Testing the Physical Activity Self-Definition Model among low-active adults participating in a physical activity intervention |
| |
Affiliation: | 1. School of Kinesiology and Health Studies, Queen’s University, Kingston, Ontario, Canada;2. Faculty of Kinesiology and Recreation Management, University of Manitoba, Winnipeg, Manitoba, Canada;3. Institute of Cardiovascular Sciences, St. Boniface General Hospital Albrechtsen Research Centre, Winnipeg, Manitoba, Canada;4. Department of Medicine, Dalhousie University, Halifax, Nova Scotia, Canada;5. Department of Kinesiology and Physical Education, McGill University, Montréal, Québec, Canada;6. School of Education and Human Development, University of Virginia, Charlottesville, Virginia, USA;1. Cricket Australia, National Cricket Centre, Brisbane, Qld, Australia;2. Queensland University of Technology, Brisbane, Qld, Australia;3. University of Canberra, Canberra, ACT, Australia;4. Faculty of Science Health and Education, University of the Sunshine Coast, Sippy Downs, Qld, Australia;1. University of Konstanz, Germany;2. Humboldt University of Berlin, Germany;3. University of Trier, Germany;1. Université des Antilles, Laboratoire “Adaptation Au Climat Tropical, Exercice & Santé”, Faculté des Sciences Du Sport de Pointe-à-Pitre, France;2. Université de Poitiers, Université de Tours, Centre National de La Recherche Scientifique, Centre de Recherches sur La Cognition et L’Apprentissage (UMR 7295), Poitiers, France;1. Department of Psychology, University of Portsmouth, PO1 2DT, UK;2. School of Sport, Health and Exercise Science, University of Portsmouth, PO1 2DT, UK |
| |
Abstract: | Seeing oneself as a physically active person is one of the strongest predictors of physical activity behaviour and self-regulatory strategies. Determining whether and how physical activity self-perceptions can be stimulated may help low-active individuals who do not see themselves as a physically active person become more active. Cross-sectional research has tested the Physical Activity Self-Definition (PASD) model among active samples; longitudinal studies among low-active adults have yet to be done. The purpose of this study was to test the predictive power of the PASD model among low-active adults over a 16-week physical activity intervention. Participants completed surveys of validated questionnaires in-person at baseline (pre-intervention) and at 16-weeks (end of intervention) at one of two primary care facilities. The final sample included 119 low-active adults. Partial least squares-structural equation modeling indicated that the original model had small-medium predictive power (Q2 = 0.22; SRMR = 0.13 [0.05, 0.07]; RMSE = 1.13; MAE = 0.9; BIC = 1348.40). Two paths were added in the revised model (perceived wanting—PASD; perceived ability—perceived commitment), which explained an additional 4% and 5% of the variance in perceived commitment (R2 = 0.62 [0.48, 0.72]) and PASD (R2 = 0.74 [0.64, 0.80]; all p’s < 0.001), respectively. The revised model had medium predictive power (Q2 = 0.25; SRMR = 0.11 [0.05, 0.06]; RMSE = 1.1; MAE = 0.87; BIC = 1332.84) All path coefficients remained positive and significant at p ≤ .001. Among low-active adults, perceived wanting and perceived ability may be more salient when engaging in physical activity and regarding themselves as a physically active person. Findings may support practitioners and health care professionals in designing physical activity interventions to foster PASD among low-active adult populations. |
| |
Keywords: | Physical activity Exercise Identity Schema Self-definition Partial least squares-structural equation modeling AVE" },{" #name" :" keyword" ," $" :{" id" :" kwrd9876" }," $$" :[{" #name" :" text" ," _" :" average variance extracted BIC" },{" #name" :" keyword" ," $" :{" id" :" kwrd9876a" }," $$" :[{" #name" :" text" ," _" :" Bayesian information criterion CI" },{" #name" :" keyword" ," $" :{" id" :" kwrd0045" }," $$" :[{" #name" :" text" ," _" :" confidence interval ENCOURAGE" },{" #name" :" keyword" ," $" :{" id" :" kwrd0055" }," $$" :[{" #name" :" text" ," $$" :[{" #name" :" underline" ," _" :" EN" },{" #name" :" __text__" ," _" :" hancing Primary Care " },{" #name" :" underline" ," _" :" COU" },{" #name" :" __text__" ," _" :" nseling and " },{" #name" :" underline" ," _" :" R" },{" #name" :" __text__" ," _" :" eferrals to Community-Based Physical " },{" #name" :" underline" ," _" :" A" },{" #name" :" __text__" ," _" :" ctivity Opportunities for Sustained Lifestyle Chan" },{" #name" :" underline" ," _" :" GE HTMT" },{" #name" :" keyword" ," $" :{" id" :" kwrd9876t" }," $$" :[{" #name" :" text" ," _" :" heterotrait-monotrait MAE" },{" #name" :" keyword" ," $" :{" id" :" kwrd9876as" }," $$" :[{" #name" :" text" ," _" :" mean absolute error PACES" },{" #name" :" keyword" ," $" :{" id" :" kwrd9876cc" }," $$" :[{" #name" :" text" ," _" :" physical activity enjoyment scale PASD" },{" #name" :" keyword" ," $" :{" id" :" kwrd0065" }," $$" :[{" #name" :" text" ," _" :" physical activity self-definition PLS-SEM" },{" #name" :" keyword" ," $" :{" id" :" kwrd98708" }," $$" :[{" #name" :" text" ," _" :" partial least squares-structural equation modeling RMSE" },{" #name" :" keyword" ," $" :{" id" :" kwrd0075" }," $$" :[{" #name" :" text" ," _" :" root mean squared error SRMR" },{" #name" :" keyword" ," $" :{" id" :" kwrd0065s" }," $$" :[{" #name" :" text" ," _" :" standardized root mean square residual VIF" },{" #name" :" keyword" ," $" :{" id" :" kwrd0065a" }," $$" :[{" #name" :" text" ," _" :" variance inflation factor |
本文献已被 ScienceDirect 等数据库收录! |
|