Profiling physical activity motivation based on self-determination theory: a cluster analysis approach

TLDR

Understanding motivation in people who do not meet physical activity guidelines is essential for promoting activity uptake and maintenance. The study examined motivational profiles among 2,473 adults who do not meet activity guidelines. Cluster analysis of motivational regulation scores identified three clusters, and ANOVA compared them on demographics, activity level, motivation, and experience. The autonomous motivation cluster showed higher activity levels, stronger motivation, and better subjective experience, supporting autonomous motivation’s role and suggesting tailored interventions.

Abstract

In order to promote physical activity uptake and maintenance in individuals who do not comply with physical activity guidelines, it is important to increase our understanding of physical activity motivation among this group. The present study aimed to examine motivational profiles in a large sample of adults who do not comply with physical activity guidelines.The sample for this study consisted of 2473 individuals (31.4% male; age 44.6 ± 12.9). In order to generate motivational profiles based on motivational regulation, a cluster analysis was conducted. One-way analyses of variance were then used to compare the clusters in terms of demographics, physical activity level, motivation to be active and subjective experience while being active.Three motivational clusters were derived based on motivational regulation scores: a low motivation cluster, a controlled motivation cluster and an autonomous motivation cluster. These clusters differed significantly from each other with respect to physical activity behavior, motivation to be active and subjective experience while being active. Overall, the autonomous motivation cluster displayed more favorable characteristics compared to the other two clusters.The results of this study provide additional support for the importance of autonomous motivation in the context of physical activity behavior. The three derived clusters may be relevant in the context of physical activity interventions as individuals within the different clusters might benefit most from different intervention approaches. In addition, this study shows that cluster analysis is a useful method for differentiating between motivational profiles in large groups of individuals who do not comply with physical activity guidelines.