Chronotype and sleep patterns among para-medical students at Kerman university of medical sciences: a cross-sectional study

Abstract

Introduction

Sleep patterns, including sleep quality, daytime sleepiness, and chronotype preferences, are critical to the academic performance and well-being of healthcare students. This study examines these factors among para-medical students to understand their sleep behaviors comprehensively.

Methods

This cross-sectional study was conducted in 2022 among 187 operating room and anesthesia students at Kerman University of Medical Sciences. Validated tools, including the Pittsburgh Sleep Quality Index (PSQI), Epworth Sleepiness Scale (ESS), and Morningness-Eveningness Scale (MES), were used to assess sleep quality, daytime sleepiness, and chronotype preferences, respectively. Demographic data were also collected. Non-parametric tests, including Mann-Whitney U, Kruskal-Wallis, and Spearman’s rank correlation, were applied due to the non-normal distribution of scores. Analyses were conducted using SPSS, with statistical significance set at P < 0.05.

Results

The mean scores for ESS, MES, and PSQI were 6.7 (SD = 4.2), 50.5 (SD = 5.1), and 6.3 (SD = 2.9), respectively. Most students (79.1%) did not report daytime sleepiness, and 90.9% were classified as having an intermediate chronotype. However, 52.9% had poor sleep quality (undesirable PSQI scores). Significant correlations were found between PSQI and ESS (P < 0.001, r = 0.320, R² = 0.10) and PSQI and MES (P = 0.010, r = 0.189, R² = 0.04). Poor sleep quality was significantly associated with older age, non-native status, and mental preoccupations.

Conclusion

Poor sleep quality is prevalent among para-medical students and is significantly linked to chronotype and daytime sleepiness. These findings highlight the need for educational institutions to promote healthy sleep habits and provide mental health support. Further studies should investigate the effectiveness of interventions aimed at improving sleep quality and academic performance in healthcare students.

Introduction

Sleep is considered an important biological and behavioral aspect of human beings and is essential for restoration and maintaining overall health (Alqudah et al. 2019). Sleep patterns, encompassing sleep quality, daytime sleepiness, and chronotype preferences, play a crucial role in the academic performance and well-being of healthcare students (Khaksarian et al. 2020).

Sleep quality is a complex and multifactorial construct that refers to an individual’s subjective perception of the adequacy and restorative nature of their sleep. It is influenced by various factors, including sleep duration, continuity, depth, and the absence of disturbances, such as frequent awakenings or vivid dreams. Although defining and measuring sleep quality in a standardized manner remains challenging, research suggests that deeper, uninterrupted sleep with fewer disturbances typically correlates with higher sleep quality (Azizzadeh forouzi et al. 2014). Poor sleep quality has been linked to a range of psychological, behavioral, and lifestyle factors, which are often exacerbated by life pressures and academic stress, particularly among university students (Li et al. 2020). In addition to challenges related to sleep quality, university students, especially those from Generation Z, frequently struggle with sleepiness. Sleepiness, characterized by an increased tendency to fall asleep during the day, is a significant concern that further impacts academic performance and daily functioning (Demir 2017).

Sleepiness is defined as a tendency to fall asleep and can be categorized as mild, moderate, or severe. Daytime sleepiness is associated with stressful work conditions and occupational fatigue (Alaminos-Torres et al. 2023). In a study among medical students in Iran, it was found that half of the medical students suffer from sleepiness (Masoodzade et al. 2006). The results of Shayegan’s 2012 study on the effect of teaching methods and instructors’ appearance on students’ sleepiness showed that 27.8% of students were significantly sleepy during classes, and 72% of students were sleepy during lecture-style teaching (Shayegan 2012).

Another factor related to sleep quality is an individual’s chronotype, i.e., the morningness-eveningness type, which is one of the factors influencing sleep quality (Alimirzaei et al. 2015). People who wake up very early in the morning are very active at the beginning of the day and tend to go to sleep early at night. They belong to the morning group. The evening type, on the other hand, consists of people who are at the opposite end of the spectrum. It is difficult for them to wake up early in the morning, and they tend to be much more active in the second half of the day than in the first half (Shareinia et al. 2021).

Studies in India and Pakistan have reported a prevalence of poor sleep quality ranging from Studies in India and Pakistan have reported a prevalence of poor sleep quality ranging from 17.3 to 39.5% (Giri et al. 2013; Surani et al. 2015). Findings from a meta-analysis of 76 studies involving a sample of 112,939 students reported the prevalence of sleep disturbance to be 25.7%, ranging from 18.1–24.1%(Li et al. 2018). A recent study indicates that poor sleep quality is not only a common issue among medical students, but also has a higher prevalence than among non-medical students and the general population. Factors such as medical students’ attitudes toward sleep and their knowledge of sleep, along with the demands of their studies, have been identified as contributing factors. However, other potential mechanisms remain incompletely understood (Azad et al. 2015). A recent study indicates that poor sleep quality is not only a common issue among medical students, but also has a higher prevalence than among non-medical students and the general population. Factors such as medical students’ attitudes toward sleep and their knowledge of sleep, along with the demands of their studies, have been identified as contributing factors. However, other potential mechanisms remain incompletely understood (Shafiee et al. 2024). Kawyannejad and et al. (2019) Kawyannejad et al. (2019) reported that a significant proportion of university students tend to be chronically sleep-deprived, sleeping less than 7 h per night on weekdays, especially during their first year of university and in the weeks leading up to exams(Kawyannejad et al. 2019).

Previous research highlights several factors influencing sleep patterns in medical students, such as limited awareness of sleep deprivation, poor sleep hygiene, and academic and clinical demands (Azad et al. 2015; Magnavita et al. 2019; Norbury and Evans 2019). These factors reveal a gap in understanding how sleep quality, sleepiness, and chronotype affect students’ health and academic performance. Sleep quality, sleepiness, and chronotype are interconnected factors that significantly influence students’ well-being and academic outcomes. While other variables like sleep deprivation and insomnia are discussed in the literature, this study emphasizes these three, as they offer a comprehensive framework for understanding students’ sleep challenges. The health of students is vital for societal intellectual development, making it essential to address sleep habits and quality. Although numerous studies have examined sleep issues among medical students, limited research focuses on para-medical students, a group with distinct academic and clinical responsibilities. This population faces unique challenges, including rigorous schedules and hands-on training, which may adversely impact sleep patterns and overall health. Therefore, this study aims to explore sleep quality, sleepiness, and chronotype among para-medical students at Kerman University of Medical Sciences.

Method

Study design and sample size

This cross-sectional study was conducted in 2022 at Kerman University of Medical Sciences (KUMS) to investigate sleep quality, sleepiness, and their relationship with morning and evening chronotypes among paramedical students. Since the total number of anesthesiology and operating room students at KUMS was 224, a census sampling method was used to include all students in the study. Of the 224 students, 20 (9%) were excluded due to inconsistent responses, and 17 (8.3%) were excluded for taking medication for mental health issues or sleep aids. After exclusions, the final sample consisted of 187 students. It should be noted that the relatively high exclusion rate could introduce selection bias, which may affect the generalizability of the findings. However, since the sampling was conducted using a census approach, it is expected that the results will be more reflective of the specific population under study. Data collection was carried out by distributing the questionnaires directly to the participants in paramedical school classes or by visiting the education office. Participants were informed about the study objectives, and written informed consent was obtained before participation. Efforts were made to minimize bias during data collection, such as ensuring that questionnaires were completed in a quiet and private setting. Additionally, researchers provided clear instructions to the participants to ensure accurate and consistent responses. Inclusion criteria included completion of an academic semester, not taking medication for mental health problems or sleep aids, and exclusion criteria included incomplete completion of the questionnaire by the participant.

Data collection instruments

The present study used four questionnaires:

Demographic information

This includes age, gender, marital status, field of study, duration of sleep during the day, caffeine consumption, etc.

Pittsburgh sleep quality index (PSQI)

The Pittsburgh Sleep Quality Index (PSQI), developed by Buysse et al. (1989), consists of 19 items across seven subscales: sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleep medication, and daytime dysfunction. Items are scored on a 4-point Likert scale, with higher scores indicating poorer sleep quality. A score above 5 indicates poor sleep quality. The PSQI has shown high internal consistency (Cronbach’s α = 0.83) and construct validity (α = 0.73 for the Persian version) (Chehri et al. 2020).

Epworth sleepiness scale (ESS)

The Epworth Sleepiness Scale (ESS) assesses the likelihood of dozing off in eight daily activities. Each item is scored from 0 to 3, with higher scores indicating greater sleepiness. A total score > 10 indicates excessive daytime sleepiness, and a score > 15 indicates severe sleepiness (Perotta et al. 2021). The ESS has demonstrated high reliability (Cronbach’s α > 0.8) and strong validity in Persian (α = 0.93) (Alimirzaei et al. 2015; Sadeghniiat Haghighi et al. 2013).

Morningness–eveningness scale (MES)

The Morningness-Eveningness Scale (MES) includes 19 items assessing sleep-wake habits, activity timing preferences, and alertness. Scores range from 16 to 86, with specific scoring for different questions. Participants are categorized into morning, evening, or intermediate chronotypes. Researchers often focus on two or three main groups: definitely evening type (Kawyannejad et al. 2019; Magnavita et al. 2019; Norbury and Evans 2019; Chehri et al. 2020; Perotta et al. 2021; Sadeghniiat Haghighi et al. 2013; Hidalgo et al. 2009; Hirshkowitz et al. 2015; Zailinawati et al. 2009; Alsaggaf et al. 2016; Alotaibi et al. 2020; Irwan et al. 2024; Aristawati and Puspitasari 2021; Roenneberg and Merrow 2016; Foster and Kreitzman 2013; Horne and Ostberg 1976; Nakie et al. 2024; Schmickler et al. 2023; Li et al. 2022; Colrain and Baker 2011; Illingworth 2020; Atadokht 2015; Farzaneh et al. 2018; Querstret and Cropley 2012; Laethem et al. 2019; Matti et al. 2024), intermediate type (42–58), and definitely morning type (58> ) (Hidalgo et al. 2009). In Alimirzaei et al.‘s (2015) study, the MES’s validity was confirmed with coefficients of 0.76–0.80, and internal consistency reliability was high, with a Cronbach’s alpha of 0.92, supporting its use in academic research.

(Alimirzaei et al. 2015).

Statistical analysis

Categorical variables are presented as frequencies (percentages), while continuous variables are expressed as means (± SD) for normally distributed data and as medians (interquartile range) for non-normally distributed data. To assess normality, the Shapiro-Wilk test was used, and to assess the homogeneity of variances, Levene’s test was applied. For comparisons between two independent groups, the Mann-Whitney U test was used when the data were non-normally distributed. For comparisons among three or more groups, the Kruskal-Wallis test was employed. To assess the strength and direction of the relationship between variables, Spearman’s rank correlation coefficient was applied. All statistical analyses were performed using SPSS V.19 (IBM Statistics, New York, NY, USA), with a significance level of 0.05.

Result

Demographic characteristics

The average age of students in this study was 21.3 years (SD = 1.8). Most participants were female (70.1%), single (91.4%), native (63.6%), non-dormitory residents (51.9%), unemployed (78.4%), and undergraduates (98.9%). Nearly all participants were undergraduates (98.9%), with only two pursuing master’s degrees. Additionally, 89.3% of participants were non-smokers, 22.5% were coffee drinkers, 48.1% were tea drinkers, and 46.6% listened to music before bedtime. Additional information is provided in Table 1.

The relationship between PSQI scores and demographic characteristics

The results indicated a significant relationship between the mean PSQI score and age (P = 0.025), native status (P = 0.006), and mental preoccupations (P = 0.010). The mean PSQI score increased with age, and participants aged 23 or older had the highest PSQI score (7.3, SD = 3.2). According to the Mann-Whitney U test with Bonferroni’s correction, there was a difference in scores between those aged 18–20 years and those aged 23 years or older (P = 0.020). Figure 1 shows that the most common types of mental preoccupations among students were personal issues (70.1%) and economic concerns (42.8%). The use of the Mann-Whitney U test did not reveal a significant relationship between the total PSQI score and the type of mental preoccupations (P > 0.05).

Correlations between PSQI, ESS and MES

The mean scores for the ESS, MES, and PSQI were 6.7 (SD = 4.2), 50.5 (SD = 5.1), and 6.3 (SD = 2.9), respectively. Twenty point 9% (20.9%) of students reported symptoms of daytime sleepiness and had an intermediate MES score (90.9%). However, 52.9% of the participants obtained an undesirable PSQI score. Spearman correlation revealed a significant relationship between PSQI and SSE (P < 0.001, r = 0.320), with SSE explaining 10% of the variance in PSQI scores (R² = 0.10). It also showed a relationship between the total PSQI score and the MES (P = 0.010, r = 0.189), with each explaining 4% of the other’s variance (R² = 0.04).

Table 1 The relationship between the mean scores of PSQI with ESS, MES scores and demographic characteristics

Fig. 1

Type of mental preoccupations (%)

Description of PSQI subscales

Table 2 presents the descriptive statistics for the PSQI subscales. The highest mean scores were observed for sleep latency (1.4, SE = 0.1) and daytime dysfunction (1.4, SE = 0.07), while the lowest mean score was reported for the use of sleep medication (0.2, SE = 0.04).

Table 2 Descriptive statistics of PSQI subscales

Discussion

The aim of the present study was to find a relationship between sleep quality and sleepiness chronotype in allied medical sciences students. Findings of this study reveal a significant relationship between the total PSQI score and sleepiness among participants. This correlation suggests that as the PSQI score increases, indicating poorer sleep quality, there is a corresponding increase in reported sleepiness levels. The observed relationship aligns with existing literature that emphasizes the detrimental effects of poor sleep quality on cognitive function and alertness. For instance, studies have shown that inadequate sleep can lead to increased daytime sleepiness, which may impair academic performance and clinical skills in medical students (Hirshkowitz et al. 2015). Furthermore, the prevalence of sleep disturbances among medical students is well-documented, with factors such as academic pressure and irregular schedules contributing to compromised sleep quality (Azad et al. 2015). The results of present study were consistent with the observations made by Zailinawati et al. (Zailinawati et al. 2009).

Alseggaf et al. reported that EES scores were a good estimate of self-reported quality of sleep among medical students in clerkship years (Alsaggaf et al. 2016). Study results show lower level of sleep quality predicted a greater likelihood of daytime sleepiness among college students. Another study showed some variables that can predict the quality of sleep and daytime sleepiness in a university population, with emphasis on academic stress (Alotaibi et al. 2020).

College and university students often experience unbalanced sleep-wake cycles, and complex lecture schedules and other activities can result in physical problems such as fatigue (Irwan et al. 2024). Lifestyle factors, such as the use of electronic devices before bedtime, may also contribute to poor sleep quality (Aristawati and Puspitasari 2021).

The findings of this study indicate a significant relationship between sleep quality, as measured by PSQI, and the intermediate chronotype among allied medical students. The observed P-value of less than 0.001 suggests a statistical significance, while the correlation coefficient (r = 0198) indicates a small effect size. This implies that students with an intermediate chronotype tend to experience poorer sleep quality compared to their counterparts with other chronotypes. Chronotype, which refers to an individual’s natural preference for being active during certain times of the day, has been shown to influence various aspects of health and well-being, including sleep patterns (Roenneberg and Merrow 2016). Previous research has highlighted the impact of chronotype on sleep quality and overall health. For instance, studies have shown that evening chronotype often report poorer sleep quality and greater sleep disturbances (Foster and Kreitzman 2013). Conversely, morning chronotypes tend to align better with societal norms, potentially leading to improved sleep outcomes (Horne and Ostberg 1976). The current stud’y focus on allied medical students is particularly relevant, as this population often experiences high levels of stress and irregular schedules, which can exacerbate sleep issues (Nakie et al. 2024).

The findings of this study suggest a positive correlation between age and sleep disturbance, as measured by the PSQI. Specifically, the mean PSQI score increased with age, with individuals aged 23 years and older demonstrating the highest level of sleep disturbance. This pattern is consistent with previous research. Li et al.(Li et al. 2020), and Schmickier et al. (Schmickler et al. 2023) reported that older age in university students is a predictor of poor sleep quality. Other studies have shown that age-related changes in neuroendocrine function are associated with alterations in sleep quality and sleep architecture with normal aging (Li et al. 2022). The increased PSQI scores observed in older students may be attributed to several factors. One possible explanation is the increasing academic and clinical demands that often accompany later years. The increased PSQI scores observed in older students may be attributed to several factors. One possible explanation is the increasing academic and clinical demands that often accompany later years. Students in this age group may be balancing more intense coursework, clinical rotations, and practical responsibilities, which can lead to higher levels of stress and anxiety—both of which are known to negatively affect sleep quality (Colrain and Baker 2011; Illingworth 2020).

The results indicated a significant relationship between the mean PSQI score and native status. The finding that non-native students had the highest PSQI scores is important and points to the potential role of acculturation and cultural stressors in influencing sleep quality. Previous research has shown that non-native students tend to have poorer sleep quality (Atadokht 2015; Farzaneh et al. 2018). This finding may be because native students often face unique challenges related to adjusting to a new cultural, academic, and social environment. These challenges can result in heightened levels of stress, anxiety, and homesickness, all of which are known to negatively affect sleep. The challenges faced by non-native students are not limited to the academic environment. Many non-native students also encounter external pressures, such as financial instability, a sense of isolation, and a lack of social support networks. These factors can exacerbate stress and anxiety, which are known to interfere with sleep. Furthermore, the need to balance multiple responsibilities, such as part-time work or family obligations, may contribute to poor sleep quality and insufficient sleep, leading to higher PSQI scores.

The results indicated a significant relationship between the mean PSQI score and mental preoccupations. Students with fewer mental preoccupations reported better sleep quality, which is consistent with other studies (Querstret and Cropley 2012; Laethem et al. 2019). Mental preoccupation refers to ruminative thoughts about academic performance, exams, or future professional responsibilities, which are significant contributors to poor sleep quality (Matti et al. 2024). Medical allied students, due to the demanding nature of their courses, often experience cognitive overload, where their minds remain hyperactive long after they attempt to sleep. This cognitive overload may lead to delayed sleep onset and increased wakefulness during the night. It should be noted that cognitive preoccupation interferes with the brain’s ability to enter deeper stages of restorative sleep, such as slow-wave sleep and REM sleep, which are vital for memory consolidation and emotional regulation.

Limitation

This cross-sectional study was limited by a small sample size of anesthesiology and operating room students, which restricts the generalizability of the findings to other healthcare students with varying stress levels and schedules. The tools used in the current study—PSQI, ESS, and MES—are self-report-based. Therefore, objective methods in addition to these tools could improve the study’s findings. Furthermore, the study was conducted during the COVID-19 pandemic, so external factors such as increased stress, remote learning, and altered schedules may have affected students’ sleep patterns. Additionally, since the census method was used to select the sample due to the limited number of eligible participants, the study faced an attrition rate of 17%. This exclusion rate, while necessary to maintain data integrity, may have introduced selection bias and slightly limited the generalizability of the findings to the broader population. Future research should address these issues with larger, more diverse samples and objective assessments.

Conclusion

The study revealed that while most students had moderate sleep quality and no significant daytime sleepiness, a substantial portion reported poor sleep quality. Addressing these issues through educational programs on sleep hygiene, flexible academic scheduling, peer support networks, and balanced workloads could improve sleep quality among paramedical students. Tailored interventions targeting the challenges faced by non-native students, such as homesickness and cultural adjustment, are also recommended. These measures could enhance students’ overall well-being, and further research is needed to identify additional factors affecting sleep quality in this population.