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Objective <p>University students’ 24-h movement behaviours, including moderate-to-vigorous physical activity (MVPA), light-intensity physical activity (LIPA), sedentary behaviour (SB), and sleep (SLP), are interdependent and may jointly shape physical fitness. However, their combined relationship with physical fitness remains unclear, as most previous studies have focused on single behaviours or variable-centred associations and have rarely identified latent subgroups based on multidimensional behavioural patterns. Therefore, this study aimed to identify latent categories of 24-h movement behaviour patterns among university students using latent profile analysis (LPA), characterise these categories, and further examine their associations with physical fitness.</p> Methods <p>A total of 5,849 university students aged 18–23 years were recruited from 12 universities in Tianjin, China, including 2,267 males and 3,582 females. Time spent in MVPA, LIPA, SB, and SLP was assessed using the Chinese versions of the International Physical Activity Questionnaire and the Pittsburgh Sleep Quality Index. Physical fitness was evaluated according to the National Student Physical Fitness Standard (2014 Revised Edition), and total physical fitness score was used as the outcome variable. Latent profile analysis (LPA) was conducted separately by sex to identify 24-h movement behaviour patterns, and the BCH approach was used to compare differences in total physical fitness scores across latent classes.</p> Results <p>A four-class model was identified as the optimal solution for both sexes. Among males, the four profiles were low-activity / long-sleep (9.88%), higher light-intensity activity / low-sedentary (29.16%), low-activity / high-sedentary (52.89%), and high-activity / low-sedentary (8.07%). Among females, the four profiles were higher light-intensity activity / low-sedentary (13.51%), low-activity / long-sleep (12.98%), low-activity / high-sedentary / short-sleep (68.48%), and high-activity / low-sedentary (5.03%). Significant overall differences in total physical fitness scores were observed across latent classes in both males (χ² = 30.435, <i>P</i> &lt; 0.001) and females (χ² = 26.215, <i>P</i> &lt; 0.001). In both sexes, profiles characterised by lower sedentary behaviour and greater daily movement tended to show more favourable physical fitness scores, whereas low-activity and high-sedentary profiles showed poorer performance.</p> Conclusion <p>University students showed clear heterogeneity in 24-h movement behaviour patterns, and these patterns were significantly associated with physical fitness. These findings support the development of sex-specific and profile-based intervention strategies to improve physical fitness among university students.</p>

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Classification of 24-h movement behaviour patterns among university students and their relationship with physical fitness: a latent profile analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月29日

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  • Yunfeng Song,
  • Ming Liu,
  • Liquan Cao,
  • Yang Liu,
  • Chi Xu

摘要

Objective

University students’ 24-h movement behaviours, including moderate-to-vigorous physical activity (MVPA), light-intensity physical activity (LIPA), sedentary behaviour (SB), and sleep (SLP), are interdependent and may jointly shape physical fitness. However, their combined relationship with physical fitness remains unclear, as most previous studies have focused on single behaviours or variable-centred associations and have rarely identified latent subgroups based on multidimensional behavioural patterns. Therefore, this study aimed to identify latent categories of 24-h movement behaviour patterns among university students using latent profile analysis (LPA), characterise these categories, and further examine their associations with physical fitness.

Methods

A total of 5,849 university students aged 18–23 years were recruited from 12 universities in Tianjin, China, including 2,267 males and 3,582 females. Time spent in MVPA, LIPA, SB, and SLP was assessed using the Chinese versions of the International Physical Activity Questionnaire and the Pittsburgh Sleep Quality Index. Physical fitness was evaluated according to the National Student Physical Fitness Standard (2014 Revised Edition), and total physical fitness score was used as the outcome variable. Latent profile analysis (LPA) was conducted separately by sex to identify 24-h movement behaviour patterns, and the BCH approach was used to compare differences in total physical fitness scores across latent classes.

Results

A four-class model was identified as the optimal solution for both sexes. Among males, the four profiles were low-activity / long-sleep (9.88%), higher light-intensity activity / low-sedentary (29.16%), low-activity / high-sedentary (52.89%), and high-activity / low-sedentary (8.07%). Among females, the four profiles were higher light-intensity activity / low-sedentary (13.51%), low-activity / long-sleep (12.98%), low-activity / high-sedentary / short-sleep (68.48%), and high-activity / low-sedentary (5.03%). Significant overall differences in total physical fitness scores were observed across latent classes in both males (χ² = 30.435, P < 0.001) and females (χ² = 26.215, P < 0.001). In both sexes, profiles characterised by lower sedentary behaviour and greater daily movement tended to show more favourable physical fitness scores, whereas low-activity and high-sedentary profiles showed poorer performance.

Conclusion

University students showed clear heterogeneity in 24-h movement behaviour patterns, and these patterns were significantly associated with physical fitness. These findings support the development of sex-specific and profile-based intervention strategies to improve physical fitness among university students.