Our Health In Motion youth obesity prevention program is targeted at teens and focuses on three areas:
- Increasing physical activity
- Eating more fruits and vegetables
- Reducing TV time
This online program includes voice-overs read by teens, ten full-motion videos of teens working through exercise, eating and TV time issues, and interactive Flash animations to keep students’ interest.
Background
Development of this program was supported by grant R43 HL074482 from the National Institutes of Health (NHLBI). An effectiveness trial conducted in schools in Tennessee, New York, Massachusetts, and Rhode Island was completed in December 2007. Outcomes will be available in 2009.
Results Presented
Castle, P.H., Paiva, A.L., Mauriello, L.M., Sherman, K.J. & Prochaska, J.M. (2009). Multiple behavior risk reduction and risk acquisition: Results from an adolescent obesity prevention program. Paper presented at the annual conference of the Society of Behavioral Medicine, Montreal, Canada. abstract
Individuals with multiple health behavior risks have the highest likelihood for morbidity and premature mortality. The present study examines risk reduction as part of an adolescent obesity prevention program. A TTM-based computer intervention offering fully tailored feedback for physical activity and optimally tailored feedback for fruit and vegetable consumption and television viewing was delivered over 14-months in 8 high schools. Participants (N=864) completing all time points were included in the following repeated measure analyses. The mean number of behavioral risks at baseline was 1.87 and 1.84 for treatment and control groups, respectively. Those in the treatment group reported significantly (p< .001) less risks compared to the control group at 2 months, F(1, 861)=95.61, ?²=.10 (1.34 vs. 1.87), 6 months F(1, 861)=30.41, ?²=.03 (1.46 vs. 1.76), and 12 months F(1, 861)=51.731, ?²=.06 (1.46 vs. 1.83). Among individuals with at least one risk at baseline (n=809), the mean number of risks at baseline was 1.97 and 2.03 for treatment and control groups, respectively. Among these students, those in the treatment group displayed significantly fewer (p<.001) risks compared to the control group at 2 months, F(1, 807)=72.24, ?²=.05 (1.39 vs. 1.97), 6 months F(1, 807)=27.73, ?²=.03 (1.51 vs. 1.86), and 12 months F(1, 807)=44.70, ?²=.05 (1.51 vs. 1.93). Among those individuals with zero risks at baseline (N=55), the treatment group reported less acquisition of risks compared to the control group at 2 months, F (1, 53)=18.59, p<.001 ?²=.26 (.22 vs. .94), 6 months F(1, 53)=6.63, p<.05 ?²=.11 (.30 vs. .78), and 12 months F(1, 53)=5.68, p<.05 ?²=.10 (.35 vs..88). Findings indicate the effectiveness of treating multiple risks simultaneously, while also reducing relapse and risk acquisition. The intervention’s high level impact of 65.5% on multiple behavior changes will be discussed and demonstrates that interventions focusing on clusters of at-risk behaviors, rather than single behaviors, are likely to produce the largest population-based impact.
Sherman, K.J., Mauriello, L.M., Paiva, A.L., Driskell, M.M. & Castle, P.H. (2009). Co-variation of multiple behavior change: Synergistic effects of an obesity prevention program. Paper presented at the annual conference of the Society of Behavioral Medicine, Montreal, Canada. abstract
A computer-delivered program offering fully tailored feedback based on the Transtheoretical Model of Behavior Change for physical activity (PA) and optimally tailored feedback for fruit and vegetable consumption (FV) and television viewing (TV) was tested over 14-months in 8 schools. The majority of participants (N=1800) were White (71.5%), female (50.8%), and 16 years old.
Logistic regression was conducted to determine the likelihood of participants moving to criteria (action or maintenance stage) for another behavior if they had moved to criteria for one behavior. Findings show that progress on one behavior led to progress on another behavior among treatment but not control group participants. The treatment group exhibited significant co-variation among behavior pairs at each time point. At two months odds ratios were 4.20 for PA and FV, 2.60 for PA and TV, and 2.13 for FV and TV. At six months odds ratios were 3.36 for PA and FV, 2.08 for PA and TV, and 1.99 for FV and TV. At 12 months for PA and FV odds ratios were 2.66. The control group did not exhibit co-variation for any behavior pair at any time point.
The findings of this study indicate the synergistic effects possible when impacting multiple behaviors. Implications are wide reaching in that such population-based programs can efficiently and effectively treat multiple risks among student populations. Discussion will focus on how various levels of tailoring can effectively achieve synergistic effects in multiple behavior interventions.
Mauriello, L.M., Sherman, K.J., Paiva, A.L., Ciavatta (Driskell), M.M., Castle, P.H., Johnson, J.J. & Prochaska, J.M. (2009). 12-month outcomes of a multimedia obesity prevention program for adolescents. Paper presented at the annual conference of the Society of Behavioral Medicine, Montreal, Canada. abstract
Eight schools throughout the US were randomized to either treatment or control group and participated in a 14-month trial. Treatment group participants received three intervention sessions and two follow-up assessments, while control group participants received four assessment-only sessions. The majority of participants (N=1800) were White (71.5%), female (50.8%), and 16 years old (SD=.94).
For each behavior 2X3 repeated measures ANCOVA’s were conducted with group (treatment and control) and time (2mo, 6mo, and 12mo). Post-hoc comparisons were performed using the Bonferroni adjustment for multiple comparisons.
The main effect for group was significant for PA, F(1, 477)=15.13,p<.001,?²=.03, for FV F(1,693)=88.14, p< .001,?² =.11., and for SB, F(1,434)=7.96, p< .01,?² =.02. For PA the treatment group reported significantly more days exercising per week compared to the control group at 2 months (3.44 vs. 2.69) and 12 months (3.42 vs. 2.79). For FV, the treatment group reported consuming more servings of fruits and vegetables per day compared to the control group at 2 months (4.0 vs. 2.94), 6 months (3.69 vs. 2.66), and 12 months (3.78 vs. 2.84). For SB, the treatment group reported significantly less hours of TV per day compared to the control group at 2 months (3.48 vs. 4.21) and 12 months (3.34 vs. 3.81). For all three behaviors significantly less treatment group participants were at-risk for all three behaviors at follow-up timepoints.
Longitudinal analyses indicate the effectiveness of Health in Motion to initiate behavior change for all three behaviors and its ability to enhance energy balance behaviors within a cost-effective, science-based, and easily deliverable intervention. Schools are a viable channel by which this program can reach large groups of adolescents. Due to the numerous effects that obesity has on youth, school administrators as well as teachers play a crucial role in bringing evidence-based interventions to schools to help curb the obesity epidemic.
Results Published
Driskell, M.M., Dyment, S.J., Mauriello, L.M., Castle, P.H., & Sherman, K.J. (2008). Relationships among multiple behaviors for childhood and adolescent obesity prevention. Preventive Medicine, 46, 209-215. abstract
Abstract:
Background: Curbing the epidemic of childhood and adolescent obesity requires impacting multiple behaviors. This article examines the interrelationships of physical activity, fruit and vegetable consumption, and limiting television time among elementary, middle, and high school students.
Methods: Nationwide samples of students in grades 4 through 12 (n=4,091) completed self-administered questionnaires assessing Transtheoretical Model (TTM) constructs and behavioral indicators for physical activity, fruit and vegetable consumption, and limiting television time. Analyses were conducted to compare the prevalence of students at-risk for the target behaviors across the age groups and to examine the interrelationships of the target behavior risks.
Results: Across the three age groups, physical activity and fruit and vegetable consumption declined, while limiting TV time increased. In addition, high school students had the greater number of behavioral risks. Across all three samples, being at-risk for one behavior almost always significantly increased the odds of being at-risk for another behavior.
Conclusion: The findings of this study provide further evidence for the need for early promotion of healthy lifestyle behaviors. The relationships among the target behaviors in three samples strongly support a multiple behavior approach for obesity prevention. TTM-based tailored interventions are now being used to address multiple behaviors without overwhelming students.
Mauriello, L.M., Sherman, K.J., Driskell, M.M., & Prochaska, J.M. (2007). Using interactive behavior change technology to intervene on physical activity and nutrition with adolescents. Adolescent Medicine: State of the Art Reviews, 18, 383-399. abstract
Abstract: Interactive technologies have emerged as a promising means for developing and disseminating health behavior change interventions. Interactive health behavior change programs are most often computer-delivered via the World Wide Web, a CD-ROM/DVD, or a stand-alone kiosk. There are many benefits of programs using these technologies. They allow the incorporation or rich media such as audio, animated graphics, and video. On-screen assessments and programming allow for immediate feedback with extensive opportunities for tailoring to participant responses. The embedded interactivity gives the user an active role with more control over their participation. Users enjoy the appeal these features offer and the flexibility of engaging in the program at their convenience. Researchers and program implementers appreciate the fidelity to treatment offered with consistent and reliable feedback delivered to participants. With improved program retention, wider reach, and less reliance on staff for delivery, interactive technologies offer a cost-effective means of delivering behavior change interventions. They are acclaimed by researchers to be an innovative, powerful, and promising way of improving the efficacy and dissemination of behavior change intervention.
Mauriello, L.M., Driskell, M.M., Sherman, K.J., Johnson, S.S., Prochaska, J.M., & Prochaska, J.O. (2006). Acceptability of a school-based intervention for the prevention of adolescent obesity. Journal of School Nursing, 22, 269-277. abstract
Abstract: This article describes the development and pilot testing of a computer-based, multiple-behavior obesity prevention program for adolescents. Using the Transtheoretical Model as a framework, this intervention offers individualized feedback based on readiness to engage in physical activity, to consume fruits and vegetables, and to limit television viewing. Focus groups and interviews with students, teachers, school administrators, and experts guided the development. Forty-five students participated in a baseline intervention session and completed a 16-item acceptability measure. Ratings were positive, with item means ranging from 3.60-4.75 on a 5-point scale. Student responses to open-ended questions aided in the enhancement of the intervention, for which an effectiveness trial begins in September 2006. This formative work demonstrated the acceptability of this school-based intervention approach, which can be promoted and prescribed by school nurses. Further, if found effective, it can be disseminated as an efficient, low-cost, population-based approach designed to address the epidemic of obesity.