Risk Factors Associated With Social Media Addiction: An Exploratory Study

Problematic usage of the internet among Hungarian elementary school children: a cross-sectional study

INTRODUCTION

Problematic usage of the internet (PUI) is perhaps one of the most frequently studied phenomena of the 21st century receiving increasing attention in both scientific literature and the media. Despite intensive research there have been relatively few meaningful studies among elementary school students in Hungary and worldwide, who may be considered as a high-risk population with regard to problematic internet use. The aim of our study was to carry out a complex research focusing on the prevalence and risk factors of PUI among elementary school children aged 10-15 years (Grade 5-8).

METHODS

Demographics included were gender, age, place of stay, type of residence, family type, parental education, start of internet use, used devices, daily internet use, purpose of internet use, internet accounts, ways of keeping in touch with friends and sporting activities. PUI was evaluated using the paper-based version of the Potentially Problematic Use of the Internet Questionnaire.

RESULTS

Overall, 2000 paper-based questionnaires were successfully delivered and the final analysis included 1168 responses (overall response rate 58.4%). Mean age was 12.55 ± 1.24 years. Female gender (OR = 2.760, p = 0,006, CI 95% 0.065 to 0.384), younger age (11-12 years) (OR = 3.812, p < 0.001, 95% CI: 1.747-4.731), early exposure to the internet (OR = 3.466, p = 0.001, 95% CI 1.535-5.446), living in a small village (OR = 1.081, p = 0.002, 95% CI 1.041-1.186) urgency to answer online (OR = 4.677, p < 0.001, 95% CI: 2.714-6.639), decreased frequency of personal contact with friends (OR = 2.897, p = 0.004, 95% CI: 1.037-1.681), spending more than 6 h online (OR = 12.913, p < 0.001, 95% CI: 10.798-14.892), morning and nighttime internet use (OR = 3.846, p < 0.001, CI 95% 1.886-5.810) and never doing any sports (OR = 2.016, p = 0.044, 95% CI: 1.050-3.354) were independently associated with problematic internet use.

CONCLUSIONS

Based on the results of our questionnaire survey more than 10% seemed to be problematic users in our study population, which is a relatively high rate. Early exposure to the internet as well as younger age were strongly related to this phenomenon. Duration of being online as well as daily time interval of internet use are important predisposing factors. Scarcely studied social factors such as being online at the expense of personal relationships and the lack of physical activity should be payed more attention to prevent the development of PUI.

Screen time and addictive use of gaming and social media in relation to health outcomes

Introduction

This study examined associations between screen time and addictive use (i.e., heavy involvement and negative consequences) of gaming and social media, and their independent effects on health outcomes.

Methods

Survey data were collected from 2,265 participants (mean age = 21.57). Internet Gaming Disorder (IGD) and Social Media Disorder (SMD) were measured with the Gaming and Social Media Questionnaire (GSMQ-9), with separate measures for heavy involvement and negative consequences. Screen time was measured by weekly hours of gaming and social media. Assessed health outcomes were psychological problems, low self-concept, social problems, sleep problems, and sleep time.

Results

Screen time and addictive use were significantly associated for both gaming and social media, with associations being stronger for symptoms of heavy involvement compared to symptoms of negative consequences. However, despite significant associations, a substantial proportion of the participants with a high screen time did not meet any or just one symptom of addiction. More importantly, it was primarily negative consequences that had independent effects on health outcomes, except for sleep. High levels of heavy involvement in gaming, were even related to lower, not higher, levels of psychological problems.

Conclusion

The present findings study show that screen time is a poor indicator of addictive use of gaming and social media. Given that it was primarily negative consequences of gaming or social media that had effects on health outcomes, our study also emphasizes the need to distinguish between different types of addictive use and to further examine the diagnostic validity of the nine IGD symptom criteria.

Relationships between Depression, Fear of Missing Out and Social Media Addiction: The Mediating Role of Self-Esteem

The present study examines the relationships between depression, self-esteem, fear of missing out, online fear of missing out, and social media addiction in a sample of 311 Italian young adults (66.2% women and 33.8% men), ages 18-35 yrs. (M = 23.5; SD = 3.5). The following hypotheses were tested: that depression is positively correlated with fear of missing out, online fear of missing out, and social media addiction, while being significantly negatively correlated with self-esteem; that depression, self-esteem, fear of missing out, and online fear of missing out explain social media addiction scores; that self-esteem mediates the relationship between depression and social media addiction; and that, among Italian participants between the ages of 18 and 35, younger women report higher scores on fear of missing out, online fear of missing out, and social media addiction. Results strongly supported the hypotheses. Taken together, our findings not only contribute to the growing body of research on online addictive behaviors and individuals' well-being, but also provide support for prevention programs in the field.

Attentional Bias Is Associated with Negative Emotions in Problematic Users of Social Media as Measured by a Dot-Probe Task

OBJECTIVE

Social media (SM) have flourished and are affecting human lives on an unprecedented scale. Problematic social media use (PSMU) is a recently emerging problematic behavior that affects both physical and mental health. The purpose of this study was to investigate whether problematic users of SM display attentional bias (AB) toward SM-related cues, as well as the relationships between AB, the severity of PSMU, and negative emotions.

METHOD

1000 college students were recruited through classes and online forums and then tested using the Bergen Social Media Addiction Scale (BSMAS). Eventually, 60 participants, identified by the cut-off point for BSMAS, consented to participate in the study and were divided into two groups (i.e., a problematic use group and a typical use group). The severity of PSMU and negative emotions (i.e., anxiety, depression, social fear, and loneliness) were evaluated by self-report questionnaires. AB was assessed by an addiction Stroop task and a dot-probe task (DPT).

RESULTS

PSMU was found to be positively associated with anxiety (r = 0.28, p < 0.05), depression (r = 0.35, p < 0.01), and social fear (r = 0.38, p < 0.01), but not with loneliness (r = 0.19, p = 0.15). Participants with a tendency toward PSMU displayed AB toward SM-related cues in the DPT [F (1, 58) = 26.77, p < 0.001, ηp2 = 0.32], but not in the Stroop task [F (1, 58) = 0.61, p = 0.44, ηp2 = 0.01]. Moreover, AB toward SM-related stimuli was found to be positively correlated with the severity of PSMU (r = -0.51, p < 0.001), anxiety (r = -0.37, p < 0.01), depression (r = -0.51, p < 0.001), and social fear (r = -0.30, p < 0.05) in the DPT.

CONCLUSIONS

Problematic users of SM show AB towards SM-related cues in the DPT, which is more reliable for assessing AB than the Stroop task. Moreover, it is suggested that in clinical interventions we should work to change the AB towards SM-related stimuli and improve negative emotions to decrease risks of PSMU.

The Mediating Roles of Anxiety, Depression, Sleepiness, Insomnia, and Sleep Quality in the Association between Problematic Social Media Use and Quality of Life among Patients with Cancer

The present study examined the mediating role of anxiety, depression, sleepiness, insomnia, and sleep quality in the association between problematic social media use and quality of life (QoL) among patients with cancer. This cross-sectional survey study recruited 288 patients with cancer to respond to measures on anxiety, depression, sleepiness, insomnia, sleep quality, problematic social media use, and QoL. Structural Equation Modeling was used for the mediation analysis. There were significant relationships between all of the variables used in the study. It was revealed that problematic social media use did not directly influence the QoL of patients with cancer except via anxiety, depression, sleepiness, and insomnia. Sleep quality did not mediate the association between problematic social media use and QoL. Healthcare workers managing cancer should pay attention to the mental health needs of their patients even as they treat their cancer so as to improve their quality of life. Future studies may examine other variables that affect the QoL of patients with cancer as well as other mediating and moderating variables.

Applying network analysis to understand the relationships between impulsivity and social media addiction and between impulsivity and problematic smartphone use

BACKGROUND

Prior studies have revealed the relationships between impulsivity and social media addiction (SMA) and between impulsivity and problematic smartphone use (PSU) based on total scores on standardized self-report scales. However, there has been a lack of studies exploring how the dimensions of impulsivity and components of SMA or PSU are interrelated. The present study aimed to investigate the structural relationships between the dimensions of impulsivity and components of SMA and PSU and determine the critical bridge node using network analysis.

METHODS

A total of 325 healthy adults aged 18-36 years participated in the study. SMA and PSU were assessed using the Bergen Social Media Addiction Scale (BSMAS) and Smartphone Application-Based Addiction Scale (SABAS), respectively. Impulsivity was measured by the Barratt Impulsiveness Scale Version 11 (BIS-11). Network analysis was used to construct an SMA-Impulsivity network and a PSU-Impulsivity network. Bridge centrality (bridge expected influence, BEI) was estimated to identify influential bridge nodes.

RESULTS

In addition to relationships within each community, network analysis revealed that the dimensions of impulsivity were closely associated with the components of SMA and PSU. Particularly, I2 "motor impulsivity" had a relatively strong connection with SMA3 "mood modification" and SMA4 "relapse" in the SMA-Impulsivity network, and with PSU2 "conflict" and PSU5 "withdrawal" in the PSU-Impulsivity network. Moreover, I2 "motor impulsivity" was identified as the most critical bridge node in both networks.

CONCLUSION

These findings demonstrate potential pathways between different dimensions of impulsivity and the components of SMA and PSU, providing new evidence relevant to understanding the underlying mechanisms that account for how highly impulsive individuals develop SMA and PSU, and highlight the critical bridge node-motor impulsivity-that may be a promising and effective target for the prevention and treatment of SMA and PSU.