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Bounce Back Now! Protocol of a population-based randomized controlled trial to examine the efficacy of a Web-based
Kenneth J Ruggiero1, Tatiana M Davidson2, Jenna McCauley3
1Technology Applications Center for Healthful Lifestyles, College of Nursing, Medical University of South Carolina (MUSC), Charleston, SC, United States; Ralph H. Johnson Veterans Affairs Medical Center, 109 Bee Street, Charleston, SC, 29401, United States.
Abstract:
Disasters have far-reaching and potentially long-lasting effects on youth and families. Research has consistently shown a clear increase in the prevalence of several mental health disorders after disasters, including depression and posttraumatic stress disorder. Widely accessible evidence-based interventions are needed to address this unmet need for youth and families, who are underrepresented in disaster research. Rapid growth in Internet and Smartphone access, as well as several Web based evaluation studies with various adult populations has shown that Web-based interventions are likely to be feasible in this context and can improve clinical outcomes. Such interventions also are generally cost-effective, can be targeted or personalized, and can easily be integrated in a stepped care approach to screening and intervention delivery. This is a protocol paper that describes an innovative study design in which we evaluate a self-help Web-based resource, Bounce Back Now, with a population-based sample of disaster affected adolescents and families. The paper includes description and justification for sampling selection and procedures, selection of assessment measures and methods, design of the intervention, and statistical evaluation of critical outcomes. Unique features of this study design include the use of address-based sampling to recruit a population-based sample of disaster-affected adolescents and parents, telephone and Web-based assessments, and development and evaluation of a highly individualized Web intervention for adolescents. Challenges related to large-scale evaluation of technology-delivered interventions with high-risk samples in time-sensitive research are discussed, as well as implications for future research and practice.
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