INTRODUCTION
Sleep is a state of reduced physical activity and diminished conscious awareness, facilitating mental and physical restoration [1]. The underlying purposes and mechanisms of sleep are only partially understood and remain an active field of research [1]. In modern societies, many individuals follow schedules that are misaligned with natural light–dark cycles, often going to bed late and waking at irregular times, which can disrupt circadian rhythms and sleep quality [2,3].
Sleep is a key component of daily biological rhythms and is essential for maintaining normal human physiology, including thermoregulation, energy conservation, metabolic homeostasis, and immune function [4,5]. Disturbances in sleep habits have been associated with adverse effects on body physiology and increased risks of cardiometabolic disease, mental health problems, and mortality [3,6,7]. Adequate sleep is beneficial for cardiovascular health, weight regulation, cognitive performance, and overall well-CIMbeing, whereas poor sleep quality is strongly linked to impaired physical, social, and mental health [4,8].
A range of biobehavioral and environmental factors influence sleep habits among adolescents and emerging adults, including the internal circadian clock, homeostatic sleep processes, college or work schedules, social demands, and exposure to artificial light at night [1,2,5,9]. Sleep timing and quality are shaped by chronotype, that is, an individual’s circadian phase preference for morning or evening activity [1,10]. Evening chronotype has been associated with delayed sleep timing, shorter sleep duration on workdays, and poorer subjective sleep quality [10,11].
In females, circadian disruption and poor sleep have been linked to endocrine and reproductive disturbances, including menstrual irregularities, dysmenorrhea, mood changes, and altered reproductive outcomes [12,13]. Night-shift work and irregular schedules have also been associated with increased risk of breast cancer and cardiometabolic disorders in women [12,14]. Therefore, maintaining and improving sleep quality and quantity is an important issue in women’s health and in modern lifestyles more broadly [5,12].
Although college students are often considered a physically healthy population, they are exposed to multiple risk factors for sleep disturbance, such as academic pressure, social activities, and increased use of digital devices and social media [15,16]. The transition to college life involves substantial curricular and extracurricular demands, extended study hours, and emotional stress, which can delay bedtimes, reduce sleep duration, and fragment sleep [15,17,18]. Young female college students may be particularly vulnerable to disturbed sleep–wake habits due to combined academic load, social expectations, and hormonal fluctuations, which can adversely affect their health, mood, and learning ability [16,17,19].
Previous studies from different countries have reported a high prevalence of poor sleep quality among university students, often in the range of 30%–60%, and have linked poor sleep to evening chronotype, irregular sleep schedules, and lifestyle behaviors such as late-night internet use [3,15,18,20]. However, there is limited population-specific information on sleep quality and sleep habits among female college students in central India, particularly using standardized chronotype and sleep quality instruments.
Therefore, the objective of the present study was to assess sleep quality in relation to sleep habits, chronotype, internet use, and menstrual cycle characteristics among young female college students under their normal routine life in Raipur, Chhattisgarh, India.
METHODS
Study design and participants
This cross-sectional, observational, questionnaire-based study investigated sleep–wake behavior and sleep quality under usual daily conditions in female college students. A total of 1,186 female students (age [mean±SE]: 19.08±0.05 years; range: 17–31 years) were randomly selected from undergraduate and postgraduate programs in colleges located in Raipur (19°50'–21°53' N, 81°25'– 83°38' E), Chhattisgarh, India.
Participants completed a general information form and signed a written informed consent form prior to enrolment. Inclusion criteria were: 1) female, 2) currently enrolled in undergraduate or postgraduate courses, 3) apparently healthy, and 4) willing to participate. Exclusion criteria included: 1) regular alcohol or tobacco use, 2) current use of illicit drugs or medications known to affect sleep, 3) self-reported psychological or psychiatric disorders, and 4) regular shift or night work, including part-time night jobs that could alter habitual sleep–wake schedules. Students attended college regularly during the study phase, following a relatively regimented schedule determined by college timings, whereas they lived at home or in hostels with more flexible schedules during holidays and vacations.
The study protocol was approved by the Institutional Ethics Committee for Human Research, Pt. Ravishankar Shukla University, Raipur, Chhattisgarh, India (Approval No. IEC REF. NO. 96/133/156/IEC/PRSU/2016).
Instruments and data collection procedures
Data collection was carried out in classrooms during regular college hours. Questionnaires were distributed to students, completed in the presence of an investigator, and collected immediately after completion. Each survey session required approximately 20–30 minutes per class.
General questionnaire
A self-administered general questionnaire obtained biographical and anthropometric information, including age (yr), height (m), and weight (kg). Body mass index (BMI) was calculated as weight (kg) divided by height (m2). Body surface area (BSA) was estimated using the Du Bois formula [21]. Additional questions assessed internet use (yes/no) [22], sports participation (yes/no), self-reported sleep duration categories (<7 h, 7–8 h, >8 h), and menstrual cycle regularity (regular/irregular).
Standard questionnaires to record sleep–wake behavior and sleep quality
Munich Chronotype Questionnaire
The Munich Chronotype Questionnaire was used to assess sleep–wake behavior on college days, including bedtime, sleep onset, time of awakening, and time of getting up [10]. Midsleep was calculated using the following standard formula:
where sleep duration was defined as the interval between sleep onset and final awakening [10].
Pittsburgh Sleep Quality Index
Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI), a widely used and validated instrument for evalu-ating subjective sleep quality over the preceding month [24]. The global PSQI score ranges from 0 to 21. In this study, participants with a global PSQI score of 0-5 were classified as having good sleep quality, whereas those with scores >5-21 were classified as having poor sleep quality. The Hindi version of the PSQI was used with permission from MAPI Research Trust, France [24]
Menstrual cycle phase at the time of questionnaire completion was not recorded; thus, phase-specific analyses of sleep parame-ters were not possible and are acknowledged as a limitation.
Statistical analyses
Data were entered into Microsoft Excel and analyzed using SPSS version 16.0 (SPSS Inc.) and the Analysis Tool Pak in Microsoft Excel. Descriptive statistics (mean±SE, percentages) were calculated for age, BMI, BSA, bedtime, wake time, midsleep, sleep duration, MEQ score, and global PSQI score.
Participants were categorized into good and poor sleep quality groups based on global PSQI scores (0–5 vs. >5). One-way analysis of variance (ANOVA) was used to compare age, BMI, BSA, bedtime, wake time, midsleep, sleep duration, and MEQ score between the two sleep quality groups. Where distributional assumptions were not met, Mann–Whitney U tests were used as non-parametric alternatives to compare these variables between groups [25].
Pearson correlation coefficients were calculated to examine associations between the global PSQI score and continuous variables, including MEQ score, bedtime, wake time, and sleep duration. Chi-square (χ2) tests were used to evaluate associations between sleep quality (good vs. poor) and categorical variables including bedtime category (≤22:30 vs. >22:30), wake time category (≤06:00 vs. >06:00), internet use (yes vs. no), menstrual cycle regularity (regular vs. irregular), sports participation (yes vs. no), sleep duration category (<7 h, 7–8 h, >8 h), and timing of meals.
Binary logistic regression analysis was performed to identify independent predictors of poor sleep quality (dependent variable), with bedtime category, wake time category, internet use, and menstrual cycle regularity entered as independent variables. Odds ratios (ORs) and 95% confidence intervals (CIs) were computed. A two-tailed p-value <0.05 was considered statistically significant [25,26].
RESULTS
Sample characteristics and distribution of sleep quality
Descriptive statistics for the total sample are presented in Table 1. The mean BMI and BSA were within the normal range for young adult females. Based on PSQI scores, 318 students (27%) were classified as having good sleep quality, whereas 868 students (73%) were classified as having poor sleep quality. This indicates a predominance of poor sleep quality in the studied cohort.
Sleep–wake variables and chronotype by sleep quality group
One-way ANOVA comparing good and poor sleep quality groups showed significant differences in bedtime and MEQ score, whereas age, BMI, BSA, wake time, midsleep, and sleep duration did not differ significantly between groups (Table 2). Poor sleepers tended to have later bedtimes and lower MEQ scores (more evening-type preference) than good sleepers.
Binary logistic regression was used to identify predictors of poor sleep quality (PSQI>5). The model was statistically significant (p=0.001). MEQ score emerged as a significant predictor, with lower (evening-type) scores associated with poorer sleep quality.
Mann–Whitney U tests (used due to non-normal distributions) confirmed that the good sleep quality group had significantly earlier mean bedtime, earlier wake time, and earlier midsleep compared with the poor sleep quality group (Table 3). In contrast, MEQ scores were significantly higher in the good sleep quality group than in the poor sleep quality group, indicating a stronger morning preference among good sleepers.
Correlations between sleep quality and sleep–wake parameters
Pearson correlation analysis revealed a significant negative correlation between global PSQI score and MEQ score (r=-0.097, p<0.01), indicating that lower MEQ scores (more evening-type) were associated with poorer sleep quality (higher PSQI scores). Global PSQI score also showed a modest but significant positive correlation with bedtime (r=0.075, p<0.01), suggesting that later bedtimes were associated with worse sleep quality. No significant correlations were observed between global PSQI score and age, BMI, BSA, wake time, midsleep, or sleep duration.
Associations of sleep quality with behavioral and menstrual variables
Chi-square tests showed that sleep quality was significantly associated with bedtime, wake time, internet use, and menstrual cycle regularity (Table 4). Poor sleep quality was more prevalent among students with late bedtimes (>22:30), late wake times (>06:00), internet use, and irregular menstrual cycles. In contrast, playing sports, sleep duration, and timing of first and last daily meals were not significantly associated with sleep quality.
Predictors of poor sleep quality
Binary logistic regression analysis identified late bedtime, late wake time, internet use, and irregular menstrual cycle as significant independent predictors of poor sleep quality (Table 5). Compared with early bedtime (≤22:30), late bedtime was associated with higher odds of poor sleep quality (OR=1.41, 95% CI 1.09– 1.83; p=0.009). Similarly, late wake time (>06:00) was associated with increased odds of poor sleep quality compared with early wake time (OR=1.42, 95% CI 1.09–1.85; p=0.008). Internet users had higher odds of poor sleep quality than non-users (OR=1.45, 95% CI 1.11–1.89; p=0.006). Students with irregular menstrual cycles had the highest odds of poor sleep quality compared with those with regular cycles (OR=1.88, 95% CI 1.15–3.08; p=0.011).
DISCUSSION
In this large sample of female college students, the prevalence of poor sleep quality was high (73%), with only about one-quarter of participants meeting criteria for good sleep quality [3]. This proportion is higher than some earlier studies in university pop-ulations but falls within the broad range of 30%–60% poor sleepers reported in other cohorts [3,15,18,20]. Differences in prevalence across studies may reflect variation in social and cultural contexts, academic load, lifestyle behaviors, and institutional schedules.
The present study demonstrated that poor sleep quality is associated with later bedtimes, later wake times, and more eveningtype chronotype, as indicated by lower MEQ scores [3]. These findings are consistent with previous work showing that evening chronotype and delayed sleep timing are linked to poorer subjective sleep quality, greater sleep–wake irregularity, and increased psychological difficulties in adolescents and young adults [3,10,11,27]. Morning-type individuals tend to maintain more regular and stable sleep–wake schedules and are more likely to report better sleep quality, particularly in environments with early morning academic or work demands [10,28].
Internet use emerged as an independent predictor of poor sleep quality in this study [3]. This corroborates growing evidence that high levels of screen time and late-night internet use are associated with delayed bedtimes, shorter sleep duration, and increased insomnia symptoms in students [18,20,29]. Excessive evening exposure to screens and stimulating online content may delay melatonin onset, increase cognitive and emotional arousal, and reduce sleep efficiency [5,29]. These mechanisms are likely to contribute to the higher odds of poor sleep among internet users observed in the present sample.
Irregular menstrual cycles were also strongly associated with poor sleep quality and predicted higher odds of poor sleep [3]. Menstrual irregularities, dysmenorrhea, and associated mood changes are common in young women and have been linked to sleep disturbances, fatigue, and reduced quality of life [12,13,22]. Bidirectional relationships between sleep, circadian rhythms, and reproductive hormones may help explain these associations [12,13]. The present findings support the notion that menstrual health and sleep health are closely intertwined in young female populations.
Interestingly, reported sleep duration categories (<7 h, 7–8 h, >8 h) were not significantly associated with sleep quality in this study, suggesting that timing and regularity of sleep, as well as subjective quality, may be more critical than duration alone within this relatively narrow age group [3]. Prior work has shown that regularity of sleep timing and stable circadian phase are important determinants of subjective sleep quality and daytime functioning across the lifespan [28,30].
Taken together, the results highlight the importance of maintaining regular sleep–wake schedules and promoting morningoriented habits among female college students. Interventions aimed at encouraging earlier bedtimes and wake times, limiting late-night internet use, and supporting menstrual health could plausibly improve sleep quality and, in turn, academic performance, mental health, and overall well-being [4,16,27,29]. Colleges and universities may consider implementing sleep education programs, digital hygiene guidelines, and screening for sleep and menstrual problems within student health services [16,27].
Limitations and future directions
This study has several limitations. First, all measures were selfreported, which may introduce recall bias and misclassification, and objective assessments such as actigraphy or hormonal circadian markers were not included [3]. Second, the sample consisted solely of female students from one city in central India, which may limit generalizability to males, non-students, or students from other cultural and institutional contexts [3,15]. Third, menstrual cycle phase at the time of data collection was not recorded, precluding more detailed analysis of phase-dependent variations in sleep parameters [12,22].
Future studies should incorporate objective measures of sleep and circadian rhythms, such as actigraphy and salivary melatonin, while also including both male and female participants and diverse academic disciplines [10,27]. Longitudinal designs would help clarify temporal relationships between chronotype, internet use, menstrual characteristics, academic stress, and trajectories of sleep quality over the course of college life [11,27,30].
In conclusion, this cross-sectional study of female college students in Raipur, India, found a high prevalence of poor sleep quality and identified late bedtime, late wake time, internet use, and irregular menstrual cycles as significant predictors of poor sleep. The findings underscore the importance of regular sleep– wake schedules, morning-oriented habits, and healthy digital and menstrual behaviors in promoting good sleep quality and wellbeing in this population.
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