What this is
- This research investigates daytime sleepiness in elementary school students attending afternoon classes.
- It examines how sleep quality and influence daytime sleepiness.
- The study involves 363 Brazilian students and assesses various sleep-related factors.
Essence
- affects 52.1% of elementary school students despite adequate sleep duration. Poor sleep quality and evening are significant predictors.
Key takeaways
- Daytime sleepiness is prevalent among students with sufficient sleep duration, with 52.1% scoring 15 or more on the pediatric daytime sleepiness scale.
- Quality of sleep (β = 0.417) and (β = 0.174) are stronger predictors of daytime sleepiness than time in bed (β = -0.091).
- 41.1% of students exhibit very altered sleep quality, indicating a significant issue that may impact academic performance.
Caveats
- The study lacks data on socioeconomic conditions and lifestyle factors that could influence sleep quality and daytime sleepiness.
- The reliance on self-reported questionnaires may introduce bias in the assessment of sleep patterns and quality.
Definitions
- Excessive daytime sleepiness (EDS): Scores of 15 or more on the pediatric daytime sleepiness scale indicate EDS.
- Chronotype: Individual preference for being active in the morning or evening, influencing sleep patterns.
AI simplified
INTRODUCTION
The literature has well documented the incompatibility between adolescents’ sleep patterns and morning school schedules1,2. Its more evident consequence is chronic sleep deprivation associated with a negative impact on academic performance3,4. Already discussed and implemented countermeasures have focused on reducing these undesirable consequences5. Basically, we can see two main trends: sleep education6 and changes in school start times7. School-based sleep education programs can improve sleep habits, but most produce only short-term benefits8. The need to delay school start times have become a consensus among sleep researchers1,9,10, a concern which has also grown in public policy discussions11. Alternatively, switching classes from mornings to afternoons also seems to benefit students’ sleep. Mello et al.12 (2001) showed a one-hour increase on sleep duration in adolescents after the transition from morning to the afternoon schedule. Anacleto et al.13 (2014) found similar results when comparing preadolescent children attending morning and afternoon classes. Recently, Carvalho-Mendes et al.14 (2020) have shown that students attending evening classes (starting at 12:30 p.m.) had greater sleep duration than those attending morning classes (starting at 7:00 a.m.).
One of the consequences of reduced hours of sleep is daytime sleepiness15. Pereira et al.16 (2015) pointed out that a short sleep duration is the main predictor of excessive daytime sleepiness (EDS) in Brazilian students. However, other studies have shown that daytime sleepiness is not only associated with sleep duration. In healthy individuals, other factors, such as poor sleep quality17, low stress control, and lower self-rated health18, as well as physical activity habits, use of social media, and consumption of processed foods19, seem to be associated with the perception of daytime sleepiness.
In Brazil, classes are divided into schedules, and students who attend school in the afternoon do not usually have their sleep duration affected by school hours. However, throughout pedagogical practice, we can observe the occurrence of daytime sleepiness in students who attend school in the afternoon. We designed this study encouraged by the results of previous studies and by the reports of teachers who observed the occurrence of daytime sleepiness in students who apparently have regular sleep regimens. This study aimed to investigate the occurrence of daytime sleepiness and its associated sleep factors in a sample of elementary school students who attended school in the afternoon schedule.
METHODS
Subjects
The sample consisted of 363 subjects (206 girls), aged 10–16 years (mean 12.78 ± 1.36 years), of which most were aged between 11 and 14 years (86.7%). All subjects were elementary school students from 15 different public schools in Curitiba, in the state of Paraná, in Southern Brazil. As for their ethnic distribution, 52.7% of students recognized themselves as white, 43.1%, as black, and 4.2% as indigenous or Asian. Most students reported belonging to families with average family incomes (B2)20, and 5.5% working. All subjects attended school in the afternoon schedule, with classes starting between 1:00 and 1:20 p.m., depending on the school.
Sample Selection, Data Collection, and Analyzed Variables
The data analyzed in this study were extracted from a database collected in 2014 (March to October), which were part of a broader survey that evaluated the sleep pattern of students who attended school in the morning and afternoon. This research was designed with the aim of obtaining a wide and distributed sample of Curitiba students. For this purpose, two schools from each of the nine teaching regions of the municipality were chosen by drawing lots, which totaled 18 schools located in various central and peripheral neighborhoods. For this study, only data from subjects who attended school in the afternoon were analyzed. Out of the 18 schools visited, one had classes only in the morning and another two requested that collections must be performed only with students in the morning schedule. Thus, the subjects who made up the sample of this article came from 15 different schools in the city.
This research was approved by the ethics committee in research of the Universidade Federal do Paraná (number 504.532).
Data Collection and Analyzed Variables
The classes in which data were collected were those indicated by the board and the pedagogical team of each school. These classes were initially visited by the researchers and all students present at that time were invited to participate in the study, after having been instructed that participation would be voluntary, that data would be obtained by individual responses to a questionnaire, and that all data collected would be kept confidential. All students who were interested in participating in the study received an informed consent form. The next day, data collection was performed and all students who were present in the classrooms and who had the consent forms signed by their parents could participate. All data (sociodemographic, and related to work, health, substance use, and sleep patterns) were obtained by self-report questionnaires filled out in the classrooms under the supervision of the experimenter. All questionnaires answered by students were collected, and the following exclusion criteria were applied: users of medication that could affect their sleep/wake cycle, users of psychoactive drugs, diagnosed psychiatric disorders, and incorrectly filled out questionnaires.
Data on work, health, and substance use were obtained by the questions:
Three instruments were used to evaluate sleep patterns:
Except for mid-sleep on school days, the investigated variables showed a non-parametric distribution. Differences between school days and weekends on the average sleep time, wake up time, time in bed, and mid-sleep were compared by the Wilcoxon test. Correlations between PDSS scores, age, and sleep variables were estimated by the Pearson’s correlation test. To evaluate the variables that could be predictive of daytime sleepiness, a hierarchical multiple logistic regression analysis was performed. The PDSS score was used as a dependent variable, whereas the MSQ score, the MSFsc, mid-sleep on school days, and time in bed were used hierarchically as independent variables.
Data were analyzed in the IBM SPSS Statistics software, version 20.0. For all analyses, a significance probability of 5% was considered.
RESULTS
The sample consisted of adolescents who maintained an average time in bed longer than nine hours both on school days and weekends (Table 1). We found a delay in the time of going to bed and waking up on weekends without any variation in total time in bed. The magnitude of social jetlag in the sample was 1.43 ± 1.26h.
We found an average PDSS score of 14.65 (6.01) points, and 52.1% of the students in the sample showed EDS. The average MSQ score was 29.3 (8.4), and 29% of subjects had scores compatible with good sleep quality. We also observed that 41.1% of students had scores indicative of very altered sleep.
We found that 5.5% of students reported working. Among students who recognized themselves as workers, their average daily working time was 3.89 (2.08) hours and the average number of days worked during the week was 3.4 (1.93) days.
We found by a correlation analysis (Table 2) that PDSS scores correlated with MSQ scores (r = 0.471; p < 0.001), the MSFsc (r = 0.362; p < 0.001), and mid-sleep on school days (r = 0.35; p < 0.001). Thus, we can claim that the greater the daytime sleepiness, the worse the quality of sleep and the greater the tendency for eveningness. We also observed correlations among PDSS scores, time in bed (r = - 0.124; p = 0.019), and social jetlag (r = 0.148; p = 0.005). Thus, the shorter the time spent in bed and the greater the social jetlag, the greater the daytime sleepiness.
Considering the results found, we performed a hierarchical multiple linear regression analysis which included PDSS scores as its dependent variable (Table 3). We sequentially inserted MSQ scores, the MSFsc, and mid-sleep and time in bed on school days (in that order) as independent variables. Model 1 considered only MSQ scores as a predictor (R2 = 24.1%; p < 0.001); model 2, MSQ scores and the MSFsc as predictors (R2 = 30.7%; p < 0.001); model 3, MSQ scores, the MSFsc, and mid-sleep on school days as predictors (R2 = 31.5% ; p = 0.026); and model 4, MSQ scores, the MSFsc, and mid-sleep and time in bed on school days as predictors (R2 = 32.2% ; p = 0.04). Model 4 proved to be the superior model (R2 = 0.008; p = 0.04), resulting in a statistically significant model [F (4.348) = 42.724; p < 0.001; R2 = 0.329]. Thus, we can claim that MSQ scores (β = 0.417; t = 9.194; p < 0.001), the MSFsc (β = 0.174; t = 2.851; p = 0.005), mid-sleep on school days (β = 0.138; t = 2.257; p = 0.025) and time in bed on school days (β = -0.091; t = -2.067; p = 0.04) were predictors of PDSS scores. By comparing the variables included in model 4, we could observe that the time in bed on school days had a reduced predictive role for daytime sleepiness (β = -0.091).
| School days | Weekends | Z; p | |
|---|---|---|---|
| Sleep timea | 0:01 (1:58) | 1:47 (2:11) | 14.564; < 0.001d |
| Wake up timea | 9:18 (1:52) | 10:62 (1:75) | 13.199; < 0.001d |
| Time in bedb | 9.17 (1.46) | 9.16 (1.71) | 0.372; 0.710 |
| Mid-sleepb | 4.59 (1.37) | 6.00 (1.65) | 14.996; < 0.001d |
| PDSS scorec | 14.65 (6.01) | ||
| EDS (%) | 52.1 | ||
| MSQ scorec | 29.30 (8.40) | ||
| Good sleep - MSQ (%) | 29 | ||
| Social jetlaga | 1.43 (1.26) | ||
| Variables | r | p |
|---|---|---|
| Age | 0.061 | 0.25 |
| MSQ | 0.471 | < 0.001a |
| Time in bed (school days) | - 0.124 | 0.019a |
| Mid-sleep (school days) | 0.35 | < 0.00a |
| Social jetlag | 0.148 | 0.005a |
| Chronotype (MSFsc) | 0.362 | < 0.001a |
| PDSS (%)a | β | Min. | Max. | p | |
|---|---|---|---|---|---|
| Model 1 | |||||
| MSQ | 24.1 | 0.471 | 0.292 | 0.424 | < 0.00a |
| Model 2 | |||||
| MSQ | 30.7 | 0.435 | 0.251 | 0.381 | < 0.001a |
| MSFsc | 0.268 | 0.621 | 1.243 | < 0.001a | |
| Model 3 | |||||
| MSQ | 31.5 | 0.423 | 0.243 | 0.373 | < 0.001a |
| MSFsc | 0.175 | 0.191 | 1.03 | 0.004a | |
| Mid-sleep (school days) | 0.137 | 0.074 | 1.156 | 0.026a | |
| Model 4 | |||||
| MSQ | 0.417 | 0.238 | 0.368 | < 0.001a | |
| MSFsc | 32.2 | 0.174 | 0.188 | 1.023 | 0.005a |
| Mid-sleep (school days) | 0.138 | 0.08 | 1.157 | 0.025a | |
| Time in bed (school days) | -0.091 | -0.739 | -0.018 | 0.04a | |
DISCUSSION
In this study, we could observe the occurrence of daytime sleepiness in subjects whose average time in bed was in accordance with the recommendations of the National Sleep Foundation (headquartered in Virginia, USA) for their age group26. By a hierarchical multiple regression analysis, we could observe that the perception of sleep quality, mid-sleep on school days, and the MSFsc could be variables with greater predictive power for daytime sleepiness in our sample than time in bed. Unlike what we might expect, based on previous studies describing associations between short sleep duration and daytime sleepiness27,28, a longer time in bed was insufficient to prevent EDS. According to what Meyer et al.23 (2018) proposed, more than half (52.1%) of the students in our sample had EDS (PDSS scores equal to or greater than 15 points). Also, in line with this same study, low sleep quality was the parameter most strongly associated with the occurrence of daytime sleepiness23.
This study confirms that daytime sleepiness may correlate with factors other than inadequate sleep duration28,29. Thus, our findings are congruent with those in Ferrari Junior et al.30 (2019) since these authors found no correlations between daytime sleepiness, as the PDSS assesses, and length of time in bed. The authors found that the perceived need for more sleep and mid-sleep on school days were predictive of daytime sleepiness30. In our data, the perception of sleep quality showed the best predictive power for daytime sleepiness, reinforcing the idea that sufficient or ideal sleep covers different aspects - many of which are subjective and susceptible to change throughout life - in addition to time in bed. Owens et al.31also emphasized the idea that factors other than sleep duration influence daytime sleepiness. In a literature review, Felden et al.32(2015), drew attention to the occurrence of associations between socioeconomic indicators and sleep quality in adolescents. Malheiros et al.19 (2021), in a recent survey of Brazilian students (16.4 ± 1.2 years) found an association among daytime sleepiness and low level of physical activity, high consumption of processed foods, and high use of social media.
Regarding sleep quality, we observed that the percentage of students in the sample who obtained scores consistent with poor sleep quality was higher than that observed in other studies. In their validation study of the MSQ scale, Falavigna et al.25 applied the scale to a group of 1,108 students with an average age of 22 years and found that 40.4% of their subjects had a score consistent with good sleep quality, with an average score of 26 points. A study by Cayres et al.33 (2019) showed that among 120 students aged between 11 and 14 years who answered the MSQ scale, 52.1% had a score equal to or greater than 25 points, which the authors considered as poor sleep. Compared with these two studies, our sample had a lower percentage of subjects with good sleep quality (29%) and a higher mean MSQ score (29.3 points). Although the authors’ data on sleep quality diverged from ours, note that 60.5% of the Brazilian students in the study by Meyer et al.23 (2018) perceived their quality of sleep as poor.
In addition to the perception of sleep quality, the MSFsc and mid-sleep on school days also showed predictive power for daytime sleepiness. In our study, we found positive correlations between PDSS scores and mid-sleep point, as well as between PDSS scores and the MSFsc. Thus, we can infer that subjects with greater daytime sleepiness showed a tendency for eveningness. Previous studies observed similar results31,34,35. Liu et al.35 (2019) conducted a study with 10,086 Hong Kong students and observed that an evening chronotype was the factor most strongly associated with EDS. Martin et al.34 (2016) compared morning-type with evening-type students who attended school in different schedules. Both in morning and afternoon schedules, evening-type students had higher PDSS scores than morning-type ones. Owens et al.31 (2016) also found a correlation between daytime sleepiness and morningness/eveningness scale for children scores regarding afternoon preferences.
In addition to the chronotype, the occurrence of sleep inertia could be associated with daytime sleepiness observed in our subjects. Roenneberg et al.36 (2003) verified the occurrence of sleep inertia in their subjects. In this study, sleep inertia was higher in evening chronotypes, but it was also associated with shorter sleep duration. Although our study ignored the evaluation of sleep inertia, we cannot dismiss the possibility that evening-type subjects start classes under the effect of sleep inertia - which reflect higher PDSS scores. According to previous studies, sleep inertia could take up to two hours to dissipate37.
Thus, our results draw attention to the fact that daytime sleepiness is associated with variables other than sleep duration. Therefore, interventions aimed at reducing it, especially in school environments, need to pay attention to this wide range of factors associated with daytime sleepiness. Our data reflect the sleep patterns and daytime sleepiness of a sample of public school students, many of them located on the outskirts of the municipality in which this study was conducted. Many students reported belonging to an economic class whose family income was below 4 minimum wages per month. The findings of Felden et al.32 (2015) reinforce that our results represent the sleep patterns and daytime sleepiness of students from lower economic classes, which is an important point for the study of possible intervention measures in Brazil. Due to these results, we suggest that further studies should follow in this direction.
Bearing this socioeconomic scenario in mind, we must mention the limitations of our study, such as our lack of data on living conditions, sleeping places, food, and patterns of physical activity. Moreover, future studies should consider obtaining data on informal38 and domestic work, living conditions, and family members’ work shift regimens since they could impact children and adolescents’ quality of sleep and temporal allocation of nighttime sleep episodes.
Moreover, our results serve as an important warning: delaying school start times is a necessary but insufficient intervention to reduce daytime sleepiness and consequently improve academic performance in adolescents. Evening-type adolescents seem to suffer more from daytime sleepiness even when they have the opportunity to extend the duration of their sleep. Maybe some of them could benefit from sleep education and improve their sleep times. Others may need to attend classes even later, starting in the middle of afternoon. The improvement of sleep education programs - seeking long-term effects - and the formulation of a plan to implement schools with flexible times in a digital world have emerged as alternatives for moving toward more inclusive schools.
Funding Statement
Funding: Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq- process 442332/2014-9). Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Capes - scholarship to TSA).