Objective: Regression analyses are commonly used for selecting determinants to target in behavior change interventions, but the aim of this article is to explain why regression analyses are not suitable for this purpose (i.e. the regression trap). Methods: This aim is achieved by providing (1) a theoretical rationale based on overlap among determinants; (2) a mathematical rationale based on the formulas that are used to calculate regression coefficients; and (3) examples based on real-world data. Results: First, the meaning of regression coefficients is commonly explained as expressing the association between a determinant and a target behavior ‘holding all other predictors constant.’ We explain that this often boils down to ‘neglecting a part of the psyche.’ Second, we demonstrate that the interpretation of regression coefficients is distorted by correlations between determinants. Third, the examples provided demonstrate the impact this has in practice. This results in interventions targeting determinants that are less relevant and, thereby, have less impact on behavior change. Conclusion: There are theoretical, mathematical, and practical reasons why regression analyses, and by extension multivariate analyses relying on correlations, are not suitable to select determinants to target in behavior change interventions. Instead, intervention developers should consider univariate distributions and bivariate association estimates simultaneously and there are freely accessible tools available to do so.