Materials & Methods
This cross-sectional study employed stratified cluster random sampling to select medical university students in Zhejiang Province in China, between April to September 2021 ( Ding et al., 2022 ). All students were first stratified by specialty (clinical medicine, nursing, and others) to ensure a representative sample from each group. Following stratification, classes were randomly selected by grade level within each specialty using a computer-generated random number table. The number of classes chosen was proportional to the number of students in each specialty relative to the total student population. All students enrolled in the selected classes were recruited in the survey. To verify the sample’s representativeness, we compared the final sample’s characteristics (gender, age, and grade distribution) with those of the overall student population, finding no significant differences ( P > 0.05).

Participation in the survey was voluntary and information was collected after obtaining written informed consent. Inclusion criteria were age 18 and above, and majoring in medicine. Among the 1,635 participants surveyed ( Ding et al., 2022 ), 24 were excluded for missing information on most variables, 85 for missing SRH data, and 130 for sleep latency data. The final sample comprised 1,396 medical students from over 12 majors. The Ethics Committee of Wenzhou Medical University approved this study (Ethics Approval Document No 2021-022).

Sleep latency was determined by the question “During the past month, how long (in minutes) has it taken you to fall asleep each night?” Previous studies have demonstrated that self-reported sleep latency has been widely used and scientifically validated in sleep epidemiology studies ( Siddiquee et al., 2023 ; Zhang et al., 2023 ). Prior studies have suggested that self-reported sleep latency is highly correlated with actigraphy-assessed sleep latency ( r = 0.667, P < 0.001) ( Biddle et al., 2015 ). To assess the test-retest reliability and calibration validity of our measurement, a repeat survey was conducted on 163 medical students, approximately 10 months after the initial survey. The median ( P 25 , P 75 ) sleep latency remained consistent, and the test–retest reliability was 0.72. Additionally, 60 participants were asked to record their sleep diary for five consecutive days (at least one weekend day), to confirm the consistency between diary-reported and questionnaire-reported sleep latency, which was presented through the Bland-Altman plot ( Figure 1 ). The sleep diary data were aggregated into representative values for each participant through a weighting process. The weighted average sleep latency for each participant was calculated as: average sleep latency on weekdays ×5/7 + average sleep latency on rest days or the value of a single rest day ×2/7 ( Dashti et al., 2022 ; Kim et al., 2016 ). The intraclass correlation coefficient (ICC) for the repeatability of sleep latency recorded in the sleep diaries was 0.900 ( P < 0.001), indicating good agreement. In this study, the restricted cubic spline (RCS) demonstrated that sleep latency had a dose–response relationship with suboptimal SRH ( P for overall <0.001, P for non-linear = 0.200). According to the curve fit and previous literature ( Albqoor & Shaheen, 2021 ; Lemma et al., 2012 ; Lohsoonthorn et al., 2013 ), sleep latency was categorized into four groups: <10 min, 10–19 min, 20–29 min, and ≥30 min.

SRH, which provides a comprehensive reflection of a person’s subjective and objective health, has been widely used in health surveys of both adolescents and adults ( Johnson & Richter, 2002 ; Kim et al., 2013 ; Li et al., 2019 ). SRH was assessed through the question “How would you perceive your health status?” using a 5-point Likert-type scale in this study, where “very good” and “good” indicated good health, while “fair”, “poor”, and “very poor” represented suboptimal SRH, as previously suggested ( Ding et al., 2022 ; Stenholm et al., 2016 ). Through a repeat survey of 163 medical students, we observed a consistent response rate of 70.4%, and the test–retest reliability of SRH was 0.50, with a Cohen’s kappa coefficient of 0.41 (95% CI [0.27–0.55]). To evaluate the validity of SRH, the Self-Rated Health Measurement Scale (SRHMS) ( Xu et al., 2010 ) was utilized among 104 medical students, and the correlation coefficient between SRH and SRHMS was 0.74. These results suggested that the SRH assessment method in our study was valid and reliable.

Demographic characteristics such as gender, grade, residence, parental education, being a single child, and lifestyle factors such as BMI (kg/m 2 ), smoking, alcohol consumption, physical activity, sitting time, and midnight snack habits were gathered through a self-reported questionnaire. BMI was calculated as weight (kg) divided by height squared (m 2 ), and according to Chinese standards ( National Health and Family Planning Commission of the People’s Republic of China (NHFPC China), 2013 ), body type was categorized into underweight (BMI < 18.5 kg/m 2 ), normal weight (18.5 ≤ BMI < 24.0 kg/m 2 ), overweight (24.0 ≤ BMI < 28.0 kg/m 2 ), and obese (BMI ≥ 28.0 kg/m 2 ). Based on the association between body shape and suboptimal SRH in this study, we divided body shape into two groups: overweight (BMI ≥ 24.0 kg/m 2 ) and non-overweight (BMI < 24.0 kg/m 2 ). Current drinking was defined as consuming alcohol three or more times a week for at least six months, and smokers were classified as individuals who smoked at least one cigarette per day for six months or longer. Additionally, sleep behavior factors such as sleep duration, chronotype, and nap were collected. Sleep duration was assessed by the question “During the past month, how many hours of actual sleep do you usually get per night? (not equal to bedtime)”, and the options were categorized into seven groups: ≤5 h, 6 h, 7 h, 8 h, 9 h, 10 h, and ≥11 h ( Ma et al., 2020 ; Shi et al., 2021 ). Following previous studies ( Liu et al., 2016 ; Shi et al., 2021 ), sleep duration was categorized into three groups: ≤6 h, 7 h, and ≥8 h. It has been found that there is a good correlation between self-measured sleep duration and the evaluation results of objective methods such as polysomnography ( Biddle et al., 2015 ). Chronotype, was assessed with the question “People can be categorized into morning-type and evening-type, which type do you think you belong to?” The response options included five categories: morning-type (early to bed, early to wake), morning-type more than evening-type, intermediate-type, evening-type more than morning-type, and evening-type (late to bed, late to wake). The Cronbach’s alpha coefficient for this evaluation method has been confirmed to be 0.71 ( Zhou et al., 2021 ). Considering the comparability with other studies and the sample size of each group, chronotype was combined into three groups: intermediate-type, morning-type, evening-type ( Zhai et al., 2021 ). We chose to assess chronotype using a single question, which is consistent with the method of evaluating chronotype in previous studies ( e.g. , Nurses’ Health Study II) ( Kianersi et al., 2023 ). Afternoon nap is divided into two categories: yes or no. The reliability and validity of these variables have been reported in prior studies ( Lauderdale et al., 2008 ; Selvi et al., 2018 ).

Quantitative variables were reported as mean (SD) or median ( P 25 , P 75 ), and qualitative variables as frequency (percentage). We conducted one-way ANOVA, Chi-square, and rank-sum tests to compare covariates among students with different sleep latencies. A RCS was used to explore the form of the relationship between sleep latency and suboptimal SRH. For node selection, we employed an automatic method based on the minimum Akaike Information Criterion (AIC) value to determine node locations ( Huang et al., 2022 ; Paluch et al., 2022 ). This approach, a widely used criterion for node selection, reduces the potential bias of manual node selection and improves model fit and reliability. We used logistic regression to estimate OR and 95% confidence intervals ( CI ) to investigate the independent association between sleep latency and suboptimal SRH. The reference group was “sleep latency ≤10 min”. Model 1 was adjusted for gender, grade, residence, parental education, and single child, while Model 2 was further adjusted for BMI, afternoon nap, midnight snack habit, sitting time, physical activity, sleep duration, and chronotype. The P for trend was obtained by modeling the median within each sleep latency group as a continuous variable. Additionally, considering that medical students may have the habit of eating late night snacks due to studying too late and that late night snacks can affect the quality of sleep ( Faris et al., 2022 ), we examined the combined effect of sleep latency and midnight snack habit on suboptimal SRH. In this multivariable-adjusted logistic model, we combined four groups of sleep latency and two groups of midnight snack habits into eight subgroups, taking participants with sleep latency <10 min and no midnight snack habits as a reference.

Stratified analysis was conducted to test the consistency of the relationship between sleep latency and suboptimal SRH across different subgroups of medical students, including afternoon nap (yes, no), midnight snack habit (yes, no), BMI (<24 kg/m 2 , ≥24 kg/m 2 ), sleep duration (≤6 h, 7 h, ≥8 h), and chronotype (intermediate-type, morning-type, evening-type). The P -values for the interaction effects were calculated using the likelihood ratio test.

Sensitivity analysis was conducted to test the robustness of the results by excluding individuals who smoked or consumed alcohol, as these factors are significant confounding factors potentially associated with both sleep latency and suboptimal SRH. Additionally, sleep latency was further included as a continuous variable in the logistic regression model. To assess the robustness of the results to the choice of classification criteria, we reanalyzed the data by categorizing sleep latency into three groups (≤15 min, 15–30 min, and >30 min), in line with previous studies ( Siddiquee et al., 2023 ).

All statistical analyses were conducted using SPSS 23.0 and Empower ® ( http://www.empowerstats.com ; X & Y solutions, Inc. Boston MA), with a significance level of 0.05 (two-sided).