Trajectories of depression and anxiety in Chinese high school freshmen: Associations with Internet addiction

Gao, Tingting; Qin, Zeying; Hu, Yueyang; Fei, Junsong; Cao, Renyi; Liang, Leilei; Li, Chuanen; Mei, Songli; Meng, Xiangyi

doi:10.1016/j.jad.2021.02.074

Cited by 16 publications

(12 citation statements)

References 37 publications

(43 reference statements)

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“…Previous studies have yielded discrepant results regarding the association between depression and internet addiction. One study showed that depression and anxiety are risk factors for internet addiction [4], while another showed that depression was a protective factor against smartphone addiction [27]. The present study suggests that anxiety plays a more important role than depression in the psychopathology underlying the development of internet addiction in adolescents.…”

Section: Discussionmentioning

confidence: 48%

“…The present study also demonstrates that students with higher levels of anxiety are more prone to problematic internet use. Gao et al [4] suggested that adolescents with depression and anxiety have an increased risk of developing internet addiction. Several other studies showed that anxiety symptoms were significantly associated with compulsive internet use, and anxiety was also shown to be a risk factor for internet addiction [23,24,27].…”

Section: Discussionmentioning

confidence: 99%

“…characterized by excessive or uncontrollable desires and behaviors for internet use and has a disruptive influence on various aspects of daily life. Recent studies demonstrated that internet addiction is associated with sleep problems, a disturbed circadian rhythm, depression, and anxiety in adolescents [2][3][4]. Moreover, sleep disturbances and internet addiction may affect suicidal ideation among depressive adolescents [5].…”

Section: Cimmentioning

confidence: 99%

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Poor Sleep Quality, Eveningness, and Anxiety Are Associated With Internet/Smartphone Addiction in Korean Adolescents

et al. 2022

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Objective: Internet addiction and smartphone addiction are emerging mental health concerns in adolescents. To determine the factors related to these conditions, we investigated their associations with sleep quality, chronotype, sleep patterns, daylight exposure, depression, and anxiety.Methods: Self-rated questionnaires, including the Internet Addiction Proneness Scale-Short Form, Smartphone Addiction Scale–short version, Insomnia Severity Index, Morningness–Eveningness Questionnaire, Epworth Sleepiness Scale, sleep-related questionnaires, and Hospital Anxiety and Depression Scale, were administered in a study population of 1,610 Korean high school students. Multiple linear regression analyses were performed to evaluate the associations with internet addiction and smartphone addiction.Results: Multiple linear regression showed significant associations of internet addiction with poor sleep quality (β=0.094, p<0.001), eveningness (β=-0.071, p=0.006), less daylight exposure (β=-0.066, p=0.005), anxiety (β=0.252, p<0.001), and older age (β=0.295, p<0.001). Smartphone addiction was significantly associated with poor sleep quality (β=0.076, p=0.005), eveningness (β=-0.164, p<0.001), excessive daytime sleepiness (β=0.109, p<0.001), longer time in bed on weekdays (β=0.057, p=0.024), shorter weekend oversleep (β=-0.081, p=0.005), anxiety (β=0.192, p<0.001), and younger age (β=-0.145, p<0.001).Conclusion: Our findings suggest that poor sleep quality, eveningness, and anxiety can serve as predictors of both internet addiction and smartphone addiction in adolescents. Internet addiction seems to be negatively associated with daylight exposure, whereas smartphone addiction showed significant associations with sleep patterns. Additional studies regarding the causal relations between these factors are needed.

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Section: Discussionmentioning

confidence: 48%

Section: Discussionmentioning

confidence: 99%

Section: Cimmentioning

confidence: 99%

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Poor Sleep Quality, Eveningness, and Anxiety Are Associated With Internet/Smartphone Addiction in Korean Adolescents

et al. 2022

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“…To identify distinct trajectories of VT in Chinese college students, Mplus software (version 8.4) was adopted for GMM analysis. The fit indices for the accuracy and optimal model were Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), adjusted BIC (aBIC), Entropy value, Lo-Mendell-Rubin Likelihood ratio test (LMR), and Bootstrapped Likelihood ratio test (BLRT) (27,28). The smaller values of AIC, BIC, and aBIC represent a better fitting effect for the model.…”

Section: Discussionmentioning

confidence: 99%

Trajectories and predictors of vicarious traumatization in Chinese college students during the COVID-19 pandemic: A longitudinal study

Hong

Yu²,

Li³

et al. 2022

Front. Psychiatry

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ObjectivesThis longitudinal study aimed to identify the trajectories and the predictors among sociodemographic and psychosocial variables at baseline of vicarious traumatization (VT) in Chinese college students during the COVID-19 pandemic.Materials and methodsA total of 544 Chinese college students enrolled in a public University in central China, majored in Clinical Medicine, Nursing, Musicology, Physics, etc., participated in this longitudinal study lasting 19 months. Three-wave (wave 1: February 2020; wave 2: November 2020; wave 3: September 2021) of data were collected. Resourcefulness Scale and the 10-item Kessler scale (K10) were only assessed in the first-wave survey, and the Event Scale-Revised (IES-R) was repeatedly measured in all three-wave surveys. A link to an online survey created by Questionnaire Star (https://www.wjx.cn/) was sent to the students to collect data. The Growth mixture modeling (GMM) and multiple logistic regression were used to identify the trajectories of VT and predictors for the distinct trajectories.ResultsThe incidence of VT at each wave varied from 9.9% at wave 1, 4.0% at wave 2, to 2.6% at wave 3. Three trajectories of VT were the medium-level escalating group (3.0%), medium-level maintaining group (32.3%), and the low-level descending group (64.7%). Seniors (OR = 1.575, 95% CI: 1.059–2.341; OR = 1.161, 95% CI: 1.043–1.293) and those with poor mental health status (OR = 1.101, 95% CI: 1.030–1.177; OR = 1.083, 95% CI: 1.060–1.106) at baseline were more likely to be classified into the medium-level escalating group and medium-level maintaining group, respectively. Additionally, females (OR = 3.601, 95% CI: 1.311–9.887) were more likely to be included in the medium-level escalating group.ConclusionTargeted psychological interventions are urgently needed for students vulnerable to VT. Further studies with more representative samples, longer period of follow-up, and predictors based on scientific theoretical framework, are needed to update the findings.

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“…The sample consisted of 453 participants after excluded cases with too many missing values in variables used in the present study. The detailed process of sample selection and this sample has been described in the previous studies for different research (Gao et al, 2020(Gao et al, , 2021, while the main variables in the present study were not analyzed. Variables including baseline demographics of the sample, perceived parental marital conflict and parent-child cohesion at each time point were used in the present study.…”

Section: Participants and Proceduresmentioning

confidence: 99%

Latent transitions across perceived parental marital conflict and family cohesion profiles in high school students

Gao

Liang

et al. 2022

Front. Psychol.

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This study aimed to explore the latent profiles across perceived parental marital conflict and family cohesion, as well as the transition patterns within-person and within-sample profiles over time. We conducted a 1-year follow-up study with a sample of first-year high school students from China. A total of 453 participants were included in the present analysis. We identified the following three latent profiles: high parental conflict and poor family cohesion profile, moderate parental conflict and family cohesion profile, and low parental conflict and good family cohesion profile. Female students and those who not lived with parents together were more likely to perceive more parental marital conflict and less cohesion in the family. The majority of students with high transition probability remained in the same profiles over time. The counts of latent transition pattern also demonstrated that students remaining in the primary profile over time accounted for the large proportion. The present study advances empirical bases for confirming the family system theory’s notion that the family is not static, but dynamic. Findings provide the optimal timing of interventions toward healthy transition.

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Trajectories of depression and anxiety in Chinese high school freshmen: Associations with Internet addiction

Cited by 16 publications

References 37 publications

Poor Sleep Quality, Eveningness, and Anxiety Are Associated With Internet/Smartphone Addiction in Korean Adolescents

Poor Sleep Quality, Eveningness, and Anxiety Are Associated With Internet/Smartphone Addiction in Korean Adolescents

Trajectories and predictors of vicarious traumatization in Chinese college students during the COVID-19 pandemic: A longitudinal study

Latent transitions across perceived parental marital conflict and family cohesion profiles in high school students

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