2.1 Participants

This study adopted a cross-sectional research design, and eight questionnaire investigators were trained before the data collection, all of whom were undergraduate counselors from various colleges of Nantong University. Nantong University was established in 1912 and is located in Nantong City, Jiangsu Province, China. In the 2023 China University Evaluation by the China Academy of Management Sciences, it was ranked 102nd. The study utilized data from the 2022 Chinese College Health Tracking Survey, and the survey was conducted in September 2022. The participants were full-time freshmen, sophomores, juniors, and seniors of Nantong University. All students signed electronic informed consent forms before participating. All students signed an electronic informed consent form prior to participation. The participant demographics are shown in Table 1.

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Table 1. Participant demographics.

https://doi.org/10.1371/journal.pone.0307162.t001

This study used a stratified random cluster sampling method, distributing 5,980 related questionnaires and receiving 4,670 valid questionnaires, with an response rate of 78%. The research plan was approved by the Ethics Committee of Nantong University (2022(70)). The minimum sample size was calculated using Formula (1) [21], where the Type I error α was set to 0.05, the allowable error δ was set to 0.01, and the sampling rate ρ was set to 0.05. Given that the total population of Yangzhou University is 48,209 people (updated data in 2023), the limited total population N was set at 75% of the student population, which is approximately 36,157 people. The calculated minimum sample size required for this study was 1,738 [22].

(1)

2.2 Measurement methods

2.3 Statistical analysis

Data processing was conducted by using IBM SPSS Statistics 26.0 and WPS Excel software(11.1.0.14309), which consisted of the following four steps: (1) Use Excel software to preprocess the data obtained from the questionnaire, and retest or delete the missing or problematic data. (2) After preprocessing the data, conduct a general analysis of the collected student data. The analysis content comprises sleep quality and physical exercise status. The Chi-square test was used to analyze the differences in physical exercise and sleep quality among students of different genders and grades. One-way ANOVA was used to analyze the differences between sleep quality and physical exercise in male and female university students of various grades based on the effect size, η². (3) A correlation analysis was performed to examine the correlation between the tendency of mobile phone addiction among university students (including withdrawal symptoms, highlight behavior, social comfort, and mood changes) and physical exercise, as well as sleep quality. (4) A linear regression analysis was used to test the effect of mobile phone addiction on sleep quality among university students, with physical exercise included as a moderating variable. Before calculating the moderation effect, the variables (Z-score) were standardized.

3.1 General trends

Table 2 shows that the proportion of male students who engaged in moderate (21.4%) and high levels (25.3%) of physical exercise was much higher than that of female students who engaged in moderate (9.5%) and high levels (4.9%) of physical exercise. Moreover, there was a significant difference in physical exercise between male and female students (p < 0.001, Cramer’s V = 0.365). From a grade perspective, a significant difference exists in physical exercise among university students in different grades (p < 0.001, Cramer’s V = 0.080). The number of freshmen who had good sleep quality (52.4%) was significantly higher than that of students in other grades. No significant difference exists in sleep quality between male and female students (p = 0.240, Cramer’s V = 0.400), but a significant difference exists in sleep quality among university students in different grades (p <0.001, Cramer’s V = 0.770).

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Table 2. General situation of physical exercise and sleep quality among college students.

https://doi.org/10.1371/journal.pone.0307162.t002

In Table 3, it can be seen that there was no significant difference in mobile phone addiction between male and female students (F = 0.240, p = 0.624, η² <0.001). However, a significant difference exists in mobile phone addiction among university students in different grades (F = 17.998, p<0.001, η²<0.001).

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Table 3. Descriptive analysis of electronic addiction and physical exercise among college students.

https://doi.org/10.1371/journal.pone.0307162.t003

3.2 Correlation analysis

Table 4 indicates a significant correlation (P<0.01) between mobile phone addiction and sleep quality among college students, with dimensions ranging from 0.255 to 0.287. Additionally, a significantly negative correlation (P<0.01) exists between sleep quality and physical exercise, with a correlation coefficient of -0.037. A significant difference (P<0.01) exists between physical exercise and mobile phone addiction, with dimensions ranging from -0.084 to -0.112. All the variables involved in Table 4 are categorical variables.

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Table 4. Correlation analysis results.

https://doi.org/10.1371/journal.pone.0307162.t004

3.3 Test for moderating effects

Table 5 presents the results of the effect of sports exercise regulation. Model 1 shows that with sleep quality as the dependent variable, the explanation rate of independent mobile addiction and regulating sports exercise variables is 18.9%. In Model 2, the predictive ability improved by 19.4%, meaning that the contribution rate of regulating effect is 19.4% under the condition that mobile addiction independent variable and sports exercise regulation variable remain unchanged. In addition, the significant F change in both models is less than 0.001. The mobile addiction independent variable and sports exercise regulation variable have a significant impact on the sleep quality dependent variable. In the analysis of variance, the P value is less than 0.001, indicating that the regulatory effect of sports exercise regulation variable on the mobile addiction independent variable is significant. Therefore, sports exercise is a regulatory factor for mobile addiction and sleep quality.

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Table 5. Results of moderating effects.

https://doi.org/10.1371/journal.pone.0307162.t005

4.1 Analysis of the variability in physical exercise among college students

The proportion of male university students with moderate and high exercise levels is much higher than that of female university students. This is consistent with the findings of a previous study from the United States. Trevor Egli MS determined that the main factors driving exercise motivation among American male college students are strength, competition, and challenge, while the main factors driving exercise motivation among American female college students are weight management and appearance [27]. We believe that the reason for this phenomenon in China may be that the exercise motivations of different genders of college students have changed. Chinese male university students engage in moderate and high exercise levels more often, to pursue strength, competition and challenge, and thus most of them choose high-intensity exercise. The feudal society in China for thousands of years has given women the stereotypical label of quiet, gentle and peaceful, which has not been completely removed. Today, the aesthetic pursuit of women in China is based on to be thin for beauty, wherein more female university students engage in low-intensity aerobic exercise to maintain their beauty of body shape, which may be the reason why there are differences in physical exercise among Chinese university students based on gender.

This study found that there were significant differences in physical exercise among university students in different grades. This is consistent with Wu’s research [28], which found that senior students face with graduation and future career or study planning, and can only spend less time on physical exercise. The proportion of students in the first year engaging in moderate and high levels of exercise was slightly higher than that of students in the second, third, and fourth years. However, the proportion of freshmen was significantly lower than that of sophomore, junior, and senior students in terms of low-intensity exercise. We believe that the reasons for this phenomenon may be related to the education system in Chinese universities. students in the first year have less academic pressure and apart from the daily class time, they have no worries about other things. They have sufficient time for physical exercise without worrying about the lack of physical and mental strength which will affect other affairs. However, in the second, third, and fourth years, students not only face academic pressure, but the pressure of future career planning, further education and other aspects, which make them begin to use their extracurricular time to make up for their deficiencies. Therefore, students turn to low-intensity exercise to minimize physical exertion. In fourth year, the proportion of students engaging in moderate and high levels of exercise is higher than that of the second and third years. The reason for this may be that some fourth-year students have found jobs or obtained qualifications for further education, leading to an increase in the proportion of students engaging in moderate and high levels of exercise. This pressure is particularly prominent during the pandemic [29]. Additionally, it is crucial to understand that university students in China are in a critical evolutionary phase of transitioning from adolescence to adulthood, a period characterized by significant psychological and lifestyle changes [30]. This transition phase influences not only their academic and professional choices but also impacts their psychological well-being and daily behaviors including exercise habits. Undergraduate education in Chinese universities lasts only for four years (except for medical majors), and three out of the four years of college students enrolled in 2019 have been affected by the pandemic. Their schools cannot guarantee normal teaching, which leads to a lack of solid mastery of professional knowledge and skills, greatly increasing the students’ pressure for the future. Therefore, most sophomore and junior students chose low-intensity exercise to ensure that they have sufficient time and energy to study.

4.2 Analysis of the differences in sleep quality among college students

This study determined that 51.9% of Chinese university students had problems with sleep quality. This is consistent with Hu’s research result, which found that more than half of the sample had sleep problems [31]. According to previous studies, long-term use of mobile phones and less physical exercise may be associated with poor sleep quality among university students [32].WANG suggested that university students’ sleep quality is susceptible to different risk factors, and the main determinants need to be considered when designing interventions to improve their sleep quality [33]. The university students’ sleep quality can be affected by various factors, and with the development of technology, smartphone addiction has gradually become an important factor affecting university students’ sleep quality [34]. This study mainly explores the influence of smartphone addiction on university students’ sleep quality, and smartphone addiction is considered as the main risk factor to investigate. Elizabeth B. Dowdell reported that a quarter of college students have a habit of using smartphones before sleep, which is associated with memory decline. Moreover, the trend of using smartphones before sleep shows an increasing tendency among college students [35]. Bartel, K study also mentioned that restricting the use of mobile phones in young people would enable them to sleep longer and have higher sleep quality [36]. All aforementioned studies have proved that smartphone addiction has an impact on sleep quality.

No significant gender differences exist in sleep quality among university students. This is different from previous studies where gender differences were found to play an important role in sleep quality, with higher sleep problem rates among females [37]. However, Zhang’s research found that sleep quality among East Asian women was better than that of women in other regions of the world [38]. This phenomenon may be related to cross-regional cultural differences. In terms of grade level, the sleep quality of freshmen was significantly better than that of students in the other three grades, which is related to the education system in China. In high school, Chinese students face heavy academic pressure, and the prevalence of sleep problems among Chinese high school students was 42.3%, according to Cao’s research [39]. However, in university, the decrease in academic pressure in the first year leads to an increase in students’ sleep time and improvement in sleep quality. However, students begin to consider their own development and future plans starting from second year, and the increasing pressure forces them to sacrifice more time in academic and skill learning. Therefore, from the data, it can be observed that the proportion of university students with poor sleep quality decreased significantly from first to third year. The reason for the improvement in sleep quality of senior students is that some of them have already found jobs with graduation approaching, and they no longer have to worry about academic and graduation pressures. Therefore, the sleep quality of students in this phase begins to improve.

4.3 Analysis of the variability in mobile phone addiction among college students

No significant difference exists in mobile phone addiction between students of different genders. This is different from previous studies which indicated that females are more susceptible to mobile phone addiction [40, 41]. However, no difference exists in this paper. The possible reason for this may be that the epidemic has led to a decrease in the entertainment methods of college students. Frequent online classes, school closures and dormitory lockdowns have forced college students to spend more time indoors [42]. During this period, mobile phones became the most convenient way for communication and entertainment for students, regardless of their gender, which led to the result that no difference exists in mobile phone addiction between male and female students in this study.

A significantly positive correlation exists between the four dimensions of mobile phone addiction in college students (withdrawal symptoms, salience, social comfort, and mood changes) and their sleep quality scores. A higher score on the PSQI represents lower sleep quality, which means that the higher the degree of mobile phone addiction among college students, the worse their sleep quality would be, which is consistent with the findings of Fengd’s study [43]. From December 2019 to December 2022, under the impact of COVID-19, China implemented normalized prevention and control policies, which forced many universities to adopt online teaching measures once positive cases were found in the cities where they were located. If students were in close contact with positive cases on campus, they had to undergo a seven-day home quarantine. In quarantine, smartphones became their only means of communication and information acquisition with the outside world. Social interaction is a universal basic human motivation [44]. Saadeh and H’s research investigation found that 85% of people in quarantine increased their mobile phone usage during the isolation period, with 42% using their phones for more than 6 hours per day [45]. Long-term quarantine can provide strong psychological pressure. Therefore, in long-term medical isolation situations, corresponding measures must be taken to ensure the psychological health of the quarantined individuals, avoiding negative mental problems. Previous studies have shown that sleep quality is influenced by many factors, such as lifestyle, environmental factors, work conditions, social life, economic conditions, physical health status, stress, and so on [46]. Mobile phone addiction, as an environmental factor, has a negative impact on sleep quality [47–49], which is consistent with the results of the correlation analysis in this study. Poor sleep quality can lead to depression and stress [50], affecting the health of college students in a significant negative way [51, 52]. Therefore, Chinese universities should take measures to reduce the use of smartphones by students.

4.4 Correlation discussion

A significantly negative correlation exists between the total score of college students’ physical exercise level and their sleep quality, indicating that physical exercise may improve college students’ sleep quality [53, 54]. In Dolezal’s study, regardless of the type and intensity of physical activity, exercise can improve subjective sleep quality [54]. Physical exercise promotes the college students’ physical and mental health. Severe mobile phone addiction is a psychological illness, and physical exercise has a significant positive impact on psychological improvement [55]. Physical exercise can reduce the impact of psychological stress on emotional states, promote emotional health [56], and provide a sense of fulfillment and satisfaction. Through physical exercise, college students can increase knowledge, develop skills, and feel a sense of fulfillment [57], which can enable them to focus their attention on real-life rather than seeking fulfillment in the virtual world.

4.5 Moderating effect discussion

Through the analysis of the moderating effect, physical exercise has a significant moderating effect on the impact of mobile phone addiction on college students’ sleep quality. From a physiological perspective, the long-term use of smartphones by college students increases the amount of microwave radiation that the body receives. The central nervous system is the most sensitive target for microwave radiation. Therefore, microwave radiation’s impact on the human body is primarily through the nervous system [58]. The main cause of neurasthenia syndrome caused by electromagnetic radiation from smartphones is long-term close contact with the body [59]. This may be the reason why sleep quality is affected by mobile phone addiction. However, an explosion of information exists in today’s society, and smartphones are the most economical, convenient, and fastest way for college students to obtain external information. Continuous theory holds that the brain’s mental activity is continuous, and dreams at night unconsciously reflect daytime activity [60]. It is also important to consider that university students are in a crucial developmental phase transitioning from adolescence to adulthood, characterized by significant psychological and lifestyle changes. This transition not only influences their academic and career choices but also their sleep patterns and behaviors, including smartphone use [61]. During the COVID-19 epidemic, the most common information college students obtained through short videos was related to the epidemic, increasing their anxiety. Therefore, prolonged use of smartphones can cause college students to unconsciously recall the information they searched for about the epidemic during the day [62], which is also one of the reasons for poor sleep quality in college students. Understanding the developmental stage of university students can provide additional context to analyze how factors like smartphone addiction impact their sleep quality.

The significant moderating effect of physical exercise on sleep quality in college students may be due to the following reasons: (1) When college students engage in physical exercise, they reduce the time of using smartphones, which reduces the impact of microwave radiation on their central nervous system [58]. (2) Through physical exercise, college students gain a sense of fulfillment and satisfaction, reducing their need to seek fulfillment in the virtual world of smartphones [63]. (3) Physical exercise can release the stress that college students experience in their studies and daily life, thus improving their sleep quality [64].

The limitations of this study comprise the following: (1) Mobile phone addiction is caused by multiple factors, and there are many interfering factors in the exploration process. The impact of each variable must be explored through quasi-experimental and longitudinal studies, which can be future research directions. (2) The scope of the investigation needs to be further expanded, and the research subjects should be representative to verify the universality and rationality of this study. (3) This study only discusses the impact mechanism of physical exercise on mobile phone addiction and sleep quality, without delving into the discussion of causal relationships. (4) The study did not collect information on participant characteristics and potential confounding factors during data collection, which is a limitation of this study.