F E AT U R E S
Predictors of Adolescent Health Risk Behaviors ■
Seyhan C. Saritas, PhD, RN ■ Behice Erci, PhD, RN The aim of this study was to determine the predictors of health risk behaviors of adolescents. A cross-sectional and descriptive design was used. A convenience sample of 436 undergraduate students was recruited from departments and faculties of Ataturk ¨ University. The researcher visited university departments 5 working days each week to conduct interviews with the students. The students were asked to complete a questionnaire in their classrooms. The data analysis used multivariate testing to identify predictors of health risk behaviors. The mean score on the health risk behavior scale indicated that the participants sometimes engaged in risky behaviors concerning diet, anger, stress, and disease prevention. The adolescents frequently engaged in risky behavior concerning medical compliance and beliefs about masculinity. Demographically, age, gender, income, and education level of demographic characteristics of the adolescents and, in terms of health status, health behaviors and the experience of a serious disease were significant predictors of adolescent health risk behaviors. In this study, some demographic characteristics were predictors for health risk behaviors, in general, of adolescents. KEY WORDS: adolescent, health behavior, health risk, predictors Holist Nurs Pract 2014;28(3):208–216
Adolescent health risk behaviors are becoming a serious health care problem,1 as adolescents frequently engage in health-compromising behaviors in several crucial domains.2,3 Research has shown that around the world many college students engage in various risky health behaviors, including alcohol use, tobacco use, physical inactivity, and unhealthy dietary practices, ignoring safety habits such as wearing helmets, seat belts, and/or condoms, and engage in excessive sun exposure. All these behaviors may have long-term implications for their health.4-6 Binge drinking has also been associated with other negative health behaviors including smoking,7 risky sexual behavior,8 having multiple sexual partners,9 injuries,
Author Affiliations: School of Health Sciences, Departments of Medical Nursing (Dr Saritas) and Public Health Nursing (Dr Erci), Inonu University, Malatya, Turkey. The authors sincerely thank all the participants. The authors received no financial support for the research and/or authorship of this article. The authors declared no potential conflicts of interest with respect to the authorship and/or publication of this article. Correspondence: Seyhan C. Saritas, PhD, RN, School of Health Sciences, Department of Medical Nursing, Inonu University, Malatya, Turkey +90 44280 ([email protected]). DOI: 10.1097/HNP.0000000000000022
and inadequate seat belt use.10 In addition, college students often fail to meet current physical activity and/or dietary intake recommendations.4,6,11 In fact, the highest rate of decline in physical activity occurs in early adulthood between 18 and 24 years of age.3 This failure of college students to engage in healthier lifestyle practices is not unique to the United States. Recently, research has shown that adolescent and university students in both Europe and North America engage in problem behaviors such as smoking, alcohol use, inadequate physical exercise, injuries, physical fights, poor academic performance, and sugar and fat consumption; have misguided beliefs about the importance of health risk behaviors; and lack an awareness of the influence of these behaviors on heart disease.12-15 Remarkably, sugar consumption significantly decreases during this period, while physical exercise and fat consumption remains the same. In addition, Steptoe et al6 reported that changes in the prevalence of behavior were not correlated with changes in risk awareness. So, adolescents should be made aware of health risk behaviors since many of them are acquired during adolescence and continue during adulthood, leading to poor health outcomes.16-18 Since today’s youth represent the future adult population; the prevention of chronic diseases by studying adolescent health risk behaviors
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Predictors of Adolescent Health Risk Behaviors
is warranted. Health professionals, who often play a vital role in developing health promotion and disease prevention programs, can use such information to develop and/or enhance programs targeted at adolescents and college students.19 Thus, identifying the factors that influence adolescent health protective behaviors merits further attention.19 The aim of this study was to determine the predictors of Turkish adolescents’ health risk behaviors.
METHODS Sample A convenience sample of 436 undergraduate students, ranging in age from 18 to 25 years, was recruited from Atat¨urk University in Turkey. The G-Power software program (Copyright 2010-2013 Heinrich-HeineUniversit¨at D¨usseldorf) for Windows was used to determine the study’s sample size.20
Procedure The data were collected using the health risk behavior scale developed by Courtenay et al21 in 2002. The health risk behavior scale formed a total of 21 items. The 6 behavioral and health belief domains were diet (5 items: eg, “I avoid chips and fried foods by choosing foods that are baked, broiled, boiled, poached, or stewed”); substance abuse (4 items: eg, “I smoke cigarettes”); disease preventive care (5 items: eg, “I have physical and dental examinations every year”); anger and stress (3 items: eg, “Things build up inside until I lose my temper”); beliefs about masculinity (2 items: eg, “I believe a person should always try to control his or her emotions”); and medical compliance (2 items: eg, “I fill my medicine prescriptions immediately.”). The items were written in the form of statements worded in the first person. The respondents were asked to rate the extent to which each item was descriptive of them, using a scale from 1 (always) to 5 (never). Items were written so that they reflected both health risk behaviors or beliefs and health-promoting behaviors or beliefs.21 These latter items were reverse coded so that high scores on all items indicated a greater degree of health risk. First, the questionnaire was translated into Turkish and was reviewed by 2 experts for clarity and cultural sensitivity. Then, 3 experts in both languages translated the Turkish questionnaire into English. No
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modification or change was made to the health risk behavior items. Finally, the instrument was tested for comprehensiveness. The internal consistency (reliability) of the scale was determined with Cronbach α method. The α coefficient for the total health risk behavior scale was 0.73, while the internal reliability coefficients of these 6 subscales ranged from 0.60 to 0.75. Pearson product moment correlation of the scale’s items ranged between 0.25 and 0.54. Factor analysis was conducted on the total data set. An initial exploratory principal component analysis suggested a 6-factor solution. Factor loadings of the items were above 0.40. Factor analysis explained 59.8% of the variance in these items. The Turkish version of the scale consisted of 21 items on a 5-point Likert Scale: from always (5) to frequently (4), sometimes (3), rarely (2), and never (1). High scores on all items indicated a greater degree of health risk. The researchers visited university departments on 5 working days each week and conducted interviews with the students in 2011. The health risk behavior questionnaire was explained to the participants, who then read it and marked their answers on the sheets. The questionnaire took approximately 20 minutes to complete and could be understood by people with minimal reading ability. The students were asked to complete the questionnaire in their classrooms. All participants completed the questionnaire. This study was approved by the ethics committee at the Health Science Faculty of Atat¨urk University and informed consent was obtained from each participant. The students were informed about the purpose of the research and assured of their right to refuse to participate or to withdraw from the study at any stage. Anonymity and confidentiality were guaranteed.
Statistical analysis The data analysis used multivariate testing to identify the predictors of health risk behaviors.
RESULTS The demographic characteristics of the participants are shown in Table 1. The mean age was 21.51 (SD: 1.92) years. The majority of the sample was single, female (59.9%). The students reported a mean monthly income of 369.82 Turkish liras (SD: 253.91). The majority of the adolescents’ general health status was good. In their opinions, their health
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HOLISTIC NURSING PRACTICE • MAY/JUNE 2014
TABLE 1. Distribution of Demographic Characteristics (n: 436) Characteristics
Gender Female Male Marital status Married Single Age, y Monthly income (TL)
N
261 175 10 426 Mean 21.12 369.82
TABLE 2. Distribution of Health Status Features (n: 436) %
59.9 40.1 2.3 97.7 SD 3.0 253.91
Abbreviation: TL, Turkish Liras.
behaviors were good (72%). Only 27.3% of the participants had experienced disease or injury. The rate of hospitalization was 21.6% (Table 2). The mean score of the health risk behaviors demonstrated that the participants sometimes engaged in risky behaviors concerning diet, anger, stress, and disease prevention. The adolescents frequently engaged in risky behaviors in terms of medical compliance and held misguided (unhealthy) beliefs about masculinity (Table 3). Examining the effect of the demographic characteristics on health risk behaviors revealed that age was a powerful predictor of medical compliance (F = 4.324, P = .038). The entire set of demographic variables explained 5.2% of the total variance in disease prevention (F = 2.99, P = .007, R2 = 0.052, adjusted R2 = 0.005). Gender was found to be an independent predictor of diet (F = 6.48, P = .011), and the entire set of variables were predictors of diet with 2.8% variance (F = 1.56, P = .157, R2 = 0.028, adjusted R2 = 0.015). Gender was also an effective predictor for substance abuse (F = 15.19, P = .000) and for total health risk (F = 6.11, P = .014). The entire set of demographic variables explained 9.6% of the total variance in substance abuse (F = 5.73, P = .000, R2 = 0.096, adjusted R2 = 0.027) and 3.4% of the total variance in total health risk (F = 1.87, P = .085, R2 = 0.034, adjusted R2 = 0.002). Gender and income were significant predictors of disease prevention (F = 17.62, P = .000; F = 8.49, P = .004, respectively). The entire set of demographic variables explained 9.5% of the total variance in disease prevention (F = 5.67, P = .000, R2 = 0.095, adjusted R2 = 0.024). Gender, income, and the education level of the adolescents all affected beliefs about
General Health Status
N
Health Status Features Excellent 75 Good 296 Fair 54 Poor 11 Health Behaviors in General Very good 63 Good 314 Fair 46 Poor 12 The Experience of a Serious Disease Very frequently 8 Frequently 16 Not very frequently 144 Infrequently 268 Serious Accident or Injury Very frequently 6 Frequently 15 Not very frequently 97 Little frequently 318 Hospitalization Yes 94 No 342 Sexual Disease Experience Yes 12 No 424 Experience of Disease/Injury Yes 119 No 316
%
17.2 67.9 12.4 2.5 14.4 72.0 10.6 2.8 1.8 3.7 33.0 61.5 1.4 3.4 22.2 72.9 21.6 78.4 2.8 97.2 27.3 72.6
masculinity (F = 4.40, P = .037; F = 6.15, P = .014; F = 4.94, P = .027, respectively). Jointly, the set of variables predicted beliefs about masculinity with 5.1% variance (F = 2.92, P = .009, R2 = 0.051, adjusted R2 = 0.019) (Table 4). Health behaviors and the experience of a serious disease were found to be independent predictors of diet (F = 4.94, P = .027; F = 4.94, P = .027, respectively). Multivariate analysis found that, collectively, health status features explained 3.1% of the total variance in diet (F = 1.96, P = .058, R2 = 0.031, adjusted R2 = 0.015). The entire set of health behaviors was a significant predictor of disease prevention (F = 4.12, P = .043). The set of health status variables explained 4.0% of the total variance in disease prevention (F = 2.51, P = .015, R2 = 0.040, adjusted R2 = 0.024). The experience of a serious disease was a powerful predictor of beliefs about masculinity (F = 5.68, P = .018). The entire set of health status variables predicted beliefs about
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Predictors of Adolescent Health Risk Behaviors
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TABLE 3. Parental Health Risk Scale: Minimum and Maximum Scores, Score Range, Scale Mean Health Risk Subscale
Diet Anger and stress Disease prevention Medical compliance Substance abuse Beliefs about masculinity Total
Items
Minimum
Maximum
Mean (SD)
5 3 5 2 4 2 21
5 3 0 0 0 0 9
25.0 15.0 25.0 10.0 26.0 10.0 93.0
13.3 (4.2) 7.4 (2.4) 12.9 (4.4) 7.6 (2.0) 5.9 (3.5) 7.9 (2.2) 55.3 (9.2)
masculinity, with 3.5% variance (F = 2.20, P = .033, R2 = 0.035, adjusted R2 = 0.019). Jointly, health status variables predicted substance abuse with 4.3% variance (F = 2.71, P = .009, R2 = 0.043, adjusted R2 = 0.027), although no single health status feature effectively predicted substance abuse (Table 5).
DISCUSSION The majority of the adolescents’ general health status was good. In their opinions, their health behaviors were good. Only 27.3% of the participants had experienced disease or injury. The rate of hospitalization was low (Table 2). The mean score of the health risk behaviors stated that the participants showed sometime risky behaviors concerning diet, anger and stress, and disease prevention. The adolescents showed sometime risky behavior in medical compliance and beliefs about masculinity (Table 3). Courtenay et al21 found that adolescents showed risky behaviors related to diet, anger and stress, and beliefs about masculinity. A previous study determined that health risk behaviors with highest prevalence rates included engagement in physical fight (32.1%); threatened or injured with a weapon (19.9%); feelings of despair or hopelessness (32.2%); and current cigarette use (13.6%).22 In this study, multivariate analysis showed that the demographic variables together explained 5.2% of the total variance for medical compliance, and age was a powerful predictor of medical compliance. Gudas et al23 claimed that perceived compliance was related to age, with younger children reporting greater perceived compliance to medication regimens. Better perceived compliance was found to be associated with higher levels of optimism and more knowledge about the disease in question.23
The entire set of variables predicted diet with 2.8% variance, and gender was found to be an independent predictor of diet. Courtenay et al21 demonstrated that gender was an important predictor of diet, and that men had riskier dietary habits than women. Women also tended to report that they engaged in risky behavior less frequently than men.24 Another study reported that responses from young men and women differed significantly for “trouble in sticking to a healthy diet” and “trouble choosing healthy foods when they eat out with family or friends,” with about 10% more females considering these items important. Significantly, most females considered the expense of a healthy diet and the use of food as a reward to be important barriers to healthy eating.25 The set of demographic variables explained 9.6% of the total variance in substance abuse and 3.4% of the total variance in total health risk. This study found that gender was an important predictor of both substance abuse and total health risk. Gender-based differences in risk perception were paralleled by significant male-female differences in reported risk taking (again in all domains except social risk), with female respondents being less likely to engage in risky behaviors.26 A previous study stated that responses differed significantly by gender, only in relation to alcohol advertising, which was considered important by 3.5% of the males and less than 1% of females in a sample of 568 adolescents.25 Puskar and a group of colleagues found that boys used tobacco slightly more than girls in both studies of a sample of 624 adolescents.27 A relationship between gender and health risk behaviors was found only for alcohol consumption and drug use. Male adolescents showed higher rates of risk behavior. Males also scored higher on the cumulative health risk behaviors than females. The differences between males and females were significant. The female adolescents scored lower than males on the cumulative risk score.28 Tuinstra et al29
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HOLISTIC NURSING PRACTICE • MAY/JUNE 2014
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TABLE 4. Predictor Effect of the Demographic Characteristics on Health Risk Behaviors Dependent Variable
Type III Sum of Squares
df
Mean Square
F
Significance
Corrected model
Diet Anger and stress Prevention Medical compliance Substance use Beliefs about masculinity Total health risk
165.12a 44.71b 645.32c 78.32d 401.81e 89.51f 963.47g
6 6 6 6 6 6 6
27.52 7.45 107.55 13.05 66.96 14.91 160.57
1.56 1.21 5.67 2.99 5.73 2.92 1.87
0.157 0.296 0.000 0.007 0.000 0.009 0.085
Intercept
Diet Anger and stress Prevention Medical compliance Substance use Beliefs about masculinity Total health risk
217.64 4.41 219.43 126.45 1.02 29.29 2434.01
1 1 1 1 1 1 1
217.64 4.41 219.43 126.45 1.02 29.29 2434.01
12.37 0.72 11.57 28.98 0.08 5.74 28.39
0.000 0.396 0.001 0.000 0.768 0.017 0.000
Age
Diet Anger and stress Prevention Medical compliance Substance use Beliefs about masculinity Total health risk
0.37 15.54 38.33 18.86 6.97 9.33 2.28
1 1 1 1 1 1 1
0.37 15.54 38.33 18.86 6.97 9.33 2.28
0.02 2.54 2.02 4.32 0.59 1.82 0.02
0.884 0.112 0.156 0.038 0.440 0.177 0.870
Gender
Diet Anger and stress Prevention Medical compliance Substance use Beliefs about masculinity Total health risk
114.01 5.48 334.24 0.03 177.45 22.48 524.49
1 1 1 1 1 1 1
114.01 5.48 334.24 0.03 177.45 22.48 524.49
6.48 0.89 17.62 0.00 15.19 4.40 6.11
0.011 0.344 0.000 0.932 0.000 0.037 0.014
Income
Diet Anger and stress Prevention Medical compliance Substance use Beliefs about masculinity Total health risk
0.02 0.90 24.52 12.57 43.49 31.42 12.20
1 1 1 1 1 1 1
0.02 0.90 24.52 12.57 43.49 31.42 12.20
0.00 0.14 1.29 2.88 3.72 6.15 0.14
0.972 0.701 0.256 0.091 0.054 0.014 0.706
No. of person in family
Diet Anger and stress Prevention Medical compliance Substance use Beliefs about masculinity Total health risk
1.56 3.18 0.39 0.98 5.26 1.02 1.31
1 1 1 1 1 1 1
1.56 3.18 0.39 0.98 5.26 1.02 1.31
0.08 0.52 0.02 0.22 0.45 0.20 0.01
0.766 0.471 0.885 0.634 0.503 0.655 0.902
Marital status
Diet Anger and stress Prevention Medical compliance Substance use Beliefs about masculinity Total health risk
9.33 11.09 8.11 0.55 3.84 4.97 7.02
1 1 1 1 1 1 1
9.33 11.09 8.11 0.55 3.84 4.97 7.02
0.53 1.81 0.42 0.12 0.32 0.97 0.08
Source
0.467 0.179 0.513 0.722 0.567 0.324 0.775 (continues)
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Predictors of Adolescent Health Risk Behaviors
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TABLE 4. Predictor Effect of the Demographic Characteristics on Health Risk Behaviors (Continued) Source
Class
Dependent Variable
Type III Sum of Squares
df
Mean Square
F
Significance
Diet Anger and stress Prevention Medical compliance Substance use Beliefs about masculinity Total health risk
20.46 0.46 161.07 1.53 17.29 25.22 249.65
1 1 1 1 1 1 1
20.46 0.46 161.07 1.53 17.29 25.22 249.65
1.16 0.07 8.49 0.35 1.48 4.94 2.91
0.282 0.782 0.004 0.554 0.224 0.027 0.089
R 2 = 0.028 (adjusted R 2 = 0.015). R 2 = 0.022 (adjusted R 2 = −0.003). c 2 R = 0.095 (adjusted R 2 = 0.024). d 2 R = 0.052 (adjusted R 2 = 0.005). e 2 R = 0.096 (adjusted R 2 = 0.027). f 2 R = 0.051 (adjusted R 2 = 0.019). g 2 R = 0.034 (adjusted R 2 = 0.002). a b
found a significant relationship between gender and health risk behaviors only for alcohol consumption and drug use. Male adolescents also scored higher on the combined score of health risk behaviors than females. Gender also affected substance abuse.30 This study found that the set of independent variables explained 9.5% of the total variance for disease prevention. Gender and income were important predictors for disease prevention. One study determined that gender, income, and family structure explained no more than 10% of the variance in each of the risk behaviors of younger adolescents.31 Boyce and Dallago32 found that health risk behaviors such as smoking, poor diet, physical activity, and alcohol use were directly linked to both socioeconomic status and adult health outcomes. Jointly, this study’s variables predicted beliefs about masculinity, with 5.1% variance. The students’ gender, income, and education levels were powerful predictors of beliefs about masculinity (Table 4). Courtenay et al21 determined that men reported riskier health-related beliefs than women. The results of Booth and Nolen’s33 study show that gender differences in preferences for risk taking were sensitive to the gender mix of the sample, with girls being more likely to choose risky outcomes when assigned to all-girl groups. A previous study stated that health risk behaviors were more prevalent in subjects with lower education, income, or occupational status.34 Risk factors are expected to increase with age during adolescence. These factors were positively correlated with education levels.35 Multivariate analysis found that, collectively, health status features explained 3.1% of the total variance in
diet. Health behaviors in general and the experience of a serious disease were found to be independent predictors of diet. The set of health status variables explained 4.0% of the total variance for disease prevention, and health behaviors in general were a significant predictor of disease prevention. The set of health status variables predicted beliefs about masculinity with 3.5% variance. Jointly, the variables were predictors with 4.3% variance for substance abuse, although no single health status feature was an effective predictor of substance abuse. The experience of a serious disease was a powerful predictor of beliefs about masculinity (Table 5). This study has shown that some demographic characteristics and health behaviors in general of adolescents were predictors for health risk behaviors. The information gained from this study should increase awareness among public health nurses and health care professionals about adolescent health risk behaviors and may help them to target adolescent groups for specific interventions. These findings help us to better understand how to promote healthy behaviors to adolescents. Predictors for health risk behaviors should be measured, and healthy behaviors should be encouraged. In addition, identifying these predictors may improve clinical research. It is essential for designing interventions that encourage adolescents to adopt healthy behaviors. The results of this study should be of value for the protection and promotion of health and health care by professionals. Professionals need to understand which adolescents engage in health risk behaviors. Since health professionals who care for adolescents should
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HOLISTIC NURSING PRACTICE • MAY/JUNE 2014
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TABLE 5. Predictor Effect of the Health Status Features on Health Risk Behaviors Source
Dependent Variable
Type III Sum of Squares
df
Mean Square
239.33a 33.59b 343.48c 40.42d 235.23e 78.52f 646.38g
6 6 6 6 6 6 6
34.19 4.79 49.06 5.77 33.60 11.21 92.34
1.96 0.82 2.51 1.33 2.71 2.20 1.10
0.058 0.570 0.015 0.230 0.009 0.033 0.360
F
Significance
Corrected model
Diet Anger and stress Prevention Medical compliance Substance use Beliefs about masculinity Total health risk
Intercept
Diet Anger and stress Prevention Medical compliance Substance use Beliefs about masculinity Total health risk
618.83 113.41 967.39 80.27 152.13 75.89 9338.20
1 1 1 1 1 1 1
618.83 113.41 967.39 80.27 152.13 75.89 9338.20
35.63 19.39 49.48 18.61 12.28 14.89 111.48
0.000 0.000 0.000 0.000 0.001 0.000 0.000
General health status
Diet Anger and stress Prevention Medical compliance Substance use Beliefs about masculinity Total health risk
17.73 0.18 13.63 6.01 36.49 11.83 0.05
1 1 1 1 1 1 1
17.73 0.18 13.63 6.01 36.49 11.83 0.05
1.02 0.03 0.69 1.39 2.94 2.32 0.00
0.313 0.859 0.404 0.238 0.087 0.128 0.979
Health behaviors in general
Diet Anger and stress Prevention Medical compliance Substance use Beliefs about masculinity Total health risk
85.91 7.64 80.58 1.50 13.66 0.07 250.30
1 1 1 1 1 1 1
85.91 7.64 80.58 1.50 13.66 0.07 250.30
4.94 1.30 4.12 0.34 1.10 0.01 2.98
0.027 0.254 0.043 0.555 0.294 0.906 0.085
Frequency of a serious disease experienced
Diet Anger and stress Prevention Medical compliance Substance use Beliefs about masculinity Total health risk
85.96 0.40 0.00 2.72 1.05 28.94 15.61
1 1 1 1 1 1 1
85.96 0.40 0.00 2.72 1.05 28.94 15.61
4.94 0.06 0.00 0.63 0.08 5.68 0.18
0.027 0.793 0.991 0.427 0.771 0.018 0.666
Frequency of a serious accident or injury experienced
Diet Anger and stress Prevention Medical compliance Substance use Beliefs about masculinity Total health risk
3.67 0.17 54.41 13.78 8.93 0.53 68.19
1 1 1 1 1 1 1
3.67 0.17 54.41 13.78 8.93 0.53 68.19
0.21 0.03 2.78 3.19 0.72 0.10 0.81
0.646 0.862 0.096 0.075 0.396 0.746 0.367
Hospitalization
Diet Anger and stress Prevention Medical compliance Substance use Beliefs about masculinity Total health risk
5.44 0.07 16.36 6.36 0.02 0.00 1.04
1 1 1 1 1 1 1
5.44 0.07 16.36 6.36 0.02 0.00 1.04
0.31 0.01 0.83 1.47 0.00 0.00 0.01
0.576 0.907 0.361 0.225 0.966 0.971 0.911 (continues)
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Predictors of Adolescent Health Risk Behaviors
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TABLE 5. Predictor Effect of the Health Status Features on Health Risk Behaviors (Continued) Source
Dependent Variable
Sexual disease experience Diet Anger and stress Prevention Medical compliance Substance use Beliefs about masculinity Total health risk
Type III Sum of Squares
df
Mean Square
F
Significance
2.82 0.44 42.39 0.04 22.78 0.09 183.29
1 1 1 1 1 1 1
2.82 0.44 42.39 0.04 22.78 0.09 183.29
0.16 0.07 2.16 0.01 1.84 0.01 2.18
0.687 0.783 0.142 0.921 0.176 0.895 0.140
R 2 = 0.031 (adjusted R 2 = 0.015). R 2 = 0.013 (adjusted R 2 = −0.003). c 2 R = 0.040 (adjusted R 2 = 0.024). d 2 R = 0.022 (adjusted R 2 = 0.005). e 2 R = 0.043 (adjusted R 2 = 0.027). f 2 R = 0.035 (adjusted R 2 = 0.019). g 2 R = 0.018 (adjusted R 2 = 0.002). a b
provide comprehensive care that addresses diet, substance abuse, preventive care, anger and stress, beliefs about masculinity, and the medical compliance of adolescents, they can provide consultations with them to minimize health risk behaviors. This study provides the foundation for future studies of health risk behavior predictors in adolescents. The implications for research include replicating the study in other geographic areas with diverse samples of adolescents.
REFERENCES 1. DiClemente RJ, Hansen WB, Ponton LE. Adolescents at risk: a generation in jeopardy. In:Di Clemente RJ, Hansen WB, Ponton LE, eds. Handbook of Adolescent Health Risk Behaviour. New York, NY: Plenum; 1996:1-4. 2. Resnick MD, Bearman PS, Blum RW, et al. Protecting adolescents from harm findings from the National Longitudinal Study on Adolescent Health. JAMA. 1997;278:823-832. 3. US. Department of Health and Human Services. Healthy People 2010 With Understanding and Improving Health and Objectives for Improving Health. 2nd ed. Washington, DC: U.S. Government Printing Office; 2000. 4. Centers for Disease Control and Prevention. Fertility, Family Planning, and Women’s Health: New Data From the 199S National Survey of Family Growth (DHHS Publication No. PHS 97-1995). Washington, DC: US. Government Printing Office; 1997. 5. Steptoe A, Wardle J. Health behaviour, risk awareness and emotional well-being in students from Eastern Europe and Western Europe. Soc Sci Med. 2001;53:1621-1630. 6. Steptoe A, Phil D, Wardle J, et al. Trends in smoking, diet, physical exercise, and attitudes toward health in European university students from 13 countries, 1990–2000. Prev Med. 2002;35:97-104. 7. Jones SE, Oeltmann J, Wilson TW, Brener ND, Hill CV. Binge drinking among undergraduate college students in the United States: implications for other substance use. J Am Coll Health. 2001;50(1):33-38. 8. Ichiyama MA, Kruse MI. The social contexts of binge drinking among private university freshmen. J Alcohol Drug Edu. 1998;44(1): 18-33.
9. Wechsler H, Rigotti NA, Gledhill-Hoyt J, Lee H. Increased levels of cigarette use among college students: a cause for national concern. JAMA. 1998;280:1673-1678. 10. Everett SA, Warren CW, Santelli JS, Kann L, Collins JL, Kolb LT. Use of birth control pills, condoms, and withdrawal among U.S. high school students. J Adoles Health. 2000;27(2):112-118. 11. Dinger MK, Waigandt A. Dietary intake and physical activity behaviors of male and female college students. Am J Health Promot. 1999;11(5):360-362. 12. Buchmann AF, Schmid B, Blomeyer D, et al. Impact of age at first drink on vulnerability to alcohol-related problems: testing the marker hypothesis in a prospective study of young adults. J Psychiatr Res. 2009;43:1205-1212. 13. Hingson RW, Edwards EM, Heeren T, Rosenbloom D. Age of drinking onset and injuries, motor vehicle crashes, and physical fights after drinking and when not drinking. Alcohol Clin Exp Res. 2009;33:783790. 14. Komro KA, Tobler AL, Maldonado-Molina MM, Perry CL. Effects of alcohol use initiation patterns on high-risk behaviors among urban, low-income, young adolescents. Prev Sci. 2010;11:14-23. 15. Kuntsche E, Rossow I, Simons-Morton B, Ter Bogt T, Kokkevi A, Godeau E. Not early drinking but early drunkenness is a risk factor for problem behaviors among adolescents from 38 European and North American countries. Alcohol Clin Exp Res. 2013;37(2): 308-314. 16. Rowland TW. Is there a scientific rational supporting the value of exercise for the present and future cardiovascular health of children? The con argument. Pediatr Exer Sci. 1996;8:303-309. 17. Kann L, Kinchen SA, Williams BI, et al. Youth risk behavior surveillance-United States, 1999. J Sch Health. 2000;70: 271-285. 18. Perry CL. Preadolescent and adolescent influences on health. In: Smedley BD, Syme SL, eds. Promoting Health. Intervention Strategies From Social and Behavioral Research. Washington, DC: The National Academies Press; 2000:217-253. 19. Von AHD, Ebert S, Ngamvitroj A, Park N, Kang DH. Predictors of health behaviours in college students. J Adv Nurs. 2004;48(5): 463-474. 20. Faul F, Erdfelder E, Lang AG. G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav Res Methods. 2007;39:175-191. 21. Courtenay WH, McCreary DR, Merighi JR. Gender and ethnic differences in health beliefs and behaviors. J Health Psychol. 2002;7(3): 219-231.
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22. Springer AE, Selwyn BJ, Kelder SH. A descriptive study of youth risk behavior in urban and rural secondary school students in El Salvador. BMC Int Health Hum Rights. 2006;6(3):1-11. 23. Gudas LJ, Koocher GP, Wypij D. Perceptions of medical compliance in children and adolescents with cystic fibrosis. J Dev Behav Pediatr. 1991;12(4):236-242. 24. Miles JA, Naumann SE. The effects of religiosity, gender, and at-risk behaviors on students’ perceived leadership ability. J Edu Human Dev. 2007;1(2):1-12. 25. Milligan RAK, Burke V, Beilin LJ, et al. Health-related behaviours and psycho-social characteristics of 18 year-old Australians. Soc Sci Med. 1997;45(10):1549-1562. 26. Slovic P. Trust, emotion, sex, politics, and science: surveying the riskassessment battlefield. In: Bazerman M, Messick D, Tenbrunsel A, et al. Psychological Perspectives to Environmental and Ethical Issues in Management. San Francisco, CA: Jossey-Bass; 1997:277-313. 27. Puskar KR, Tusaie-Mumford K, Sereika S, Lamb J. Health concerns and risk behaviors of rural adolescents. J Community Health Nurs. 1999;16(2):109-119. 28. Burke V, Milligan AK, Beilin LJ, et al. Clustering of health-related behaviors among 18-year-old Australians. Prev Med. 1997;26:724-733.
29. Tuinstra J, Groothoff JW, Van Den Heuvel WJA, Post D. Socioeconomic differences in health risk behavior in adolescence: do they exist? Soc Sci Med. 1998;47(1):67-74. 30. Lam CW, Ng HY, Boey KW. Measuring drug abuse: the development of the Chinese drug involvement scale in Hong Kong. Res Soc Work Pract. 2002;12(4):525-533. 31. Blum RW, Bearinger LH, Resnick MD. The effects of race/ethnicity, income, and family structure on adolescent risk behaviors. Am J Public Health. 2000;90:1879-1884. 32. Boyce W, Dallago L. Young people’s health in context Health Behaviour in School-aged Children (HBSC) study: international report from the 2001/2002 survey. In:Currie C, Roberts C, Morgan A, et al., eds. Health Policy for Children and Adolescents. No. 4. World Health Organization; 2004:13-25. 33. Booth AL, Nolen PJ. Gender Differences in Risk Behaviour: Does Nurture Matter? Discussion Paper Series IZA DP 2009; No. 4026. 34. Nocon M, Keil T, Willich SN. Education, income, occupational status and health risk behaviour. J Public Health. 2007;15:401-405. 35. Arthur MW, Hawkins DJ, Pollard JA, Catalano RF, Baglioni JR. Measuring risk and protective factors for substance use, delinquency, and other adolescent problem behaviors. Eval Rev. 2002;26(6):575-601.
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Predictors of Adolescent Health Risk Behaviors ■
Seyhan C. Saritas, PhD, RN ■ Behice Erci, PhD, RN The aim of this study was to determine the predictors of health risk behaviors of adolescents. A cross-sectional and descriptive design was used. A convenience sample of 436 undergraduate students was recruited from departments and faculties of Ataturk ¨ University. The researcher visited university departments 5 working days each week to conduct interviews with the students. The students were asked to complete a questionnaire in their classrooms. The data analysis used multivariate testing to identify predictors of health risk behaviors. The mean score on the health risk behavior scale indicated that the participants sometimes engaged in risky behaviors concerning diet, anger, stress, and disease prevention. The adolescents frequently engaged in risky behavior concerning medical compliance and beliefs about masculinity. Demographically, age, gender, income, and education level of demographic characteristics of the adolescents and, in terms of health status, health behaviors and the experience of a serious disease were significant predictors of adolescent health risk behaviors. In this study, some demographic characteristics were predictors for health risk behaviors, in general, of adolescents. KEY WORDS: adolescent, health behavior, health risk, predictors Holist Nurs Pract 2014;28(3):208–216
Adolescent health risk behaviors are becoming a serious health care problem,1 as adolescents frequently engage in health-compromising behaviors in several crucial domains.2,3 Research has shown that around the world many college students engage in various risky health behaviors, including alcohol use, tobacco use, physical inactivity, and unhealthy dietary practices, ignoring safety habits such as wearing helmets, seat belts, and/or condoms, and engage in excessive sun exposure. All these behaviors may have long-term implications for their health.4-6 Binge drinking has also been associated with other negative health behaviors including smoking,7 risky sexual behavior,8 having multiple sexual partners,9 injuries,
Author Affiliations: School of Health Sciences, Departments of Medical Nursing (Dr Saritas) and Public Health Nursing (Dr Erci), Inonu University, Malatya, Turkey. The authors sincerely thank all the participants. The authors received no financial support for the research and/or authorship of this article. The authors declared no potential conflicts of interest with respect to the authorship and/or publication of this article. Correspondence: Seyhan C. Saritas, PhD, RN, School of Health Sciences, Department of Medical Nursing, Inonu University, Malatya, Turkey +90 44280 ([email protected]). DOI: 10.1097/HNP.0000000000000022
and inadequate seat belt use.10 In addition, college students often fail to meet current physical activity and/or dietary intake recommendations.4,6,11 In fact, the highest rate of decline in physical activity occurs in early adulthood between 18 and 24 years of age.3 This failure of college students to engage in healthier lifestyle practices is not unique to the United States. Recently, research has shown that adolescent and university students in both Europe and North America engage in problem behaviors such as smoking, alcohol use, inadequate physical exercise, injuries, physical fights, poor academic performance, and sugar and fat consumption; have misguided beliefs about the importance of health risk behaviors; and lack an awareness of the influence of these behaviors on heart disease.12-15 Remarkably, sugar consumption significantly decreases during this period, while physical exercise and fat consumption remains the same. In addition, Steptoe et al6 reported that changes in the prevalence of behavior were not correlated with changes in risk awareness. So, adolescents should be made aware of health risk behaviors since many of them are acquired during adolescence and continue during adulthood, leading to poor health outcomes.16-18 Since today’s youth represent the future adult population; the prevention of chronic diseases by studying adolescent health risk behaviors
208 Copyright © 2014 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
Predictors of Adolescent Health Risk Behaviors
is warranted. Health professionals, who often play a vital role in developing health promotion and disease prevention programs, can use such information to develop and/or enhance programs targeted at adolescents and college students.19 Thus, identifying the factors that influence adolescent health protective behaviors merits further attention.19 The aim of this study was to determine the predictors of Turkish adolescents’ health risk behaviors.
METHODS Sample A convenience sample of 436 undergraduate students, ranging in age from 18 to 25 years, was recruited from Atat¨urk University in Turkey. The G-Power software program (Copyright 2010-2013 Heinrich-HeineUniversit¨at D¨usseldorf) for Windows was used to determine the study’s sample size.20
Procedure The data were collected using the health risk behavior scale developed by Courtenay et al21 in 2002. The health risk behavior scale formed a total of 21 items. The 6 behavioral and health belief domains were diet (5 items: eg, “I avoid chips and fried foods by choosing foods that are baked, broiled, boiled, poached, or stewed”); substance abuse (4 items: eg, “I smoke cigarettes”); disease preventive care (5 items: eg, “I have physical and dental examinations every year”); anger and stress (3 items: eg, “Things build up inside until I lose my temper”); beliefs about masculinity (2 items: eg, “I believe a person should always try to control his or her emotions”); and medical compliance (2 items: eg, “I fill my medicine prescriptions immediately.”). The items were written in the form of statements worded in the first person. The respondents were asked to rate the extent to which each item was descriptive of them, using a scale from 1 (always) to 5 (never). Items were written so that they reflected both health risk behaviors or beliefs and health-promoting behaviors or beliefs.21 These latter items were reverse coded so that high scores on all items indicated a greater degree of health risk. First, the questionnaire was translated into Turkish and was reviewed by 2 experts for clarity and cultural sensitivity. Then, 3 experts in both languages translated the Turkish questionnaire into English. No
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modification or change was made to the health risk behavior items. Finally, the instrument was tested for comprehensiveness. The internal consistency (reliability) of the scale was determined with Cronbach α method. The α coefficient for the total health risk behavior scale was 0.73, while the internal reliability coefficients of these 6 subscales ranged from 0.60 to 0.75. Pearson product moment correlation of the scale’s items ranged between 0.25 and 0.54. Factor analysis was conducted on the total data set. An initial exploratory principal component analysis suggested a 6-factor solution. Factor loadings of the items were above 0.40. Factor analysis explained 59.8% of the variance in these items. The Turkish version of the scale consisted of 21 items on a 5-point Likert Scale: from always (5) to frequently (4), sometimes (3), rarely (2), and never (1). High scores on all items indicated a greater degree of health risk. The researchers visited university departments on 5 working days each week and conducted interviews with the students in 2011. The health risk behavior questionnaire was explained to the participants, who then read it and marked their answers on the sheets. The questionnaire took approximately 20 minutes to complete and could be understood by people with minimal reading ability. The students were asked to complete the questionnaire in their classrooms. All participants completed the questionnaire. This study was approved by the ethics committee at the Health Science Faculty of Atat¨urk University and informed consent was obtained from each participant. The students were informed about the purpose of the research and assured of their right to refuse to participate or to withdraw from the study at any stage. Anonymity and confidentiality were guaranteed.
Statistical analysis The data analysis used multivariate testing to identify the predictors of health risk behaviors.
RESULTS The demographic characteristics of the participants are shown in Table 1. The mean age was 21.51 (SD: 1.92) years. The majority of the sample was single, female (59.9%). The students reported a mean monthly income of 369.82 Turkish liras (SD: 253.91). The majority of the adolescents’ general health status was good. In their opinions, their health
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HOLISTIC NURSING PRACTICE • MAY/JUNE 2014
TABLE 1. Distribution of Demographic Characteristics (n: 436) Characteristics
Gender Female Male Marital status Married Single Age, y Monthly income (TL)
N
261 175 10 426 Mean 21.12 369.82
TABLE 2. Distribution of Health Status Features (n: 436) %
59.9 40.1 2.3 97.7 SD 3.0 253.91
Abbreviation: TL, Turkish Liras.
behaviors were good (72%). Only 27.3% of the participants had experienced disease or injury. The rate of hospitalization was 21.6% (Table 2). The mean score of the health risk behaviors demonstrated that the participants sometimes engaged in risky behaviors concerning diet, anger, stress, and disease prevention. The adolescents frequently engaged in risky behaviors in terms of medical compliance and held misguided (unhealthy) beliefs about masculinity (Table 3). Examining the effect of the demographic characteristics on health risk behaviors revealed that age was a powerful predictor of medical compliance (F = 4.324, P = .038). The entire set of demographic variables explained 5.2% of the total variance in disease prevention (F = 2.99, P = .007, R2 = 0.052, adjusted R2 = 0.005). Gender was found to be an independent predictor of diet (F = 6.48, P = .011), and the entire set of variables were predictors of diet with 2.8% variance (F = 1.56, P = .157, R2 = 0.028, adjusted R2 = 0.015). Gender was also an effective predictor for substance abuse (F = 15.19, P = .000) and for total health risk (F = 6.11, P = .014). The entire set of demographic variables explained 9.6% of the total variance in substance abuse (F = 5.73, P = .000, R2 = 0.096, adjusted R2 = 0.027) and 3.4% of the total variance in total health risk (F = 1.87, P = .085, R2 = 0.034, adjusted R2 = 0.002). Gender and income were significant predictors of disease prevention (F = 17.62, P = .000; F = 8.49, P = .004, respectively). The entire set of demographic variables explained 9.5% of the total variance in disease prevention (F = 5.67, P = .000, R2 = 0.095, adjusted R2 = 0.024). Gender, income, and the education level of the adolescents all affected beliefs about
General Health Status
N
Health Status Features Excellent 75 Good 296 Fair 54 Poor 11 Health Behaviors in General Very good 63 Good 314 Fair 46 Poor 12 The Experience of a Serious Disease Very frequently 8 Frequently 16 Not very frequently 144 Infrequently 268 Serious Accident or Injury Very frequently 6 Frequently 15 Not very frequently 97 Little frequently 318 Hospitalization Yes 94 No 342 Sexual Disease Experience Yes 12 No 424 Experience of Disease/Injury Yes 119 No 316
%
17.2 67.9 12.4 2.5 14.4 72.0 10.6 2.8 1.8 3.7 33.0 61.5 1.4 3.4 22.2 72.9 21.6 78.4 2.8 97.2 27.3 72.6
masculinity (F = 4.40, P = .037; F = 6.15, P = .014; F = 4.94, P = .027, respectively). Jointly, the set of variables predicted beliefs about masculinity with 5.1% variance (F = 2.92, P = .009, R2 = 0.051, adjusted R2 = 0.019) (Table 4). Health behaviors and the experience of a serious disease were found to be independent predictors of diet (F = 4.94, P = .027; F = 4.94, P = .027, respectively). Multivariate analysis found that, collectively, health status features explained 3.1% of the total variance in diet (F = 1.96, P = .058, R2 = 0.031, adjusted R2 = 0.015). The entire set of health behaviors was a significant predictor of disease prevention (F = 4.12, P = .043). The set of health status variables explained 4.0% of the total variance in disease prevention (F = 2.51, P = .015, R2 = 0.040, adjusted R2 = 0.024). The experience of a serious disease was a powerful predictor of beliefs about masculinity (F = 5.68, P = .018). The entire set of health status variables predicted beliefs about
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Predictors of Adolescent Health Risk Behaviors
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TABLE 3. Parental Health Risk Scale: Minimum and Maximum Scores, Score Range, Scale Mean Health Risk Subscale
Diet Anger and stress Disease prevention Medical compliance Substance abuse Beliefs about masculinity Total
Items
Minimum
Maximum
Mean (SD)
5 3 5 2 4 2 21
5 3 0 0 0 0 9
25.0 15.0 25.0 10.0 26.0 10.0 93.0
13.3 (4.2) 7.4 (2.4) 12.9 (4.4) 7.6 (2.0) 5.9 (3.5) 7.9 (2.2) 55.3 (9.2)
masculinity, with 3.5% variance (F = 2.20, P = .033, R2 = 0.035, adjusted R2 = 0.019). Jointly, health status variables predicted substance abuse with 4.3% variance (F = 2.71, P = .009, R2 = 0.043, adjusted R2 = 0.027), although no single health status feature effectively predicted substance abuse (Table 5).
DISCUSSION The majority of the adolescents’ general health status was good. In their opinions, their health behaviors were good. Only 27.3% of the participants had experienced disease or injury. The rate of hospitalization was low (Table 2). The mean score of the health risk behaviors stated that the participants showed sometime risky behaviors concerning diet, anger and stress, and disease prevention. The adolescents showed sometime risky behavior in medical compliance and beliefs about masculinity (Table 3). Courtenay et al21 found that adolescents showed risky behaviors related to diet, anger and stress, and beliefs about masculinity. A previous study determined that health risk behaviors with highest prevalence rates included engagement in physical fight (32.1%); threatened or injured with a weapon (19.9%); feelings of despair or hopelessness (32.2%); and current cigarette use (13.6%).22 In this study, multivariate analysis showed that the demographic variables together explained 5.2% of the total variance for medical compliance, and age was a powerful predictor of medical compliance. Gudas et al23 claimed that perceived compliance was related to age, with younger children reporting greater perceived compliance to medication regimens. Better perceived compliance was found to be associated with higher levels of optimism and more knowledge about the disease in question.23
The entire set of variables predicted diet with 2.8% variance, and gender was found to be an independent predictor of diet. Courtenay et al21 demonstrated that gender was an important predictor of diet, and that men had riskier dietary habits than women. Women also tended to report that they engaged in risky behavior less frequently than men.24 Another study reported that responses from young men and women differed significantly for “trouble in sticking to a healthy diet” and “trouble choosing healthy foods when they eat out with family or friends,” with about 10% more females considering these items important. Significantly, most females considered the expense of a healthy diet and the use of food as a reward to be important barriers to healthy eating.25 The set of demographic variables explained 9.6% of the total variance in substance abuse and 3.4% of the total variance in total health risk. This study found that gender was an important predictor of both substance abuse and total health risk. Gender-based differences in risk perception were paralleled by significant male-female differences in reported risk taking (again in all domains except social risk), with female respondents being less likely to engage in risky behaviors.26 A previous study stated that responses differed significantly by gender, only in relation to alcohol advertising, which was considered important by 3.5% of the males and less than 1% of females in a sample of 568 adolescents.25 Puskar and a group of colleagues found that boys used tobacco slightly more than girls in both studies of a sample of 624 adolescents.27 A relationship between gender and health risk behaviors was found only for alcohol consumption and drug use. Male adolescents showed higher rates of risk behavior. Males also scored higher on the cumulative health risk behaviors than females. The differences between males and females were significant. The female adolescents scored lower than males on the cumulative risk score.28 Tuinstra et al29
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HOLISTIC NURSING PRACTICE • MAY/JUNE 2014
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TABLE 4. Predictor Effect of the Demographic Characteristics on Health Risk Behaviors Dependent Variable
Type III Sum of Squares
df
Mean Square
F
Significance
Corrected model
Diet Anger and stress Prevention Medical compliance Substance use Beliefs about masculinity Total health risk
165.12a 44.71b 645.32c 78.32d 401.81e 89.51f 963.47g
6 6 6 6 6 6 6
27.52 7.45 107.55 13.05 66.96 14.91 160.57
1.56 1.21 5.67 2.99 5.73 2.92 1.87
0.157 0.296 0.000 0.007 0.000 0.009 0.085
Intercept
Diet Anger and stress Prevention Medical compliance Substance use Beliefs about masculinity Total health risk
217.64 4.41 219.43 126.45 1.02 29.29 2434.01
1 1 1 1 1 1 1
217.64 4.41 219.43 126.45 1.02 29.29 2434.01
12.37 0.72 11.57 28.98 0.08 5.74 28.39
0.000 0.396 0.001 0.000 0.768 0.017 0.000
Age
Diet Anger and stress Prevention Medical compliance Substance use Beliefs about masculinity Total health risk
0.37 15.54 38.33 18.86 6.97 9.33 2.28
1 1 1 1 1 1 1
0.37 15.54 38.33 18.86 6.97 9.33 2.28
0.02 2.54 2.02 4.32 0.59 1.82 0.02
0.884 0.112 0.156 0.038 0.440 0.177 0.870
Gender
Diet Anger and stress Prevention Medical compliance Substance use Beliefs about masculinity Total health risk
114.01 5.48 334.24 0.03 177.45 22.48 524.49
1 1 1 1 1 1 1
114.01 5.48 334.24 0.03 177.45 22.48 524.49
6.48 0.89 17.62 0.00 15.19 4.40 6.11
0.011 0.344 0.000 0.932 0.000 0.037 0.014
Income
Diet Anger and stress Prevention Medical compliance Substance use Beliefs about masculinity Total health risk
0.02 0.90 24.52 12.57 43.49 31.42 12.20
1 1 1 1 1 1 1
0.02 0.90 24.52 12.57 43.49 31.42 12.20
0.00 0.14 1.29 2.88 3.72 6.15 0.14
0.972 0.701 0.256 0.091 0.054 0.014 0.706
No. of person in family
Diet Anger and stress Prevention Medical compliance Substance use Beliefs about masculinity Total health risk
1.56 3.18 0.39 0.98 5.26 1.02 1.31
1 1 1 1 1 1 1
1.56 3.18 0.39 0.98 5.26 1.02 1.31
0.08 0.52 0.02 0.22 0.45 0.20 0.01
0.766 0.471 0.885 0.634 0.503 0.655 0.902
Marital status
Diet Anger and stress Prevention Medical compliance Substance use Beliefs about masculinity Total health risk
9.33 11.09 8.11 0.55 3.84 4.97 7.02
1 1 1 1 1 1 1
9.33 11.09 8.11 0.55 3.84 4.97 7.02
0.53 1.81 0.42 0.12 0.32 0.97 0.08
Source
0.467 0.179 0.513 0.722 0.567 0.324 0.775 (continues)
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Predictors of Adolescent Health Risk Behaviors
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TABLE 4. Predictor Effect of the Demographic Characteristics on Health Risk Behaviors (Continued) Source
Class
Dependent Variable
Type III Sum of Squares
df
Mean Square
F
Significance
Diet Anger and stress Prevention Medical compliance Substance use Beliefs about masculinity Total health risk
20.46 0.46 161.07 1.53 17.29 25.22 249.65
1 1 1 1 1 1 1
20.46 0.46 161.07 1.53 17.29 25.22 249.65
1.16 0.07 8.49 0.35 1.48 4.94 2.91
0.282 0.782 0.004 0.554 0.224 0.027 0.089
R 2 = 0.028 (adjusted R 2 = 0.015). R 2 = 0.022 (adjusted R 2 = −0.003). c 2 R = 0.095 (adjusted R 2 = 0.024). d 2 R = 0.052 (adjusted R 2 = 0.005). e 2 R = 0.096 (adjusted R 2 = 0.027). f 2 R = 0.051 (adjusted R 2 = 0.019). g 2 R = 0.034 (adjusted R 2 = 0.002). a b
found a significant relationship between gender and health risk behaviors only for alcohol consumption and drug use. Male adolescents also scored higher on the combined score of health risk behaviors than females. Gender also affected substance abuse.30 This study found that the set of independent variables explained 9.5% of the total variance for disease prevention. Gender and income were important predictors for disease prevention. One study determined that gender, income, and family structure explained no more than 10% of the variance in each of the risk behaviors of younger adolescents.31 Boyce and Dallago32 found that health risk behaviors such as smoking, poor diet, physical activity, and alcohol use were directly linked to both socioeconomic status and adult health outcomes. Jointly, this study’s variables predicted beliefs about masculinity, with 5.1% variance. The students’ gender, income, and education levels were powerful predictors of beliefs about masculinity (Table 4). Courtenay et al21 determined that men reported riskier health-related beliefs than women. The results of Booth and Nolen’s33 study show that gender differences in preferences for risk taking were sensitive to the gender mix of the sample, with girls being more likely to choose risky outcomes when assigned to all-girl groups. A previous study stated that health risk behaviors were more prevalent in subjects with lower education, income, or occupational status.34 Risk factors are expected to increase with age during adolescence. These factors were positively correlated with education levels.35 Multivariate analysis found that, collectively, health status features explained 3.1% of the total variance in
diet. Health behaviors in general and the experience of a serious disease were found to be independent predictors of diet. The set of health status variables explained 4.0% of the total variance for disease prevention, and health behaviors in general were a significant predictor of disease prevention. The set of health status variables predicted beliefs about masculinity with 3.5% variance. Jointly, the variables were predictors with 4.3% variance for substance abuse, although no single health status feature was an effective predictor of substance abuse. The experience of a serious disease was a powerful predictor of beliefs about masculinity (Table 5). This study has shown that some demographic characteristics and health behaviors in general of adolescents were predictors for health risk behaviors. The information gained from this study should increase awareness among public health nurses and health care professionals about adolescent health risk behaviors and may help them to target adolescent groups for specific interventions. These findings help us to better understand how to promote healthy behaviors to adolescents. Predictors for health risk behaviors should be measured, and healthy behaviors should be encouraged. In addition, identifying these predictors may improve clinical research. It is essential for designing interventions that encourage adolescents to adopt healthy behaviors. The results of this study should be of value for the protection and promotion of health and health care by professionals. Professionals need to understand which adolescents engage in health risk behaviors. Since health professionals who care for adolescents should
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HOLISTIC NURSING PRACTICE • MAY/JUNE 2014
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TABLE 5. Predictor Effect of the Health Status Features on Health Risk Behaviors Source
Dependent Variable
Type III Sum of Squares
df
Mean Square
239.33a 33.59b 343.48c 40.42d 235.23e 78.52f 646.38g
6 6 6 6 6 6 6
34.19 4.79 49.06 5.77 33.60 11.21 92.34
1.96 0.82 2.51 1.33 2.71 2.20 1.10
0.058 0.570 0.015 0.230 0.009 0.033 0.360
F
Significance
Corrected model
Diet Anger and stress Prevention Medical compliance Substance use Beliefs about masculinity Total health risk
Intercept
Diet Anger and stress Prevention Medical compliance Substance use Beliefs about masculinity Total health risk
618.83 113.41 967.39 80.27 152.13 75.89 9338.20
1 1 1 1 1 1 1
618.83 113.41 967.39 80.27 152.13 75.89 9338.20
35.63 19.39 49.48 18.61 12.28 14.89 111.48
0.000 0.000 0.000 0.000 0.001 0.000 0.000
General health status
Diet Anger and stress Prevention Medical compliance Substance use Beliefs about masculinity Total health risk
17.73 0.18 13.63 6.01 36.49 11.83 0.05
1 1 1 1 1 1 1
17.73 0.18 13.63 6.01 36.49 11.83 0.05
1.02 0.03 0.69 1.39 2.94 2.32 0.00
0.313 0.859 0.404 0.238 0.087 0.128 0.979
Health behaviors in general
Diet Anger and stress Prevention Medical compliance Substance use Beliefs about masculinity Total health risk
85.91 7.64 80.58 1.50 13.66 0.07 250.30
1 1 1 1 1 1 1
85.91 7.64 80.58 1.50 13.66 0.07 250.30
4.94 1.30 4.12 0.34 1.10 0.01 2.98
0.027 0.254 0.043 0.555 0.294 0.906 0.085
Frequency of a serious disease experienced
Diet Anger and stress Prevention Medical compliance Substance use Beliefs about masculinity Total health risk
85.96 0.40 0.00 2.72 1.05 28.94 15.61
1 1 1 1 1 1 1
85.96 0.40 0.00 2.72 1.05 28.94 15.61
4.94 0.06 0.00 0.63 0.08 5.68 0.18
0.027 0.793 0.991 0.427 0.771 0.018 0.666
Frequency of a serious accident or injury experienced
Diet Anger and stress Prevention Medical compliance Substance use Beliefs about masculinity Total health risk
3.67 0.17 54.41 13.78 8.93 0.53 68.19
1 1 1 1 1 1 1
3.67 0.17 54.41 13.78 8.93 0.53 68.19
0.21 0.03 2.78 3.19 0.72 0.10 0.81
0.646 0.862 0.096 0.075 0.396 0.746 0.367
Hospitalization
Diet Anger and stress Prevention Medical compliance Substance use Beliefs about masculinity Total health risk
5.44 0.07 16.36 6.36 0.02 0.00 1.04
1 1 1 1 1 1 1
5.44 0.07 16.36 6.36 0.02 0.00 1.04
0.31 0.01 0.83 1.47 0.00 0.00 0.01
0.576 0.907 0.361 0.225 0.966 0.971 0.911 (continues)
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Predictors of Adolescent Health Risk Behaviors
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TABLE 5. Predictor Effect of the Health Status Features on Health Risk Behaviors (Continued) Source
Dependent Variable
Sexual disease experience Diet Anger and stress Prevention Medical compliance Substance use Beliefs about masculinity Total health risk
Type III Sum of Squares
df
Mean Square
F
Significance
2.82 0.44 42.39 0.04 22.78 0.09 183.29
1 1 1 1 1 1 1
2.82 0.44 42.39 0.04 22.78 0.09 183.29
0.16 0.07 2.16 0.01 1.84 0.01 2.18
0.687 0.783 0.142 0.921 0.176 0.895 0.140
R 2 = 0.031 (adjusted R 2 = 0.015). R 2 = 0.013 (adjusted R 2 = −0.003). c 2 R = 0.040 (adjusted R 2 = 0.024). d 2 R = 0.022 (adjusted R 2 = 0.005). e 2 R = 0.043 (adjusted R 2 = 0.027). f 2 R = 0.035 (adjusted R 2 = 0.019). g 2 R = 0.018 (adjusted R 2 = 0.002). a b
provide comprehensive care that addresses diet, substance abuse, preventive care, anger and stress, beliefs about masculinity, and the medical compliance of adolescents, they can provide consultations with them to minimize health risk behaviors. This study provides the foundation for future studies of health risk behavior predictors in adolescents. The implications for research include replicating the study in other geographic areas with diverse samples of adolescents.
REFERENCES 1. DiClemente RJ, Hansen WB, Ponton LE. Adolescents at risk: a generation in jeopardy. In:Di Clemente RJ, Hansen WB, Ponton LE, eds. Handbook of Adolescent Health Risk Behaviour. New York, NY: Plenum; 1996:1-4. 2. Resnick MD, Bearman PS, Blum RW, et al. Protecting adolescents from harm findings from the National Longitudinal Study on Adolescent Health. JAMA. 1997;278:823-832. 3. US. Department of Health and Human Services. Healthy People 2010 With Understanding and Improving Health and Objectives for Improving Health. 2nd ed. Washington, DC: U.S. Government Printing Office; 2000. 4. Centers for Disease Control and Prevention. Fertility, Family Planning, and Women’s Health: New Data From the 199S National Survey of Family Growth (DHHS Publication No. PHS 97-1995). Washington, DC: US. Government Printing Office; 1997. 5. Steptoe A, Wardle J. Health behaviour, risk awareness and emotional well-being in students from Eastern Europe and Western Europe. Soc Sci Med. 2001;53:1621-1630. 6. Steptoe A, Phil D, Wardle J, et al. Trends in smoking, diet, physical exercise, and attitudes toward health in European university students from 13 countries, 1990–2000. Prev Med. 2002;35:97-104. 7. Jones SE, Oeltmann J, Wilson TW, Brener ND, Hill CV. Binge drinking among undergraduate college students in the United States: implications for other substance use. J Am Coll Health. 2001;50(1):33-38. 8. Ichiyama MA, Kruse MI. The social contexts of binge drinking among private university freshmen. J Alcohol Drug Edu. 1998;44(1): 18-33.
9. Wechsler H, Rigotti NA, Gledhill-Hoyt J, Lee H. Increased levels of cigarette use among college students: a cause for national concern. JAMA. 1998;280:1673-1678. 10. Everett SA, Warren CW, Santelli JS, Kann L, Collins JL, Kolb LT. Use of birth control pills, condoms, and withdrawal among U.S. high school students. J Adoles Health. 2000;27(2):112-118. 11. Dinger MK, Waigandt A. Dietary intake and physical activity behaviors of male and female college students. Am J Health Promot. 1999;11(5):360-362. 12. Buchmann AF, Schmid B, Blomeyer D, et al. Impact of age at first drink on vulnerability to alcohol-related problems: testing the marker hypothesis in a prospective study of young adults. J Psychiatr Res. 2009;43:1205-1212. 13. Hingson RW, Edwards EM, Heeren T, Rosenbloom D. Age of drinking onset and injuries, motor vehicle crashes, and physical fights after drinking and when not drinking. Alcohol Clin Exp Res. 2009;33:783790. 14. Komro KA, Tobler AL, Maldonado-Molina MM, Perry CL. Effects of alcohol use initiation patterns on high-risk behaviors among urban, low-income, young adolescents. Prev Sci. 2010;11:14-23. 15. Kuntsche E, Rossow I, Simons-Morton B, Ter Bogt T, Kokkevi A, Godeau E. Not early drinking but early drunkenness is a risk factor for problem behaviors among adolescents from 38 European and North American countries. Alcohol Clin Exp Res. 2013;37(2): 308-314. 16. Rowland TW. Is there a scientific rational supporting the value of exercise for the present and future cardiovascular health of children? The con argument. Pediatr Exer Sci. 1996;8:303-309. 17. Kann L, Kinchen SA, Williams BI, et al. Youth risk behavior surveillance-United States, 1999. J Sch Health. 2000;70: 271-285. 18. Perry CL. Preadolescent and adolescent influences on health. In: Smedley BD, Syme SL, eds. Promoting Health. Intervention Strategies From Social and Behavioral Research. Washington, DC: The National Academies Press; 2000:217-253. 19. Von AHD, Ebert S, Ngamvitroj A, Park N, Kang DH. Predictors of health behaviours in college students. J Adv Nurs. 2004;48(5): 463-474. 20. Faul F, Erdfelder E, Lang AG. G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav Res Methods. 2007;39:175-191. 21. Courtenay WH, McCreary DR, Merighi JR. Gender and ethnic differences in health beliefs and behaviors. J Health Psychol. 2002;7(3): 219-231.
Copyright © 2014 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
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HOLISTIC NURSING PRACTICE • MAY/JUNE 2014
22. Springer AE, Selwyn BJ, Kelder SH. A descriptive study of youth risk behavior in urban and rural secondary school students in El Salvador. BMC Int Health Hum Rights. 2006;6(3):1-11. 23. Gudas LJ, Koocher GP, Wypij D. Perceptions of medical compliance in children and adolescents with cystic fibrosis. J Dev Behav Pediatr. 1991;12(4):236-242. 24. Miles JA, Naumann SE. The effects of religiosity, gender, and at-risk behaviors on students’ perceived leadership ability. J Edu Human Dev. 2007;1(2):1-12. 25. Milligan RAK, Burke V, Beilin LJ, et al. Health-related behaviours and psycho-social characteristics of 18 year-old Australians. Soc Sci Med. 1997;45(10):1549-1562. 26. Slovic P. Trust, emotion, sex, politics, and science: surveying the riskassessment battlefield. In: Bazerman M, Messick D, Tenbrunsel A, et al. Psychological Perspectives to Environmental and Ethical Issues in Management. San Francisco, CA: Jossey-Bass; 1997:277-313. 27. Puskar KR, Tusaie-Mumford K, Sereika S, Lamb J. Health concerns and risk behaviors of rural adolescents. J Community Health Nurs. 1999;16(2):109-119. 28. Burke V, Milligan AK, Beilin LJ, et al. Clustering of health-related behaviors among 18-year-old Australians. Prev Med. 1997;26:724-733.
29. Tuinstra J, Groothoff JW, Van Den Heuvel WJA, Post D. Socioeconomic differences in health risk behavior in adolescence: do they exist? Soc Sci Med. 1998;47(1):67-74. 30. Lam CW, Ng HY, Boey KW. Measuring drug abuse: the development of the Chinese drug involvement scale in Hong Kong. Res Soc Work Pract. 2002;12(4):525-533. 31. Blum RW, Bearinger LH, Resnick MD. The effects of race/ethnicity, income, and family structure on adolescent risk behaviors. Am J Public Health. 2000;90:1879-1884. 32. Boyce W, Dallago L. Young people’s health in context Health Behaviour in School-aged Children (HBSC) study: international report from the 2001/2002 survey. In:Currie C, Roberts C, Morgan A, et al., eds. Health Policy for Children and Adolescents. No. 4. World Health Organization; 2004:13-25. 33. Booth AL, Nolen PJ. Gender Differences in Risk Behaviour: Does Nurture Matter? Discussion Paper Series IZA DP 2009; No. 4026. 34. Nocon M, Keil T, Willich SN. Education, income, occupational status and health risk behaviour. J Public Health. 2007;15:401-405. 35. Arthur MW, Hawkins DJ, Pollard JA, Catalano RF, Baglioni JR. Measuring risk and protective factors for substance use, delinquency, and other adolescent problem behaviors. Eval Rev. 2002;26(6):575-601.
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