WHAT MAKES A GOOD STAGING ALGORITHM - EXAMPLES FROM REGULAR EXERCISE

Purpose. This study retrospectively compared subjects from three unrel ated studies using eight algorithms to stage exercise behavior. Subjec ts and settings. Study One included 936 employees involved in a smokin g cessation study at four worksites-a medical center; retail store, ma nufacturing firm, and a government agency. Study Two included 19,212 m embers of a New England HMO; and Study Three included a convenience sa mple 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. Res ults. Algorithms using longer; more precise definitions of exercise re sulted in larger numbers of subjects being staged in precontemplation and contemplation in comparison to algorithms using shorter definition s, which tended to stage subjects in preparation and action. Maintenan ce was the most and preparation the least consistently described stage across algorithms. Conclusions. Alteration of the descriptions of sta ge and the definition of exercise has consequences for the staging of subjects. Definitions need to be explicit, stating all parameters need ed to meet criterion, and subjects must be able to assess themselves. Either a 5-Choice or a true/false response format is effective in asse ssing stage.