Filipe Rodrigues, Diogo Teixeira

Testing Assumptions of the Physical Activity Adoption and Maintenance Model: A Longitudinal Perspective of the Relationships Between Intentions and Habits on Exercise Adherence

  • Sensory Systems
  • Experimental and Cognitive Psychology

In this study, we aimed to examine empirically the Physical Activity Adoption and Maintenance model (PAAM). We collected data on these variables at baseline (T0) and 6-months later (T1). We recruited 119 participants (42 male, 77 female) aged 18–81 years old ( Mage = 44.89, SD = 12.95). who reported, at baseline, that they exercised an average of 3.76 days per week ( SD = 1.33) in training periods lasting 15–60 minutes ( M = 38.69; SD = 23.28). We conducted hierarchical multiple regression analysis to test the association between each determinant (intentions, habits, and frequency) and future exercise adherence. We tested four models by entering blocks of predictors according to PAAM assumptions. The variance change ( R2) between the first and fourth models (Δ R2 = .391) was statistically significant, showing that the fourth model accounted for 51.2% of variance for future exercise adherence, F (6, 112) = 21.631, p < .001, R2 = .73, adjusted R2 = .512. Exercise intention at T1 maintained its significant association ( p = .021) with exercise frequency at T1 in all tested models. Exercise frequency at T0 was the most significant predictor ( p < .01) of future exercise adherence, with past experience the second most significant predictor ( p = .013). Interestingly, exercise habits at T1 and T0 did not predict exercise frequency at T1 in the fourth model. Among the variables we studied, having constantly high exercise intentions and high regular exercise frequency are significantly associated with maintaining or increasing regular future exercise behavior.

Need a simple solution for managing your BibTeX entries? Explore CiteDrive!

  • Web-based, modern reference management
  • Collaborate and share with fellow researchers
  • Integration with Overleaf
  • Comprehensive BibTeX/BibLaTeX support
  • Save articles and websites directly from your browser
  • Search for new articles from a database of tens of millions of references
Try out CiteDrive

More from our Archive