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. Author manuscript; available in PMC: 2022 May 1.
Published in final edited form as: J Pediatr Health Care. 2021 Jan 29;35(3):285–291. doi: 10.1016/j.pedhc.2020.11.002

Psychometric Properties of the Healthy Lifestyle Beliefs Scale for Adolescents

Bernadette Mazurek Melnyk 1, Stephanie Kelly 2, Alai Tan 3
PMCID: PMC8140993  NIHMSID: NIHMS1668426  PMID: 33518442

Abstract

Background:

Healthy lifestyle behaviors are at the core of maintaining health. Understanding cognitive factors that influence these behaviors may be key in assisting individuals to adopt healthy lifestyle behaviors.

Objectives:

The aim of this study was to analyze the psychometric properties of the 16-item Healthy Lifestyle Beliefs Scale (HLB) that allows measurement of a person’s beliefs about their ability to live a healthy lifestyle.

Methods:

Descriptive statistics were used to summarize sample characteristics and item characteristics of the 16 HLB scale items. Exploratory factor analysis (EFA) was conducted to identify the number of factors and factor structure underlying the HLB scale. Confirmatory factor analysis (CFA) was used to verify the factor structure derived from the EFA. Measurement invariance across gender and ethnic groups was examined by testing a sequence of nested multi-group CFA models by adding further constraints in each successive model to test three levels of measurement invariance.

Results:

Cronbach’s alpha of the total scale was 0.894. The EFA scree plot identified two factors with eigenvalues greater than one. All factor loadings for factors 1 and 2 were greater than 0.40 and there were no items cross-loaded. A two-factor solution was retained for CFA. The final CFA model with correlated items had a better model fit, compared to the model with no correlated items. The measurement invariance results suggested that there was no gender difference in HLB measure in terms of factor structure, factor loading, threshold, and residual variances.

Discussion:

The HLB scale was found to have adequate internal consistency and fit with the data. Findings show it is valid and acceptable for both boys and girls.

Keywords: Adolescent, Surveys and Questionnaires, health

INTRODUCTION

Encouraging a healthy lifestyle has been a world-wide priority for the past several decades as rates of obesity and chronic diseases have soared. In 2015/2016, 40.7% of children aged 2 to 19 years in the United States were at or above the 85th percentile based on the Centers for Disease Control and Prevention 2000 growth charts (Fryar, Caroll, & Ogden, 2018). The current rates of childhood obesity are still rising and are well above healthy levels despite national campaigns, changes in food policies, and food industry modifications to their products. Overweight teens have multiple adverse health outcomes, including depression, anxiety, poor social skills and academic problems (Freedman, Dietz, Srinivasan, Berenson, 1999; Sagar & Gupta, 2018; Steinberger et. al, 2018). Multiple factors influence overweight/obesity in teens, including inadequate physical activity, poor nutrition, and psychosocial and emotional distress (Sahoo et. al, 2015; Kumar & Kelly 2017). Only 24% of children age 6 to 17 participate in 60 minutes of physical activity daily (Center for Disease Control and Prevention, 2020).

Our current environment is not supportive of healthy lifestyles. For example, the built environment does not support the need for physical activity with features such as elevators, escalators, lack of walkability, and abundant use of screen time (Meldrum, Morris, & Gambone, 2017). Additionally, the wide availability of inexpensive, highly processed food is detrimental to health (Meldrum, Morris, & Gambone, 2017). In the United States, according to National Health and Nutrition Examination Survey (NHANES) data from 2005 to 2014, ultra-processed foods provided nearly 60% of calories and 89% of added sugars (Juul, Martinez-Steele, Parekh, Monteiro, & Chang, 2018). These issues highlight the need for teens to be given the tools to assist them in developing personal healthy lifestyle habits. Similarly, mental health disorders are a significant cause of morbidity and mortality in youth in the United States. Estimates of the prevalence of a mental health disorder in children and adolescents range from 16.5% to 17.7% (Ghandour et. al., 2019; Whitney & Peterson, 2019) with up to 49.4% not receiving treatment from a mental health provider (Whitney & Peterson, 2019). Suicide is the second leading cause of death in 10 to 34 year olds (National Institute of Mental Health, 2019). Developing healthy mental health habits are as important as developing healthy physical habits, such as regular physical activity and healthy nutrition.

Theoretical Framework

Theoretical frameworks are important and have been utilized to understand behaviors. Behaviors occur through a complex interaction of numerous determinants. One factor consistently correlated with an individual’s behaviors are their beliefs (Beck, 2011). In developing cognitive theory (CT), Beck was influenced by writings during the cognitive revolution in psychology of George Kelly, PhD and Albert Ellis, PhD in the 1950s and 1960s (Beck, 2005). At the time of Beck’s initial writings, it was widely believed that depression was an affective disorder (Beck, 1963). Beck sought to determine the “prevalence of a thought disorder in depression” and identified its characteristics (Beck, 1963, p. 324). CT was developed in relation to the thinking disorders he observed in depressed patients (Beck, 2005). Cognitive theory acknowledges the influence of an individual’s thoughts on their affect and behavior (Beck, 1967).

Cognitive Theory includes how a person cognitively appraises or structures the world largely influences a person’s affects and behaviors (Beck, Rush, Shaw, & Emery, 1979). When an individual’s thoughts are distorted, they can lead to maladaptive information processing (Arora, 2015). Hence, changing dysfunctional thoughts are a key target for cognitive theory based interventions. Additionally, there is a “reciprocal relationship between ones’ cognitive processes (what one thinks), affect (emotional experience), physiology, and behavior” (Nehra, Sharma, Kumar, & Nehra, 2013, p. 274). From this theoretic perspective, a person who has negative thoughts or beliefs is more likely to have negative emotions and display negative behaviors. Hence, a person who has negative beliefs about their ability to lead a healthy lifestyle would have negative emotions towards a healthy lifestyle and perform less healthy lifestyle behaviors.

Therapeutic skills taught in cognitive behavior therapy (CBT) include cognitive restructuring, coping skills, and problem solving (Nehra, Sharma, Kumar, & Nehra). Cognitive therapy is commonly used synonymously with CBT although CBT is a cognitive therapy module used in combination with a set of behavioral modules (Beck, 2005). Today, CBT is a treatment option in a wide range of conditions including depression, anxiety, post-traumatic stress disorder, obsessive-compulsive disorder, bulimia nervosa, sleeping disorders, pain, fatigue, phobias, and substance misuse (Blane, Williams, Morrison, Wilson, Mercer, 2013).

In CT, beliefs are paramount in influencing behaviors and can be targeted in intervention studies to modify behavior. Healthy lifestyle beliefs are a person’s belief in their ability to live a healthy lifestyle. Recent research with 779 teens supports cognitive theory in that “adolescents who have more positive thoughts about engaging in healthy lifestyle behaviors reported less negative feelings and engaged in more healthy lifestyle behaviors” (McGovern et al., 2018, p. 476).

Measures to assess beliefs or self-efficacy, a similar construct proposed by Bandura (1977), regarding healthy lifestyle behaviors often only measure one construct and have primarily been created for adults (i.e. nutrition, physical activity, cardiovascular disease; Sallis, Pinski, Grossman, Patterson & Nader, 1988; Schwarzer & Renner, 2009; Tovar, Rayens, Clark & Nguyen, 2010). Sallis and colleagues have created measures for adolescents assessing cognitive domains such as stages of change, change strategies, pros and cons, confidence, social support, and enjoyment, for physical activity, sedentary behavior, fruit and vegetable intake, and dietary fat (Prochaska, Sallis, & Long, 2001; Prochaska, Sallis, & Rupp, 2001; Prochaska & Sallis, 2004; Norman et al., 2005). Additionally, researchers have created broad healthy lifestyle measures for adolescents assessing physical activity, nutrition, stress and other components such as health responsibility, identity awareness, social support, and spiritual health ranging from 24 to 97 questions (Chen, Wang, Yang, & Liou, 2003; Gillis, 1997; Hiew, Chin, Chan, & Mohd, 2015; Mahon, Yarcheski, & Yarcheski, 2002; Scoloveno, 2016; Taymoori, Moeini, Lubans, & Bharami, 2012; Walker, Sechrist, & Pender, 1995). Sallis and colleagues have created numerous measures related to healthy lifestyles including the built environment and can be found at https://drjimsallis.org/measures.html.

The intervention, for which this scale was created, is a multi-component intervention with emphasis on mental health, stress reduction, healthy eating, and physical activity. A scale was not identified that addressed beliefs regarding all of these constructs and hence the scale was created. Previously completed pilot studies utilizing the Healthy Lifestyle Beliefs scale have assessed correlation with measures pertinent to a teen’s health including the Beck Youth Inventories (2nd edition, BYI-II; Beck et al., 2005) and the Perceived Healthy Lifestyle Difficulty Scale (Melnyk & Small, 2003).

Purpose

The purpose of this study was to assess the psychometric properties of the Healthy Lifestyle Beliefs Scale for Adolescents, which was used in a randomized controlled trial (RCT) designed to assess the efficacy of the Creating Opportunities for Personal Empowerment (COPE) Healthy Lifestyles TEEN (Thinking, Emotions, Exercise and Nutrition) Program versus an attention control program with high school adolescents (Melnyk et. al, 2013). The Healthy Lifestyle Beliefs Scale for Adolescents is a 16-item scale designed to assess a teen’s beliefs about the ability to self-regulate emotions and engage in healthy lifestyle behaviors. The analysis was conducted to assess reliability/internal consistency, convergent and discriminant validity through exploratory factor analysis, how items represent the constructs through confirmatory factor analysis, and measurement invariance.

METHODS

Sample and Setting

Teens ages 14 to 16 years from 11 high schools and two school districts who were enrolled in randomly selected sections of a required health course were invited participate in the RCT which was located in a large metropolitan city in the southwest United States. Students were primarily in 9th or 10th grades. Nearly 70% of the teens were Hispanic and about 52% were female. Details of the study methods have been published previously (Melnyk et al., 2013).

Procedure

Research team personnel invited students to participate in the study during the first week of class. To participate, teens gave written assent and brought home consents for parents to complete. All health class students received the intervention or control group sessions one time per week for fifteen weeks. Only students who completed assent and had parental consent completed questionnaires. Baseline measures were analyzed for this study for all study participants.

Item development.

The Healthy Lifestyles Beliefs Scale (Melnyk, 2003) was patterned after scales assessing parent’s beliefs in their ability to care for their sick infants and children (Melnyk et al, 2006a) and was used in pilot studies of the intervention with good reliability (Melnyk, et al., 2006b). During item development, the face and content validity were assessed by 10 adolescents and 10 content experts. Participants responded to 16-items on a 5-point Likert scale that ranges from ‘Strongly Disagree” to “Strongly Agree” with the middle response being neutral “Neither Agree nor Disagree”. Examples of items included, “I know how to deal with things in a healthy way that bother me.”, “I believe that I can be more active.”, “I am sure that I can spend less time watching TV.”, and “ I am certain that I will make healthy food choices.” Scoring of the instrument consisted of summing all 16 items for a total possible score ranging between 16 and 80.

Statistical Analysis

Descriptive statistics were used to summarize sample characteristics and item characteristics of the 16 HLB scale items. We first conducted exploratory factor analysis (EFA) to identify the number of factors and factor structure underlying the HLB scale. Oblique (promax) rotation was used for the EFA to accommodate inter-correlation between the factors. Next, confirmatory factor analysis (CFA) was used to verify the factor structure derived from the EFA. A good model fit is indicated by a comparative fit index (CFI) of ≥0.95, a Tucker Lewis Index (TLI) of ≥0.95, and a root mean square error of approximation (RMSEA) of <0.08. To examine measurement invariance across gender and ethnic groups, we tested a sequence of nested multi-group CFA models by adding further constraints in each successive model to test three levels of measurement invariance: 1) configural invariance – the number of factors and items of each factor are identical across groups; 2) metric invariance – factor loadings are equal across groups; 3) strong invariance – factor thresholds are equal across groups; and 4) strict invariance – residual variances are equal across groups. The same model fit indices (CFI, TLI, and RESEA) and cutoffs as described above were used to evaluate model fit at each stage. Chi-square difference tests were used to compare fit of nested models. We used weighted least-square estimation methods for both EFA and CFA to take account of the ordinal nature of the HBL items (5-point Likert scale). Lastly, we used Cronbach’s α to examine the internal consistency of the total scale and subscales. Mplus version 7.4 was used for all the analyses.

RESULTS

Sample Demographics

The 779 students had a mean age of 14.7 years (SD=0.7) and were almost equally distributed by gender (48.4% male; see Table 1). Two-thirds (67%) were Hispanics and one-third (31.1%) were non-Hispanics. Most of the students were 9th or 10th graders (49.9% and 37.9%, respectively). Seventy-six percent of the students provided information on whether their family received public assistance (41.6%) or not (34.5%).

Table 1.

Sample Characteristics (N=779)

Characteristics Mean±SD or N (%)
Age in years 14.7± 0.7
Gender
 Male 377 (48.4)
 Female 402 (51.6)
Ethnicity
 Hispanics 522 (67.0)
 Non-Hispanics 242 (31.1)
 Missing 15 (1.9)
Grade
 9th 389 (49.9)
 10th 295 (37.9)
 11th 89 (11.4)
 12th 6 (0.8)
Family receive public assistance
 Yes 324 (41.6)
 No 269 (34.5)
 Missing 186 (23.9)
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Exploratory Factor Analysis (EFA)

The HLB items had mean scores ranging from 3.55 for item 16 (I am able to talk to my parents/family about things that bother/upset me) to 4.26 for item 13 (I believe that my parents and family will help me to reach my goals. The scree plot identified two factors with eigenvalues greater than one – the first factor had an eigenvalue of 7.29 and the second factor had an eigenvalue of 1.34. The first two factors accounted for 53.9% of the total variance (factor-1: 45.5%; factor-2: 8.4%). All factor loadings for factors 1 and 2 were greater than 0.40 and there were no item cross-loaded. Therefore, a two-factor solution was retained for confirmatory factor analysis. The first factor contained 9 items regarding beliefs about behaviors that impact physical health. The second factor contained 7 items regarding beliefs about coping and family support that impact mental health. (Table 2)

Table 2.

Item description and factor loadings from exploratory factor analysis (EFA)

Item label Mean SD Factor loadings
Factor-1 Factor-2
1 I am sure that I will do what is best to lead a healthy life. 4.15 0.80 0.663 0.180
2 I believe that exercise & being active will help me to feel better about myself. 4.24 0.81 0.889 −0.142
3 I am certain that I will make healthy food choices. 3.69 0.83 0.542 0.180
7 I believe that I can be more active. 4.25 0.83 0.496 0.133
8 I am sure that I will do what is best to keep myself healthy. 4.05 0.80 0.648 0.231
9 I am sure that I can spend less time watching tv. 3.74 1.09 0.421 0.074
10 I know that I can make healthy snack choices regularly. 3.76 0.89 0.481 0.204
14 I am sure that I will feel better about myself if I exercise regularly. 4.11 0.89 0.829 −0.080
15 I believe that being active is fun. 4.21 0.84 0.610 0.084
4 I know how to deal with things in a healthy way that bother me. 3.64 0.93 0.173 0.568
5 I believe that I can reach my goals. 4.25 0.81 0.340 0.440
6 I am sure that I can handle my problems well. 3.94 0.86 0.048 0.706
11 I deal with pressure in positive ways. 3.89 0.92 0.031 0.689
12 I know what to do when things bother or upset me. 3.77 0.99 −0.097 0.823
13 I believe that my parents and family will help me to reach my goals. 4.26 0.92 0.186 0.518
16 I am able to talk to my parents/family about things that bother/upset me. 3.55 1.28 0.030 0.649
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Confirmatory Factor Analysis (CFA)

Figure 1 shows the final model from CFA. Factor loadings were high, ranging from 0.59 to 1.06. The model allowed correlated items (items 2 [I believe that exercise and being active will help me to feel better about myself] and 14 [I am sure that I will feel better about myself if I exercise regularly] in factor-1; items 13 [I believe my parents and family will help me to reach my goals] and 16 [I am able to talk to my parents and family about things that bother or upset me] in factor-2). These correlations were suggested by an initial model that did not allow for such correlation (modification index = 183.8 for items 2 and 14; and 117.8 for items 13 and 16). The final model with these correlated items had better model fit, compared to the model with no correlated items (CFI = 0.946 vs. 0.917; TFL = 0.936 vs. 0.903; and RMSEA [90% confidence interval] = 0.080 [0.074, 0.086] vs. 0.099 [0.093, 0.105]).

Figure 1.

Figure 1.

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Confirmatory factor analysis (RMSEA=0.080, CFI=0.946, TLI=0.936)

Measurement Invariance

Table 3 summarizes the tests for measurement invariance by gender (male vs. female) and ethnicity (Hispanics vs. non-Hispanics), comparing across four levels of measurement invariance models. For gender, all models showed excellent fit. While the strong invariance model had the best fit indices (CFI=0.955, TLI=0.961, RMSEA = 0.068), the strict invariance across gender was also supported with CFI=0.953, TLI=0.958, RMSEA (90% CI) = 0.071 (0.065–0.077), and a significant Chi-square difference test (vs. strong invariance model, P <0.001). The results suggested that there was no gender difference in HLB measure in terms of factor structure, factor loading, threshold, and residual variances. For ethnicity, we stopped adding more constraint to the strong invariance model which had excellent fit (CFI=0.0961, TLI=0.967, and RMSEA = 0.060) but showed no significant difference compared to the metric invariance model (P = 0.173). The results suggested that the HBL was equivalent across ethnic groups in terms of factor structure, factor loading, and threshold; but different in residual variances.

Table 3.

Test of measurement invariance across gender (female vs male) and ethnicity (Hispanics or non-Hispanics)

Configural Metric Strong Strict
Sex
CFI 0.948 0.955 0.955 0.953
TLI 0.939 0.951 0.961 0.958
RMSEA 0.085 0.076 0.068 0.071
90% CI of RMSEA 0.078, 0.091 0.070, 0.082 0.062, 0.073 0.065, 0.077
Chi-square test for Difference
 Value - 56.517 84.986 48.301
 DF - 16 58 16
 P-value - <0.001 0.012 <0.001
Ethnicity
CFI 0.953 0.960 0.961 -
TLI 0.945 0.956 0.967 -
RMSEA 0.077 0.068 0.060 -
90% CI of RMSEA 0.070, 0.083 0.062, 0.075 0.054, 0.066 -
Chi-square test for Difference -
 Value - 47.045 68.001 -
 DF - 16 58 -
 P-value - <0.001 0.173 -
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Internal Consistency

The Cronbach’s alpha of the total scale was 0.894 (see Table 4). The 16 items were separated intro two subscales based on findings from the EFA and CFA. The Cronbach’s alpha was 0.845 for the first subscale (beliefs about leading a physical healthy lifestyle) and 0.827 for the second subscale (beliefs about coping and family support).

Table 4.

Descriptive Statistics and Internal Consistency of Healthy Lifestyle Beliefs Scale and Subscales

Mean (SD) Range Cronbach’s α
Subscale
 Factor 1 (9 items) 36.2 (5.2) 9 – 45 0.845
 Factor 2 (7 items) 27.3 (4.7) 7 – 45 0.827
Total Scale 63.5 (9.0) 18 – 90 0.894
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DISCUSSION

The HLB scale was created to assess one’s beliefs in their ability to live a healthy lifestyle. The scale factored into two sub-scales with items focusing on nutrition and exercise as factor 1 and items regarding mental health as factor 2. The Cronbach’s alpha of the total scale was 0.894 with sub-scale internal consistency each greater than 0.82.

Factor 1 contains nine items regarding beliefs about behaviors that impact physical health, and include what is best to lead a healthy life, exercise, making healthy food choices, and watching less television. These are all key aspects in obesity prevention and treatment programs. The second factor contains seven items regarding beliefs about coping and family support that impact mental health. Key beliefs that promote mental health are assessed, including dealing with stress in healthy ways, belief about the ability to reach goals, and having support to reach goals or discuss problems.

The EFA scree plot identified two factors with eigenvalues greater than one. All factor loadings for factors 1 and 2 were greater than 0.40 and there was no item cross-loadings. A two-factor solution was retained for CFA. The final CFA model with correlated items had better model fit, compared to the model with no correlated items. The measurement invariance results suggested that there was no gender differences in HLB measure in terms of factor structure, factor loading, threshold, and residual variances.

Healthy lifestyle beliefs have been significantly correlated with other factors impacting a teen’s health. For example, healthy lifestyle beliefs and perceived difficulty in living a healthy lifestyle had a Pearson’s r correlation of −0.57 (p<0.01) in the same sample as this study, −0.49 (p<0.01) in a study of 404 high school teens (Kelly et al., 2011), and −0.37 (p<0.05) in a sample of 45 youth age 10–12 years (O’Haver et al., 2014). Similarly, healthy lifestyle beliefs and healthy lifestyle behaviors were significantly correlated in this sample at 0.63 (p<0.01) and 0.43 (p<0.01) in a sample of 45 youth age 10–12 years (O’Haver et al., 2014).

Healthy lifestyle beliefs also have been significantly correlated (p<0.01) with mental health measures, including subscales of the Beck Youth Inventories (2nd edition, BYI-II; Beck et al., 2005). For example, in this same sample, Pearson’s r correlations for healthy lifestyle beliefs with self-concept was 0.53, anxiety −0.25, depression −0.35, anger −0.35, and disruptive behavior −0.37. That is, the stronger the beliefs, the less negative mental health attributes. These findings support cognitive theory in that how an adolescent thinks (i.e., beliefs) affects his/her healthy lifestyle behaviors and emotions.

The Healthy Lifestyle Beliefs Scale when compared to other available self-efficacy measures are similar in that they all measure beliefs or self-efficacy regarding nutrition and exercise. They also differ in several ways. For example, the self-efficacy scales regarding nutrition and exercise created by Schwarzer and Renner (2009) assess one’s self-efficacy regarding overcoming barriers (e.g. “How certain are you that you could overcome the following barriers?” I can manage to stick to healthful foods even if I need a long time to develop the necessary routines.) whereas the Healthy Lifestyle Beliefs scale focuses on positively framing the ability to do healthy lifestyle behaviors. In comparison to the Healthy Lifestyle Beliefs Scale, the self-efficacy scales created by Sallis and colleagues (1988) include more questions (20 items for nutrition and 12 items for physical activity) which are also very specific to aspects regarding diet and exercise (e.g. How sure are you that you can do these things? Get up early, even on weekends, to exercise, Eat salads for lunch. Eat smaller portions of food at a party.). Finally, the scale created by Tovar and colleagues (2010) is different in that a portion of the questions address behaviors in the context of cardiovascular disease (e.g. Eating a healthy diet will decrease my chances of dying from cardiovascular disease). It also includes four subscales tapping concepts of susceptibility, severity, benefits, and barriers.

The scales created by Sallis and colleagues for youth are similar in that cognitive skills are tapped regarding nutrition and physical activity although beliefs are not included (Prochaska, Sallis, & Long, 2001; Prochaska, Sallis, & Rupp, 2001; Prochaska & Sallis, 2004; Norman et al., 2005). The scales differ as additional factors influencing healthy lifestyles are also measured including stages of change, change strategies, pros and cons, confidence, social support, and enjoyment. The general scales assessing healthy lifestyles do not specifically assess beliefs (Chen, Wang, Yang, & Liou, 2003; Gillis, 1997; Hiew, Chin, Chan, & Mohd, 2015; Mahon, Yarcheski, & Yarcheski, 2002; Scoloveno, 2016; Taymoori, Moeini, Lubans, & Bharami, 2012; Walker, Sechrist, & Pender, 1995). The scales are longer than the HLB scale with several having more constructs assessed.

CONCLUSION

The Healthy Lifestyle Beliefs Scale taps a person’s beliefs about their ability to engage in healthy lifestyle behaviors and regulate emotions. The HLB scale has adequate validity, internal consistency, and fit with the data. Findings show it is valid and acceptable for both boys and girls.

Acknowledgement:

Research reported in this publication was supported by the National Institutes of Health/National Institute of Nursing Research (grant number 1R01NR012171; PI: Bernadette Melnyk). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Footnotes

Conflict of Interest: The authors have no conflicts of interest to report.

Ethical Conduct of Research: The research was conducted in a safe and ethically responsible manner. Approval was received from the university IRB in addition to approval from each school district.

Ethical Statement

The study was approved by the University Institutional Review Board and each participating school district. None of the authors have published, posted or submitted any related papers from this study.

Clinical Trial Registration: ClinicalTrials.gov Identifier NCT01704768. Date of registration October 11, 2012. First participants enrolled January 2010. Study link: https://clinicaltrials.gov/ct2/show/NCT01704768.

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Contributor Information

Bernadette Mazurek Melnyk, The Ohio State University College of Nursing.

Stephanie Kelly, The Ohio State University College of Nursing, Columbus, OH, United States.

Alai Tan, The Ohio State University College of Nursing, Columbus, OH, United States.

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