currency, thereby fostering a high level of acceptance among athletes [12]. Another possible explanation is that high alcohol consumption is frequently intertwined with aggressive behaviors and masculinity norms inherent in sports culture\\u003c/p\\u003e \\u003cp\\u003e[27],These cultural factors may collectively reinforce the normalization and legitimization of Alcohol use within sports teams.\\u003c/p\\u003e \\u003cp\\u003eSecond, regarding traditional cigarette use, our study identified a significant protective role of sports participation. This finding aligns with the classic research of Wichstr\\u0026oslash;m and Wichstr\\u0026oslash;m[28], who observed that sports participation during adolescence serves as an effective deterrent against future tobacco use. This is largely because the immediate detrimental impact of smoking on cardiorespiratory function is fundamentally in conflict with athletes' goals of maintaining physical fitness and performance. Furthermore, coaches play a pivotal role in the physical, psychological, and social health of sports participants [29]. For instance, a study demonstrated that participation in team sports with a coach significantly reduces the risk of smoking initiation among adolescents[30]. As core managers of the sports environment, coaches not only regulate behavior by establishing strict team norms but, more crucially, the anti-smoking messages they convey during training effectively enhance adolescents' self-efficacy to refuse smoking, thereby reinforcing their smoking refusal attitudes[31].\\u003c/p\\u003e \\u003cp\\u003eHowever, it is worth noting that e-cigarette use exhibits a trend directly opposite to that of traditional cigarettes; namely, it is positively associated with sports participation. The findings of Veliz et al. [32] corroborate this, confirming that while sports participation protects adolescents from traditional cigarette use, it conversely increases the risk of e-cigarette use. Recent qualitative research provides insight into the mechanisms underlying this paradox. Interviews with adolescent athletes reveal that many harbor a \\\"health illusion,\\\" believing that e-cigarettes, unlike traditional cigarettes, do not impair sports performance or physical fitness[33].Consequently, within sports club culture, e-cigarettes have successfully circumvented the defense mechanisms associated with the notion that \\\"smoking damages health,\\\" thereby establishing themselves as a widely accepted mode of nicotine delivery. Future educational efforts must be strengthened to explicitly highlight the adverse effects of nicotine on specific health metrics, such as cardiorespiratory endurance and muscle recovery, in order to effectively curb e-cigarette use.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec19\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eDrug Use Behaviors\\u003c/h2\\u003e \\u003cp\\u003eRegarding marijuana use, the declining trend confirmed by this study aligns closely with the conclusions of previous systematic reviews. A systematic review of longitudinal studies confirmed that while sports participation may increase alcohol use, it is generally associated with a reduction in illicit drug use, particularly marijuana systematic review reached the same conclusion, noting a significant negative association between sports participation and illicit drug use[34]. This protective effect has been corroborated by longitudinal research focusing on adolescents. These studies found that participation in organized sports significantly reduced the rate of increase in marijuana and tobacco use during late adolescence[28]. One potential explanation for this phenomenon is that the detrimental impact of marijuana on reaction time and cardiorespiratory function conflicts with athletes' goals of optimizing performance. Another contributing factor involves strict coach supervision and institutionalized drug testing policies within sports teams, which substantially increase the social costs and the risk of detection associated with marijuana use.\\u003c/p\\u003e \\u003cp\\u003ePrevious research examining the relationship between adolescent sports and illicit drug use has predominantly suggested that sports participation serves as a protective factor. For instance, Veliz et al. [35] noted that while sports participation generally functions as a protective factor against heroin use, they explicitly identified a significant increase in the risk of heroin use among adolescents involved in high-contact sports. This stands in contrast to our findings. This discrepancy may be attributed to differences in sample size and the selection of indicators. To explain the elevated risk identified in the present study, we propose two potential explanatory mechanisms. First, pain management pathways following sports injuries may constitute a \\\"gateway\\\" to heroin use. Research by Veliz, Boyd, and McCabe indicated that adolescents involved in sports face a significantly increased risk of non-medical use of prescription opioids (NMUPO) due to frequent injuries [36].Given that heroin is pharmacologically classified as an opioid and is lower in cost, the early abuse of prescription analgesics often serves as a gateway to heroin use. A systematic review by Ekhtiari et al. further underscores the severity of this issue, reporting that the lifetime prevalence of opioid use among high school athletes is extremely high[37].Secondly, the rise in drug use may be linked to the extreme measures taken by adolescent athletes in their quest for enhanced sports performance and an ideal muscular physique.Research by Ganson et al. [38] indicates that adolescents engaging in \\\"muscle-enhancing behaviors\\\" face a higher risk of polysubstance use, a phenomenon often rooted in the excessive internalization of masculinity norms.A study examining adolescent anabolic-androgenic steroid (AAS) use revealed that team sports participants are at a higher risk of using and injecting steroids in their pursuit of muscle strength. Crucially, the study identified a significant association between steroid use and injection drug use (IDU) [39]. This implies that the extreme physical demands of competitive sports may lower adolescents' psychological barriers to injection, thereby rendering them more susceptible to high-risk behaviors associated with injection drug use.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec20\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eSexual Risk Behaviors\\u003c/h2\\u003e \\u003cp\\u003eThe observed rise in sexual activity aligns with previous studies. It is worth noting that the prevalence of sexual activity increased sharply among adolescents involved in three or more sports teams. Research by Habel et al. [40]found that students participating in daily sports activities had a significantly higher likelihood of engaging in sexual intercourse compared to non-participants. Miller et al.[41] provided a deeper explanation for this phenomenon through the lens of \\\"Jock Identity\\\" theory. Their study indicated that sports team participation enhances adolescents' social activity; mediated by \\\"dating frequency,\\\" this participation increases opportunities for potential sexual contact, thereby indirectly leading to a higher prevalence of sexual behavior.\\u003c/p\\u003e \\u003cp\\u003eRegarding condom use, sports participation demonstrated a significant protective effect, highlighting athletes' advantages in health decision-making. Habel et al.[40] found that athletes had a higher likelihood of using a condom during their last sexual intercourse. The study by Savage and Holcomb, primarily focused on the female population, further corroborates this trend. Their research indicates that adolescent female athletes exhibit heightened protective awareness in sexual decision-making, with significantly higher rates of condom use during their most recent sexual intercourse compared to the general U.S. female adolescent population[42]. The underlying mechanism for this protective behavior may lie in the significant enhancement of adolescents' empowerment and self-efficacy through sports participation[43]. This self-confidence and sense of bodily control, stemming from sports training, equip adolescents with the capacity to negotiate with partners and insist on condom use during sexual decision-making, thereby resulting in a lower risk of \\\"unprotected sexual behavior.\\\" This protective effect may also be attributed to the health education function inherent in the sports environment. Existing research has confirmed that sports participation effectively enhances adolescents' HIV/AIDS prevention knowledge and reduces related stigma [44][45]. Integrating sports with sexual health education has also demonstrated immense potential in increasing condom use and reducing unintended pregnancies[46].This implies that organized sports environments serve not only as venues for socialization but also as effective vehicles for disseminating norms regarding safe sexual behavior.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec21\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eImplication\\u003c/h2\\u003e \\u003cp\\u003eOur findings reveal that adolescent sports team participants are subject to distinct patterns of risk exposure. Educators must prioritize addressing the significantly elevated rates of dangerous driving, alcohol abuse, and sexual risk behaviors within this population, as well as the emerging risk of illicit injection drug use among high-intensity participants. Furthermore, this study identifies a significant shift in nicotine consumption patterns: while sports participation exerts a protective effect against traditional cigarette smoking, it is associated with a contrasting upward trend in e-cigarette use. These findings underscore the need for future interventions to implement more targeted health education strategies.\\u003c/p\\u003e \\u003cp\\u003eCurrently, distracted driving has emerged as a critical public health crisis in the United States. Despite various promotional and educational initiatives launched by government agencies to heighten public awareness, data indicate that this issue remains acute among adolescents. Future intervention strategies should leverage models such as the NHTSA's \\\"Distraction-Free Pledge.\\\" This initiative, when spearheaded by coaches, integrates safe driving education into daily training routines, effectively establishing it as an internal team norm. Current evidence demonstrates that this approach significantly enhances adolescents' willingness to adhere to safe driving behaviors and reduces the prevalence of self-reported distracted driving[47].\\u003c/p\\u003e \\u003cp\\u003eStanding in contrast to the national decline, our findings substantiate the protective role of sports participation in promoting condom use. Notably, even among multi-team participants associated with heightened sexual activity, there was a concurrent increase in the rate of condom use. This finding suggests that policymakers should leverage the \\\"protective advantage\\\" of the sports environment, transforming it into a vehicle for sexual health education. We suggest training coaches to recognize risk indicators and to seamlessly integrate sexual health discussions into informal contexts to effectively strengthen adolescents' consciousness regarding sexual safety. Given that the athlete population has demonstrated higher compliance with condom use, schools should not view them solely as a risk group but rather as potential \\\"Peer Educators.\\\" Leveraging athletes' high social status within the adolescent subculture to position them as opinion leaders for promoting safe sexual behaviors could help reverse the current downward trend in condom use rates among adolescents.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec22\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStrength and weakness\\u003c/h2\\u003e \\u003cp\\u003eThis study represents one of the largest investigations to date examining the association between adolescent sports team participation and risk behaviors. A key strength lies in the utilization of a large-scale, nationally representative dataset derived from the YRBSS, spanning a 24-year period from 1999 to 2023. Furthermore, unlike previous studies that have predominantly focused on isolated risk domains (e.g., restricting analysis to substance use alone), this study adopts a holistic perspective by simultaneously examining four distinct dimensions: dangerous driving, tobacco and alcohol consumption, illicit drug use, and sexual behaviors. This approach facilitates a more nuanced understanding of the differential impacts of sports participation across various adolescent health behaviors.\\u003c/p\\u003e \\u003cp\\u003eNotwithstanding its significance, this study is subject to several limitations. First, the cross-sectional design precludes the establishment of causal relationships. Second, the absence of subgroup analyses based on gender, socioeconomic status (SES), and sport type (e.g., contact vs. non-contact) limits our ability to explore heterogeneity within specific populations. Third, the study relies on self-reported data. Despite the implementation of anonymity, the findings remain susceptible to recall bias and social desirability bias; specifically, students may underreport sensitive risk behaviors due to privacy concerns, potentially leading to an underestimation of the results.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec23\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003eFuture direction\\u003c/h2\\u003e \\u003cp\\u003eBased on these findings, future research should advance along four key trajectories. First, to transcend mere correlational associations, researchers should employ causal inference methodologies to rigorously examine the specific impact of sports participation on risk behaviors. Second, more granular subgroup analyses are critical. Future studies should stratify data by sport type (e.g., contact vs. non-contact; individual vs. team), competitive level, gender, and socioeconomic status to uncover heterogeneous effects within the athlete population. Third, longitudinal research designs should be implemented to elucidate temporality, thereby disentangling whether sports participation confers a causal protective benefit or if observed patterns are driven by self-selection bias. Fourth, to mitigate the limitations of self-reporting, future scholarship should adopt a multi-informant approach, collecting data through diverse channels (e.g., peer, parental, or coach assessments) to triangulate findings and ensure data reliability.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eDrawing on nationally representative YRBSS data from 1999 to 2023, this study demonstrates that adolescent sports team participation is associated with a mixed pattern of health risk behaviours rather than a uniformly protective effect. Greater participation was linked to higher engagement in risky driving, alcohol use, and sexual activity, while showing protective associations with cigarette smoking and marijuana use, alongside increased use of electronic cigarettes and certain high-risk drug behaviours among highly involved youth. These findings highlight the dual role of organised sport as both a developmental asset and a context of elevated risk exposure, underscoring the need for targeted, behaviour-specific prevention strategies embedded within sports settings to maximise health benefits and mitigate unintended harms.\\u003c/p\\u003e\"},{\"header\":\"Abbreviations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eAAS\\u003c/strong\\u003e: Anabolic-androgenic steroid\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAIDS\\u003c/strong\\u003e: Acquired Immunodeficiency Syndrome\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAOR\\u003c/strong\\u003e: Adjusted Odds Ratio\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eBMI\\u003c/strong\\u003e: Body Mass Index\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eBMIPCT\\u003c/strong\\u003e: Body Mass Index Percentile\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCDC\\u003c/strong\\u003e: Centers for Disease Control and Prevention\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCI\\u003c/strong\\u003e: Confidence Interval\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eHIV\\u003c/strong\\u003e: Human Immunodeficiency Virus\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eIDU\\u003c/strong\\u003e: Injection drug use\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eNHTSA\\u003c/strong\\u003e: National Highway Traffic Safety Administration\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eNMUPO\\u003c/strong\\u003e: Non-medical use of prescription opioids\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eOR\\u003c/strong\\u003e: Odds Ratio\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eRSA\\u003c/strong\\u003e: Responsible Service of Alcohol\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eSD\\u003c/strong\\u003e: Standard Deviation\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eSES\\u003c/strong\\u003e: Socioeconomic status\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eYRBS\\u003c/strong\\u003e: Youth Risk Behavior Survey\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eYRBSS\\u003c/strong\\u003e: Youth Risk Behavior Surveillance System\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eEthics approval and consent to participate\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis study involves the secondary analysis of publicly available data from the Youth Risk Behavior Surveillance System (YRBSS). Since the dataset is completely de-identified and contains no personally identifiable information, ethical approval or Institutional Review Board (IRB) review was not required for this study in accordance with relevant laws and regulations. The original data collection protocol was approved by the Institutional Review Board of the Centers for Disease Control and Prevention (CDC). Informed consent was obtained from all participants during the primary data collection, comprising parental permission and student assent.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAvailability of data and materials\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe datasets generated and/or analyzed during the current study are publicly available in the CDC YRBSS repository at [https://www.cdc.gov/yrbs].\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare that they have no competing interests.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors received no specific funding for this work.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthors' contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eHS: Conceptualization, Methodology, Formal analysis, Software, Writing-original draft. A: Data curation, Investigation. GL: Data curation, Investigation. JZ (corresponding author): Conceptualization, Methodology, Supervision, Project administration, Writing-review and editing. All authors contributed to the drafting of the manuscript. The authors have reviewed and endorsed the final submission.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors would like to thank the Centers for Disease Control and Prevention (CDC) for the design and management of the Youth Risk Behavior Surveillance System (YRBSS) and for making the data publicly available for research. We also extend our gratitude to all the students, teachers, and school administrators who participated in the survey and data collection process.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eBozzini AB, Bauer A, Maruyama J, Sim\\u0026otilde;es R, Matijasevich A: Factors associated with risk behaviors in adolescence: a systematic review. Brazilian Journal of Psychiatry 2020, 43:210\\u0026ndash;221.\\u003c/li\\u003e\\n\\u003cli\\u003eBall J, Grucza R, Livingston M, Ter Bogt T, Currie C, de Looze M: The great decline in adolescent risk behaviours: Unitary trend, separate trends, or cascade? Social science \\u0026amp; medicine 2023, 317:115616.\\u003c/li\\u003e\\n\\u003cli\\u003eMytton OT, Donaldson L, Goddings A-L, Mathews G, Ward JL, Greaves F, Viner RM: Changing patterns of health risk in adolescence: implications for health policy. 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Addictive behaviors 2010, 35(3):235\\u0026ndash;241.\\u003c/li\\u003e\\n\\u003cli\\u003eRowland B, Toumbourou J, Allen F: Drink-driving in community sports clubs: Adopting the Good Sports alcohol management program. Accident Analysis \\u0026amp; Prevention 2012, 48:264\\u0026ndash;270.\\u003c/li\\u003e\\n\\u003cli\\u003eGill SK, Shults RA, Cope JR, Cunningham TJ, Freelon B: Teen driving in rural North Dakota: A qualitative look at parental perceptions. Accident Analysis \\u0026amp; Prevention 2013, 54:114\\u0026ndash;121.\\u003c/li\\u003e\\n\\u003cli\\u003eSpence H, Buckley L, Truelove V: A systematic review of the role of peer passengers on young driver behaviour. Transportation Research Part F: Traffic Psychology and Behaviour 2026, 116:103406.\\u003c/li\\u003e\\n\\u003cli\\u003eOuimet MC, Pradhan AK, Brooks-Russell A, Ehsani JP, Berbiche D, Simons-Morton BG: Young drivers and their passengers: a systematic review of epidemiological studies on crash risk. Journal of Adolescent Health 2015, 57(1):S24\\u0026ndash;S35. e26.\\u003c/li\\u003e\\n\\u003cli\\u003eCurry AE, Mirman JH, Kallan MJ, Winston FK, Durbin DR: Peer passengers: how do they affect teen crashes? J Adolesc Health 2012, 50(6):588\\u0026ndash;594.\\u003c/li\\u003e\\n\\u003cli\\u003eWalczak B, Walczak A, Tricas-Sauras S, Kołodziejczyk J: Does sport participation protect adolescents from alcohol consumption? A scoping review. International journal of environmental research and public health 2023, 20(7):5417.\\u003c/li\\u003e\\n\\u003cli\\u003eWichstr\\u0026oslash;m T, Wichstr\\u0026oslash;m L: Does sports participation during adolescence prevent later alcohol, tobacco and cannabis use? Addiction 2009, 104(1):138\\u0026ndash;149.\\u003c/li\\u003e\\n\\u003cli\\u003eT\\u0026eacute;zier B, Barros K, Geidne S, Bardid F, Grieco S, Johnson S, Kokko S, Lambe B, Lefebvre A, Lane A et al: The health promoting sports coach: theoretical background and practical guidance. BMC Sports Science, Medicine and Rehabilitation 2025, 17(1):17.\\u003c/li\\u003e\\n\\u003cli\\u003eAdachi-Mejia AM, Gibson Chambers JJ, Li Z, Sargent JD: The Relative Roles of Types of Extracurricular Activity on Smoking and Drinking Initiation Among Tweens. Academic Pediatrics 2014, 14(3):271\\u0026ndash;278.\\u003c/li\\u003e\\n\\u003cli\\u003eMcGee CE, Trigwell J, Fairclough SJ, Murphy RC, Porcellato L, Ussher M, Foweather L: Effect of a sport-for-health intervention (SmokeFree Sports) on smoking-related intentions and cognitions among 9-10 year old primary school children: a controlled trial. BMC Public Health 2016, 16(1):445.\\u003c/li\\u003e\\n\\u003cli\\u003eVeliz P, McCabe SE, McCabe VV, Boyd CJ: Adolescent sports participation, e-cigarette use, and cigarette smoking. American journal of preventive medicine 2017, 53(5):e175\\u0026ndash;e183.\\u003c/li\\u003e\\n\\u003cli\\u003eYazidjoglou A, Watts C, Joshy G, Banks E, Freeman B: The relationship between sports performance, physical activity and e-cigarette use among Australian adolescents: A qualitative study. Tobacco Induced Diseases 2025, 23:10.18332/tid/199474.\\u003c/li\\u003e\\n\\u003cli\\u003eLisha NE, Sussman S: Relationship of high school and college sports participation with alcohol, tobacco, and illicit drug use: A review. Addictive Behaviors 2010, 35(5):399\\u0026ndash;407.\\u003c/li\\u003e\\n\\u003cli\\u003eVeliz P, Boyd CJ, McCabe SE: Nonmedical Use of Prescription Opioids and Heroin Use Among Adolescents Involved in Competitive Sports. Journal of Adolescent Health 2017, 60(3):346\\u0026ndash;349.\\u003c/li\\u003e\\n\\u003cli\\u003eVeliz PT, Boyd C, McCabe SE: Playing Through Pain: Sports Participation and Nonmedical Use of Opioid Medications Among Adolescents. American Journal of Public Health 2013, 103(5):e28\\u0026ndash;e30.\\u003c/li\\u003e\\n\\u003cli\\u003eEkhtiari S, Yusuf I, AlMakadma Y, MacDonald A, Leroux T, Khan M: Opioid use in athletes: a systematic review. Sports health 2020, 12(6):534\\u0026ndash;539.\\u003c/li\\u003e\\n\\u003cli\\u003eGanson KT, Rodgers RF, Murray SB, Nagata JM: Associations between muscle-building exercise and concurrent e-cigarette, cigarette, and cannabis use among US adolescents. PLoS one 2022, 17(12):e0278903.\\u003c/li\\u003e\\n\\u003cli\\u003eSchneider KE, Webb L, Boon D, Johnson RM: Adolescent Anabolic-Androgenic Steroid Use in Association with Other Drug Use, Injection Drug Use, and Team Sport Participation. J Child Adolesc Subst Abuse 2020, 29(4-6):246\\u0026ndash;251.\\u003c/li\\u003e\\n\\u003cli\\u003eHabel MA, Dittus PJ, De Rosa CJ, Chung EQ, Kerndt PR: Daily participation in sports and students\\u0026apos; sexual activity. 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Journal of Integrative Medicine and Research 2024, 2(3):137\\u0026ndash;142.\\u003c/li\\u003e\\n\\u003cli\\u003eKaufman Z, Spencer T, Ross D: Effectiveness of sport-based HIV prevention interventions: a systematic review of the evidence. AIDS and Behavior 2013, 17(3):987\\u0026ndash;1001.\\u003c/li\\u003e\\n\\u003cli\\u003eSaphir MN, Salem MK, Tahir P, Devanaboyina VL, Decker M: Integrated sports and sexual and reproductive health education for young people: A global scoping review. 2023.\\u003c/li\\u003e\\n\\u003cli\\u003eNicolls M, Truelove V, Stefanidis KB: Examining the impact of interventions in reducing self-reported engagement in distracted driving: A systematic review. Accident Analysis \\u0026amp; Prevention 2024, 202:107608.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-public-health\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"pubh\",\"sideBox\":\"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/pubh/default.aspx\",\"title\":\"BMC Public Health\",\"twitterHandle\":\"@BMC_series\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Adolescents, Sports team participation, Health risk behaviors, YRBSS\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-8586391/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-8586391/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eBackground\\u003c/h2\\u003e \\u003cp\\u003eSports team participation is widely regarded as a protective factor for adolescent development. However, current evidence remains inconsistent regarding its association with specific health risk behaviors. This study aimed to characterize the divergent patterns of substance use, risky driving, and sexual behaviors among U.S. adolescents participating in sports teams over a 24-year period.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e \\u003cp\\u003eData from the Youth Risk Behavior Surveillance System (YRBSS) Combined Datasets (1999\\u0026ndash;2023) were analyzed, comprising a nationally representative sample of high school students (Grades 9\\u0026ndash;12). The primary exposure was the frequency of sports team participation. Multivariable logistic regression models were utilized to estimate for four domains of risk behaviors (driving, substance use, illicit drug use, and sexual activity), adjusting for covariates such as gender, age, race/ethnicity, and BMI percentile. Results were expressed as predictions with 95% confidence intervals (95% CIs).\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e \\u003cp\\u003eSports team participation was significantly negatively associated with cigarette smoking and most illicit drug use behaviors. In contrast, it was significantly associated with higher rates of texting while driving and alcohol consumption. With respect to sexual behavior, students who participated in sports teams were more likely to report recent sexual activity; however, they also demonstrated significantly higher compliance with condom use.\\u003c/p\\u003e\\u003ch2\\u003eConclusions\\u003c/h2\\u003e \\u003cp\\u003eSports team participation shows distinct associations with adolescent risk behaviors, highlighting the potential importance of interventions. Greater participation was linked to higher engagement in risky driving, alcohol use, and sexual activity, while showing protective associations with cigarette smoking and the majority of illicit drug use, and exhibiting higher compliance with condom use. These findings underscore the necessity of integrating targeted, behavior-specific prevention strategies into sports settings to maximize health benefits while mitigating unintended harms.\\u003c/p\\u003e\",\"manuscriptTitle\":\"The associations between sports team participation and adolescent health risk behaviors in the United States\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2026-02-12 18:16:56\",\"doi\":\"10.21203/rs.3.rs-8586391/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2026-02-09T03:52:56+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvited\",\"content\":\"\",\"date\":\"2026-01-16T05:38:39+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2026-01-14T06:27:26+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2026-01-14T06:27:08+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"BMC Public Health\",\"date\":\"2026-01-13T02:18:08+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-public-health\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"pubh\",\"sideBox\":\"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/pubh/default.aspx\",\"title\":\"BMC Public Health\",\"twitterHandle\":\"@BMC_series\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"23dca139-5ef5-4ad2-ae5f-b6947e14a528\",\"owner\":[],\"postedDate\":\"February 12th, 2026\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"under-review\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-02-12T18:16:56+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2026-02-12 18:16:56\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-8586391\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-8586391\",\"identity\":\"rs-8586391\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}