Zhao Ying, Wu Qinghua, Zheng Wei
School of Physical Education and Health, Sanming University, Sanming, Fujian, China.
Graduate School, Guangzhou Sport University, Guangzhou, Guangdong, China.
PLoS One. 2025 Jul 30;20(7):e0328383. doi: 10.1371/journal.pone.0328383. eCollection 2025.
Despite known links between motivation and physical activity, latent profiles of motivation among Chinese adolescents remain unexamined. Using the person-centered approach, this paper explores the relationship between adolescent motivation and physical activity. We aim to identify the latent motivation profiles and examine how these profiles differential predict physical activity (PA) levels, with attention to gender and age variations. This study recruited 571 adolescents (M age = 11.995, SD = 1.519) in southern China by the scale of Sport Motivation Scale and International Physical Activity Questionnaire-Short Form. We conducted latent profile analysis (LPA) to classify motivation subgroups using Mplus. MANOVA and ANOVA were employed to compare PA differences across profiles, genders, and education levels. The results indicate that three profile model is the optimal model: Low Motivation-High Amotivation (8.45%), Moderate Motivation-High Amotivation (60.61%), and High Autonomous Motivation (30.94%). The subgroup with higher scores of intrinsic motivation and external motivation reported more PA. Moreover, male's PA is significantly more active than female adolescents, while older adolescents have less PA than younger adolescents. This study identified adolescents with different motivation profiles and PA. Findings suggest the need for more personalized strategies to promote adolescent participation in PA and provide a novel insight into intervention for adolescents with low motivation. Further research could be measured by objective methods and long-term design.
尽管动机与体育活动之间的联系已为人所知,但中国青少年的潜在动机特征仍未得到研究。本文采用以个体为中心的方法,探讨青少年动机与体育活动之间的关系。我们旨在识别潜在的动机特征,并研究这些特征如何差异预测体育活动(PA)水平,同时关注性别和年龄差异。本研究通过运动动机量表和国际体育活动问卷简表,在中国南方招募了571名青少年(年龄均值M = 11.995,标准差SD = 1.519)。我们使用Mplus进行潜在剖面分析(LPA)以对动机亚组进行分类。采用多变量方差分析(MANOVA)和方差分析(ANOVA)来比较不同特征、性别和教育水平之间的PA差异。结果表明,三剖面模型是最优模型:低动机 - 高无动机(8.45%)、中等动机 - 高无动机(60.61%)和高自主动机(30.94%)。内在动机和外在动机得分较高的亚组报告的PA更多。此外,男性的PA显著比女性青少年更活跃,而年龄较大的青少年的PA比年龄较小的青少年少。本研究识别出了具有不同动机特征和PA水平的青少年。研究结果表明需要更多个性化策略来促进青少年参与PA,并为低动机青少年的干预提供了新的见解。进一步的研究可以采用客观方法和长期设计来进行测量。