{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T17:11:55Z","timestamp":1793293915732,"version":"4.5.0"},"reference-count":59,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T00:00:00Z","timestamp":1785110400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Psychol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Exercise adherence is a key challenge in promoting sustainable physical activity among college students. Although self-determination theory has been widely used to explain exercise motivation, less is known about how different forms of motivation coexist within individuals and combine to support high exercise adherence.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aimed to identify latent exercise motivation profiles among Chinese college students and examine motivational configurations associated with high exercise adherence.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A cross-sectional survey was conducted among 2,677 college students from 20 universities in Sichuan Province, China. Exercise motivation was measured using the Chinese-translated Behavioral Regulation in Exercise Questionnaire-2, and self-reported exercise adherence was assessed using the total score of the Exercise Adherence Questionnaire. Latent profile analysis was used to identify motivational profiles based on amotivation, external regulation, introjected regulation, identified regulation, and intrinsic motivation. The Bolck, Croon, and Hagenaars method was used to compare exercise adherence across profiles, with Holm-Bonferroni adjustment for pairwise comparisons. Fuzzy-set qualitative comparative analysis was used to identify motivational configurations associated with high and non-high exercise adherence.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Four latent motivation profiles were identified: intrinsic-dominant motivation profile, amotivated\u2013controlled motivation profile, high autonomous motivation profile, and identified-regulation dominant profile. Exercise adherence differed significantly across the four profiles. After Holm-Bonferroni adjustment, students in the high autonomous motivation profile and intrinsic-dominant motivation profile showed higher exercise adherence than those in the amotivated\u2013controlled motivation profile and identified-regulation dominant profile, whereas the two higher-adherence profiles did not differ significantly from each other. The fsQCA results indicated that no single motivational condition was necessary for high exercise adherence. One sufficient configuration for high exercise adherence was identified: the absence of amotivation, the absence of external regulation, and the presence of intrinsic motivation. Non-high exercise adherence was explained by the opposite configuration.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Exercise adherence among college students is associated with both motivational profiles and motivational configurations. Promoting adherence requires reducing amotivation and external control while strengthening intrinsic motivation, enjoyment, autonomy, and personally meaningful exercise experiences.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpsyg.2026.1886854","type":"journal-article","created":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T15:15:01Z","timestamp":1785165301000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["From \u201cwanting to exercise\u201d to \u201csticking with exercise\u201d: motivational profiles and configurational pathways to high exercise adherence among college students\u2014an LPA and fsQCA analysis"],"prefix":"10.3389","volume":"17","author":[{"given":"Jia","family":"Zheng","sequence":"first","affiliation":[{"name":"Sichuan Sports College","place":["Chengdu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyue","family":"Chang","sequence":"additional","affiliation":[{"name":"Sichuan Sports College","place":["Chengdu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yawei","family":"Ren","sequence":"additional","affiliation":[{"name":"Physical Education, Sichuan Normal University","place":["Chengdu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,27]]},"reference":[{"key":"B1","article-title":"Auxiliary Variables in Mixture Modeling: Using the BCH Method in Mplus to Estimate a Distal Outcome Model and an Arbitrary Secondary Model","author":"Asparouhov","year":"2014"},{"key":"B2","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1080\/10705511.2014.955104","article-title":"Robustness of stepwise latent class modeling with continuous distal outcomes.","volume":"23","author":"Bakk","year":"2016","journal-title":"Struct. 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