Background: Unhealthy behavior patterns such as smoking, alcohol use, physical inactivity, and poor diet often being in adolescence and worsen over time. A peak time for interventions to prevent these patterns is when students are in middle school; prior to increased reluctance to change that occurs in high school. While tobacco and alcohol abuse prevention programs in schools have had mixed success, evidence of effective interventions is growing. Similarly, diet and physical activity interventions are promising within the school-age population. Computer or technology-based interventions are being used as a means to implement prevention programs, particularly with this tech-savvy generation of students. The current study combines the Transtheoretical Model of Behavior Change (TTM) with a computer-based intervention for multiple risk behaviors in middle school students. Current study: This study reports the 36-month results of a randomized two-arm comparison trial of TTM tailored, multiple behavior change interventions delivered to entire populations of middle school students (20 schools and 4,518 students) conducted between 2007 and 2011. Each participant interacted with five 30-min computerized TTM-tailored intervention sessions that were group specific: one in sixth grade, three times in seventh grade, and one in eighth grade. Students in both groups completed a total of four computerized health risk assessments early in each year of the project (grades 6, 7, 8, and 9) to determine outcomes by stage of change. The smoking and alcohol prevention program targeted smoking and alcohol acquisition. The energy balance intervention targeted physical activity (at least 60 min on at least 5 days per week), fruit and vegetable consumption (at least five servings of fruits and vegetables each day), and limited TV viewing (2 h or less of TV each day). Results: Analyses assessed group differences in smoking and alcohol acquisition among those who were nonsmokers and nondrinkers at baseline between the energy balance intervention (EB) and the substance use prevention intervention (SP). The EB group had less smoking acquisition than the SP group at 12, 24, and 36 months. The EB group had less alcohol acquisition than the SP group at 12, 24, and 36 months. There were too few students (1–2%) who were smoking or using alcohol at baseline to perform a meaningful analysis. Analyses assessed group differences in movement to action or maintenance stages (A/M) among those in a pre-action stage at baseline for each energy balance behavior. The EB group had greater percentages than the SP group progressing to A/M for physical activity at 12 and 36 months. The EB group had greater percentages than the SP group in fruit and vegetable consumption progressing to A/M at 12, 24, and 36 months. The EB group had greater percentages than the SP group in limiting TV viewing progressing to A/M at 12, 24, and 36 months. In looking at relapse, the EB group had more stability with behavior changes than the SP group at 36 months in physical activity, fruit and vegetable consumption, and limiting TV viewing. Bottom Line: The energy balance intervention was not only effective in initiating and maintaining energy balance behaviors, but also in reducing smoking and alcohol acquisition in these early adolescents. These findings add to a growing body of evidence indicating that computer technology interventions are an efficacious way to improve energy balance behaviors among adolescents at the population level. Policy implications Policies at the district and school level can facilitate the development and testing of multiple risk factor interventions in this population. Education policies should embrace prevention of unhealthy behaviors through evidence-based and innovative interventions. Funding for intervention programs to continue beyond the scope of research within schools is necessary for sustainability and positive long-term outcomes. Research implications Enhancing the efficiency of delivery and effectiveness of outcome through computer technology interventions should be a research priority. Future research should include tailoring and testing effective interventions to different groups (e.g., high school to middle school), different modes of delivery (e.g. computer technology), and different settings (e.g., community centers).
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Jan 31, 2018
Davy Vancampfort + 8
Open Access