The Associations between Daytime Physical Activity, While-in-Bed Smartphone Use, Sleep Delay, and Sleep Quality: A 24-h Investigation among Chinese College Students

The effects of mobile phone addiction on bedtime procrastination in university students: the masking effect of physical activity and anxiety

PURPOSE

Good sleep is one of the necessary conditions to ensure the normal performance of the physiological and psychological functions of college students. This study aimed to explore the relationship between mobile phone addiction and bedtime procrastination among Chinese college students and the mediating mechanisms of physical exercise and anxiety between the two, with a view to seek ways to prevent and intervene in college students' sleep procrastination and improve their sleep quality.

METHODS

Using SPSS 29.0 analysis with Bootstrap's method, 3,800 first-year students, sophomores, and juniors were given the Mobile Phone Addiction Tendency Scale, Bedtime Procrastination Scale, Physical Activity Scale, and Anxiety Scale. The results of the analyses included mediation tests and effect analyses of anxiety and physical activity.

RESULTS

The correlation analysis revealed significant positive correlations between mobile phone addiction and bedtime procrastination (r = 0.149, p < 0.01) as well as anxiety (r = 0.497, p < 0.01). Additionally, there was a significant negative correlation between mobile phone addiction and physical activity (r = -0.447, p < 0.01). Physical activity was also found to have significant negative correlations with anxiety (r = -0.506, p < 0.01) and bedtime procrastination (r = -0.424, p < 0.01). Furthermore, anxiety showed a significant positive correlation with bedtime procrastination (r = 0.334, p < 0.01). Physical activity and anxiety acted as substantial mediators between mobile phone addiction and nighttime procrastination. Both mediators had considerable masking effects, with the mediating effect amounting to 50.3% and 25.1%, respectively. Physical exercise and anxiety played a chain mediating role between mobile phone addiction and bedtime procrastination, and the masking effect was also significant, with a mediating effect size of 13.4%.

CONCLUSIONS

This study reveals the special characteristics of the influencing factors and pathways of bedtime procrastination in this group of college students, providing targeted evidence for the prevention and intervention of bedtime procrastination in college students. It also has an important reference value for the effects of exercise and comprehensive intervention to improve bedtime procrastination and enhance the quality of sleep in college students.

Could physical activity alleviate smartphone addiction in university students? A systematic literature review

Objectives

Recently, the widespread surge in smartphone addiction (SA) has raised major global health concerns and prompted researchers to scrutinize the inverse relationship between physical activity (PA) and the risk of SA. This systematic literature review aims to synthesize the empirical research on the relationship between PA and SA among university students representing the most affected age group.

Methods

Adopting the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines, we explored five databases: PubMed, Social Sciences Research Network (SSRN), Oxford Research Archive, Journal Storage (JSTOR), and Google Scholar. We used the Mixed Methods Appraisal Tools (MMAT) for quality assessment.

Results

Thirty-one studies met the inclusion criteria. Twenty-eight of them were cross-sectional, and three were experimental. The 31 studies emerged from 12 countries, most stemming (45.16%) from China. Their findings suggest an inverse relationship between PA and SA in the examined population. However, the direct relationship may be weak based on correlational studies, while intervention research yields noteworthy effects. Still, other factors like resilience may mediate the studied relationship. Methodological concerns render the results of correlational studies tentative.

Conclusions

Regular PA could be a promising preventive measure for SA. Future work should use objective PA indices in longitudinal research designs while assessing the type and duration of smartphone applications used via device meters. In correlational studies, interviews should follow up on the high SA risk or too much device use. In conclusion, moderate evidence indicates that PA can reduce SA among university students.

The relationship between nighttime exercise and problematic smartphone use before sleep and associated health issues: a cross-sectional study

OBJECTIVE

Physical exercise has the potential to mitigate addictive behaviors and relevant health issues. However, the nighttime exercise has not been studied regarding this research topic. This study aims to explore the association between nocturnal physical exercise and problematic smartphone use before sleep, as well as related health issues.

METHODS

To explore the association between nighttime physical exercise and problematic smartphone use before sleep as well as related health issues, we conducted a cross-sectional survey among 1,334 college students. Their daily exercise behaviors (including timeframe, rationale, frequency, and duration), smartphone use before sleep, sleep quality, smartphone addiction, anxiety, and depression were measured by questionnaires. The associations were assessed using generalized linear models.

RESULTS

Our findings indicate that nearly 70% of participants chose to perform exercise at nighttime. Among these individuals who exercised at nighttime, the frequency and duration of nighttime exercise were significantly associated with decreased probabilities of smartphone use before sleep. Additionally, the frequency and duration of nighttime exercise were associated with lower levels of smartphone addiction and anxiety disorders.

CONCLUSION

Nighttime Exercise behaviors can effectively reduce sleep delays caused by problematic smartphone use before bedtime. These findings contribute to understanding the potential effects of nighttime exercise on problematic smartphone use and relevant health issues. Future research should employ more precise methodologies to examine these associations.

Can restricting while-in-bed smartphone use improve sleep quality via decreasing pre-sleep cognitive arousal among Chinese undergraduates with problematic smartphone use? Longitudinal mediation analysis using parallel process latent growth curve modeling

BACKGROUND AND AIM

Problematic smartphone use (PSU) has become a global public health problem. Excessive while-in-bed smartphone use may result in poor sleep quality. The present study aimed to examine whether restricting while-in-bed smartphone use can improve sleep quality and the mediating role of pre-sleep cognitive arousal in this process.

METHODS

A total 152 Chinese undergraduates with symptoms of PSU (Smartphone Addiction Scale-Short Version [SAS-SV] > 31) were recruited and randomly assigned to the intervention group (N = 76) and the control group (N = 76) for a 4-week follow-up experiment. Sleep quality (assessed by the Pittsburgh Sleep Quality Index) and pre-sleep cognitive arousal (assessed by the Pre-sleep Arousal Scale-Cognitive Arousal) were measured at baselime, Week1, Week2, Week3, and Week4. The longitudinal mediation model was analyzed using parallel process latent growth curve modeling. The significance of indirect effect was tested by 95% biascorrected accelerated confidence intervals on the basis of 5000 bootstrap samples.

RESULTS

The initial level of sleep quality was positively associated with the initial level of pre-sleep cognitive arousal. The intervention decreased pre-sleep cognitive arousal and increased sleep quality. The slope of pre-sleep cognitive arousal was positively associated with the slope of sleep quality. The mediating effect of pre-sleep cognitive arousal was significant.

DISCUSSION

Restricting while-in-bed smartphone use can improve sleep quality via decreasing pre-sleep cognitive arousal among Chinese undergraduates with symptoms of PSU. These results suggests that restricting while-in-bed smartphone use is a potential remedy for sleep disturbance induced by PSU.

Development and validation of the while-in-bed-smartphone-use-induced sleep procrastination scale (WSPS) in Chinese undergraduates with/without problematic smartphone use

PURPOSE

Problematic smartphone use (PSU) has become a global public health problem. Excessive while-in-bed smartphone use may result in sleep procrastination and other negative outcomes. The present study aimed to develop and validate a new scale called WSPS to assess while-in-bed-smartphone-use-induced sleep procrastination among undergraduates.

METHODS

In total, 910 Chinese undergraduates completed the collection of WSPS, smartphone addiction scale-short version (SAS-SV), bedtime procrastination scale, Pittsburgh sleep quality index, and Zung self-rating depression scale. The whole sample were randomly splited in the exploratory factor analysis (EFA) sample (n = 455) and confirmatory factor analysis (CFA) sample (n = 455). 40 undergraduates with PSU (SAS-SV > 31) and 40 without PSU were asked to keep sleep diary for 2 weeks and complete the WSPS again.

RESULTS

EFA and CFA supported a six-item unidimensional structure of the WSPS. The WSPS demonstrated acceptable internal consistency among undergraduates. The WSPS showed good concurrent validity with other relevant variables including PSU, BP, sleep quality, and depression. Scalar invariance of the WSPS between undergraduates with/without PSU was supported, as well as scalar invariance across gender. The WSPS showed good convergent validity with self-report everyday while-in-bed smartphone use duration and good discriminant validity with sleep duration and sleep onset latency recorded by sleep diary. The WSPS also presented good test-retest reliability among undergraduates with/without PSU.

CONCLUSION

The WSPS is a reliable and valid measure of while-in-bed-smartphone-use-induced sleep procrastination in undergraduates with/without PSU.

Effect of Nighttime Exercise on Sleep Quality Among the General Population in Riyadh, Saudi Arabia: A Cross-Sectional Study

Introduction Engaging in physical activity has been proven to have health benefits, with a positive impact on sleep quality. While the timing of exercise plays a significant role in determining its effect on sleep, nighttime exercise still needs to be explored, especially in Saudi Arabia. This study assessed the effect of nighttime exercise on sleep quality among the general population in Riyadh, Saudi Arabia. Methods A cross-sectional study was conducted on people performing physical exercises in the gym, training, and walking places using a self-administered questionnaire evaluating exercising behaviors and sleep quality. Comparisons were performed using the Chi-square test and ANOVA, and p<0.05 was considered for significance. Results We enrolled 385 participants, among whom 53.8% were male, and 47.2% were female. The mean age was 28.2±7.85 years, and mostly aged 25-29 years (24.7%), followed by 20-24 years old (21.3%). Most had university degrees (61.3%) and were also employed (60%). Of all participants, the majority were overweight and obese (61.3%). Most (n=225) participants practiced vigorous physical exercise, and the mean Pittsburgh Sleep Quality Index (PSQI) global score was 7.37±3.49 points. Evening vigorous (r= 0.25, p=0.038) and moderate (r=0.30, p=0.025) physical exercise sessions lasting > 90 min had a significant positive correlation with poor sleep quality (high PSQL score). There was no statistically significant correlation between sleep quality and other variables (p>0.05). Conclusion This study found that participants had poor sleep quality, and lengthy, intense evening exercises had a negative effect on sleep. Incorporating regular exercise tailored to individual preferences and encouraging people to widen the time interval between exercise and bedtime could improve sleep quality.

Longitudinal association between problematic smartphone use and sleep disorder among Chinese college students during the COVID-19 pandemic

BACKGROUND

Problematic smartphone use (PSU) and sleep disorders (SD) are common public health problems among college students. While previous cross-sectional studies have found a relationship between PSU and SD, the causal direction of this relationship remains unclear. This study aims to examine the longitudinal changes of PSU and SD during the COVID-19 pandemic, determine the causal relationship between them, and identify confounding factors that affect this association.

METHODS

The study sample consisted of 1186 Chinese college students (47.7% male) with a mean age of 18.08 years. Participants completed the Smartphone Addiction Scale - Short Version (SAS-SV) and the Pittsburgh Sleep Quality Index (PSQI) at both baseline and follow-up surveys, conducted one year apart. The cross-lagged panel model (CLPM) was used to examine the causal relationship between PSU and SD, stratified by gender and duration of daily physical activity. The fixed effect panel regression was used to confirm the findings of CLPM.

RESULTS

The results of the CLPM analysis showed a significant bidirectional relationship between PSU and SD for the overall sample, which was consistent with the fixed effects model findings. However, subgroup analyses revealed that the bidirectional association disappeared among males or those who engaged in daily physical activity for more than 1 h.

CONCLUSIONS

Our study shows a significant bidirectional association between PSU and SD, with variations across gender and daily physical activity levels. Encouraging physical activity may serve as a potential intervention to disrupt the bidirectional association between PSU and SD, which has important implications for public health strategies aimed at reducing the negative consequences of PSU and SD.

Profiling Physical Fitness of Physical Education Majors Using Unsupervised Machine Learning

The academic curriculum has shown to promote sedentary behavior in college students. This study aimed to profile the physical fitness of physical education majors using unsupervised machine learning and to identify the differences between sexes, academic years, socioeconomic strata, and the generated profiles. A total of 542 healthy and physically active students (445 males, 97 females; 19.8 [2.2] years; 66.0 [10.3] kg; 169.5 [7.8] cm) participated in this cross-sectional study. Their indirect VO2max (Cooper and Shuttle-Run 20 m tests), lower-limb power (horizontal jump), sprint (30 m), agility (shuttle run), and flexibility (sit-and-reach) were assessed. The participants were profiled using clustering algorithms after setting the optimal number of clusters through an internal validation using R packages. Non-parametric tests were used to identify the differences (p < 0.05). The higher percentage of the population were freshmen (51.4%) and middle-income (64.0%) students. Seniors and juniors showed a better physical fitness than first-year students. No significant differences were found between their socioeconomic strata (p > 0.05). Two profiles were identified using hierarchical clustering (Cluster 1 = 318 vs. Cluster 2 = 224). The matching analysis revealed that physical fitness explained the variation in the data, with Cluster 2 as a sex-independent and more physically fit group. All variables differed significantly between the sexes (except the body mass index [p = 0.218]) and the generated profiles (except stature [p = 0.559] and flexibility [p = 0.115]). A multidimensional analysis showed that the body mass, cardiorespiratory fitness, and agility contributed the most to the data variation so that they can be used as profiling variables. This profiling method accurately identified the relevant variables to reinforce exercise recommendations in a low physical performance and overweight majors.