Association between the pattern of mobile phone use and sleep quality in Northeast China college students

Dose-response analysis of smartphone usage and self-reported sleep quality: a systematic review and meta-analysis of observational studies

STUDY OBJECTIVES

Several studies have recently reported on the association between smartphone usage and self-reported sleep quality. However, no systematic review or meta-analysis has yet been performed. We aimed to analyze the association between smartphone usage time and self-reported sleep quality.

METHODS

We searched for articles published up to January 13, 2022, using the Embase and Medline databases. All observational studies were eligible for inclusion. The Newcastle-Ottawa scale was used to evaluate the risk of bias within studies. We used restricted cubic spline analysis to perform a dose-response analysis.

RESULTS

Seventeen studies with a total of 36,485 participants were included. The pooled odds ratio was 2.28 (confidence interval [CI]: 1.81-2.89; P < .001) and heterogeneity was 80%. In the dose-response analysis conducted on 5 studies, the regression coefficient between daily smartphone usage time (hours/day) and poor sleep quality was 1.042 (1.027-1.058), which confirmed a significantly positive association.

CONCLUSIONS

Smartphone overuse was closely associated with poor self-reported sleep quality, sleep deprivation, and sleep latency prolongation. Further studies using a more structured method and high-quality evidence (cohort or case-control) should be conducted.

SYSTEMATIC REVIEW REGISTRATION

CRD42022303371.

CITATION

Chu Y, Oh Y, Gwon M, et al. Dose-response analysis of smartphone usage and self-reported sleep quality: a systematic review and meta-analysis of observational studies. J Clin Sleep Med. 2023;19(3):621-630.

Study on lifestyle habits affecting sleep disorders at the undergraduate education stage in Xuzhou City, China

Background

In China, undergraduate students face both academic and career selection pressures, sleep is an important physiological process for them. Investigate the physical exercise, sleep quality of undergraduate students in the education stage in Xuzhou City, and analyze the factors affecting their sleep quality, to promote the health education and psychological health of undergraduate students.

Materials and methods

The Physical Activity Rating Scale-3 (PARS-3), the Pittsburgh Sleep Quality Index (PSQI), and the demographic information questionnaire were used to survey a whole-group sample of four undergraduate colleges and universities (Xuzhou Institute of Engineering, Xuzhou Medical University, China University of Mining and Technology, Jiangsu Normal University) in Xuzhou by cluster sampling, the general characteristics including gender, grade, height, weight, domicile, race, economic income, etc., were collected, and the data were analyzed and processed using chi-square tests and multi-factor logistic regression.

Results

3,366 valid questionnaires were collected from four undergraduate colleges and universities, including 1,355 males and 2,011 females. The detection rate of exercise in Jiangsu Normal University was lower than that in other universities, and the detection rate of sleep disorders was higher than that in other universities. Xuzhou Medical University, the highest detection rate of large exercise, Xuzhou Institute of Technology, the lowest detection rate of sleep disorders. There were significant differences in the detection rate of large amount of exercise among college students of different genders, grades, body types, and majors (χ2 = 259.172, P < 0.001; χ2 = 34.473, P < 0.001; χ2 = 36.026, P < 0.001; χ2 = 57.908, P < 0.001). There were significant differences in the detection rate of sleep disorders among college students with different gender, grade, family economic status, daily cell phone use time, cell phone purposeless usage, and exercise level (χ2 = 5.806, P = 0.016; χ2 = 47.5, P < 0.001; χ2 = 28.949, P < 0.001; χ2 = 55.866, P < 0.001; χ2 = 147.101, P < 0.001; χ2 = 9.129, P = 0.010). Multivariate logistic regression analysis showed that grade, family economic status, cell phone use time, cell phone purposeless usage is the main influencing factors of sleep disorders in college students.

Conclusion

The sleep problems of undergraduates are serious, especially in Jiangsu Normal University. Scientific and appropriate exercise is an important measure to solve the sleep problems of undergraduates. Colleges and universities should actively carry out health education, college students living habits such as cell phone use should be guided training.

Deteriorated sleep quality and influencing factors among undergraduates in northern Guizhou, China

Background

The sleep quality of undergraduates is considerably worse than that in general population, a cross sectional study was conducted to evaluate sleep quality and identify related factors.

Methods

All participants from the freshmen to senior were recruited by the stratified cluster sampling from December 1, 2018 to January 12, 2019. The questionnaire used in this research was primarily composed of three sections: demographic characteristics, Pittsburgh Sleep Quality Index (PSQI) questionnaire and influencing factors of sleep quality. The data were analyzed using SPSS 18.0.

Results

A total of 1,063 valid questionnaires were collected. Among them, 53.7% subjects suffered poor sleep quality. PSQI general score was 5.94 ± 2.73. There were significantly differences in sleep quality in sex, majors and grades. The survey reported that women suffered worse sleep quality than that of men, and medical students suffered worse sleep quality than non-medical students. Meanwhile, it also found that freshmen had better sleep quality than that of sophomores and juniors, sophomores suffered worst sleep quality. The logistic regression analysis showed that bad physical condition (OR (Odds ratio): 2.971 (2.034∼4.339)) and smoking (OR: 1.754 (1.258∼2.446)) were associated with poor sleep quality in males. However, more factors associated with poor sleep quality among females were found, including noisy dormitory environment (OR: 2.025 (1.354-3.030)), skipping breakfast more times per week (OR: 1.332 (1.031∼1.721)), drinking coffee before sleep (OR: 2.111 (1.155∼3.861)), playing with mobile phones for more than 45 minutes before sleep (OR: 1.745 (1.210∼2.515)), more time spent playing games per day (OR: 1.347 (1.048∼1.730)), bad physical condition (OR: 2.507 (1.797-3.497)), and severe academic stress (OR: 1.561 (1.126-2.166)).

Conclusion

About half of college students experienced poor sleep, and poor sleep quality was prevalent in women, medical students, and sophomores. Moreover, there were more risk factors associated with the poor sleep quality of women than with men. Health policymakers should fully consider these factors in improving the sleep quality of college students.

Sleep latency and sleep disturbances mediates the association between nighttime cell phone use and psychological well-being in college students

To examine sleep latency and sleep disturbance as mediators between nighttime cell phone use variables (cell phone use for unstructured leisure activities and for accessing emotionally charged media content before sleep: CPU_BeforeBed and CPU_Arousal) and psychological well-being (PWB) of college students. 521 (74% female) undergraduate students from a large public university were surveyed using a validated self-report quantitative questionnaire assessing CPU variables, sleep quality, and PWB. Pearson correlation analyses were used to compute the correlation between CPU_BeforeBed, CPU_Arousal, sleep latency, and sleep disturbance. Ordinary least-squares regressions were conducted to assess the estimates of the relationships within the models. One-way ANOVA was used to see the difference between the groups. The partial eta squared was used to determine the effect size between the groups. The PROCESS method was used to perform mediation analyses. The sample consisted of undergraduate students between 18 and 29 years old, with an average age of 20 years (SD = 3.18). The sample was diverse in terms of ethnicity (49% Caucasian, 24% Latinx, 19% Asian, 3% African American, 1% Native American, 3% identified as "other") and the number of years the participants had been attending a 2 year or 4 year higher institution (38% incoming freshman, 19% sophomore, 17% junior, 14% senior, and 13% returning senior). The correlation between CPU_BeforeBed and PWB (α = - 0.044, p = 0.615), and the correlation between CPU_Arousal and PWB (α = - 0.061, p = 0.228) were not statistically significant. However, the correlation between sleep latency and PWB (α = - 0.140, p = 0.001), and the correlation between sleep disturbance and PWB (α = - 0.121, p = 0.005) were statistically significant. The mediation effect of sleep latency on the association between CPU_BeforeBed and PWB (Effect = - 0.0325, SE = 0.0145, p < 0.05), and the mediation effect of sleep disturbance on the association between CPU_Arousal and PWB (Effect = - 0.0214, SE = 0.0086, p < 0.05) were statistically significant. Sleep latency and sleep disturbance act as a mediator on the association between CPU_BeforeBed and PWB, and the association between CPU_Arousal and PWB. However, CPU_BeforeBed and CPU_Arousal did not have a direct impact on their PWB. These findings may help college students in regulating CPU habits before going to bed. These findings may also help medical practitioners make informed decisions about the use of cell phones for patients with sleep-related disorders.

Supplementary Information

The online version contains supplementary material available at 10.1007/s41105-022-00388-3.

Perceived Academic Stress and Depression: The Mediation Role of Mobile Phone Addiction and Sleep Quality

BACKGROUND

Although academic stress is a well-known risk factor for students' depression, little is known about the possible psychological mechanisms underlying this association. In this study, we investigated the prevalence of depression and sleep disturbance among Chinese students, examined the relationship between perceived academic stress and depression, considered if mobile phone addiction and sleep quality is a mediator of this relationship, and tested if mobile phone addiction and sleep quality together play a serial mediating role in the influence of perceived academic stress on depression.

METHOD

A cross-sectional survey was conducted among students from September to December 2018 in Heilongjiang Province, China. The final analysis included 5,109 students. Mobile phone addiction, sleep quality, and depressive symptoms were assessed using the Mobile Phone Addiction Index, Pittsburgh Sleep Quality Index, and Center for Epidemiologic Studies-Depression scales, respectively. The serial mediation model was used to analyse the relationship between perceived academic stress, mobile phone addiction, sleep quality, and depression.

RESULTS

Among all participants, the prevalence of depressive symptoms and sleep disturbance was 28.69 and 27.95%, respectively. High school students showed the highest scores of perceived academic stress (2.68 ± 1.06), and the highest prevalence of depressive symptoms (33.14%) and sleep disturbance (36.47%). The serial mediation model indicated that perceived academic stress was a significant predictor of depression (B = 0.10, SE = 0.02, 95% CI = 0.06 - 0.13). Additionally, mobile phone addiction (B = 0.08, 95% boot CI = 0.06-0.11) and sleep quality (B = 0.27, 95% boot CI = 0.22-0.33) played a mediating role between perceived academic stress and depression. Mobile phone addiction and sleep quality together played a serial mediating role in the influence of perceived academic stress on depression (B = 0.11, 95% boot CI = 0.08-0.14). Furthermore, the indirect effect (i.e., the mediating effect of mobile phone addiction and sleep quality) was significant and accounted for 64.01% of the total effect.

CONCLUSIONS

Our research results underscore the need for stakeholders-including family members, educators, and policy makers-to take preventative intervention measures to address depression among Chinese students, especially high school students.

Nighttime cell phone use and sleep quality in young adults

Young adults are particularly vulnerable to sleep disturbances related to their cell phone use (CPU). The purpose of this study was to test Sleep Displacement and Psychological Arousal theories of CPU-led sleep disruption in relation in a sample of university students. CPU for unstructured leisure activities before sleep (CPU_BeforeBed), as well as CPU for accessing explicit or emotionally charged media content before sleep (CPU_Arousal), were both measured. 525 (75% female) undergraduate students from a large public institution participated in this study during fall 2019. The data were collected using a battery of validated self-report questionnaires. Questionnaires included the Pittsburgh Sleep Quality Index scale, which was used to measure sleep latency and sleep difficulty, and a contemporary questionnaire developed to measure CPU_BeforeBed and CPU_Arousal. A crude odds ratio analysis was implemented to analyze findings. Sleep Displacement data showed higher odds of sleep latency occurring with CPU_BeforeBed [Exp (B) = 1.091, p < 0.001]. Psychological Arousal data showed higher odds of sleep difficulty occurring with CPU_Arousal [Exp (B) = 1.065, p < 0.001]. CPU_BeforeBed significantly predicted sleep latency [Exp (B) = 1.062, p < 0.01], and CPU_Arousal significantly predicted the sleep difficulty [Exp (B) = 1.069, p < 0.001] of undergraduate students. Young adults who used cell phones before sleep and access emotionally charged content before going to bed were more likely to report trouble sleeping. They took more time to fall asleep at night as their sleep hours were compressed due to CPU. These findings support the Sleep Displacement and Arousal theories of sleep disruption and provide further insight into possible mechanisms for sleep disturbance in young adults.