Abstract
Background
Sleep is a vital component for cognitive function, emotional well-being, and overall health, yet it is frequently disrupted by external stressors, such as war and conflict. University students, particularly those in conflict zones, face increased risks of poor sleep quality due to academic pressure, psychological stress, and trauma exposure.
Objectives
This study aims to assess the prevalence of poor sleep quality, sleep latency, and sleep efficiency among university students in Lebanon during the 2023–2024 war conflicts, and the direct and indirect effects of war exposure.
Materials and methods
A cross-sectional study was conducted on 631 university students in Lebanon using the Pittsburgh Sleep Quality Index (PSQI) to evaluate sleep quality. Sociodemographic characteristics and the type of war exposure (direct or indirect) were also assessed. Inferential statistical tests were performed to identify correlations between sleep quality and sociodemographic factors, as well as sleep quality and war exposure. The Prevalence Rate Ratio (PRR) was calculated to identify risk and protective factors for poor sleep quality in each socio demographic group. A binary regression model was applied to predict the most significant risk factors for compromised sleep quality.
Results
73.5% of Lebanese university students had poor sleep quality (PSQI > 5), with a mean Global PSQI score of 8.20 (± 3.95). Students with direct war exposure had the highest mean score of 10.57 (p < 0.001), compared to 8.04 for those with indirect exposure. The average sleep latency PSQI score was 1.46 ± 1.04, and the average sleep efficiency PSQI score was 1.00 ± 1.00. Compared to no exposure, indirect exposure resulted in a 22% higher prevalence (PRR = 1.22) of poor sleep, while direct exposure showed a 60% higher prevalence (PRR = 1.60). The binary exposure showed that female gender increased the odds of poor sleep by 2.71 (p = 0.035), and direct war exposure increased the odds by 19.76 times (p = 0.001).
Conclusion
This study highlights the profound impact of war exposure on sleep quality among Lebanese university students, with direct exposure having the greatest risk. Poor sleep quality in war-affected zones can lead to severe disruptions in academic performance, cognitive function, and overall mental and physical health. The findings underscore the need for targeted interventions, including mental health support and stress management programs, to limit sleep disturbances in conflict-affected populations. Further research is warranted to explore effective coping mechanisms and long-term sleep health in similar contexts.
Keywords: Sleep, Sleep disturbances, Sleep quality, University students, War conflict, PSQI, Mental health, Psychological stress, Lebanon
Introduction
Sleep plays a crucial role in emotional regulation and stress recovery, thus, poor sleep is linked to a higher risk of developing trauma-related distress [1]. Lebanon has been experiencing its worst crisis since the 2006 war, struggling with deep political, economical and financial challenges, a situation compounded by a triple crisis that started in 2019, consisting of the COVID-19 pandemic, the Beirut port explosion on August 4, 2020, and a further economic collapse [2]. The impact of this triple crisis has further aggravated student’s mental health [3]. The prevalence of PTSD in university students was 31.5% in response to the COVID-19 pandemic, 26.0% following the 2024 Beirut Blast, and 40.3% due to the ongoing economic crisis [4].
Since October 2023, the escalation of violence in Lebanon has resulted in ongoing airstrikes, mass displacement, and widespread fear, according to Human Rights Watch (2024) [5]. According to a statement by the Middle East Studies Association in January 2025, the 2023–2024 war had a large impact on Lebanese Universities [6]. Educational activities at universities were suspended between 28 September 2024, up until 6 October 2024, a period during which 80,000 university students had been displaced. For example, Université Saint Joseph de Beirut (USJ) reported that 20% of its staff and one third of its students were displaced from their houses. On November 9, 2024, the main campus of the Lebanese University in Hadath, South Beirut, was damaged in an airstrike [6]. Social stability and essential services have steadily declined, further weakened by internal and external pressures [7]. These factors have been shown to significantly affect sleep, overall health, and quality of life [8, 9].
University students, a particularly vulnerable group, face numerous challenges, including academic pressure, economic instability, and psychological stress. Within this context, sleep quality, insomnia, and stress are closely interconnected, significantly impacting their academic performance and mental well-being [10]. A recent study in Germany showcased that 49% of university students experienced poor sleep quality [11], in China, the prevalence was 30.1% [12], while a study conducted in Jordan reported a prevalence of 74% [13]. In Lebanon, before the 2023–2024 war, the proportion of university students experiencing poor sleep quality ranged from 52.7% to 64.2% [14–16], demonstrating that sleep issues are prevalent among Lebanese university students and may negatively impact their daily functioning [16].
Given the high prevalence of poor sleep quality among university students in Lebanon, the situation may be worsened by the war conflicts of 2023–2024. In fact, sleep disturbances, such as insomnia, nightmares, and hyperarousal, are common among individuals who were exposed to severe trauma, particularly in conflict zones [17, 18]. Unlike typical sleep disruptions, those caused by trauma are closely related to neurophysiological and psychological mechanisms triggered by prolonged exposure to violence [17, 18]. In war zones, sleep shifts into a state of hypervigilance, as trauma intensifies autonomic nervous system activity, leading to recurring nightmares and flashbacks. This disruption impairs the brain’s ability to attain restorative sleep, particularly during the REM phase [19–21]. Studies on the Israeli-Palestinian conflict have documented high rates of poor sleep quality, excessive daytime sleepiness, severe depression, and overall diminished quality of life among Palestinian adults living in the Gaza Strip [22]. While university students generally face psychosocial and academic distress even under normal circumstances [23, 24], war further exacerbates these challenges and may compromise their well-being if they lack the necessary coping skills [25]. Several studies have highlighted the negative impact of war on university students’ health and well-being, leading to psychological distress [26], which can affect academic performance negatively. A study done in Ukraine showed that severe depression symptoms, anxiety symptoms and sleep problems are associated with 17.4%, 12.2% and 11.0% decrease in academic performance of university students, respectively [27].
Understanding the correlation between war exposure and sleep disturbances will provide insight into the prevalence of poor sleep among university students in a war-conflict area, and whether students with direct exposure to war conflicts have poorer sleep quality than those with indirect exposure. Previous studies have explored sleep quality among Lebanese university students, often focusing on sociodemographic characteristics and lifestyle factors [14–16, 28]. However, they did not focus on the influence of external stressors such as armed conflict and economic instability. In particular, no research has examined how the 2023–2024 war has impacted the sleep quality among Lebanese university students, marking a significant gap in the literature. Our aim is to assess the patterns of sleep quality and the effect of direct and indirect exposure to war stressors on sleep quality among university students in Lebanon during the 2023–2024 war conflicts.
Materials and methods
This cross-sectional study was conducted in Lebanon among university students aged 18–30 who lived in Lebanon during the 2023–2024 war events. A total of 631 participants (n = 631) were asked to complete a self-administered, anonymous, online questionnaire that was distributed during the academic year through university networks and social media to students from all universities in Lebanon. We worked with a convenient sample that was set based on feasibility and consistency with similar studies in Lebanon, ensuring adequate representation [15, 16]. Concerning the sample size, according to standard recommendations [29], a sample size of around 384 participants is typically needed for reliable results at 80% statistical power with a medium effect size. Our sample (n = 631) significantly exceeds this recommendation, ensuring strong statistical reliability. A specific statistical power analysis confirmed this adequacy. Using a medium effect size (Cohen’s d = 0.5) and a significance level of 0.05, our calculated statistical power was nearly 99%. This high power indicates our sample size is very effective at detecting meaningful differences and greatly reduces the likelihood of false-negative findings [29]. The questionnaire detailed sociodemographic characteristics, war exposure, subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleep medication, and daytime dysfunction.
The questionnaire considered demographic information including age, gender, living arrangements, household size, marital status, employment, level of education and yearly income. War exposure was assessed using a multiple-choice question that allowed participants to select all applicable experiences. The direct aspects were: lived close to a bombarded area, house damaged by bombardments, house was totally bombarded, lost someone due to war, and displaced to shelter. The indirect aspects were: exposed through social media and news, and heard bombardment sounds and breaking of sound barriers. Participants could choose more than one option to reflect the range of their experiences and based on their responses, participants were subsequently categorized as directly or indirectly exposed. Indirect exposure was defined as exposure in which the individual’s house was not bombarded, no physical damage or casualties were visible from their location, but experienced the war through auditory cues (hearing explosions, etc.) or through indirect sources such as social media and news reports. Any other type of exposure, in which the individual witnessed the events or experienced direct damage including visible destruction or casualties, was considered direct exposure. The war exposure question was developed specifically for this study based on context-specific considerations and was not pilot-tested prior to data collection. Sleep quality was evaluated using the Pittsburgh Sleep Quality Index (PSQI), a 19-item self-reported questionnaire that evaluates sleep quality within the past month. The PSQI, a retrospective tool, comprises seven components that assess sleep habits and these included: sleep duration (in hours), sleep disturbance, sleep latency, estimates of habitual sleep efficiency, use of sleep-enhancing medication, daytime dysfunction due to sleepiness, and overall perceived sleep quality [30, 31]. Each component is scored from 0 to 3, with a global score ranging from 0 to 21; scores ≥ 5 indicate poor sleep quality. Participants were categorized as good (score < 5) or poor sleepers (score ≥ 5). Previous studies have documented the reliability and validity of the PSQI for detecting sleep disturbances [31]. Sleep quality was assessed using the original, validated English version of the PSQI. Although a validated Arabic version exists, the English version was used because the study targeted university students, for whom English fluency was assumed. Lifestyle accommodations were evaluated using a multiple-choice question assessing changes participants adopted to improve their sleep. They were asked to choose which of the following accommodations they used to enhance sleep quality during the period of war. The choices were: hanging out with friends, exercising, meditating, journaling, therapy, and listening to music. We analyzed students who used each coping method and determined what percentage of them were good sleepers.
Inclusion criteria were university students aged 18–30 years old, enrolled in a Lebanese University, residing in Lebanon and being directly or indirectly exposed to the 2023–2024 war, and providing informed consent. Exclusion criteria included non-students, individuals outside the target age range, those living abroad during the study period, and duplicate or incomplete submissions.
Data were analyzed using IBM SPSS Statistics for Windows, version 27. The dependent variable was sleep quality status (Good vs. Poor Sleep Quality), derived from the Global Pittsburgh Sleep Quality Index (PSQI) with a threshold of ≤ 5 indicating good sleep quality and > 5 indicating poor sleep quality. All categorical demographic variables, such as age group, gender, living arrangement, household size, work status, household income, war exposure, and accommodation type were appropriately coded into categorical variables. Continuous variables were also properly formatted. They were verified to ensure numeric formatting in SPSS for accurate statistical analysis. Variables were categorized into meaningful groups (example: age and income brackets) to simplify interpretation and clearly communicate practical differences among groups. Descriptive analyses included frequency tables to summarize categorical demographic variables, while means and standard deviations summarized continuous data (example: Global PSQI scores). Inferential statistical analysis was conducted to examine the data. Normality testing, including skewness and kurtosis, indicated that data distributions were within acceptable limits (± 2 range), supporting the use of parametric tests. Analysis of Variance (ANOVA) and Independent Samples t-tests were performed to compare mean PSQI scores across groups for normally distributed variables. Pearson correlation was used to assess relationships between continuous variables, such as household size and PSQI scores. Given the small sample sizes in certain groups (for example in the living situation: in a dormitory alone n = 13, alone n = 16, with a partner n = 7), Kruskal-Wallis, non-parametric tests were applied to evaluate differences in PSQI scores among demographic categories. In statistical practice, a small sample size generally refers to group sizes less than 30 individuals, where the assumption of normality required for parametric tests (such as ANOVA) often cannot be reliably assessed or met [32]. Prevalence Rate Ratios (PRR) were calculated to quantify the relative prevalence of poor sleep quality across demographic groups. The Prevalence Rate Ratio (PRR) quantifies how many times higher (or lower) the prevalence of poor sleep quality is among those exposed to a certain factor compared to those unexposed. For a PRR > 1, the exposure is associated with a higher prevalence of poor sleep quality and the factor is considered a risk factor for poor sleep quality. On the other hand, for a PRR < 1, the exposure is associated with a lower prevalence of poor sleep quality and the factor is considered a protective factor for poor sleep quality. Finally, for a PRR = 1, no association between the factor and poor sleep quality is found. Additionally, Binary Logistic Regression was employed to model the likelihood of poor sleep quality based on demographic and war exposure variables. Predictors were included using the Enter method, with categorical variables coded using indicator contrasts. Model diagnostics included the Omnibus test, Hosmer–Lemeshow test, and classification tables to assess predictive accuracy. All tests were considered statistically significant at a p-value < 0.05. The results were interpreted in terms of Odds Ratios (OR), confidence intervals, and statistical significance. To preserve the credibility and statistical validity of the study, certain data were necessarily excluded.
Ethical approval and consent
The study was performed in accordance with the Declaration of Helsinki, and was reviewed and approved by the Research Ethics Committee of the Higher Center for Research at the Holy Spirit University of Kaslik - USEK, under HCR/EC 2025-016.
Informed consent to participate was obtained from all of the participants in the study, and they were informed before participating about their role in the study, as well as the measures ensuring their anonymity and confidentiality throughout data collection, analysis, and publication.
Results
Descriptive data
The study included 631 university students, the majority of whom were between 18 and 21 years old. As seen in Table 1, the sample was predominantly female, and most students lived with their parents. Employment status varied, with more than half of students unemployed, 26.9% engaged in part-time work, and 12.4% working full-time. Regarding education level, most were undergraduate students. 82.73% of students reported indirect exposure to war, primarily through social media exposure. Direct exposure was reported by 10.3% of students, with most residing in bombarded areas. Only 6.97% of students reported no exposure at all (Table 1).
Table 1.
Descriptive characteristics of socio-demographic indicators and war exposure
| Variable | Category | n (%) |
|---|---|---|
| Age Group | 18–21 years | 493 (78.1%) |
| 22–25 years | 116 (18.4%) | |
| 26–30 years | 22 (3.5%) | |
| Gender | Female | 426 (67.5%) |
| Male | 205 (32.5%) | |
| Living Arrangement | With parents | 565 (89.5%) |
| Dormitory with roommates | 30 (4.8%) | |
| Living alone | 16 (2.5%) | |
| Dormitory alone | 13 (2.1%) | |
| With partner | 7 (1.1%) | |
| Household Size | Lives alone | 17 (2.7%) |
| 2 people | 32 (5.1%) | |
| 3 people | 81 (12.8%) | |
| 4 people | 210 (33.3%) | |
| 5 people | 206 (32.6%) | |
| 6 + people | 85 (13.5%) | |
| Marital Status | Never married | 606 (96.0%) |
| Married | 10 (1.6%) | |
| Living with partner | 7 (1.1%) | |
| Separated | 6 (0.9%) | |
| Divorced | 1 (0.2%) | |
| Widowed | 1 (0.2%) | |
| Employment Status | Unemployed | 361 (57.2%) |
| Part-time/casual work | 170 (26.9%) | |
| Full-time work | 78 (12.4%) | |
| Unable to work | 19 (3.0%) | |
| Retired | 3 (0.5%) | |
| Education Level | Undergraduate | 516 (81.8%) |
| Graduate | 109 (17.3%) | |
| PhD | 6 (1.0%) | |
| Income Level | Did not disclose | 249 (39.5%) |
| ≤ $10,000 | 139 (22.0%) | |
| $10,001 – $20,000 | 98 (15.5%) | |
| $20,001 – $40,000 | 66 (10.5%) | |
| $40,001 – $60,000 | 36 (5.7%) | |
| $60,001 – $80,000 | 24 (3.8%) | |
| >$80,000 | 19 (3.0%) | |
| War Exposure | Indirect exposure | 522 (82.73%) |
| - Through social media/news | 478 (75.8%) | |
| - Hearing bombardments | 447 (70.8%) | |
| Direct exposure | 65 (10.3%) | |
| - Resided in bombarded areas | 156 (24.7%) | |
| - House was damaged | 38 (6.0%) | |
| - House totally bombarded | 11 (1.7%) | |
| - Displaced | 21 (3.3%) | |
| - Lost someone due to war | 32 (5.1%) | |
| No exposure | 44 (6.97%) |
Analysis of PSQI scores
73.5% of students experienced poor sleep quality (PSQI > 5), with a mean Global PSQI score of 8.20 (± 3.95). In Table 2, to simplify interpretation, we grouped scores 0 and 1 under good sleep and scores 2 and 3 under poor sleep for each PSQI component. This dichotomization helps convey the overall distribution of sleep quality in the sample. Half of the students rated their subjective sleep quality as fairly good, but 38.9% reported poor or very poor sleep, indicating a significant portion experiencing sleep issues. Sleep latency was a challenge for many, with 47.85% taking longer than 30 min to fall asleep. Sleep duration was also problematic, as 48.7% of students reported getting less than 6 h of sleep per night. Sleep efficiency was relatively better, with 39.0% having high efficiency, but 28.5% had moderate to severe issues. Sleep disturbances were common, with 34.07% experiencing them at least twice a week. Despite these issues, 83.36% did not use sleep medication, though 8.08% reported regular use. Daytime dysfunction was notable, with 41.36% reporting difficulty staying alert or maintaining enthusiasm (Table 2).
Table 2.
Sleep quality patterns in the overall study sample (n = 631)
| PSQI Component | Score 0 | Score 1 | Score 2 | Score 3 | n | Mean | Std. Deviation | ||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| N | % | N | % | N | % | N | % | ||||
| Subjective Sleep Quality a | 69 | 10.90% | 317 | 50.20% | 196 | 31.10% | 49 | 7.80% | 631 | 1.36 | 0.78 |
| Sleep Latency a, b | 136 | 21.55% | 193 | 30.60% | 178 | 28.20% | 124 | 19.65% | 631 | 1.46 | 1.04 |
| Sleep Duration c | 159 | 25.20% | 165 | 26.10% | 176 | 27.90% | 131 | 20.80% | 631 | 1.44 | 1.08 |
| Sleep Efficiency d | 246 | 39.00% | 205 | 32.50% | 112 | 17.70% | 68 | 10.80% | 631 | 1.00 | 1.00 |
| Sleep Disturbances b | 61 | 9.67% | 355 | 56.26% | 188 | 29.79% | 27 | 4.28% | 631 | 1.29 | 0.70 |
| Use of Sleeping Medication b | 526 | 83.36% | 54 | 8.56% | 25 | 3.96% | 26 | 4.12% | 631 | 0.29 | 0.73 |
| Daytime Dysfunction b | 126 | 19.97% | 244 | 38.67% | 167 | 26.47% | 94 | 14.89% | 631 | 1.36 | 0.96 |
| Global PSQI Score | – | – | – | – | – | – | – | – | – | 8.20 | 3.95 |
a0: very good; 1: fairly good; 2: fairly bad; 3: very bad
b0: not during past month; 1: less than once a week; 2: once or twice a week; 3:three or more times a week
c0:>7 h; 1: 6–7 h; 2: 5–6 h, 3: < 5 h
d0: >85%; 1: 75–84%, 2: 65–74%; 3: < 65%
Inferential statistics
Comparison of global PSQI scores by sociodemographic characteristics
In Table 3, we compared the Global PSQI Score across demographic variables using parametric tests such as independent samples t-tests and ANOVA. The findings were considered statistically significant when the p-value is less than 0.05.
Table 3.
Descriptive characteristics of socio-demographic indicators stratified by PSQI mean scores
| Sociodemographic Characteristics | N | Mean (PSQI) | p-value |
|---|---|---|---|
| Age Group a | |||
| 18–21 | 493 | 7.96 | 0.01 |
| 22–25 | 116 | 8.90 | |
| 26–30 | 22 | 9.86 | |
| Gender b | |||
| Female | 426 | 8.56 | < 0.001 |
| Male | 205 | 7.45 | |
| Living Situation c | |||
| With your parents | 565 | 8.16 | 0.04 |
| In a dormitory with roommates | 30 | 7.17 | |
| In a dormitory alone | 13 | 8.38 | |
| Alone | 16 | 10.38 | |
| With a partner | 7 | 10.57 | |
| Work Status c | |||
| Part-time/Casual employment | 170 | 8.16 | 0.70 |
| Unemployed | 361 | 8.06 | |
| Full-time employed | 78 | 8.58 | |
| Unable to work (disability/invalid) | 19 | 9.53 | |
| Level of Education c | |||
| Undergraduate | 516 | 8.17 | 0.95 |
| Graduate | 109 | 8.32 | |
| Post-graduate (PhD) | 6 | 8.67 | |
| Household Income c | |||
| Up to $10,000 | 139 | 9.18 | 0.03 |
| $10,001 - $20,000 | 98 | 7.78 | |
| $20,001 - $40,000 | 66 | 7.85 | |
| $40,001 - $60,000 | 36 | 9.25 | |
| $60,001 - $80,000 | 24 | 7.17 | |
| More than $80,000 | 19 | 8.63 | |
aOne-Way ANOVA
bIndependent Samples t-test
cKruskal-Wallis test
A one-way ANOVA indicated a significant difference in sleep quality among age groups (p = 0.01). Older students reported poorer sleep quality compared to younger students.
An independent sample t-test revealed a significant gender difference (p < 0.001), with female students reporting poorer sleep quality than male students.
A Kruskal-Wallis test showed a significant effect of living situation on sleep quality (p = 0.04). Students living with a partner exhibited the poorest sleep quality compared to those living with parents. The test also showed a significant effect of household income on sleep quality (p = 0.03). Students from lower and mid-range income households reported poorer sleep quality compared to those from higher-income brackets.
The “Retired” category (n = 3) in the mean PSQI score and the PRR analysis is not statistically appropriate due to the extremely small sample size. With such a limited number of observations, the estimate would lack reliability and could produce misleading or unstable results. Therefore, it was excluded from PRR calculations and other inferential tests to maintain the validity and robustness of the findings.
In addition, we acknowledge that 39.5% of participants (n = 249) did not disclose their household income; however, the remaining 382 participants still provide a sufficiently large and diverse subsample for meaningful and statistically valid income-related comparisons.
Comparison of global PSQI scores by war exposure
In Table 4, we compared the Global PSQI Score across war exposure groups using the One-Way ANOVA Test. The findings were considered statistically significant when the p-value is less than 0.05. Individuals with direct exposure reported the highest PSQI scores (poorest sleep quality) (p < 0.001), while those with no exposure reported the lowest scores (best sleep quality) (p < 0.001). (Table 4).
Table 4.
Descriptive characteristics of war exposure types stratified by PSQI mean scores
| War Exposure | N | Mean (PSQI) | p-value |
|---|---|---|---|
| Direct Exposure | 65 | 10.57 | < 0.001 |
| Indirect Exposure | 522 | 8.04 | |
| None | 44 | 6.59 |
Indirect exposure: Exposed through social media and news, heard bombardment sounds and breaking of sound barrier, lived close to a bombarded area
Direct exposure: House damaged by bombardments, House was bombarded, lost someone due to war, displaced to shelter
Comparison of global PSQI scores by sleeping accommodation type
In Table 5, we compared the Global PSQI Score across sleeping accommodation groups using the Kruskal-Wallis Test. The findings will be considered statistically significant when the p-value is less than 0.05. Analysis revealed that students sharing a bed with a partner had significantly higher PSQI scores (p = 0.03), indicating poorer sleep quality compared to those sleeping alone, or with a roommate in a separate room (Table 5).
Table 5.
Descriptive characteristics of accommodation types stratified by PSQI mean scores
| Sleeping Accommodation Type | N | Mean (PSQI) | p-value |
|---|---|---|---|
| No bed partner or roommate | 419 | 8.23 | 0.03 |
| Partner in same room, but not same bed | 150 | 7.77 | |
| Partner/Roommate in other room | 46 | 8.35 | |
| Partner in same bed | 16 | 10.57 |
Comparison of global PSQI scores with bedtime behaviors (partner observations)
In Table 6, global PSQI scores were compared across accommodation groups using Pearson correlation, with significance set at p < 0.05. All examined sleep disturbances reported by bed partners showed statistically significant positive correlations with PSQI scores (p < 0.001). Higher frequency of these behaviors is associated with higher PSQI scores, indicating poorer sleep quality. The correlations are weak-to-moderate in strength, with “Episodes of disorientation or confusion” and “Long pauses between breaths while asleep” showing the strongest relationships with poorer sleep quality (Table 6).
Table 6.
Descriptive characteristics of sleep behaviors (Reported by Partner) stratified by PSQI mean scores
| Sleep Behavior (Reported by Partner) | N | Pearson Correlation | p-value |
|---|---|---|---|
| Loud snoring | 631 | 0.18 | < 0.001 |
| Long pauses between breaths while asleep | 631 | 0.26 | < 0.001 |
| Legs twitching or jerking during sleep | 631 | 0.25 | < 0.001 |
| Episodes of disorientation or confusion during sleep | 631 | 0.29 | < 0.001 |
| Other restlessness during sleep | 631 | 0.16 | < 0.001 |
Comparison of global PSQI scores by activities that improved sleep
Table 7 shows the descriptive characteristics of activities that were used to improve sleep, stratified by sleep quality. No statistical test was performed. Only descriptive cross tabulations were conducted, since the question about Activities that Improved Sleep allowed multiple responses. This descriptive study showed that the majority of participants across all activities reported poor sleep quality, with journaling, listening to music and therapy showing slightly higher percentages reporting poor sleep quality (Table 7).
Table 7.
Descriptive characteristics of activities that improved sleep stratified by sleep quality
| Activity | Good Sleep Quality (%) | Poor Sleep Quality (%) | Total (N) |
|---|---|---|---|
| Hanging out with friends | 27.9% | 72.1% | 315 |
| Exercising | 28.0% | 72.0% | 230 |
| Meditating | 29.6% | 70.4% | 71 |
| Journaling | 25.0% | 75.0% | 35 |
| Therapy | 25.7% | 74.3% | 35 |
| Listening to music | 24.1% | 75.9% | 299 |
Prevalence rate ratio (PRR)
Prevalence rate ratio (PRR) of poor sleep quality by demographic variables
Table 8 presents the Prevalence Rate Ratio (PRR) of poor sleep quality for each socio demographic group.
Table 8.
Prevalence rate ratio (PRR) of poor sleep quality by sociodemographic characteristics for the whole sample (n = 631)
| Sociodemographic Characteristics | Mean PSQI | PRR | Interpretation |
|---|---|---|---|
| Age Group | |||
| 18–21* | 7.96 | 1.00 | Reference group |
| 22–25 | 8.90 | 1.12 | 12% higher prevalence |
| 26–30 | 9.86 | 1.24 | 24% higher prevalence |
| Gender | |||
| Male* | 7.45 | 1.00 | Reference group |
| Female | 8.56 | 1.15 | 15% higher prevalence |
| Living Situation | |||
| With your parents* | 8.16 | 1.00 | Reference group |
| Dormitory with roommates | 7.17 | 0.88 | 12% lower prevalence |
| Dormitory alone | 8.38 | 1.03 | 3% higher prevalence |
| Alone | 10.38 | 1.27 | 27% higher prevalence |
| With a partner | 10.57 | 1.30 | 30% higher prevalence |
| Work Status | |||
| Unemployed* | 8.06 | 1.00 | Reference group |
| Part-time/Casual | 8.16 | 1.01 | 1% higher prevalence |
| Full-time employed | 8.58 | 1.06 | 6% higher prevalence |
| Unable to work (disability) | 9.53 | 1.17 | 17% higher prevalence |
| Education | |||
| Undergraduate* | 8.17 | 1.00 | Reference group |
| Graduate | 8.32 | 1.02 | 2% higher prevalence |
| Post-graduate (PhD) | 8.67 | 1.06 | 6% higher prevalence |
| Income Level | |||
| $60,001–$80,000* | 7.17 | 1.00 | Reference group |
| $10,001–$20,000 | 7.78 | 1.09 | 9% higher prevalence |
| $20,001 - $40,000 | 7.85 | 1.09 | 9% higher prevalence |
| More than $80,000 | 8.63 | 1.20 | 20% higher prevalence |
| Up to $10,000 | 9.18 | 1.28 | 28% higher prevalence |
| $40,001 - $60,000 | 9.25 | 1.29 | 29% higher prevalence |
*: Reference Group
Concerning age, according to the PRR, those aged 22–25 years old have a 12% higher prevalence of poor sleep quality while those aged 26–30 have a 24% higher prevalence of poor sleep quality, all compared to those aged 18–21 years old.
As for the gender, according to the PRR, females have a 15% higher prevalence of poor sleep quality compared to males.
Regarding the living situation, according to the PRR, those living in a dormitory alone have a 3% higher prevalence, while those living with a partner have a 30% higher prevalence of poor sleep quality, all compared to those living with their parents.
Students with household incomes of ≤$10,000 and $40,001–$60,000 had the highest prevalence of poor sleep quality (28% and 29%, respectively). Those in the $10,001–$40,000 and >$80,000 brackets also showed increased prevalence (9% and 20%) compared to the $60,001–$80,000 group.
Prevalence rate ratio (PRR) of poor sleep quality by war exposure
Concerning war exposure, according to the PRR in Table 9, those with indirect exposure have a 22% higher prevalence of poor sleep quality than those with no exposure, while those with direct exposure have a 60% higher prevalence of poor sleep quality than those with no exposure (Table 9).
Table 9.
Prevalence rate ratio (PRR) of poor sleep quality by war exposure for the whole sample (n = 631)
| War Exposure | Mean PSQI | PRR | Interpretation |
|---|---|---|---|
| None* | 6.59 | 1.00 | Reference group |
| Indirect Exposure | 8.04 | 1.22 | 22% higher prevalence |
| Direct Exposure | 10.57 | 1.60 | 60% higher prevalence |
*: Reference Group
Prediction model
In Table 10, a logistic regression model was run on a cleaned subsample of 382 participants after excluding cases with missing or non-informative income data (not stated income). The model identified significant predictors of poor sleep quality. Female gender increased the odds of poor sleep by 1.85 times (p = 0.019), indirect war exposure increased the odds by 2.71 (p = 0.035), and direct war exposure increased the odds by 19.76 times (p = 0.001). Living situation also proved to be overall significant (p = 0.028). Household income, work status, and education level were however not significant predictors. The model explained 14.3% of the variance in sleep quality (Nagelkerke R² = 0.143) and had an overall classification accuracy of 75.4% (Table 10).
Table 10.
Binary logistic regression test results for predictors of poor sleep quality (n = 382)
| Predictor | B | SE | Wald | p-value | OR [Exp(B)] | 95% CI for OR | Significant |
|---|---|---|---|---|---|---|---|
| Age Group | 0.296 | 0.288 | 1.057 | 0.304 | 1.345 | 0.764–2.366 | No |
| Gender (Female) | 0.615 | 0.262 | 5.517 | 0.019 | 1.85 | 1.11–3.09 | Yes |
| Living Situation (Overall) | – | – | 7.175 | 0.028 | – | – | Yes |
| Dormitory w/roommates | 0.857 | 0.605 | 2.008 | 0.156 | 2.355 | 0.720–7.701 | No |
| Dormitory alone | –0.646 | 0.781 | 0.684 | 0.408 | 0.524 | 0.114–2.421 | No |
| Household Size | –0.209 | 0.125 | 2.796 | 0.094 | 0.812 | 0.635–1.037 | No |
| Work Status (Overall) | – | – | 0.097 | 0.999 | – | – | No |
| Income (Grouped) | 0.012 | 0.182 | 0.004 | 0.947 | 1.012 | 0.709–1.444 | No |
| War Exposure (Overall) | – | – | 11.462 | 0.003 | – | – | Yes |
| Indirect Exposure | 1.002 | 0.474 | 4.468 | 0.035 | 2.72 | 1.08–6.90 | Yes |
| Direct Exposure | 2.984 | 0.890 | 11.233 | 0.001 | 19.76 | 3.45–113.13 | Yes |
| Bed Partner/Roommate (Overall) | – | – | 2.306 | 0.511 | – | – | No |
| Constant | 18.22 | 40195.26 | 0.00 | 1 | 81572406.1 |
Discussion
The findings indicate a high prevalence of poor sleep quality among university students in Lebanon, with 73.5% of students experiencing disrupted sleep and a mean Global PSQI score of 8.20 (± 3.95). In contrast, studies from Austria (32.1% and mean PSQI score of 4.87 (± 2.22)) [33], Canada (36.8%) [34], and Germany (48.7%) [11], reported significantly lower rates of poor sleep quality. Unlike our population, these countries were not experiencing war-related stressors, which likely contributed to better sleep outcomes. Other studies conducted in war-torn regions have reported a high prevalence of poor sleep quality, reinforcing the significant impact of armed conflicts on sleep. For instance, in Syria, the prevalence of poor sleep was reported at 67.7% [35], while in Gaza it was 52.8% when defined as PSQI ≥ 6 and 30.5% when defined as PSQI ≥ 8 [36]. However, these studies did not stratify participants based on their level or type of war exposure. In contrast, our study differentiated between direct exposure and indirect exposure. This stratification allows for a more nuanced understanding of how varying levels of war-related stressors contribute to sleep disturbances. Additionally, a study conducted in Ukraine highlighted widespread insomnia, depression, and overall poor sleep quality among its population [37]. These findings align closely with our reported prevalence of poor sleep (73.5%), suggesting that conflict zones exhibit consistently high rates of sleep disturbances. The common factor among these regions is prolonged exposure to war, bombardments, and continuous stress, all of which have been shown to disrupt sleep patterns. It has been demonstrated that frequent exposure to airstrikes and bombings leads to increased sleep fragmentation, heightened nighttime awakenings, and an overall decline in sleep efficiency [38, 39]. Prior to the war and severe socio-economic instability, several studies conducted in Lebanon reported lower PSQI scores than our findings. A study reported a PSQI mean score of 6.65, (± 3.24) [40] and another one a prevalence of poor sleep quality of 52.7% in 2014 [16]. The significant increase in sleep disturbances observed in more recent studies is most likely attributable to the compounded effects of the ongoing war and the severe economic crisis.
Our study revealed a significantly higher percentage of students (47.85%) experiencing prolonged sleep latency, taking more than 30 min to fall asleep, compared to previous findings [16]. The proportion of students sleeping less than six hours per night was nearly four times higher than that reported in a similar study conducted previously in Lebanon in 2014 [28]. Our findings also indicate a significant impact on daytime functioning, with 41.36% of students reporting difficulties staying alert and maintaining enthusiasm, and this reflects a higher prevalence than the 2014 study [16], where approximately 30% of participants reported experiencing a lack of enthusiasm more than once per week. Several factors could account for these discrepancies, with the most plausible explanation being the war environment and the worsening socio-political and economic conditions in Lebanon [41].
The results of the sociodemographic characteristics revealed that older students (22–30 years) experienced poorer sleep quality than younger ones. This could be attributed to the increased responsibilities and stressors faced by older students, as highlighted by previous research [11]. Our study also showed a gender difference in sleep quality, with female students reporting significantly higher PSQI scores (M = 8.56) compared to males (M = 7.45). This shows that both genders have a difference in stress processing and vulnerability to sleep disturbances which also aligns with previous study about poor sleep quality, insomnia symptoms and stress in undergraduate students [42]. Our results indicate a significant effect of living arrangements on sleep quality. Students living alone (M = 10.38) or with a partner (M = 10.57) experienced the poorest sleep quality, likely due to the absence of familial support systems during crises. In contrast, students living in dormitories with roommates (M = 7.17) reported better sleep quality, possibly benefiting from increased social interaction and emotional support from peers facing similar challenges. There is a significant non- linear relationship between household income and sleep quality. PRR analysis shows that mid - range income students ($60,001 - $80,000) have better sleep quality than both lower (≤$10,000) and higher income groups (more than $80,000). The mid-income individuals may experience better sleep quality due to relative financial stability. Low-income groups face considerable financial challenges, especially during war conflicts, including stress to meet financial needs, balancing work and studies, uncertainty of the future, etc. These stressors usually lead to stress and burnout, which increase the risk of poor sleep quality and aligns with similar studies done in Lebanon, correlating low-income with high levels of stress and burnout in university students [43]. Meanwhile, high-income groups face different challenges like stress of wealth preservation, higher expectations, and uncertainty of the future. These stressors, while different from the ones low-income students are exposed to, can also lead to stress and burnout, which increase the risk of poor sleep quality. Other studies found financial stress to be a major contributor to poor sleep quality [43], however most studies focus on its association with low income. The relationship between high income and stress or sleep quality, particularly in conflict-affected regions, remains largely unexplored.
Additionally, sharing a sleeping space (bed, bedroom, etc.) led to poor sleep quality (p = 0.03). In Lebanon, it is common for young adults and university students to live with their families, even into adulthood. Shared bedrooms among siblings or same-gender relatives are culturally acceptable due to limited space in low income households, or due to strong familial bonds; which contrasts with Western norms, where individual sleep space is the standard. And during the 2023–2024 war, many individuals displaced by violence or instability moved to relatively safer areas, often staying with relatives or friends. While these arrangements can provide emotional safety, especially during conflict, it can also lead to sleep disruptions due to overcrowding and noises. Other studies also found stressors related to overcrowding, lack of privacy, noise and movement disruptions to impair sleep quality [15, 16]. These results highlight a potential tension between cultural norms and psychological needs during war-conflicts. Our findings are in accordance with other findings: sleep disturbances such as disorientation, breathing pauses, and irregular routines further contribute to poorer sleep, particularly in high-stress settings [44].
In response to these challenges, individuals often adopt coping mechanisms to improve sleep quality. For instance, listening to music before bed was associated with lower PSQI scores, aligning with studies on its relaxing effects. A study found that sleep-assisting music led to improvements in subjective sleep quality, sleep latency, and daytime dysfunction among university students [45]. However, in high-stress environments, coping mechanisms were insufficient to significantly improve sleep quality, as the psychological burden of war had a stronger impact [10, 16].
Our results demonstrate that direct exposure to war was significantly associated with poorer sleep quality compared to indirect exposure or no exposure. Table 4 showed that university students with direct exposure to war had significantly higher PSQI scores (M = 10.57) compared to those with indirect exposure (M = 8.04) and no exposure (M = 6.59), with a highly significant p-value (p < 0.001). Therefore, direct exposure to conflict significantly worsens sleep quality. Direct exposure to war-related trauma is strongly linked to psychological stressors like PTSD and grief, leading to severely impaired sleep. Indirect exposure through sounds of bombardment or distressing content on social media also affects sleep but to a lesser extent. The direct exposure to war through the bombarding or damage of houses, displacement, and loss of a family member act as chronic stressors that can trigger or exacerbate conditions such as PTSD, anxiety, and depression. A relevant theoretical framework to understand the link between war exposure and sleep disturbances is the Stress-Diathesis Model [46], which suggests that individuals facing severe stressors, such as armed conflict, are more likely to develop psychological disorders if predisposed. Similar findings following the Beirut Blast and economic crisis support our results [4]. These psychological states disrupt sleep through mechanisms like hyperarousal, intrusive thoughts, and dysregulated cortisol secretion. Additionally, according to Trauma Theory [47], the exposure to traumatic events (explosions, etc.), hearing bombardment sounds, or even exposure through social media, disrupt one’s sense of safety and stability, which are essential for restful sleep. Similarly, a study among Ukrainian university students during times of conflict with nearly half of the sample (45%) reported direct exposure to bomb explosions, showed high levels of insomnia (49%) among the participants [48]. Also, the network analysis in this study found that greater war exposure correlated with more severe PTSD symptoms, which were centrally linked to insomnia [48]. Furthermore, another study reported that 82.4% of Palestinian adults in Gaza exposed to war had poor sleep quality, with a mean PSQI score of 9.2, which is comparable to our direct exposure group [36]. We found that indirectly exposed university students compared to directly exposed had a lower mean PSQI score. Another study examined in more depth the relation between media exposure and insomnia, using the war-related media exposure scale (WarMES) [49]. The results found an indirect association between war media exposure and insomnia: war media exposure increases stress and anxiety, which consequently leads to insomnia [50]. Overall, our findings align with studies from conflict zones like Ukraine and Gaza, confirming that direct war exposure worsens sleep.
Concerning the logistic regression analysis, gender, living situation, and war exposure were identified as significant predictors of poor sleep quality among university students. Female students were significantly more likely to experience poor sleep (OR = 1.85, p = 0.019), with 85% higher odds of reporting poor sleep compared to males. This suggests potential gender-related vulnerabilities, possibly because women are about twice as likely as men to experience anxiety and depression, with sex hormone fluctuations playing a critical role in this increased vulnerability [51]. War exposure was the most influential factor: students indirectly exposed to conflict had 2.71 times greater odds of poor sleep (p = 0.035), while those with direct exposure had an exceptionally elevated risk, with 19.76 times greater odds (p = 0.001). This extremely high odds ratio strongly implicates trauma as a major driver of sleep disturbances. However, the wide confidence interval (3.45–113.13), along with the unusually large model constant (B = 18.22, SE = 40195.26), indicates complete separation, where direct exposure perfectly or near-perfectly predicts poor sleep, which could lead to slightly inflated odds ratios and less precise p-values. While students’ living situation showed a significant overall association with sleep quality (p = 0.028), none of the specific categories were individually significant, suggesting that environmental or social context may affect sleep in more complex ways.
Limitations and strengths
Strengths
This study is the first to determine the prevalence of sleep quality during periods of armed conflict in Lebanon and to examine the impact of war-related stressors on sleep quality among this specific population, particularly in the aftermath of the 2023–2024 conflicts. Notably, the research employs a standardized and validated sleep quality metric to evaluate sleep quality. The findings in this study are based on a large dataset that was collected and analysed, thereby enhancing the reliability of the findings.
Limitations
This study employs a cross-sectional design, so the findings cannot be generalised. A self-administered questionnaire was used, which may have introduced self-reporting bias. Other limitations include the restricted scope of variables assessed in the conflict exposure questionnaire, which may not fully capture the complexity of participants’ experiences. Additionally, the dataset was unbalanced, with a disproportionately higher number of female participants compared to males, potentially limiting the representativeness of the sample in reflecting the broader student population in Lebanon. Finally, the complete separation and low explanatory power of the logistic model slightly affected the model’s stability and accuracy. Future studies with more advanced methods and more varied data to get clearer, more accurate results are needed.
Conclusion
This study sheds light on the profound impact of war on the sleep health of Lebanese university students, revealing how direct exposure to armed conflict, financial hardship, and the unique challenges faced by students place them at greater risk. Poor sleep quality, as observed, is not just a symptom, but a critical health concern with implications for cognitive function, academic performance, and long-term mental and physical well-being. Specifically, inadequate sleep impairs concentration, memory retention, and decision-making abilities, which are critical for academic success. It also increases vulnerability to anxiety, depression, and other psychological disorders. These findings suggest the need for targeted interventions, including mental health support, stress management programs, and increased awareness of sleep quality and its associated risk factors. Furthermore, the results have broader implications for public health policy, highlighting the importance of integrating sleep and mental health considerations into emergency response planning, enhancing mental health services, and strengthening community support systems in conflict-affected regions.
Acknowledgements
N/A.
Abbreviations
- PSQI
Pittsburgh sleep quality index
- PRR
Prevalence rate ratio
- PTSD
Post-traumatic stress disorder
- REM
Rapid eye movement
- IBM
International business machines corporation
- SPSS
Statistical Package for the Social Sciences
- ANOVA
Analysis of variance
- OR
Odds ratios
- Std Deviation
Standard deviation
- SE
Standard error
- CI
Confidence interval
- M
Mean
- WarMES
War-related media exposure scale
Author contributions
All authors (Hanna El Haddad, Magalie Estanom, Michel Gergi, Christelle Khalil, Maria Angela Labaki, Rita Youssef, Shafika Assaad) contributed equally to this manuscript. The corresponding author is Maria Angela Labaki. Her email is: mariaangela.g.labaki@net.usek.edu.lb.
Funding
This research was self-funded by the authors.
Data availability
The data used and analyzed in the current study are available upon request. Contact the corresponding author Maria Angela Labaki: mariaangela.g.labaki@net.usek.edu.lb.
Declarations
Ethics approval and consent to participate
The study was performed in accordance with the Declaration of Helsinki, and was reviewed and approved by the Research Ethics Committee of the Higher Center for Research at the Holy Spirit University of Kaslik - USEK, under HCR/EC 2025-016. Informed consent to participate was obtained from all of the participants in the study, and they were informed before participating about their role in the study, as well as the measures ensuring their anonymity and confidentiality throughout data collection, analysis, and publication.
Consent for publication
N/A.
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.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data used and analyzed in the current study are available upon request. Contact the corresponding author Maria Angela Labaki: mariaangela.g.labaki@net.usek.edu.lb.