Discussion
This cross-sectional study found that after adjusting for potential covariates, prolonged sleep latency was associated with higher odds of suboptimal SRH compared to sleep latency less than 10 min. These associations were consistently observed in stratified analyses by lifestyle factors and in the sensitivity analyses. To the best of our knowledge, this is the first study to demonstrate an association between sleep latency and suboptimal SRH among medical students.

Our study found a high prevalence of prolonged sleep latency among medical students (27.9%). This is similar to the results of a meta-analysis that included 57 studies involving 82,055 Chinese university students, which found a 25.5% prevalence of prolonged sleep latency ( Li et al., 2017 ). The incidence of prolonged sleep latency in other countries/regions is also roughly in the range of 20%–35% ( Albqoor & Shaheen, 2021 ; Lohsoonthorn et al., 2013 ; Sweileh et al., 2011 ). However, Lemma et al. (2012) found a 48.5% prevalence of prolonged sleep latency among 2,230 college students in Ethiopia. This result is significantly higher than studies in other countries and regions and may be closely related to the prevalent caffeine intake habits of college students in the region and its relationship with sleep latency. Additionally, the differences in the detection rate of longer sleep latency across countries may be attributed to variations in education systems, academic disciplines, and sleep latency measurement methods, all of which can influence students’ sleep habits ( Albqoor & Shaheen, 2021 ). In the context of China’s “involution” culture, it is well established that college students, particularly medical students, tend to study for extended hours, experience higher academic pressure, often engage in studying before bedtime, and report poorer sleep quality ( Preisegolaviciute, Leskauskas & Adomaitiene, 2010 ). Furthermore, discrepancies may exist between objective sleep latency, as measured by actigraphy, and self-reported sleep latency.

In addition, we observed variation in the prevalence of suboptimal SRH among university students from different countries and regions. For instance, a study of 2,312 Chinese university students showed a suboptimal SRH rate of 71% ( Li et al., 2019 ), while a longitudinal study of 568 Italian university students found a prevalence of 20.5% ( Porru et al., 2022 ). A cross-sectional study of 326 Hungarian international medical students showed a prevalence rate of 32.5% ( Umami, Paulik & Molnar, 2022 ); and a study of 27.3% in a cross-sectional study of 689 young Chicagoans ( Stefan et al., 2017 ); and a study encompassing 24 countries, 27 universities, and 17,465 undergraduates showed a prevalence of 12% ( Steptoe, Peacey & Wardle, 2006 ). These discrepancies may stem from differing definitions of suboptimal SRH, with some studies categorizing SRH as “average”, “poor”, or “very poor” as suboptimal, while others include only “poor” or “very poor”. Furthermore, although the Steptoe study reported a suboptimal SRH rate of 12% among college students ( Steptoe, Peacey & Wardle, 2006 ), the study population consisted of students in non-medical or health-related majors. We also note that university students (especially medical students) may face unique challenges, such as intense academic pressure and high social expectations ( Bayram & Bilgel, 2008 ; Mao et al., 2019 ).

Our study found that prolonged sleep latency was associated with a higher likelihood of experiencing suboptimal SRH, supporting previous research. For instance, Kim et al. (2021) reported that in a Korean adult population, individuals with prolonged sleep latency had a higher risk of subjective cognitive decline. Additionally, a cohort study highlighted the relationship between prolonged sleep latency and depression ( Williams et al., 2020 ). Studies have also linked sleep latency to hypertension, with a Swedish study and a Chinese study both finding longer sleep latency in individuals with hypertension ( Sahlin et al., 2009 ). Our study confirmed the association between prolonged sleep latency and poorer health, even after accounting for confounding factors like chronotype and sleep duration. While further research is necessary to fully validate these findings, our study underscores the importance of addressing sleep latency as a means of promoting health.

There are some potential mechanisms for the association between prolonged sleep latency and suboptimal SRH. Firstly, habitual prolongation of sleep latency may lead to accumulation of sleep debt. For example, if a person needs 8 h of sleep per night, but only actually sleeps 7 h, a sleep debt of 1 h is accumulated. Sleep debt can accumulate over multiple nights and negatively impact a person’s health ( Spiegel, Leproult & Van Cauter, 1999 ). Secondly, prolonged sleep latency is positively associated with anxiety ( Narisawa, 2013 ), which may activate the sympathetic nervous system and elevate cortisol levels ( Vgontzas & Chrousos, 2002 ), contributing to adverse health outcomes such as hypertension, depression, and metabolic syndrome ( Duman, Malberg & Nakagawa, 2001 ; Lappin & Whaley, 1985 ; Miskiewicz et al., 2018 ). Thirdly, sleep efficiency is calculated as total sleep time divided by time in bed, multiplied by 100%. Time in bed is defined as sleep onset latency, total sleep time, time awake after initial sleep onset but before final awakening, and time spent attempting to sleep after final awakening ( Reed & Sacco, 2016 ). When time in bed is constant, prolonged sleep latency directly reduces total sleep time, thereby decreasing sleep efficiency. Furthermore, prolonged sleep latency may indirectly impair sleep efficiency through the following mechanisms: (a) increased sleep fragmentation ( Hermans et al., 2021 ), characterized by more frequent nighttime awakenings, which further reduces total sleep time; and (b) the induction of sleep-related anxiety ( De Almondes, Castro & Paiva, 2022 ), leading to sympathetic nervous system overactivation, suppression of melatonin secretion ( Li et al., 2023 ), and increased wakefulness time ( Huang, Li & Leng, 2020 ). These factors collectively contribute to extended wakefulness during time in bed, ultimately diminishing sleep efficiency. Poor sleep efficiency has been shown to be associated with adverse health outcomes such as cardiovascular metabolic disease, cognitive impairment, psychological distress, and inflammatory response ( Meerlo, Sgoifo & Suchecki, 2008 ; Irwin, 2019 ; Moon et al., 2022 ; Yiallourou & Carrington, 2021 ). Fourthly, excessive sleep latency may also be a manifestation of difficulty falling asleep (insomnia), and systematic reviews have consistently shown that insomnia is associated with multiple adverse health outcomes ( Bos & Ferreira, 2019 ; Perlis et al., 2022 ). Finally, medical students with a prolonged sleep latency are more likely to be evening-type students, a group associated with higher anxiety levels and poorer sleep quality compared to morning-type students ( Silva, Magalhaes & Duarte, 2020 ), which may negatively affect SRH.

Our study revealed that participants with both prolonged sleep latency and midnight snack habits had an increased risk of suboptimal SRH. Midnight snacks, as an unhealthy lifestyle, may disrupt biological circadian rhythms and increase metabolic imbalance ( Gladka, Le Couteur & Simpson, 2023 ), thereby adversely affecting nighttime sleep, increasing the risk of overweight/obesity ( Hess, Jonnalagadda & Slavin, 2016 ), and ultimately affecting one’s overall health evaluation.

One of our key strengths is conducting groundbreaking research examining the relationship between sleep and suboptimal SRH in the context of Chinese medical students. However, this study has several limitations. As a cross-sectional study, we cannot determine the temporal sequence between prolonged sleep latency and suboptimal SRH, therefore it is difficult to discern whether prolonged sleep latency is a coping mechanism for poor SRH. Future studies should consider longitudinal study designs to explore the association between sleep latency and suboptimal SRH. Additionally, the self-reporting of sleep latency and suboptimal SRH may have introduced measurement bias. Nonetheless, our measurements showed high test-retest reliability and strong associations with objective measures ( Biddle et al., 2015 ; Tamura et al., 2018 ). Moreover, it is important to note that self-reported sleep latency data summarize an individual’s sleep patterns over the past month, in contrast to polysomnography or actigraphy data, which are typically collected over a single night ( Siddiquee et al., 2023 ). This distinction means that self-reported data are more revealing of a person’s long-term difficulty falling asleep. Finally, this research was carried out at a medical university located in Zhejiang Province, which might restrict the generalizability of our findings to other geographical areas. Nonetheless, it’s worth noting that the university’s students come from all over the country, which partially compensates for potential limitations in sample representativeness.