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Introduction: Addictive use of the Internet among adolescents has been linked to a negative psychosocial development, but more detailed information about Internet addiction (IA) profiles is warranted. The aim of this study was to identify IA profiles in adolescents based on psychometric properties from the Internet Addiction test (IAT), and to assess the associations between the profiles and personal/social behaviors.Material and Methods: A cross-sectional study was performed at public schools from a Portuguese region, using a survey that included the IAT. We performed a latent profiling analysis to identify the profiles of adolescent based on the six IAT dimensions.Results: From the 1915 responses, students’ mean age was 15 ± 1.82 years, 53% were female. IA was found in 16.5%. Four models were estimated with latent profiling analysis. Analysis of the models by fit statistics, integrated completed likelihood and Lo-Mendell-Rubin likelihood ratio test, indicated a better solution with four profiles: Profile 1 – Worrisome lack of control users, Profile 2 – Balanced users, Profile 3 – Worrisome anticipation users, Profile 4 – Problematic users.Conclusion: This study provides a characterization of different patterns in adolescents’ traits and behaviors associated with Internet addiction. Preventive approaches may be useful to reduce IA.
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Introduction: Addictive use of the Internet among adolescents has been linked to a negative psychosocial development, but more detailed information about Internet addiction (IA) profiles is warranted. The aim of this study was to identify IA profiles in adolescents based on psychometric properties from the Internet Addiction test (IAT), and to assess the associations between the profiles and personal/social behaviors.Material and Methods: A cross-sectional study was performed at public schools from a Portuguese region, using a survey that included the IAT. We performed a latent profiling analysis to identify the profiles of adolescent based on the six IAT dimensions.Results: From the 1915 responses, students’ mean age was 15 ± 1.82 years, 53% were female. IA was found in 16.5%. Four models were estimated with latent profiling analysis. Analysis of the models by fit statistics, integrated completed likelihood and Lo-Mendell-Rubin likelihood ratio test, indicated a better solution with four profiles: Profile 1 – Worrisome lack of control users, Profile 2 – Balanced users, Profile 3 – Worrisome anticipation users, Profile 4 – Problematic users.Conclusion: This study provides a characterization of different patterns in adolescents’ traits and behaviors associated with Internet addiction. Preventive approaches may be useful to reduce IA.
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