Abstract
Background
Poor sleep quality is common among university students. However, evidence on its prevalence and associated factors among nursing undergraduates in Sri Lankan state universities remains limited.
Methods
A descriptive cross-sectional study was conducted among 412 nursing undergraduates selected through stratified random sampling. Data was collected using a validated, pre-tested online self-administered questionnaire, which included the Pittsburgh Sleep Quality Index (PSQI) and Perceived Stress Scale (PSS). Statistical analysis was performed using SPSS version 27. Chi-square tests and one-way ANOVA were applied, with p < 0.05 considered significant. For clarity, 95% confidence intervals were reported for prevalence estimates and group comparisons.
Results
The prevalence of poor sleep quality (PSQI ≥ 5) was 84% (95% Confidence Interval). Significant associations were found between sleep quality and living place, diet, electronic device use, sleep hygiene practices, medical conditions, clinical rotation schedules, and academic stress. Most participants (65.8%) reported moderate stress levels. No significant associations were observed with credit hours, assignments, Grade Point Average (GPA), or study hours.
Conclusions
Poor sleep quality is highly prevalent among nursing undergraduates in Sri Lanka and is influenced by stress, lifestyle factors, and health conditions. Interventions focused on sleep hygiene and stress reduction are recommended.
Clinical trial number
Not applicable.
Keywords: Prevalence, Sleep quality, Nursing students, Associated factors, Stress, Quantitative research
Introduction
Sleep is essential for overall well-being, cognitive functioning, and academic performance among university students [1, 2]. University students, particularly those in health-related fields, often experience disrupted sleep due to academic demands, clinical training, and irregular clinical placements, leading to fatigue, decreased cognitive function, and impaired judgment, which may compromise academic performance and clinical safety [3].
Sleep quality is influenced by various sociodemographic, lifestyle and environmental factors. Sociodemographic determinants such as low income, neighborhood violence, discrimination, and food insecurity have been associated with poor sleep among nursing students [4]. Lifestyle factors, including irregular sleep patterns, digital device use at bedtime, psychoactive substance use, smoking, alcohol consumption, and exhaustion, also contribute to sleep disturbances [5–8]. Additionally, stress, unhealthy sleep practices, anxiety, depressive symptoms, medical conditions, and environmental factors negatively affect sleep quality [2, 9–11]. The COVID-19 pandemic further disrupted sleep patterns worldwide, leading to increased stress, fatigue, and reduced psychological well-being [13]. These disruptions highlight the relevance of studying sleep determinants in nursing populations. While these determinants have been studied globally, evidence from Sri Lanka remains limited.
Globally, the prevalence of poor sleep quality among nursing students varies widely. For instance, 78.8% of nursing undergraduates in São Paulo, Brazil, reported poor sleep [4], whereas 65.6% of nurses at Apeksha Hospital, Sri Lanka, experienced sleep disturbances influenced by health issues, night shifts, workload, and long hours [12].
In Sri Lanka, nursing students in state universities follow a uniform curriculum, clinical training structure, and academic schedule as regulated by the University Grants Commission and the Ministry of Health. This consistency provides an opportunity to study sleep quality across institutions under similar academic and clinical demands. Despite evidence from other countries, limited research has explored the prevalence and associated factors of poor sleep quality among nursing undergraduates in Sri Lankan state universities.
Understanding these factors is crucial, as poor sleep can impact both the well-being of nursing students and the quality of care they provide as future healthcare professionals. Therefore, this study aimed to determine the prevalence and associated factors of poor sleep quality among nursing undergraduates in Sri Lankan state universities.
Methodology
Study design and setting
A descriptive cross-sectional study was conducted to determine the prevalence and associated factors of poor sleep quality among nursing undergraduates in state universities in Sri Lanka. The study included students from six universities offering B.Sc. Nursing degrees including University of Colombo, University of Peradeniya, University of Jayewardenepura, University of Jaffna, University of Ruhuna, and Eastern University.
Study population and sampling
Participants were selected using a proportionate stratified random sampling technique to ensure representation across all universities.
Participants were selected using a proportionate stratified random sampling technique to ensure representation across all universities. The required sample size was calculated using Daniel’s formula (1999) as referenced by Liu et al. [14].
 |
where 
is the sample size, 
is the critical value of the normal distribution at a 95% confidence level (1.96), 
is the expected proportion (0.5), and 
is the acceptable margin of error (0.05). Substituting the values:
 |
To account for a potential non-respondent rate of 10%, the sample size was increased by 10% (38 participants), resulting in a final target sample size of 422 participants.
The sample size of 412 was calculated using Daniel’s sample size formula (1999), as referenced by Liu et al. [14] The study population consisted of nursing undergraduates enrolled in state universities, with enrollment in a state university serving as an explicit inclusion criterion to ensure consistency in curriculum, clinical training, and educational environment. Eligible participants were nursing undergraduates of both genders who had completed at least the first semester of their first academic year, ensuring familiarity with university life, academic expectations, and clinical training. While workload and stress may differ by year, early-year students’ experiences provide valuable insight into the onset of sleep disturbances, allowing for identification of early risk factors. Students who did not provide informed consent, had mental or psychological disorders, were on medication affecting sleep, had completed their academic program, or had not yet commenced their studies were excluded.
The sample size for each university was determined using proportional allocation. Specifically, the required sample for a given university was calculated by dividing the number of registered undergraduates in that university by the total number of registered undergraduates across all six universities and then multiplying this ratio by the total sample size. This approach ensures that the sample distribution accurately reflects the relative size of each university’s undergraduate population.
To enhance clarity and precision, 95% confidence intervals were calculated for prevalence estimates and mean differences in addition to p-values, providing a more comprehensive understanding of the precision observed associations.
Data collection
Data were collected using a validated and pre-tested online self-administered questionnaire which consists of four sections: Section A: Sociodemographic data, Section B: Factors associated with sleep quality (academic, health-related, and lifestyle factors), Section C: Perceived Stress Scale (PSS) [14–16] and Section D: Pittsburgh Sleep Quality Index (PSQI) [17–19].
The Perceived Stress Scale (PSS) was used to assess stress levels, categorized as mild (0–13), moderate (14–26), and high (27–40). The Pittsburgh Sleep Quality Index (PSQI) was used to assess sleep quality, with scores > 5 indicating poor sleep quality.
In addition to standardized instruments, several lifestyle-related variables (e.g., healthy diet, physical activity, and screen time) were included in Section B. For this study, a “healthy diet” was defined as the regular consumption of balanced meals including fruits, vegetables, and protein sources, while limiting intake of processed foods and sugar-sweetened beverages. “Electronic device use before bed” was defined as the frequency of engaging with phones, computers, tablets, or other screens within one hour before intended sleep. “Sleep hygiene” refers to behaviors and environmental factors that promote quality sleep, including maintaining a consistent sleep schedule, avoiding caffeine/alcohol before bedtime, having a comfortable sleep environment, and limiting screen use before sleep.
These definitions were consistently applied across all participants, regardless of institution or ethnicity, to ensure uniformity in data interpretation.
Validation of research instruments
The study employed validated instruments, including the Perceived Stress Scale (PSS) and the Pittsburgh Sleep Quality Index (PSQI), both of which have demonstrated strong reliability and validity in previous research. The sociodemographic questionnaire was reviewed by subject matter experts to ensure clarity and relevance. To minimize bias, data were collected using a self-administered online questionnaire, and participant anonymity was maintained to encourage honest and accurate responses. Prior to the main study, the complete questionnaire was pre-tested among 30 nursing undergraduates from a university not included in the main sample to assess clarity, comprehension, and completion time. Reliability for the current sample was assessed using Cronbach’s α. The PSS demonstrated a Cronbach’s α of 0.82, and the PSQI demonstrated a Cronbach’s α of 0.79, indicating acceptable internal consistency for the instruments in this study.
Data analysis
Data was entered, coded, and cleaned using Microsoft Excel 365 and imported into SPSS for analysis. Google Sheets was used only for secure sharing among authorized team members. Confidentiality and privacy were ensured by securely storing information sheets. All gathered data were stored within a secure digital repository, specifically a Google Sheet, which offered limited access only to authorized personnel. In our study, individuals authorized for access included our supervisor, co-supervisor, and members of our research team. To mitigate any potential loss of data, backup copies were maintained. All participant data were anonymized by removing any identifiable information from the data collected in the field. The data have been linked using a serial number. The research data will be securely retained for a period of five years and subsequently completely removed and destroyed from storage thereafter.
Statistical analyses were conducted using SPSS Statistics version 27.0. Descriptive statistics were used to summarize categorical variables as frequencies and percentages, and continuous variables as means and standard deviations (mean ± SD). The Chi-square test was applied to assess associations between categorical variables such as sleep quality and socio-demographic, academic, health-related, stress, and lifestyle factors. Where applicable, one-way ANOVA was used to further explore significant associations among continuous variables across multiple groups. A p-value < 0.05 was considered statistically significant. Results were presented in tables and figures to illustrate key findings and highlight statistically significant relationships.
One-way ANOVA was applied to compare mean PSQI scores across groups. Given the large sample size (n = 412), ANOVA was considered robust to moderate deviations from normality, and homogeneity of variance was assumed based on comparable group sizes.
Effect sizes and odds ratios were not calculated in the present study, as the analysis focused on bivariate associations using chi-square tests and one-way ANOVA. Future studies employing multivariable regression analyses are recommended to estimate effect sizes and odds ratios, thereby providing a more comprehensive understanding of the strength of associations.
Ethical considerations
Ethical approval was obtained from the Ethics Review Committee of the Faculty of Medicine, University of Colombo (Protocol No. EC-24-089), and permissions from relevant faculties Written informed consent was obtained from all participants after explaining study objectives, confidentiality, and voluntary participation. Participants were allowed to withdraw at any time during the study without facing any penalties. The Helsinki Declaration guidelines were followed in this study.
Results
Sociodemographic characteristics
A total of 412 nursing undergraduates from Sri Lankan state universities participated in the study. The majority were female (71.1%) and aged 24–26 years (51%), with most being in their fourth academic year (35.4%). More than half of the participants were Sinhalese (60.9%) and the vast majority were single (93.4%). Over half resided in hostels (53.4%), while 46.8% lived in suburban areas. In terms of socioeconomic status, 41.0% reported a monthly family income between Rs. 50,000 and 100,000 (Fig. 1).
Fig. 1.
Sociodemographic characteristics of nursing undergraduates
Prevalence of sleep quality
Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI), which ranges from 0 to 21. A global PSQI score greater than 5 indicates poor sleep quality. Among the 412 nursing undergraduates surveyed, the prevalence of poor sleep quality was notably high at 84% (n = 346), whereas only 16% (n = 66) demonstrated good sleep quality (Table 1). The PSQI scores ranged from 0 to 16, with a mean score of 7.69 ± 3.67, indicating a general trend toward poor sleep quality in the study population (Fig. 2).
Table 1.
Prevalence of sleep quality among nursing undergraduates
| Sleep Quality | Frequency | Percentage (%) |
|---|---|---|
| Good Sleep Quality | 66 | 16 |
| Poor Sleep Quality | 346 | 84 |
Fig. 2.
Distribution of PSQI scores
The highest frequency was observed at a score of 5 (n = 52), followed closely by scores of 6 (n = 50) and 7 (n = 51). These scores fall within the poor sleep quality range, reinforcing the finding that the majority of nursing undergraduates experience suboptimal sleep.
Factors associated with sleep quality
Association with sociodemographic factors
Sleep quality was significantly associated with students’ living place based on the chi-square test (p = 0.002). In contrast, no significant associations were found between sleep quality and other sociodemographic characteristics, including age (p = 0.980), gender (p = 0.243), ethnicity (p = 0.076), academic year (p = 0.520), marital status (p = 0.064), residence type (urban/rural) (p = 0.449), or family income (p = 0.684). The highest mean PSQI score, indicating poorer sleep quality, was observed among students residing in boarding houses (8.05 ± 2.95), followed by those living in hostels (7.91 ± 3.33), while the lowest scores were seen among students living at home (6.63 ± 3.12) according to one-way ANOVA test (Table 2).
Table 2.
Mean PSQI scores across associated factors
| Factors | Category | Mean PSQI (± SD) | One way ANOVA test P-Value |
|---|---|---|---|
| Living Place | Home | 6.63 ± 3.12 | 0.040* |
| Hostel | 7.91 ± 3.33 | ||
| Boarding | 8.05 ± 2.95 | ||
| Clinical Rotation | Very Manageable | 5.43 ± 2.62 | 0.040* |
| Manageable | 6.77 ± 2.86 | ||
| Somewhat Challenging | 8.21 ± 3.22 | ||
| Very Challenging | 9.08 ± 3.30 | ||
| Diet | Very Healthy | 5.57 ± 1.90 | 0.040* |
| Healthy | 6.10 ± 2.66 | ||
| Average | 7.97 ± 3.13 | ||
| Unhealthy | 9.59 ± 3.57 | ||
| Very Unhealthy | 11.00 ± 2.76 | ||
| Electronic Device Use | Never | 6.00 ± 2.12 | 0.020* |
| Rarely | 6.18 ± 2.22 | ||
| Sometimes | 6.42 ± 2.82 | ||
| Often | 7.60 ± 3.01 | ||
| Always | 8.77 ± 3.50 | ||
| Sleep Hygiene Practices | Excellent | 6.50 ± 3.39 | 0.001* |
| Good | 6.29 ± 2.63 | ||
| Average | 8.24 ± 2.89 | ||
| Poor | 10.58 ± 3.03 | ||
| Very Poor | 10.58 ± 3.03 | ||
| Academic Stress | Low Stress | 6.49 ± 2.56 | 0.001* |
| Moderate Stress | 7.66 ± 3.07 | ||
| High Perceived Stress | 10.65 ± 3.75 |
*Significant at p < 0.05 {One way ANOVA Test}
Association with academic factors
Among academic variables, clinical rotation schedules showed a significant association with sleep quality (p = 0.002), Students who perceived their clinical rotations as somewhat challenging reported the highest proportion of poor sleep (36.7%), followed by those who found rotations very challenging (15.8%) (Table 3). Students who perceived their clinical rotations as very challenging had the highest PSQI scores (9.08 ± 3.30) (Table 2). In contrast, no significant associations were observed between sleep quality and credit hours (p = 0.104), number of assignments per month (p = 0.875), GPA (p = 0.500), study hours per week (p = 0.365), or overall academic performance (p = 0.191).
Table 3.
Association between academic factors with sleep quality
| Academic Factor | Category | Good Sleep Quality (%) | Poor Sleep Quality (%) | Chi-Square Test P-Value |
|---|---|---|---|---|
| Credit Hours | Less than 12 | 0.7 | 6.1 | 0.104 |
| 12–15 | 5.1 | 28.6 | ||
| 16–18 | 7.5 | 27.2 | ||
| More than 18 | 2.7 | 22.1 | ||
| Clinical Rotation | Very Manageable | 1.7 | 3.4 | 0.002 |
| Manageable | 8.3 | 28.2 | ||
| Somewhat Challenging | 4.4 | 36.7 | ||
| Very Challenging | 1.7 | 15.8 | ||
| Assignments per Month | 0–1 | 1.2 | 8.0 | 0.875 |
| 2–3 | 8.0 | 37.4 | ||
| 4–5 | 5.6 | 29.6 | ||
| 6–7 | 0.7 | 6.1 | ||
| 8 or more | 0.5 | 2.9 | ||
| GPA | 3.50–4.00 | 1.0 | 4.4 | 0.500 |
| 3.00–3.49 | 5.8 | 24.8 | ||
| 2.50–2.99 | 6.8 | 33.7 | ||
| 2.00–2.49 | 1.7 | 14.6 | ||
| Below 2.00 | 0.7 | 6.6 | ||
| Study Hours per Week | Less than 5 | 2.9 | 16.7 | 0.365 |
| 5–10 | 5.6 | 23.8 | ||
| 11–15 | 4.1 | 19.9 | ||
| 16–20 | 1.2 | 14.1 | ||
| More than 20 | 2.2 | 9.5 | ||
| Academic Performance | Excellent | 1.2 | 2.4 | 0.191 |
| Good | 7.5 | 33.0 | ||
| Average | 6.8 | 43.9 | ||
| Below Average | 0.5 | 3.9 | ||
| Poor | 0.0 | 0.7 |
*Significant at p < 0.05 {Chi- square Test}
Association with health-related factors
The majority (88.3%) of nursing undergraduates who participated in the study did not have any medical conditions and 92.2% were free from hormonal imbalances. The analysis revealed that medical condition had a significant association (p = 0.005) with sleep quality among nursing undergraduates, while hormonal imbalances did not show a significant association (p = 0.57) with sleep quality. Figure 3 illustrates that poor sleep quality was more prevalent among those with medical conditions (11.4%) compared to those without (72.6%).
Fig. 3.
Association between health-related factors with sleep quality
Association with lifestyle factors
Significant associations were observed between sleep quality and several lifestyle factors, notably diet (p = 0.008), electronic device usage (p = 0.01), and sleep hygiene practices (p < 0.001). Figure 4A clearly illustrates that participants with very healthy diets had the highest proportion of good sleep quality, reflected in the lowest PSQI scores (5.57 ± 1.902), while those with very unhealthy diets had the poorest sleep quality and the highest PSQI scores (11.00 ± 2.757) (Table 2).
Fig. 4.
Association between lifestyle factors with sleep quality
Similarly, non-users of electronic devices demonstrated better sleep quality (5.00 ± 2.121), whereas frequent users showed significantly poorer sleep outcomes (8.77 ± 3.496) (Fig. 4A). Sleep hygiene emerged as the strongest predictor of sleep quality, participants with excellent sleep hygiene had markedly better sleep (PSQI: 6.50 ± 1.387), while those with poor or very poor practices had substantially worse sleep (PSQI: 10.53 ± 3.031) (Table 2).
In contrast, no significant associations were found between sleep quality and physical activity, caffeine intake, alcohol consumption, tobacco use, or smoking frequency, as reflected in the relatively uniform distribution of sleep quality across these factors in Fig. 4B.
Association with stress
The majority of students (65.8%) reported moderate stress levels. A significant association was found between stress and sleep quality (p = 0.01). More than half of students with moderate academic stress experienced poor sleep. PSQI scores varied significantly across stress levels (p < 0.05), confirming that higher academic stress was strongly associated with poorer sleep quality (Table 2).
Discussion
The high prevalence of poor sleep quality observed in this study (84%) highlights that sleep disturbances are a major concern among nursing undergraduates, likely reflecting the combined impact of academic and clinical demands. Similar prevalence rates reported in other nursing populations suggest that common educational and occupational stressors may contribute to poor sleep quality across different settings [10, 12]. The higher prevalence among undergraduates compared to professional nurses may indicate that students are particularly vulnerable, as they have less experience in coping with academic and clinical pressures. Differences across studies may also reflect variations in cultural norms, lifestyle patterns, and institutional factors such as scheduling practices and academic expectations.
Sociodemographic factors
Sociodemographic variables were largely not associated with sleep quality in the present study, except for living place. Similar findings have been reported among Sri Lankan nurses, where personal demographic characteristics showed minimal influence on sleep quality [12]. Collectively, these results indicate that contextual and training-related factors may exert a stronger influence on sleep quality than individual demographic characteristics in nursing populations.
Academic-related factors
Academic workload indicators were not significantly associated with sleep quality, suggesting that perceived workload alone may be less influential than the structure and demands of clinical training. Similar observations among Chinese university students indicate that subjective academic pressure, rather than objective workload measures, may play a greater role in sleep disturbances across different educational contexts [19]. In contrast, clinical rotations showed a significant association with poor sleep quality, likely due to irregular schedules, emotional demands, and physical fatigue during clinical training [18]. Variations from international studies may be attributable to differences in rotation intensity, hospital staffing, supervision, and the availability of student support systems.
Health-related factors
In the present study, medical condition had a significant association with sleep quality. This finding aligns with evidence indicating that chronic conditions such as hypertension, diabetes, and COPD are associated with poor sleep quality, potentially due to symptom burden, psychological stress, and medication effects that interfere with normal sleep patterns [18]. The consistency across studies suggests a robust relationship, potentially mediated by symptom burden, psychological stress, or medication side effects, factors that warrant further exploration.
In the present study, self-reported hormonal imbalance did not show a statistically significant association with sleep quality based on chi-square analysis. This may be partly explained by the low proportion of participants reporting hormonal imbalance and the reliance on self-reported information rather than objective clinical or biochemical assessments. Although other studies have reported associations between hormonal factors and sleep disturbances [20], such mechanisms could not be directly evaluated in the present study due to the absence of hormonal measurements.
Lifestyle factors
Among lifestyle factors, significant associations were observed with diet, electronic device use, and sleep hygiene practices. The observed association between healthier dietary patterns and better sleep quality may reflect improved metabolic regulation and reduced gastrointestinal discomfort, both of which support restorative sleep. Similar associations among Spanish university students suggest that dietary quality plays a universal role in sleep regulation across student populations [21], supporting our findings. Similarly, nighttime electronic device use is linked to sleep difficulties, particularly among females, due to fear of missing out and disrupted circadian rhythms [22]. Frequent nighttime electronic device use can exacerbate poor sleep quality, partly due to nomophobia, ring anxiety, and technostress. Nomophobia refers to the anxiety experienced when unable to access a mobile phone, while ring anxiety is the perception of phantom notifications. Both phenomena have been linked to heightened stress, impaired sleep quality, and reduced academic performance among university students [27]. Technostress, or stress caused by information technology use, further contributes to emotional exhaustion and sleep disturbances [28].
However, in contrast to the present study, other research has shown a positive impact of physical activity on sleep quality [23] and a negative impact of caffeine and alcohol consumption [24]. Such discrepancies may reflect cultural differences, varying consumption patterns, or methodological differences across studies, suggesting that lifestyle interventions should be context-specific.
Psychological factors (Stress)
Stress was significantly associated with poor sleep quality (p = 0.002), with 65.8% of participants reporting moderate stress levels in our study. The strong association between stress and poor sleep quality observed in this study is consistent with evidence from nursing populations, where elevated stress levels have been shown to disrupt sleep through heightened physiological arousal and impaired emotional regulation [25]. University counsellors in Malaysia, who observed a marked increase in student anxiety and depression over recent years [26]. Burnout, defined by emotional exhaustion, depersonalization, and reduced personal accomplishment, is increasingly recognized among nursing students and professionals. High stress and irregular clinical schedules contribute to burnout, which can further disrupt sleep quality [29]. The strong correlation between stress and sleep disturbances underscores the importance of psychological support and stress management programs for nursing students.
The study identified modifiable factors (stress, lifestyle habits, clinical workload) and non-modifiable factors (medical conditions) influencing sleep quality. Interventions should focus on modifiable factors through multifaceted strategies, including stress management, healthy lifestyle promotion, and optimized clinical rotations, to enhance sleep quality, well-being, and academic success.
Limitations
This study has several limitations. The use of an online self-administered questionnaire may have introduced selection, response, and social desirability bias, and some responses may have been misunderstood. The reliance on self-reported data and the cross-sectional design limits causal inference. Objective measurements of sleep (e.g., actigraphy) were not included, which may affect the accuracy of the findings. Multivariable analysis was not performed to adjust for potential confounders. Additionally, the findings may not be generalizable beyond nursing undergraduates in Sri Lankan state universities. Future studies should consider longitudinal designs, interviewer-administered tools, and additional variables to improve accuracy and generalizability.
Conclusion
Poor sleep quality among nursing undergraduates is influenced by multiple factors, including living conditions, clinical rotations, medical conditions, dietary patterns, electronic device usage, sleep hygiene, and academic stress. The high levels of stress reported by students highlight the need for targeted strategies to improve sleep quality and promote mental well-being. Educational institutions should consider integrating sleep-health education into the nursing curriculum, providing accessible mental health support, and implementing programs that encourage healthy sleep habits. Such interventions may enhance student well-being, academic performance, and clinical readiness.
Author contributions
Conceptualization (MDTLG), Methodology (SM, WASCJ, MRP), Software (EMMUBE, HMT), Validation (SM, WASCJ, MRP), Formal Analysis (SM, WASCJ, MRP), Investigation (SM), Resources (MRP), Data Curation (PHWSD, MALU), Writing - Original Draft (SM), Writing - Review & Editing (MDTLG), Visualization (HMT), Supervision (MDTLG), Project Administration (MDTLG).
Funding
Not applicable.
Data availability
The data that support the findings of this are available upon reasonable request.
Declarations
Ethics approval and consent to participate
Ethical approval was obtained from the Ethics Review Committee of the Faculty of Medicine, University of Colombo (Protocol No. EC-24-089). Permission was also sought from the relevant faculty or department of each participating university. Informed consent was obtained from all participants prior to data collection. The Helsinki Declaration guidelines were followed in this study.
Consent for publication
All authors have reviewed and approved the final manuscript and consent to its publication.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
- 1.Safarzade S, Tohidinik H. The sleep quality and prevalence of sleep disorders in adolescents. J Res Health. 2019b;9(6):471–9. 10.32598/jrh.9.6.471. [Google Scholar]
- 2.Rathakrishnan B, Singh B, Kamaluddin SS, Yahaya MR, Mohd Nasir A, Ibrahim MA, F., Rahman A, Z. Smartphone addiction and sleep quality on academic performance of university students: an exploratory research. Int J Environ Res Public Health. 2021;18(16):8291. 10.3390/ijerph18168291. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Thomas CM, McIntosh CE, Lamar RA, Allen RL. Sleep deprivation in nursing students: the negative impact for quality and safety. J Nurs Educ Pract. 2017;7(5):87. 10.5430/jnep.v7n5p87. [Google Scholar]
- 4.Yusuf R, Mohamed NA, Abdi AA, Osman IM, Ali AN, Doğan S, Mohamud SM, Orhan Z. Prevalence and associated factors of poor sleep quality among nurses in a tertiary care hospital: a cross-sectional study. Risk Manag Healthc Policy. 2025;18:975–86. 10.2147/rmhp.s511543. [DOI] [PMC free article] [PubMed]
- 5.Gorgun Hattatoglu D, Aydin S, Aydin C, Yildiz BP. The effect of sleep hygiene and sleep deterioration on quality of life in shiftworking healthcare professionals. Archives Neuropsychiatry. 2020. 10.29399/npa.24827. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Navarro-Martínez R, Chover-Sierra E, Colomer-Pérez N, Vlachou E, Andriuseviciene V, Cauli O. Sleep quality and its association with substance abuse among university students. Clin Neurol Neurosurg. 2020;188:105591. 10.1016/j.clineuro.2019.105591. [DOI] [PubMed] [Google Scholar]
- 7.Pham HT, Chuang H-L, Kuo C-P, Yeh T-P, Liao W-C. Electronic device use before bedtime and sleep quality among university students. Healthcare. 2021b;9(9):1091. 10.3390/healthcare9091091. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Valerio TD, Kim MJ, Sexton-Radek K. Association of Stress, general Health, and alcohol use with poor sleep quality among U.S. College students. Am J Health Educ. 2016b;47(1):17–23. 10.1080/19325037.2015.1111173. [Google Scholar]
- 9.Owens J. Insufficient sleep in adolescents and young adults: an update on causes and consequences. Pediatrics. 2014;134(3):921–32. 10.1542/peds.2014-1696. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Segon T, Kerebih H, Gashawu F, Tesfaye B, Nakie G, Anbesaw T. Sleep quality and associated factors among nurses working at comprehensive specialized hospitals in Northwest, Ethiopia. Front Psychiatry. 2022;13. 10.3389/fpsyt.2022.931588. [DOI] [PMC free article] [PubMed]
- 11.Cândido C, Guilherme Gomes B, Karollaine A, Alves E, Fonseca R, Fonseca R, Igor Cordeiro Mendes. Factors associated with poor sleep quality in nursing students from a private university in Ceará. O Mundo Da Saúde. 2022;46:003–11. 10.15343/0104-7809.202246003011. [Google Scholar]
- 12.Gunathunga SK, Aththanayake AMSK, Jayantha TADN, Wathsala EPS, Perera JTES, Kariyawasam KHAY, Amarasekara AATD, Nisansala MWN. Sleep quality and sleep disturbing factors among nurses in a selected hospital in Colombo, Sri Lanka. Int J KIU. 2022;2022(03):98–106. 10.37966/ijkiu2022032029. [Google Scholar]
- 13.Tommaso D, Halson SL, Oehmen R. Poor sleep quality during COVID-19 pandemic restrictions associated with reduced psychological wellbeing in healthcare students. Chronobiol Int. 2023:1–12. 10.1080/07420528.2023.2186127. [DOI] [PubMed]
- 14.Yılmaz DA, Yıldız GN, Çiftçi B, Yıldız Ö. Examination of the relationship between physical activity, perceived stress and sleep quality of nursing students: A cross-sectional and correlational study. Herkes İçin Spor Ve Rekreasyon Dergisi. 2024;6(1):1–8. 10.56639/jsar.1410108. [Google Scholar]
- 15.Wu C-S, Rong J-R, Huang M-Z. Factors associated with perceived stress of clinical practice among associate degree nursing students in Taiwan. BMC Nurs. 2021;20(1). 10.1186/s12912-021-00602-6. [DOI] [PMC free article] [PubMed]
- 16.Song Y, Yang F, Sznajder K, Yang X. Sleep quality as a mediator in the relationship between perceived stress and job burnout among Chinese nurses: A structural equation modeling analysis. Front Psychiatry. 2020;11:566196. 10.3389/fpsyt.2020.566196. [DOI] [PMC free article] [PubMed]
- 17.Safarzade S, Tohidinik H. The sleep quality and prevalence of sleep disorders in adolescents. J Res Health. 2019;9(6):471–9. 10.32598/jrh.9.6.471. [Google Scholar]
- 18.Wang P, Song L, Wang K, Han X, Cong L, Wang Y, Zhang L, Yan Z, Tang S, Du Y. Prevalence and associated factors of poor sleep quality among Chinese older adults living in a rural area: a population-based study. Aging Clin Exp Res. 2019;32(1):125–31. 10.1007/s40520-019-01171-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Liu X, Lang L, Wang R, Chen W, Ren X, Lin Y, Chen G, Pan C, Zhao W, Li T, Han C, He L, Gu Y. Poor sleep quality and its related risk factors among university students. Annals Palliat Med. 2021;10(4):4479–85. 10.21037/apm-21-472. [DOI] [PubMed] [Google Scholar]
- 20.Lisda Amalia. The relationship of cortisol levels and sleep quality in acute ischemic stroke patients: A literature review. Jurnal Neuroanestesi Indonesia. 2024;13(1):46–52. 10.24244/jni.v13i1.533. [Google Scholar]
- 21.Ramón-Arbués E, Granada-López J-M, Martínez-Abadía B, Echániz-Serrano E, Antón-Solanas I, Jerue BA. The association between diet and sleep quality among Spanish university students. Nutrients. 2022;14(16):3291. 10.3390/nu14163291. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Caba-Machado, Mcilroy D, Padilla-Adamuz FM. Night-time use of electronic devices, fear of missing out, sleep difficulties, anxiety, and well-being in UK and spain: a cross-cultural comparison. Curr Psychol. 2024. 10.1007/s12144-024-05934-5. [Google Scholar]
- 23.Yogesh M, Shihora J, Thakkar D, Makwana N. Association of physical activity with sleep quality in Indian medical students: A Mixed-Methods study. Indian J Community Med. 2025. 10.4103/ijcm.ijcm_43_24. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Bogati S, Singh T, Paudel S, Adhikari B, Baral D. Association of the pattern and quality of sleep with consumption of stimulant Beverages, cigarette and alcohol among medical students. J Nepal Health Res Counc. 2020;18(3):379–85. 10.33314/jnhrc.v18i3.2633. [DOI] [PubMed] [Google Scholar]
- 25.Cecere L, de Novellis S, Gravante A, Petrillo G, Pisani L, Terrenato I, Ivziku D, Latina R, Gravante F. Quality of life of critical care nurses and impact on anxiety, depression, stress, burnout and sleep quality: A cross-sectional study. Intensive Crit Care Nurs. 2023;79:103494. 10.1016/j.iccn.2023.103494. [DOI] [PubMed] [Google Scholar]
- 26.Marzo R, Bhattacharya S. Mental stress faced by the mental health counsellors working in a Malaysian medical university: an interpretative phenomenological analysis. J Family Med Prim Care. 2022;11(11):6772. 10.4103/jfmpc.jfmpc_271_19. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Rathoure G, Sharma P. Nomophobia, ring anxiety, and technostress: mental health implications among university students. Psychiatry Res. 2023;335:114712. 10.1016/j.psychres.2023.114712. PubMed ID: 36993001.
- 28.Bhattacharya S, Singh A, Singh A. Bound by Pixels: A Bibliometric Journey (2010–2024) through Nomophobia. 2025. 10.21203/rs.3.rs-7587735/v1.
- 29.Sultana A, Sharma R, Hossain MM, Bhattacharya S, Purohit N. Burnout among healthcare providers during COVID-19: challenges and evidence-based interventions. Indian J Med Ethics. 2020;5(4):01–4. 10.20529/ijme.2020.73. [DOI] [PubMed] [Google Scholar]
Associated Data
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Data Availability Statement
The data that support the findings of this are available upon reasonable request.