Abstract
Existing models of exercise behavior are insufficient in predicting outcomes, this point is shown by the relatively high levels of unexplained variance in exercise behavior in meta-analyses of social cognitive theories and models (Chatzisarantis et al., 2003; Hagger and Chatzisarantis, 2009). Researchers are beginning to recognize the importance of implicit, automatic processes in the prediction of health behaviors (Dimmock and Banting, 2009; Keatley et al., 2012, 2013b). The research by de Bruijn et al. (2014) is useful for highlighting the importance of automaticity in exercise behavior. We commend the authors on investigating an important approach to automaticity and exercise behavior. There were, however, some points with which we disagree. We think that the authors do not provide a clear account of what they mean by automaticity–an issue that is essential for the operationalization of the construct. Bargh (1994), for instance, suggested automaticity has four characteristics: awareness, intention, efficiency, and control; it is not clear whether de Bruijn and colleagues automaticity adheres to this. In particular, we contend that the explicit measure of automaticity used in their research is not an optimal way to assess implicit, impulsive processes. Furthermore, we contend that implicit measures, such as the implicit association test (IAT; Greenwald et al., 1998) would be better positioned as measures of non-conscious processes. The present commentary focuses on pre-behavior automatic associations, which we contend are better assessed by existing implicit measures, rather than during-behavior automatic “processes.”
Highlights
Edited by: Dietmar Heinke, University of Birmingham, UK Reviewed by: Chin Ming Hui, Chinese University of Hong Kong, China
We contend that implicit measures, such as the implicit association test (IAT; Greenwald et al, 1998) would be better positioned as measures of non-conscious processes
We suggest that the type of automaticity de Bruijn and colleagues referred to may be better measured by existing implicit measures, such as the IAT
Summary
Edited by: Dietmar Heinke, University of Birmingham, UK Reviewed by: Chin Ming Hui, Chinese University of Hong Kong, China. Researchers are beginning to recognize the importance of implicit, automatic processes in the prediction of health behaviors (Dimmock and Banting, 2009; Keatley et al, 2012, 2013b). We contend that the explicit measure of automaticity used in their research is not an optimal way to assess implicit, impulsive processes.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have