European Journal of Cardiovascular Medicine

Sleep is a fundamental biological necessity that plays a pivotal role in maintaining physical, emotional, and cognitive health. High-quality sleep is essential for optimal functioning of various physiological systems, including metabolism, immune regulation, memory consolidation, and psychological resilience. Despite its critical importance, sleep health is often neglected in public health discussions, even as global evidence indicates a disturbing rise in sleep disorders and compromised sleep quality across diverse populations.^1-4

In recent decades, changes in lifestyle patterns—including increased screen exposure, urbanization, occupational stress, irregular work hours, and decreased physical activity—have contributed significantly to the deterioration of sleep habits. These changes are particularly evident in developing countries like India, where societal transitions and modernization have introduced new stressors while traditional health practices wane. Poor sleep quality has been associated with a host of adverse outcomes, such as cardiovascular diseases, depression, anxiety, impaired concentration, reduced work productivity, and diminished quality of life.^5-11

Punjab, a northwestern state of India known for its agrarian roots and evolving urban infrastructure, presents a unique socio-cultural mix of rural and urban populations. As the state undergoes rapid transitions in lifestyle and occupational structures, its population is increasingly exposed to risk factors for sleep disturbances. Despite these challenges, there is a striking paucity of community-based data evaluating sleep quality and its determinants among the general adult population in Punjab. Most existing studies have focused on niche groups such as students or shift workers, limiting the understanding of sleep issues at the population level.

Given that sleep quality is influenced by a constellation of socio-demographic factors—such as age, gender, education, occupation, marital status, and place of residence—there is an urgent need for a holistic exploration of how these variables interact to impact sleep health in the general public. This study seeks to fill this knowledge gap by assessing the prevalence of poor sleep quality among adults in Punjab and examining its association with key demographic and lifestyle characteristics. The findings are intended to inform public health stakeholders and support the development of targeted interventions aimed at promoting sleep hygiene and overall well-being.

Study Design

This study was conducted as a community-based, descriptive, cross-sectional survey to assess the sleep quality and its socio-demographic correlates among the general population of Punjab, India. A structured and pre-validated questionnaire, including the standardized Pittsburgh Sleep Quality Index (PSQI), was used to evaluate the sleep patterns and quality of adult participants.

Study Area and Population

The research was carried out in the state of Punjab, which comprises both urban and rural populations across diverse socio-economic backgrounds. Participants included residents aged 18 years and above, representing different districts such as Ludhiana, Amritsar, Patiala, Bathinda, and Jalandhar. Both rural and urban settings were covered to ensure a representative understanding of sleep behaviors in the population.

Study Duration

The data collection period spanned three months, from March to May 2025, ensuring adequate outreach and diversity in responses across seasons and regions.

Sample Size and Sampling Technique

A total sample size of 400 participants was targeted based on standard cross-sectional sampling formulas, assuming a 50% prevalence of poor sleep quality (in absence of prior state-specific data), 95% confidence level, and 5% margin of error. An additional 10% was accounted for potential incomplete responses. A convenience sampling approach was adopted due to the online nature of data collection, utilizing digital distribution channels to maximize reach.

Inclusion and Exclusion Criteria

Inclusion Criteria:

• Individuals aged 18 years or older
• Permanent residents of Punjab
• Ability to understand and respond in English, Hindi, or Punjabi
• Voluntary participation with informed digital consent

Exclusion Criteria:

• Known psychiatric or neurological illnesses affecting sleep
• Shift workers or individuals with diagnosed sleep disorders
• Incomplete or inconsistent responses

Data Collection Tool

The data was collected using a structured online questionnaire developed through Google Forms. The form was circulated via social media platforms (e.g., WhatsApp, Facebook, Instagram), email groups, and community forums to ensure wide participation.

The questionnaire consisted of the following sections:

1. Socio-Demographic Data – Age, gender, education, occupation, marital status, residence (urban/rural), and income group.
2. Pittsburgh Sleep Quality Index (PSQI) – A widely validated tool to assess sleep quality over the previous one-month period. The PSQI includes 19 self-rated items grouped into seven components:
   • Subjective sleep quality
   • Sleep latency
   • Sleep duration
   • Habitual sleep efficiency
   • Sleep disturbances
   • Use of sleep medication
   • Daytime dysfunction

Each component is scored 0–3, yielding a global PSQI score ranging from 0 to 21. A score >5 indicates poor sleep quality.

Lifestyle and Behavioral Factors – Questions regarding screen time before bed, caffeine intake, physical activity, chronic health conditions, and use of mobile devices in bed.

Prior to launch, the questionnaire was pilot-tested on 30 individuals from the target population to assess clarity, comprehension, and technical functionality. Modifications were made based on feedback before mass dissemination.

Ethical Considerations

Ethical approval was obtained from the Institutional Ethics Committee prior to data collection. Participation was entirely voluntary, and digital informed consent was obtained at the beginning of the survey. Anonymity and confidentiality of data were maintained throughout the study.

Statistical Analysis

Data were cleaned, coded, and analyzed using IBM SPSS Version 26. Descriptive statistics (frequency, percentage, mean, standard deviation) were used to summarize socio-demographic characteristics and sleep quality indicators. Chi-square tests were employed to assess associations between poor sleep quality (PSQI >5) and socio-demographic variables. A p-value of <0.05 was considered statistically significant.

The data collected from 400 participants were analyzed to explore the prevalence of poor sleep quality and its association with various socio-demographic factors.

Table 1 presents the socio-demographic profile of the 400 participants surveyed across various districts of Punjab. The age distribution reveals that the majority of respondents were between 26–35 years (33.0%), followed by 36–45 years (28.0%), while younger adults aged 18–25 comprised 19.0%, and individuals aged 46 and above constituted 20.0%. Females represented a slightly higher proportion of the sample (52.0%) compared to males (48.0%). Regarding education, the largest segments held secondary school (34.0%) or undergraduate (34.5%) qualifications, while a small portion had no formal education (4.5%). Occupationally, the sample included homemakers (22.0%), private sector employees (19.5%), students (18.0%), self-employed individuals (15.5%), government employees (15.0%), and others (10.0%). A greater proportion of participants resided in rural areas (56.5%) than in urban settings (43.5%), ensuring a broad representation of Punjab's diverse population.

Table 1: Socio-Demographic Characteristics of Participants (n = 400)

Table 2 outlines the component-wise distribution of Pittsburgh Sleep Quality Index (PSQI) scores, highlighting which aspects of sleep were most affected among participants. While a majority reported good subjective sleep quality (67.0%) and did not rely on sleep medication (76.5%), other components showed notable concern. Nearly half of the participants had poor scores for sleep disturbances (47.0%) and habitual sleep efficiency (44.5%), indicating frequent interruptions and low effectiveness in their sleep routines. Sleep latency (41.5%) and daytime dysfunction (38.0%) were also considerable issues, reflecting difficulties in falling asleep and impaired daytime functioning, respectively. These findings suggest that even if individuals perceive their sleep to be satisfactory, objective markers reveal multiple underlying disturbances.

Table 2: Component-wise Distribution of PSQI Scores (n = 400)

Table 3 summarizes the overall sleep quality among participants, based on their global PSQI scores. A significant proportion of the sample (66.5%) were classified as having poor sleep quality (PSQI > 5), while only 33.5% were found to have good sleep quality (PSQI ≤ 5). This high prevalence of poor sleep highlights a pressing public health concern in the region and indicates the need for widespread awareness and targeted interventions to improve sleep hygiene across different population groups in Punjab.

Table 3: Global Sleep Quality Classification Based on PSQI Score (n = 400)

Table 4 examines the association between sleep quality and key socio-demographic variables using Chi-square analysis. Age was significantly associated with sleep quality (p = 0.002), with older adults (particularly those aged 46 and above) reporting the highest prevalence of poor sleep (81.6%). Gender was not significantly associated with sleep quality (p = 0.148), although more females reported poor sleep (70.8%) than males (61.7%). Education level showed a statistically significant relationship with sleep quality (p = 0.021), with higher education correlating with better sleep; for example, 44.8% of postgraduates had good sleep quality versus only 26.5% among those with secondary education. Occupation did not show a significant association (p = 0.336), though students and government employees exhibited relatively better sleep than self-employed individuals and those in the 'others' category. Residence type was significantly associated with sleep quality (p = 0.047), with urban dwellers reporting better sleep (38.4%) compared to rural residents (29.8%). These associations underscore the multifactorial influences on sleep health and emphasize the role of education, age, and place of residence in shaping sleep patterns.

Table 4: Association Between Sleep Quality and Socio-Demographic Variables (n = 400)

This cross-sectional study aimed to evaluate sleep quality and its socio-demographic correlates among the general adult population of Punjab using the validated Pittsburgh Sleep Quality Index (PSQI). The findings reveal a concerning picture of sleep health in the region, with a significant majority (66.5%) of the participants experiencing poor sleep quality. This reinforces the notion that sleep disturbances are increasingly becoming a public health issue, even in states like Punjab where both rural traditions and modern urban lifestyles coexist.

The high prevalence of poor sleep quality (PSQI > 5) reported in our study aligns with global and national trends that indicate an upward trajectory in sleep-related problems. The prevalence observed (66.5%) is notably higher than some prior Indian studies conducted in specific groups like students, healthcare professionals, or shift workers, underscoring the widespread nature of the issue even in the general community. This elevated prevalence may be attributed to ongoing transitions in Punjab's socio-economic landscape, such as increased work stress, changes in dietary and activity patterns, growing digital dependence, and declining adherence to traditional health practices that once emphasized circadian discipline.^12,13

The component-wise analysis of PSQI sheds light on the dimensions most affected. While most respondents rated their subjective sleep quality as good (67.0%) and showed low dependence on sleep medications (76.5%), a large proportion reported poor scores in sleep disturbances (47.0%), habitual sleep efficiency (44.5%), and sleep latency (41.5%). These results suggest that although individuals may perceive their sleep as acceptable, objective indicators reveal significant interruptions, inefficiencies, and delays in sleep initiation. This gap between perceived and actual sleep quality highlights the potential for under-recognition of sleep issues in routine healthcare consultations.

Among the socio-demographic variables, age was strongly and significantly associated with poor sleep quality (p = 0.002). The prevalence of poor sleep increased progressively with age, peaking among those aged 46 and above (81.6%). This trend is consistent with existing literature indicating that advancing age is often accompanied by changes in sleep architecture, decreased melatonin production, increased comorbidities, and more frequent nocturnal awakenings. These physiological and psychosocial factors may contribute to sleep fragmentation and daytime fatigue, ultimately reducing overall sleep quality.^14-16

Gender, though not statistically significant (p = 0.148), revealed an observable trend—females reported poorer sleep quality (70.8%) compared to males (61.7%). This may be explained by gender-specific stressors such as caregiving responsibilities, hormonal fluctuations (particularly during menstruation or menopause), and higher prevalence of anxiety and depressive symptoms among women. The absence of statistical significance could be due to sample size or variations in individual coping strategies.

Educational attainment was significantly associated with sleep quality (p = 0.021). Participants with higher levels of education, particularly postgraduates, were more likely to report good sleep (44.8%) compared to those with only secondary education (26.5%). Education may influence health literacy, including awareness about sleep hygiene, stress management, and digital consumption habits. Furthermore, better-educated individuals might have more structured routines and improved access to healthcare, indirectly supporting better sleep outcomes.

Interestingly, occupation did not demonstrate a significant association with sleep quality (p = 0.336), although some patterns emerged. Students and government employees reported better sleep compared to self-employed individuals and those categorized as 'others'—possibly reflecting greater job stability, regular schedules, or fewer economic pressures. Self-employed individuals and informal workers often face irregular hours, financial uncertainty, and a lack of work-life boundaries, all of which may contribute to chronic sleep disturbances.

Place of residence showed a statistically significant relationship with sleep quality (p = 0.047), with urban dwellers reporting better sleep (38.4%) than rural residents (29.8%). This finding challenges some conventional beliefs that rural populations enjoy more natural and less stressful lifestyles. It is possible that rural communities are now increasingly burdened with financial instability, poor access to healthcare, and digital overload without the coping mechanisms or infrastructure available in urban areas. Additionally, the proliferation of smartphones and internet use in rural regions may have disrupted traditional sleep cycles without adequate awareness or regulation.

Implications for Public Health and Policy

The findings of this study carry important implications for public health planning in Punjab and similar socio-cultural regions. The high prevalence of poor sleep across age groups and education levels indicates the need for sleep health to be incorporated into broader non-communicable disease (NCD) prevention strategies. Community-level interventions—such as sleep hygiene education campaigns, workplace wellness programs, and behavioral counseling—should be developed and tailored for different population segments. In particular, rural populations and older adults may benefit from focused outreach through primary healthcare workers and village-level health committees.^17-20

Moreover, incorporating sleep assessments into routine health checkups could help in early identification of sleep issues. Primary care physicians and frontline health workers should be trained to recognize and address sleep-related complaints, especially since they may be masked as fatigue, irritability, or poor concentration.

Limitations and Future Directions

Despite its strengths, this study is not without limitations. The use of convenience sampling and online data collectionmay have introduced selection bias, potentially underrepresenting those without internet access, particularly the elderly and marginalized populations. Self-reported data is subject to recall bias and may not reflect actual sleep behavior. Additionally, while the PSQI is a validated tool, objective sleep metrics like actigraphy or polysomnography were not used due to feasibility constraints.

Future studies should consider longitudinal designs to establish causal relationships between socio-demographic variables and sleep quality. It would also be beneficial to examine behavioral and environmental factors—such as screen time, diet, noise pollution, and family structure—in greater depth. Regional comparisons across Indian states could shed light on how cultural and infrastructural differences impact sleep health.

This study provides vital community-level evidence that poor sleep quality is highly prevalent among the adult population in Punjab and is significantly influenced by age, education, and place of residence. These findings highlight an urgent need to integrate sleep health into public health frameworks and raise awareness about its critical role in mental and physical well-being. Tailored interventions, targeted outreach, and continued research are essential to address the growing burden of sleep disturbances and promote healthier, more resilient communities in India.

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