Abstract
The aim of this study was to investigate the sleep structure of mid-day naps in students who frequently nap, and to determine whether this structure has any impact on their subjective feelings after nap. A total of 91 college students (male, mean age 20.47 ± 2.02 years) completed one-hour mid-day nap with polysomnographic recording in a sleep laboratory. Upon awakening, self-ratings of sleep quality and sleepiness were assessed using questionnaires. The sleep structure of mid-day naps varied among participants: 47.3% participants experienced stage-1, -2, and -3 non-rapid eye movement (NREM) sleep in sequence in the one-hour nap, while rapid eye movement (REM) sleep occurred between 40 and 60 min after lights off in 27.5% participants, and less than 15 min in 16.5% participants. After nap, participants who achieved stage-3 sleep reported better sleep quality, but the subjective sleepiness was not influenced by the sleep stage contained in nap or the sleep stage at awakening. These findings highlight the diversity in the sleep structure of mid-day naps, suggesting that this variability may need to be considered when studying the effects of napping.
Keywords: Mid-day nap, Sleep structure, Sleep quality, Subjective sleepiness, College students
Introduction
Daytime nap, especially mid-day nap, is widely used to counteract the negative consequences of poor nocturnal sleep and post-lunch dip [1]. In a random sample of 171 American teenagers, 53% reported napping at least once on weekdays, while 33% on weekends, with the average duration of nap was 43 min on weekdays and 46 min on weekends [2]. In another cross-sectional study involving 0.5 million adults in China, 21% reported taking daytime naps [3].
Although mid-day nap is common, there is no consistent conclusion on its physiological functions. Plenty of work showed that mid-day nap was beneficial to reducing subjective sleepiness, improving cognitive performance, and enhancing subsequent learning [4, 5]. Nevertheless, in other studies, daytime nap was linked with negative outcomes, including self-rated fatigue and cognitive decline [6, 7].
Why does daytime nap lead to these seemingly paradoxical findings? One possible explanation is the sleep structure of nap may influence the consequence. For example, stage 3 of non-rapid eye movement (NREM) sleep, also known as slow-wave sleep (SWS), during a daytime nap was essential for protection from subsequent interference and enhancing long-term retention [8]. Moreover, the positive effect of daytime nap on working memory was related to the duration of rapid eye movement (REM) sleep [9]. These findings suggest that stage 3 and REM sleep play pivotal roles in the benefits derived from napping. Additionally, people who abruptly awakened from SWS might encounter more sleep inertia, a transitional state between sleep and wakefulness, associated with lowered arousal and cognitive performance decrements [10]. Therefore, clearly describing the sleep stages experienced during a mid-day nap can offer a better framework for interpreting the diverse effects of daytime naps and developing strategies to enhance nap efficiency.
A large number of literatures have reported the characteristics of nocturnal sleep, which has a typical pattern: sleepers usually follow the stages of NREM sleep (stages 1, 2 and 3), then back through the light sleep, and enter REM sleep before a new cycle [11]. However, the structure of daytime nap is unlike nocturnal sleep. In fact, the proportion of stage 3 and REM sleep in nap changes throughout the day [12]. Meanwhile, a few studies have reported that the nap sleep structure of habitual nappers, those who frequently take daytime nap, was different from non-habitual nappers [13, 14]. However, the typical pattern of the sleep architecture of mid-day nap is still veiled. Given the consistent engagement in napping behavior, habitual nappers are likely to exhibit more pronounced effects of napping compared to non-habitual nappers [15, 16]. By obtaining a comprehensive understanding of the mid-day nap in habitual nappers, we can gain deeper insights into sleep pattern and its implications for those most likely to experience the effects of regular napping. Thus, this study takes an exploratory approach to observe the sleep architecture of one-hour mid-day naps in habitual napping college students, characterizing the distribution and sequence of sleep stages during the nap. In addition, as subjective feelings of sleep quality and sleepiness are crucial outcomes of napping and can affect an individual's performance later in the day [7], we aimed to investigate whether these post-nap feelings are influenced by sleep stages achieved during a nap.
Materials and methods
Participants
College students were recruited by study advertisements. Participants were selected based on the following criteria: (1) having a regular sleep–wake schedule (spending 6–8 h of time in bed per night, with bedtime between 11:00 pm and 12:30 am, awaking time between 6:30 am and 7:00 am, and habitually napping between 1:00 pm and 2:00 pm); (2) napping ≥ 4 times per week [14, 16]; (3) no subjective complaint about sleep disorder, and Pittsburgh Sleep Quality Index (PSQI) < 6 [17]; (4) body mass index (BMI) < 24 kg/m2; (5) no diagnosed physical and mental disease; (6) nonsmoker; (7) daily consumption of fewer than 700 ml caffeinated beverages [14]. Participants were required to maintain their regular sleep–wake schedule and complete sleep diaries for one week before the study [18]. During experimental period, they were not allowed for intense exercise, or intake of alcohol, caffeine, nicotine or the medications that affect sleep. The study was conducted throughout the entire year to account for potential seasonal influences: 19 participants (20.9%) were observed in spring, 3 (3.3%) in summer, 46 (50.5%) in autumn, and 23 (25.3%) in winter. The protocol was approved by the Ethics Committee of Army Medical University. All participants were voluntary, and provided written informed consent prior to joining the experiment.
Procedure
To observe the characteristics of mid-day nap, participants underwent polysomnographic (PSG) recording for two consecutive mid-day naps and two nights of nocturnal sleep in sleep laboratory. For the mid-day naps, participants arrived at the laboratory at 12:30 pm and had a one-hour nap opportunity starting at 1:00 pm. For nocturnal sleep recording, participants arrived at the laboratory at 10:30 pm and slept from about 11:45 pm according to their regular schedule. The mid-day nap on the first day served as an adaptation to the new sleep environment, while the nap on the second day was the primary focus of observation, which was compared to the first hour of sleep on the second night. The room temperature was maintained between 22 and 24 ℃, and the light intensity were less than 5 lx.
To evaluate the subjective feelings following mid-day nap on the second day, self-ratings of sleep quality and sleepiness were assessed immediately after waking up. Sleep quality was measured by question “How did you sleep just now?” with four options ranging from 1 (Very good) to 4 (Very bad) [19]. Subjective sleepiness was assessed using the 9-point scale Karolinska Sleepiness Scale (KSS) [20].
Polysomnography and sleep staging
During all sleep episodes, electroencephalography (EEG), electrooculography (EOG) and submental electromyography (EMG) were recorded using EEG-9200K (Nihon Kohden, Japan) with Polysmith. EEG was recorded from six channels (10–20-electrode system, F3, F4, C3, C4, O1, O2) referenced to the contralateral mastoids. Electrode impedance was kept below 10 kΩ. Signals were sampled at 200 HZ and band-pass-filtered between 0.3 and 35HZ (EEG and EOG) or 0.3 and 100 Hz (EMG).
Sleep stage classification was performed in 30 s epochs by two experienced technicians according to the criteria American Academy of Sleep Medicine (AASM) Manual [21]. The following sleep parameters were calculated in minutes: total sleep time (TST), sleep-onset latency (SOL, time from lights off to the occurrence of sleep), stages 1, 2, 3, and REM sleep. Time in bed (TIB) was defined as the time from lights off to the time the subject got up in mid-day nap. Sleep efficiency (SE) was calculated as the percentage of TST to TIB.
Statistical analysis
Statistical analysis was performed with SPSS Statistics 20 and Sigmaplot 14.0. All the figures were drawn with GraphPad Prism 8, Microsoft Visio and Adobe Illustrator CS6. The hierarchical cluster method (Ward’s method) was used to categorize sleep architecture into different patterns based on three binary categorical variables: the presence of stage 3, the presence of REM, and whether REM sleep-onset latency (SOL) was less than 15 min [22]. Shapiro–Wilk test was used to assess data normality. Based on the results, data were presented as mean ± standard deviation for normally distributed data and median (interquartile range) for non-normal distributed data. Demographics and sleep history were compared among different patterns of sleep structure using one-way ANOVA for normally distributed data or Kruskal–Wallis one-way analysis of variance on ranks for non-normal distributed data. To examine sleep quality and KSS scores, we initially applied Box–Cox transformation to normalize the data due to their non-normal distribution [23]. Subsequent analyses were conducted using two-way mixed ANCOVA to assess the effects of stage 3 and REM sleep, and one-way ANCOVA to investigate the impact of arousal from various sleep stages. In these analyses, BMI, age, PSQI, and TST of the previous night were included as covariates to control for their potential confounding effects. Post hoc analyses were conducted using the Bonferroni test. Statistical significance was set as p < 0.05.
Results
Features of mid-day nap
While 96 participants satisfied the inclusion and completed PSG recording, 5 subjects did not finish their sleep diary, so the final sample consisted of 91 participants (mean age 20.47 ± 2.02 years, ranging from 17 to 28). In the one-hour mid-day nap of day 2, five patterns of sleep architecture were observed (Fig. 1). 15 subjects experienced SOREM periods (REM SOL < 15 min [22], classified as pattern (1); 17 subjects went through the three stages of NREM sleep in sequence, then repeated stage 2, and subsequently entered REM sleep (classified as pattern 2); 43 subjects went through stage 1, 2 and 3 NREM sleep in the one-hour nap (classified as pattern 3); 8 subjects only showed stage 1 and 2 sleep (classified as pattern 4); and 8 subjects had a long period of light sleep, and then entered REM without stage-3 sleep (classified as pattern 5). Table 1 shows the sleep variables during nap for each group.
Fig. 1.
Sleep architecture of mid-day nap. A Representative sleep hypnograms of mid-day nap. B Dendrogram illustrating the clustering of sleep architecture patterns, based on three binary categorical variables: the presence of stage 3, the presence of REM, and whether REM sleep-onset latency was less than 15 min. C Proportion of each pattern of sleep architecture in nap, n = 91. N1 stage-1 sleep, N2 stage-2 sleep, N3 stage-3 sleep, REM rapid eye movement sleep
Table 1.
Sleep parameters for five patterns of sleep architecture
| Pattern 1 (n = 15) | Pattern 2 (n = 17) | Pattern 3 (n = 43) | Pattern 4 (n = 8) | Pattern 5 (n = 8) | |
|---|---|---|---|---|---|
| TIB (min) | 60.0 (57.0–60.0) | 60.0 (59.5–60.0) | 60.0 (58.0–60.0) | 58.3 (53.0–60.0) | 60.0 (60.0–60.0) |
| TST (min) | 56.0 (53.0–58.0) | 57.5 (54.6–58.8) | 56.0 (52.5–58.0) | 52.0 (49.0–57.8) | 53.0 (50.3–56.75) |
| SE (%, TST/TIB) | 93.3 (91.7–96.7) | 95.8 (94.9–98.3) | 94.4 (91.7–97.5) | 93.3 (88.4–96.3) | 88.3 (83.7–94.6) |
| SOL (min) | 3.5 (1.5–4.0) | 1.5 (0.8–3.0) | 3.0 (1.5–5.0) | 2.3 (2.0–4.4) | 7.0 (2.9–8.3) |
| Stage 1 (% TST) | 14.2 (8.5–21.4) | 17.8 (10.6–20.4) | 16.3 (9.3–26.4) | 36.4 (23.7–67.3) | 29.7 (15.3–32.8) |
| Stage 2 (% TST) | 39.9 ± 14.84 | 50.6 ± 13.32 | 44.4 ± 13.59 | 57.6 ± 57.61 | 54.2 ± 20.81 |
| Stage 3 (% TST) | 29.9 ± 14.1 | 18.4 ± 11.6 | 36.3 ± 16.1 | – | – |
| REM SOL | 8.0 (6.0–9.5) | 51.5 (46.3–54.0) | – | – | 53.8 (47.1–56.4) |
| REM (% TST) | 15.5 (7.1–19.6) | 12.4 (8.6–20.8) | – | – | 10.3 (6.0–23.6) |
Data were presented as mean ± standard deviation for normally distributed data and median (interquartile range) for non-normal distributed data
TIB time in bed, TST total sleep time, SE sleep efficiency, SOL sleep-onset latency
As our participants showed so many sleep architectures in mid-day nap, we next compared their sleep architecture of mid-day nap with the first hour of nocturnal sleep that is known to have reliable characteristics (n = 91). In contrast to the diversity in the sleep pattern of mid-day nap, only patterns 2, 3 and 4 were observed in the first hour of nocturnal sleep (respectively 4, 84, and 3 subjects; Fig. 2).
Fig. 2.
Sleep architecture of the first hour of nocturnal sleep. A Dendrogram illustrating the clustering of sleep architecture patterns, based on three binary categorical variables: the presence of stage 3, the presence of REM, and whether REM sleep-onset latency was less than 15 min. B Diagram illustrating the pattern transition of individuals from mid-day nap to their first hour of nocturnal sleep. The lettered circle (P1, P2, P3, P4, P5) represents patterns 1–5, respectively. The numbers adjacent to circle denote the number of individuals exhibiting that pattern during naps and their corresponding patterns during nocturnal sleep
Comparison of sample characteristics
To explore the potential factors contributing to the diverse patterns of sleep architecture, demographics and sleep history derived from sleep diary were compared among patterns, but no significant difference was found (all p > 0.05; Table 2).
Table 2.
Comparisons of sample characteristics among five patterns of sleep architecture
| Variables | Pattern 1 (n = 15) | Pattern 2 (n = 17) | Pattern 3 (n = 43) | Pattern 4 (n = 8) | Pattern 5 (n = 8) | F(H), p value |
|---|---|---|---|---|---|---|
| Age | 20 (19–21) | 20 (19–22) | 20 (19–22) | 19 (19–23) | 20 (19–20) |
H = 1.938 p = 0.747 |
| BMI | 21.6 ± 1.54 | 22.0 ± 2.06 | 21.4 ± 1.73 | 21.1 ± 1.24 | 21.1 ± 1.33 |
F = 0.716 p = 0.583 |
| PSQI | 3 (2–4) | 3 (2–5) | 3 (2–4) | 4 (3–5) | 4 (3–5) |
H = 7.611 p = 0.107 |
| Average TST in previous week | 466.6 (457.2–499.1) | 462.5 (446.3–480.3) | 478.2 (461.0–496.4) | 472.1 (451.8–516.8) | 473.3 (454.1–498.2) |
H = 1.619 p = 0.805 |
| Number of naps in previous week | 7 (6–7) | 7 (7–7) | 7 (6–7) | 7 (6–7) | 7 (6–7) |
H = 2.776 p = 0.596 |
| TST of previous night | 475.4 ± 29.48 | 485.7 ± 30.74 | 494.3 ± 35.43 | 480.9 ± 53.02 | 484.0 ± 32.70 |
F = 0.935 p = 0.488 |
Data were presented as mean ± standard deviation for normally distributed data and median (interquartile range) for non-normal distributed data. Comparisons performed using one-way ANOVA for normally distributed data or Kruskal–Wallis one-way analysis of variance on ranks for non-normal distributed data
BMI body mass index, PSQI Pittsburg Sleep Quality Index, TST total sleep time
Subjective feelings after mid-day nap
To examine whether the naps containing stage 3 and REM sleep influence the subjective feelings following nap, participants completed the self-rating questionnaires immediately after waking up. There were 75 subjects obtaining stage-3 sleep in nap (32 subjects experienced REM sleep, 43 subjects did not), and 16 subjects having no stage-3 sleep (8 subjects experienced REM sleep, 8 subjects did not). Two-way mixed ANCOVA analysis of sleep quality revealed no significant interaction between stage-3 and REM sleep (F(1, 90) = 3.21, p = 0.08). The groups with and without REM sleep in nap did not differ in the self-rated sleep quality (F(1, 90) = 0.23, p = 0.63), but the subjects having stage-3 sleep in nap reported better sleep qualities than the others having no stage-3 sleep (F(1, 90) = 6.53, p = 0.01, Fig. 3A). For subjective sleepiness, there was no significant interaction between stage 3 and REM sleep (F(1, 90) = 0.88, p = 0.35). No significant difference was found either between groups with and without stage-3 sleep (F(1, 90) = 3.18, p = 0.08) or between groups with and without REM sleep (F(1, 90) = 0.11, p = 0.74, Fig. 3B).
Fig. 3.
Effects of stage 3 and REM sleep in nap on subjective ratings of sleep quality (A) and sleepiness (B). * indicates p < 0.05, comparisons performed using two-way mixed ANOVAs, with data normalized through the application of Box–Cox transformation. N3 stage-3 sleep, REM rapid eye movement sleep
Self-reported sleep quality and sleepiness were also compared among groups with different sleep stages at awakening. One-way ANCOVA analysis indicated that arousal from stages 1, 2, 3 or REM sleep (n = 9, 45, 11, 26, respectively) did not lead to a significant difference in rating of sleep quality (F(1, 90) = 0.11, p = 0.95). Although post-nap sleepiness varied after arousal from different stages (F(1, 90) = 3.00, p = 0.04), no significant differences were found in multiple comparisons using Bonferroni.
Discussion
The present study was conducted to explore the characteristics of mid-day nap in college students. Previous studies have shown that there are different sleep architectures in mid-day nap [24]. Extending to a larger sample, the current findings revealed that 47.3% subjects (pattern 3) exhibited sleep patterns of nap similar to the first cycle of nocturnal sleep, in which sleep progresses through stages 1, 2, and 3 in one hour. Moreover, 44.0% participants had REM sleep in the one-hour nap (patterns 1, 2, and 5), with 16.5% experienced SOREM (pattern 1). This is in agreement with the early work on the diurnal variation of REM sleep in nap, showing that the REM SOL increased and the percentage of SOREM decreased continuously from morning to evening [12, 25].
We further tried to find the potential factors that may influence the architecture of mid-day nap. The individual differences in nap sleep structure have been attributed to napping habits [13, 14]. Compared to non-nappers, habitual nappers had more stage-1 sleep and less SWS. However, all of our participants reported napping at least four days a week, and the number of naps in previous week did not differ among the five patterns, so napping habits may not be the main reason for the diverse sleep structure in the present study. Additionally, neither sleep duration in the previous week or night nor PSQI scores showed significant differences in relation to the mid-day nap's sleep structure, suggesting that these factors may not be the primary determinants of the varied sleep patterns observed. However, it should be noted that our exclusion criterion of a PSQI score below 6, while effective in identifying participants without subjective sleep complaints, may not fully capture issues related to insufficient sleep or accumulated sleep debt, which could potentially influence the sleep structure. Moreover, sleep structure can be affected by many other elements, such as presleep negative emotion [26] and intensive cognitive activity [27]. In addition, the trait-like interindividual differences in sleep physiology, which are thought to be related to genetic factors [28], and the individual differences in chronotype that could influence daytime sleepiness may also contribute to the various sleep pattern in different subjects [29]. Therefore, whether these factors are associated with the variation in sleep structure of mid-day nap remains to be elucidated.
Regarding the subject feelings following nap, the current study found that the subjects who had stage-3 sleep in nap reported better sleep qualities in comparison to those who had not, while the self-reported sleep quality did not differ between the groups with and without REM sleep. These results were in line with the previous findings that SWS is one of the strongest predictors of sleep quality [30]. However, we did not observe a significant difference in post-nap sleepiness among groups awakened from different sleep stages as indicated by the results of multiple comparisons. It seemed to be contradictory to some studies indicating that awakenings from SWS lead to greater sleep inertia which is often associated with post-nap sleepiness [10]. Yet, a few other studies found no effect of sleep stage prior to awakening on the severity of sleep inertia [31, 32]. These discrepancies may be related to the different methods to evaluate sleep inertia. In fact, it has been demonstrated that the impacts of sleep inertia varied depending on the types of tasks [33]. Additionally, an imaging study reported that awakening from stage-3 sleep induced more robust functional disruption in brain networks than from stage-2 sleep though there was no difference in either EEG spectral power or behavioral performance [34].
There are a few limitations in this study. First, to exclude the factors that may affect sleep structure, such as age, gender, and napping habit [13, 14, 35], the current study was focused on male college students that were habitual nappers. Caution should be exercised when extrapolating these results to other demographic groups as their nap habits and socioeconomic backgrounds may differ significantly. To address these limitations, future research should aim to include a more diverse sample, encompassing various age groups and lifestyles. This would help establish a more comprehensive understanding of nap sleep architecture and its determinants across different populations. Second, we acknowledge the limitations posed by the small sample sizes within certain groups, such as those in patterns 4 and 5. This limitation could affect the generalizability of our results as smaller groups may not capture the full variability present in the broader population. Therefore, future studies should aim to include larger and more diverse samples across each sleep pattern. Such studies would help confirm our findings and enhance their applicability to different populations. Third, the current study lacks objective evaluation on performance after mid-day nap. Interindividual variation may lead to bias in the scores of self-rating questionnaire, and the subjective feelings may not be sensitive enough to display the subtle difference in cognitive function [36]. Hence, future work is needed to explore the influence of different sleep structure on post-nap objective tasks, especially on real-world tasks, like learning performance for students.
In summary, the present study showed that even in the habitual nappers, there are diverse patterns of sleep structures in a one-hour mid-day nap though the underlying mechanisms remain unclear. Moreover, the participants obtaining stage-3 sleep in nap reported better sleep qualities, but the subjective sleepiness did not differ among groups awaking from different sleep stages. These findings may help expand the existing knowledge of daytime nap feature and provide a new perspective for the study on the behavioral effects of a one-hour nap.
Acknowledgements
We would like to thank Feng Hu, Chunqian Liu, Xinyue Lan, Shanshan Liang, Xiangming Li, and Ping Liu for assistance in data collection and analysis.
Author contributions
Conceptualization: Zhian Hu; Formal analysis and investigation: Qin Xiao, Xueqi Sun, Guoxin Zeng and Xin Luo, Qian Jiang; Writing—original draft preparation: Qin Xiao and Jie Yan; Writing—review and editing: Rong Ren, Xiangdong Tang, Chao He and Fenlan Luo; Funding acquisition: Zhian Hu; Supervision: Zhian Hu and Fenlan Luo.
Funding
This work was supported by grants from the National Natural Science Foundation of China (No. 31921003).
Declarations
Conflict of interest
The authors have no conflicts of interest relevant to this article to disclose.
Ethical approval
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. The protocol of study was approved by the Ethics Committee of Army Medical University.
Consent to participate
Informed consent was obtained from all individual participants included in the study.
Consent to publication
All participants agreed with publishing the data as a manuscript.
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Qin Xiao and Xueqi Sun contributed equally to this work and share first authorship.
Contributor Information
Fenlan Luo, Email: fenlanluo@163.com.
Jie Yan, Email: yanjie@tmmu.edu.cn.
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