Smartphone Addiction and Its Relationship with Cyberbullying Among University Students

Qudah, Mohammad Farhan Al.; Albursan, Ismael Salamah; Bakhiet, Salaheldin Farah Attallah; Hassan, Elsayed Mohammed Abu Hashem; Alfnan, Ali A.; Aljomaa, Suliman S.; Al-Khadher, Mohammed M.

doi:10.1007/s11469-018-0013-7

Cited by 45 publications

(45 citation statements)

References 54 publications

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“…The count model verified that smartphone dependency could also intensify the frequency of cyberbullying perpetration. This supports the findings of previous studies conducted among university students and adolescents [ 36 , 37 ]. Indeed, smartphones constitute a major medium for cyberbullying, with severe aggressive and/or delinquent behaviors seen in adolescents who are at risk for smartphone addiction [ 38 ].…”

Section: Discussionsupporting

confidence: 92%

Risk Factors Influencing Cyberbullying Perpetration among Middle School Students in Korea: Analysis Using the Zero-Inflated Negative Binomial Regression Model

Kang

Kim

2021

IJERPH

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This cross-sectional descriptive study identified risk factors and predictors related to the perpetration of and potential for cyberbullying among adolescents, respectively. The analysis included a zero-inflated negative binomial regression model. Data were assessed from 2590 middle-school student panels obtained during the first wave of the Korean Child and Youth Panel Survey 2018. Of these respondents, 63.7% said they had not experienced the perpetration of cyberbullying. However, a subsequent count model analysis showed that several factors were significantly associated with cyberbullying, including offline delinquency, aggression, smartphone dependency, and smartphone usage on weekends (either 1–3 h or over 3 h). A logit model analysis also showed several predictive factors that increased the likelihood of cyberbullying, including gender (boys), offline delinquency, aggression, smartphone usage during weekdays (1–3 h), computer usage during weekends (1–3 h), and negative parenting. These identified risks and predictors should be useful for interventions designed to prevent the perpetration of cyberbullying among middle school students.

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Section: Discussionsupporting

confidence: 92%

Risk Factors Influencing Cyberbullying Perpetration among Middle School Students in Korea: Analysis Using the Zero-Inflated Negative Binomial Regression Model

Kang

Kim

2021

IJERPH

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“…5 College students are generally highly motivated to use smartphones and update them quickly, and they are also the fastest adopters and users of new applications and new programs. 6 Recent researches show that college students generally own smartphones. 7 , 8 What’s more, college students are more likely to be addicted to mobile phones, leading to problematic smartphone usage.…”

Section: Introductionmentioning

confidence: 99%

Effects of Online Social Support and Perceived Social Support on the Relationship Between Perceived Stress and Problematic Smartphone Usage Among Chinese Undergraduates

Zhao¹,

Xu²,

Lai³

et al. 2021

PRBM

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Purpose Recent research has identified a relationship between perceived stress and problematic smartphone usage. However, little is known about the underlying mediating and moderating mechanisms. The present study aimed to test the relationship between perceived stress and problematic smartphone usage as well as the mediating role of online social support and the moderating role of perceived social support among Chinese undergraduates. Patients and Methods A sample of 1123 college students participated in the study. The Perceived Stress Scale, Mobile Phone Addiction Index Scale, Online Social Support Questionnaire, and Perceived Social Support Scale were used. Results Perceived stress was significantly and positively associated with problematic smartphone usage, and online social support partially mediated the relationship between perceived stress and problematic smartphone usage. Moderated mediation analysis suggested that perceived social support moderated the association between perceived stress and online social support. This relationship became stronger for college students with lower levels of perceived social support. Conclusion The results of this study can contribute to a better understanding of how perceived stress increases the risk of problematic smartphone usage. The importance of reducing online social support and enhancing perceived social support in preventing undergraduates’ problematic smartphone usage is highlighted. The findings should be taken with caution given the convenience sampling and cross-sectional study design.

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“…Several studies have reported a high prevalence of smartphone addiction: the prevalence of smartphone addiction in a Malaysian medical school was 46.9% [16], Saudi Arabian university students was 48% [18], Saudi Arabian dental students was 71.9% [19] and Indian medical students was 85.4% [20]. Conversely, some studies have reported a low prevalence of smartphone addiction: the prevalence of smartphone addiction in Chinese medical college students was 29.8% [4], Saudi Arabian students was 33.2% [21] and Saudi Arabian medical students was 36.5% [22]. These results suggested more than a quarter of students in higher education experienced smartphone addiction that requires further exploration of possible factors contributing to it as well as its consequences on students' wellbeing.…”

Section: Introductionmentioning

confidence: 99%

The relationship of smartphone addiction with psychological distress and neuroticism among university medical students

et al. 2020

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Background Smartphone plays a vital role in higher education as it serves as a device with multiple functions. Smartphone addiction was reported on the rise among college and university students. The addiction may result in unwanted consequences on their academic performance and psychological health. One factor that consistently relates to psychological distress and smartphone addiction is the neurotic personality trait. This study explored the relationship of smartphone addiction with psychological health and neuroticism among USM medical students. Methods A cross-sectional study was carried out on medical students in a public medical school. DASS-21, the neuroticism-subscale of USMaP-i and SAS-SV were administered to measure psychological distress, neuroticism, and smartphone addiction of the medical students. Spearman correlation was performed to examine the correlation between smartphone addiction with psychological distress and neuroticism. Simple linear regression was performed to investigate relationship factors of smartphone addiction. Results A total of 574 medical students participated in this study. The prevalence of smartphone addiction was 40.6%. It was higher among male (49.2%) compared to female (36.6%) medical students. The result showed a fair positive correlation between smartphone addiction and psychological health (rdepression = 0.277, p-value < 0.001; ranxiety = 0.312, p-value < 0.001; rstress = 0.329, p-value < 0.001). However, there was a poor positive correlation between smartphone addiction and neuroticism (r = 0.173, p-value < 0.001). The simple linear regression showed a significant increase in the levels of depression, anxiety, stress and neuroticism upon one unit increase in smartphone addiction (bdepression = 0.101, p-value < 0.001; banxiety = 0.120, p-value < 0.001; bstress = 0.132, p-value < 0.001; bneuroticism = 0.404, p-value < 0.05). These results indicated significant relationships between smartphone addiction, psychological health and neuroticism. Conclusion This study suggested a high prevalence of smartphone addiction among medical students, particularly in male medical students. The smartphone addiction might lead to psychological problems and the most vulnerable group is the medical student with the neurotic personality trait.

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Smartphone Addiction and Its Relationship with Cyberbullying Among University Students

Cited by 45 publications

References 54 publications

Risk Factors Influencing Cyberbullying Perpetration among Middle School Students in Korea: Analysis Using the Zero-Inflated Negative Binomial Regression Model

Risk Factors Influencing Cyberbullying Perpetration among Middle School Students in Korea: Analysis Using the Zero-Inflated Negative Binomial Regression Model

Effects of Online Social Support and Perceived Social Support on the Relationship Between Perceived Stress and Problematic Smartphone Usage Among Chinese Undergraduates

The relationship of smartphone addiction with psychological distress and neuroticism among university medical students

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