“I Am So Bored!”: Prevalence Rates and Sociodemographic and Contextual Correlates of High Boredom Among American Adolescents

Abstract

Adolescent boredom is associated with maladaptation and negative developmental outcomes, yet little is known about the prevalence and correlates of high boredom. Drawing from a broad psychosocial framework, the present study examined rates of high boredom and sociodemographic and contextual correlates among nationally representative samples of 8th and 10th graders (N = 21,173; 51.8% female) from the Monitoring the Future survey. Results indicate that approximately 20% of adolescents reported high levels of boredom. Those who were more likely to report high boredom were eighth graders; females; youth who identified as Black, Biracial, or Native American/Native Hawaiian/Pacific Islander; rural youth; and youth of lower socioeconomic status. Results of multivariable logistic regression analyses show significant associations between high boredom and many elements of school, parent, peer, and extracurricular contexts, controlling for sociodemographic characteristics. Findings highlight the pervasiveness of high boredom among American youth and may benefit prevention and intervention efforts by identifying multiple contextual associations with adolescent boredom.

Keywords

emotion education parenting peers extracurricular activity

Boredom is a central adolescent experience, often reflected through popular culture and sentiments expressed by teachers, parents, and especially adolescents themselves. The existing research literature on adolescent boredom, although sparse and often outdated, corroborates the notion that boredom is common among youth (e.g., Hunter & Csikszentmihalyi, 2003; Larson & Richards, 1991; Macklem, 2015; Pekrun, Goetz, Daniels, Stupnisky, & Perry, 2010). Using an experience sampling method to assess the frequency of boredom among middle school students at random moments throughout the day, Larson and Richards (1991) found that students reported feeling bored nearly a quarter of their waking hours. Despite its apparent ubiquity, adolescent boredom is associated with negative developmental and health outcomes. Studies have shown that boredom is associated with, and may contribute to, school difficulties (Caldwell & Smith, 2006; Pekrun et al., 2010; Pekrun, Hall, Goetz, & Perry, 2014), juvenile delinquency (Newberry & Duncan, 2001), risky sexual behavior (Miller et al., 2014), and alcohol and marijuana use (Lee, Neighbors, & Woods, 2007; Patrick, Schulenberg, O’Malley, Johnston, & Bachman, 2011). These findings highlight the pervasiveness of boredom and its association with detrimental outcomes. By identifying adolescents most at risk for and the contexts that contribute to high levels of boredom, the present study sets the stage for efforts to reduce boredom and its negative consequences.

Boredom can be viewed generally as “the aversive state of wanting, but being unable, to engage in satisfying activity” (Eastwood, Frischen, Fenske, & Smilek, 2012, p. 483). Boredom has been characterized either as an inherent trait or a situational state (Macklem, 2015; Mercer-Lynn, Bar, & Eastwood, 2014; Vogel-Walcutt, Fiorella, Carper, & Schatz, 2012). As a trait, certain individuals may have personalities especially prone to boredom. As a state, boredom arises in situations that provoke a temporary period of disinterest (Fahlman, Mercer-Lynn, Flora, & Eastwood, 2013) and offer inadequately engaging activities; here boredom is characterized by monotony and nonoptimal arousal (Eastwood et al., 2012; Harris, 2000). Thus, context plays a central role in the experience of boredom. Bronfenbrenner’s Ecological Systems Theory provides a framework for studying the role of contexts on psychosocial functioning (Bronfenbrenner, 1979; Bronfenbrenner & Morris, 2006). This theory posits that human development occurs within multiple levels of bidirectional contexts. The microsystem, which includes contexts most proximal to the individual, may contribute to adolescent boredom through the dynamic interplay between individual and social contexts. Social contexts relevant to adolescence, such as the school, parent, peer, and extracurricular, are often highly influential on adolescents’ psychosocial functioning, and these contexts likely contribute to boredom. Only a handful of studies, however, have examined the link between social contexts and boredom. Of these studies, most have examined boredom in the school context (e.g., Macklem, 2015) or focused solely on leisure boredom (concerning boredom during free time; for example, Iso-Ahola & Weissinger, 1990; Spaeth, Weichold, & Silbereisen, 2015; Wegner & Flisher, 2009), which may limit the generalizability of findings beyond such specific contexts and situations. Examining the prevalence and contextual correlates of more general boredom, as done in the present study, is needed to provide a broader perspective on adolescents’ experiences of boredom.

Contexts of Boredom

School Context

Boredom in the school context appears to be relatively common. Using data from the High School Survey of Student Engagement (HSSSE) that assessed students across 103 schools in 27 states, Yazzie-Mintz (2010) found that 49% of students reported being bored on a daily basis and 17% indicated being bored during every class. The extent of boredom at school is especially concerning, considering the association between boredom and detriments to academic achievement (Belton & Priyadharshini, 2007; Pekrun et al., 2010; Pekrun et al., 2014; Vogel-Walcutt et al., 2012). Pekrun et al. (2010) found a positive association between boredom and attention problems and a negative association between boredom and intrinsic motivation and academic performance. Students who perceived a greater sense of control during academic tasks and who valued courses as relevant and intrinsically motivating were less likely to report boredom, compared with students with lower perceptions of control and value.

Boredom experienced at school tends to arise in response to inadequate challenge from school tasks, low motivation, low autonomy, and low value for school (Vogel-Walcutt et al., 2012). Thus, boredom is likely to occur during passive activities that require both low skill and low challenge. In other words, boredom is the near opposite of “flow.” Flow describes a psychological state of complete focus and absorption during an activity, when a balance occurs between the activity challenge level and one’s skills to accomplish the required task (Csikszentmihalyi, 2000; Harris, 2000; Nakamura & Csikszentmihalyi, 2002). Conversely, boredom is characterized by monotonous situations with a poor fit between the needs of the individual and contextual affordances.

Parent Context

The parent context is also likely associated with boredom, although research in this area is limited primarily to leisure boredom and findings are often mixed. In one sense, a certain level of parental control provides a structured environment that fosters development of self-regulation and staves off feelings of boredom (Wegner & Flisher, 2009). Parents who set clear, reasonable limits for their adolescents, while also being emotionally available and engaged in their adolescent’s life, provide a structured environment associated with low leisure boredom (Caldwell, Darling, Payne, & Dowdy, 1999). Parents who enforce control parameters that are too rigid may negatively influence their adolescents’ interest and motivation, which can contribute to leisure boredom (Sharp, Caldwell, Graham, & Ridenour, 2006). Because of the limitation of generalizability when studying leisure boredom, we know little about associations between parenting (e.g., parental involvement and supervision) and more general forms of adolescent boredom.

Peer Context

Peers play an integral part in adolescents’ achievement of developmental tasks, such as identity formation, autonomy from parents, and intimacy (Steinberg & Morris, 2001). Partaking in activities with peers can provide engaging experiences that promote positive adolescent development (Smetana, Campione-Barr, & Metzger, 2006). This contrasts directly with the monotony, disinterest, and a generally negative affective state that characterize boredom (Eastwood et al., 2012; Harris, 2000; Hunter & Csikszentmihalyi, 2003; Martin, Sadlo, & Stew, 2006). Furthermore, it is possible that the likelihood of experiencing boredom may be contingent upon the specific types of peer interactions that occur. Peer interactions can be quite variable, ranging from in-person socializing to virtual exchanges. Social networking illustrates an increasingly popular method of interacting with peers. According to the Pew Research Center (Lenhart, 2015), 71% of American adolescents use Facebook, followed by 52% who use Instagram, and 41% who use Snapchat. Although social networking allows for adolescents to connect with peers, it can be a relatively passive way of interacting. One of the few existing studies that investigated the relation between social networking and adolescent boredom focused primarily on the alleviation of boredom as a motivating factor for social networking (Gilbert & Barton, 2013). It is less clear, however, if the association between social networking and boredom might occur in the opposite direction. That is, a greater amount of time spent social networking may actually lead to higher boredom.

Extracurricular Context

Involvement in different extracurricular contexts, such as athletics and volunteering, allows adolescents to partake in engaging experiences and gain a sense of satisfaction from setting and achieving goals. In addition, extracurricular contexts also tend to provide structured time use (Fredricks & Eccles, 2006). The study of leisure boredom is one particular area of research specifically focused on the experience of boredom during free time, including time spent in extracurricular contexts (Caldwell et al., 1999; Iso-Ahola & Weissinger, 1990). In their analysis of leisure boredom among adolescents, Caldwell et al. (1999) found a negative association between boredom and extracurricular contexts that provided opportunities for autonomy development. Involvement in extracurricular contexts, such as community service and athletics, may lessen boredom by offering opportunities for physical and mental stimulation that promote positive psychosocial development. Despite the fact that involvement in extracurricular contexts, such as community service activities and athletics, may be protective against high boredom and its negative outcomes, the association between extracurricular contexts and more general boredom, beyond that of boredom in the leisure context, remains unknown.

Research Questions

Given the dearth of empirical research on adolescent boredom, coupled with the association between boredom and adolescent difficulties, the purpose of the present study is to provide a broad analysis of the prevalence and correlates of high boredom among nationally representative samples of U.S. 8th and 10th graders. Students in the 8th and 10th grades are approximately between the ages of 13 and 16 years, which coincides with the typical escalation and peak in levels of boredom (Caldwell et al., 1999; Larson & Richards, 1991). Due to evidence indicating that boredom can be a common experience during adolescence (Hunter & Csikszentmihalyi, 2003; Larson & Richards, 1991), we focus on the less common, but more problematic, level of high boredom. Our research questions are as follows:

Research Question 1: How prevalent is high boredom among American adolescents, broadly?

Research Question 2: To what extent are adolescents’ sociodemographic characteristics, including grade level, gender, race/ethnicity, urbanicity, and parent education (as a proxy for socioeconomic status [SES]), associated with high boredom?

Research Question 3: Controlling for sociodemographic characteristics, which social contexts (school, parent, peer, and extracurricular) are associated with high boredom?

Addressing these research questions will identify youth most vulnerable for high levels of boredom and provide a greater understanding of sociocontextual risk factors that may be informative to reduce boredom and its associated harms.

Method

Study Design

Data were drawn from the national Monitoring the Future (MTF) study, an ongoing study of adolescents and young adults (Miech, Johnston, O’Malley, Bachman, & Schulenberg, 2015). MTF selects national samples of 8th- and 10th-grade students through a three-stage sampling procedure, where geographic areas, schools in each geographic area, and specific classes within each school are randomly selected. Sampling weights were used to correct for differential probabilities of selection. Approximate response rates were 90% for 8th graders and 86% for 10th graders. Less than 1% of students refused to complete the questionnaire, and the large majority of nonresponse was due to absenteeism on day of data collection. Students completed a self-administered, machine-readable paper questionnaire in their classroom during normal school hours. Participants were randomly assigned within classrooms to complete one of four questionnaire forms. The boredom questions were included on only one form, which was the form used for the present study. All procedures are reviewed and approved on an annual basis by the University of Michigan’s Institutional Review Board (IRB) for compliance with federal guidelines for the treatment of human subjects.

Sample

To include study participants falling within the age range when boredom is most common (Caldwell et al., 1999; Larson & Richards, 1991), the present study included three consecutive cohorts of 8th- and 10th-grade students from 2009 through 2011. The total sample included 21,173 students, which consisted of 48.7% 8th graders and 51.3% 10th graders (modal ages 14 and 16, respectively). The combined sample of 8th and 10th graders was 51.8% female and 65.6% White, 11.3% Black, 11.2% Hispanic, 6.3% Biracial, 3.4% Asian, and 2.1% Native Peoples. In our sample, 24.4% of students lived in a rural area, 29.5% lived in an urban area, and 46.2% lived in a suburban area. Overall, 74.8% of participants reported at least one parent had at least some college education. Additional sample information is shown in Table 1.

Table 1.

Sample Sizes by Sociodemographic Subgroups.

Sociodemographic characteristics	n	%
Grade
8th	10,313	48.71
10th	10,860	51.29
Gender
Male	10,197	48.16
Female	10,976	51.84
Race/ethnicity
White	13,895	65.63
Black	2,400	11.34
Hispanic	2,370	11.19
Biracial	1,335	6.31
Asian	725	3.42
Native	448	2.12
Urbanicity
Rural	5,156	24.35
Urban	6,246	29.50
Suburban	9,771	46.15
Parent education
≥Some college	15,834	74.79
≤High school degree	5,338	25.21

Note. Weighted N = 21,173.

Measures

Outcome variable: Boredom

Boredom was measured with two items on the MTF survey (Miech et al., 2015): “I am often bored” and “I often find myself with nothing to do.” These two items were added to the MTF survey in 2009, following consultation with an expert in the field of boredom research (L. L. Caldwell, personal communication, September 22, 2008) and by referencing the Boredom Proneness Scale (Farmer & Sundberg, 1986). Both items were asked within the overall question of “How much do you agree or disagree with the each of the following statements?” Response options were 1 = disagree, 2 = mostly disagree, 3 = neither, 4 = mostly agree, or 5 = agree. The mean of these two items (α = .84) formed the boredom scale. Responses to boredom were nearly evenly spread across response option quintiles. High boredom (coded as 1) was defined as a mean greater than or equal to 4.5, and medium/low boredom (referred to as low boredom in the remainder of this article) was defined as a mean of 4.0 or less (coded as 0). Sensitivity analyses revealed that changing the cut point to 4.5 to dichotomize the boredom scale slightly altered the coefficients but not significance levels or directionality of associations.

Sociodemographic measures

Sociodemographic measures included (a) grade level (8th or 10th grade), (b) gender (male or female), (c) race/ethnicity (White, Black, Hispanic, Asian, Biracial, or Native Peoples), (d) urbanicity (rural, urban, or suburban) and (e) parents’ highest level of education (at least some college vs. high school degree or less). To report race/ethnicity, the questionnaire instructed students to answer the following question: “How do you describe yourself? Select one or more responses.” Response options were coded as White (White or Caucasian), Black (Black or African American), Hispanic (Mexican American or Chicano, Cuban American, Puerto Rican, Other Hispanic or Latino), Asian (Asian American), or Native Peoples (American Indian/Alaskan Native or Native Hawaiian/Other Pacific Islander). Students who selected two race/ethnicity categories were identified as Biracial.

The urbanicity measure was formed by a combination of a self-report question asking students to indicate the type of residential area where they currently lived and a school-level population density variable used in the sampling process as a stratification variable. For the self-report question, “Where are you living now?”, the response options were “on a farm,” “in the country not on a farm,” or “in a city or town.” The population density variable consisted of categorizations used by the U.S. Census Bureau that include non-Metropolitan Statistical Area (MSA; rural areas), other MSA (suburban area), and large MSA (large urban area). Categories indicating rural, urban, and suburban areas were created for analysis. For more information on the MTF measure of population density, see Appendix B in Miech et al. (2015).

Parent education was used as a proxy for SES. Previous findings show strong validity for using parent education as a proxy for SES with survey data (Patrick et al., 2011; Ver Ploeg & Perrin, 2004). To assess parent education, students were instructed and asked, “The next questions ask about your parents. If you were raised mostly by foster parents, stepparents, or others, answer for them. For example, if you have both a stepfather and a natural father, answer for the one that was most important in raising you. What is the highest level of schooling your father/mother completed?” Response options included completed grade school or less, some high school, completed high school, some college, completed college, graduate or professional school after college, or don’t know or does not apply. We formed the parent education measure by combining valid data for one or two parents and then dichotomizing the highest reported parent education into at least some college versus high school degree or less.

School context

We included three measures of the school context: school bonding, average letter grades, and school difficulty. School bonding was measured with a mean of a three-item scale (α = .75): “How often did you (1) enjoy being in school?; (2) hate being in school? (reverse coded); and (3) find your schoolwork interesting?” Responses ranged from 1 = never to 5 = almost always. The school bonding scale has been used in previous MTF analyses (Bryant, Schulenberg, Bachman, O’Malley, & Johnston, 2000; Dever et al., 2012). One item measured average letter grades in the current school year (1 = D or lower to 9 = A+) and another single item measured school difficulty, “How often do you find the schoolwork too hard to understand?” with response options from 1 = never to 5 = almost always. Correlations among school context measures are shown in Table 2, along with correlations among all social context measures and high boredom.

Table 2.

Intercorrelations Among Social Context Items and High Boredom.

	1.	2.	3.	4.	5.	6.	7.	8.	9.	10.	11.
School
1. School bonding	1
2. Average letter grade	.29	1
3. School difficulty	−.20	−.34	1
Parent
4. Parental involvement	.25	.19	−.08	1
5. After-school supervision	.10	.13	−.09	.17	1
Peer
6. Peer socializing	−.03	−.09	.06	−.04	−.12	1
7. Evenings out with friends	−.05	−.07	.01^†	−.05	−.13	.44	1
8. Social networking	−.07	−.10	.09	−.12	−.08	.25	.15	1
Extracurricular
9. Community service	.24	.20	−.08	.19	.05	.09	.04	.01^†	1
10. Athletic involvement	.13	.20	−.10	.16	.05	.20	.18	.03	.19	1
11. High boredom	−.23	−.17	.13	−.16	−.05	−.18	−.15	.02	−.14	−.19	1

Note. All correlations are significant (p < .001) except where indicated (^†).

Parent context

We included two measures of the parent context: parental involvement and after-school supervision. Parental involvement was measured with the mean of a four-item scale (α = .63). Items included the following: “How often do your parents (or stepparents or guardians) (1) check on whether you have done your homework?; (2) provide help with your homework when it’s needed?; (3) require you to do work or chores around the home?; and (4) limit the amount of time you can spend watching TV?” Response options were 1 = never to 4 = often for each of the four questions. The scale has been used in previous analyses of MTF data (e.g., Pilgrim, Schulenberg, O’Malley, Bachman, & Johnston, 2006). After-school supervision was included as an additional parent context item. This item asked, “How many hours after school each day are spent home alone with no adult present?” Response options ranged from 1 = none or almost none to 6 = more than 5 hours. This item was reverse coded to indicate greater after-school supervision. Correlations among parent context measures are shown in Table 2.

Peer context

We included three measures of the peer context: peer socializing, evenings out with friends, and online social networking. Peer socializing was measured by a two-item scale (α = .56). These items indicate how often students reported that they (1) “Get together with friends informally (in your free time)” and (2) “Go to parties or other social affairs.” Response options ranged from 1 = never to 5 = almost every day. Also included in the peer context was evenings out with friends, which was assessed by the following question: “During a typical week, on how many evenings do you go out for fun and recreation? (Don’t count things you do with your parents or other adult relatives).” Response options ranged from 1 = less than one evening per week to 6 = six or seven evenings per week. Evenings out with friends was not included in the peer socializing scale due to its differing response scale. An additional peer context item included a measure of how often students report using social networking websites, such as Facebook. Response options include 1 = never to 5 = almost every day. Correlations among peer context measures are shown in Table 2.

Extracurricular context

We included two measures of the extracurricular context: community service and athletic involvement. First, we included frequency of participation in community affairs or volunteer activities (e.g., Syvertsen, Wray-Lake, Flanagan, Osgood, & Briddell, 2011), referred to in the present study as community service. For this item, students were asked how often they “Participate in community affairs or volunteer work.” Second, we used an item on frequency of participation in sports, athletics, or exercising (e.g., Dever et al., 2012), referred to as athletic involvement. Students were asked how often they “actively participate in sports, athletics, or exercising.” Response options for both questions ranged from 1 = never to 5 = almost every day. Correlations among extracurricular context measures are shown in Table 2.

Data Analyses

All data analyses were performed using SAS version 9.3 (SAS Institute, Inc., Cary, NC, USA). Pearson’s correlations were used to examine associations between social context items and high boredom. PROC SURVEYLOGISTIC was used for the bivariate and multivariable logistic regression models to account for the complex sample design and weight the sample for differential probabilities of selection. The NOMCAR (not missing completely at random) option of the SURVEYLOGISTIC procedure was included to allow for the analysis of nonmissing and missing values as separate domains and address the issue of missing data. Percentage of missing values across all variables ranged from 2.90% (peer socializing measure) to 9.83% (parent education measure). Students who reported three or more races/ethnicities were coded as missing data, due to the heterogeneity and low sample size (2.8% of the total sample) of this group.

To examine prevalence rates of boredom among the study sample, PROC SURVEYMEANS was used to compute mean rates of high boredom among the full sample and by sociodemographic subgroups (grade level, gender, race/ethnicity, urbanicity, parents’ education). Bivariate logistic regression analyses were conducted to assess differences in high boredom within each sociodemographic subgroup. Multivariable logistic regression was used to test associations between all sociodemographic subgroups and high boredom (Model 1). We then examined relationships between social contexts (school, parent, peer, and extracurricular) and high boredom through multivariable logistic regression (Model 2). Due to multiple contextual influences commonly experienced during adolescence (Bronfenbrenner, 1979), constructs within the school, parent, peer, and extracurricular contexts were added simultaneously within our analytic model. Model 2 builds on Model 1 to ensure that sociodemographic variation in boredom is accounted for when examining the associations between social context indices and high boredom.

Results

National Rates of High Boredom Among Adolescents

To address our first research question on prevalence of high boredom among American adolescents, broadly, we found that the total sample prevalence rate of high boredom was 19.90%. To address our second research question, as shown in Figure 1, we found considerable sociodemographic variation. Results of bivariate logistic regression showed that high boredom rates were significantly higher for 8th graders (21.16%, standard error [SE] = 0.61) than 10th graders (18.70%, SE = 0.49) (odds ratio [OR] = 1.17, p < .01) and for females (20.75%, SE = 0.55) than for males (18.99%, SE = 0.48) (OR = 1.12, p < .01). Regarding racial/ethnic variation, high boredom rates were greatest for Native youth (28.40%; SE = 2.64), followed by Biracial (23.16%; SE = 1.47), Black (22.44%; SE = 1.14), Hispanic (20.56%; SE = 1.03), White (18.87%; SE = 0.50), and Asian (17.81%; SE = 1.41) youth. Native youth had significantly higher rates than Whites (OR = 1.71, p < .001), Blacks (OR = 1.37, p < .05), Asians (OR = 1.83, p < .001), and Hispanics (OR = 1.53, p < .01); Biracial youth had significantly higher rates than Whites (OR = 1.30, p < .01) and Asians (OR = 1.39, p < .01); and Black youth had significantly higher rates than Whites (OR = 1.25, p < .01) and Asians (OR = 1.34, p < .05). High boredom rates were significantly greater among rural youth (23.92%, SE = 0.86) than among urban (18.01%, SE = 0.64) and suburban (18.98%, SE = 0.60) youth (OR = 1.43, p < .001, OR = 1.34, p < .001, respectively) and among adolescents whose parents reported lower education (25.52%, SE = 0.70) than higher education (18.00%, SE = 0.44; OR = 1.56, p < .001).

Sociodemographic and Contextual Associations With High Boredom

Our second research question pertained to sociodemographic differences in high boredom, including grade level, gender, race/ethnicity, urbanicity, and parent education. Model 1 in Table 3 shows associations between sociodemographic characteristics and high boredom. Results from Model 1 similarly reflect the significant bivariate differences previously described, suggesting differences were largely independent. Including all sociodemographic correlates simultaneously in Model 1, we found that the odds of reporting high boredom were significantly greater for 8th graders compared with 10th graders. Differences by gender indicate that females had higher odds of reporting high boredom than males. Compared with White youth, youth of Black, Biracial, and Native race/ethnicity showed greater odds of high boredom. Among urbanicity groups, urban and suburban youth showed significantly lower odds of high boredom compared with rural students. Finally, youth with parents who completed a high school degree or less showed significantly greater odds of high boredom than those with parents who completed at least some college.

Table 3.

Multivariable Logistic Regression Models for High Adolescent Boredom.

	Model 1	Model 2
	OR (95% CI)	OR (95% CI)
Sociodemographic groups
Grade
8th	1.16 [1.06, 1.27]**	1.28 [1.17, 1.41]***
10th	—	—
Gender
Male	—	—
Female	1.10 [1.01, 1.19]*	1.06 [0.97, 1.16]
Race/ethnicity
White	—	—
Black	1.23 [1.07, 1.41]**	1.17 [1.02, 1.35]*
Hispanic	1.02 [0.88, 1.18]	0.99 [0.85, 1.14]
Biracial	1.28 [1.08, 1.53]**	1.27 [1.06, 1.52]**
Asian	1.00 [0.81, 1.22]	0.98 [0.80, 1.22]
Native	1.57 [1.19, 2.06]**	1.47 [1.08, 2.00]*
Urbanicity
Rural	—	—
Urban	0.72 [0.64, 0.82]***	0.76 [0.67, 0.86]***
Suburban	0.77 [0.68, 0.86]***	0.80 [0.71, 0.90]***
Parent education
≥Some college	—	—
≤High school degree	1.51 [1.37, 1.66]***	1.23 [1.11, 1.36]***
Social contexts
School
School bonding		0.71 [0.67, 0.76]***
Average letter grade		0.94 [0.92, 0.96]***
School difficulty		1.07 [1.03, 1.13]**
Parent
Parental involvement		0.81 [0.76, 0.87]***
After-school supervision		0.96 [0.93, 0.99]*
Peer
Peer socializing		0.74 [0.70, 0.78]***
Evenings out with friends		0.88 [0.85, 0.91]***
Social networking		1.08 [1.05, 1.11]***
Extracurricular
Community service		0.92 [0.88, 0.97]**
Athletic involvement		0.91 [0.88, 0.94]***

Note. Weighted N = 21,173. High boredom measured as a mean greater than or equal to 4.5 on a boredom scale ranging from 1 (low) to 5 (high) and dichotomized to 1 (high) versus 0 (low) for analysis; Model 1 Nagelkerke R² = .02; Model 2 Nagelkerke R² = .11. OR = odds ratio; CI = confidence interval; — = reference group.

p < .05. **p < .01. ***p < .001.

Results of analyses based on our third research question, concerning the extent to which school, parent, peer, and extracurricular contexts are associated with high boredom, are shown in Model 2 of Table 3. Among the school context constructs, adolescents who reported lower school bonding and lower grades had significantly greater odds of high boredom. In addition, adolescents who perceived school as more difficult had greater odds of high boredom. Within the parent context, adolescents whose parents were more involved had lower odds of high boredom. Similarly, greater after-school parental supervision predicted lower odds of high boredom. In the peer context, higher peer socializing was associated with lower odds of high boredom. Likewise, spending more evenings out with friends was associated with lower odds of high boredom. In contrast, greater online social networking predicted greater odds of high boredom. Regarding the extracurricular context, adolescents who reported more frequent participation in community service had lower odds of high boredom, and those who reported more frequent athletic involvement also showed lower odds of high boredom.

Discussion

To our knowledge, the present study provides the first population-level analysis of prevalence rates, sociodemographic differences, and associations between multiple social contexts and adolescent boredom. Using a U.S. national sample, we focused specifically on high boredom because it is most likely to be associated with maladaptation and problematic outcomes. Among the full sample included in the present study, we found that approximately 20% of youth reported high boredom. This finding is concerning, suggesting that high boredom is relatively common among American adolescents. In the following sections, we provide interpretations of our findings on the prevalence of high boredom within sociodemographic subgroups and on the sociodemographic and contextual associations with high boredom.

High Boredom by Sociodemographic Characteristics

In relation to grade level, we found that 8th graders were more likely to report high boredom than 10th graders, suggesting an age curve of declining boredom from middle to late adolescence. This finding builds upon previous research from Larson and Richards (1991) who found boredom tends to peak between 7th grade and 8th grade and then decline thereafter. The many individual and contextual changes during this time likely contribute to this age curve. High school students, such as the 10th-grade students included in the present study, may be more likely to perceive greater freedom and autonomy compared with middle school students. The high school context, compared with middle school, tends to offer more opportunities for interactions with peers, autonomous decision making in class schedule (i.e., electives), and involvement in extracurricular contexts. A study on psychosocial adjustment during the transition from middle school to high school found that adolescents rated increased freedom, making new friends, and engaging in social events as the most beneficial aspects of high school (Akos & Galassi, 2004). Thus, 10th-grade students may be less likely than 8th-grade students to report boredom, because high school may provide more stimulating and enriching experiences.

In terms of gender, females were significantly more likely to be highly bored in comparison with males, although this relationship was no longer significant when we added contextual measures to our model. Thus, gender showed relatively weak associations with boredom. This finding aligns with evidence showing inconsistencies in gender as a correlate of boredom. For example, using the Leisure Boredom Scale (Iso-Ahola & Weissinger, 1990), Wegner and Flisher (2009) found that females were more likely to report leisure boredom compared with males. Conversely, other studies using the Boredom Proneness Scale (Farmer & Sundberg, 1986) show that males tend to have greater tendencies toward boredom (Newberry & Duncan, 2001; Vodanovich et al., 2011; Watt & Vodanovich, 1999). According to the present study, using population-level data and considering boredom more generally, gender does not appear to be a strong correlate of adolescent boredom after controlling for other relevant variables.

Our results indicate several differences in adolescent boredom among racial/ethnic groups. After accounting for other sociodemographic differences, those who identified as Native Peoples (including American Indian/Alaskan Native or Native Hawaiian/Other Pacific Islander) reported the highest rate of high boredom, and Asians and Whites reported the lowest rates. Those who identified as two racial/ethnic groups, which we coded as Biracial, tended to have greater odds of high boredom, but given the heterogeneity of the combinations of races/ethnicities represented in this group, this finding should be considered with caution. We also found that Black adolescents reported relatively high rates of high boredom, a finding consistent with what has been found regarding leisure boredom (e.g., in South Africa; Wegner, Flisher, Muller, & Lombard, 2006). Few studies have examined rates of boredom by race/ethnicity in the United States, and we are aware of no other study that has examined race/ethnicity specifically at high levels of boredom and including such a wide range of racial/ethnic groups as included in the present study. Thus, our findings provide novel information on high boredom among a nationally representative sample of adolescents.

In relation to urbanicity, rural adolescents reported higher rates of high boredom than urban and suburban adolescents. These results corroborate previous findings on adolescents living in rural versus urban settings (Gordon & Caltabiano, 1996; Patterson, Pegg, & Dobson-Patterson, 2000). Urban settings tend to be characterized by an array of activities and resources within close proximity to city residents. Suburban areas also may offer more resources than rural settings and often include similar resources that one would find in a city. The present study is unique in its look at differences in high boredom associated with urbanicity among a large, nationally representative sample.

We found a strong link between parent education and adolescent boredom, whereby higher education was associated with lower boredom. This finding corroborates research on leisure boredom, suggesting that limited resources may be associated with boredom (Wegner & Flisher, 2009). It is important to note that these differences by parent education, a proxy for SES, remained when all contextual measures including parental involvement and supervision were included. Thus, the link between parent education and high boredom may be attributable to differences in opportunities and resources, similar to associations with urbanicity and high boredom.

Contextual Associations With High Boredom

After accounting for sociodemographic characteristics, elements in each of the four contexts included in the present study predicted high levels of boredom. Within the school context, lower school bonding, greater school difficulty, and lower grades were significantly associated with high boredom. In other words, adolescents who held negative views toward their school and were inappropriately challenged by schoolwork experienced a greater likelihood of high boredom. Our findings may reflect the theory of flow (Csikszentmihalyi, 2000; Nakamura & Csikszentmihalyi, 2002), in which adolescents who perceive inappropriate challenge in relation to their skill level may be at a heightened risk for boredom. In addition, the negative association between school bonding and high boredom found in the present study corroborates existing research on the link between higher boredom and lower academic control and value (Pekrun et al., 2010; Pekrun et al., 2014).

In the parent context, we found that lower levels of parental involvement and less after-school parental supervision predicted high boredom. Our findings support research showing that parental support, in combination with enforcement of appropriate control parameters, is protective against adolescent boredom (Caldwell et al., 1999; Sharp et al., 2006). Thus, it is possible that authoritative parenting practices, characterized by parents who are highly responsive yet set appropriate limits (Baumrind, 1991), might be related to lower boredom. Requiring their adolescent child to do chores, homework, and limiting television, coupled with parents who are frequently present in the home, may minimize opportunities for boredom.

Our findings on boredom in the peer context suggest that low peer socializing and fewer evenings out with friends predict high boredom. Considering the importance of peers to facilitate healthy adolescent development (Steinberg & Morris, 2001), it is not surprising that lower levels of peer interactions resulted in high boredom. Yet, although spending time with peers may alleviate boredom, unsupervised peer time may also contribute to delinquent behavior, such as risk-taking and drug use (Albert, Chein, & Steinberg, 2013; Andrews, Tildesley, Hops, & Li, 2002). Also within the peer context, we found that adolescents who spent more time engaging in online social networking were more likely to report high boredom. Although previous research suggests that the intent to alleviate boredom is a reason for greater social networking (Gilbert & Barton, 2013), the passive nature of social networking itself may also contribute to high boredom. Due to the popularity and use of social networking, the circumstances that affect the direction of these effects warrant additional empirical inquiry.

Within the extracurricular context, our study showed that greater involvement with community service and athletics was associated with lower boredom. However, the association between extracurricular contexts and boredom may be contingent upon intrinsic motivation for participation, reflecting the main focus of social control theory (Caldwell & Smith, 2006). Social control theory posits that boredom arises from feeling forced by parents, teachers, coaches, or other adults to take part in activities within the extracurricular context that do not align with their personal goals and desires. For example, Eastwood et al. (2012) described “constraint and disordered agency” as central tenets of boredom (p. 488). Feeling forced to participate in community service, through enforcement from school or the judicial system, may be less effective to reduce boredom. Likewise, pressure to partake in athletics from parents and other adults may be similarly less effective than participation due to an adolescent’s own interests.

Strengths and Limitations

An important strength of the present study is its use of large, nationally representative samples of adolescents, allowing us to consider important subgroups and also to focus on a relatively extreme group reporting high boredom. Because adolescents commonly experience low to moderate levels of boredom, focusing on high boredom identifies those adolescents at greatest risk for maladaptation and problem behavior. Another strength of this study is the breadth of contextual correlates included, allowing us to gain some leverage on the extent to which high boredom is multiply determined. The present study provides a needed broad overview on adolescent boredom and its contextual correlates, setting the stage for future measurement intensive and longitudinal studies that can better examine mechanisms of adolescent boredom.

The main limitations of the present study include the use of cross-sectional data and reliance on self-report measures of limited depth. Cross-sectional data do not allow for consideration of temporal precedence, limiting what can be concluded about causal connections and mechanisms. In addition, our measures are limited, both in terms of being self-report, which can artificially inflate associations, and lacking depth. Our boredom measure included only two items, and the questionnaire offered only a limited number of constructs regarding contextual correlates. Limitations attributable to self-report methods and questionnaire depth are common in large-scale survey data and suggest caution in drawing conclusions about the contextual correlates of boredom.

Conclusion

Overall, findings from the present study indicate that a subset of adolescents may experience boredom more intensely than others. In fact, we found that nearly one in five adolescents report particularly high levels of boredom. This is concerning, considering that boredom may lead to developmental, social, and educational difficulties. To identify factors that likely contribute to high boredom, researchers must account for the dynamic, developmental contexts in which adolescents are embedded. Each of these contexts, including school, parent, peer, and extracurricular domains, plays a crucial role in adolescent development and, more specifically, in the experience of boredom. As demonstrated in the present study, high boredom among adolescents is likely multiply determined by these contexts. Determining which adolescents are most at risk to experience high boredom and which contexts are associated with high boredom provides valuable insight for strategies to promote optimal adolescent development.

Footnotes

Acknowledgements

The second author gratefully acknowledges support from the Center for Human Growth and Development, University of Michigan. The authors thank Linda L. Caldwell for assistance in developing the boredom questions used on the Monitoring the Future survey.

Authors’ Note

The content here is solely the responsibility of the authors and does not necessarily represent the official views of the sponsors.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The research conducted in this article was funded by support from the National Institute on Drug Abuse (R01 DA001411 to L. Johnston and T32 DA007267 to P. Gnegy).

Author Biographies

Meghan E. Martz is a doctoral candidate in Developmental Psychology at the University of Michigan and a pre-doctoral trainee on the T32 Ruth L. Kirschstein Institutional National Research Service Award through the National Institutes of Health. Her research investigates psychosocial and neural mechansisms involved in substance use and wellbeing during adolescence through early adulthood.

John E. Schulenberg is a research professor at the Institute for Social Research, professor of Developmental Psychology at the University of Michigan, and president of the Society for Research on Adolescence. His research interests include substance use and psychosocial development during adolescence and transition to adulthood. He helps direct the national monitoring the future study.

Megan E. Patrick is a research assistant professor at the University of Michigan’s Institute for Social Research. Her published research focuses on adolescent and young adult risk behaviors, including alcohol use, drug use, and risky sexual behaviors.

Deborah D. Kloska is a statistician with the Youth & Social Issues Program at the University of Michigan. Her analytical interests focus on the application of statistical methods and procedures in the area of substance use, especially as related to longitudinal data analysis and complex survey design.

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