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<p>With the continuous penetration of mobile Internet to rural residents, mobile phone dependence (MPD) is highly prevalent among rural adolescents. Previous research mainly adopted variable-centered analyses, which overlooked different latent patterns. Moreover, few studies focused on the relationship between MPD and left-behind status and family cohesion among adolescents within the Chinese context. This study aims to explore the MPD among rural adolescents in China and examine the potential predictors of their severity categorization. We recruited 913 rural middle school students in China and used questionnaires to collect data. Latent Profile Analysis (LPA) was employed to identify subgroups within the sample. Subsequently, we used multinomial logistic regression to explore the factors predicting those subgroups. The LPA results revealed three subgroups: the “Mild“(the least severe class), “Moderate“(moderate severity class) and “Severe“(the highest severity class). The regression results indicated that poor family cohesion and left-behind status were significant predictors of classification into more severe classes. The findings suggest that educators should assess rural adolescents more accurately and implement tailored intervention measures for the different severity levels of MPD among Chinese rural adolescents.</p>

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Mobile phone dependence associated with left-behind status and family cohesion: latent profile analysis and outcome prediction among Chinese rural adolescents

  • 期刊论文

  • 发表日期：

    2025年3月1日

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  • Lilan Chen,
  • Wenyuan Jiang

摘要

With the continuous penetration of mobile Internet to rural residents, mobile phone dependence (MPD) is highly prevalent among rural adolescents. Previous research mainly adopted variable-centered analyses, which overlooked different latent patterns. Moreover, few studies focused on the relationship between MPD and left-behind status and family cohesion among adolescents within the Chinese context. This study aims to explore the MPD among rural adolescents in China and examine the potential predictors of their severity categorization. We recruited 913 rural middle school students in China and used questionnaires to collect data. Latent Profile Analysis (LPA) was employed to identify subgroups within the sample. Subsequently, we used multinomial logistic regression to explore the factors predicting those subgroups. The LPA results revealed three subgroups: the “Mild“(the least severe class), “Moderate“(moderate severity class) and “Severe“(the highest severity class). The regression results indicated that poor family cohesion and left-behind status were significant predictors of classification into more severe classes. The findings suggest that educators should assess rural adolescents more accurately and implement tailored intervention measures for the different severity levels of MPD among Chinese rural adolescents.