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Abstract：
To understand the latent classes and distribution of an adolescent eye care behavior, and to provide a basis for the formulation of appropriate adolescent vision health management interventions.
Information on eye behavior and eye health of primary and secondary school students in Wuhan was collected by multistage stratified cluster sampling. The latent class analysis (LCA) method was used to analyze the students\' eye care behavior, and the latent class model (LCM) was built.
A total of 6,130 students were enrolled in this study, of which 53.56% were males, aged from 6 to 17 years old, with an average age of 10.33 ± 2.60. The latent class results classified the adolescents\' eye care behaviors into bad behaviors, moderate behaviors, and healthy behaviors. The model fitting results were as follows: Akaike Information Criterion (AIC) was 36,698.216, Bayesian Information Criterion (BIC) was 36,906.565, Adjusted Bayesian Information Criterion (aBIC) was 36,808.056, and entropy was 0.838.Compared with the healthy behaviors class, the bad behaviors class was more prevalent in high schools (p = 0.003), non-demonstration schools (p = 0.001), and most of this group had astigmatism (p = 0.002). The moderate behaviors class predominately consisted of females (p = 0.001), 15-17 years old (p = 0.005, 6~8 years old as the reference), from non-demonstration schools (p < 0.001), and most had myopia (p = 0.009).
There were differences in basic demographic characteristics, visual acuity development level, and family visual environment among different classes. In the management and intervention of an adolescent vision health, we should continue to promote the visual health management of adolescents based on visual monitoring and realize the early intervention and guidance of individuals in bad behaviors class.
Information on eye behavior and eye health of primary and secondary school students in Wuhan was collected by multistage stratified cluster sampling. The latent class analysis (LCA) method was used to analyze the students\' eye care behavior, and the latent class model (LCM) was built.
A total of 6,130 students were enrolled in this study, of which 53.56% were males, aged from 6 to 17 years old, with an average age of 10.33 ± 2.60. The latent class results classified the adolescents\' eye care behaviors into bad behaviors, moderate behaviors, and healthy behaviors. The model fitting results were as follows: Akaike Information Criterion (AIC) was 36,698.216, Bayesian Information Criterion (BIC) was 36,906.565, Adjusted Bayesian Information Criterion (aBIC) was 36,808.056, and entropy was 0.838.Compared with the healthy behaviors class, the bad behaviors class was more prevalent in high schools (p = 0.003), non-demonstration schools (p = 0.001), and most of this group had astigmatism (p = 0.002). The moderate behaviors class predominately consisted of females (p = 0.001), 15-17 years old (p = 0.005, 6~8 years old as the reference), from non-demonstration schools (p < 0.001), and most had myopia (p = 0.009).
There were differences in basic demographic characteristics, visual acuity development level, and family visual environment among different classes. In the management and intervention of an adolescent vision health, we should continue to promote the visual health management of adolescents based on visual monitoring and realize the early intervention and guidance of individuals in bad behaviors class.
摘要：
为了了解青少年眼部护理行为的潜在类别和分布，为制定适宜的青少年视力健康管理干预措施提供依据。
采用多阶段分层整群抽样方法收集武汉市中小学生眼行为和眼健康状况信息。采用潜在类别分析(LCA)方法对学生的眼部护理行为进行分析，建立了潜在类模型(LCM)。
共有6130名学生参加了这项研究，其中53.56%是男性，年龄从6岁到17岁，平均年龄10.33±2.60岁。潜在的分类结果将青少年的眼部护理行为归类为不良行为，适度的行为，和健康的行为。模型拟合结果如下:Akaike信息准则(AIC)为36,698.216,贝叶斯信息准则(BIC)为36,906.565,调整贝叶斯信息准则(aBIC)为36,808.056,熵为0.838。与健康行为类相比，不良行为课堂在高中更为普遍(p=0.003)，非示范学校(p=0.001)，该组大部分有散光(p=0.002)。中等行为类别主要由女性组成(p=0.001)，15-17岁（p=0.005，6~8岁为参考），来自非示范学校(p<0.001)，大多数患有近视（p=0.009）。
基本人口学特征存在差异,视力发展水平，以及不同类别之间的家庭视觉环境。在青少年视力健康的管理和干预中，继续推进以视觉监测为基础的青少年视觉健康管理,实现对不良行为阶层个体的早期干预和引导。
采用多阶段分层整群抽样方法收集武汉市中小学生眼行为和眼健康状况信息。采用潜在类别分析(LCA)方法对学生的眼部护理行为进行分析，建立了潜在类模型(LCM)。
共有6130名学生参加了这项研究，其中53.56%是男性，年龄从6岁到17岁，平均年龄10.33±2.60岁。潜在的分类结果将青少年的眼部护理行为归类为不良行为，适度的行为，和健康的行为。模型拟合结果如下:Akaike信息准则(AIC)为36,698.216,贝叶斯信息准则(BIC)为36,906.565,调整贝叶斯信息准则(aBIC)为36,808.056,熵为0.838。与健康行为类相比，不良行为课堂在高中更为普遍(p=0.003)，非示范学校(p=0.001)，该组大部分有散光(p=0.002)。中等行为类别主要由女性组成(p=0.001)，15-17岁（p=0.005，6~8岁为参考），来自非示范学校(p<0.001)，大多数患有近视（p=0.009）。
基本人口学特征存在差异,视力发展水平，以及不同类别之间的家庭视觉环境。在青少年视力健康的管理和干预中，继续推进以视觉监测为基础的青少年视觉健康管理,实现对不良行为阶层个体的早期干预和引导。
