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Background and aims
The aim of the study is to investigate (a) whether probable depression status assessed at baseline prospectively predicted new incidence of Internet addiction (IA) at the 12-month follow-up and (b) whether IA status assessed at baseline prospectively predicted new incidence of probable depression at follow-up.
Methods
We conducted a 12-month cohort study (n = 8,286) among Hong Kong secondary students, and derived two subsamples. The first subsample (n = 6,954) included students who were non-IA at baseline, using the Chen Internet Addiction Scale (≤63), and another included non-depressed cases at baseline (n = 3,589), using the Center for Epidemiological Studies Depression Scale (<16).
Results
In the first subsample, 11.5% of the non-IA cases developed IA during follow-up, and probable depression status at baseline significantly predicted new incidence of IA [severe depression: adjusted odds ratio (ORa) = 2.50, 95% CI = 2.07, 3.01; moderate: ORa = 1.82, 95% CI = 1.45, 2.28; mild: ORa = 1.65, 95% CI = 1.32, 2.05; reference: non-depressed], after adjusting for sociodemographic factors. In the second subsample, 38.9% of those non-depressed participants developed probable depression during follow-up. Adjusted analysis showed that baseline IA status also significantly predicted new incidence of probable depression (ORa = 1.57, 95% CI = 1.18, 2.09).
Discussion and conclusions
The high incidence of probable depression is a concern that warrants interventions, as depression has lasting harmful effects in adolescents. Baseline probable depression predicted IA at follow-up and vice versa, among those who were free from IA/probable depression at baseline. Healthcare workers, teachers, and parents need to be made aware of this bidirectional finding. Interventions, both IA and depression prevention, should thus take both problems into consideration.
Background and aims
The aim of this study is to estimate the longitudinal associations between online social networking addiction (OSNA) and depression, whether OSNA predicts development of depression, and reversely, whether depression predicts development of OSNA.
Methods
A total of 5,365 students from nine secondary schools in Guangzhou, Southern China were surveyed at baseline in March 2014, and followed up 9 months later. Level of OSNA and depression were measured using the validated OSNA scale and CES-D, respectively. Multilevel logistic regression models were applied to estimate the longitudinal associations between OSNA and depression.
Results
Adolescents who were depressed but free of OSNA at baseline had 1.48 times more likely to develop OSNA at follow-up compared with those non-depressed at baseline [adjusted OR (AOR): 1.48, 95% confidence interval (CI): 1.14–1.93]. In addition, compared with those who were not depressed during the follow-up period, adolescents who were persistently depressed or emerging depressed during the follow-up period had increased risk of developing OSNA at follow-up (AOR: 3.45, 95% CI: 2.51–4.75 for persistent depression; AOR: 4.47, 95% CI: 3.33–5.99 for emerging depression). Reversely, among those without depression at baseline, adolescents who were classified as persistent OSNA or emerging OSNA had higher risk of developing depression compared with those who were no OSNA (AOR: 1.65, 95% CI: 1.01–2.69 for persistent OSNA; AOR: 4.29; 95% CI: 3.17–5.81 for emerging OSNA).
Conclusion
The findings indicate a bidirectional association between OSNA and depression, meaning that addictive online social networking use is accompanied by increased level of depressive symptoms.
Abstract
Background and aims
Problematic online social networking use is prevalent among adolescents, but consensus about the instruments and their optimal cut-off points is lacking. This study derived an optimal cut-off point for the validated Online Social Networking Addiction (OSNA) scale to identify probable OSNA cases among Chinese adolescents.
Methods
A survey recruited 4,951 adolescent online social networking users. Latent profile analysis (LPA) and receiver operating characteristic curve (ROC) analyses were applied to the validated 8-item OSNA scale to determine its optimal cut-off point.
Results
The 3-class model was selected by multiple criteria, and validated in a randomly split-half subsample. Accordingly, participants were categorized into the low risk (36.4%), average risk (50.4%), and high risk (13.2%) groups. The highest risk group was regarded as “cases” and the rest as “non-cases”, serving as the reference standard in ROC analysis, which identified an optimal cut-off point of 23 (sensitivity: 97.2%, specificity: 95.2%). The cut-off point was used to classify participants into positive (probable case: 17:0%) and negative groups according to their OSNA scores. The positive group (probable cases) reported significantly longer duration and higher intensity of online social networking use, and higher prevalence of Internet addiction than the negative group.
Conclusions
The classification strategy and results are potentially useful for future research that measure problematic online social networking use and its impact on health among adolescents. The approach can facilitate research that requires cut-off points of screening tools but gold standards are unavailable.
