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Volume 4: No. 3, July 2007
ORIGINAL RESEARCH
Are Diet and Physical
Activity Patterns Related to Cigarette Smoking in Adolescents? Findings From
Project EAT
Nicole I. Larson, MPH, RD, Mary Story, PhD, RD, Cheryl L. Perry, PhD, Dianne Neumark-Sztainer, PhD, MPH, RD, Peter J. Hannan, MStat
Suggested citation for this article: Larson NI, Story M, Perry CL, Neumark-Sztainer D, Hannan PJ. Are diet and physical activity patterns related to cigarette smoking in adolescents? Findings from Project EAT. Prev Chronic Dis [serial online] 2007 Jul [date cited]. Available from: http://www.cdc.gov/pcd/issues/2007/
jul/06_0053.htm.
PEER REVIEWED
Abstract
Introduction
An inadequate diet and physical inactivity may compound the many deleterious effects of smoking on health. Some research indicates that smoking behavior is related to other health behaviors, but little research has examined how smoking may be related to dietary intake of key nutrients, consumption of fast food, sedentary lifestyle, or weight status. The purpose of this study was to describe
smoking frequency among adolescents and its relationship to physical activity and dietary patterns.
Methods
The research study employed a cross-sectional, population-based design. Adolescents self-reported cigarette smoking, physical activity, and eating behaviors on the Project EAT
(Eating Among Teens) survey and reported dietary intake on a food frequency questionnaire completed in school classrooms. The sample included 4746 middle school and high school students from Minneapolis-St. Paul public schools. Mixed-model
regression, which was controlled for sex, race and ethnicity, socioeconomic status, grade level (middle school or high school), and school, was used to examine the association of smoking with diet and physical activity patterns.
Results
Overall, reported smoking frequency was inversely related to participating in team sports, eating regular meals, and consuming healthful foods and nutrients. Smoking frequency was directly related to frequency of fast-food and soft drink consumption.
Conclusion
Adolescents who smoke cigarettes may be less likely to engage in health-promoting lifestyle behaviors. Interventions are needed to prevent smoking and the unhealthy dietary practices and physical activity behaviors that may be associated with it.
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Introduction
Cigarette smoking, poor diet, and physical inactivity are prevalent among adolescents in the United States and are major contributors to preventable morbidity and mortality (1). These behaviors continue into adulthood (2) and increase the risk of heart disease, osteoporosis, stroke, certain cancers, and other chronic diseases (1). National surveys (3) indicate that 8% of middle school students
and 23% of high school students used cigarettes on one or more of the past 30 days. Nine percent of high school students report frequent, current cigarette smoking on 20 or more of the past 30 days (4). Survey data further indicate that the majority of adolescents do not consume diets that meet the Dietary Guidelines for Americans 2005 (http://www.health.gov/dietaryguidelines/dga2005/document/),
and many young people are physically inactive (1,5).
An inadequate diet and physical inactivity may compound the deleterious effects of smoking on health. For example, some research indicates that adolescents who smoke may be less likely to exercise or to consume adequate amounts of calcium-rich foods and beverages (6). As smoking cigarettes, inactivity, and poor dietary intake of calcium during adolescence are independent risk factors for
osteoporosis, people who begin smoking during adolescence may be at particular risk for osteoporosis later in life (7). Smokers are at increased risk for atherosclerosis and heart disease because of substances in cigarette smoke that promote the formation of free radicals and plaques (8). Vitamin C and other antioxidants neutralize free radicals and may help to prevent
oxidative damage. Because of the effect of smoking on free-radical formation, smokers have higher metabolic requirements for vitamin C; therefore, the
negative health impact of poor or marginal diets is greater for smokers than for nonsmokers. Observational studies examining patterns of health behavior covariation in adolescents have related smoking behavior to irregular meal patterns (9), greater intakes of soft
drinks (10), and lower intakes of fruits, vegetables, and dairy foods (6,11). In addition, findings from cross-sectional and longitudinal research suggest an inverse relationship between physical activity and cigarette smoking during adolescence (6,12).
Although previous studies have reported covariation of smoking behavior with other health behaviors, little research has examined how smoking may be related to dietary intake of key nutrients (e.g., calcium, vitamin C, iron), frequency of fast-food meals, sedentary lifestyle behaviors, or weight status. Furthermore, few studies have been able to consider the association between smoking and a
comprehensive range of behaviors within a single population of adolescents. A clear understanding of the interrelationships between these behaviors would help to inform future interventions, such as coordinated school health programs (CSHPs). CSHPs combine nutrition education, physical education, health education, and health services in a climate that supports healthy lifestyle choices in order to
promote and maintain the well-being of students (13). Such programs are ideally suited to addressing the intersection of risk behaviors, teaching healthy lifestyle skills, and developing self-efficacy to improve or maintain one’s own health.
Our study built on previous research by examining how smoking is related to
physical activity patterns, eating behaviors, and diet within a diverse,
population-based sample of adolescents in Minnesota. The specific research
questions we addressed were the following:
1) Are patterns of physical activity (vigorous and moderate activity and participation in team sports) and sedentary activity (watching television and videos) related to cigarette
smoking among adolescents?
2) Are eating behaviors (frequency of eating breakfast, lunch, dinner, family meals, and fast-food meals) related to smoking frequency?
3) Is diet quality (intake of foods and nutrients) related to cigarette
smoking among adolescents?
The study’s hypothesis was that healthy behaviors (i.e., vigorous and moderate physical activity; participation in team sports; eating regular meals; participation in family meals;
and consumption of fruits, vegetables, grains, fiber, and micronutrients) would be inversely related to frequency of cigarette smoking and that unhealthy behaviors (i.e., sedentary activity; fast-food meals; and
intakes of fat, soft drinks, and caffeine) would be directly related to smoking frequency.
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Methods
Study design
Data for this analysis were drawn from Project EAT (Eating Among Teens), a study designed to investigate socioenvironmental, personal, and behavioral correlates of dietary intake among adolescents
aged 11 through 18 years (14). (In the context of this study, we define adolescents as
young people aged 11 through 18 years.) Following approval of the study by the University of Minnesota Human Subjects’ Committee and by the research boards of participating school districts, a trained staff collected survey
and anthropometric data in health, physical education, and science classrooms during the 1998–1999 school year. Students completed surveys and had their height and weight measured within a private area of the school. Student assessments included the Project EAT survey and the Youth and Adolescent Food Frequency Questionnaire (YAQ), which measures usual dietary intake. Additional details about the
development of the Project EAT survey can be found in previous publications (15).
Study sample
The overall study sample of students who completed the Project EAT survey (N
= 4746 adolescents, response rate = 81.5%) was ethnically and socioeconomically
diverse. Students aged 11 through 18 years from 31 public middle and high
schools in the Minneapolis-St. Paul area participated in the study. Student participants were equally divided by sex, with 2377 (50.1%) males and 2357 (49.7%) females (12 students
[0.2%] did not indicate their
sex). The mean age of students in the study sample was 14.9 years (range 11 to 18 years); 33.9% (1608 students) were in middle school, and 64.8% (3074 students) were in high school (64 students
[1.3%] did not indicate their grade). The racial and ethnic backgrounds of study participants were as follows: 2264 (47.7%) white, 887 (18.7%) African American, 896 (18.9%) Asian American, 273 (5.8%) Hispanic, 165
(3.5%) Native American, and 186 (3.9%) mixed or other (75 students [1.5%] did not indicate their background).
Measures
Demographics and weight status
Study participants self-reported sex, race and ethnicity, and socioeconomic status on the Project EAT survey. Race and ethnicity were assessed with the following question: “Do you think of yourself as (a) white, (b) black or African American, (c) Hispanic, (d) Asian American, (e) Hawaiian or Pacific Islander, or (f) American Indian or Native American.” Subjects could choose more
than one category; those responses indicating multiple categories were coded as mixed or other. Because there were few participants who identified themselves as Hawaiians or Pacific Islanders, these participants were also included in the mixed or other category.
Classification tree methodology (16) was used to generate five categories of socioeconomic status (SES). The prime determinant of SES was parental educational level, defined by the higher level of either parent. Secondary variables were family eligibility for public assistance, student eligibility for free or reduced-cost school meals, and parental employment status (14).
Trained research staff measured heights and weights using standardized equipment and procedures. Body mass index (BMI) was calculated
by dividing weight in kilograms by the square of height in meters (i.e., [weight
in kilograms]/[height in meters]2). Respondents were classified according to sex- and age-specific cut-off points as
not overweight (BMI <85th percentile), moderately overweight (BMI ≥85th to <95th percentile), or very overweight (BMI ≥95th percentile).
Behaviors
Study participants self-reported smoking, physical activity, and sedentary activity on the Project EAT survey. A single item modified from the Minnesota Adolescent Health Survey assessed how often students had smoked cigarettes during the past year (17). Response categories were never, a few times, monthly, weekly, or daily.
On the basis of self-reported smoking
frequency, adolescents were then categorized into one of three groups roughly corresponding to the categories employed in analyses of
surveys from the Youth Risk Behavior Surveillance System (YRBSS) (5): 1) nonsmokers reported never smoking or smoking only a few times, 2) current smokers reported smoking at least monthly but less often than once per day, and 3) daily smokers reported
smoking daily during the past year.
A modified version of the Leisure Time Exercise Questionnaire was used to measure physical activity (18). Students were asked to report how many hours they spent during a usual week in strenuous exercises (e.g., biking fast, aerobic dancing, running, jogging, swimming laps, rollerblading, skating, lacrosse, tennis, cross-country skiing, soccer, basketball, football) and moderate exercises
(e.g., walking quickly, baseball, gymnastics, easy bicycling, volleyball, skiing, dancing, skateboarding, snowboarding). Response options for these questions were 0, <0.5, 0.5 to 2, 2.5 to 4, 4.5 to 6, and >6 hours per week and were recoded to 0, 0.3, 1.3, 3.3, 5.3, and 8 hours per week. Total hours of vigorous physical activity and moderate physical activity per week were calculated by
summing the recoded values.
As a measure of sedentary behavior, students were asked to report the average number of hours they watched television and videos on weekdays and on weekends. Response options for these questions were 0, 0.5, 1, 2, 3, 4, and ≥5 hours. An average number of hours spent watching television and videos per week was computed from responses. A single item adapted from the YRBSS asked students to
indicate the number of sports teams (0, 1, 2, or ≥3) they played on during the past year (19). Participation in team sports was defined as playing on one or more teams. Pretesting of physical activity and inactivity measures in a sample of 161 adolescents found test-retest reliability coefficients of r = .63 for vigorous physical activity, r = .52 for moderate physical
activity, r = .81 for weekday television and video watching, and r = .69 for weekend television and video watching.
Eating behaviors assessed were dietary intake, eating breakfast, eating lunch, eating dinner, participating in family meals, and consuming fast-food meals. We employed the 149-item YAQ to assess dietary intakes of total energy (kcals), fruits and vegetables (servings), grains (servings), soft drinks (servings), total fat (% of total energy), fiber (g), calcium (mg), iron (mg), zinc (mg),
vitamin A (IU), vitamin C (mg), folate (mcg), and caffeine (mg). Other research has documented the validity and reliability of the YAQ for use in adolescents (20,21). Among a sample of adolescents aged 9 to 18 years, the mean correlation between energy-adjusted nutrient intakes assessed with the YAQ and with three 24-hour recalls was 0.45 (20). The mean intake of energy as assessed by the YAQ was
higher than for recalls but within 1% (20). Reproducibility coefficients for responses on two YAQs administered one year apart were 0.48 for fruits and vegetables, 0.48 for grains, and 0.57 for soft drinks (21). Self-report measures assessing past-week frequency of eating breakfast, lunch, and dinner; participating in family meals; and consuming meals from a fast-food restaurant were included on
the Project EAT survey (22). Response options ranged from never to every day or more than 7 times.
Statistical analyses
Descriptive statistics were calculated to examine the association of frequency of smoking with demographic characteristics in our sample. We used chi-square tests to test bivariate associations between smoking and characteristics of adolescents.
We used mixed model linear regression, controlling for sex, grade level (middle school or high school), race and ethnicity (in six categories), and SES (in five categories) on physical activity and eating behaviors to generate adjusted prevalences by smoking status. Outcomes were dichotomized: meeting recommendations for moderate physical activity (30 minutes on ≥5 days or
≥2.5 hours per week), meeting recommendations for vigorous physical activity (20 minutes on ≥3 days or ≥1 hour per week), participating in team sports (on at least one team), exceeding the maximum recommended for hours of television or video watching (≥14 hours per week), eating regular meals (eating breakfast, lunch, and dinner 5–7 days per week), participating
regularly in family meals (5–7 days per week), and frequently eating fast-food meals (≥3 meals per week). School was included in the models as a random effect to control for possible intracluster correlation of responses from students in the same school. Identical models
but with the logistic link function and binomial error were used to generate P values for testing
differences in adjusted prevalences over the three categories of smoking status.
We used the same mixed model linear regression described above with total energy as an added covariate to generate adjusted mean dietary intakes of adolescents by smoking status and P values for testing differences. An adjustment for total energy intake was applied to examine differences in diet quality between smokers and nonsmokers because the average total energy intake of
smokers was greater than the average energy intake of nonsmokers (23). For dietary outcomes that exhibited positive skewness, identical models but with the square-root transformation were used to generate P values. A 95% confidence level was used to interpret the statistical significance of probability tests. All analyses were conducted using the Statistical Analysis System (SAS), version 8.2 (SAS Institute, Cary, NC,
2001).
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Results
Characteristics of adolescents who smoke cigarettes
In the study sample, nearly 7% of adolescents reported smoking at least monthly but less frequently than once per day, and another 10.3% reported smoking daily (Table 1). Middle school students reported lower smoking rates than high school students. The percentage of high school students smoking at least monthly, 22.1% (639/2887 students), was nearly three times higher than that of middle
school students smoking as often, 7.4% (109/1477 students). (Denominators were based on the total numbers in high school and middle school, respectively, that responded to both items assessing cigarette use and grade level. These numbers match the denominators in the first column of Table 1.) Rates of smoking were lowest among African American and Asian students. More young people of middle and
low-middle SES reported frequent smoking than did those of low SES or high SES. Smoking
frequency was similar among males and females, and no
significant differences in smoking frequency were observed according to weight status based on BMI.
Physical activity and eating behaviors by smoking status
Table 2 presents adjusted prevalences of physical activity and eating behaviors according to smoking status. Participation in team sports was significantly and inversely related to smoking frequency. Of 457 students who reported daily smoking, only 46.0% had played on a sports team during the past year compared with 64.5% of 3657 nonsmokers. We observed no significant differences
between frequent smokers and nonsmokers for moderate
physical activity or hours of television and video watching. However, differences in the observed prevalences of participants meeting the recommendation for vigorous physical activity according to smoking status were at the cut-point of statistical significance (P = .05). A higher percentage of nonsmokers (79.6% of 3657 students) met the recommendation
for participation in weekly vigorous physical activity compared with students who reported
daily smoking (71.0% of 457).
Different eating patterns were also observed among adolescents who reported more frequent smoking. Smoking frequency was significantly and inversely related to regularly eating breakfast, lunch, and dinner. Frequent consumption of fast-food meals was significantly and directly related to smoking frequency. More than one third of daily smokers reported three or more fast-food meals per week
while fewer than one fifth of nonsmokers reported this number. An inverse association of borderline statistical significance (P = .06) was found between
participation in family meals and adolescent smoking frequency.
Diet Quality by Smoking Status
Table 3 presents means of daily energy intake and dietary intake of foods and nutrients adjusted for energy intake according to smoking status. Smoking was associated with higher energy intake: daily smokers reported mean intakes of an additional 264 kcals per day compared with nonsmokers. Before adjustment of food and nutrient intake for total daily energy intake (data not shown), smoking was
associated with higher intakes of grains, soft drinks, and caffeine and lower intakes of vitamin A. After adjusting for total energy intake, greater smoking frequency was associated with
several indicators of a lower-quality diet. Consumption of fruits and vegetables, grains, fiber, calcium, iron, zinc, vitamin A, vitamin C, and folate were significantly and inversely related to smoking
frequency. Intakes of soft drinks and caffeine were significantly and directly related to smoking frequency.
Because of the substantially lower rates of smoking among middle school
versus high school students and differences between the dietary habits of
male and female adolescents in our sample, interactions were tested to
assess for effect modification by grade level or sex. No statistically
significant interactions were identified and thus are not discussed further.
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Discussion
This study described cigarette smoking behaviors among a diverse sample of middle and high school students and the association of cigarette smoking status with a range of lifestyle behaviors, including physical activity, eating patterns, and dietary intake. Self-reported current (past month) cigarette smoking rates in the sample were similar but somewhat lower than rates reported by national
surveys of middle school students (7% versus 9%) and high school students (22% versus 28%) in 1999 (24). In the present study, smoking was associated with grade level (high school), race and ethnicity (i.e., Native American, white, Hispanic), and with low-middle and middle SES but not with sex or weight status. These findings
about the relationship of smoking with sex, race and ethnicity, and
grade level are consistent with national surveillance data (3). However, the lack of a relationship with weight status was not consistent with other findings that have shown higher rates of smoking among overweight adolescents (25).
Overall, we found that adolescents who smoke were less likely to have healthful eating and physical activity habits. In agreement with other research, smoking frequency was inversely related to team sport participation (26), but we did not observe strong associations with moderate or vigorous physical activity. Other research studies have reported strong inverse associations between physical
activity and smoking, and it is possible that our findings did not support these studies because of measurement differences (6,12). Most other studies have considered frequency rather than total hours of weekly physical activity and have focused only on vigorous physical activity that makes one breathe hard (6,12). Our study also found an association suggesting reduced vigorous physical activity
in adolescents who smoke more frequently; however, this association was at the cut-off point of statistical significance (P = .05). Observations indicating a potential inverse association between smoking frequency and vigorous physical activity in adolescents
are of particular concern because of the tendency for physical activity to decline as adolescents transition to young adulthood and
because research has linked declining activity levels to increases in BMI (27,28).
Because few studies have examined associations between smoking frequency and sedentary behaviors, in this study we considered whether a relationship
exists between smoking and hours of watching television or videos. Although we found that the prevalence of exceeding the maximum recommended for hours of television- and video-watching was high in each category of smoking, exceeding the
recommendation was unrelated to smoking frequency. These results contradicted our hypothesis, which we based on a prospective study of media use and smoking initiation in young people (29). Given the observation in previous research of a direct, dose-response relationship between television viewing hours and rates of smoking initiation (29), we hypothesized that more frequent smoking would be
directly related to time spent watching television and videos. Although use of these media may be related to smoking initiation, in this study it did not appear to be related to the frequency of smoking among adolescents.
We observed associations between smoking and eating behaviors with greater consistency than associations between smoking and physical activity. One strong finding among students in our study was a direct association of smoking frequency with irregular meal patterns at breakfast, lunch, and dinner. Irregular meal patterns have been related to poorer intakes of key micronutrients
(e.g., calcium, zinc, vitamin C, iron) and greater consumption of sugar (30). Few other studies have investigated whether smoking frequency is related to fast-food consumption or family meal patterns (31,32). The results of our study were in agreement with another study, which indicated that adolescent smoking frequency is directly related to more frequent
intake of fast food (32). In a previous
analysis of the Project EAT data, we found associations between participation in family meals and a number of substance-use behaviors, including smoking (31). Frequent
intake of fast-food meals and irregular family meal patterns are of concern as these behaviors have also been related to diets of lower nutritional quality in adolescents and because fast-food intake has been shown to prospectively
predict risk of increasing BMI in adolescent females (33-35).
To investigate relationships between smoking and diet quality, we examined whether smoking frequency is associated with food-group servings and intake of selected nutrients among adolescents. In general, smoking frequency was inversely related to healthful food selections and to nutrient intakes adjusted for total energy intake. These findings build on previous studies that have reported
similar associations between food choices and smoking behavior in adolescents (6,36). The more comprehensive assessment of dietary patterns in this study demonstrated that, compared with the diets of nonsmokers, the diets of adolescents who reported frequent smoking were lower in several key nutrients important for their roles in the prevention of chronic disease (e.g., calcium for the prevention
of osteoporosis).
One of the strengths of this study was its large and diverse sample of adolescents, which allowed us to assess for the effect of modification by sex and grade level. Other strengths were the range of physical activity and eating behaviors evaluated within this sample and the comprehensive assessment of dietary intake accomplished using a validated instrument (YAQ) (20). The
response rate of 81.5% was relatively high, and the demographic makeup of the study sample approximated the makeup of the source school populations.
In interpreting the results of this study, certain limitations should also be considered that may have interfered with our ability to observe the total effect of associations between smoking, physical activity, and nutrition behaviors. For example, parents’ level of education is a widely used indicator of SES, but the economic benefits of higher education are not uniform, and this
indicator may have produced incomplete adjustment for SES (37). Despite efforts to further adjust associations for sex, grade level, and race and ethnicity, it is also possible that factors other than the covariates included in our models are related to the associations between smoking and patterns of poor diet and physical activity. This also could have influenced the observed pattern of
associations.
Finally, using only a single item to assess cigarette use may have led to some misclassification of smoking status. Misclassification would have attenuated associations of smoking with physical and sedentary activity, dietary patterns, and BMI. Additional measures of lifetime smoking habits would have allowed for analyses to consider whether established habitual patterns of smoking behavior
are associated with unhealthy lifestyle behaviors. Future research should use stronger measures of smoking behavior and
use longitudinal study designs to clarify the temporal order of increases in smoking and decreases in healthful eating and activity behaviors.
Collectively, this study and previous research suggest the importance of early intervention in adolescents on smoking, physical activity, and dietary patterns. It is possible that the observed covariation in health behaviors is due to a greater belief in chance as an influence on health and a lower perceived benefit of engaging in healthful behaviors (38,39). Researchers are still learning how
adolescents receive, process, and prioritize information about health risks and how they modify their behavior as a result. Therefore, young people who smoke may benefit from comprehensive, coordinated interventions that address self-efficacy to improve or maintain personal health by engaging in regular physical activity, healthful eating behaviors, and other positive lifestyle behaviors in
addition to reducing cigarette smoking. CSHPs are one example of how messages may be combined to address and reinforce multiple lifestyle behaviors through classroom instruction and environmental interventions (40). These programs can involve all areas of a school in promoting good nutritional habits, physical activity, and a nonsmoking lifestyle. Programs should be
designed not only to teach adolescents about the benefits of engaging in healthful behaviors but also the behavioral skills necessary to carry them out (40). In addition, the school environment should be supportive of healthy lifestyle behaviors (e.g., healthy food available in the cafeteria and in vending machines, teachers and staff serving as role models for a physically active, smoke-free
lifestyle). To have the greatest impact on reducing risk for chronic disease in adulthood, prevention programs need to target those at highest risk for unhealthy behaviors, engage youth early in adolescence, and focus on specific behaviors so that young people
can observe and learn the benefits of adopting healthful lifestyle behaviors.
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Acknowledgments
This study was supported by grant MCJ-270834 from the Maternal and Child Health Bureau (Title V, Social Security Act), Health Resources and Service Administration, and the U.S. Department of Health and Human Services. Analyses were supported by
grant T01-DP000112 from the Centers for Disease Control and Prevention (CDC). The content of the manuscript is solely the responsibility of the
authors and does not necessarily represent official views of CDC.
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Author Information
Corresponding Author: Nicole I. Larson, MPH, RD, Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, 1300 South 2nd
Street, Suite 300, Minneapolis, MN 55454. Telephone: 612-625-5881. E-mail: hans1621@umn.edu.
Author Affiliations: Mary Story, Dianne Neumark-Sztainer, Peter J. Hannan, Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, Minn; Cheryl L. Perry, School of Nursing, The University of Texas at Austin, Austin, Tex.
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