The Relationship Between Sleep Quality and Internet Addiction Among Female College Students

Abstract

Background: Over 40% of Taiwanese College students experience sleep problems that not only impair their quality of life but also contribute to psychosomatic disorders. Of all the factors affecting the sleep quality, internet surfing is among one of the most prevalent. Female college students are more vulnerable to internet-associated sleep disorders than their male counterparts. Therefore, this study aims to investigate (1) the relationship between internet addiction and sleep quality, and (2) whether significant variations in sleep quality exist among students with different degrees of internet use. Methods: This structured questionnaire-based cross-sectional study enrolled students from a technical institute in southern Taiwan. The questionnaire collected information on the following three aspects: (1) demography, (2) sleep quality with Pittsburgh Sleep Quality Index (PSQI), and (3) severity of internet addiction using a 20-item Internet Addiction Test (IAT). Multiple regression analysis was performed to examine the correlation between PSQI and IAT scores among the participants. Logistic analysis was used to determine the significance of association between PSQI and IAT scores. Results: In total, 503 female students were recruited (mean age 17.05 ± 1.34). After controlling for age, body mass index, smoking and drinking habits, religion, and habitual use of smartphone before sleep, internet addiction was found to be significantly associated with subjective sleep quality, sleep latency, sleep duration, sleep disturbance, use of sleep medication, and daytime dysfunction. Worse quality of sleep as reflected by PSQI was noted in students with moderate and severe degrees of internet addiction compared to those with mild or no internet addiction. Logistic regression analysis of the association between scores on IAT and sleep quality, demonstrated significant correlations between quality of sleep and total IAT scores (odds ratio = 1.05:1.03 ∼ 1.06, p < 0.01). Conclusion: The results of this study demonstrated significant negative association between the degree of internet addiction and sleep quality, providing reference for educational institutes to minimize adverse effects associated with internet use and improve students' sleep quality.

Sleep Pattern Changes in Nursing Students during the COVID-19 Lockdown

“…Sleep latency refers to the time it took students to fall asleep, with subjects asked how often they had trouble getting to sleep within 30 minutes. In university students, this parameter is related to internet addiction [ 38 ]. These data, together with recent studies showing that the population increased their use of technology before going to bed during lockdown [ 18 ], explain our findings.…”

Effect of Internet Addiction on Sleep Quality in University Students

“…In the study of Yılmaz, Tanrıkulu and Dikmen [25], it was found that nursing students had a poor sleep quality and a mean score of 6.52 ± 3.17. Lin et al [26] examined the relationship between internet addiction and sleep quality among university students and found that sleep quality was poor. Similarly, Ji and Wang [27] evaluated sleep quality of university students in China and found that the students had poor sleep quality and the PSQI scores above seven.…”

The association between Internet Usage and Overweight/Obesity modified by Gender: Evidence from a nationally representative survey in Nepal

“…Consistent with this, one of the greatest changes in the last decade in Nepalese lifestyle is that internet use has increased substantially in more than half of the total population [25]. Possible mechanisms through which internet use may cause obesity include a disrupted sleeping schedule leading to weight gain as well as effects on an individuals' regular food consumption [10,11,37].…”

“…Studies have shown that excessive internet use and internet-based activities, especially during weekends and leisure hours, affect individuals' regular food consumption patterns, standard sleep hours, and sleeping schedules that are associated with weight gain [9][10][11]. One study showed that night-time internet use, especially for video games and complementary day-time sleeping, is significantly associated with increased body mass index (BMI) [12].…”