Document details

Tell me what You’ve done, and I’ll predict what You’ll do : the role of motivation and past behavior in exercise adherence

Author(s): Cid, Luis ; Monteiro, Diogo ; Bento, Teresa ; Jacinto, Miguel ; Vitorino, Anabela ; Teixeira, Diogo dos Santos ; Duarte-Mendes, Pedro ; Bastos, Vasco Alambre ; Couto, Nuno

Date: 2025

Persistent ID: http://hdl.handle.net/10437/15496

Origin: ReCiL - Repositório Científico Lusófona

Subject(s): SPORT; MOTIVATION; EXERCISE; PHYSICAL ACTIVITY; SELF-DETERMINATION THEORY; DESPORTO; MOTIVAÇÃO; EXERCÍCIO FÍSICO; ATIVIDADE FÍSICA; TEORIA DA AUTODETERMINAÇÃO


Description

Introduction: The main purpose of this study was to test a hierarchical model of motivation that integrates Achievement Goal Theory and Self-Determination Theory to explain and predict exercise adherence. Method: In total, 2180 exercisers (1020 female, 1160 male) aged between 18 and 60 years, from different gyms and health clubs, completed several scales validated in exercise settings, regarding perceived motivational climate, basic psychological need satisfaction, behavioral regulation, and exercise adherence. For the last measure, weekly computer access to a control system over a 6-month period before and after data collection was consulted. Results: Through structural equation models (SEM), it was verified that (1) task-involving climate positively predicted basic psychological needs. In turn, the satisfaction of these needs predicted autonomous motivation, which led to a positive prediction of adherence; (2) a small variation in exercise adherence was explained by the motivational model under analysis. Nevertheless, models significantly improved their analytical power when past adherence was inserted in the model increasing the explained variance in future behavior from 9.2% to 64%. Conclusions: In conclusion, autonomous motivation can predict people’s exercise adherence, and past behavior increases that predictive effect. The present study brings scientific evidence to the popular saying “tell me what you’ve done and, and I’ll predict what you’ll do”.

Document Type Journal article
Language English
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