Determinants of sleep quality and its association with cognitive functions in undergraduate students: A cross-sectional study

Study design and participants

A cross-sectional observational study was conducted at Apollo University, Chittoor, Andhra Pradesh, from August to December 2024. Ethical approval was obtained from the Institutional Ethics Committee, and administrative permissions were secured from the Vice Chancellor and Dean. Students were approached in classrooms, informed about the study, and those who consented completed an electronic self-administered questionnaire capturing sociodemographic data, the Pittsburgh Sleep Quality Index (PSQI), and the Sleep Hygiene Index (SHI).

The PSQI is a 19-item self-report scale by Buysse et al. (1989)^[6] assessing sleep quality over 1 month through seven components: Subjective sleep quality, sleep latency, duration, efficiency, disturbances, medication use, and daytime dysfunction. Component scores (0–3) yield a global score (0–21), with >5 indicating poor sleep quality. The PSQI demonstrates good reliability (Cronbach’s α = 0.83) and validity in distinguishing good and poor sleepers.^[7,8]

The SHI is a 13-item tool measuring behavioral and environmental sleep factors, rated on a 5-point scale (0 = Never to 4 = Always), with total scores of 0–52; higher scores reflect poorer hygiene.^[9] It has good internal consistency (Cronbach’s α = 0.64) and test–retest reliability. A cutoff score of 16 showed optimal sensitivity (77.0%) and specificity (47.5%) for identifying poor sleepers per PSQI criteria.^[10]

Each participant then underwent the Montreal Cognitive Assessment (MoCA), administered individually to ensure standardized cognitive evaluation. The MoCA, developed by Nasreddine (2005),^[11] screens for mild cognitive impairment (MCI) across multiple domains, with 30 items scored out of 30. Administration requires 10–15 min; scores ≥26 are normal, 18–25 suggest mild, 10–17 suggest moderate, and <10 suggest severe cognitive impairment. The MoCA shows high reliability (Cronbach’s α = 0.83) and strong correlations with the MMSE and neuropsychological measures.^[12]

Statistical analysis

Data were analyzed using Jamovi (The Jamovi Project, 2024). Non-parametric tests were used due to the non-normality of the main variables. The Shapiro–Wilk test indicated significant deviations from normality for PSQI, SHI, and MoCA (all P < 0.001). Skewness values showed mild positive skew for PSQI (0.638) and SHI (0.514), and a strong negative skew for MoCA (–1.65), supporting the use of non-parametric methods.

Descriptive statistics summarized demographic and key study variables. Group comparisons used the Mann– Whitney U-test, while the Kruskal–Wallis H-test examined differences in PSQI, SHI, and MoCA across perceived stress categories. Kendall’s tau-b correlation assessed associations among cognitive function, sleep hygiene, and sleep quality.

To identify behavioral predictors of sleep quality, generalized linear models (GLMs) with a gamma distribution and a log link function were performed using the PSQI Global Score as the dependent variable. Missing data were addressed through listwise deletion, excluding cases with incomplete values for any variable involved in a given analysis, ensuring analytic consistency and data integrity.

All statistical tests were two-tailed, with P < 0.05 considered statistically significant.

Descriptive statistics

Of the 610 participants, 57.4% were female (n = 350) and 42.6% were male (n = 260). A total of 64.3% (n = 392) were enrolled in the MBBS program, and 35.7% (n = 218) were enrolled in B.Sc. programs. Reported mood states included pleasant (28.9%), euthymic (23.0%), happy (16.1%), stressed (10.2%), neutral (8.9%), unpleasant (6.6%), sad (3.9%), and unstable (2.6%). Regarding perceived stress, 76.7% of participants reported no stress, 10.2% reported examination-related stress, 9.2% reported unknown stress, 2.3% reported academic stress, and 1.6% reported relationship-related stress.

According to the PSQI, 57.7% of students demonstrated poor sleep quality, and 42.3% reported good sleep quality. Based on the SHI, 31.1% practiced good sleep hygiene, 61.0% moderate hygiene, and 7.9% poor hygiene. On the MoCA, 94.4% demonstrated normal cognitive function, and 5.6% were identified as having MCI.

Mean (standard deviation) scores were PSQI = 6.24 (2.72), SHI = 19.0 (7.48), and MoCA = 28.3 (1.53), indicating mildly compromised sleep quality, moderate sleep hygiene, and overall normal cognition within the sample.

Normality testing

Skewness values indicated mild positive skew for PSQI (0.64) and SHI (0.51) and strong negative skew for MoCA (–1.65). The Shapiro–Wilk test confirmed significant deviation from normality for all three variables (P < 0.001). Therefore, non-parametric tests were applied: Mann–Whitney U for group comparisons, Kruskal–Wallis H for multigroup analyses, and Kendall’s τ_β for correlations.

Group comparisons

Course of study

B.Sc. students exhibited significantly higher PSQI scores (median = 7.00) compared with MBBS students (median = 5.00), U = 32,552, P < 0.001, r =.24. SHI scores were also higher among B.Sc. students (median = 20.00) than MBBS students (median = 18.00), U = 36,006, P = 0.001, r =.16.

Cognitive performance differed significantly by course, with MBBS students (median = 29.00) scoring higher than B.Sc. students (median = 28.00), U = 28,814, P < 0.001, r = 0.33 [Table 1].

Mood states

Students reporting positive mood states demonstrated significantly better sleep quality, U = 28,798, P = 0.015, r = 0.13, and higher MoCA scores, U = 29,268, P = 0.026, r = 0.12, compared with those reporting negative moods. No significant difference was observed in SHI scores between the groups, U = 31,592, P= 0.373 [Table 1].

Perceived stress and its impact

The Kruskal–Wallis H-test revealed significant effects of perceived stress on sleep and cognition [Table 2].

For sleep quality (PSQI), χ² (9) = 62.5, P < 0.001, ε² =.10. Post hoc Dwass–Steel–Critchlow–Fligner comparisons showed that students reporting examination stress had significantly poorer sleep quality than those with no stress (P < 0.001).

For sleep hygiene (SHI), χ² (9) = 26.9, P = 0.001, ε² = 0.04. Participants with “unknown stress” had poorer hygiene than those without stress (P = 0.031).

For cognitive function (MoCA), χ² (9) = 42.6, P < 0.001, ε² = 0.07. Students with relationship-related stress scored lower than those reporting no stress (P = 0.007) and examination stress (P = 0.046).

Correlations among sleep and cognition

Kendall’s τ_β correlations [Table 3] showed a weak-negative association between cognitive function and sleep hygiene (τ_β = –0.08, P = 0.010). The correlation between cognitive function and sleep quality was not significant (τ_β = 0.01, P = 0.796). A small positive correlation was observed between sleep hygiene and sleep quality (τ_β = 0.16, P < 0.001).

Predictors of sleep quality

GLM using a gamma distribution and log link was performed to identify predictors of PSQI global score [Table 4].

Daytime naps ≥2 h were not significant predictors, χ² (4) = 7.43, P = 0.115. Bedtime variability was marginally non-significant, χ² (4) = 9.03, P = 0.060; participants who “never” (β = –0.16, P = 0.047) or “rarely” (β = –0.13, P = 0.027) varied their bedtime reported better sleep.

Wake-up time variability significantly predicted sleep quality, χ² (4) = 10.9, P =.028; “never” (P = 0.015) and “rarely” (P = 0.009) varying wake times were associated with better sleep.

Pre-bedtime exercise was significant, χ² (4) = 12.1, P = 0.017; participants who “sometimes” exercised within 1 h of bedtime reported better sleep (β = –0.23, P= 0.012). Staying in bed longer than necessary was also significant, χ² (4) = 12.0, P = 0.017; “never,” “rarely,” and “sometimes” were associated with better sleep than “always.”

Substance use before bedtime was marginally non-significant, χ² (4) = 9.00, P = 0.061, though “rare” users tended toward better sleep (P = 0.064). Stimulating activities before bed were not significant, χ² (4) = 4.19, P = 0.381.

Emotional state before bed had a strong effect, χ² (4) = 37.1, P < 0.001; participants who “never,” “rarely,” or “sometimes” went to bed feeling angry, upset, or nervous reported better sleep. Bed use for non-sleep activities was non-significant overall, χ² (4) = 7.53, P = 0.110, though “never” using the bed for non-sleep activities was associated with better sleep (P = 0.026).

Sleeping on an uncomfortable bed significantly affected sleep quality, χ² (4) = 24.9, P < 0.001; participants who “never” experienced discomfort reported better sleep (P = 0.003). Bedroom discomfort (temperature, noise, light) was also significant, χ² (4) = 21.6, P < 0.001; those reporting “never” (P = 0.001) or “rarely” (P = 0.045) experiencing discomfort had better sleep.

Important work before bedtime was not significant overall, χ² (4) = 4.95, P = 0.293, although “frequently” engaging showed a minor improvement (P = 0.042).

Thinking, planning, or worrying in bed had a strong negative association with sleep quality, χ² (4) = 41.8, P < 0.001; participants who “never” (P < 0.001) or “rarely” (P < 0.001) engaged in these behaviors reported better sleep.

Overall, consistent bed and wake times, emotional stability, and a comfortable sleep environment were significantly associated with better sleep quality, though the overall explained variance across models remained small, highlighting the multifactorial nature of sleep.

Prevalence of poor sleep quality

This study found that 57.7% of undergraduate medical and paramedical students reported poor sleep quality, which is higher than reports from Chinese (31.0%; Li et al, 2020),^[13] Indian (33.9%; Awasthi et al, 2020),^[14] and German (48.7%; Schmickler et al, 2023)^[15] university students but slightly lower than the 63.39% reported by Chatterjee and Kar (2021).^[16] These differences likely reflect contextual variations in academic pressure, lifestyle behaviors, and cultural norms related to sleep hygiene.

Gender differences

Male students in the present study reported significantly poorer sleep quality and sleep hygiene than females, although no gender differences were observed in cognitive function. This contrasts with prior findings (Kaur, 2018; Buboltz et al., 2001).^[17,18] which indicated a greater vulnerability to sleep disturbances among female students. However, the findings align with those of Schmickler et al (2023),^[15] suggesting that sex differences in sleep may vary by cultural or academic context.

Program-based differences were observed, with MBBS students exhibiting better sleep quality, healthier sleep hygiene, and higher cognitive scores than paramedical students did. This finding diverges from studies suggesting that medical students typically experience poorer sleep due to greater academic stress (Brick et al, 2010; Mahida et al, 2025),^[19,20] and the better outcomes in MBBS students may reflect greater awareness of health practices and structured routines within the medical curriculum.

Mood and stress influences

Positive mood was associated with significantly better sleep quality and cognitive function, but not with sleep hygiene. This supports the evidence that mood states can affect subjective sleep perception and cognition but may not necessarily alter behavior patterns. Examination-related stress is significantly associated with poor sleep quality, corroborating studies on academic stress and disrupted sleep patterns (Ahrberg et al., 2012; Carpi et al., 2022).^[21,22] In addition, students experiencing ambiguous or relationship-related stress exhibited poorer sleep hygiene and lower cognitive function, respectively, highlighting the nuanced effects of stressor type on sleep and cognition (Yuen et al., 2012).^[23]

Interrelationships among sleep hygiene, sleep quality, and cognition

A weak but significant negative correlation was observed between sleep hygiene and cognitive function, while a positive correlation was found between sleep hygiene and sleep quality. No significant correlation was observed between sleep quality and cognitive performance. These findings support previous research that emphasizes the role of sleep hygiene in sleep quality (Ali et al, 2023; Lakshme SV et al., 2025),^[24,25] while indicating that short-term fluctuations in sleep quality may not directly impair cognitive functioning in high-performing students (Alfonsi et al, 2020; Curcio et al, 2006).^[26,27] Poor sleep hygiene, however, may gradually impact cognitive performance (Khan and AlJahdali, 2023).^[28]

Behavioral predictors of sleep quality

Using GLMs, several behaviors were found to be significant predictors of sleep quality. Consistency in sleep-wake timing, emotional regulation before bed, and environmental comfort were strong predictors, aligning with research on circadian regulation and sleep quality (Beattie et al, 2015).^[29] Contrary to older views, occasional pre-sleep exercise was associated with better sleep (Stutz et al, 2019),^[30] and emotional arousal before bed was a significant negative predictor (Harvey, 2002; Åkerstedt et al, 2012).^[31,32] Poor sleep environments were associated with worse sleep (Lan et al., 2017; Carter et al, 2012).^[33,34]

Implications

The findings underscore the multifactorial nature of sleep quality, with modest effect sizes indicating contributions from unmeasured variables such as lifestyle, diet, and genetics. The strengths of this study include the use of validated instruments (PSQI, SHI, and MoCA) and robust, non-parametric analyses.

Limitations

Limitations include its cross-sectional design, the study’s reliance on self-reported questionnaires for assessing sleep quality and sleep hygiene may introduce recall bias and social desirability effects, which could influence the accuracy of responses. The findings are limited to undergraduate students enrolled in medical and paramedical courses at a single university and may not be generalizable to students from other academic disciplines or cultural backgrounds. In addition, the study did not account for potential confounding factors such as dietary habits or undiagnosed psychological conditions. Future studies may consider incorporating objective sleep measures, such as actigraphy or polysomnography, and can include more diverse student populations to enhance external validity.