A latent class analysis of health lifestyles and suicidal behaviors among US adolescents

Journal of Affective Disorders 255 (2019) 116–126

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Research paper

A latent class analysis of health lifestyles and suicidal behaviors among US adolescents

T

Yunyu Xiaoa,b, , Meghan Romanellia,b, Michael A. Lindseya,b ⁎

a b

Silver School of Social Work, New York University, New York, USA McSilver Institute for Poverty Policy and Research, New York, USA

ARTICLE INFO

ABSTRACT

Keywords: Suicidal behaviors Adolescents Health lifestyles Health behaviors Latent class analysis

Background: Previous studies have documented the link between individual health behaviors and suicide, but little is known about the influence of health lifestyles on suicide among adolescents. This study aims to identify the unobserved patterns of health behaviors and to examine their associations with adolescent suicidal behaviors to inform screening of suicidality. Methods: Data were derived from a nationally representative sample of adolescents (n = 14,506, ages 12–18, 50.9% female) in the national school-based 2017 Youth Risk Behavior Survey. Latent class analysis was performed based on 13 health behaviors related to diet (e.g., frequency of consuming breakfast, fruits/vegetables, soda), physical activity (frequencies of physical activity, sports team participation), sleep, and media use (TV/ computers). Suicidal behaviors were measured by three dichotomized variables, including suicidal ideation, plan, and attempts. Multivariate logistic regressions were used to examine associations between identified classes and suicidal behaviors. Results: Four classes of health lifestyles were identified. Class 1 (23.6%) consistently engaged in health-promoting behaviors, including eating breakfast daily, high intake of fruits/vegetables, physically active, and infrequent use of TV/computers. Class 2 (37.7%) had an irregular diet, moderate exercise, and high computer use. Class 3 (31.8%) had moderate diet, frequent exercise, and moderate sleep. Class 4 (6.9%) had the lowest engagement in health-promoting behaviors. Class 4 had higher odds of suicide plan than Class 1 (OR = 1.50, 95% CI = 1.10–2.05). Notably, Class 2 and 3 were less likely to attempt suicide than Class 1 (OR = 0.74, 95% CI = 0.57–0.95 for Class 2; OR = 0.65, 95% CI = 0.48–0.89 for Class 3). Limitations: Due to the cross-sectional design, no causal inference can be drawn. Conclusions: Both Class 1 (consistent) and Class 4 (lowest) engagement in health-promoting behaviors were associated with increased suicidal behaviors. Suicide prevention efforts that examine both lifestyles are keys to early detection of suicidal ideation and plans, and prevention of suicide attempts.

1. Introduction

to mention social and economic costs on the society at large (GoldmanMellor et al., 2014). The identification of risk factors for suicide is imperative to advancing our understanding of youth suicidal behaviors and suicide prevention (Nock et al., 2008; U.S. Department of Health and Human Services and Office of the Surgeon General and National Action Alliance for Suicide Prevention, 2012). Previous studies have examined psychological, sociological, biological, and environmental risk factors for adolescent suicide (Bridge et al., 2006; Cha et al., 2018), including depression, peer influence, molecular (e.g., alterations in serotonin

Suicide is a significant public health crisis (Bridge et al., 2014; Trent and Cheng, 2010), ranking as the second leading cause of death among adolescents between age 15 and 19 in the United States (Heron and National Center for Health Statistics, 2016). In 2016, a total of 6159 people aged 10–24 died by suicide in the U.S, accounting for 17.3% total deaths within this age group (Centers for Disease Control and Prevention, 2016). Each suicide has devastating physical and psychological impacts on families, friends, and communities of the victim, not

Abbreviations: YRBS, Youth Risk Behavior Survey; YRBSS, Youth Risk Behavior Surveillance System; MHBC, Multiple Health Behavior Change; LCA, latent class analysis; CDC, The Centers for Disease Control and Prevention; PA, Physical Activity; MVPA, Moderate and Vigorous Physical Activity; MSEs, Muscle-Strengthening Exercise; PE, Physical Education; STP, Sports Team Participation ⁎ Corresponding author. E-mail address: [email protected] (Y. Xiao). https://doi.org/10.1016/j.jad.2019.05.031 Received 19 October 2018; Received in revised form 31 January 2019; Accepted 18 May 2019 Available online 20 May 2019 0165-0327/ © 2019 Elsevier B.V. All rights reserved.

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function), and access to lethal means of suicide. Relatively few studies, however, have systematically examined patterns of health behaviors preceding or concurrent with suicidal behaviors.

encompass both health-promoting behaviors (e.g., nutritious diet, regular exercise, sufficient sleep) and health-risk behaviors (e.g., unhealthy diet, sedentary behaviors, deficient sleep; Lawrence et al., 2017; Mulye et al., 2009; Prochaska et al., 2008). To present a more comprehensive picture of health lifestyles among U.S. adolescents, the current study considers health behaviors from four key domains pertaining to habitual daily living activities that play a significant role in suicidal behaviors. These domains include dietary behaviors (Li et al., 2009; Solnick and Hemenway, 2014), physical activities (Lowry et al., 2014; Taliaferro et al., 2008), sleep (BlascoFontecilla et al., 2011; Guo et al., 2017; Lowry et al., 2014; McKnightEily et al., 2011; Weaver et al., 2018), and media use (Carli et al., 2014; Lowry et al., 2014). For example, past research shows that higher levels of soft drink consumption are associated with suicidal thoughts (Solnick and Hemenway, 2014). Insufficient sleep and an excessive amount of time playing video games are related to higher risk of suicidal behaviors among high school students (Lowry et al., 2014). On contrary, frequent sports participation might protect against adolescent suicidality (Taliaferro et al., 2008). Thus, it is important to account for these multifaceted features of health behaviors to examine their associations with suicidal behaviors among adolescents. As such, this study includes a comprehensive assessment of the extent to which adolescents engaged in each health behaviors. We aim to understand how the underlying patterns emerged from the observed behaviors might influence suicidal behaviors. Examining the relationship between health lifestyles and suicidal behaviors adds value to the existing research in several ways. First, focusing on health lifestyles provides us with a psychosocial framework to investigate the proneness for engaging in health behaviors, including a set of personality variables (e.g., values, expectations, beliefs, selfesteem, attitudes, and orientation; Jessor, 1991, 2016). Additionally, identifying atypical health lifestyles can help disaggregate the complex relationship among health behaviors (Laska et al., 2009), which is important for suicide prevention as this may enable an integrative intervention approach across multiple sectors (U.S. Department of Health and Human Services and Office of the Surgeon General and National Action Alliance for Suicide Prevention, 2012). Indeed, recent research suggested that multiple health behavior change (MHBC) interventions (i.e., efforts to promote two or more health behaviors; Pronk et al., 2004) outperformed single-behavior interventions in health promotion (Prochaska et al., 2008; Wilson et al., 2015). Hence, if certain health lifestyles are associated with suicidal behaviors, mental health professionals may need to take a more integrative approach in suicide prevention among adolescents (Paul, 2016).

1.1. Health lifestyles and suicidal behaviors Health lifestyles are defined as collective and organized patterns of broad and interrelated behaviors that are derived from unobservable knowledge and norms (Bourdieu, 1984; Cockerham, 2005; Sobel, 2013). According to the health lifestyle theory, people's health lifestyles are often based on health options available to them, which are in accordance with their life circumstances, socioeconomic status, and cultural backgrounds (Abel and Frohlich, 2012; Bourdieu, 1984; Cockerham, 2013). For example, health lifestyles can be distinguished by age (Lawrence et al., 2017), gender (Cockerham, 2013), race/ethnicity (Cockerham, 2005; Cockerham et al., 2017; Krueger et al., 2011), and cultural preferences (Saint Onge and Krueger, 2011). Hence, investigating patterns of behaviors, rather than single behaviors, may provide more insight into the distinct ways that health behaviors coalesce into meaningful patterns, and how lifestyles are structured by sociodemographic contexts. Extant research documenting the association between health behaviors and suicidal behaviors has routinely focused on single behaviors or one category of health-related behaviors (e.g., smoking, drinking, and drug use, which all fall under the domain of 'substance use'; Peltzer and Pengpid, 2015; Saint Onge and Krueger, 2017). Health lifestyle theory, however, suggests that analyzing the influence of a single health risk behavior offers limited explanatory power to understand the etiology of suicidal behaviors because it does not account for the fact that behaviors are usually not isolative, but co-occur with one another (Ames and Leadbeater, 2018; Ellis and Trumpower, 2008; VermeulenSmit et al., 2015). Furthermore, this approach does not account for how health behaviors tend to cluster in ways that reflect the social and structural contexts of individuals, and influence their health outcomes (Cockerham, 2005). For example, Blacks have lower levels of vigorous exercise than their white counterparts, a phenomenon which can be explained by racial and ethnic disparities that Blacks experience, such as the higher level of segregation of Black people living in disadvantaged neighborhood and having limited access to after-school programming (Cubbins and Buchanan, 2009; Grzywacz and Marks, 2001). Previous studies examining patterns of health behaviors have predominantly focused on health-risk behaviors (e.g., Ellis and Trumpower, 2008; Juan, 2010; Zweig et al., 2001). Additionally, these studies were mostly conducted among adults (Morris, 2016; Paul, 2016; Vermeulen-Smit et al., 2015). There is a paucity of research that quantitatively examines how multiple health behaviors may cluster together to create health lifestyles among adolescents (Burdette, 2017). Moreover, while prior research has associated health lifestyles with physical health (Burdette, 2017; Lawrence et al., 2017), Axis-I disorders (Vermeulen-Smit et al., 2015), and psychological distress (Conry, 2011), only a handful of studies have examined the influences of health lifestyles on suicidal behaviors among adolescents.

1.3. The present study This study expands upon past research by 1) using latent class analysis (LCA) to identify and characterize health lifestyles, 2) exploring demographic and psychological characteristics of the identified classes, and 3) examining associations between health lifestyles and suicidal behaviors among a nationally representative sample of high school students. Rather than analyzing each health behavior in isolation, LCA allows for a person-centered approach to understand how a wide range of health behaviors may cluster together to reflect distinct health lifestyles within a large and heterogeneous population. The current study is unique as it includes various domains of health behaviors and levels of engagement in them, rather than setting cutoffs to predetermine health-risk or health-promoting behaviors. Understanding the association between health lifestyles and suicidal behaviors is imperative for detecting the most at-risk subgroups. Through matching tailored programs to different subgroups and targeting high-risk adolescents, resources can be leveraged more effectively for suicide prevention.

1.2. Measurement of health lifestyles A focus on health lifestyles as a constellation of multiple health behaviors allows for a person-centered perspective, i.e., an aggregation of individuals based on the behaviors they perform (Burdette, 2017; Saint Onge and Krueger, 2017). Existing studies on health lifestyles, however, attempted to measure health behaviors using narrow questions centered on one category of health-risk behaviors (e.g., substance use; Gilreath et al., 2014; Peltzer and Pengpid, 2015). For instance, the pattern of more intense smoking was found to associated with higher risk of suicidal behaviors (Gilreath et al., 2012). Yet, health behaviors 117

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2. Methods

previous literature (Guo et al., 2017; McKnight-Eily et al., 2011),. Media use contained two dichotomized variables about whether the students watched TV or played computer/video games for more than three hours on an average school day, a categorization that is in line with the recommendations by the American Academy of Pediatrics (Strasburger et al., 2013). The original questions and coding mechanisms of the health behaviors are provided in Appendix Table 1.

2.1. Design and participants The Centers for Disease Control and Prevention (CDC) developed the Youth Risk Behavior Surveillance System (YRBSS) to monitor a wide range of prioritized health-risk behaviors among youth in the U.S. The National Youth Risk Behavior Survey (YRBS) is a school-based cross-sectional survey that has been conducted biennially since 1991. In 2017, a three-stage cluster sampling design was used to produce a nationally representative sample of 9th- through 12th- grade students attending public and private schools in the 50 states and the District of Columbia. A weighting factor was applied to each record to adjust for nonresponse and the oversampling of Black and Hispanic/Latino students. The institutional review board at the CDC approved the protocol of national YRBS. Student participation in the survey was anonymous and voluntary through a self-administered computer-scannable questionnaire, and local parental permission procedures were used. The school response rate was 75%, the student response rate was 81%, and the overall response rate was 60%. Usable data were returned by 14,765 students. More details regarding the sampling strategies and psychometric properties of the YRBS questionnaire were reported elsewhere (Kann et al., 2018). The analytic sample in this study consisted of 14,506 students in 2017 YRBS, which excluded individuals who had missing information on all the 13 questions of health behaviors.

2.2.2. Suicidal behaviors Suicidal behaviors were assessed with the following three questions: (1) “During the past 12 months, did you ever seriously consider attempting suicide?” (suicidal ideation), (2) “During the past 12 months, did you make a plan about how you would attempt suicide?” (suicide plan), and (3) “During the past 12 months, how many times did you actually attempt suicide?” (suicide attempts). Response options were dichotomized into yes or no. These questions have been demonstrated with substantial reliability (Brener et al., 2002). 2.2.3. Covariates Covariates included demographic and psychological variables. Demographic characteristics included age (continuous, from 12–18 years old), gender (females = 1), and self-reported race/ethnicity (nonHispanic White, non-Hispanic Black, Hispanic, Asian, and all other racial/ethnic groups), which have been suggested to influence health lifestyles by health lifestyle theory (Cockerham, 2005, 2013), and have been found to affect suicidal thoughts and behaviors among adolescents (Cha et al., 2018). To adjust for the impact of psychological distress, one variable assessing depressed affect was included. Students were asked, “During the past 12 months, did you ever feel so sad or hopeless almost every day for two weeks or more that you stopped doing some usual activities?” Response options were dichotomized into yes or no. Although YRBS does not provide diagnostic criteria for major depressive episodes, this question has been widely used as an indicator of depressed mood at school (Peña et al., 2016). Besides, “sadness” and “hopelessness” were two of the descriptors used in the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5), and the twoweek period is in line with the required time frame of the DSM-5 to meet the criteria for diagnosing MDE (American Psychiatric Association, 2013). The reliability and validity of this construct have been reported previously (Brener et al., 1999, 2002, 2013).

2.2. Measurements 2.2.1. Health behaviors indicators Thirteen items in the 2017 YRBS were recoded to encompass four domains of health behaviors, including (1) diet (consumption of breakfast, fruits, fruit juices, vegetables, milk, water, and soda); (2) physical activities (moderate and vigorous physical activity [MVPA], muscle-strengthening exercise [MSEs], and sports team participation [STP]); (3) sleep; and (4) media use (television [TV], computer/video games). These four domains of health behaviors were topics and objectives covered in the Healthy People 2020 (National Center for Health Statistics, 2016). All the cutoff scores were based on recommendations from federal guidelines and significant thresholds used in previous studies on health behaviors and suicidal behaviors (e.g., Carli et al., 2014; Lowry et al., 2014; McKnight-Eily et al., 2011; Solnick and Hemenway, 2014; Swahn et al., 2009; Taliaferro et al., 2008). For breakfast consumption, responses were categorized as eating 0 days, not eating daily, and eating daily per week, which was consistent with previous studies (Fleary, 2017; Kim et al., 2016). Consumptions of fruits and fruit juices were categorized as eating 0 days, not eating daily, eat once per day, and eat more than twice per day, while for vegetables the last category was eating more than three times per day. Frequencies of drinking water, milk and soda were categorized into 0 days, drink but not daily, drink 1–2 times per day, and drink more than three times per day. These cutoffs were based on the 2015–2020 Dietary Guidelines for Americans and previous studies (Fleary, 2017; Jones et al., 2011; Kim et al., 2016; Lowry et al., 2015; U.S. Department of Health and Human Services and U.S. Department of Agriculture, 2015). MVPA was categorized as 0 days, 1–2 days, 3–4 days, and more than 5 days per week. MSEx and the number of sports team were categorized as 0, 1–2, and more than three days (teams). Doing MVPA at least 5 times per week, MSEx more than 3 days, and participating at least one sports team were recommended by the 2008 Physical Activity Guidelines for Americans (U.S. Department of Health and Human Services, 2008) and previous research (Carli et al., 2014; Eaton et al., 2011; Fleary, 2017; Lowry et al., 2014; Taliaferro et al., 2008). Sleep hours were categorized into three groups: less than 5 h, 6–8 h, and more than 9 h. This is based on the National Sleep Foundation's Updated Sleep Duration Recommendations (Hirshkowitz et al., 2015) and

2.3. Statistical analysis 2.3.1. Descriptive statistics Descriptive statistics for the demographic characteristics and depressed affect of the study sample and comparisons across groups were analyzed using Stata version 13.0 (StataCorp., College Station, TX). To ensure the representativeness of model estimates, complex survey design was accounted for by employing sampling weights, stratum, and clustering (primary sampling units). 2.3.2. Latent class analysis LCA was conducted using Mplus version 7.4 (Muthén and Muthén, 1998–2015) to identify and describe unobserved classes of health lifestyles among adolescents. LCA is a person-centered methodological approach that helps to elucidate population heterogeneity within observed data through the identification of underlying subgroups of individuals, thus allowing the empirical examination of distinct health lifestyles while addressing the diverse nature of population (Laska et al., 2009). Membership in subgroups is based on the similarities in individual responses to questions related to a set of observed behaviors. A 3-step approach to modeling was utilized so that the measurement model remained fixed when introducing the covariates (Asparouhov and Muthén, 2013; Vermunt, 2010). Health behavior indicators were treated as categorical variables. Since the exact number of latent classes 118

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representing health lifestyles was unknown, an exploratory approach was used, which started with the most parsimonious 1-class model and fitted successive models with increasing numbers of classes. Each latent class solution was replicated 20 times beginning at random starting values. This method included a close examination of item loadings and model fit indices for estimated latent classes (Asparouhov and Muthén, 2013; Vermunt, 2010). The final number of classes was determined based on the conceptual meaning, smallest estimated class proportions (Nylund et al., 2007), entropy (Asparouhov and Muthén, 2013), and statistical model fit indices (Nylund et al., 2007), such as the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and adjusted BIC. Latent classes with less than 5% of the total sample were not considered due to the possibility of class over-extraction in the presence of non-normal data (Bauer and Curran, 2003) and poor generalizability (Finch and Bolin, 2017). Maximum likelihood estimation with robust standard errors incorporating all available data was used to deal with missing data and to estimate parameters. Mplus accounted for the complex survey design by correcting the standard errors and Chi-square tests of model fit (Muthén and Muthén, 1998–2015).

is too small to be generalized to a broader population (Bauer and Curran, 2003; Finch and Bolin, 2017). Among the remaining models (2, 3 and 4 classes), the 4-class model had the lowest BIC and adjusted BIC. The entropy of the 4-class model (0.724) was beyond the criteria for good class separation (i.e., entropy = 0.60; Asparouhov and Muthén, 2013) and was the highest among all estimated models. Given that the 4-class solution also provided the most conceptually coherent description of health lifestyles, it is chosen as the most appropriate solution. Table 3 shows the estimated item probabilities for the four identified latent classes. Class 1 (consistent engagement in health-promoting behaviors; 23.6%) included adolescents reporting the highest probabilities of a healthy dietary pattern (i.e., eating breakfast daily, consuming fruits/fruit juices once and ≥2 times/day, eating vegetables once and ≥3 times/day, drinking milk 1–2 times and ≥3 times/day, drinking water ≥3 times/day, and not drinking soda), frequent physical activity (participating more than three STPs, high probability of MVPA and MSEx over 3 days), sleep more than 9 h, but the lowest probability of watching TV or playing computer/video games for ≥3 h/ day. Class 2 (irregular diet, moderate exercise, high computer use; 37.7%) consisted of adolescents who had an irregular diet (i.e., eating breakfast but not daily, lowest intake of fruit juices and milk, drinking soda but not daily), moderate levels of exercise (particularly MVPA 1–4 days and MSEx 1–2 days), and the highest probability of playing computer/video games for ≥3 h/day. Class 3 (moderate diet, frequent exercise, moderate sleep; 31.8%) contained adolescents who had a moderate dietary pattern (least chance of not eating breakfast, eating fruits, vegetables, fruit juices, and milk but not daily), frequent exercises (MVPA ≥5 days, MSEx ≥3 days, participating 1–2 sports team), and moderate number of sleep hours (6–8 h). Class 4 (lowest engagement in health-promoting behaviors; 6.9%) consisted of adolescents who had the lowest probabilities of eating breakfast, fruits, vegetables, milk, and water. They had the highest probabilities of drinking soda excessively (≥3 times/day), the lowest frequency of physical activity participation (0 days of MVPA and MSEx, 0 sports team), as well as the highest probability of insufficient sleep (≤5 h) and high TV use (≥3 h/day).

2.3.3. Bivariate analyses and multivariate logistic regression After identifying the appropriate number of latent classes, a series of cross-tabulations and bivariate analyses (through Chi-square tests and Analysis of variance [ANOVA]) were conducted to examine the distribution of and associations with suicidal behaviors, depressed affect, and demographic characteristics by classes. Prior to the multivariate logistic regressions, intraclass correlation (ICC) was calculated by dividing the random intercept variance by the sum of the random intercept and residual variances in a null multilevel logistic model to determine whether there is a need to apply a multilevel model. The cutoff points of the decision were consistent with previous suggestions (Cohen, 1988), in which an ICC value lower than 0.059, between 0.059 and 0.138, above 0.138 indicated low, medium and high ICC, respectively. Low ICC indicates that there is no need to consider a multilevel approach (Cohen, 1988). Multivariate logistic regressions were performed using the identified classes to predict suicidal ideation, plan, and attempts sequentially, adjusting for demographic characteristics and depressed affect. Statistical significance was taken as a 2-sided p < .05.

3.3. Distribution of suicidal behaviors and covariates by health lifestyle classes

3. Results 3.1. Descriptive statistics Of 14,765 adolescents who participated in the 2017 YRBS, 14,506 participants without missing values in the 13 health behavior indicators were included (missingness < 5.0%). The excluded sample (n = 259) were younger, had more male, Black and Hispanic adolescents than the included sample (p <0.001, See Table 1). Students in the included sample had relatively equal distribution in gender (49.2% male). The majority of the participants were White students (53.8%), followed by 22.8% Hispanics, 13.1% Blacks, 4.3% Asian, and 6.0% students who reported other race/ethnicity. The mean age of students was 16.01 (SD=1.25). Significant differences were found between gender and racial/ethnic groups across all suicidal behaviors. Females reported higher rates of suicidal behaviors than males. Blacks were less likely to have suicidal ideation but higher chances of attempting suicides than White and Hispanic students.

Based on Table 4, there are significant differences in the distribution of suicidal behaviors, demographic and psychological characteristics across the identified classes. Class 4 (lowest engagement in health-promoting behaviors) had the highest percentages of suicidal ideation (23.3%), suicide plan (20.4%) and suicide attempts (14.7%) than other classes (p <0.001), while Class 3 (moderate diet, frequent exercise, moderate sleep) had the lowest rates of suicidal behaviors (13.0%, 10.7% and 5.5% for ideation, plan, and attempts, respectively). The percentage of depressed affect was the highest in Class 4 (37.4%), followed by 36.1% in Class 2 (irregular diet, moderate exercise, high computer use), 28.4% in Class 1 (consistent engagement in health-promoting behaviors), and 27.0% in Class 3. Concerning demographic characteristics, Class 4 had the highest percentages of older (Mage = 16.06, SD = 1.37), Black (20.8%) and Hispanic (23.0%) adolescents, while Class 3 and Class 1 had the highest percentages of White (55.3%) and Asian adolescents (4.7%), respectively.

3.2. Latent class models of adolescent health lifestyles

3.4. Health lifestyles and suicidal behaviors

The AIC, BIC and adjusted BIC decreased until an 11-class solution was found (Table 2). Although the AIC, BIC and adjusted BIC were slightly better for 5- to 11- group solutions, each of them included a latent class that contained a very small percentage of the sample, which

Prior to examining the predictors of suicidal behaviors, results of ICC indicated a value of 0.0078 (p <0.05, 95% CI = 0.001–0.014). An ICC lower than 0.059 indicated that the within-subject variation is very small and can be ignored. Hence, there is no need to consider a 119

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Table 1 Descriptive statistics and group comparisons.a

Age (M ± SD) Gender, n (%) Female Male Race/ethnicity, n (%) White Black Hispanic Asian All other race Depressed affect (%)

Total (n = 14,765)

Included (n = 14,506)

Excluded (n = 259)

pb

Suicidal ideation (n = 2532, 17.7%)

pc

16.01 (1.26)

16.01 (1.25)

15.62 (1.33)

16.03 (1.28)

50.7 49.3

50.9 49.2

33.2 66.9

<0.001 <0.010

.544 <0.001

53.5 13.4 22.9 4.3 6.0 31.5

53.8 13.1 22.8 4.3 6.0 31.5

14.8 50.4 29.4 2.0 3.4 30.0

<0.001

.742

65.6 34.4 54.5 11.3 21.8 4.4 8.0 81.3

<0.01

<0.001

Suicide plan (n = 1990, 13.9%)

64.5 35.5 50.8 12.4 22.7 5.4 8.8 79.2

pc .613 <0.001 <0.01

<0.001

Suicide attempts (n = 827, 7.8%)

66.3 33.7 47.6 14.5 25.2 3.8 8.9 83.2

pc <0.05 <0.001 <0.01

<0.001

Note: In Table 1, the left side presents the comparison between included and excluded sample. The right side shows the bivariate analysis between suicidal behaviors and demographic characteristics as well as depressed affect. a All means and percentages are weighted to be representative of the state population of high school students by student gender, age, and race/ethnicity. Sex, age, and race/ethnicity are based on participant self-report. b Wilcoxon rank-sum (Mann-Whitney) test (for age) and Pearson Chi-squared statistics with the second-order correction of Rao-Scott Chi-square test (for gender, race/ethnicity) between included and excluded samples, correcting for the complex survey design. c Wilcoxon rank-sum (Mann-Whitney) test (for age) and Pearson Chi-squared statistic with the second-order correction of Rao-Scott Chi-square test (for gender, race/ethnicity) between having and not having suicidal behaviors, correcting for the complex survey design.

suicide (OR = 1.64, 95% CI = 1.16–2.32) than their White counterparts. Besides, Asian adolescents and those from other racial/ethnic groups were 1.51 times (95% CI = 1.05–2.17) and 1.47 times (95% CI = 1.11–1.96) more likely to have a suicide plan compared to White adolescents. Having depressed affect was significantly associated with higher odds of suicidal ideation (OR = 15.28, 95% CI = 13.05–17.89), plan (OR = 11.54, 95% CI = 10.09–13.21) and attempt (OR = 12.15, 95% CI = 9.01–16.39).

Table 2 Summary of latent class model identification and fit statistics. No. of classes

AIC

BIC

Adjusted BIC

1 2 3 4 5 6 7a 8 9 10 11

359,145.0 347,820.2 343,665.0 341,261.0 340,019.4 338,969.0 338,275.1 337,682.1 337,078.9 336,734.3 336,343.3

359,380.0 348,297.9 344,385.4 342,223.9 341,225.0 340,417.2 339,965.9 339,615.6 339,255.1 339,153.1 339,004.7

359,281.5 348,097.7 344,083.5 341,820.3 340,719.7 339,810.3 339,257.2 338,805.2 338,343.0 338,139.3 337,889.2

Smallest class, %

Entropy

48.6% 25.3% 6.9% 6.3% 5.9% 1.6% 1.7% 1.2% 1.1% 1.2%

0.692 0.696 0.724 0.716 0.688 0.696 0.678 0.672 0.666 0.663

4. Discussion This study identified four distinct health lifestyles using LCA with 13 health behavior indicators among U.S. high school students. Patterns of health behaviors are characterized by various levels of engagement in health behaviors across multiple domains and are differentially associated with suicidal behaviors. The presence of underlying and distinct health lifestyles among adolescents contributes substantially to the theoretical and empirical literature (Cockerham, 2013; Jessor and Jessor, 1977; Lawrence et al., 2017; Mize, 2017). Differences in demographic characteristics and depressed affect were observed across heterogeneous classes, supporting the health lifestyle theory that structural factors (e.g., age, gender, and race/ethnicity) influence individuals’ health lifestyles (Cockerham, 2005, 2013). To our knowledge, our study is one of the first studies to identify associations between health lifestyles and suicidal behaviors among adolescents using LCA with nationally representative data.

Note. AIC, Akaike information criterion; BIC, Bayesian information criterion. Bolded row represents the identified model. a Error message obtained during model estimation stating that the best loglikelihood value was not replicated and that there may be problems with model identification or with local maxima. The best likelihood was replicated after increasing the number of random starts to 200 (model fit shown in the table), and still obtained when the random starts increased to 400.

multilevel approach (Cohen, 1988). Table 5 presents the results of multivariate logistic regressions examining associations between health lifestyles and suicidal behaviors, adjusting for covariates. Compared to Class 1 (consistent engagement in health-promoting behaviors), individuals with the lowest engagement in health-promoting behaviors (Class 4) were 1.5 times more likely to have suicide plan (95% CI = 1.10–2.05). Adolescents in Class 2 (irregular diet, moderate exercise, high computer use) and Class 3 (moderate diet, frequent exercise, moderate sleep), however, showed lower odds of suicide attempts than respondents in Class 1 (Odds Ratio [OR] = 0.74, 95% CI = 0.57–0.95 for Class 2; OR = 0.65, 95% CI = 0.48–0.89 for Class 3). Moreover, adolescents in Class 3 reported a lower chance of considering suicide (OR = 0.78, 95% CI = 0.66–0.92). In addition, older adolescents reported lower odds of suicide attempts (OR = 0.84, 95% CI = 0.78–0.91). Compared to males, female adolescents were more likely to have suicidal ideation (OR = 1.25, 95% CI = 1.06–1.47) and plan (OR = 1.21, 95% CI = 1.03–1.42). Black and Hispanic adolescents had lower odds of suicidal ideation (OR = 0.79, 95% CI = 0.69–0.89 for Blacks; OR = 0.81, 95% CI = 0.70–0.94 for Hispanics), while Black adolescents reported higher odds of attempting

4.1. Consistent engagement in health-promoting behaviors and suicidal behaviors Notably, in our study, we found that adolescents in Class 1 (consistent engagement in health-promoting behaviors) had higher risks of suicidal attempts than those in Class 2 and Class 3, as well as higher odds of suicidal ideation than adolescents in Class 3, even after adjusting for demographic characteristics and depressed affect. Instead of the focus on individual health-promoting behaviors (e.g., Taliaferro et al., 2008), our findings suggest an underlying group of adolescents at risk for suicide—those who engage in high levels of health-promoting behaviors across domains. In a recent meta-analysis, perfectionism, defined as having high standards of performance accompanied by overly critical evaluations of one's behavior (O'Connor, 2007; Smith et al., 2018), was positively associated with suicide attempts (Smith et al., 2018). Given these findings, it is plausible that 120

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Table 3 Four-class model: estimated probabilities by latent class membership.a Health behavior indicators Breakfast 0 Days Eat, not daily 7 Days Fruits 0 Day Eat, not daily Eat 1 time/day Eat ≥2 times/day Fruit Juices 0 Day Drink, not daily Drink, 1 time/day Drink ≥2 times/day Vegetables 0 Day Eat, not daily Eat 1 time/day Eat ≥3 times/day Milk 0 Day Drink, not daily Drink, 1–2 times/ day Drink, ≥3 times/day Water 0 Day Drink, not daily Drink, 1–2 times/ day Drink, ≥3 times/day Soda 0 Day Drink, not daily Drink, 1–2 times/ day Drink, ≥3 times/day MVPA 0 Day 1–2 Days 3–4 Days ≥ 5 Days MSEx 0 Day 1–2 Days ≥3 Days STP 0 Team 1–2 Teams ≥3 Teams Sleep Duration ≤5 h 6–8 h ≥9h High TV Use Yes No High Computer Use Yes No

Population proportion (weighted)

Class 1 [Consistent engagement in HPBs] (n = 3428, 23.6%)

Class 2 [Irregular diet, moderate exercise, high computer use] (n = 5464, 37.7%)

Class 3 [Moderate diet, frequent exercise, moderate sleep] (n = 4617, 31.8%)

Class 4 [Lowest engagement in HPBs] (n = 996, 6.9%)

0.141 0.506 0.353

0.095 0.392 0.513

0.140 0.605 0.255

0.093 0.520 0.387

0.480 0.342 0.178

0.111 0.552 0.105 0.231

0.027 0.121 0.158 0.694

0.097 0.697 0.096 0.111

0.075 0.770 0.092 0.064

0.572 0.331 0.040 0.056

0.270 0.524 0.066 0.140

0.198 0.332 0.124 0.346

0.273 0.608 0.049 0.070

0.227 0.646 0.049 0.077

0.632 0.235 0.041 0.092

0.072 0.627 0.141 0.160

0.010 0.228 0.269 0.493

0.042 0.765 0.125 0.067

0.060 0.825 0.087 0.029

0.443 0.432 0.035 0.090

0.267 0.421 0.234

0.184 0.281 0.358

0.306 0.475 0.185

0.202 0.502 0.233

0.602 0.273 0.075

0.079

0.177

0.034

0.063

0.050

0.038 0.208 0.241

0.012 0.049 0.156

0.018 0.274 0.294

0.015 0.240 0.261

0.318 0.269 0.176

0.513

0.783

0.413

0.485

0.237

0.279 0.534 0.116

0.376 0.381 0.143

0.232 0.602 0.117

0.236 0.625 0.098

0.369 0.321 0.104

0.071

0.100

0.048

0.041

0.206

0.154 0.168 0.212 0.465

0.029 0.053 0.199 0.718

0.270 0.390 0.259 0.080

0.000 0.012 0.199 0.789

0.601 0.106 0.092 0.200

0.287 0.202 0.511

0.076 0.136 0.788

0.550 0.327 0.123

0.040 0.142 0.817

0.761 0.064 0.176

0.457 0.432 0.112

0.269 0.519 0.212

0.679 0.302 0.019

0.255 0.581 0.164

0.831 0.151 0.018

0.213 0.727 0.060

0.206 0.700 0.094

0.217 0.745 0.037

0.152 0.789 0.060

0.466 0.473 0.060

0.207 0.793

0.192 0.808

0.200 0.800

0.200 0.800

0.310 0.690

0.430 0.570

0.330 0.670

0.517 0.483

0.386 0.614

0.510 0.490

Note. Latent class analysis (LCA) adjusted for clustering and sampling design. MVPA, moderate and vigorous physical activity; MSEx, muscle-strengthening exercise; STP, sports team participation. a Bolded indices are the highest probabilities (underlined) and the lowest probabilities (italicized) in the rows.

adolescents in Class 1 have set very high standards for themselves, which manifests, in part, in their practices of health-promoting behaviors. This hyper-focus on perfection might cause further distress, skewing their emotional regulation systems toward self-harming behaviors (Ruggiero et al., 2003; Slaney et al., 2001; Swahn et al., 2009), and in turn, lead to more pronounced suicidal behaviors. Other possible explanations could be attributed to specific health

behaviors characterizing Class 1. Adolescents in Class 1 had the highest probability of having long sleep (≥ 9 h). Consistent with Guo et al. (2017), there existed a U-shape association between sleep duration and suicide attempts, where sleep at unusually long or short periods was positively related to suicidal behaviors. It should be noted, however, that since the current study lacks information on personality and psychological characteristics of adolescents, these plausible 121

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Table 4 Suicidal behaviors, depressed affect, and demographic characteristics by latent classes. Health lifestyles Class 1 [Consistent engagement in HPBs] (n = 3428, 23.6%) Suicidal ideation, n (%) No 2742 (84.1) Yes 536 (15.9) Suicide plan, n (%) No 2835 (87.0) Yes 442 (13.0) Suicide attempt, n (%) No 2277 (92.3) Yes 210 (7.7) Depressed affect, n (%) No 2319 (71.6) Yes 960 (28.4) Age (M ± SD) 15.97 (1.25) Sex, n (%) Female 1481 (43.1) Male 1802 (56.9) Race/ethnicity, n (%) White 1475 (54.3) Black 539 (12.1) Hispanic 833 (22.9) Asian 186 (4.7) All other race 211 (6.0)

Class 2 [Irregular diet, moderate exercise, high computer use] (n = 5464, 37.7%)

Class 3 [Moderate diet, frequent exercise, moderate sleep] (n = 4617, 31.8%)

Class 4 [Lowest engagement in HPBs] (n = 996, 6.9%)

4410 (79.5) 1155 (20.5)

3777 (87.0) 575 (13.0)

843 (76.7) 266 (23.3)

4691 (84.9) 861 (15.1)

3894 (89.3) 455 (10.7)

879 (79.6) 232 (20.4)

3740 (92.4) 318 (7.6)

3160 (94.5) 192 (5.5)

601 (85.3) 107 (14.7)

3503 (63.9) 2035 (36.1) 16.04 (1.25)

3214 (73.0) 1141 (27.0) 15.86 (1.23)

696 (62.6) 412 (37.4) 16.06 (1.37)

3525 (63.3) 2064 (36.7)

1884 (43.1) 2503 (56.9)

535 (45.7) 587 (54.3)

2321 (53.5) 1109 (13.5) 1417 (22.6) 305 (4.6) 363 (5.9)

2037 (55.3) 691 (11.7) 1067 (22.8) 224 (3.9) 305 (6.2)

396 (47.2) 330 (20.8) 260 (23.0) 40 (3.2) 68 (5.7)

p-value

< 0.001a < 0.001a < 0.001a < 0.001a < 0.001b < 0.001a < 0.050a

Note:. a Chi-square (χ2). b ANOVA. Table 5 Model estimates predicting suicidal behaviors.

Class (Ref: Class 1 - Consistent engagement in HPBs) Class 2 (Irregular diet, moderate exercise, high computer use) Class 3 (Moderate diet, frequent exercise, moderate sleep) Class 4 (Lowest engagement in HPBs) Age Sex (Ref: Male) Female Race/ethnicity (Ref: White) Black Hispanic Asian All other races Depressed affect Constant

Suicidal ideation OR [95% CI]

Suicide plan OR [95% CI]

Suicide attempt OR [95% CI]

1.08 0.78 1.30 1.01

0.95 0.83 1.50 1.01

0.74 0.65 1.46 0.84

[0.90–1.30] [0.66–0.92]⁎⁎ [0.99–1.72] [0.95–1.07]

[0.75–1.19] [0.68–1.01] [1.10–2.05]* [0.95–1.07]

[0.57–0.95]* [0.48–0.89]⁎⁎ [0.97–2.18] [0.78–0.91]⁎⁎⁎

1.25 [1.06–1.47]⁎⁎

1.21 [1.03–1.42]*

1.22 [0.94–1.59]

0.79 [0.69–0.89] 0.81 [0.70–0.94]⁎⁎ 1.04 [0.78–1.40] 1.18 [0.91–1.54] 15.28 [13.05–17.89]⁎⁎⁎ 0.06 [0.04–0.08]⁎⁎⁎

1.03 [0.81–1.32] 0.98 [0.78–1.24] 1.51 [1.05–2.17]* 1.47 [1.11–1.96]⁎⁎ 11.54 [10.09–13.21]⁎⁎⁎ 0.05 [0.03–0.06]⁎⁎⁎

1.64 [1.16–2.32]⁎⁎ 1.23 [0.88–1.71] 1.10 [0.54–2.24] 1.54 [1.12–2.12]⁎⁎ 12.15 [9.01–16.39]⁎⁎⁎ 0.05 [0.03–0.08]⁎⁎⁎

⁎⁎⁎

Note. HPB, health-promoting behavior; OR, odds ratio; CI, confidence interval. ⁎ p < 0.05. ⁎⁎ p < 0.01. ⁎⁎⁎ p < 0.001.

underlying factors could not be examined. For example, pain insensitivity and acquired capability for suicide (i.e., reduced self-preservation instinct due to repeated struggles with suicidal ideation) were found to mediate the association between vigorous excise (as a means to control weight) and suicidality (Smith et al., 2013). Future research is needed to examine the mechanisms through which consistent engagement in health-promoting behaviors is associated with suicidal behaviors.

behaviors reported higher levels of depressive symptoms (Ames and Leadbeater, 2018; Vermeulen-Smit et al., 2015). Moreover, low fruit/vegetable intake and reduced breakfast consumption have been associated with greater stress (Cartwright et al., 2003). Both depression and stress were consistently found to be significant correlates of adolescent suicidal behaviors (Cha et al., 2018). The present study extends the existing literature by providing evidence that not only individual health-risk behaviors, but the health lifestyle with the lowest engagement in health-promoting behaviors, can be associated with adolescent suicide.

4.2. Lowest engagement in health-promoting behaviors and suicidal behaviors

4.3. Moderate engagement in health-promoting behaviors and suicidal behaviors

Consistent with previous research, individuals with the lowest engagement in health-promoting behaviors (Class 4) were associated with higher odds of formulating a suicide plan (Carli et al., 2014; Lowry et al., 2014). Studies among adults showed that individuals with fewer health-promoting

In addition, the lower odds of suicide attempts found among youth in Class 2 (irregular diet, moderate exercise, high computer use) and Class 3 122

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(moderate diet, frequent exercise, moderate sleep), relative to those in Class 1, suggests that moderation in practicing health-promoting behaviors may have a protective impact on adolescents’ suicidal behaviors. For example, previous research found that frequent exercise could improve mood, while intense exercise might lead to the deterioration of mood (Tao et al., 2007). Similarly, moderate level of sleep duration (i.e., 7–9 h) has been found to associate with lower suicidal risks than either short or long sleep duration (Guo et al., 2017). Recent evidence has indicated that moderate hours of using TV and computers are not intrinsically harmful. Instead, it might be advantageous for engaging adolescents in a digitally connected society (Przybylski and Weinstein, 2017). Future research is encouraged to examine the specific levels of engagement in health-promoting behaviors that could induce beneficial effects on adolescent health.

4.6. Strengths and limitations The strengths of this study included the use of a person-centered approach (i.e., LCA) that concurrently examines the health lifestyles of adolescents and their associations with suicidal behaviors, and the use of a nationally representative data with a comprehensive range of health behaviors. Our results highlight that (1) early prevention of suicidal behaviors that integrate strategies targeting different health lifestyles may be beneficial, (2) screening for health lifestyles, especially the differences in the levels of engaging in health-promoting behaviors, may signal the onset of suicidal behaviors, and (3) demographic differences in patterns of health behaviors and suicidal behaviors may necessitate tailored interventions. The LCA perspective provides important insight into how suicide prevention programs may be targeted for or tailored to different subgroups to improve effectiveness. Limitations of the present study must be considered when interpreting the findings. First, this study used a cross-sectional design based on retrospective data, which suggests that causal links between health lifestyles and suicidal behaviors could not be examined due to the lack of longitudinal data. However, the current data is advantageous for its most recent, large, and nationally representative sample and a broad set of health behavior indicators to serve the study purposes. Future research would benefit from employing longitudinal data to address the impact of dynamic trajectories of health lifestyles on adolescent health. Specifically, do health lifestyles change over the course of adolescents’ developmental stages? How do the different classes of health lifestyles affect suicidal behaviors over time? These are important yet understudied areas. Second, the assessment of health behaviors and suicidal behaviors in YRBS reflected different timeframes, and the responses were self-reported, which may introduce social desirability bias and retrospective recall bias. Previous studies, however, found that while health-risk behaviors are affected by cognitive and situational factors, their validities are not threatened (Brener et al., 2002, 2013). Besides, the use of these measures is consistent with previous research on related topics (e.g., Lowry et al., 2014; Swahn et al., 2009). While questions related to suicidal behaviors in YRBS demonstrated good convergent and discriminant validity, with social desirability bias (May and Klonsky, 2011), the proportion of suicidal behaviors in this study may be underestimated. To increase the measurement accuracy, these measures should be validated internally and externally before data collection. Furthermore, depressed affect was measured with a single item, which may not have the specificity (i.e., clinical cut points) to account for a more nuanced understanding of adolescent mental health. Future studies are encouraged to use diagnostic criteria to measure depression. Third, while our analysis adjusted for common demographic and psychological factors (Bridge et al., 2006), other well-known risk factors for adolescent suicide were not measured in the YRBS (Bridge et al., 2006; Cha et al., 2018). Factors such as socioeconomic characteristics, academic pressures, family histories, interpersonal relationships, and access to lethal methods should be considered in future studies to understand the complex mechanisms through which health lifestyles influence suicidal behaviors. Finally, since the sampling frame of YRBS was based on schools, findings of this study only apply to adolescents who attend school and are not representative of all individuals in this age group (Kann et al., 2018). Nationwide, approximately 5% persons aged 16–17 years were not enrolled in high school and lacked a high school credential in 2013 (McFarland et al., 2016). Specifically, sexual minority youth might be disproportionately represented in the pool of high school dropouts and absence (Burton et al., 2014). It is plausible that students who dropped out of school may be more likely to report health risk behaviors (Lansford et al., 2016), but were not accounted for in the current study.

4.4. Demographic differences across classes Differences in demographic profiles across the four health lifestyles reflect meaningful health disparities. Aligning with previous literature (Cockerham, 2005; Leech et al., 2014; Mulye et al., 2009; Trent et al., 2009), older, Black and Hispanic students were disproportionately represented in the pattern with the lowest engagement in health-promoting behaviors (Class 4). The influence of age was supported by the findings that physical activities and diet turned worse while people are aging (Grzywacz and Marks, 2001; Harris et al., 2006). Research on adult populations found that the probability of exhibiting a harmful pattern of health-risk behaviors (e.g., short/long sleep duration, no physical activity) is higher among Blacks and Hispanics than among Whites (Saint Onge and Krueger, 2017). Asian students had a higher probability of consistent engagement in health-promoting behaviors (Class 1), which was consistent with a previous finding showing that Asian participants had less unhealthy behaviors (e.g., marijuana use, binge drinking) than adolescents from other racial/ethnic groups (Harris et al., 2006). Nevertheless, the higher probability of suicidal behaviors for Class 1 underlined the need to dispel the model minority myth and address the unique health disparities (e.g., stigma, shame, family discord) faced by Asian adolescents (Wyatt et al., 2015). 4.5. Implications Clinically, the results of this study provide evidence for using integrative models in adolescent medical settings that focus on assessing patterns of health behaviors (Trent and Cheng, 2010), rather than individual health behaviors, to achieve a more effective suicide prevention. Specifically, different patterns of health behaviors are associated with varied suicidal behaviors. Adolescents engaging in a moderate diet with frequent exercises were found to be less likely to attempt suicide than those engaged in the high levels of multiple health-promoting behaviors. In other words, one size does not fit all: Researchers should take account of the nuanced differences in health lifestyles among adolescents when designing suicide prevention and intervention programs so that better efficiency can be achieved. Clinicians and health professionals may consider incorporating information about personality characteristics (e.g., perfectionism) and psychosocial changes (e.g., stress and depressive affect) when identifying at-risk adolescents (Cartwright et al., 2003; Smith et al., 2018). Recognizing these traits may facilitate the development of more effective interventions to improve mental health. Clinicians could also identify at-risk adolescents for suicide based on their health lifestyles, with a particular focus on both behavioral patterns and levels of engagement in health behaviors. Taken together, these results address the importance of examining individual and cultural differences in the association of health lifestyles and suicidal behaviors (Cha et al., 2018). Greater attention needs to be paid to developing suicide prevention programs tailored to target the sociodemographic and psychological characteristics of at-risk adolescents. For instance, incorporating culturally-specific protective factors of suicidal behaviors, such as supportive networks and family environments, could be promising intervention strategies when developing intervention programs targeting Black adolescents (Lindsey and Xiao, 2019).

5. Conclusion The present study identified four classes of health lifestyles and highlighted their associations with suicidal behaviors among adolescents. Class 2 (irregular diet, moderate exercise, high computer use) and Class 3 (moderate 123

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diet, frequent exercise, moderate sleep) showed lower odds of suicide attempts than Class 1 (consistent engagement in health-promoting behaviors). Class 4 (lowest engagement in health-promoting behaviors) were more likely to have suicide plan relative to Class 1. The associations between health lifestyles and suicidal behaviors emphasize the importance of screening the patterns of health behaviors as a promising step to more effectively detect and prevent suicidal behaviors among at-risk adolescents.

included in the study. CRediT authorship contribution statement Yunyu Xiao: Data curation, Writing - original draft, Writing - review & editing. Meghan Romanelli: Conceptualization, Investigation, Writing - review & editing. Michael A. Lindsey: Conceptualization, Investigation, Writing - review & editing.

Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Acknowledgments We thank the support from Behavioral and Intervention Service Research in Context Lab, McSilver Institute for Poverty Policy and Research. The first author thanks Mr. Chengbo Zeng (University of South Carolina) for statistical advice and support.

Informed consent Informed consent was obtained from all individual participants Appendix

Appendix Table 1 Coding mechanisms of health behaviors indicators. Health behaviors indicators

Questionnaire itemsa

Analytic coding

During the past 7 days, on how many days did you eat breakfast?

0 Days Eat, not daily 7 Days (eating daily) 0 Day Eat, not daily Eat 1 time/day Eat ≥2 times/day 0 Day Drink, not daily Drink 1 time/day Drink ≥2 times/day

b

Dietary behaviors Breakfast Fruits

During the past 7 days, how many times did you eat fruit? (Do not count fruit juice.)

Fruit Juices

During the past 7 days, how many times did you drink 100% fruit juices such as orange juice, apple juice, or grape juice? (Do not count punch, Kool-Aid, sports drinks, or other fruit-flavored drinks.) During the past 7 days, how many times did you eat green salad? During the past 7 days, how many times did you eat potatoes? (Do not count French fries, fried potatoes, or potato chips.) During the past 7 days, how many times did you eat carrots? During the past 7 days, how many times did you eat other vegetables? (Do not count green salad, potatoes, or carrots.) During the past 7 days, how many glasses of milk did you drink? (Count the milk you drank in a glass or cup, from a carton, or with cereal. Count the half pint of milk served at school as equal to one glass.) During the past 7 days, how many times did you drink a bottle or glass of plain water? (Count tap, bottled, and unflavored sparkling water.) During the past 7 days, how many times did you drink a can, bottle, or glass of soda or pop, such as Coke, Pepsi, or Sprite? (Do not count diet soda or diet pop)

Vegetables

Milk Water Soda Physical activitiesc Moderate and vigorous physical activity (MVPA) Muscle-strengthening exercise (MSEx) Sports team participation (STP) Sleepd Sleep duration Media Usee High computer/video game use (≥ 3 h/day) High TV use (≥ 3 h/day)

0 Day Eat, not daily Eat 1 time/day Eat ≥3 times/day

0 Day Drink, not daily Drink 1–2 times/ day Drink ≥3 times/day 0 Day Drink, not daily Drink 1–2 times/ day Drink ≥3 times/day 0 Day Drink, not daily Drink 1–2 times/ day Drink ≥3 times/day

During the past 7 days, on how many days were you physically active for a total of at least 60 min per day? (Add up all the time you spent in any kind of physical activity that increased your heart rate and made you breathe hard some of the time.) During the past 7 days, on how many days did you do exercises to strengthen or tone your muscles, such as push-ups, sit-ups, or weight lifting? During the past 12 months, on how many sports teams did you play? (Count any teams run by your school or community groups.)

0 Day 1–2 Days 3–4 Days ≥ 5 Days

On an average school night, how many hours of sleep do you get?

≤ 5 h 6–8 h ≥ 9 h

On an average school day, how many hours do you play video or computer games or use a computer for something that is not school work? (Include activities such as Xbox, PlayStation, Nintendo DS, iPod touch, Facebook, and the Internet.) On an average school day, how many hours do you watch TV?

Yes vs. No

0 Day 1–2 Days ≥3 Days 0 Day 1–2 Days ≥3 Days

Yes vs. No

a Questions adapted from Centers for Disease Control and Prevention (CDC). 2017 YRBS Data User's Guide. Division of Adolescent and School Health, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention; 2018. b The categorizations of dietary intake were based on the 2015–2020 Dietary Guidelines for Americans by U.S. Department of Agriculture (U.S. Department of Health and Human Services and U.S. Department of Agriculture, 2015) and the cutoff points from previous literature (Lowry et al., 2014; McKnight-Eily et al., 2011). c The categorizations of MVPA, PE class, MSEx, STP were based on the 2008 Physical Activity Guidelines for Americans by U.S. Department of Health and Human Services (U.S. Department of Health and Human Services, 2008) and the thresholds used in previous studies (Lowry et al., 2014). d The thresholds of sleep duration was based on National Sleep Foundation's Updated Sleep Duration Recommendations (Hirshkowitz et al., 2015) and previous research (Guo et al., 2017; McKnight-Eily et al., 2011). e The categorization of high media use (≥ 3 h per day) was based on the recommendation by the American Academy of Pediatrics (Strasburger et al., 2013).

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