Improving measurement methods for behavior change interventions: opportunities for innovation.

Theoretically based behavioral interventions have demonstrated effectiveness in stopping smoking, increasing physical activity and improving nutrition in order to prevent major chronic diseases such as cardiovascular disease, cancer and diabetes [1–7]. Despite the development of effective behavior change programs, the magnitude of change in these behaviors has been relatively modest [8, 9]. The rising rates of diabetes and obesity, for example, indicate there is still much to learn about the mechanisms of behavior change and how to maintain newly acquired behavioral skills. One problem that has slowed behavioral intervention research is that the validity and reliability of our measures has sometimes lagged other innovations such as the development of effective tailored interventions [10, 11] or analytical techniques for assessing moderators and mediators of behavior change, hence making it difficult to understand the mechanisms of behavior change and making it difficult to improve our interventions [12–15]. This special issue of Health Education Research provides an opportunity to consider an important advancement in our behavioral measurement methods, specifically, how we can apply item response models to improve our psychometric methods in health education and health behavior research and practice. Although item response modeling (IRM) has been used in educational testing over the last three decades [16], it is an emerging method in health education and health behavior research [17]. As, for example, in health research, IRM is being widely adopted to improve and revise quality of life questionnaires [18–20]. There are many other innovative applications of IRM and as the nine papers in this issue show us, these applications can aid our understanding of the psychometric properties of scales beyond making questionnaires shorter and beyond what most of us learned in our survey development courses based on classical test theory (CTT). In this brief afterword, we highlight current and future applications of IRM and discuss how these methods might help us improve the efficiency of our research. We believe these methods could lead to considerable improvements in intervention methods, understanding the mechanisms of behavior change and developing and refining theoretical models to make them more parsimonious as well as provide a foundation for considering the feasibility of computerized adaptive testing for measures of behavioral constructs. For those who are not familiar with IRM methods, the two papers by Wilson colleagues [21, 22] provide an excellent tutorial by presenting an example using the Rasch one-parameter model to examine a measure of self-efficacy using both dichotomous and polytomous models and then comparing IRM and CTT methods. In the first paper, readers learn the basics of IRM, including the relationship of individual items as well as the role of item difficulty response patterns. In other words, item response models estimate a result of the underlying construct Klein Buendel, Inc., 1667 Cole Boulevard, Suite 225, Golden, CO 80401, USA, Health Behavior and Health Education School of Public Health, University of Michigan, Ann Arbor, MI, USA and Department of Epidemiology and Cancer Control, St Jude Children’s Research Hospital, Memphis, TN, USA *Correspondence to: A. L. Dunn. E-mail: [email protected]