The unique role of smartphone addiction and related factors among university students: a model based on cross-sectional and cross-lagged network analyses

Smartphone addiction is a global problem affecting university students. Previous studies have explored smartphone addiction and related factors using latent variables. In contrast, this study examines the role of smartphone addiction and related factors among university students using a cross-sectional and cross-lagged panel network analysis model at the level of manifest variables. A questionnaire method was used to investigate smartphone addiction and related factors twice with nearly six-month intervals among 1564 first-year university students (M = 19.14, SD = 0.66). The study found that procrastination behavior, academic burnout, self-control, fear of missing out, social anxiety, and self-esteem directly influenced smartphone addiction. Additionally, smartphone addiction predicted the level of self-control, academic burnout, social anxiety, and perceived social support among university students. Self-control exhibited the strongest predictive relationship with smartphone addiction. Overall, self-control, self-esteem, perceived social support, and academic burnout were identified as key factors influencing smartphone addiction among university students. Developing prevention and intervention programs that target these core influencing factors would be more cost-effective.

Comorbidity of posttraumatic stress disorder and depression among adolescents following an earthquake: A longitudinal study based on network analysis

BACKGROUND

High comorbidity between posttraumatic stress disorder (PTSD) and depression among adolescents often follows severe traumatic events. Models on the pathway to comorbidity dispute greatly and how PTSD and depression get comorbidity, remain unclear.

METHODS

A follow-up investigation was conducted of 424 adolescent survivors of the Jiuzhaigou earthquake at 12 months (T1) and 27 months (T2).

RESULTS

Contemporaneous network analysis and cross-lagged panel network analysis showed that PTSD and depression are two separate disorders with strong associations via links between dysphoric symptoms of PTSD and somatic or non-somatic symptoms of depression. However, the association weakened from T1 to T2, and internal connections between symptoms within each disorder became stronger.

LIMITATION

We only measured the comorbidity of PTSD and depression at two time points following the earthquake, which may limit the long-term applicability of our findings following trauma.

CONCLUSIONS

The findings also showed that the centrality in contemporaneous networks may indicate node connectivity rather than the influence or potential causality among nodes. These results help to elucidate the relationship between PTSD and depression and could contribute to the development of appropriate therapies.

Cross-sectional and longitudinal AUD symptom networks: They tell different stories

Modern theoretical models of Alcohol Use Disorder (AUD) highlight the different functional roles played by various mechanisms associated with different symptoms. Symptom network models (SNMs) offer one approach to modeling AUD symptomatology in a way that could reflect these processes and provide important information on the progression and persistence of disorder. However, much of the research conducted using SNMs relies on cross-sectional data, which has raised questions regarding the extent they reflect dynamic processes. The current study aimed to (a) examine symptom networks of AUD and (b) compare the extent to which cross-sectional network models had similar structures and interpretations as longitudinal network models. 17,360 participants from Wave 1 (2001-2002) and Wave 2 (2003-2004) of the National Epidemiological Survey on Alcohol and Related Conditions (NESARC) were used to model cross-sectional and longitudinal AUD symptom networks. The cross-sectional analyses demonstrate high replicability across waves and central symptoms consistent with other cross-sectional studies on addiction networks. The longitudinal network shared much less similarity than the cross-sectional networks and had a substantially different structure. Given the increasing attention given to the network perspective in psychopathology research, the results of this study raise concerns about interpreting cross-sectional symptom networks as representative of temporal changes occurring within a psychological disorder. We conclude that the psychological symptom network literature should be bolstered with additional research on longitudinal network models.