What Makes a Good Staging Algorithm: Examples from Regular Exercise

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Abstract
Purpose.: This study retrospectively compared subjects from three unrelated studies using eight algorithms to stage exercise behavior. Subjects and settings.: Study One included 936 employees involved in a smoking cessation study at four worksites—a medical center, retail store, manufacturing firm, and a government agency. Study Two included 19,212 members of a New England HMO; and Study Three included a convenience sample of 327 adult New Englanders. Measures.: The eight algorithms used different descriptions of stages based on the transtheoretical model, as well as different definitions of exercise and response formats. Results.: Algorithms using longer, more precise definitions of exercise resulted in larger numbers of subjects being staged in precontemplation and contemplation in comparison to algorithms using shorter definitions, which tended to stage subjects in preparation and action. Maintenance was the most and preparation the least consistently described stage across algorithms. Conclusions.: Alteration of the descriptions of stage and the definition of exercise has consequences for the staging of subjects. Definitions need to be explicit, stating all parameters needed to meet criterion, and subjects must be able to assess themselves. Either a 5-Choice or a true/false response format is effective in assessing stage.