517275 research-article2014
HPQ0010.1177/1359105313517275Journal of Health PsychologyLange et al.
Article
The relationship between weight and smoking in a national sample of adolescents: Role of gender
Journal of Health Psychology 201X, Vol. XX(X) 1–10 © The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/1359105313517275 hpq.sagepub.com
Krista Lange, Sneha Thamotharan, Madeline Racine, Caroline Hirko and Sherecce Fields
Abstract This study sought to investigate the role of weight status and body mass index percentile in risky smoking behaviors in male and female adolescents. Analyses of the data obtained in the 2011 Youth Risk Behavior Surveillance System were conducted. The national sample size included 15,425 adolescents. Questions addressing weight status and smoking behaviors were used in analyses. Significant effects of perceived weight status, weight change status, and body mass index percentile on smoking behaviors were found for both genders. The current findings indicate the importance of accounting for both gender and weight status when developing prevention and cessation programs targeting smoking behaviors.
Keywords adolescents, body mass index percentile, dieting, smoking, weight perception
Introduction Tobacco use remains the leading cause of preventable death in the United States and is responsible for approximately 443,000 deaths annually (Centers for Disease Control and Prevention (CDC), 2002). Despite its risks, the use of tobacco remains one of the leading causes of death and disability among youth; thus, intervention and prevention efforts aimed at decreasing tobacco use are vital (CDC, 2002). This issue is especially prevalent within adolescent populations as research has found that 3600 individuals aged 12–17 years initiate tobacco use daily. Furthermore, 1100 of these adolescents will become daily cigarette smokers (Substance Abuse and Mental Health Services Administration, Center for Behavioral Health Statistics and Quality, 2008). Understanding the
characteristics that may predict enhanced risk of engaging in smoking behaviors is essential to the development of effective treatment and prevention programs. Previous research has indicated that attitudes toward smoking can influence the likelihood of initiating smoking and the endorsement of habitual smoking behaviors (Otten et al., 2008). The perception that smoking can aid in weight loss or the promotion of a slimmer figure has Texas A&M University, USA Corresponding author: Sherecce Fields, Health Behavior Research Group, Department of Psychology, Texas A&M University, MS4235, College Station, TX 77843, USA. Email: [email protected]
Downloaded from hpq.sagepub.com at Monash University on November 14, 2015
2
Journal of Health Psychology XX(X)
been a robust finding in the literature. Individuals with weight concerns may be more likely to smoke cigarettes when compared with individuals unconcerned with their weight (Crisp et al., 1999; Gibbons and Gerrard, 1996). This conclusion is further supported by studies revealing that individuals monitoring their weight are likely to engage in smoking with the goal of maintaining and losing weight (Camp et al., 1993; French and Jeffery, 1995; McKee et al., 2005; Tomeo et al., 1999), and adolescents who believe smoking can assist in weight control are at an increased risk to start smoking 1 year following the endorsement of this belief (Harakeh et al., 2010). Within pediatric obesity treatment programs, the Expert Committee on Childhood Obesity has identified a pressing need for smoking cessation programs (Barlow and Dietz, 1998). An increased likelihood of smoking is observed in individuals with a poor body image (Kendzor et al., 2009; Lopez et al., 2008; Pomerleau and Saules, 2007), which may be of concern as body mass index (BMI) has been shown to be strongly correlated to body image (Goldfield et al., 2010; Yates, Edman, & Aruguete, 2004). Although the relationship between weight status and smoking patterns has been investigated previously, little research has investigated the effect of adolescent weight status and current dieting status on observed smoking patterns. Therefore, the purpose of this study was to conduct analyses of the data obtained in the 2009 Youth Risk Behavior Surveillance System (YRBSS), a national survey conducted by the CDC, to investigate the relationship of weight perception, weight change status, and BMI percentile to risky smoking behaviors.
The secondary aim of this study was to investigate whether gender differences existed in the hypothesized relationship of weight status and smoking patterns, as past research has shown differences in smoking motivation for males and females. It was hypothesized that weight concerns would predict smoking behaviors. Specifically, it was hypothesized that adolescents desiring to lose weight would have a greater likelihood of endorsing all smoking behaviors as past research has indicated that many individuals, especially females, believe that smoking will aid both weight loss and maintenance. BMI percentile and the perception of being overweight were also hypothesized to predict smoking behaviors in both males and females. Although little research has investigated the role of actually being overweight, there has been some conflicting evidence to indicate a positive correlation between BMI and smoking initiation for males compared to females (Cawley et al., 2004; Tucker, 1983). Additional inconsistencies have been found in numerous empirical studies investigating characteristics that motivate smoking behaviors of males and females, and these differences are especially prevalent when focused on the role of body image and current weight status in smoking likelihood (Saarni et al., 2004; Strauss and Mir, 2001). Through the use of a diverse, nationally representative sample of adolescents, we aimed to achieve a greater understanding of the role of weight status on reported smoking behaviors of male and female adolescents.
Method
Hypotheses
Participants
The first aim of this study was to determine whether a relationship between weight status and smoking behaviors exists. This relationship was addressed by both objective (weight change status and BMI percentile) and subjective (weight perception) responses to further elucidate predictors of risky smoking patterns.
Participants were sampled from the YRBSS from the CDC for the reporting period of September 2010 to December 2011. The YRBSS monitors priority health-risk behaviors among youth and young adults including obesity and asthma through the use of the youth risk behavior survey (YRBS). The YRBS
Downloaded from hpq.sagepub.com at Monash University on November 14, 2015
3
Lange et al. includes national, state, territorial, tribal, and local school-based surveys of representative samples of 9th- through 12th-grade students and provides data in public and private schools in the United States (CDC, 2011). The national student sample size included 15,425 respondents and comprised 50.2 percent female and 49.8 percent male participants. In regard to school classification, 24.6 percent were in 9th grade, 24.1 percent were in 10th grade, 27.0 percent were in 11th grade, 24.1 percent were in 12th grade, and 0.2 percent were ungraded or other. Further information regarding demographic variables is presented in Table 1.
Procedure In order to obtain a representative national sample, a three-stage cluster design was implemented. The initial sample included 1276 primary sampling units (PSUs) which were divided into 16 strata in accordance with their metropolitan statistical area as well as the percentage of black and Hispanic students. Of these 1276 PSUs, 57 were sampled with probability proportional to the school enrollment size observed overall for the PSU. In the second stage of sampling, schools with any of grades 9–12 were sampled with probability proportional to school enrollment size. In the third sampling stage, intact classes from either a required subject (e.g. English or social studies) or a required period (e.g. homeroom or second period) were sampled randomly, and all students in the sampled classes were eligible to participate (Eaton et al., 2012). Survey procedures were designed to protect students’ privacy by allowing for anonymous and voluntary participation. Data collection procedures are similar for national, state, and local surveys. Students completed the selfadministered questionnaire following the CDC’s Institutional Review Board approved protocol for the national YRBS. In the national survey, students who were absent on the day of data collection were allowed to complete questionnaires at a later date and had their questionnaire administered by the data collector or
Table 1. Descriptive statistics of YRBS respondents (N = 15,425). Variable Sex Male Female Age 12 years old or younger 13 years old 14 years old 15 years old 16 years old 17 years old 18 years old or older Classification 9th grade 10th grade 11th grade 12th grade Ungraded/Other Race/ethnicity American Indian/Alaska Native Asian Black of African American Native Hawaiian/Other Pacific Islander White Hispanic/Latino Multiple—Hispanic Multiple—non-Hispanic
Percent of frequency 50.2% 49.8% (Missing = 61) 0.3% 0.2% 10.2% 22.6% 26.4% 25.5% 14.9% (Missing = 62) 24.6% 24.1% 27.0% 24.1% 0.2% (Missing = 99) 1.9% 3.2% 18.3% 0.8% 40.8% 14.7% 15.9% 4.3% (Missing = 315)
YRBS: Youth Risk Behavior Surveillance. All percentages are valid percentages to account for missing N.
school personnel. This allowed an increase in student response rates which averaged 71 percent overall (Eaton et al., 2012). Additionally, because frequently absent students are more likely to engage in health-risk behaviors than students who are not frequently absent, these
Downloaded from hpq.sagepub.com at Monash University on November 14, 2015
4
Journal of Health Psychology XX(X)
procedures help provide data that are representative of all high school students (CDC, 2004). Further explanation of the administration and methodology of the YRBS questionnaire are described elsewhere (Eaton et al., 2012).
Measures The YRBS included items which addressed demographic variables, sexual behaviors, substance use, physical activity, and diet behaviors. Of interest to this study were responses to questions regarding weight status and smoking behavior. Weight status was indicated by questions which addressed perceived weight status, weight change status, and BMI percentile. Smoking behaviors were assessed by responses to questions addressing having ever smoked, age of first cigarette, number of cigarettes smoked per day, number of days engaged in smoking, and smoking daily. Response options for each question included in our analyses are presented in Table 2.
Statistical analyses To investigate the role of weight status in predicting risky smoking behaviors, logistic regressions were used for all smoking questions with dichotomous response items (yes/ no), while linear regressions were utilized to assess the remaining responses. All analyses were completed using the Statistical Analysis Software (SAS) SURVEYLOGISTIC procedure. A Taylor Series Linearization method was used in all analyses to adjust for sample procedure including PSU, cluster sampling, and weighted design. Age, ethnicity, and sampling procedure (national, state, or local) were controlled for by including these variables as covariates in our analyses. Ethnicity was recoded such that individuals noting they were Caucasian were used as the reference group for analyses. Additionally, weight change status was recoded such that those endorsing they were “not trying to do anything” about their weight were used as the reference group and remaining variables were dummy coded. Age
of first cigarette, although categorical in the questionnaire format, was recoded to make this response continuous such that those who had never smoked were given the highest dummy code. Furthermore, regression analyses were completed with both males and females to view the effects of weight status on smoking behaviors, and interactions were also analyzed to determine gender differences in this relationship.
Results A summary of the results for regression analyses predicting smoking behavior is presented in Table 3. When analyses included both males and females, endorsing a higher perceived weight status was a significant predictor of ever smoking. Contrary to our hypothesis, perceived weight status was not a significant predictor of age smoked, number of days smoked in past month, number of cigarettes daily, or smoking daily. Compared to adolescents not attempting to change their weight, adolescents reporting wanting to lose weight were 1.411 times more likely to endorse ever having smoked, and adolescents endorsing a desire to lose weight, compared to not wanting to make any changes to one’s weight, were predictive of smoking more cigarettes per day. Weight change status was not significant in predicting days smoked in the past month, age of first cigarette use, or daily smoking. Tests examining the influence of BMI percentile on smoking indicated that a higher BMI percentile was predictive of endorsing ever having smoked, daily smoking, number of days one smoked in the past month, and cigarettes smoked per day. Upon further investigation of the interaction of weight status and gender, gender differences were found such that female respondents were shown to have a greater number of significant relationships of weight status and risky smoking behaviors compared to males. Regression analyses revealed a number of gender differences in the relationship of weight status and reported smoking behaviors (detailed tables containing all values for the regression
Downloaded from hpq.sagepub.com at Monash University on November 14, 2015
5
Lange et al. Table 2. Response items addressing weight status and smoking patterns. Response item
Frequency of endorsement
Which of the following are you trying to do about your weight? Lose weight 46.9% Gain weight 17.2% Stay the same weight 18.2% 17.7% Not trying to do anything (Missing = 241) How do you describe your weight? Very underweight 2.3% Slightly underweight 11.9% About the right weight 55.8% Slight overweight 25.7% Very overweight 4.3% (Missing = 285) Have you ever smoked cigarettes daily, that is, at least one cigarette every day for 30 days? Yes 9.4% No 90.6% (Missing = 1485) How old were you when you first smoked? 66.2% Never smoked a cigarette 8 years old or younger 2.7% 9 or 10 years old 2.4% 11 or 12 years old 5.2% 13 or 14 years old 10.4% 15 or 16 years old 10.2% 17 years old or older 2.9% (Missing = 580) During the past 30 days, how many days did you smoke cigarettes? 0 days 82.8% 1–2 days 5.3% 3–5 days 2.7% 6–9 days 1.7% 10–19 days 1.8% 20–29 days 1.5% All 30 days 4.1% (Missing = 682) Have you ever tried cigarette smoking, even one or two puffs? Yes 46.0% No 54.0%
Table 2. (Continued) Response item
Frequency of endorsement
(Missing = 241) During the past 30 days, on the days you smoked, how many cigarettes did you smoke per day? 82.4% Did not smoke cigarettes Less than 1 cigarette 4.5% 1 cigarette 3.7% 2–5 cigarettes 6.4% 6–10 cigarettes 1.7% 11–20 cigarettes 0.7% 0.7% More than 20 cigarettes (Missing = 1611) Variable BMI percentile 63.0 ± 28.4 BMI: body mass index. All percentages are valid percentages to account for missing N. BMI percentile is presented as mean and standard deviation.
model and simple slopes can be obtained by contacting the authors directly). The regression model investigating perceived weight status significantly predicted having ever smoked (Wald χ2 = 337.80, p < 0.01), number of days smoked in the last 30 days (R2 = 0.04, p = 0.01), cigarettes smoked per day (R2 = 0.04, p < 0.01), smoking daily (Wald χ2 = 183.74, p < 0.01), and age of smoking initiation (R2 = 0.02, p < 0.01). The interaction of gender and perceived weight status significantly predicted having ever smoked (B = 0.12, Wald χ2 = 3.25, p = 0.07) and smoking daily (B = 0.11, Wald χ2 = −2.23, p = −2.23). Investigation of simple slopes for gender revealed that the simple effects were only significant among females in the prediction of having ever smoked (B = 0.16, Wald χ2 = 13.14, p < 0.01) and smoking daily from perceived weight status (B = 0.06, Wald χ2 = 5.24, p = 5.24). In order to examine nonsignificant interactions, regression analyses were completed without the interaction term to examine the main effects of perceived weight status and gender on smoking behaviors. Regression
Downloaded from hpq.sagepub.com at Monash University on November 14, 2015
6
Journal of Health Psychology XX(X)
Table 3. Regression analyses examining the relationship of weight status and smoking behaviors. Males and females Perceived weight status Ever smoked Smoking daily Age of first cigarette Days smoked in last 30 days Cigarettes per day Weight change status Ever smoked Smoking daily Age of first cigarette Days smoked in last 30 days Cigarettes per day BMI percentile Ever smoked Smoking daily Age of first cigarette Days smoked in last 30 days Cigarettes per day
β
OR
R2
F
0.107 0.048 0.029 0.033 0.033
1.113 1.049 – – –
– – 0.003 0.037 0.034
– –
0.344 0.035 0.004 0.083 0.076
1.411 1.036 – – –
– – 0.003 0.037 0.034
– –
0.005 0.003 0.001 0.002 0.002
1.005 1.003 – – –
– – 0.003 0.042 0.040
– –
p
1.710 (1, 43) 1.190 (1, 43) 1.850 (1, 42)
0.007* 0.395 0.198 0.281 0.181
0.080 (1, 43) 3.170 (1, 43) 6.310 (1, 42)
HPQ0010.1177/1359105313517275Journal of Health PsychologyLange et al.
Article
The relationship between weight and smoking in a national sample of adolescents: Role of gender
Journal of Health Psychology 201X, Vol. XX(X) 1–10 © The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/1359105313517275 hpq.sagepub.com
Krista Lange, Sneha Thamotharan, Madeline Racine, Caroline Hirko and Sherecce Fields
Abstract This study sought to investigate the role of weight status and body mass index percentile in risky smoking behaviors in male and female adolescents. Analyses of the data obtained in the 2011 Youth Risk Behavior Surveillance System were conducted. The national sample size included 15,425 adolescents. Questions addressing weight status and smoking behaviors were used in analyses. Significant effects of perceived weight status, weight change status, and body mass index percentile on smoking behaviors were found for both genders. The current findings indicate the importance of accounting for both gender and weight status when developing prevention and cessation programs targeting smoking behaviors.
Keywords adolescents, body mass index percentile, dieting, smoking, weight perception
Introduction Tobacco use remains the leading cause of preventable death in the United States and is responsible for approximately 443,000 deaths annually (Centers for Disease Control and Prevention (CDC), 2002). Despite its risks, the use of tobacco remains one of the leading causes of death and disability among youth; thus, intervention and prevention efforts aimed at decreasing tobacco use are vital (CDC, 2002). This issue is especially prevalent within adolescent populations as research has found that 3600 individuals aged 12–17 years initiate tobacco use daily. Furthermore, 1100 of these adolescents will become daily cigarette smokers (Substance Abuse and Mental Health Services Administration, Center for Behavioral Health Statistics and Quality, 2008). Understanding the
characteristics that may predict enhanced risk of engaging in smoking behaviors is essential to the development of effective treatment and prevention programs. Previous research has indicated that attitudes toward smoking can influence the likelihood of initiating smoking and the endorsement of habitual smoking behaviors (Otten et al., 2008). The perception that smoking can aid in weight loss or the promotion of a slimmer figure has Texas A&M University, USA Corresponding author: Sherecce Fields, Health Behavior Research Group, Department of Psychology, Texas A&M University, MS4235, College Station, TX 77843, USA. Email: [email protected]
Downloaded from hpq.sagepub.com at Monash University on November 14, 2015
2
Journal of Health Psychology XX(X)
been a robust finding in the literature. Individuals with weight concerns may be more likely to smoke cigarettes when compared with individuals unconcerned with their weight (Crisp et al., 1999; Gibbons and Gerrard, 1996). This conclusion is further supported by studies revealing that individuals monitoring their weight are likely to engage in smoking with the goal of maintaining and losing weight (Camp et al., 1993; French and Jeffery, 1995; McKee et al., 2005; Tomeo et al., 1999), and adolescents who believe smoking can assist in weight control are at an increased risk to start smoking 1 year following the endorsement of this belief (Harakeh et al., 2010). Within pediatric obesity treatment programs, the Expert Committee on Childhood Obesity has identified a pressing need for smoking cessation programs (Barlow and Dietz, 1998). An increased likelihood of smoking is observed in individuals with a poor body image (Kendzor et al., 2009; Lopez et al., 2008; Pomerleau and Saules, 2007), which may be of concern as body mass index (BMI) has been shown to be strongly correlated to body image (Goldfield et al., 2010; Yates, Edman, & Aruguete, 2004). Although the relationship between weight status and smoking patterns has been investigated previously, little research has investigated the effect of adolescent weight status and current dieting status on observed smoking patterns. Therefore, the purpose of this study was to conduct analyses of the data obtained in the 2009 Youth Risk Behavior Surveillance System (YRBSS), a national survey conducted by the CDC, to investigate the relationship of weight perception, weight change status, and BMI percentile to risky smoking behaviors.
The secondary aim of this study was to investigate whether gender differences existed in the hypothesized relationship of weight status and smoking patterns, as past research has shown differences in smoking motivation for males and females. It was hypothesized that weight concerns would predict smoking behaviors. Specifically, it was hypothesized that adolescents desiring to lose weight would have a greater likelihood of endorsing all smoking behaviors as past research has indicated that many individuals, especially females, believe that smoking will aid both weight loss and maintenance. BMI percentile and the perception of being overweight were also hypothesized to predict smoking behaviors in both males and females. Although little research has investigated the role of actually being overweight, there has been some conflicting evidence to indicate a positive correlation between BMI and smoking initiation for males compared to females (Cawley et al., 2004; Tucker, 1983). Additional inconsistencies have been found in numerous empirical studies investigating characteristics that motivate smoking behaviors of males and females, and these differences are especially prevalent when focused on the role of body image and current weight status in smoking likelihood (Saarni et al., 2004; Strauss and Mir, 2001). Through the use of a diverse, nationally representative sample of adolescents, we aimed to achieve a greater understanding of the role of weight status on reported smoking behaviors of male and female adolescents.
Method
Hypotheses
Participants
The first aim of this study was to determine whether a relationship between weight status and smoking behaviors exists. This relationship was addressed by both objective (weight change status and BMI percentile) and subjective (weight perception) responses to further elucidate predictors of risky smoking patterns.
Participants were sampled from the YRBSS from the CDC for the reporting period of September 2010 to December 2011. The YRBSS monitors priority health-risk behaviors among youth and young adults including obesity and asthma through the use of the youth risk behavior survey (YRBS). The YRBS
Downloaded from hpq.sagepub.com at Monash University on November 14, 2015
3
Lange et al. includes national, state, territorial, tribal, and local school-based surveys of representative samples of 9th- through 12th-grade students and provides data in public and private schools in the United States (CDC, 2011). The national student sample size included 15,425 respondents and comprised 50.2 percent female and 49.8 percent male participants. In regard to school classification, 24.6 percent were in 9th grade, 24.1 percent were in 10th grade, 27.0 percent were in 11th grade, 24.1 percent were in 12th grade, and 0.2 percent were ungraded or other. Further information regarding demographic variables is presented in Table 1.
Procedure In order to obtain a representative national sample, a three-stage cluster design was implemented. The initial sample included 1276 primary sampling units (PSUs) which were divided into 16 strata in accordance with their metropolitan statistical area as well as the percentage of black and Hispanic students. Of these 1276 PSUs, 57 were sampled with probability proportional to the school enrollment size observed overall for the PSU. In the second stage of sampling, schools with any of grades 9–12 were sampled with probability proportional to school enrollment size. In the third sampling stage, intact classes from either a required subject (e.g. English or social studies) or a required period (e.g. homeroom or second period) were sampled randomly, and all students in the sampled classes were eligible to participate (Eaton et al., 2012). Survey procedures were designed to protect students’ privacy by allowing for anonymous and voluntary participation. Data collection procedures are similar for national, state, and local surveys. Students completed the selfadministered questionnaire following the CDC’s Institutional Review Board approved protocol for the national YRBS. In the national survey, students who were absent on the day of data collection were allowed to complete questionnaires at a later date and had their questionnaire administered by the data collector or
Table 1. Descriptive statistics of YRBS respondents (N = 15,425). Variable Sex Male Female Age 12 years old or younger 13 years old 14 years old 15 years old 16 years old 17 years old 18 years old or older Classification 9th grade 10th grade 11th grade 12th grade Ungraded/Other Race/ethnicity American Indian/Alaska Native Asian Black of African American Native Hawaiian/Other Pacific Islander White Hispanic/Latino Multiple—Hispanic Multiple—non-Hispanic
Percent of frequency 50.2% 49.8% (Missing = 61) 0.3% 0.2% 10.2% 22.6% 26.4% 25.5% 14.9% (Missing = 62) 24.6% 24.1% 27.0% 24.1% 0.2% (Missing = 99) 1.9% 3.2% 18.3% 0.8% 40.8% 14.7% 15.9% 4.3% (Missing = 315)
YRBS: Youth Risk Behavior Surveillance. All percentages are valid percentages to account for missing N.
school personnel. This allowed an increase in student response rates which averaged 71 percent overall (Eaton et al., 2012). Additionally, because frequently absent students are more likely to engage in health-risk behaviors than students who are not frequently absent, these
Downloaded from hpq.sagepub.com at Monash University on November 14, 2015
4
Journal of Health Psychology XX(X)
procedures help provide data that are representative of all high school students (CDC, 2004). Further explanation of the administration and methodology of the YRBS questionnaire are described elsewhere (Eaton et al., 2012).
Measures The YRBS included items which addressed demographic variables, sexual behaviors, substance use, physical activity, and diet behaviors. Of interest to this study were responses to questions regarding weight status and smoking behavior. Weight status was indicated by questions which addressed perceived weight status, weight change status, and BMI percentile. Smoking behaviors were assessed by responses to questions addressing having ever smoked, age of first cigarette, number of cigarettes smoked per day, number of days engaged in smoking, and smoking daily. Response options for each question included in our analyses are presented in Table 2.
Statistical analyses To investigate the role of weight status in predicting risky smoking behaviors, logistic regressions were used for all smoking questions with dichotomous response items (yes/ no), while linear regressions were utilized to assess the remaining responses. All analyses were completed using the Statistical Analysis Software (SAS) SURVEYLOGISTIC procedure. A Taylor Series Linearization method was used in all analyses to adjust for sample procedure including PSU, cluster sampling, and weighted design. Age, ethnicity, and sampling procedure (national, state, or local) were controlled for by including these variables as covariates in our analyses. Ethnicity was recoded such that individuals noting they were Caucasian were used as the reference group for analyses. Additionally, weight change status was recoded such that those endorsing they were “not trying to do anything” about their weight were used as the reference group and remaining variables were dummy coded. Age
of first cigarette, although categorical in the questionnaire format, was recoded to make this response continuous such that those who had never smoked were given the highest dummy code. Furthermore, regression analyses were completed with both males and females to view the effects of weight status on smoking behaviors, and interactions were also analyzed to determine gender differences in this relationship.
Results A summary of the results for regression analyses predicting smoking behavior is presented in Table 3. When analyses included both males and females, endorsing a higher perceived weight status was a significant predictor of ever smoking. Contrary to our hypothesis, perceived weight status was not a significant predictor of age smoked, number of days smoked in past month, number of cigarettes daily, or smoking daily. Compared to adolescents not attempting to change their weight, adolescents reporting wanting to lose weight were 1.411 times more likely to endorse ever having smoked, and adolescents endorsing a desire to lose weight, compared to not wanting to make any changes to one’s weight, were predictive of smoking more cigarettes per day. Weight change status was not significant in predicting days smoked in the past month, age of first cigarette use, or daily smoking. Tests examining the influence of BMI percentile on smoking indicated that a higher BMI percentile was predictive of endorsing ever having smoked, daily smoking, number of days one smoked in the past month, and cigarettes smoked per day. Upon further investigation of the interaction of weight status and gender, gender differences were found such that female respondents were shown to have a greater number of significant relationships of weight status and risky smoking behaviors compared to males. Regression analyses revealed a number of gender differences in the relationship of weight status and reported smoking behaviors (detailed tables containing all values for the regression
Downloaded from hpq.sagepub.com at Monash University on November 14, 2015
5
Lange et al. Table 2. Response items addressing weight status and smoking patterns. Response item
Frequency of endorsement
Which of the following are you trying to do about your weight? Lose weight 46.9% Gain weight 17.2% Stay the same weight 18.2% 17.7% Not trying to do anything (Missing = 241) How do you describe your weight? Very underweight 2.3% Slightly underweight 11.9% About the right weight 55.8% Slight overweight 25.7% Very overweight 4.3% (Missing = 285) Have you ever smoked cigarettes daily, that is, at least one cigarette every day for 30 days? Yes 9.4% No 90.6% (Missing = 1485) How old were you when you first smoked? 66.2% Never smoked a cigarette 8 years old or younger 2.7% 9 or 10 years old 2.4% 11 or 12 years old 5.2% 13 or 14 years old 10.4% 15 or 16 years old 10.2% 17 years old or older 2.9% (Missing = 580) During the past 30 days, how many days did you smoke cigarettes? 0 days 82.8% 1–2 days 5.3% 3–5 days 2.7% 6–9 days 1.7% 10–19 days 1.8% 20–29 days 1.5% All 30 days 4.1% (Missing = 682) Have you ever tried cigarette smoking, even one or two puffs? Yes 46.0% No 54.0%
Table 2. (Continued) Response item
Frequency of endorsement
(Missing = 241) During the past 30 days, on the days you smoked, how many cigarettes did you smoke per day? 82.4% Did not smoke cigarettes Less than 1 cigarette 4.5% 1 cigarette 3.7% 2–5 cigarettes 6.4% 6–10 cigarettes 1.7% 11–20 cigarettes 0.7% 0.7% More than 20 cigarettes (Missing = 1611) Variable BMI percentile 63.0 ± 28.4 BMI: body mass index. All percentages are valid percentages to account for missing N. BMI percentile is presented as mean and standard deviation.
model and simple slopes can be obtained by contacting the authors directly). The regression model investigating perceived weight status significantly predicted having ever smoked (Wald χ2 = 337.80, p < 0.01), number of days smoked in the last 30 days (R2 = 0.04, p = 0.01), cigarettes smoked per day (R2 = 0.04, p < 0.01), smoking daily (Wald χ2 = 183.74, p < 0.01), and age of smoking initiation (R2 = 0.02, p < 0.01). The interaction of gender and perceived weight status significantly predicted having ever smoked (B = 0.12, Wald χ2 = 3.25, p = 0.07) and smoking daily (B = 0.11, Wald χ2 = −2.23, p = −2.23). Investigation of simple slopes for gender revealed that the simple effects were only significant among females in the prediction of having ever smoked (B = 0.16, Wald χ2 = 13.14, p < 0.01) and smoking daily from perceived weight status (B = 0.06, Wald χ2 = 5.24, p = 5.24). In order to examine nonsignificant interactions, regression analyses were completed without the interaction term to examine the main effects of perceived weight status and gender on smoking behaviors. Regression
Downloaded from hpq.sagepub.com at Monash University on November 14, 2015
6
Journal of Health Psychology XX(X)
Table 3. Regression analyses examining the relationship of weight status and smoking behaviors. Males and females Perceived weight status Ever smoked Smoking daily Age of first cigarette Days smoked in last 30 days Cigarettes per day Weight change status Ever smoked Smoking daily Age of first cigarette Days smoked in last 30 days Cigarettes per day BMI percentile Ever smoked Smoking daily Age of first cigarette Days smoked in last 30 days Cigarettes per day
β
OR
R2
F
0.107 0.048 0.029 0.033 0.033
1.113 1.049 – – –
– – 0.003 0.037 0.034
– –
0.344 0.035 0.004 0.083 0.076
1.411 1.036 – – –
– – 0.003 0.037 0.034
– –
0.005 0.003 0.001 0.002 0.002
1.005 1.003 – – –
– – 0.003 0.042 0.040
– –
p
1.710 (1, 43) 1.190 (1, 43) 1.850 (1, 42)
0.007* 0.395 0.198 0.281 0.181
0.080 (1, 43) 3.170 (1, 43) 6.310 (1, 42)
