Appearance- and performance-enhancing drugs (APEDs) constitute a wide range of substances, including anabolic-androgenic steroids, nonsteroidal anabolics, and licit and illicit ergo/thermogenics. A great deal of heterogeneity exists in APED use patterns among weight-lifting men, and, consequently, little is known about how these patterns are related to side effect profiles or risk potential. In the current study, a sample of 400 adult men who were regular APED users completed an interactive Web-based instrument detailing information about APED use, side effects, and related indicators of risk. To explore the heterogeneity of APED use patterns, the authors subjected data on use patterns to (a) latent class analysis (LCA), (b) latent trait analysis (LTA), and (c) factor mixture analysis to determine the best model of APED use. Results indicated that a 4-class factor mixture model provided a better fit than LCA and LTA models. The authors also found that severity and latent class were uniquely associated with negative outcomes. Each of the 4 classes was associated with unique side effects, motivations, and participant use patterns. Implications for identifying pathological forms of APED use are discussed.

Long-term use of anabolic-androgenic steroids (AASs) is associated with both positive and negative effects. The authors examined possible mechanisms by which these effects contribute to AAS satisfaction and predict intentions for future AAS use. Five hundred male AAS users completed an interactive Web-based instrument assessing the psychological and physical effects of AAS use. Covariance structure modeling was used to evaluate both direct and indirect effects of AAS consequences on satisfaction with AASs and intentions for future AAS use. Results suggest that gain in muscle mass and psychological benefits from AAS use uniquely contributed to both AAS satisfaction and intentions for future use. Side effects from AAS use also uniquely contributed to AAS satisfaction, but ancillary drug use was found to partially mediate this relationship, suggesting that the satisfaction of experienced AAS users is enhanced by their mastery of side effects through the use of ancillary drugs. The final model explained 29% of the variance in intentions for future AAS use. Mechanisms for sustained AAS use and implications for intervention and prevention strategies are discussed.

ur overall objective is to persuade the reader that social psychological theory can play a critical role in improving the understanding of the behavioral issues 0 surrounding detection, control, treatment, and recovery from cancers. In addition, we hope to make the following, more specific points. First, we believe that the relationship between behavioral research on cancer and "basic" psychological theory is a two-way street, in which each side informs and influences the other. Thus, we do not believe that behavioral research on cancer is merely an applied endeavor in which investigators use "basic" psychological theory to understand behaviors related to cancer. Rather, we believe that behavioral research in the cancer domain makes important contributions to basic, psychological theory.Second, we argue that a multilevel self-regulation model is needed for the science of modeling and the practice of influencing behaviors in the cancer domain. A fully specified, self-regulation model of this type will capture people's perceptions of cancer ( e g , its perceived symptoms, causes, consequences, time frames, control) and their perceptions of the procedures for controlling the threat of cancer, and it will conceptualize the relationships of these perceptions to the individual's personal and sociocultural context. We propose further that sociocultural variables and personal characteristics are contextual factors or moderators whose primary effects on behavior are through their influence on the perception of disease threats and the procedures used for threat management (Leventhal, Diefenbach, & Leventhal, 1992; Leventhal, Leventhal, & Cameron, in press), although we do not rule out the We thank Andrew Baum for his encouragement and helpful comments. The work was supported by Grants AG12072 and AGO3501 from the National Institute on Aging.

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