A lean method for selecting determinants when developing behavior change interventions

Abstract

ABSTRACT When developing behavior change interventions in a systematic way, it is important to select determinants relevant to the target behavior. Data is needed to gain insight into the determinant structures (the relative strengths of associations between determinants and behavior) and their univariate distributions. This insight is crucial to select the most relevant determinants, but at the same time institutions tasked with behavior change (e.g. prevention organizations, municipal health services) often operate under prohibitive resource constraints, which also extend to how easily they can collect data from a sample. This paper introduces CIBERlite – an approach that furnishes the intervention developer with an idea of the relevance of a limited number of determinants using short measurements informed by theory. The first study (N = 401) in a series of three explores the convergent validity of short and full measurements of determinants derived from the Reasoned Action Approach. The short measurements are used in the main study (N = 415) that serves as a proof-of-concept for the CIBERlite plot, an efficient visualization combining data of determinant structures and their univariate distributions for eight behaviors. The unexpected patterns detected in the main study led to an expert estimation study (N = 45), which shows that individual experts have difficulty in predicting how people score on determinants. This stresses the importance of conducting determinant studies and CIBERlite is a valuable alternative to do so if resources are limited.