{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T04:55:41Z","timestamp":1790916941840,"version":"4.1.0"},"reference-count":39,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T00:00:00Z","timestamp":1790899200000},"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:p>\n                    Understanding why adolescents discontinue organized sport is central to sustaining the physical and mental health benefits of sport participation. Most research has examined psychological correlates of dropout one variable at a time, yet burnout, amotivation, and competitive anxiety may co-occur in meaningful configurations. This study applied a person-centered latent profile analysis (LPA) to identify psychological risk profiles among adolescent athletes and used the R3STEP three-step approach to examine whether dropout status was associated with baseline profile membership over a 12-month follow-up. A total of 864 competitive adolescent athletes (46.5% girls; mean age = 14.6 years, SD = 1.8) from Jiangsu Province, China, completed measures of athlete burnout (emotional\/physical exhaustion, reduced sense of accomplishment, sport devaluation), amotivation, and multidimensional competitive anxiety (somatic, worry, concentration disruption). Dropout status was recorded 12 months later. Model selection indices supported a three-profile solution: a low-risk profile (52.8%), characterized by uniformly favorable scores; a moderate-risk profile (33.4%); and a high-risk profile (13.8%), marked by pronounced elevations across all seven indicators. Twelve-month dropout rates rose monotonically across profiles (8.6%, 19.4%, and 35.3%, respectively), \u03c7\n                    <jats:sup>2<\/jats:sup>\n                    (2) = 45.87,\n                    <jats:italic>p<\/jats:italic>\n                    &amp;lt; 0.001. In R3STEP analyses controlling for age, sex, and weekly training hours, dropouts were 3.86 times more likely to belong to the high-risk rather than the low-risk profile (OR = 3.86,\n                    <jats:italic>p<\/jats:italic>\n                    &amp;lt; 0.001) and 1.94 times more likely to belong to the moderate-risk profile (OR = 1.94,\n                    <jats:italic>p<\/jats:italic>\n                    &amp;lt; 0.01). Older age and greater weekly training hours were also associated with higher-risk membership. These findings indicate that burnout, amotivation, and anxiety cluster within identifiable subgroups of adolescent athletes, and that membership in a high-risk psychological profile is strongly associated with subsequent sport dropout. These profiles may inform the future development of risk-assessment approaches for retaining adolescent athletes, pending prospective validation of their predictive performance.\n                  <\/jats:p>","DOI":"10.3389\/fpsyg.2026.1972994","type":"journal-article","created":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T04:35:51Z","timestamp":1790915751000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Psychological predictors of sport dropout in adolescents: a latent profile analysis of risk profiles and R3STEP auxiliary regression"],"prefix":"10.3389","volume":"17","author":[{"given":"Tianzhi","family":"Zhan","sequence":"first","affiliation":[{"name":"College of Physical Education and Health, Jiangsu Normal University","place":["Xuzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lu","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Physical Education and Health, Jiangsu Normal University","place":["Xuzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,10,2]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1080\/10705511.2014.915181","article-title":"Auxiliary variables in mixture modeling: Three-step approaches using Mplus.","volume":"21","author":"Asparouhov","year":"2014","journal-title":"Struct. 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