Dataset Information

A bidirectional association between smartphone addiction and depression among college students: A cross-lagged panel model

ABSTRACT:

Background

Smartphone addiction (SA) is associated with adverse consequences, especially for freshmen. Evidence indicates that SA is associated with depression, and it is necessary to conduct a longitudinal study to explore the association further.

Methods

SA (measured by the Smartphone Addiction Scale-Short Version) and depression (measured by the Zung's Self-Rating Depression Scale) among 1,186 freshmen were surveyed at baseline and a respective 12-month follow-up for each participant. The application of a cross-lagged panel model approach (CLPM) revealed an association between SA and depression after adjusting for demographic variables.

Results

The CLPM results showed a significant path from baseline SA to follow-up depression (β = 0.08, P < 0.001) and a significant path from baseline depression to follow-up SA (β = 0.08, P < 0.001). Compared with the overall cross-lagged model, the cross-lagged coefficient of the path from baseline SA to follow-up depression increased in the female group (β = 0.10, P = 0.015), and the cross-lagged coefficient of the path from baseline depression to follow-up SA also increased significantly (β = 0.15, P < 0.001). In contrast, the cross-lagged model in the male group showed no predictive effect between SA and depression (P > 0.05).

Conclusions

The current study showed a significant bidirectional association between smartphone addiction and depression among freshmen, but only in the female population.

SUBMITTER: Zhang K

PROVIDER: S-EPMC9902510 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:ObjectivesThe study aims to assess prevalence of smartphone addiction symptoms, and to ascertain whether depression or anxiety, independently, contributes to smartphone addiction level among a sample of Lebanese university students, while adjusting simultaneously for important sociodemographic, academic, lifestyle, personality trait, and smartphone-related variables.MethodsA random sample of 688 undergraduate university students (mean age = 20.64 ±1.88 years; 53% men) completed a survey composed of a) questions about socio-demographics, academics, lifestyle behaviors, personality type, and smartphone use-related variables; b) 26-item Smartphone Addiction Inventory (SPAI) Scale; and c) brief screeners of depression and anxiety (PHQ-2 and GAD-2), which constitute the two core DSM-IV items for major depressive disorder and generalized anxiety disorder, respectively.ResultsPrevalence rates of smartphone-related compulsive behavior, functional impairment, tolerance and withdrawal symptoms were substantial. 35.9% felt tired during daytime due to late-night smartphone use, 38.1% acknowledged decreased sleep quality, and 35.8% slept less than four hours due to smartphone use more than once. Whereas gender, residence, work hours per week, faculty, academic performance (GPA), lifestyle habits (smoking and alcohol drinking), and religious practice did not associate with smartphone addiction score; personality type A, class (year 2 vs. year 3), younger age at first smartphone use, excessive use during a weekday, using it for entertainment and not using it to call family members, and having depression or anxiety, showed statistically significant associations with smartphone addiction. Depression and anxiety scores emerged as independent positive predictors of smartphone addiction, after adjustment for confounders.ConclusionSeveral independent positive predictors of smartphone addiction emerged including depression and anxiety. It could be that young adults with personality type A experiencing high stress level and low mood may lack positive stress coping mechanisms and mood management techniques and are thus highly susceptible to smartphone addiction.

Project description:IntroductionStudies consistently link excessive smartphone use to poor sleep quality, depression, anxiety, and stress. This study specifically aimed to investigate these associations among medical students in Belgrade and Nis (Central Serbia).Materials and methodsThe cross-sectional study included a sample of 761 students, who were selected from both the Faculties of Medicine at the University of Belgrade and the University of Nis. Questionnaires, including the International Physical Activity Questionnaire - Short Form (IPAQ-SF), Smartphone Addiction Scale - Short Version (SAS-SV), the Pittsburgh Sleep Quality Index (PSQI), and the Depression, Anxiety, and Stress Scale - 21 items (DASS-21), were completed by the participants. Statistical analysis techniques, such as the Chi-square test, student's t-test, and logistic regression, were employed to examine the relationship between smartphone addiction, physical activity, sleep quality, depression, anxiety, and stress.ResultsThe findings indicated a prevalence of smartphone addiction among medical students at 21.7%, with rates of 22.9% among males and 21.1% among females. Females exhibited significantly higher scores on the SAS-SV scale compared to males (p = 0.032). Univariate logistic regression analysis revealed significant associations between smartphone addiction and spending over 4 h daily on smartphones (OR = 2.39; p < 0.001), poor sleep quality (OR = 1.65; p = 0,005), as well as elevated levels of stress (OR = 1.75; p = 0.003), anxiety (OR = 2.04; p < 0.001), and depression (OR = 2.29; p < 0.001). Multivariate regression analysis identified spending more than 4 h daily on smartphones (OR = 2.39; p < 0.001) and increased levels of depression (OR = 2.51; p < 0.001) as independent significant factors associated with smartphone addiction.ConclusionThis study sheds light on the prevalence of smartphone addiction among medical students, with spending excessive time on smartphones and higher levels of depression standing out as significant factors. Future research should delve into the underlying mechanisms and causal relationships between smartphone addiction and these psychosocial factors. Understanding these connections will aid in developing effective interventions and strategies to tackle this growing public health concern.