Session: Identifying Effective Intervention Components: How Microtrials and Idiographic Research Can Benefit Prevention Research (Society for Prevention Research 22nd Annual Meeting)

4-013 Identifying Effective Intervention Components: How Microtrials and Idiographic Research Can Benefit Prevention Research

Schedule:
Friday, May 30, 2014: 8:30 AM-10:00 AM
Regency D (Hyatt Regency Washington)
Theme: Innovative Methods and Statistics
Symposium Organizer:
Patty Leijten
Discussants:
David P. MacKinnon and Ingrid C. Wurpts
This symposium seeks to illustrate the value of small scale, focused intervention studies (i.e., microtrials and idiographic research) to the field of prevention and intervention research. The current field of intervention research is dominated by traditional randomized controlled trials of comprehensive intervention programs. Although proven extremely valuable for our knowledge on the prevention of a wide range of (mental) health problems, the predominance of tradition randomized controlled trials has led intervention scientists to focus on the effectiveness of “program packages”, rather than on understanding what individual intervention-components (e.g., specific techniques or strategies taught) actually contribute. By identifying the essential components of interventions( and the ways through which these components operate), we will gain unique opportunities to optimize the effectiveness of interventions.

Three series of studies are presented that illustrate the potential of small and focused intervention studies. Presentation 1 focuses on when and why prevention programs have long lasting effects. Based on self-stabilization theory, the authors use microtrials to test whether intervention components that include strategies for inoculation against setbacks and components for developing coherent action plans are important for long-term effects. As an example, they take prevention programs designed to reduce the risk of depressive symptoms in adults facing loss of employment and economic decline. Presentation 2 focuses on the empirical merit of a key component of family interventions. Based on clinical presumptions that praise is more effective when it includes explicit reference to the desired behavior of the child (i.e., labeled praise), families in well-established parenting interventions are encouraged to use labeled over unlabeled praise to reinforce child behavior. Three microtrial field experiments are presented that suggest that labeled praise may actually not be superior to unlabeled praise, and even inferior to unlabeled praise for some families. Presentation 3 demonstrates how traditional statistical techniques can be used to study within-person change in small sample studies, and how this approach informs us on which intervention components are effective for which families. For example, the authors present a study on a program to improve communication skills of child with autism in terms of how well the outcomes of family member-led intervention compares to the impact of speech pathologist-led outcomes. Overall, this symposium illustrates the potential of microtrials and idiographic research to complement traditional prevention and intervention research.


* noted as presenting author
420
Using Microtrials to Test Preventive Intervention Components for Stabilizing Positive Change, Based on Dynamic Systems Extensions of Intervention Action Theory
George W. Howe, PhD, George Washington University; Anna P. Hornberger, MPhil, George Washington University
421
What Good Is Labeling What's Good? Experimental Field Studies on the Empirical Merit of Labeled Praise As a Key Parenting Intervention Element
Patty Leijten, PhD, University of Oxford, UK; Sander Thomaes, PhD, University of Southampton; Geertjan Overbeek, PhD, University of Amsterdam; Maartje A. J. Raaijmakers, PhD, Utrecht University; Bram Orobio de Castro, PhD, Utrecht University; Walter Matthys, MD, Utrecht University
422
Demonstration of Two Traditional Statistical Techniques for Use with Small Sample, within-Person Experiments: Unified Structural Equations Modeling and Mixed Model Trajectory Analysis
Ty Andrew Ridenour, PhD, University of Pittsburgh; Szu-Han Chen, MA, University of Pittsburgh; Hsin-yi Liu, MA, University of Pittsburgh; Katya Hill, PhD, University of Pittsburgh; Rory A. Cooper, PhD, University of Pittsburgh