Journal of affective disorders · 2022
Demographic characteristics, family environment and psychosocial factors affecting internet addiction in Chinese adolescents.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Zhang Wanling, Pu Jianlin, He Ruini, Yu Minglan, Xu Liling, He Xiumei, Chen Ziwen, Gan Zhiqin, Liu Kezhi, Tan Youguo, Xiang Bo
Аннотация
BACKGROUND: Internet addiction of adolescents has aroused social concern recently. The present study aims to identify predicting factors of internet addiction on adolescents.
METHODS: The demographic characteristics and psychological characteristics of 50, 855 middle school students were investigated through Internet Gaming Disorder Scale- Short Form(IGDS9-SF), Smartphone Application-Based Addiction Scale (SABAS), Bergen Social Media Addiction Scale (BSMAS), Strengths and Difficulties Questionnaire-students (SDQS), 16-Item Version of the Prodromal Questionnaire (PQ-16), Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder 7-item (GAD-7), Multidimensional Peer Victimization Scale (MPVS), Warwick-Edinburgh Mental Well-being Scale (WEMWBS), and Connor-Davidson Resilience Scale (CD-RISC10) were used to analyze factors associated with internet addiction by Pearson correlation coefficient and multiple hierarchical regression.
RESULTS: IGDS9-SF, SABAS and BSMAS are positively correlated with SDQS, PQ-16, PHQ-9, GAD-7 and MPVS (r-values ranging from 0.180 to 0.488, p < 0.01). IGDS9-SF, SABAS and BSMAS are negatively correlated with WEMWB and CD-RISC (r-values ranging from -0.242 ~ -0.338, p < 0.01). Multiple hierarchical regression shown gender, one-child, twins, left-behind, rural, education (father), drink (father), smoke (father), CD-RISC-10, SDQS, PQ-16, PHQ-9, GAD-7 and MPVS predicted 32.7 % of the variance in internet gaming disorder (IGD) (F = 1174.949, p < 0.001). Group (junior and senior), Gender, Age, One-Child, Twins, Village, Education (father), Drink (father), Drink (mother), Smoke (father), WEMWBS, CD-RISC-10, SDQS, PQ-16, PHQ-9, GAD-7 and MPVS predicted 28.9 % of the total variance in social media addiction (SMA) (F = 982.932, p < 0.001). Fifteen variables [Gender, Age, Twins, Left-behind, Residence, Residence, Education (mother), Drink(father), Drink (mother), Smoke (father), WEMWBS, CD-RISC-10, PHQ-9, GAD-7 and MPVS] predicted 30.7 % of the variance in smartphone addiction (SA) (F = 1076.02, p < 0.001).
CONCLUSION: The present study found that demographic characteristics, family environment and psychosocial factors were associated with internet gaming addiction, social media addiction and smartphone addiction. Negative psychological factors (such as anxiety and depression) play an important role in different behavioral addictions.
METHODS: The demographic characteristics and psychological characteristics of 50, 855 middle school students were investigated through Internet Gaming Disorder Scale- Short Form(IGDS9-SF), Smartphone Application-Based Addiction Scale (SABAS), Bergen Social Media Addiction Scale (BSMAS), Strengths and Difficulties Questionnaire-students (SDQS), 16-Item Version of the Prodromal Questionnaire (PQ-16), Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder 7-item (GAD-7), Multidimensional Peer Victimization Scale (MPVS), Warwick-Edinburgh Mental Well-being Scale (WEMWBS), and Connor-Davidson Resilience Scale (CD-RISC10) were used to analyze factors associated with internet addiction by Pearson correlation coefficient and multiple hierarchical regression.
RESULTS: IGDS9-SF, SABAS and BSMAS are positively correlated with SDQS, PQ-16, PHQ-9, GAD-7 and MPVS (r-values ranging from 0.180 to 0.488, p < 0.01). IGDS9-SF, SABAS and BSMAS are negatively correlated with WEMWB and CD-RISC (r-values ranging from -0.242 ~ -0.338, p < 0.01). Multiple hierarchical regression shown gender, one-child, twins, left-behind, rural, education (father), drink (father), smoke (father), CD-RISC-10, SDQS, PQ-16, PHQ-9, GAD-7 and MPVS predicted 32.7 % of the variance in internet gaming disorder (IGD) (F = 1174.949, p < 0.001). Group (junior and senior), Gender, Age, One-Child, Twins, Village, Education (father), Drink (father), Drink (mother), Smoke (father), WEMWBS, CD-RISC-10, SDQS, PQ-16, PHQ-9, GAD-7 and MPVS predicted 28.9 % of the total variance in social media addiction (SMA) (F = 982.932, p < 0.001). Fifteen variables [Gender, Age, Twins, Left-behind, Residence, Residence, Education (mother), Drink(father), Drink (mother), Smoke (father), WEMWBS, CD-RISC-10, PHQ-9, GAD-7 and MPVS] predicted 30.7 % of the variance in smartphone addiction (SA) (F = 1076.02, p < 0.001).
CONCLUSION: The present study found that demographic characteristics, family environment and psychosocial factors were associated with internet gaming addiction, social media addiction and smartphone addiction. Negative psychological factors (such as anxiety and depression) play an important role in different behavioral addictions.