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

The definition of sleep disturbances

Sleep disturbances encompassing a wide range of sleep complaints, such as difficulty in initiating sleep (DIS), difficulty in maintaining sleep (DMS), early morning awakenings (EMA), non-restorative sleep (NRS) and poor sleep quality, are a major health problem (Li et al., 2018).

Prevalence in college students

Previous studies demonstrated that sleep disturbances are common in all around the world, sleep problems might be a unrecognized public health issue in low- and middle-income and emerging economy countries, especially among young adults and university student populations (Peltzer & Pengpid, 2015). For many college students, the transition from adolescence to adulthood poses numerous new challenges, such as roommates/dorm life, erratic living schedules, coping with high academic and social pressures, work obligations, and new level of independence, etc. This transition may provide the necessary stress for development or exacerbation of sleep disorder, which is highly prevalent in this population (Jiang et al., 2015; Peltzer & Pengpid, 2015).

The effect of poor sleep

Sleep disorder is detrimental to physical and mental health of the young generation. The consequences of poor sleep in this population are increasingly salient, as several recent studies have shown poor sleep is associated with poor physical and psychological health outcomes, such as mood disturbances and decreased cognitive performance (Li et al., 2018). Also, poor sleep has been associated with worse academic performance, which can result in dropout or underemployment (Barahona-Correa et al., 2018). Further, there is evidence for a causal association between sleep problems and general overweight or obesity (Fatima, Doi & Al Mamun, 2018; Öztürk & YabancıAyhan, 2017).

The factors of poor sleep quality

A systematic review reported that 18.5% (95% CI (confidence interval): 11.2∼28.8%) of adolescents had sleep quality problems worldwide, considerably higher than rates of 7.4% reported in general population (Jiang et al., 2015). Research has indicated that sleep quality is linked to smartphone use (Wang et al., 2019). Lifestyle and behaviors, such as eating nutritious foods, may be able to influence sleep quality (St-Onge, Mikic & Pietrolungo, 2016). A study conducted by Sawah et al. (2015) revealed that higher odds of developing poor sleep quality were associated with coffee intake. In a study of 1,044 university students, Kenney et al. (2013) found that students with poor sleep quality had a greater likelihood of alcohol abuse. In addition, the level of sleep quality is also linked to poor physical health and risky health behaviors (Swinkels et al., 2013), excessive Internet use (Shadzi, Salehi & Vardanjani, 2020), and smoking (Cohrs et al., 2014) and physical activity (Lee, 2017). Moreover, many studies showed that cold indoor environments negatively affected sleep comfort and quality (Chimed-Ochir et al., 2021).

To our knowledge, although the foregoing studies verified that poor sleep quality was prevalent among college students and associated with various factors, there was few studies related to sleep quality among undergraduates conducted in the southwest of China, especially in Guizhou province, which is located on the Yunnan-Guizhou Plateau, with a low economic level and relative laggard in many aspects compared to the eastern part of China; moreover, there were significant differences of lifestyle compared with other parts of China. A previous meta-analysis reported that there was one related study conducted in Guiyang published in Chinese in 2011 (Li et al., 2018). Therefore, it is important to evaluate the prevalence of poor sleep quality and explore whether sleep quality is associated with a variety of behaviors and external factors. For this purpose, a cross-sectional study was conducted in a university located at the north of Guizhou Province, China; participants filled out a questionnaire which included evaluation of PSQI scores and related behaviors and external factors. Moreover, previous studies showed there were some difference of life habits between genders, such as smoking (Li et al., 2022; Yu et al., 2022), drinking, personal hygiene, the amount and frequency of exercise (Yang et al., 2022) and the frequency of playing games (Yang et al., 2022), which were all associated with sleep quality; thus, the influence factors between genders should be considered respectively. We hope that the results of this study can lead to a better understanding of sleep quality among college students and what health-promoting activities can improve sleep quality.

Study population and procedure

The cross-sectional study was conducted between December 1, 2018 and January 12, 2019, and the stratified cluster sampling was used to recruit students who were enrolled in Zunyi Medical University, Guizhou province, China, a comprehensive university, including medical major and non-medical major, stratification was carried out according to different grades and majors. Before the survey, the research assistants accepted a unified training, then the research assistants entered the classes, explained the objectives and details of the project and distributed questionnaires, students were given sufficient time to answer the questions independently, and questionnaires were collected on the spot. All participants provided informed written consent and they received monetary compensation for taking part in this study, which was approved by the Medical Ethics Committee of Zunyi Medical University (No.: ZMU-IRB[2018] 1-067). Participants who did not provide informed written consent or did not complete the study questionnaire were excluded from the study. A total of 1,200 questionnaires were distributed, 1,063 valid questionnaires were regained, and the valid rate was 88.6%.

Questionnaire

A standardized and unified self-report closed-end questionnaire was used for the survey. The questionnaire consisted of three parts, including basic demographic data, the Pittsburgh Sleep Quality Index (PSQI) questionnaire, and the influence factors questionnaire.

The first part included the basic demographic data of age, sex, grade, height, weight and major.

The second part was the PSQI questionnaire which is the most common measure of sleep quality (Pilz et al., 2018). The PSQI is an 18-item self-report questionnaire designed to assess overall sleep quality over last one month period. The first four items relate to sleeping habits and are in free response format (e.g., “How many hours of actual sleep do you get at night?”). The remaining items are related to sleep disturbances and daytime impairments. The response choices include four options referring to the last month (e.g., 0 = Not during the past month, 1 = Less than once a week, 2 = Once or twice a week, 3 = Three or more times a week). The 18 items are scored nonlinearly to generate seven component scores, namely, subjective sleep quality (hereafter referred to as Quality), sleep latency (Latency), sleep duration (Duration), habitual sleep efficiency (Efficiency), sleep disturbances (Disturbances), use of sleeping medication (Medication), and daytime dysfunction (Dysfunction). Each component score has a possible range of zero to three, with higher scores indicating worse sleep. The sum of the component scores yields a PSQI general score of sleep quality which ranges from zero to 21 (Raniti et al., 2018). It is common to classify subjects into two groups: good (PSQI ≤ 5) and poor sleepers (PSQI > 5) (Sawah et al., 2015; Wang et al., 2019; Buysse et al., 1989).

The third part was a self-designed closed-end questionnaire. Previous studies reported that sleep quality was associated with electronic products, physical activity, living conditions, and lifestyle, etc. According to the results of communicating with students, almost all college students have electronic products, and some students did not eat breakfast. Additionally, tea and tobacco and alcohol are the three pillar industries of Guizhou province, which can be bought by students easily. Therefore, we hold a point on that the questionnaire should encompass influence factors as follows, such as smoking habits, drinking habits, and coffee intake before sleep (e.g., 1 = Never, 2 = Occasional (Less than twice a month), 3 = Often); night snack intake habits and skipping breakfast habits (e.g., 1 = Never, 2 = One or twice per week, 3 = More than three times per week), physical condition (e.g., 1 = Good, 2 = General, 3 = Bad), monthly expenses of using mobile phones (e.g., 1 = Less than 50 yuan, 2 = More than 50 yuan), how long did you spent on mobile phones before sleep, time spent on playing games (e.g., 1 = Never, 2 = One to two hours per day, 3 = Three to four hours per day, 4 = More than four hours per day), physical exercise (e.g., 1 = More than five times per week, 2 = Three to four times per week, 3 = One or twice per week, 4 = Never), dormitory noise and bright dormitory light (e.g., 1 = No, 2 = Yes), dormitory environmental hygiene (e.g., 1 = Good, 2 = General, 3 = Bad), relationship with classmates and lover (e.g., 1 = Harmony, 2 = General, 3 = Strained), academic stress and employment stress (e.g., 1 = Mild, 2 = Moderate, 3 = Severe) and family economic conditions (e.g., 1 = Affluence, 2 = General, 3 = Poverty).

Additionally, Cronbach’s alpha coefficient was 0.566 for the third part of questionnaire, the KMO coefficient was 0.670, close to 0.70 (P < 0.001). The reliability and validity was acceptable.

Sample size calculation

We assumed that the unknown prevalence rate of sleep disorder was 50 percent, the precision (two-sided) was 0.1, and the confidence level was 0.95, a sample size of 482 could be calculated by PASS 15.0 (NCSS, LLC, Kaysville, Utah, USA). Considering the sample method was cluster sampling, the sample size should be increased by 50 percent, so we needed to recruit at least 723 participants.

Statistical analyses

Epidata 3.0 (EpiData Association, Odense, Denmark; http://www.epidata.dk/, ) software was used for data entry and error detection, SPSS 18.0 (IBM Corp., Armonk, NY, USA) was used for statistical analyses. All measures variables followed a normal distribution determination. Normal distribution measurement data was described by mean and standard deviation, differences between two groups were tested by two-independent samples t-test, one way analysis of variance (one way ANOVA) was used to test differences of multiple groups, followed by LSD or Tamhane multiple comparisons tests. Abnormal distribution measurement data was described by average rank, differences between two groups were tested by Mann–Whitney U test, differences of multiple groups were tested by Kruskal-Wallis test, followed by adjusted test level through Bonferroni multiple comparisons tests. Count data was described by rate. Logistic regression analysis was conducted to determine the impact of variables on sleep quality, and the influence factors were hierarchically analyzed by gender. α = 0.05 was taken as the level of significance.

Demographic statistics

A total of 1,063 valid questionnaires were collected. Participants were between 17 and 26 years old (mean 19.8 ± 1.3 years): 646 were females and 417 males, 738 were medical students and 325 were non-medical, numbers of freshmen, sophomore, junior and senior were 344, 458, 240, 21, respectively.

The analysis of influence factors related to sleep quality

The results of univariate logistic regression analysis of influence factors for male showed that there were statistically significant differences in dormitory noise, dormitory environmental hygiene, monthly expenses of using mobile phones, time spent on mobile phones before sleep, smoking, coffee intake before sleep, night snack intake, physical condition and frequency of participating in physical exercise (Table 3). Factors with statistically significant differences in univariate analysis were taken as independent variables and sleep quality (PSQI ≤5 = 1, PSQI >5 = 2) as dependent variable. The results of multivariate logistic regression analysis showed that higher odds of developing poor sleep quality were associated with physical condition (OR: 2.971 (2.034∼4.339)) and smoking (OR: 1.754 (1.258∼2.446)) (Table 4).

The results of univariate logistic regression analysis of influence factors for females indicated statistically significant differences in dormitory noise, bright dormitory light, dormitory environmental hygiene, time spent on mobile phones, time spent on playing games per day, drinking, smoking, coffee intake before sleep, skipping breakfast, physical condition, frequency of participating in physical exercise, relationship with classmates, academic stress, and family economic conditions (Table 3). Factors with statistically significant differences in univariate analysis were taken as independent variables and sleep quality (PSQI ≤5 = 1, PSQI >5 = 2) as dependent variable. The results of multivariate logistic regression analysis showed that the significant influence factors consisted of dormitory noise, skipping breakfast, drinking coffee before sleeping, time spent on mobile phones before sleep, time spent on playing games per day, physical condition and academic stress. 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 going to bed (OR: 2.111 (1.155∼3.861)), playing mobile phones more than 45 min before going to bed (OR = 1.745 (1.210∼2.515)), more time spent on 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)) were not conducive to ensuring sleep quality (Table 4).