中国体育科技

目的：运用潜在剖面分析识别中小学生的24 h活动行为模式类别，探讨其类别特征，分析其与体质健康的关联。方法：选取武汉市、长沙市共1 090名6～18岁中小学生，采用加速度传感器测量24 h活动行为时间，依据《国家学生体质健康标准(2014年修订)》对体质健康进行测试，基于成分数据，通过潜在剖面分析确定中小学生24 h活动行为模式类别，采用多元线性回归分析24 h活动行为模式类别与体质健康的关系。结果：1)小学生24 h活动行为模式类别分为高活跃低久坐、低活跃高久坐短睡眠及长睡眠3个类别；中学生24 h活动行为模式类别分为高活跃低久坐长睡眠、低活跃及高久坐短睡眠3个类别；2)小学生不同活动行为模式类别在年龄(χ²=37.888,P<0.001)、性别(χ²=6.227,P=0.044)、父母受教育程度(χ²=21.744,P<0.001)上存在显著差异；中学生不同活动行为模式类别在年龄(χ²=23.556, P<0.001)和性别(χ²=53.291,P<0.001)上存在显著差异；3)小学生中，相较于低活跃高久坐短睡眠组，高活跃低久坐组[β=9.193,95%CI:(5.948,12.438),P<0.001]、长睡眠组[β=5.191,95%CI:(1.718,8.664),P=0.003]与体质健康总分显著正相关；以高活跃低久坐组为参照，长睡眠组与体质健康总分显著负相关[β=-4.002,95%CI:(-6.461,-1.543),P<0.001];4)中学生中，相较于高久坐短睡眠组，高活跃低久坐长睡眠组与体质健康总分显著正相关[β=3.540,95%CI:(0.738,6.343),P=0.013]，而低活跃组与体质健康总分无显著相关[β=0.895,95%CI:(-1.490,3.280),P=0.461]；以高活跃低久坐长睡眠组为参照，低活跃组与体质健康总分显著负相关[β=-2.645,95%CI:(-4.879,-0.411),P=0.020]。结论：小学生高活跃低久坐组的体质健康水平最高，其次为长睡眠组，低活跃高久坐短睡眠组的体质健康水平最低；中学生高活跃低久坐长睡眠组的体质健康水平显著高于低活跃组和高久坐短睡眠组，后两组的体质健康水平无显著差异。建议根据不同群体的行为类型差异制定具有针对性的干预策略。

Objective: To identify 24 h movement behavior typologies among primary and middle school students by using latent profile analysis, explore the characteristics of these categories, and analyze their associations with physical fitness. Methods: A total of 1,090 primary and middle school students aged 6 to 18 years old from Wuhan and Changsha were included. The 24 h movement behavior duration was measured by using an accelerometer, physical fitness was tested according to the National Standards for Students' Physical Health(2014 Revision). Based on compositional data, latent profile analysis was used to identify 24 h movement behavior typologies, and multiple linear regression was performed to analyze the relationship between these typologies and physical fitness. Results: 1) Three typologies were identified among primary school students: “highly active and low sedentary”, “low active, high sedentary and short sleep” and “long sleep”. Three typologies were also identified among middle school students: “highly active, low sedentary and long sleep”, “low active”, and “high sedentary and short sleep”. 2) For primary school students, significant differences existed across categories in age(χ²=37.888, P<0.001), gender(χ²=6.227, P=0.044), and parental education level(χ²=21.744, P<0.001). For middle school students, significant differences existed across categories in age(χ²=23.556, P<0.001) and gender(χ²=53.291, P<0.001). 3) For primary school students, compared with the “low active, high sedentary and short sleep” group, the “highly active and low sedentary” [β=9.193, 95% CI:(5.948,12.438), P<0.001] and the “long sleep” group [β=5.191, 95% CI:(1.718, 8.664), P=0.003] were significantly positively associated with the physical fitness score. Taking the “highly active and low sedentary” group as a reference, the “long sleep” group was significantly negatively associated with the physical fitness score [β=-4.002, 95% CI:(-6.461,-1.543), P< 0.001]. 4) For middle school students, compared with the “high sedentary and short sleep” group, the “highly active, low sedentary and long sleep” group was significantly positively associated with the physical fitness score [β =3.540, 95% CI:(0.738, 6.343), P=0.013], while the “low active” group had no significant association with the physical fitness score [β=0.895, 95% CI:(-1.490, 3.280), P=0.461]. Taking the “highly active, low sedentary and long sleep” group as a reference, the “low active” group was significantly negatively associated with the physical fitness score [β =-2.645, 95% CI:(-4.879,-0.411), P=0.020]. Conclusions: Among primary school students, the “highly active and low sedentary” group exhibited the highest physical fitness level, followed by the “long sleep” group, and the “low active, high sedentary and short sleep” group had the lowest physical health level. Among middle school students, the physical fitness level of the “highly active, low sedentary and long sleep” group was significantly higher than both the “low active” and “high sedentary and short sleep” groups. There was no significant difference in physical fitness between the latter two groups. It is recommended to formulate targeted intervention strategies based on the behavioral type differences among different groups.