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An Internet Obesity Prevention Program for Adolescents

Published:September 27, 2012DOI:https://doi.org/10.1016/j.jadohealth.2012.07.014

      Abstract

      Purpose

      To compare the effectiveness of two school-based internet obesity prevention programs for diverse adolescents on body mass index (BMI), health behaviors, and self-efficacy, and to explore moderators of program efficacy. It was hypothesized that the addition of coping skills training to a health education and behavioral support program would further enhance health outcomes.

      Methods

      A randomized clinical trial with cluster randomization by class and repeated measures with follow-up at 3 and 6 months was conducted (n = 384). BMI was assessed by use of standard procedures. Sedentary behavior, physical activity, nutrition behavior, self-efficacy, and satisfaction were assessed with self-report measures. Data analysis consisted of mixed model analyses with autoregressive covariance structure for repeated data by use of intent-to-treat procedures.

      Results

      The mean age of students was 15.31 years (±0.69), with a mean BMI of 24.69 (±5.58). The majority were girls (62%) and of diverse race/ethnicity (65% non-white). There were no significant differences between groups on any outcomes and no change in BMI over time. There were significant improvements in health behaviors (sedentary behavior, moderate and vigorous physical activity, healthy eating, fruit and vegetable intake, sugar beverages, and junk food intake) and self-efficacy. Gender and lesson completion moderated select health outcomes. There was excellent participation and high satisfaction with the programs.

      Conclusions

      School-based internet obesity prevention programs are appealing to adolescents and improve health behaviors. The differential effect of coping skills training may require longer follow-up.

      Keywords

      Implications and Contribution
      This study provides support for the use of a school-based internet obesity prevention program to promote health behaviors in adolescents. Participants were very receptive to the program, had high participation, and improved health behaviors. The findings can be applied to the development of other school-based internet programs.
      The prevalence of overweight and obesity in adolescents has increased dramatically in the past several decades, with more than 18% of adolescents in the United States obese and 30% of adolescents overweight or obese. African-American and Hispanic adolescents are disproportionately affected with higher rates of overweight and obesity compared with white adolescents (21% vs. 15%) [
      • Ogden C.L.
      • Carroll M.D.
      • Curtin L.R.
      • et al.
      Prevalence of high body mass index in US children and adolescents, 2007-2008.
      ,
      • Flegal K.M.
      • Carroll M.D.
      • Ogden C.L.
      • Curtin L.R.
      Prevalence and trends in obesity among US adults, 1999-2008.
      ]. Overweight adolescents are at risk for serious health consequences such as asthma, hyperlipidemia, hypertension, and type 2 diabetes. Overweight and obesity in adolescents have also been associated with psychological consequences such as low self-esteem, stigma, and depression [
      • Reilly J.J.
      • Methven E.
      • McDowell Z.C.
      • et al.
      Health consequences of obesity.
      ]. Adolescents, particularly minority youth, are an underserved population with respect to nutrition and health education [
      • Casazza K.
      • Ciccazzo M.
      The method of delivery of nutrition and physical activity information may play a role in eliciting behavior changes in adolescents.
      ,
      • Power T.G.
      • Bindler R.C.
      • Goetz S.
      • Daratha K.B.
      Obesity prevention in early adolescence: Student, parent, and teacher views.
      ]. Thus, adolescence is a particularly critical developmental phase for obesity prevention programs.
      Prevention is widely advocated as an important strategy to address the rising prevalence of obesity in adolescents [
      • Koplan J.P.
      • Liverman C.T.
      • Kraak V.I.
      Preventing childhood obesity: Health in the balance: executive summary.
      ,
      • Baranowski T.
      • Cullen K.W.
      • Nicklas T.
      • et al.
      School-based obesity prevention: a blueprint for taming the epidemic.
      ,
      • Stice E.
      • Shaw H.
      • Marti C.N.
      A meta-analytic review of obesity prevention programs for children and adolescents: The skinny on interventions that work.
      ], inasmuch as once youth become obese, treatment is difficult [
      • Caballero B.
      Obesity prevention in children: Opportunities and challenges.
      ]. School-based obesity prevention programs are one approach to reach adolescents at risk for overweight and obesity as well as engage adolescents in learning strategies to improve health behaviors. Schools also have an existing infrastructure to integrate obesity prevention education into the curriculum. Research on school-based obesity prevention programs has proliferated in the past 2 decades. The majority of programs have been multifaceted and comprehensive and include health education (diet and physical activity), behavioral strategies (i.e., goal setting), parental support, environmental modification, and/or policy change. Programs vary in curriculum, implementation, length, and supplemental components, such as parental support. The results of numerous meta-analyses and systematic reviews indicate that more than 75% of the programs resulted in significant improvements in knowledge, self-efficacy, and health behavior (physical activity, sedentary behavior, dietary intake) [
      • Flodmark C.-
      • Marcus C.
      • Britton M.
      Interventions to prevent obesity in children and adolescents: A systematic literature review.
      ,
      • Gonzalez-Suarez C.
      • Worley A.
      • Grimmer-Somers K.
      • Dones V.
      School-based interventions on childhood obesity: A meta-analysis.
      ]. However, the impact on body mass index (BMI) has been mixed [
      • Gittelsohn J.
      • Kumar M.B.
      Preventing childhood obesity and diabetes: Is it time to move out of the school?.
      ,
      • Kanekar A.
      • Sharma M.
      Meta-analysis of school-based childhood obesity interventions in the U.K. and U.S.
      ,
      • Katz D.L.
      School-based interventions for health promotion and weight control: not just waiting on the world to change.
      ]. Change in BMI is challenging to demonstrate in obesity prevention programs for youth, who are predominately of normal weight at baseline, particularly if programs and follow-up were short. Lack of improvement in BMI or health behavior may also be because programs were not implemented as intended and the full “dose” of the program was not provided.
      One promising solution to the challenge of implementing school-based obesity prevention programs with fidelity is to provide the program using interactive multimedia (i.e., internet). With interactive multimedia, the program delivery is standardized, and the burden to schools is dramatically reduced [
      • Baranowski T.
      • Cullen K.W.
      • Nicklas T.
      • et al.
      School-based obesity prevention: a blueprint for taming the epidemic.
      ,
      • Nguyen B.
      • Kornman K.P.
      • Baur L.A.
      A review of electronic interventions for prevention and treatment of overweight and obesity in young people.
      ]. Adolescents are very technologically savvy; more than 93% are active users of the internet [

      Lenhart A, Arafeh S, Smith A, Macgill A. The lives of teens and their technology. http://www.pewinternet.org/Reports/2008/Writing-Technology-and-Teens/04-The-Lives-of-Teens-and-Their-Technology/02-Nearly-all-teens-use-the-internet.aspx. Updated 2008. Accessed February 22, 2012.

      ]. Internet obesity prevention programs for youth have demonstrated significant improvements in dietary behaviors [
      • Casazza K.
      • Ciccazzo M.
      The method of delivery of nutrition and physical activity information may play a role in eliciting behavior changes in adolescents.
      ,
      • Ezendam N.P.
      • Brug J.
      • Oenema A.
      Evaluation of the web-based computer-tailored FATaintPHAT intervention to promote EnergyBalance among adolescents: Results from a school cluster randomized trial.
      ,
      • Mauriello L.M.
      • Ciavatta M.M.H.
      • Paiva A.L.
      • et al.
      Results of a multi-media multiple behavior obesity prevention program for adolescents.
      ,
      • Frenn M.
      • Malin S.
      • Brown R.L.
      • et al.
      Changing the tide: an internet/video exercise and low-fat diet intervention with middle-school students.
      ], physical activity [
      • Casazza K.
      • Ciccazzo M.
      The method of delivery of nutrition and physical activity information may play a role in eliciting behavior changes in adolescents.
      ,
      • Mauriello L.M.
      • Ciavatta M.M.H.
      • Paiva A.L.
      • et al.
      Results of a multi-media multiple behavior obesity prevention program for adolescents.
      ,
      • Frenn M.
      • Malin S.
      • Brown R.L.
      • et al.
      Changing the tide: an internet/video exercise and low-fat diet intervention with middle-school students.
      ], and BMI [
      • Casazza K.
      • Ciccazzo M.
      The method of delivery of nutrition and physical activity information may play a role in eliciting behavior changes in adolescents.
      ], thus demonstrating the potential of this approach in this population. However, few programs have specifically targeted adolescents or evaluated the effect on BMI. One study with adolescents that compared an internet obesity prevention program with traditional classroom education indicated better behavioral and psychosocial outcomes with the internet program. Adolescents also reported that they preferred the media-based education over print materials and lectures [
      • Casazza K.
      • Ciccazzo M.
      The method of delivery of nutrition and physical activity information may play a role in eliciting behavior changes in adolescents.
      ].
      The complexity of obesity prevention will require multifaceted and comprehensive community programs [
      • Gittelsohn J.
      • Kumar M.B.
      Preventing childhood obesity and diabetes: Is it time to move out of the school?.
      ]; however, there needs to be a theoretically based and developmentally appropriate health education and behavior change program at the center. Adolescents need to be knowledgeable about healthy foods and the risks of inactivity, they need to be cognizant of their own behaviors, and they need behavioral skills to promote behavior change [
      • Hoelscher D.M.
      • Evans A.
      • Parcel G.S.
      • Kelder S.H.
      Designing effective nutrition interventions for adolescents.
      ]. The HEALTH[e]TEEN program was developed to provide interactive education and behavioral support on healthy eating and physical activity to reduce overweight and obesity in adolescents based on principles of interactive technology, social learning theory, and behavior change. Interactive technology contributes to experiential learning by including self-assessments, simulations, problem solving, repetition, and feedback [
      • Lieberman D.A.
      Interactive video games for health promotion.
      ]. Social learning theory posits that personal factors of knowledge, self-efficacy, and skill development are critical to initiating and maintaining behavior change [
      • Bandura A.
      The anatomy of stages of change.
      ]. Self-efficacy and skill development are enhanced through mastery of behavioral skills (goal setting and self-monitoring), by observing others who are successful with the targeted behavior change (social modeling), and by verbal encouragement (social persuasion of peers or professionals).
      Because social learning theory also posits that the development of coping skills aimed at moderating psychological responses (i.e., stress reduction) can further assist individuals to carry out healthy lifestyle behaviors [
      • Bandura A.
      The anatomy of stages of change.
      ], additional lessons on coping skills training (CST) were added to the HEALTH[e]TEEN program and tested in this study. Previous research has demonstrated the efficacy of an in-person CST program in improving metabolic control and quality of life in adolescents with type 1 diabetes [
      • Grey M.
      • Boland E.A.
      • Davidson M.
      • Li J.
      • Tamborlane W.V.
      Coping skills training for youth with diabetes mellitus has long-lasting effects on metabolic control and quality of life.
      ] and improving health behaviors and insulin resistance in youth who are at risk for type 2 diabetes [
      • Grey M.
      • Jaser S.S.
      • Holl M.G.
      • et al.
      A multifaceted school-based intervention to reduce risk for type 2 diabetes in at-risk youth.
      ]. Key theoretical components and their operationalization in the HEALT[e]TEEN programs are identified in Table 1.
      Table 1Theoretical components of program
      Principles of program developmentTheory componentHow operationalized in programPrograms
      Theory of interactive technologyInteractive technology promotes experiential learning and tailored/individualized feedbackLessons include self-assessment, simulations, problem-solving, repetition, and individualized feedback

      Health coaching and social networking provide individualized feedback;
      Both
      Social learning theoryKnowledge provides necessary, but insufficient precondition to behavior change

      Confidence to perform a specific task (self-efficacy) is important to behavior change

      Self-efficacy is enhanced by mastery of skills (goal setting & self-monitoring)

      Self-efficacy is enhanced by social modeling and social persuasion
      Lessons provide content

      Goal setting and self-monitoring included

      A reality television concept of the program includes diverse relatable characters who demonstrate typical situations (social modeling) in videos, text, and lesson commentary

      Health coaching and social networking provide encouragement and social persuasion;
      Both
      Social learning theorySelf-efficacy is enhanced by the moderation of psychologic responses (ie, stress reduction)Lessons provide content on stress reduction, assertive communication, conflict resolution, and social problem solving as it relates to healthy eating and physical activity

      HEALTH[e]TEEN + CST only
      CST = coping skills training.

      Purpose

      The purpose of this study was to compare the effectiveness of two school-based internet obesity prevention programs, HEALTH[e]TEEN and HEALTH[e]TEEN + CST, in diverse adolescents on BMI, health behaviors (nutrition, physical activity, sedentary behavior), and self-efficacy at 3 and 6 months. It was hypothesized that the addition of CST would enhance the ability of adolescents to make positive health behavior changes and thus improve health outcomes compared with the internet obesity prevention program alone. Program participation and satisfaction were also compared. A secondary aim was to explore moderators of intervention efficacy for the two programs.

      Method

      A randomized clinical trial with cluster randomization by class and repeated measures was conducted. A convenience sample was recruited from three high schools in two cities in the northeast between October 2010 and January 2011. Sample size was determined by a power analysis, based on data from a previous study of an in-person obesity prevention program [
      • Grey M.
      • Jaser S.S.
      • Holl M.G.
      • et al.
      A multifaceted school-based intervention to reduce risk for type 2 diabetes in at-risk youth.
      ].
      For 80% power to test the primary hypothesis, 392 pupils would be required at alpha = .05.

      Procedure

      Approvals were obtained from the Yale Institutional Review Board and the Boards of Education before study implementation. Informed consent was obtained from a parent or guardian, and assent was obtained from adolescents.
      Students enrolled in health or biology classes were eligible to participate. Students were excluded if cognitive functioning prohibited them from completing study questionnaires and program materials, as identified by teachers. All students in the targeted classes participated in the program assigned to their class (n = 604), although only students who returned consent forms participated in the research study (i.e., data collection) (n = 384). In total, students from 35 classes across all three schools participated in the study, with 66% of students who were approached consenting to participate in the study (Figure 1). Study participants received a gift card for completion of data collection ($25.00 at time 1; $30.00 at times 2 and 3). Two schools provided the program in class (n = 26 classes), and one school provided the program as homework (n = 9 classes). Attrition at 6 month follow-up was very low at 5%.
      Teachers and school administrators were involved in all decisions about study protocols to assure optimal implementation. Teachers were provided access to the websites and guidelines to promote student participation. The program was developed to be self-standing, with teacher involvement required only to help students log onto the program and monitor student activity to assure that students were participating in the program (rather than exploring other websites). Teachers were also instructed to prompt students to complete lessons and self-monitoring as well as explore all components of the program. The research team was available for any questions or problems encountered during class time. Student participation was monitored bimonthly by the research team and reported to classroom teachers. If class participation was low, the research team discussed strategies to enhance participation with teachers.

      Programs

      The major components of the HEALTH[e]TEEN program were lessons, goal setting, self-monitoring, health coaching, and social networking. There were eight lessons on the topics of nutrition, physical activity, metabolism, and portion control. Lessons were highly interactive, and students received individualized feedback via self-assessments and questions on content. Students were encouraged to record their food intake and physical activity each time they logged on, and the program provided a visual display of their progress. Students also set goals and monitored progress with completing goals. A blog by a “coach,” the opportunity to interact with a health coach (graduate nursing student) and other students, and a personal journal section were other components of the program. The HEALTH[e]TEEN + CST included all the aforementioned components and the addition of four lessons on coping skills training (total of 12 lessons). CST lessons included social problem solving, stress reduction, assertive communication, and conflict resolution [
      • Grey M.
      • Boland E.A.
      • Davidson M.
      • et al.
      Coping skills training for youths with diabetes on intensive therapy.
      ].

      Measures

      Data collection procedures

      Demographic information was collected from parents at the time of informed consent. Adolescent height, weight, and BMI data were collected in private locations at each school by trained research personnel. Adolescents also completed self-report questionnaires on health behaviors (nutrition, physical activity, sedentary behavior) and self-efficacy at baseline, 3 months, and 6 months. Satisfaction data were collected at 3 months. Self-report questionnaires were completed either online or by paper-and-pencil forms depending on the accessibility of the computer rooms at the time data collection was due. All paper forms were double-entered into password-protected and secure electronic databases. Data comparisons were run to obtain 100% accuracy.

      Primary outcome

      Body mass index

      Height was obtained using one wall-mounted stadiometer (Health O Meter Metal Height Rod), calibrated in 1-centimeter intervals, and was rounded up to the nearest centimeter. Weight in kilograms was measured to the nearest .1 kilogram using a floor scale (Omron HBF-400 Body Fat Monitor and Scale). BMI was calculated according to the formula BMI = kg/m2. Unadjusted BMI scores were used in all analyses based on recent recommendations for evaluating BMI longitudinally in youth [
      • Berkey C.S.
      • Colditz G.A.
      Adiposity in adolescents: Change in actual BMI works better than change in BMI z score for longitudinal studies.
      ].

      Secondary outcomes

      Sedentary behavior

      Sedentary behavior was measured using an adapted version of a sedentary behavior questionnaire [
      • Robinson T.N.
      • Killen J.D.
      Ethnic and gender differences in the relationships between television viewing and obesity, physical activity, and dietary fat intake.
      ]. Items include how many hours per day adolescents spent "watching television or movies," "playing video games," and “working on the computer” separately for a weekday and a weekend. Content validity had been established, and survey questions were similar to those used and validated in epidemiologic studies.

      Physical activity

      Physical activity was measured using the Exercise survey items of the Youth Risk Behaviors Survey, a survey used since 1990 to assess health behaviors in youth [

      Centers for Disease Control and Prevention. Youth risk behavior survey. 2009. http://www.cdc.gov/yrbs.

      ]. Six items evaluate days per week of moderate, vigorous, stretching, and strengthening exercise. Adequate test–retest reliability with select items has been reported [
      • Brener N.D.
      • Kann L.
      • McManus T.
      • et al.
      Reliability of the 1999 youth risk behavior survey questionnaire.
      ].

      Nutrition behavior

      Nutrition behavior was measured with a 22-item survey adapted from the After School Student Questionnaire that elicits information on typical food and drink intake. Items are consistent with the Healthy People 2020 goals, and selected items (i.e., fruit and vegetable intake) have evidence of adequate test-retest reliability. In addition to key items evaluated, a total score was calculated by coding items in such a manner that a higher score was reflective of better nutrition behavior. The total score range is from 0 (unhealthy behavior) to 97 (healthy behavior). Validity has been demonstrated with significant correlations with select items and food record data [
      • Prochaska J.J.
      • Sallis J.F.
      Reliability and validity of a fruit and vegetable screening measure for adolescents.
      ].

      Self-efficacy

      Self-efficacy for healthy eating and physical activity was measured with two subscales from the After School Student Questionnaire [
      • Kelder S.
      • Hoelscher D.M.
      • Barroso C.S.
      • et al.
      The CATCH kids club: a pilot after-school study for improving elementary students' nutrition and physical activity.
      ]. The subscales consist of 12 items on how likely an individual is to eat healthfully (eight items) and exercise (four items). Higher scores are indicative of higher self-efficacy. Cronbach’s alpha was .70 for the dietary subscale and .82 for the exercise subscale in this study.

      Satisfaction

      A program satisfaction survey had six items on enjoyment, helpfulness, ability to navigate website, practice content, and overall satisfaction on a Likert scale. Mean scores were calculated with a range of 0 (not satisfied) to 5 (highly satisfied). The scale had adequate reliability, with a Cronbach’s alpha of .80 in this study.

      Usage

      Usage data included lesson participation (i.e., percent of lessons completed relative to the program assigned) and self-monitoring (number of times students completed self-monitoring). These data were generated from programming that linked student identifications with user statistics.

      Data analysis

      Analyses were conducted using SAS 9.2 (SAS Institute, Cary, NC). Descriptive statistics were calculated, and groups were compared on baseline characteristics using t tests for continuous variables and Fisher’s exact tests for categorical variables. To compare the effects of the two programs, mixed effect model analyses with autoregressive covariance structure for repeated data were used, with two groups, three time points, and intent-to-treat procedures. Differences in rates of change between the groups, based on an interaction of group-by-time in the regression model, were used. For an overall effect of time, the group-by-time interaction was removed. Analyses were adjusted for age, gender, and race/ethnicity. Weight and BMI were not normally distributed; therefore, these variables were transformed using log-transformation. To describe how strongly outcome changes of participants in the same school and class were correlated, the intraclass correlation coefficients (ICCs) were obtained from a repeated mixed model with a random effect of school or class. The ICCs were ignorable in the change of most outcomes, with the highest ICC observed on vigorous exercise (.03) and moderate exercise (.02) for school and on diet self-efficacy (.02), stretching (.03), muscle strengthening (.02), and eating breakfast (.02) for class. To account for the variability among the three schools and classes, a random effect of school or class was added to the model if it was significant. Program participation and satisfaction with programs were compared using t tests. Because program implementation was different in some classes (homework vs. classroom), we also ran mixed model analyses exploring the effect of implementation by program.
      Potential moderators of program efficacy included adolescent characteristics (gender, race/ethnicity, and weight status at baseline) and program usage (percent lessons completed). To examine whether program efficacy was influenced by a potential moderator, an interaction moderator-by-time was tested for each outcome in the regression model. For the program usage moderation analysis, program usage was dichotomized into two groups consisting of students who completed 100% of lessons (n = 233) versus students who completed less than 100% of lessons (n = 152).

      Results

      The mean age of students was 15.31 years (+.69), with a mean BMI of 24.69 (+5.58). The majority were girls (62%) and of diverse race/ethnicity (65% nonwhite). Thirty-eight percent of adolescents were overweight or obese, with 16% obese. Participants reported high sedentary behavior, moderate physical activity, and poor eating behaviors. There were no significant differences between groups at baseline (Tables 2 and 3). There were significant differences between schools with respect to gender and race/ethnicity; therefore, these variables were controlled in all subsequent analyses.
      Table 2Baseline characteristics
      HEALTH[e]TEEN + CST N = 207HEALH[e]TEEN N = 177p
      N (%)N (%)
      Gender
       M77 (37.2%)69 (39.0%).72
      p values from chi-square test for cross table.
       F130 (62.8%)108 (61.0%)
      Age
       14–15139 (67.2%)125 (70.6%).46
      p values from chi-square test for cross table.
       16–1768 (32.8%)52 (29.4%)
      Income
       <$40,00068 (42.2%)61 (45.2%).08
      p values from Cochran-Armitage Trend test.
       $40,000–$79,99954 (33.5%)58 (43.0%)
       >$80,00039 (24.2%)16 (11.8%)
      Race
       White, non-Hispanic76 (37.3%)57 (33.9%).46
      p values from chi-square test for cross table.
       White, Hispanic/Latino44 (21.6%)40 (23.8%)
       African-American59 (28.9%)42 (25.0%)
       Other25 (12.3%)29 (17.3%)
      Body mass index
       <25125 (61.0%)111 (63.8%).57
      p values from chi-square test for cross table.
       ≥2580 (39.0%)63 (36.2%)
      Adherences
       Completed 100%109 (52.7%)123 (69.5%)<.01
      p values from chi-square test for cross table.
       Completed less than 100%98 (47.3%)54 (30.5%)
      N: mean (SD)N: mean (SD)
      Parent’s education years146: 12.5 (3.0)133: 12.4 (3.3).87
      Parent’s education was tested based on Wilcoxon Rank Sum test.
      Body mass index categories were classified based on the age-adjusted percentile of body mass index for each gender (resource: Centers for Disease Control and Prevention).
      CST = coping skills training; SD = standard deviation.
      a p values from chi-square test for cross table.
      b p values from Cochran-Armitage Trend test.
      c Parent’s education was tested based on Wilcoxon Rank Sum test.
      Table 3Behavioral variables at baseline
      HEALTH[e]TEEN +CST N = 207HEALTH[e]TEEN N = 177p
      Mean (SD)Mean (SD)
      Self-efficacy
       Healthy diet (range 0–24)14.9 (3.3)15.0 (3.4).57
      p values obtained from t test comparing means between two groups.
       Healthy exercise (range 0–12)8.1 (2.4)8.3 (2.4).45
      p values obtained from t test comparing means between two groups.
      Sedentary behavior
       Weekday (∼hours/day)8.8 (2.9)9.1 (3.2).50
      p values obtained from t test comparing means between two groups.
       Weekend (∼hours/day)9.0 (3.1)9.8 (3.5).04
      p values obtained from t test comparing means between two groups.
      Physical activity
       Vigorous (days/week of 20 minutes)3.37 (2.43)3.29 (2.31).83
      p values obtained from Wilcoxon rank sum test (nonparametric test).
       Moderate (days/week of 30 minutes)3.79 (2.42)3.73 (2.42).83
      p values obtained from Wilcoxon rank sum test (nonparametric test).
       Stretching (days/week)2.49 (2.51)2.17 (2.49).21
      p values obtained from Wilcoxon rank sum test (nonparametric test).
       Muscle strengthening (days/week)2.57 (2.58)2.13 (2.41).11
      p values obtained from Wilcoxon rank sum test (nonparametric test).
      Eating behavior (children’s health behavior at baseline) (range 0–97)52.5 (12.3)53.8 (11.5).29
      p values obtained from t test comparing means between two groups.
       Fruits and vegetables (servings/day)4.39 (2.25)4.53 (2.05).53
      p values obtained from t test comparing means between two groups.
       Breakfast (times/week)3.48 (2.64)3.69 (2.56).40
      p values obtained from Wilcoxon rank sum test (nonparametric test).
       Sugar drinks (servings/day)6.47 (3.25)6.06 (2.81).20
      p values obtained from Wilcoxon rank sum test (nonparametric test).
       Fast food (times/week).89 (1.11).78 (.97).50
      p values obtained from Wilcoxon rank sum test (nonparametric test).
       Junk food (servings/day)3.10 (2.66)2.91 (2.14).97
      p values obtained from Wilcoxon rank sum test (nonparametric test).
      Weight intention was tested by chi-square test.
      a p values obtained from t test comparing means between two groups.
      b p values obtained from Wilcoxon rank sum test (nonparametric test).
      Satisfaction with the programs was high. The mean satisfaction score was 3.58 (+.68). There was no significant difference between groups with respect to satisfaction (p = .26). Participation was also high, with adolescents completing 83% of lessons (median 100%). In each group, more than half of participants completed all lessons (53% of participants in HEALTH[e]TEEN + CST and 70% in HEALTH[e]TEEN). Adolescents completed self-monitoring assessments 5.26 times (+2.75; median 5) over the eight to 12 lessons. Adolescents of the HEALTH[e]TEEN + CST completed fewer lessons (p = .001) yet had higher participation in self-monitoring (p < .001).
      In mixed model analyses, using intent-to-treat procedures and controlling for age, gender, and race/ethnicity, there were no significant differences between groups on any of the outcome variables (Table 4). However, there were significant improvements in the health behaviors of adolescents in both groups over 6 months. Adolescents demonstrated a significant increase in self-efficacy (p < .001), healthy eating behavior (p < .001), fruit and vegetable intake (p < .001), moderate and vigorous exercise (p < .001), and stretching exercises (p < .01) along with a significant decrease in sugar-sweetened drinks (p < .001), junk food intake (p < .01), and sedentary behavior (p < .001). There was no time effect with respect to muscle strengthening, eating breakfast, and junk food intake. Given that weight and BMI generally increase during adolescence, the time effect on weight and BMI change was tested against the projected increase due to change in age. There was a marginally significant decrease in weight (p = .05) but not BMI (p = .86).
      Table 4Change of outcomes over 6 months
      Change of outcomes over 6 monthsTime effect
      p values for testing the improvement of outcome in each group after adjusting for age, gender, and race.
      Baseline mean (SD)3 months mean (SD)6 months mean (SD)
      Weight & body mass index
       Weight (lb)
      All147.7 (39.2)148.5 (39.2)148.4 (38.0).05
      HEALTH[e]TEEN + CST149.4 (39.7)150.0 (38.0)150.0 (37.7).09
      HEALTH[e]TEEN145.7 (38.6)146.8 (38.4)146.3 (38.3).06
      Time × group
      Time × group presents p value for comparing the intervention effect between CST+HET and HET only after adjusting for age, gender, and race.
      .6727
       Body mass index
      All24.6 (5.6)24.8 (5.6)24.9 (5.5).86
      HEALTH[e]TEEN + CST24.9 (5.8)25.0 (5.7)25.1 (5.6).87
      HEALTH[e]TEEN24.3 (5.4)24.5 (5.4)24.6 (5.4).87
      Time × group
      Time × group presents p value for comparing the intervention effect between CST+HET and HET only after adjusting for age, gender, and race.
      .9945
      Self-efficacy
       Healthy diet (range 0–24)
      All14.9 (3.3)15.7 (3.4)16.0 (3.5)<.01
      HEALTH[e]TEEN + CST14.9 (3.4)15.5 (3.3)15.9 (3.4)<.01
      HEALTH[e]TEEN15.0 (3.3)15.9 (3.5)16.2 (3.5)<.01
      Time × group
      Time × group presents p value for comparing the intervention effect between CST+HET and HET only after adjusting for age, gender, and race.
      .7128
       Exercise (range 0–12)
      All8.2 (2.4)8.8 (2.3)8.9 (2.3)<.01
      HEALTH[e]TEEN + CST8.1 (2.4)8.7 (2.3)8.9 (2.4)<.01
      HEALTH[e]TEEN8.3 (2.4)8.9 (2.2)8.9 (2.3)<.01
      Time × group
      Time × group presents p value for comparing the intervention effect between CST+HET and HET only after adjusting for age, gender, and race.
      .4556
      Sedentary behaviors
       Weekday (∼hours/day)
      All5.9 (2.3)5.5 (2.2)5.2 (2.3)<.01
      HEALTH[e]TEEN + CST5.9 (2.2)5.4 (2.2)5.2 (2.3)<.01
      HEALTH[e]TEEN5.9 (2.5)5.6 (2.2)5.3 (2.3)<.01
      Time × group
      Time × group presents p value for comparing the intervention effect between CST+HET and HET only after adjusting for age, gender, and race.
      .9557
       Weekend (∼hours/day)
      All6.2 (2.7)5.7 (2.7)5.4 (2.7)<.01
      HEALTH[e]TEEN + CST6.1 (2.6)5.6 (2.7)5.3 (2.6)<.01
      HEALTH[e]TEEN6.4 (2.8)5.8 (2.7)5.4 (2.9)<.01
      Time × group
      Time × group presents p value for comparing the intervention effect between CST+HET and HET only after adjusting for age, gender, and race.
      .3967
      Exercise
       Vigorous (days/week of 20 minutes)
      All3.4 (2.4)3.9 (2.2)4.1 (2.1)<.01
      HEALTH[e]TEEN + CST3.4 (2.4)4.1 (2.2)4.1 (2.1)<.01
      HEALTH[e]TEEN3.4 (2.3)3.7 (2.2)4.1 (2.1)<.01
      Time × group
      Time × group presents p value for comparing the intervention effect between CST+HET and HET only after adjusting for age, gender, and race.
      .9829
       Moderate (days/week of 30 minutes)
      All3.8 (2.4)4.0 (2.2)4.3 (2.1)<.01
      HEALTH[e]TEEN + CST3.8 (2.4)4.2 (2.2)4.4 (2.1)<.01
      HEALTH[e]TEEN3.9 (2.4)3.9 (2.1)4.3 (2.0).06
      Time × group
      Time × group presents p value for comparing the intervention effect between CST+HET and HET only after adjusting for age, gender, and race.
      .5958
       Stretching (days/week)
      All2.4 (2.5)2.8 (2.4)2.9 (2.5). <.01
      HEALTH[e]TEEN + CST2.5 (2.5)3.0 (2.5)3.0 (2.6).03
      HEALTH[e]TEEN2.2 (2.5)2.5 (2.3)2.8 (2.5).03
      Time × group
      Time × group presents p value for comparing the intervention effect between CST+HET and HET only after adjusting for age, gender, and race.
      .8253
       Muscle strengthening (days/week)
      All2.4 (2.5)2.8 (2.4)2.6 (2.5).12
      HEALTH[e]TEEN + CST2.6 (2.6)2.8 (2.4)2.8 (2.5).18
      HEALTH[e]TEEN2.2 (2.4)2.7 (2.5)2.4 (2.5).40
      Time × group
      Time × group presents p value for comparing the intervention effect between CST+HET and HET only after adjusting for age, gender, and race.
      .7844
      Eating behaviors
       Fruits & vegetables (servings/day)
      All4.5 (2.2)4.9 (2.2)5.0 (2.0)<.01
      HEALTH[e]TEEN + CST4.4 (2.3)5.0 (2.3)4.9 (2.1)<.01
      HEALTH[e]TEEN4.6 (2.0)4.9 (2.0)5.1 (1.9)<.01
      Time × goup
      Time × group presents p value for comparing the intervention effect between CST+HET and HET only after adjusting for age, gender, and race.
      .9162
       Breakfast (days/week)
      All3.6 (2.6)4.1 (2.5)3.8 (2.6).18
      HEALTH[e]TEEN + CST3.4 (2.6)4.1 (2.6)3.7 (2.7).29
      HEALTH[e]TEEN3.7 (2.5)4.2 (2.4)3.9 (2.5).40
      Time × group
      Time × group presents p value for comparing the intervention effect between CST+HET and HET only after adjusting for age, gender, and race.
      .9211
       Sugar drinks (servings/day of soda and fruit juice)
      All6.3 (3.1)5.8 (2.8)5.6 (2.8)<.01
      HEALTH[e]TEEN + CST6.5 (3.3)5.9 (2.9)5.8 (2.9)<.01
      HEALTH[e]TEEN6.2 (2.8)5.6 (2.8)5.4 (2.6).01
      Time × group
      Time × group presents p value for comparing the intervention effect between CST+HET and HET only after adjusting for age, gender, and race.
      .8954
       Fast food (times/week)
      All.85 (1.05).78 (1.01).82 (1.02).78
      HEALTH[e]TEEN + CST.90 (1.12).83 (1.09).80 (1.03).18
      HEALTH[e]TEEN.78 (.96).72 (.91).85 (1.00).28
      Time × group
      Time × group presents p value for comparing the intervention effect between CST+HET and HET only after adjusting for age, gender, and race.
      .0892
       Junk food (servings/day)
      All3.0 (2.4)2.5 (2.1)2.6 (2.2)<.01
      HEALTH[e]TEEN + CST3.1 (2.7)2.5 (2.2)2.7 (2.4).01
      HEALTH[e]TEEN2.9 (2.2)2.4 (2.0)2.5 (1.9).06
      Time × group
      Time × group presents p value for comparing the intervention effect between CST+HET and HET only after adjusting for age, gender, and race.
      .7861
       Total eating behavior (range 0–97)
      All52.9 (12.0)56.7 (11.5)56.8 (11.3)<.01
      HEALTH[e]TEEN + CST52.4 (12.3)56.8 (11.9)56.4 (11.9)<.01
      HEALTH[e]TEEN53.5 (11.6)56.6 (11.1)57.2 (10.6)<.01
      Time × group
      Time × group presents p value for comparing the intervention effect between CST+HET and HET only after adjusting for age, gender, and race.
      .6011
      CST = coping skills training; HET = HEALTH[e]TEEN.
      a p values for testing the improvement of outcome in each group after adjusting for age, gender, and race.
      b Time × group presents p value for comparing the intervention effect between CST+HET and HET only after adjusting for age, gender, and race.
      With respect to program implementation, there was no difference between classroom and homework implementation by program on any outcome, with the exception of weight and BMI. For students in the HEALTH[e]TEEN + CST group, there was less increase in weight (p = .03) and BMI (p = .05) compared with the normal growth curve in the homework group compared with the in-class group. Across both programs, there were trends for students of the classroom implementation group to have greater improvements in self-efficacy, sedentary behavior, exercise, and eating behavior.
      Adolescent age, gender, race/ethnicity, weight status (normal, overweight, or obese), and program usage (percent lessons completed) were tested for moderation of program efficacy on the outcomes of healthy eating, sedentary behavior, and moderate or vigorous exercise. There was significantly greater improvement in breakfast behavior of girls compared with boys (p = .02). Girls also significantly reduced junk food intake (p < .001), whereas boys did not (p = .58). There were no other significant moderators of healthy eating, sedentary behavior, or exercise. Inasmuch as program usage (lesson participation) was different between the two groups, the moderation of program usage was tested separately for each group (Table 5). Both groups improved moderate to vigorous exercise significantly for participants who completed all lessons (p = .005) but not for participants with lower lesson participation. However, the interaction of program usage-by-time was statistically significant only on moderate exercise in the HEALTH[e]TEEN program (p = .03). There were trends for participants with high program usage to have greater improvements in weekend sedentary behavior and junk food consumption compared with those with less program usage (Table 5).
      Table 5Moderation effect of program completion (100% completion vs. less than 100%)
      OutcomesEstimated coefficient of time effect
      HEALTH[e]TEEN + CSTHEALTH[e]TEEN
      Coeff (StdErr)pCoeff (StdErr)p
      Sedentary: weekdays
       Completed all sessions−.09 (.04).0215−.12 (.04).0023
       Completed less than 100%−.11 (.04).0107−.04 (.06).5136
       Time × program usage.7785.2631
      Sedentary: weekend
       Completed all sessions−.14 (.05).0019−.18 (.04)<.0001
       Completed less than 100%−.09 (.05).0614−.11 (.07).1214
       Time × program usage.4368.3724
      Vigorous exercise
       Completed all sessions.16 (.04).0001.16 (.04)<.0001
       Completed less than 100%.08 (.04).0840.03 (.06).5574
       Time × program usage.1520.0846
      Moderate exercise
       Completed all sessions.13 (.04).0044.12 (.04).0048
       Completed less than 100%.05 (.05).2623−.05 (.06).4025
       Time × program usage.2329.0249
      Fruits & vegetables
       Completed all sessions.09 (.04).0183.06 (.03).0750
       Completed less than 100%.08 (.04).0638.12 (.05).0278
       Time × program usage.7850.3807
      Breakfast
       Completed all sessions.04 (.04).3518.00 (.04).9892
       Completed less than 100%.03 (.04).5673.10 (.07).1380
       Time × program usage.8206.2156
      Sugar drinks
       Completed all sessions−.10 (.06).0645−.12 (.04).0100
       Completed less than 100%−.10 (.06).0840−.08 (.07).2956
       Time × program usage.9907.5995
      Junk foods
       Completed all sessions−.09 (.04).0498−.09 (.04).0234
       Completed less than 100%−.07 (.05).1438−.00 (.06).9475
       Time × program usage.7717.2351
      Time × program usage represents the interaction effect between time and program completion on outcome for each group.

      Discussion

      The purpose of this study was to compare the effectiveness of two school-based obesity prevention programs for adolescents provided over the internet. The primary hypothesis, that adolescents who participated in HEALTH[e]TEEN + CST would demonstrate better self-efficacy and health behaviors and less weight gain compared with an internet educational and behavioral program alone, was not supported. The lack of differential effects of CST may have been due to the short-term follow-up or to implementation factors. CST may take longer to have effects, given that coping skills take time and practice to develop and become useful in social situations. Adolescents in the CST group also completed a lower percent of assigned lessons compared with the HEALTH[e]TEEN group, which may be related to the addition of four sessions. Standard procedures were used in implementing the programs across classes and groups. Variations in implementation occurred, however, with the program being provided as homework in some classes. In an exploratory analysis, there was some indication that program implementation may affect outcomes; however, interpretation of this must be cautious because of the small sample size of the homework implementation group and the confound that all homework implementation occurred in one school. Variations across schools that could affect implementation and outcome included scheduling media rooms, level of teacher involvement, and unanticipated school closings. Studies conducted in highly unstable environments require different approaches, especially in community-based effectiveness trials such as this [
      • Buckwalter K.C.
      • Grey M.
      • Bowers B.
      • et al.
      Intervention research in highly unstable environments.
      ].
      Despite a lack of differential effects between programs, the results of this study indicate that both programs improved adolescents’ self-efficacy and health behaviors in the short term. Thus, internet education and behavioral support have the potential to improve health outcomes in adolescents. This is an important finding because programs that contribute to healthy dietary and physical activity behaviors in adolescents are greatly needed. The complexity of obesity prevention in adolescents will require multifaceted and comprehensive programs in the future; however, central to such programs needs to be a theoretically based and developmentally appropriate program that has been systematically developed and evaluated. More research with programs of longer duration and follow-up are needed to determine the effect on BMI, inasmuch as no change in BMI was demonstrated in this study of short duration.
      The results of this study also indicate that school-based internet obesity prevention programs are appealing to adolescents, as demonstrated by high participation and satisfaction. The benefits of multimedia obesity prevention programs include the ability to present content in an engaging and interactive format that is part of the world of today’s adolescents, to provide individualized feedback, and for students to learn at their own pace [
      • Nguyen B.
      • Kornman K.P.
      • Baur L.A.
      A review of electronic interventions for prevention and treatment of overweight and obesity in young people.
      ]. Adolescents have reported that they preferred media-based obesity prevention education over print materials and lectures [
      • Casazza K.
      • Ciccazzo M.
      The method of delivery of nutrition and physical activity information may play a role in eliciting behavior changes in adolescents.
      ].
      The results of the moderation analysis indicated that girls improved selected eating behaviors compared with boys, which is consistent with previous school-based obesity prevention studies demonstrating that girls have higher participation [
      • Mauriello L.M.
      • Ciavatta M.M.H.
      • Paiva A.L.
      • et al.
      Results of a multi-media multiple behavior obesity prevention program for adolescents.
      ] and better outcomes [
      • Maes L.
      • Cook T.L.
      • Ottovaere C.
      • et al.
      Pilot evaluation of the HELENA (healthy lifestyle in Europe by nutrition in adolescence) food-O-meter, a computer-tailored nutrition advice for adolescents: a study in six European cities.
      ,
      • Haerens L.
      • Deforche B.
      • Maes L.
      • et al.
      Body mass effects of a physical activity and healthy food intervention in middle schools.
      ]. Girls may respond better to cognitive-behavioral programs based on social learning theory and may have heightened enthusiasm for programs because of concerns about their weight and body shape [
      • Kropski J.A.
      • Keckley P.H.
      • Jensen G.L.
      School-based obesity prevention programs: An evidence-based review.
      ,
      • O'Dea J.A.
      • Caputi P.
      Association between socioeconomic status, weight, age and gender, and the body image and weight control practices of 6- to 19-year-old children and adolescents.
      ,
      • Paxton S.J.
      • Wertheim E.H.
      • Gibbons K.
      • et al.
      Body image satisfaction, dieting beliefs, and weight loss behaviors in adolescent girls and boys.
      ]. There were not any other demographic or clinical characteristics that moderated program efficacy with respect to healthy eating, physical activity, or sedentary behavior. Thus, the program was equally effective across race/ethnicity, age, and adolescent weight status. There was some indication that program usage moderated improvement in selected outcomes. Participation in internet programs is important, inasmuch as several studies have shown a positive relationship between user log-ons or lesson completion and improved outcomes [
      • Frenn M.
      • Malin S.
      • Brown R.L.
      • et al.
      Changing the tide: an internet/video exercise and low-fat diet intervention with middle-school students.
      ,
      • Williamson D.A.
      • Davis Martin P.
      • White M.A.
      • et al.
      Efficacy of an internet-based behavioral weight loss program for overweight adolescent African-American girls.
      ]. A recent study indicated that adolescents were more likely to participate consistently in an internet program provided during school time than in a program designed to completed on their own time [
      • Neil A.L.
      • Batterham P.
      • Christensen H.
      • et al.
      Predictors of adherence by adolescents to a cognitive behavior therapy website in school and community-based settings.
      ]. This suggests that school-based programs may be the best way to enhance participation in internet obesity prevention programs, particularly when considering the potential issue of access to the internet for minority and low-income populations.
      The findings of this study must be interpreted in light of several limitations. The sample was from one geographical location, and although it was inclusive of adolescents of diverse race/ethnicity, 34% of adolescents declined to participate in the study. Therefore, the results of the study may not be generalizable to other adolescents. Teacher enthusiasm, technology expertise, or teaching style (i.e., classroom control, monitoring of homework completion) was not systematically evaluated in this study, which may have influenced participation and outcomes. In addition, the outcomes of self-efficacy and health behaviors were self-report measures. However, these measures were brief, have been widely used with adolescents, and have evidence of reliability and validity. As noted above, follow-up was limited to 6 months in this American Recovery and Reinvestment Act-funded study, which may have attenuated the impact of the program. Last, data on pubertal status was not collected, which affects BMI change in adolescents. Given the age of the adolescents and their BMI, it is highly likely that the majority of these youth were in advanced puberty.

      Directions for future research

      There are several important directions for future research. First, research is needed on school-based internet obesity prevention programs of longer duration that include a maintenance component. Follow-up of at least 1 year may be necessary to demonstrate a significant impact on BMI in adolescents. Second, future research on mediators and moderators to program efficacy are needed. Other intermediate outcomes, such as fitness level and body composition, should be evaluated in future research. Intrinsic factors such as academic performance, students’ preferred learning style, and motivation may influence participation, satisfaction, and outcomes of an internet obesity prevention program and should be considered in future research. Last, future research on internet obesity prevention programs for adolescents should include a systematic evaluation of implementation. Although providing an obesity prevention program via the internet standardizes content, implementation factors such as teacher enthusiasm, teacher technology skills, classroom implementation, and classroom discussion may influence outcomes.

      Acknowledgments

      NIH/NINR: RC1NR011594.

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