The Journal of School Nursing2024, Vol. 40(3) 316–328© The Author(s) 2022Article reuse guidelines:sagepub.com/journals-permissionsDOI: 10.1177/10598405221091481journals.sagepub.com/home/jsn
Adolescent behavior now occurs offline and online. Frequently studied and treated independently, the relationship between offline problem behaviors and online risk taking is not well understood. This study asked whether there are any problematic behaviors predictive of online risk taking by high school students. Using a 2009 dataset of 2,077 high school students grades 9–12, five areas of offline problematic behaviors were examined: Academic problems, anxiety, behavioral wrongdoing, bullying, and social-emotional. Nine binary results were classified as online risk: Sexting, online harassment (perpetrating and experiencing), visiting sex sites, talking about sex, receiving sexual pictures, meeting offline, anything sexual happened, feeling nervous or uncomfortable. Behavioral wrongdoing (fighting, school suspension, trouble with police, theft), emerged as a significant predictor appearing in all nine models, followed by bullying experience (bully or victim) in six models. Identifying common problem behaviors that predict online risk taking are key components in developing strategies to promote adolescent health and well-being.
Keywordsadolescents, problem behaviors, screening/risk identification, quantitative research, predictive model, social media
The natural development of adolescent risk taking is now occurring in two arenas, offline (binge drinking, smoking, sex, illicit substance use) and online (sexting, cyberaggression, cyberbullying). Additionally, for many adolescents there are other concerns such as academic problems, truancy, fear of going to school, anxiety, undiagnosed mental health problems, getting into trouble with police, and delinquency. Risk behaviors, regardless of realm, often cluster increasing the adolescent’s risk for chronic disease, addiction, victimization, psychological trauma, and other negative outcomes. Frequently studied and treated independently, the relationship between offline problem behaviors and online risk taking is not well understood and represents a gap. To address this, a study was undertaken that asked whether there are any problematic behaviors predictive of online risk taking by high school students. Problematic behaviors examined were: Academic problems (academic failure), anxiety (feeling anxious, tense, restless), behavioral wrongdoing (physical fighting, school suspension, recent trouble with the police, vandalism, and theft), bullying (being a victim and/or a bully), and social-emotional (feelings of sadness, loneliness, fearfulness, anger, isolation, rejection, and poor self-esteem). The purpose of this paper is to report findings from this study specific to which behaviors predicted online risk taking and negative experiences in a high school student population.
The single most obvious risk-relevant difference between adults and adolescents is age. Neuroimaging studies have shown significant differences in brain structure and decisionmaking patterns between adolescents and adults that account for differences in impulse control, planning, and risky behavior. Key brain regions that are responsible for self-regulation, decision making, integrating sensory information and executive functioning such as the frontal, temporal and parietal cortices are still developing during adolescence. Additionally, the frontal cortex which is primarily responsible for behavioral control, self-regulation, abstract thinking and judgement, executive functioning, decision making, long term planning, is not fully developed until the early twenties (Blakemore & Choudhury, 2006; Bonnie & Scott, 2013; Casey, 2013; Casey et al., 2008; Dir et al., 2019; Dow-Edwards et al., 2019; Streib & Schrempp, 2007).
Adolescence is a time of extraordinary maturational change in virtually all domains of development, from physical and biological to emotional, cognitive, neuro-cognitive, social, sexual, and behavioral (Bonnie & Scott, 2013; Casey et al., 2008; Iselin et al., 2009; Dow-Edwards et al., 2019; Modecki, 2008; Reyna & Farley, 2006; Steinberg, 2007; Steinberg & Scott, 2003; van den Bos, van Dijk, Westenberg, Rombouts, & Crone, 2011). This transitionary period is marked by profound physical, cognitive, social, and emotional change and growth that is peer-focused and exploratory. Puberty and sex have been found to influence brain maturation during adolescence, implying that specific stages of brain development and maturation in males and females can differ as significant brain remodeling occurs in females (Bale, & Epperson, 2017; Ernst et al., 2019; Hantsoo, & Epperson, 2017). These changes in brain functional organization imply that there may be different trajectory patterns and that the influence of puberty on brain function may also have a role in the risk taking and risk seeking differences seen between adolescent males and females (Bale, & Epperson, 2017; Ernst et al., 2019). Their developmental immaturity may undermine competent decision making, leaving adolescents more vulnerable to the influence of coercive circumstances such as provocation, duress, or threat (Bonnie & Scott, 2013; Steinberg & Scott, 2003) and susceptible to the impact of negative risk taking behaviors or experiences.
Adolescence is typically a time of good physical and mental health. Difficulties experienced during this time often relate to growth and development, childhood illnesses that continue into adolescence, school concerns, mental health disorders, and consequences of risky or illegal behaviors. For example, in 2015, nearly 40 percent of high school students reported having used marijuana one or more times, 56 percent consumed alcohol, 30 percent were sexually active with 43 percent reporting they did not use a condom, and 21 percent reported alcohol or drug use before intercourse Centers for Disease Control and Prevention (CDC, 2020).
Problematic behaviors that develop during adolescence are typically considered not acceptable and have a consistent pattern that can vary in terms of severity (Andrews et al., 2020; CDC, 2020). Common areas of problematic behaviors include academics which can appear during any grade level and can range from poor study habits, failing grades, suspension or dropping out. Adolescents who generally have poor academic achievement are more likely to engage in other high-risk behaviors, such as having unprotected sex, drug use, and engaging in violence (Andrews et al., 2020; CDC, 2020). Anxiety, which is a natural and important emotion, is now the most common psychiatric disorders in children and adolescents (Walter et al., 2020). Social anxiety tends to develop in later school-age and early adolescents with feelings of generalized anxiety, panic, and agoraphobia often accompanied with other psychiatric disorders such as depression, attention-deficit/hyperactivity disorder (ADHD), obsessivecompulsive, eating disorders, and substance use related disorders (Walter et al., 2020).
Experiences of bullying often contribute to a variety of other problems and is considered a multi-layered problematic behavior. Defined by aggressive behavior that is intentional and mean, bullying occurs repeatedly over time and within the context of a power imbalance and may include physical (e.g., hitting), verbal (e.g., name-calling), relational (e.g., social isolation), or online (cyber) aggression (CDC, 2018; Children’s Hospital of Philadelphia [CHOP], 2020). In schools, bullying and harassment tend to increase in late childhood and peak in early adolescence, specifically during middle school into high school. In-person bullying typically takes place in unstructured settings such as the cafeteria, hallways, and on the playground during recess (CHOP, 2020).
Access to the internet has changed the way we all live, work, learn and use healthcare. Adolescents and young adults are often considered to be digital natives who have grown up with technology advancement and use the internet as their virtual playground. In the United States an estimated 92 percent of adolescents aged 13–17 years old go online daily, with 95 percent using mobile devices like phones, smartwatches, and tablets to communicate (Anderson & Jiang, 2018). Most use the internet as a venue for socializing, sending pictures, viewing media or entertainment, online journaling, and blogging (Anderson & Jiang, 2018; Perrin & Jiang, 2019; Pew Research Center, 2019; Vogels & Anderson, 2020). There are many positives associated with being online, such as easily transferred money to buy things like food, music, clothes, and new technology or applications (apps). Fast and easy communication such as texting, sending pictures, posting on social media, and emailing. Access to the latest news and global developments from any part of the world without depending on TV or print newspapers makes staying connected easy as does using digitalized books, journals, videos and tutors or academies that provide support for education and adaptive learning.
This increase in use of and access to technology also comes with negative elements that can be traumatic, harmful, and exploitative. For adolescents’ harmful aspects include electronic aggression, cyberbullying, exploitation and victimization by peers and adults, as well as indirect negative outcomes that result from risky online behaviors (CDC, 2017; Dowdell & Noel, 2020; Kann et al., 2018; Lenhart, 2015; Long & Dowdell, 2018; Loh & Kanai, 2016; Mitchell et al. 2018; Noll et al., 2013). Four main areas of online risk behaviors and experiences that are of concern for adolescents, parents, and professionals include: (1) cyberbullying, harassing, or negative/hostile interactions that cause discomfort, (2) exploring sexual websites or engaging in online activities that are sexual in nature (e.g., sexting which is defined as the electronic transmission of nude or seminude images as well as sexually explicit text messages (American Academy of Pediatrics [AAP], 2016), (3) online sexual solicitations or exploitation (unwanted requests to engage in sexual activities or sexual talk or to give personal sexual information, and commercial sex trafficking or prostitution) by older peers and/or adults, and (4) meeting exclusively online acquaintances offline and in person where there is an assault or injury.
Online risk behaviors often cluster and may include the posting of personal information, corresponding online with an unknown person, meeting cyber acquaintances in person, online-initiated harassment, overriding internet filters or blocks, and engaging in online sexual behaviors (e.g., X-rated websites, sending sexual pictures depicting nudity/sexting) (Handschuh et al., 2019; Kann et al., 2018; Long & Dowdell, 2018; Long et al., 2016; Loh & Kanai, 2016; Temple et al., 2014). Baumgartner et al. (2010) identified increases in sexual awareness and sensation seeking by adolescents encompassed both online and offline behavior. Additionally, peer influence was an element associated with risk taking behaviors by adolescents who perceived peer involvement in risky sexual behavior (i.e., perception that peers are engaging in risky sexual behaviors online) predicted risky sexual online behavior (Baumgartner et al., 2010). Livingstone and Helsper (2008) observed that age (being older), gender (males), and possessing more online skills significantly predicted online risk. Mitchell et al. (2018) had similar a finding reporting that youth who were victimized online reported trauma symptoms, delinquency, and aversive life events, even after controlling for other variables.
Risky online sexual behaviors (sexting or searching online for someone to talk about sex) have been associated with an increased likelihood of negative experiences such as unwanted aggressive and sexual online solicitation (Kann et al., 2018; Mitchell et al., 2016; Mitchell et al., 2018; Temple et al., 2014). A study by Handschuh et al. (2019) reported that adolescents who send sexually explicit text messages and sexts were more likely to report sexual activity than adolescents who did not. Long and Dowdell (2018) found that high school students who reported experiences of cyberbullying as the bully, victim, and both (bullyvictim) had significant overlap and clustering of online risk taking. The two highest risk groups were the students who reported being a bully-victim and a bully suggesting that adolescents who report bullying experiences should be considered vulnerable.
The present study employed a developmental theme to identify selected offline problem behaviors and online risk behaviors in a sample of high school students. After Institutional Review Board (IRB) approval, contact was made with two public school districts and one private school located in Maryland, Massachusetts, and Pennsylvania. Each school district offered access to one high school and a meeting was held with each school’s principal to review the details of the study, including informed consent, student confidentiality, and an examination of the questions. An information sheet describing the purpose of the study and an informed consent form was sent to all parents with an explanation of the purpose of the study, seeking permission for their child to participate. A signed consent form agreeing to allow participation was required from the parent or legal guardian and assent was required from the adolescent prior to participation in the study.
The response rate was high with the suburban Philadelphia school having a 94.7% response rate (1400/ 1478), followed by the Boston urban school with 84.5% (274/324) and the Southern Maryland rural school having the lowest rate of 36.4% (403/1108). All surveys were given in-person, as paper and pencil instruments, with the first page containing a statement of informed consent/assent. Each survey contained three sections, the first included 14 questions derived from the Youth Risk Behavior Survey (YRBS) developed by the CDC to assess health risk behaviors, specifically about smoking cigarets, drinking alcohol, physical fighting, and underage driving (CDC, 2009). The YRBS has undergone multiple test- retest reliability assessments with Kappa values that range from 47.0% to 90.5%, with a mean of 60.7% and a median of 60.0% (Brener et al., 2002). The second section of the survey included 67 questions from the telephone-administered Youth Internet Safety Survey (YISS) (Finkelhor, Mitchell, & Wolak, 2000) which was adapted for use as paper and pencil administered survey. The final section asked eight socio-demographic questions such areas as age, grade, and ethnicity to collect basic information.
Items reflecting problem behaviors and common problem areas for high school students were entered into a series of Principal Component Analyses (PCA) with rotation to varimax, resulting in five components: Academic problems (academic failure), anxiety (a scale comprised of items anxious, tense, restless,), behavioral wrongdoing (a scale comprised of items such as fighting, school suspension, recent trouble with the police, vandalism, and theft), bullying experience (being a victim and/or a bully), and socialemotional (a scale that reflected primarily sadness, loneliness, fearfulness, anger, isolation, rejection, and poor self-esteem). Scales were derived from the components by taking the mean of all item responses for that component. Unanswered items (missing data) were substituted with the scale mean. All items and item loadings for each of the five components are provided in Table 1. Understanding that puberty and sex have been found to influence brain maturation in adolescence, data for males and females were analyzed separately.
Nine binary results were selected and classified as online risk behaviors and negative experiences. Self-initiated online risk behaviors include: (1) Have you ever sexted? (2) Have you ever used the internet to harass someone, (3) In the past 6 months, have you ever gone on purpose to sex sites on the internet? (4) Have you ever met anyone in person that you met first online? Negative online experiences include: (1) Have you ever been sexually harassed on the internet? (2) Has anyone on the internet ever talked to you about sex when you didn’t want to? (3) Have you received sex pictures from anyone online? (4) Did this person ever do anything that made you feel nervous of uncomfortable, and (5) Did anything sexual happen when you met? We used multivariate logistic regression models to predict self-initiating online risk behaviors and negative online experiences. The predictors were five predictor scales and sex (males vs. females), and interaction terms between sex and each of the five predictor scales for a total of 18 logistic regression models. Least-square estimate, standard error (SE), adjusted odds ratio (aOR), 95% confidence limits (CL) of aOR, p-value of significance were calculated for each of the 5 predictor scales and sex using Bonferroni multiple comparison method. C-statistic was also calculated for model predictability. Fairly good predictability for all models was evidenced by c-statistics being above 0.7 except for one case which was 0.67 (Table 2).
The sample consisted of 2,077 high students (1,151 females and 926 males), ranging in age from 15 – 18 years in grades 9–12. The average age for both males (M = 16.03, SD = 1.28) and females was 16 (M = 15.93, SD = 1.25). There were comparable proportions of males and females in all four grades: Females: 27.6% (n = 318), 21.2% (n = 244), 24.6% (n = 283), and 26.6% (n = 306), Males: 27.4% (n = 255), 22.5% (n = 204), 21.8% (n = 203), and 28.3% (n = 264). The majority of the males (74.6%, n = 690) and females (76.7%, n = 883) identified as Caucasian, 8% African American, 8% Asian, and approximately 2% Latinx. The overwhelming majority of the males (99.3%, n = 920) and the females (82.1%, n = 945) attended public schools.
Among the males, behavioral wrongdoing emerged as a significant predictor appearing in all nine risky online behavior models (Figure 1). The following two risk parameters were predicted with a high degree of accuracy (AUC = 0.817, 0.802, respectively), the model inquiring “Have you ever been sexually harassed on the internet” was strongly predicted by behavioral wrongdoing (OR = 1.77, p < .003) and social-emotional (OR = 1.25, p < .04) (Figure 2). With one additional Yes-response to the scale came with an increase of 77% ( = 1.77–1) risk of being sexually harassed. With regards to “Have you ever used the Internet to harass someone”, the critical predictor for males was again behavioral wrongdoing (OR: males = 1.65, p < .001) with bullying (OR = 1.80, p < .002) (Figure 3).
Two models having to do with in-person, offline meetings with adults only known from the internet were also predicted with considerable accuracy (c = 0.78, 0.78, 0.76, respectively): “Did the person that you met make you feel uncomfortable” was predicted by behavioral wrongdoing (OR = 1.46, p < .02) and social-emotional (OR = 1.41, p < .04); “Did anything sexual happen when you met” was predicted by behavioral wrongdoing (OR = 1.51, p < .003) and academic problems (OR = 1.78, p < .05) (Figure 4). The model asking about sexual talking, “Did anyone on the internet talk to you about sex when you didn’t want to” was again predicted by behavioral wrongdoing (OR = 1.62, p < .02) and social-emotional (OR = 1.25, p < .001).
The two models having to do with receipt of or exchange of pictures depicting nudity were more complexly predicted than any of the other models. The model, “Did you ever receive any sex pictures from anyone online” was predicted (c = 0.736) by behavioral wrongdoing (OR = 1.53, p < .0001), anxiety (OR = 1.24, p < .05) (Figure 5), academic problems (OR = 1.41, p < .01) and bullying (OR = 1.33, p < .01). Similarly, the model “Have you ever sexted” (c = 0.72) was predicted by behavioral wrongdoing (OR = 1.32, p < .0001), academic problems (OR = 1.74, p < .001) and bullying (OR = 1.30, p < .05).
There was a remarkably high degree of similarity between the female and male students with respect to prediction however, there were a few differences. Unlike the males, the model inquiring “Have you ever been sexually harassed on the Internet” had a reasonably high degree of accuracy (c = 0.856–0.779) and was predicted for females by bullying (OR = 1.94, p < .0001) (Figure 1), anxiety (OR = 1.30, p < .01) (Figure 5), and academic problems (OR = 1.58, p < .01) (Figure 2). The model for “Have you ever used the internet to harass someone” was strongly predicted by behavioral wrongdoing (OR = .53, p < .0001), bullying (OR = 1.94, p < .0001) (Figure 3) and academic problems (OR = 1.51, p < .05) (Figure 4).
Models having to do with in-person, offline meetings with adults only known from the internet were also predicted with a high degree of accuracy (c = 0.856 – 0.806). “Did the person that you met make you feel uncomfortable” was predicted by behavioral wrongdoing (OR = 1.87; p < .0001) and social-emotional (OR = 1.46; p < .01). For the question: “Did anything sexual happen when you met,” was predicted by behavioral wrongdoing (OR = 1.90, p < .0001) and academic problems (OR = 1.78; p < .02).
Two models having to do with receipt of or exchange of pictures depicting nudity were more complexly predicted than any of the other models. For females the model “Did you ever receive any sex pictures from anyone online” was predicted (c = 0.747) by behavioral wrongdoing (OR = 1.32, p < .001) and bullying (OR = 1.82, p < .0001). Similarly, the model “Have you ever sexted” (c = 0.737) was predicted by behavioral wrongdoing (OR = 1.41, p < .0001) and bullying (OR = 1.75, p < .0001). A summary of logistic regression models with p-values is provided in Table 3. It shows which composite scale is a significant predictor for which risky internet behavior.
Adolescence is recognized as a time of developmental milestones, varying levels of maturity, sex differences, and risktaking behavior. Traditionally, research on risk-taking behavior has focused on offline health-related concerns such as smoking, alcohol use, substance use, risky sexual behaviors, and teen pregnancy. The internet, however, offers an entirely new vista for risk taking. Using a sample of high school students findings include: (1) a remarkably robust predictive relationship of offline behavioral wrongdoing (fighting, school suspension, recent trouble with the police, vandalism, and theft), with all parameters of online risk behaviors and experiences; (2) the second most effective predictor of online risk behavior was bullying (victim of and/or a perpetrator of bullying), followed by academic problems, social/emotional, and anxiety; (3) offline behavioral wrongdoing, bullying and academic problems were more likely to predict sexting, meeting adults offline, and harassing others/cyberaggression; and (4) offline anxiety and experiences of bullying by females had a strong prediction relationship with online sexual harassment.
While the relationship among offline and online behaviors may not be well understood our study found that certain problematic behaviors predicted online risk behaviors. This finding indicates that both domains must be screened and evaluated by school nurses and other professionals who work with adolescents. The AAP states that all children and adolescents, regardless of academic concerns or behaviors, should be routinely screened for their amount of screen time (AAP, 2016). The advancement of technology along with increasing access and dependance by children raise the potential for risk with negative outcomes that go beyond asking about screen time. Nurses, especially school nurses at all types of schools, and other health professionals are in a prime position to ask students of all ages about online behavior, activity, and experiences.
Students who exhibit a pattern, or have a history of multiple behavioral wrongdoings tend to be well known to school staff and faculty and/or have been identified as at-risk individuals. These “red flag” school behaviors may make an adolescent more vulnerable to participation in additional risk taking. The severity and frequency of problematic behaviors can be used as guides by school nurses, educators, and other professionals. For example, frequent or clustered episodes of fighting, trouble with the police, vandalism, and theft are much more significant than isolated episodes of the same activities. School nurses, educators, and other professionals must work together within systems and with families to distinguish occasional errors of judgment from a pattern of wrongdoing that require intervention. Special attention through screening and assessment must be paid to identify all behaviors, offline and online, that place the adolescent at risk for negative outcomes.
Findings from our study suggest that behavioral wrongdoing (fighting, school suspension, recent trouble with the police, vandalism, and theft) behaviors are a predictor of online risk. School nurses can screen students who display these “red flag” type of behaviors for online risk by asking questions about online behaviors. Answers by a student may provide insight as well as a comprehensive picture of this individual. Conversations about offline and online risk behaviors or experiences may be difficult for nurses and other professionals to navigate. There can be barriers to screening for risk as some students may be unwilling to discuss openly their own behaviors or experiences, especially if they think the information might not be kept confidential. Additionally, time with each student may be limited and it can be hard to imagine fitting in one more assessment however, online behaviors, risks, and experiences are a vital piece to understanding the whole person. Asking online questions directly may start a conversation that will aide in further identifying at-risk and vulnerable students.
Consideration must also be given to students who have academic problems. Almost all children and adolescents have the experience of attending school and for most it is where they spend the majority of their day. Academic and behavioral concerns can occur throughout academic careers going from elementary school through to college (Simmons et al., 2020). Not surprisingly, academic problems were found in our study to be a predictive behavior for online risk taking. Students who have academic problems, especially in terms of under achievement or failures are typically identified by educators, school administrators, and families throughout the school year. Students who have one or more failing grades or are identified as underachieving must also be asked about their online behaviors, especially if this is a new or change from previous behavior. Failing grades and changes in behavior are often associated with cyberbullying, having sexting photos shared without permission with others after a relationship ends or being posted online (Mitchell et al., 2018; Ybarra et al., 2017). Many children and adolescents do not report or share their negative online experiences and suffer in silence while their grades drop and offline behavior changes for fear of a parental overreaction or losing or having their online privileges limited (D’Auria, 2014).
Adolescents who have experienced or are perpetrators of bullying must also be screened for online risk behavior. The relationship between the aggression of bullying and online risk behavior is not well understood, it may be reflective of this age group’s technology awareness, knowledge, ability, or may even be related to technology. Inclusion of and screening for experiences of bullying, either as the victim, the bully, the bully-victim, or as witness/bystander is vital. In our study female students reported offline bullying being a stronger predictor of online sexual harassment experiences. The culture of school violence cannot be focused only on bullies and victims but on all vulnerable and at-risk students who can be taught how to recognize, report, and refuse bullying. When nurses and professionals working with adolescents are knowledgeable about bullying, they can promote better communication among students, families, and other professionals within the school system. Students need school to be a positive environment where everyone can feel safe.
Experiencing occasional anxiety is a normal part of life but chronic, intense, or acute anxiety is an increasingly prevalent public health concern which presents differently with women experiencing a greater prevalence of anxiety disorders (Hantsoo, & Epperson, 2017). Anxiety and emotional disorders among women often precipitate or worsen during puberty and are broadly associated with poor mental health and risk-taking behaviors (Copeland et al., 2018; Gill et al., 2018; Hantsoo, & Epperson, 2017), so it may not be surprising that in our study this offline behavior was a predictor of online risk for female students. Especially given that these feelings were significantly related to being sexually harassed online, receiving sex pictures from someone online, having someone talked about sex when not interested, and that meeting someone offline made them feel uncomfortable. All experiences that appropriately increase anxiety and emotional responses.
Adolescents who experience anxiety or emotional disorders typically interact with a variety of professionals, such as primary care providers, psychologists, psychiatrists, nurse practitioners, school nurses and personnel, plus those in the community. Each professional is in a prime position to ask students who are experiencing anxiety about online behavior, activity, and encounters. It is not uncommon for adolescents experiencing anxiety or emotional disorders to be online and on social media as a way to connect to others, especially if they have self-reported loneliness (Barry et al., 2017). Inclusion of online risk assessment as part of a treatment plan for anxiety, which also includes psychotherapy and medications, may provide insight into a contributing element for the anxiety.
Across all types of schools, nurses can use questions about offline behaviors to ask questions about online experiences, feelings, risk taking, and behavior(s). Answers to these questions can open avenues for further screening, assessment, identification, and intervention. School nurses who interact with any student who has had one or more reports of fighting, being bullied or being a bully, school suspension, recent trouble with the police, anxiety, or any academic experience or concern(s) must also ask about internet risk behaviors. Our predictive models suggest that these students will be more likely to report having online risk behaviors and experiences such as sexting, encounters with cyber aggression, harassment, cyberbullying, or going purposefully to online sex sites. Screening for offline and online behaviors are an important piece of health information.
Nurses can easily include questions about an adolescent’s favorite online websites, YouTube videos, influencers, experiences on social media platforms or blogs. Responses can provide insight into student interests, activities, and behaviors as well as provide an opportunity to ask about concerns or outcomes. A systematic screening by school nurses and other professionals can operate as a tool for internet education and risk identification, using the information gathered to raise awareness of the addictiveness, risks, and repercussions of aggressive, sexualized, or threatening social media use (Clark et al., 2018). Directly asking adolescents about their online risk behaviors such as any encounters with electronic aggression, harassment, sexting, or cyberbullying must also be incorporated when working with at-risk students.
Although this study provides strong support for key predictor variables for both females and males, limitations include a dataset from 2009, reliance on solely self-report measures and a predominantly Caucasian sample. Technology and access to the internet change every year. Advances made in the past twelve years include faster internet service, sophisticated mobile phones, cloud computing, and new social media platforms. The availability of and access to the internet in addition to having multiple devices such as tablets, e-readers, smartwatches, fitness trackers, wireless earphones, and other innovations have provided opportunities for online behaviors not imagined ten years ago. This suggests that students from our dataset may have lower reports of online risk taking compared to current students, signifying that screening for online risk taking and incorporating online questions when working with students with behavioral wrongdoing, academic problems, bullying experiences, and anxiety is even more important today.
Additionally, reliance solely on self-report raises the obvious question of veridicality. Assuring confidentiality was, first and foremost, the goal of the study, so no confirmatory data were sought for domains such as behavioral wrongdoing or academic problems related to grades. Additionally, there is growing evidence which suggests that adolescents who are both a bully and a victim have worse psychosocial outcomes than victims or perpetrators. Neither role was differentiated in this study. Also, this study did not examine if these offline predictors function differently in subsets of youth that are possibly more vulnerable to exploitation and harassment, such as, African American, Latinx or students who identify as LGBTQ+. Research with more ethnically diverse samples is needed, as well as exploring whether routes of access (i.e., smartphone, tablet, home computer, etc.) influence results. Further research should also investigate if there are specific internalizing or externalizing problems that influence online behaviors.
Adolescents can exercise their developing sense of independence by questioning or challenging, and sometimes breaking rules. This study found that high school students who reported having one or more behavioral wrongdoing, bullying, academic problems, and anxious social/emotional feelings were more likely to report online risk behaviors. Experiences such as sexual exploitation, electronic aggression, and cyberbullying are of concern to schools, nurses, educators, parents, and society and will only proliferate with the onward march of technology. In our study we identified common problem behaviors that are traditionally associated with adolescents. These behaviors are plausible targets for interventions as there are empirically supported treatments to focus on behavioral wrongdoing, academic problems, and bullying. School nurses, providers, professionals, educators, and caregivers who have ongoing contact with students are uniquely positioned to identify at-risk adolescents. Integration of routine screening of online risk taking may provide an opportunity to educate about risk taking and to intervene as well as prevent or respond to any negative experiences. Protecting children and adolescents will require a continuous and multifaceted approach that includes nursing across specialties, medicine, healthcare, psychoeducation, communication, and prevention approaches that are rooted in understanding factors related to negative and risky online and offline experiences. Clearly, more comprehensive screening is required for troubleshooting across multiple areas of concern, however, as a preliminary screen for online risk behaviors, the findings reported here provide a reasonable starting place.
The authors would also like to acknowledge all of the high school students, administrators, faculty, and staff who participated in this study, and the project management expertize and support of Jeffrey Gersh, now Deputy Associate Administrator at OJJDP
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Office of Juvenile Justice and Delinquency Prevention, (grant number 2006-JW-BX-K069 ).
Elizabeth B. Dowdell https://orcid.org/0000-0001-9413-9393
American Academy of Pediatrics Council on Communications and Media. (2016). Media use in school-aged children and adolescents. Pediatrics, 138(5), e20162592. https://pediatrics.aappublications.org/content/138/5/e20162592 https://doi.org/10.1542/peds.2016-2592
Anderson, M., & Jiang, J. (2018). Teens, social media & technology 2018. Pew Research Center. https://www.pewinternet.org/2018/05/31/teens-social-media-technology-2018/
Andrews, J. L., Mills, K. L., Flournoy, J. C., Flannery, J. E., Mobasser, A., Ross, G., Durnin, M., Peake, S., Fisher, P. A., & Pfeifer, J. H. (2020). Expectations of social consequences impact anticipated involvement in health-risk behavior during adolescence. Journal of Research on Adolescence, 30(4), 1008–1024. https://doi.org/10.1111/jora.12576
Bale, T. L., & Epperson, C. N. (2017). Sex as a biological variable: Who, what, when, why, and how. Neuropsychopharmacology, 42(2), 386–396. https://doi.org/10.1038/npp.2016.215
Barry, C. T., Sidoti, C. L., Briggs, S. M., Reiter, S. R., & Lindsey, R. A. (2017). Adolescent social media use and mental health from adolescent and parent perspectives. Journal of Adolescence, 61, 1–11. https://doi.org/10.1016/j.adolescence.2017.08.005
Baumgartner, S., Valkenburg, P., & Jochen, P. (2010). Assessing causality in the relationship between adolescents’ risky sexual online behavior and their perceptions of this behavior. Journal of Youth and Adolescence, 39, 896–898.
Blakemore, S. J., & Choudhury, S. (2006). Development of the adolescent brain: Implications for executive functioning and social cognition. Journal of Child Psychology and Psychiatry, 47, 296–312. https://doi.org/10.1111/j.1469-7610.2006.01611.x
Bonnie, R. J., & Scott, E. S. (2013). The teenage brain: Adolescent brain research and the law. Current Directions in Psychological Science, 22(2), 158–161. https://doi.org/10.1177/0963721412471678
Brener, N. D., Kann, L., McManus, T., Kinchen, S. A., Sundberg, E. C., & Ross, J. G. (2002). Reliability of the 1999 youth risk behavior survey questionnaire. Journal of Adolescent Health, 31(4), 336–342. https://doi.org/10.1016/s1054-139x(02)00339-7
Casey, B. J. (2013). The teenage brain: An overview. Current Directions in Psychological Science, 22, 80–81. https://doi.org/10.1177/0963721413486971
Casey, B. J., Getz, S., & Galvan, A. (2008). The adolescent brain. Developmental Review, 28, 62–77. https://doi.org/10.1016/j.dr.2007.08.003
Centers for Disease Control and Prevention. (2009). YRBS: Youth risk behavior surveillance system. U.S. Department for Health and Human Services.
Centers for Disease Control and Prevention. (2017). Teen risk behaviors. U.S. Department of Health and Human Services. http://www.cdc.gov/parents/teens/risk_behaviors.html
Centers for Disease Control and Prevention. (2018). Youth violence: Preventing violence. U.S. Department of Health and Human Services. https://www.cdc.gov/violenceprevention/youthviolence/fastfact.html
Centers for Disease Control and Prevention. (2020). Youth risk behavior surveillance – United States, 2015. Morbidity and Mortality Weekly Report. MMWR 2019; 69(SS-01). https://www.cdc.gov/healthyyouth/data/yrbs/overview.htm
Children’s Hospital of Philadelphia. (2020). Bullying in schools. Center for Violence Prevention: Types of violence. https://violence.chop.edu/types-violence/bullying-schools
Clark, D. L., Raphael, J. L., & McGuire, A. L. (2018). HEADS4 : Social media screening in adolescent primary care. Pediatrics, 141, 1–3. https://doi.org/10.1542/peds.2017-3655
Copeland, M., Fisher, J. C., Moody, J., & Feinberg, M. E. (2018). Different kinds of lonely: Dimensions of isolation and substance use in adolescence. Journal of Youth and Adolescence, 47(8), 1755–1770. https://doi.org/10.1007/s10964-018-0860-3
D’Auria, J. P. (2014). Cyberbullying resources for youth and their families. Journal of Pediatric Health Care, 28, e19–e22. https://doi.org/10.1016/j.pedhc.2013.11.003
Dir, A. L., Hummer, T. A., Aalsma, M. C., & Hulvershorn, L. A. (2019). Pubertal influences on neural activation during risky decision-making in youth with ADHD and disruptive behavior disorders. Developmental Cognitive Neuroscience, 36, 100634. https://doi.org/10.1016/j.dcn.2019.100634
Dowdell, E. B., & Noel, J. (2020). Having a peer who self-harms: Examining risk taking behaviors in high school students. Issues in Mental Health Nursing, 41(5), 415–420. https://doi.org/10.1080/01612840.2019.1663568
Dow-Edwards, D., MacMaster, F. P., Peterson, B. S., Niesink, R., Andersen, S., & Braams, B. R. (2019). Experience during adolescence shapes brain development: From synapses and networks to normal and pathological behavior. Neurotoxicology and Teratology, 76, 106834. https://doi.org/10.1016/j.ntt.2019.106834
Ernst, M., Benson, B., Artiges, E., Gorka, A. X., Lemaitre, H., Lago, T., Miranda, R., Banaschewski, T., Bokde, A. L. W., Bromberg, U., Brühl, R., Büchel, C., Cattrell, A., Conrod, P., Desrivières, S., Fadai, T., Flor, H., Grigis, A., Gllinat, J., & Martinot, J. L. (2019). Pubertal maturation and sex effects on the default-mode network connectivity implicated in mood dysregulation. Translational Psychiatry. 9(1):103. https://doi.org/10.1038/s41398-019-0433-6
Finkelhor, D., Mitchell, K. J., & Wolak, J. (2000). Online victimization: A report on the nation’s youth (No. 6-00-020). Alexandria, VA: National Center for Missing & Exploited Children.
Gill, C., Watson, L., Williams, C., & Chan, S. W. (2018). Social anxiety and self-compassion in adolescents. Journal of Adolescence, 69, 163–174. https://doi.org/10.1016/j.adolescence.2018.10.004
Handschuh, C., La Cross, A., & Smaldone, A. (2019). Is sexting associated with sexual behaviors during adolescence? A systematic literature review and meta-analysis. Journal of Midwifery & Women‘s Health, 64(1), 88–97. https://doi.org/10.1111/jmwh.12923
Hantsoo, L., & Epperson, C. N. (2017). Anxiety disorders among women: A female lifespan approach. Focus, 15(2), 162–172. https://doi.org/10.1176/appi.focus.20160042
Iselin, A. M. R., DeCoster, J., & Salekin, R. T. (2009). Maturity in adolescent and young adult offenders: The role of cognitive control. Law and Human Behavior, 33(6), 455–469. https://doi.org/10.1007/s10979-008-9160-x
Kann, L., McManus, T., Harris, W. A., Shanklin, S. L., Flint, K. H., Queen, B., Lowry, R., Chyen, D., Whittle, L., Thornton, J., Lim, C., Bradford, D,, Yamakawa, Y., Leon, M., Brener, N., & Ethier, K. A. (2018). Youth risk behavior surveillance — United States, 2017. MMWR Surveillance Summaries, 67(8), 1–114. https://doi.org/10.15585/mmwr.ss6708a1
Lenhart, A. (2015). Teens, social media & technology overview 2015. Pew Research Center. http://www.pewinternet.org/2015/04/09/teens-social-media-technology-2015/
Livingstone, S., & Helsper, E. J. (2008). Parental mediation of children’s internet use. Journal of Broadcasting & Electronic Media, 52, 581–599. https://doi.org/10.1080/08838150802437396
Loh, K. K., & Kanai, R. (2016). How has the internet reshaped human cognition? The Neuroscientist, 22, 506–520. https://doi.org/10.1177/1073858415595005
Long, M., & Dowdell, E. B. (2018). Online and health risk behaviors in high school students: An examination of bullying. Pediatric Nursing, 44(5), 223–228.
Long, M. R., McNiel, D. E., & Binder, R. L. (2016). Minors and sexting: Legal implications. Journal of the American Academy of Psychiatry and the Law, 44(73), 73–81. PMID: 26944746.
Mitchell, K. J., Segura, A., Jones, L. M., & Turner, H. A. (2018). Poly-victimization and peer harassment involvement in a technological world. Journal of Interpersonal Violence, 33(5), 762–788. https://doi.org/10.1177/0886260517744846
Mitchell, K. J., Ybarra, M. L., Jones, L. M., & Espelage, D. (2016). What features make online harassment incidents upsetting to youth? Journal of School Violence, 15(3), 279–301. https://doi.org/10.1080/15388220.2014.990462
Modecki, K. L. (2008). Addressing gaps in the maturity of judgment literature: Age differences and delinquency. Law and Human Behavior, 32(1), 78–91. https://doi.org/10.1007/s10979-007-9087-7
Noll, J. G., Shenk, C. E., Barnes, J. E., & Haralson, K. (2013). Association of maltreatment with high-risk internet behaviors and offline encounters. Pediatrics, 131, e510–e517. https://doi.org/10.1542/peds.2012-1281
Perrin, A., & Jiang, J. (2019). About a quarter of US adults say they are ‘almost constantly’ online. Pew Research Center. https://www.pewresearch.org/fact-tank/2019/07/25/americans-goingonline-almost-constantly/
Pew Research Center. (2019). Internet/broadband fact sheet. Pew Research Center. https://www.pewresearch.org/internet/factsheet/internet-broadband/
Reyna, V. F., & Farley, F. (2006). Risk and rationality in adolescent decision making: Implications for theory, practice, and public policy. Psychological Science in the Public Interest, 7(1), 1–44. https://doi.org/10.1111/j.1529-1006.2006.00026.x
Simmons, K. X., Shah, N. N., Fakeh Campbell, M. L., Gonzalez, L. N., Jones, L. E., & Shendell, D. E. (2020). Online and in-person violence, harassment, intimidation and bullying in new jersey: 2011-2016. Journal of School Health. https://doi.org/10.1111/josh.12938
Steinberg, L. (2007). Risk taking in adolescence: New perspectives from brain and behavioral science. Current Directions in Psychological Science, 16(2), 55–59. https://doi.org/10.1111/j.1467-8721.2007.00475.x
Steinberg, L., & Scott, E. S. (2003). Less guilty by reason of adolescence. Developmental immaturity, diminished responsibility and the juvenile death penalty. American Psychologist, 58, 1009–1018. https://doi.org/10.1037/0003-066X.58.12.1009
Streib, V., & Schrempp, B. (2007). Life without parole for children. Criminal Justice, 21(Winter), 4.
Temple, J. R., Le, V. D., van der Berg, P., Ling, Y., Paul, J. A., & Temple, B. W. (2014). Brief report: Teen sexting and psychosocial health. Journal of Adolescence, 37, 33–36. https://doi.org/10.1016/j.adolescence.2013.10.008
van den Bos, W., van Dijk, E., Westenberg, M., Rombouts, S.A., & Crone, E.A. (2011). Changing brains, changing perspectives: the neurocognitive development of reciprocity. Psychological Science, 22(1), 60–70. https://doi.org/10.1177/0956797610391102. Epub 2010 Dec 16. PMID: 21164174.
Vogels, E., & Anderson, M. (2020). Dating and Relationships in the Digital Age. Pew Research Center. https://www.pewresearch.org/internet/2020/05/08/dating-and-relationships-in-the-digitalage/
Walter, H. J., Bukstein, O. G., Abright, A. R., Keable, H., Ramtekkar, U., Ripperger-Suhler, J., & Rockhill, C. (2020). Clinical practice guideline for the assessment and treatment of children and adolescents with anxiety disorders. Journal of the American Academy of Child & Adolescent Psychiatry, 59(10), 1107–1124. https://doi.org/10.1016/j.jaac.2020.05.005
Ybarra, M. L., Langhinrichsen-Rohling, J., & Mitchell, K. J. (2017). Stalking-like behavior in adolescence: Prevalence, intent, and associated characteristics. Psychology of Violence, 7(2), 192–202. https://doi.org/10.1037/a0040145
1 M. Louise Fitzpatrick College of Nursing, Villanova University, Villanova, PA, USA
2 University of Massachusetts – Dartmouth, Dartmouth, MA, USA
3 Massachusetts General Hospital, Boston, MA, USA
4 University of Massachusetts Medical School, Worcester, MA, USA
5 Justice Resource Institute, Boston, MA, USA
6 Fairleigh Dickinson University, Teaneck, NJ, USA
Corresponding Author:Elizabeth B. Dowdell, PhD, RN, FAAN, M. Louise Fitzpatrick College of Nursing, Villanova University, 800 Lancaster Ave, Driscoll Hall, Villanova, PA. 19085, USA.Email: elizabeth.dowdell@villanova.edu