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Profiling physical activity motivation based on self-determination theory: a cluster analysis approach.
Stijn Ah Friederichs1, Catherine Bolman1, Anke Oenema2
1Faculty of Psychology and Educational Sciences, Open University of the Netherlands, P.O. Box 2960, 6401 DL Heerlen, The Netherlands.
BMC Psychology
|February 14, 2015
Summary
Understanding physical activity motivation is key for those not meeting guidelines. This study identified three motivational profiles, with autonomous motivation showing the most favorable outcomes for physical activity engagement.
Area of Science:
- Behavioral Science
- Exercise Psychology
- Public Health
Background:
- Physical activity is crucial for health, yet many adults do not meet recommended guidelines.
- Understanding the motivational factors influencing physical activity is vital for promoting adherence.
- This study focuses on individuals with low physical activity levels to explore their motivational profiles.
Purpose of the Study:
- To examine motivational profiles in a large sample of adults who do not comply with physical activity guidelines.
- To identify distinct groups based on motivational regulation strategies.
- To compare these groups regarding demographics, physical activity, and subjective experiences.
Main Methods:
- Cluster analysis was used to identify motivational profiles based on motivational regulation.
- A large sample of 2473 adults not meeting physical activity guidelines participated.
- One-way ANOVAs compared clusters on key variables like physical activity level and motivation.
Main Results:
- Three distinct motivational clusters emerged: low motivation, controlled motivation, and autonomous motivation.
- Significant differences were found between clusters in physical activity behavior, motivation, and subjective experiences.
- The autonomous motivation cluster exhibited more favorable characteristics related to physical activity.
Conclusions:
- Autonomous motivation is strongly linked to positive physical activity behavior.
- The identified motivational clusters can inform tailored physical activity interventions.
- Cluster analysis is an effective method for profiling motivation in inactive populations.
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