ICAN: Infant, Child, & Adolescent Nutrition http://can.sagepub.com/
Does Behavioral Intention Predict Nutrition Behaviors Related to Adolescent Obesity? Melinda J. Ickes and Manoj Sharma ICAN: Infant, Child, & Adolescent Nutrition 2011 3: 38 DOI: 10.1177/1941406410395017 The online version of this article can be found at: http://can.sagepub.com/content/3/1/38
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ICAN: Infant, Child, & Adolescent Nutrition
Clinical Research Reports
Does Behavioral Intention Predict Nutrition Behaviors Related to Adolescent Obesity? Melinda J. Ickes, PhD, and Manoj Sharma, MBBS, CHES, PhD
Abstract: The theory of planned behavior (TPB) proposes that the single best predictor of a person’s behavior is intention to perform that behavior. Successful application of the TPB supports that attitudes, subjective norms, and perceived behavioral control are predictive factors of behavioral intention (BI). The purpose of this study was to examine the extent to which BI predicted nutrition behaviors linked to adolescent obesity. A cross-sectional design obtained a convenience sample of 318 middle school students who completed a 129-item validated instrument. Multiple regression was used to establish predictors for fruit and vegetable (FV) consumption and sweetened beverages (SBs) versus water consumption. The mean BI scores were as follows: FV consumption, M = 12.18, standard deviation [SD] = 5.74, and SB versus water consumption, M = 12.42, SD = 6.07. This denotes a moderate intent to participate in the behavior. Regression showed that BI was predictive for consumption of FV among overweight and obese students and consumption of water versus SB in normal weight students (P < .05). BI was linked to nutrition behaviors related to obesity prevention in adolescents. Differences
among those students who were considered normal weight and overweight/obese existed and should be considered when working with these populations and designing future interventions. Keywords: theory of planned behavior; modifiable behaviors; fruit and vegetables; sweetened beverages; intention; adolescents
limited to adults; of children and teenagers, an estimated 17% (almost 9 million) are obese.2 Sadly, rates of obesity have increased among all age groups in the period between 1980 and 2008: 6.5% to 19.6% among 6- to 11-year-olds and 5% to 18.1% among adolescents aged 12 to 19 years.2 Health care professionals, specifically pediatricians, pediatric nurse practitioners (PNPs), and registered dietitians (RDs) are on the front line of
“Although these are not the only nutrition-related behaviors that have the potential to influence improved dietary behaviors in children and adolescents, it has been recommended that when considering predictors responsive to change, the focus should be on those determinants that are modifiable, prevalent, and relatively easy to change in today’s youth.” Introduction Problems of overweight and obesity have reached epidemic proportions.1 Unfortunately, the obesity epidemic is not
providing health services to youth and thus are well positioned to care and provide preventive counseling for overweight and obese children.3 To do this, they must clearly understand the
DOI: 10.1177/1941406410395017. From the Department of Kinesiology and Health Promotion, University of Kentucky, Lexington, Kentucky (MJI) and the University of Cincinnati, Cincinnati, Ohio (MS). Address correspondence to Melinda J. Ickes, PhD, Department of Kinesiology and Health, University of Kentucky, 111 Seaton Building, Lexington, KY 40506; e-mail:
[email protected]. For reprints and permissions queries, please visit SAGE’s Web site at http://www.sagepub.com/journalsPermissions.nav. Copyright © 2011 The Author(s)
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complexity of the behaviors associated with obesity and in turn how to approach these behaviors. The determinants of overweight and obesity are multifaceted: lifestyle, environmental, and genetic factors all need to be taken into consideration. Yet the reason most often cited is the relationship between an excess in energy consumption and inadequate physical activity.4 Unhealthy habits associated with food consumption, both quantity and quality, have dire consequences on overall health status and are potential contributors to obesity. It has been established that many young people do not follow the recommendations of the Dietary Guidelines for Americans.5,6 Unfortunately, there has been an increase in consumption of high fat and high sugar foods, particularly among children and adolescents.7 These foods contribute little nutritional value, do not aid in meeting the Dietary Guidelines for Americans, and all the while provide excessive calories. One food group for which youth do not meet the recommended guidelines is fruits and vegetables (FVs). Studies indicate that youth ate 3.6 servings of FVs daily; 50.8% ate fewer than 1 serving of fruit per day, and 29.3% ate fewer than 1 serving per day of vegetables that were not fried.8 The impact of increased FV consumption on decreasing risk of obesity has been examined in many epidemiological studies, with conflicting results. The premise is that because of the low energy density in FVs but high nutritional value, they offer a plausible alternative to improve diets high in fat, sugar, and excess calories. For example, in a clinical trial that tested the impact of increased FV intake in families with children at risk for childhood obesity, FV intake increased by 0.72 (+1.11) servings per day, high-fat/ high-sugar foods decreased by 4.50 (+7.97) servings, and percentage of overweight decreased by 1.10 (+5.29).9 It is understood that FV consumption alone is not the “cure all” to decreasing childhood obesity but must be coupled with decreased consumption of other high-fat and high-sugar foods. Another nutrition behavior that has been directly linked to an increase in energy intake
ICAN: Infant, Child, & Adolescent Nutrition
among children and adolescents has been sweetened beverage (SB) consumption, which has increased by 300% in the past 20 years.10 In 2004, adolescents consumed an average of 300 calories per day from SBs, which accounted for 13% of their daily caloric intake. This consumption of SBs has been positively associated with obesity in children: the odds ratio of becoming obese increased 1.6 times for each additional glass (8-ounces) consumed.11 Clearly, today’s youth are not making the best choices related to their nutrition behaviors. Adolescents are viewed as an important target population because of the strong link between obesity during that time period and in adulthood because many of the unhealthy behaviors that contribute to obesity are likely to continue into adulthood.5,12 In addition, adolescents are able to understand the consequences and influences of their own choices and behaviors. Despite the alarming obesity statistics, adolescents are among the most underserved population in terms of preventive services.13 According to the American Medical Association, behaviors that have the capability of offsetting the development of childhood obesity include increasing water consumption in relation to the amount of SB consumed and eating 5 or more servings of FVs daily.14,15 Although these are not the only nutrition-related behaviors that have the potential to influence improved dietary behaviors in children and adolescents, it has been recommended that when considering predictors responsive to change, the focus should be on those determinants that are modifiable, prevalent, and relatively easy to change in today’s youth. These nutrition behaviors present a starting point with which to address individual-level behaviors related to obesity—a step forward in understanding the obesity epidemic. The theory of planned behavior (TPB) is one framework that has been used in addressing health behaviors. According to the theory, the single best predictor of a person’s behavior is the intention to perform that behavior or behavior intention (BI). Intention is an indication
of an individual’s readiness to perform a given behavior—considering how hard they are willing to try and how much effort they plan to exert toward initiation of a behavior.16 BI is the immediate antecedent to a behavior: a function of attitude toward performing the behavior, the subjective norm that expresses the person’s perception of relevant others, and perceived behavioral control or perception of ease or difficulty in carrying out a behavior. Each construct of the theory is further explained by an expectancy x value model. Ajzen16 proposed that attitudes were formed from a combination of beliefs that behavior will lead to certain consequences (behavioral beliefs) and the evaluation of these consequences. Subjective norms were determined by a combination of normative expectations of specific referent groups (normative beliefs) and the motivation to comply with those groups. Perceived behavioral control was determined by beliefs about the presence of factors that may initiate or hinder performance of behavior (control beliefs) and the perceived power of these factors. Ajzen believed that the relative importance each variable had for intentions varied among individuals and behaviors.16 Efforts to change BI should take into account whether attitudes, subjective norms, and/or perceived behavioral control carry the most weight in determining intentions and behavior. The TPB has also been recognized as a framework for understanding adolescent health behaviors.6,17-23 Successful application of the TPB has provided evidence to support that attitudes, subjective norms, and perceived behavioral control are all predictive factors of intention to engage in nutrition and physical activity behaviors9,24—those behaviors that have been directly related to obesity. However, very few applications have used the theory to develop and assess interventions that facilitate behavior change related to obesity. According to Fishbein and Ajzen,25(p24) “the ultimate test of the theory rests upon its ability to guide the development of effective behavioral change interventions.” To address obesity prevention, recommendations include the 39
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need to develop instruments that specifically measure attitudinal antecedents of behavior, and changes in constructs based on behavioral theories.26,27 The lack of structured monitoring and evaluation has hindered the development of an evidence base to identify, apply, and disseminate best practices to support continuing childhood obesity prevention approaches.28 Theory-based health behavior change programs are more effective compared with those that do not use theory29 because they aid in the development of measurable program outcomes, help in the design of interventions and effective programming strategies, and increase the likelihood of successful replication.30 In a review of approaches used by pediatricians, PNPs, and RDs, there was no mention of theory, which could limit the potential success and/or replication of their recommendations.31 Purpose
The purpose of this study was to examine the extent to which BI predicted 2 nutrition behaviors that have been linked to obesity in adolescents, with the aim of proposing implications for future obesity prevention and treatment interventions. Methods Sampling and Design
A cross-sectional design was incorporated to obtain a convenience sample of 318 middle school students. The significance criterion was set at α = .05, power was set at .80, and a population correlation coefficient of .15 was assumed. Using standard sample size calculations in relation to the total population of seventh and eighth graders in the state, a sample size of 251 was required.32 There were 719 students enrolled in the middle school; all were recruited to participate. Instrumentation
A 129-item scale (included behaviors related to both nutrition, physical activity, and sedentary behaviors) was developed and validated for face and content validity by a panel of experts in a 2-round review
process. Each behavior and construct within the TPB was constitutionally and operationally defined. Readability, content validity, and face validity of the instrument was established through this 2-round process. The instrument was also pilot tested with a middle school sample of 20 students. Recommended changes included the following: modifications to sentence construction, wording changes, elimination of items, and changes in instrument format. All the changes were incorporated except for the suggestion to change the Likert-type scales that used both positive and negative values. In staying true to the development of an instrument based on the TPB, the scale was kept in the original format. Further results for instrument validation are reported elsewhere (M.J.I. and M.S., unpublished data, 2010). The first items on the scale addressed demographics. The next 4 items related to nutrition behaviors: using a 24-hour recall, students were asked for the number of servings of FVs eaten and the number of glasses (defined as 8 ounces) of water and SBs they consumed. Students were given a handout with visual representation of serving sizes for common fruits and vegetables. BI was measured through a 3-item Likert scale—1 = strongly disagree to 7 = strongly agree—which measured intent to consume at least 5 FVs a day or intent to decrease the SBs consumed. The rating scores of the 3 items were summed to achieve a possible range of 3 to 21. Attitudes comprised 2 components that worked together—behavioral beliefs and outcome evaluations—both of which were measured through a Likert scale (5 items FVs and 4 items SBs): 1= unlikely to 7 = likely, and −3 = extremely unimportant to +3 = extremely important, respectively. For each behavioral belief, the belief score was multiplied by the relevant evaluation score, and the resulting products were summed to create an overall attitude score with a range of −105 to 105 for FV consumption and −84 to 84 for SB consumption. Subjective norms consisted of 2 components that worked in interaction: normative beliefs and motivation to comply. Normative beliefs were measured
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through a 4-item Likert scale (−3 = strongly disagree to 3 = strongly agree) as was motivation to comply (1 = strongly disagree to 7 = strongly agree). For each normative belief, the belief score was multiplied by the motivation to comply score, and the resulting products were summed to create an overall subjective norm score with a possible range of −84 to 84. Perceived behavioral control consisted of 2 components—control beliefs and influence of control beliefs—both of which were measured through a 4-item Likert scale: 1 = strongly disagree to 7 = strongly agree and 3 = less likely to +3 = more likely, respectively. For each control belief, the belief score was multiplied by the influence of control belief score, and the resulting products were summed to create an overall perceived behavior control score with a possible range of −84 to 84. Protection of Human Participants
Approval by the university’s institutional review board was obtained before starting the research study. Both the researcher and the faculty advisor completed training to ensure compliance with all ethical considerations in the handling of informed consent, participant interactions, data collection, and analysis. Data Collection
Permission was granted by the principal and the health teacher of the middle school. Data were collected during the 2008-2009 school year. Parents of all students in the middle school received a letter for parental consent. Students were instructed to return a signed letter indicating permission to participate in the research study. Students who received parental permission to participate in the research study were given a child assent form. It was explained that even though their parents had given permission, the students still had to decide whether or not they wanted to participate. The instruments were completed confidentially, and the students were required to write their school codes on the instrument for subsequent matching to the body mass index (BMI) measurements.
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To calculate BMI, height and weight were measured by the principal investigator using a calibrated Seca 700. BMI was calculated as weight in kg/height in m2 using the BMI Calculator for Child and Teen available from the Centers for Disease Control and Prevention (CDC) at http://apps.nccd.cdc.gov/dnpabmi/ Calculator.aspx. The BMI calculations were then interpreted using the appropriate BMI-for-age growth chart for children available from the CDC at http://www.cdc .gov/nccdphp/dnpa/bmi/00binaries/ bmi-tables.pdf, which define the following categories: underweight (
