{"paper_id":"139d1347-d869-4834-aa52-643ceea31f7e","body_text":"The impact of sleep quality on body weight among young adults: A cross-sectional study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Short Report The impact of sleep quality on body weight among young adults: A cross-sectional study Fatema Rahaman, Sabkat Kamal, Rajib Ul Islam, Labina Taher, Fariha Noshin, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7448615/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Nov, 2025 Read the published version in BMC Research Notes → Version 1 posted 14 You are reading this latest preprint version Abstract Objective: Obesity and poor sleep quality are rising concerns in health discussions today, especially among young adults. Young adults may be more vulnerable to weight gain due to academic pressures and lifestyle choices, making this research particularly relevant. The goal of this study is to examine how body mass index (BMI) relates to various aspects of sleep quality in young adults in Bangladesh. A cross-sectional study was conducted involving 445 participants aged 18 to 25, who were recruited online. The Pittsburgh Sleep Quality Index (PSQI) was used to assess sleep quality. Results: More than half of our participants (56.4%) reported having poor sleep quality. Those with poor sleep had a higher average BMI of 24.11 kg/m² compared to 22.36 kg/m² for participants with good sleep. There was a notable inverse relationship between BMI and sleep duration, while correlations were positive with sleep latency, sleep disturbances, and the overall PSQI score. Individuals who were overweight and obese faced significantly greater odds of poor sleep quality. Additionally, being female and sleeping less than 6 hours a night were also significant predictors. This study reveals a troubling prevalence of poor sleep quality among young adults in Bangladesh and its strong link to higher BMI, which has important implications for public health. Integrating sleep health promotion within obesity prevention initiatives could foster healthier futures for young adults. sleep quality overweight/obesity BMI young adults health promotion Introduction In recent years, obesity and sleep disturbances have emerged as pressing public health challenges, particularly among young adults. As individuals navigate busy lifestyles filled with academic pressures and social obligations, it has become clear that these two issues are closely intertwined (Sa et al., 2020 ; Shechter et al., 2014 ; Wang et al., 2024 ). Alarmingly, more than a billion people worldwide are classified as overweight/obese, with this problem becoming increasingly prevalent in low- and middle-income countries (LMICs) like Bangladesh (Edwards et al., 2019 ; World Health Organization, 2024 ). Rapid socio-economic changes in these regions can disrupt traditional lifestyles and contribute to unhealthy habits (Hossain et al., 2022 ; Islam et al., 2020 ). At the same time, poor sleep quality affects the amount and effectiveness of health, has been recognized as a significant risk factor for numerous health issues, including heart disease, diabetes, and even early death. Research from high-income countries (HICs) suggests a complicated, two-way relationship between excess weight and sleep disturbances (Bove et al., 2021 ; Figorilli et al., 2025 ). Factors like hormones, disrupted body clocks, and lifestyle choices all play a role (Ahmed & Mohammed, 2025 ; Sebastião et al., 2021 ). Overweight/obesity accounted for 3.69% of all healthcare expenditures and around 0.13% of Bangladesh’s GDP (Hoque et al., 2020 ; World Obesity, 2023 ). The growing number of non-communicable diseases (NCDs) linked to overweight/obesity in Bangladesh puts a heavy load on the healthcare system. This includes not only the cost of medical care but also indirect costs like lost work and lower productivity (Ali et al., 2022 ; Das et al., 2022 ; Paul et al., 2018 ). However, less is known about how these dynamics play out in South Asian countries, where different cultural and economic factors could influence both how well people sleep and how much they weigh. Young adults, especially, are at a heightened risk for issues with sleep. They often juggle demanding academic schedules, extensive screen time, and the stress of transitioning into adulthood, all of which can lead to insufficient sleep (Bruce et al., 2017 ; Chaput & Dutil, 2016 ; Islam et al., 2018 ). These difficulties are made worse by infrastructure and socioeconomic limitations. Many young adults in LMICs live in crowded apartments with little privacy, noisy surroundings, erratic power supplies, and no climate control, all of which can make it difficult to get a good night's sleep (Vestergaard et al., 2024 ; Xu et al., 2023 ). Competitive tests for admission and a lack of job opportunities frequently increase academic pressure, resulting in long-term stress and unstable study habits (Deng et al., 2022 ; Dudo et al., 2022 ; Mofatteh, 2021 ). Expectations from family and culture, especially for women, can also restrict leisure and personal time, which worsens the quality of sleep (Muhammad et al., 2024 ; Silva-Costa et al., 2021 ; Upoma et al., 2020 ). Additionally, in LMICs contexts, late-night screen use has increased due to rapid urbanisation and the widespread use of low-cost mobile devices, frequently without corresponding public awareness of healthy sleep practices (Hale et al., 2018 ; Maurya et al., 2022 ). These interrelated factors increase the risk of downstream health issues in addition to affecting sleep. Studies consistently show that poor sleep quality is linked to a higher BMI, a common measure of obesity (Appuhamy et al., 2023 ). These connections raise concerns, not just for physical health, but also for mental well-being and academic success. While research from HICs has confirmed these links, there is still a significant gap in data from regions like Bangladesh. Understanding how sleep and weight relate in this context is critical for developing effective health interventions. As urbanization increases and traditional lifestyles evolve in Bangladesh, more young adults are struggling with obesity (Riaz & Lodhi, 2025 ). In Bangladesh, urbanisation, changes in eating habits, and less physical activity have led to a quick epidemiological transition, and obesity is now affecting more young adults. There is a rapid change in the country's diet, and more people in cities and with higher education are becoming overweight/obese (Barth-Jaeggi et al., 2023 ; Kabir et al., 2018 ). At the same time, young adults in school are dealing with more stressors that could affect their sleep quality. Young adults are at risk for unique health problems (Cacciatore et al., 2025 ; Keramat et al., 2023 ). For example, high academic demands, irregular schedules, long periods of screen time, and eating more energy-dense foods can all make it harder to sleep and cause weight gain (Bruce et al., 2017 ; Mofatteh, 2021 ). Cultural norms and gender roles may also affect how people behave when it comes to their health. For example, women often have more trouble getting enough exercise and sleep (Al-Mamun et al., 2023 ; Muhammad et al., 2024 ; Silva-Costa et al., 2021 ). Even though these new worries are coming up, sleep health is still mostly left out of national NCDs prevention plans, and there is not much research on how it relates to obesity in Bangladeshi populations. It is crucial to identify at-risk groups and modifiable behaviours by looking at both the quantity and quality of sleep with BMI. The Social Ecological Model (SEM) serves as the foundation for this study because it acknowledges that a variety of interrelated levels affect health behaviours like sleep and weight control: societal (e.g., cultural norms, public health infrastructure), institutional (e.g., academic stress, campus resources), interpersonal (e.g., peer and family influences), and individual (e.g., biological and behavioural factors) (Chery & Moise, 2024 ; Grandner, 2019 ). To address both inadequate sleep and unhealthy weight, the SEM offers a thorough framework for determining modifiable determinants and creating multi-level interventions. This study can help pave the way for healthier futures for young people in this rapidly changing landscape by better understanding the factors that influence sleep and its relationship with body weight. Methods Study Design and Participants A cross-sectional study was conducted to examine the relationship between sleep quality and body weight among young adults in Bangladesh. This study opted for an online recruitment method, which allowed us to reach a diverse group of young adults, a critical population that often faces unique challenges related to both sleep and weight. Young adults (18 to 25 years old) and a willingness to complete an online survey in English were prerequisites for inclusion. Participants with known sleep disorders or chronic illnesses were excluded. A target sample size of at least 384 was calculated using Cochran’s formula for proportions (with 5% margin of error and a 95% confidence level), assuming a prevalence of poor sleep quality of approximately 50% based on previous literature. To account for potential non-responses or incomplete submissions, the sample was increased by 20%, resulting in a final sample of 445 participants. Data Collection Tools A semi-structured online questionnaire was distributed through digital platforms (such as Facebook Messenger and WhatsApp) to collect data between April and June 2022 (supplementary file). To assess sleep quality, this study used the PSQI. This tool is well-regarded for its ability to cover multiple facets of sleep, making it perfect for this study (Carpi, 2025 ). The PSQI helped to evaluate not just how long participants slept, but also the quality of that sleep and how rested they felt upon waking. The BMI of participants was calculated based on their self-reported height and weight. the World Health Organization (WHO) classifications were followed to categorize them as underweight, normal weight, overweight, or obese, providing a picture of how their BMI aligned with their sleep quality (World Health Organization, 2024 ). Statistical Analysis Data were analysed using SPSS version 25. To dive deeper into the relationships between BMI, various sleep parameters, and demographic factors, this study used several statistical analyses. Independent t-tests were used to compare the average BMI between those who reported good sleep quality and those who did not. Pearson's correlation was used to explore the relationships between sleep duration, sleep disturbances, and BMI scores. Finally, binary logistic regression was applied to identify which predictors, like gender, sleep duration, and BMI, were most significantly linked to poor sleep quality. This study aimed to identify the complex interplay between sleep and body weight in this specific group of young adults, ultimately shedding light on the challenges they face and the factors that influence their health. A p -value < .05 was considered statistically significant. Results This study revealed some findings about the sleep quality and body weight of young adults in Bangladesh. The analysis included 445 participants, of whom 45.8% were female and 54.2% were male (Table 1 ). Most participants (63.2%) were undergraduates, while a smaller percentage had graduate (27.6%) or postgraduate (2.7%) degrees. Most reported a family income of between 11,000 and 40,000 BDT per month (47.4%), followed by > 40,000 BDT (30.1%) and less than 10,000 BDT (13.5%). Of those underweight, 14.6% were underweight, 54.2% were normal weight, 22.5% were overweight, and 8.8% were obese. Poor sleep quality was experienced by more than half of the sample (56.4%) (PSQI > 5). This suggests that sleep issues are quite prevalent in this group, raising important questions about their overall health and well-being. Table 1 Descriptive Statistics of the Study Sample (N = 445) Variable Category n % Gender Male 241 54.2 Female 204 45.8 Education SSC 4 .9 HSC 24 5.4 Undergrad 282 63.2 Graduated 123 27.6 Postgraduate 12 2.7 Occupation Student 385 87.3 Housewife 8 1.8 Self-employed 16 3.6 Service 32 7.2 Monthly family income < 10000 60 13.5 11000–40000 211 47.4 > 40000 174 39.1 BMI Status Underweight (< 18.5) 65 14.6 Normal (18.5–24.9) 241 54.2 Overweight (25–29.9) 100 22.5 Obese (≥ 30) 39 8.8 Sleep Quality Good (PSQI ≤ 5) 194 43.6 Poor (PSQI > 5) 251 56.4 This study found that those who reported poor sleep had a significantly higher average BMI. Specifically, the mean BMI for participants with poor sleep quality was 24.11 kg/m², while those who had better sleep averaged at 22.36 kg/m². This difference was statistically significant ( p < .001) (Table 2 ). Further examination revealed relationships between sleep and weight. This study found an inverse correlation between BMI and sleep duration; participants with higher BMIs tended to sleep less ( r = − .28). Conversely, this study observed positive correlations between BMI and factors such as sleep latency (the time it takes to fall asleep, r = .22) and sleep disturbances ( r = .31), along with a strong positive correlation with the total PSQI score ( r = .38). These correlations suggest that as BMI increases, sleep quality tends to worsen. Table 2 Mean BMI Across Sleep Quality Categories Sleep Quality Mean BMI (kg/m²) SD n Good Sleep 22.36 3.27 194 Poor Sleep 24.11 3.96 251 Independent Samples t-test: t (443) ≈ − 4.98; p < .001. The logistic regression analysis shed light on significant predictors of poor sleep quality. Participants who were overweight (OR = 1.80, p < .004) and obese (OR = 2.29, p < .001) had significantly higher odds of reporting poor sleep quality than participants with a normal BMI. Poor sleep quality was more common in female participants than in male participants (OR = 1.46, p < .01). The data show a significant correlation between short sleep duration, poor overall sleep quality, and higher BMI at the individual level when mapped onto the SEM (Table 3 ). These results emphasize the importance of understanding how factors like gender and sleep duration can affect sleep quality and BMI among young adults. The high prevalence of poor sleep quality and its connection to higher body weight underscore the pressing need for targeted health interventions in this population. Table 3 Binary Logistic Regression Predicting Poor Sleep Quality Predictor B SE (Adj) OR 95% CI (Adj) p (Approx) Overweight vs Normal 0.59 0.17 1.80 1.30–2.50 .001–.004 Obese vs Normal 0.83 0.24 2.29 1.44–3.66 < .001 Female vs Male 0.38 0.15 1.46 1.09–1.96 .01 Sleep duration < 6h 0.71 0.20 2.04 1.38–3.00 .001–.003 Table 3 Key Findings on Sleep and BMI Categorized by the Social Ecological Model (N = 445) SEM Level Variable/Construct Finding Statistical Evidence Individual BMI category (overweight, obese) Higher BMI is associated with poor sleep quality OR = 1.80 [1.30–2.50] (overweight), OR = 2.29 [1.44–3.66] (obese); p < .01 Sleep duration Short sleep (< 6 hrs) is linked to poor sleep and higher BMI r = − .28, p < .01; OR = 2.04 [1.38–3.00] Sleep latency and disturbance Longer latency and higher disturbances associated with higher BMI r = .22 and .31, respectively; p < .05 and < .01 Overall sleep quality (PSQI global score) Poorer sleep associated with higher BMI r = .38, p < .01 Interpersonal Gender Female students are more likely to report poor sleep quality OR = 1.46 [1.09–1.96], p = .01 Peer/family stress (inferred from gender/study context) Psychosocial stressors may disproportionately affect female sleep and eating patterns Contextual, supported by gendered differences and literature Institutional Academic workload (proxied via sleep deprivation) Poor sleep patterns associated with academic pressures, especially sleep duration < 6 hours OR = 2.04 [1.38–3.00], p = .002 Societal Cultural norms and public health neglect (inferred) Sleep health is under-prioritized in health promotion among young adults in Bangladesh Contextual analysis based on regional health strategy gaps Table 4 Key Findings on Sleep and BMI Categorized by the Social Ecological Model (N = 445) SEM Level Variable/Construct Finding Statistical Evidence Individual BMI category (overweight, obese) Higher BMI is associated with poor sleep quality OR = 1.80 [1.30–2.50] (overweight), OR = 2.29 [1.44–3.66] (obese); p < .01 Sleep duration Short sleep (< 6 hrs) is linked to poor sleep and higher BMI r = − .28, p < .01; OR = 2.04 [1.38–3.00] Sleep latency and disturbance Longer latency and higher disturbances associated with higher BMI r = .22 and .31, respectively; p < .05 and < .01 Overall sleep quality (PSQI global score) Poorer sleep associated with higher BMI r = .38, p < .01 Interpersonal Gender Female students are more likely to report poor sleep quality OR = 1.46 [1.09–1.96], p = .01 Peer/family stress (inferred from gender/study context) Psychosocial stressors may disproportionately affect female sleep and eating patterns Contextual, supported by gendered differences and literature Institutional Academic workload (proxied via sleep deprivation) Poor sleep patterns associated with academic pressures, especially sleep duration < 6 hours OR = 2.04 [1.38–3.00], p = .002 Societal Cultural norms and public health neglect (inferred) Sleep health is under-prioritized in health promotion among young adults in Bangladesh Contextual analysis based on regional health strategy gaps Discussion The results of this study offer crucial insights into the relationship between sleep quality and body weight among young adults in Bangladesh. This connection is particularly concerning because, as previous research indicates, poor sleep can lead to hormonal changes that affect appetite and metabolism, potentially creating a cycle of weight gain and worsening sleep issues (Chaput et al., 2023 ; Figorilli et al., 2025 ; Rogers et al., 2024 ). A significant number of participants are grappling with poor sleep, which aligns with growing global concerns around sleep health. This high prevalence is particularly alarming, given that quality sleep is essential for overall physical and mental well-being. One of the standout findings is the strong link between poor sleep quality and elevated BMI. Participants who reported inadequate sleep consistently had higher BMI values, which suggests that sleep disturbances might be a contributing factor to weight gain in this population. Studies revealed that gender plays an important role in sleep quality, with female participants more likely to report poor sleep. This may reflect the additional societal pressures and stressors that young women face, such as balancing academic demands and family responsibilities. Enhancing awareness about these gender differences can help tailor interventions to meet the specific needs of young women, helping them improve both their sleep and overall health (Alosta et al., 2024 ; Fatima et al., 2016 ; Madrid-Valero et al., 2023 ). Participants who reported sleeping less than six hours a night had notably higher odds of experiencing poor sleep quality. This finding reiterates the importance of promoting healthy sleep habits among young adults, who often prioritize academics, work, and social commitments at the expense of sufficient rest. Encouraging students to make sleep a priority could yield significant benefits for their health and academic performance. Studies revealed the potential interaction of institutional factors (e.g., academic workload, dorm environments), interpersonal influences (e.g., peer stress), and individual behaviours (e.g., sleep hygiene) to affect both sleep and weight outcomes (Gallego-Gómez et al., 2021 ; Lisnyj et al., 2021 ; Wang et al., 2023 ). The findings of the study are also consistent with previous findings. These findings highlight the necessity of multifaceted, integrated approaches to health promotion that address both more general structural and cultural factors as well as modifiable behaviours. Under the direction of the SEM, this study shows a relationship between young adults' BMI and sleep quality, especially in the South Asian context. Understanding the intricate relationship between sleep and obesity requires an understanding of the SEM's premise that individual behaviours are influenced by interacting factors at several levels, including personal, interpersonal, institutional, and societal. Higher BMI is significantly correlated with poorer sleep quality, shorter sleep duration, and more disturbances. This relationship may be influenced by biological processes like altered circadian rhythms, impaired glucose metabolism, and dysregulation of leptin and ghrelin (Chaput et al., 2023 ; Figorilli et al., 2025 ; Rogers et al., 2024 ). The findings at the individual level support the global evidence. Additionally, participants who do not get enough sleep might feel more exhausted, which could make them less active and more likely to eat foods high in energy. Sleep and eating patterns may be impacted at the interpersonal level by peer pressure and family expectations. Participants, particularly women, may experience additional psychological stress in cultures like Bangladesh, where family obligations and social expectations regarding academic achievement are high. This can lead to sleep disturbances and unhealthy coping mechanisms like emotional eating. The literature on gender differences in stress reactivity and sleep disorders is consistent with our finding that female participants were more likely to report poor sleep quality. Academic workload, strict class schedules, and a dearth of facilities for student wellness can all have a detrimental effect on sleep hygiene at the institutional level. Policies and resources to support mental health, control screen time, and inform students about the importance of sleep for metabolic health are frequently lacking in university settings (Kumar et al., 2025 ; Saintila et al., 2025 ; Sedek et al., 2023 ). The bidirectional relationship between sleep deprivation and obesity, where weight gain results from inadequate sleep and vice versa, may be exacerbated by these structural limitations. Participants' health behaviours are also influenced by public health priorities and wider cultural norms at the societal level. Obesity among young adults is frequently underrecognized as a public health concern, and sleep health is not commonly incorporated into preventive health strategies in many LMICs, such as Bangladesh. This study fills a gap in the existing literature by highlighting the interplay between sleep quality and body weight in a South Asian context. Most research to date has largely focused on HICs, which means that insights from LMICs like Bangladesh are crucial for developing effective public health strategies. Overall, these findings underscore the necessity of integrating sleep health into obesity prevention efforts. By addressing sleep quality as a fundamental aspect of weight management, multifaceted health promotion programs might be created that tackle the issue from various angles. Empowering young adults with education about the importance of sleep could lead to healthier lifestyles and improved health outcomes, laying a stronger foundation for their futures. Conclusion In summary, this study sheds light on a significant public health concern facing young adults in Bangladesh: the worrying relationship between poor sleep quality and higher body weight. The fact that over half of the participants reported inadequate sleep highlights an urgent need for awareness and intervention. These findings indicate that not only is sleep quality crucial for physical health, but it also plays a vital role in managing body weight. This knowledge is instrumental in shaping targeted interventions that address both sleep and obesity, promoting healthier lifestyles. Integrating sleep health into obesity prevention strategies could be a game-changer, especially in LMICs contexts where these issues are becoming increasingly prevalent. These insights will encourage both policymakers and health professionals to consider the broader implications of sleep quality in health promotion. By prioritizing sleep health, these findings can contribute to the overall well-being of young adults, empowering them to lead healthier, more fulfilled lives. It is essential to foster a supportive environment that emphasizes the significance of quality sleep, aiding in the fight against the obesity epidemic and enhancing the health of future generations. Limitations Self-reported measures may be prone to social desirability bias or recall, and causal inference is limited by the cross-sectional design. The sample's generalisability to rural populations or young adults who are not students may be limited because it was selected from urban academic settings. Future studies should examine mediators like screen time, stress, and physical activity, as well as include objective sleep measures, longitudinal designs, and a wider range of sociodemographic groups. Implications for Practice and Policy Include sleep hygiene in health programs. In clinical settings, check for sleep issues in young adults who are at risk of obesity. In Bangladesh, national NCDs strategies and frameworks for preventing obesity should incorporate sleep health promotion. In addition to weight management programs, primary care physicians and university health programs should screen for poor sleep quality. To address young adults' dual burden of inadequate sleep and excess body weight, gender-sensitive, culturally appropriate interventions are required. Declarations Ethics approval and consent to participate The study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki . All participants were provided with detailed information about the study’s purpose, procedures, potential risks, and benefits. All responses were anonymous, ensuring data confidentiality. All participants provided their informed consent to participate in the study after being informed about the purpose of the study. The ethical aspects of this study were also reviewed and approved by the Biosafety, Biosecurity, and Ethical Review Board of Jahangirnagar University, Savar, Dhaka-1342, Bangladesh [Ref No: BBEC, JU/M 2022/ 19 (6)]. Consent for publication All authors Availability of data and material The data can be obtained from the corresponding author upon request. Competing interests None Funding This research received no specific grants from public, commercial, or non-profit funding agencies. Authors' contributions Fatema Rahaman: Conceptualization; Methodology; Data collection; Writing: review & editing. Sabkat Kamal: Data curation; Writing: review & editing. Rajib Ul Islam: Data curation; Writing: review & editing. Labina Taher: Data curation; Writing: review & editing. Fariha Noshin: Data curation; Writing: review & editing. Tabeen Taher: Data curation; Writing: review & editing. Mst Sabrina Moonajilin: Conceptualization; Methodology; Writing: original draft. 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A., Hoque, D. M., Long, K. Z., & Mamun, A. A. (2020). Health care cost of overweight-related diseases in Bangladesh. Public Health Nutrition , 23 (13), 2395-2401. https://doi.org/10.1017/s1368980020001068 Hossain, A., Bhuiya, R. A., & Ali, M. Z. (2022). The Association between Obesity and Depression, Anxiety, and Stress Disorders among University Students at Rajshahi City in Bangladesh. Journal of Psychiatry and Psychiatric Disorders , 6 . https://doi.org/10.26502/jppd.2572-519X0172 Islam, F., Kathak, R. R., Sumon, A. H., & Molla, N. H. (2020). Prevalence and associated risk factors of general and abdominal obesity in rural and urban women in Bangladesh. PLoS ONE , 15 (5), e0233754. https://doi.org/10.1371/journal.pone.0233754 Islam, T., Moonajilin, M. S., & Islam, R. U. (2018). A study on stress among university students, Bangladesh. International Journal of Academic Health and Medical Research , 2 (10). https://philpapers.org/rec/ISLASO-2 Kabir, A., Miah, S., & Islam, A. (2018). Factors influencing eating behavior and dietary intake among resident students in a public university in Bangladesh: A qualitative study. PLoS ONE , 13 (6), e0198801. https://doi.org/10.1371/journal.pone.0198801 Keramat, S. A., Alam, K., Basri, R., Siddika, F., Siddiqui, Z. H., Okyere, J., Seidu, A.-A., & Ahinkorah, B. O. (2023). Sleep duration, sleep quality and the risk of being obese: Evidence from the Australian panel survey. Sleep Medicine , 109 , 56-64. https://doi.org/https://doi.org/10.1016/j.sleep.2023.06.012 Kumar, P., Sahani, A., Hafiz, A., & Tyagi, A. (2025). The Impact of Sleep on Student Health: Exploring Its Effects on Physical Well-Being, Mental Health, and Academic Performance. Lisnyj, K., Pearl, D., McWhirter, J., & Papadopoulos, A. (2021). Exploration of Factors Affecting Post-Secondary Students’ Stress and Academic Success: Application of the Socio-Ecological Model for Health Promotion. International Journal of Environmental Research and Public Health , 18 , 3779. https://doi.org/10.3390/ijerph18073779 Madrid-Valero, J. J., Kirkpatrick, R. M., González-Javier, F., Gregory, A. M., & Ordoñana, J. R. (2023). Sex differences in sleep quality and psychological distress: Insights from a middle-aged twin sample from Spain. Journal of Sleep Research , 32 (2), e13714. https://doi.org/https://doi.org/10.1111/jsr.13714 Maurya, C., Muhammad, T., Maurya, P., & Dhillon, P. (2022). The association of smartphone screen time with sleep problems among adolescents and young adults: cross-sectional findings from India. BMC Public Health , 22 (1), 1686. https://doi.org/10.1186/s12889-022-14076-x Mofatteh, M. (2021). Risk factors associated with stress, anxiety, and depression among university undergraduate students. AIMS Public Health , 8 (1), 36-65. https://doi.org/10.3934/publichealth.2021004 Muhammad, T., Anil Kumar, A. H. S., & Sekher, T. V. (2024). Gender-specific associations between sleep quality, sleep duration and cognitive functioning among older Indians. Sleep Science and Practice , 8 (1), 6. https://doi.org/10.1186/s41606-024-00100-z Paul, R., Moonajilin, M. S., Sarker, S. K., Paul, H., Pal, S., Paul, S., Zahan, R. R., & Begum, N. (2018). Association between Serum Ferritin and Pre-eclampsia. Bangladesh Medical Journal , 47 (3), 18-24. https://doi.org/10.3329/bmj.v47i3.43494 Riaz, M., & Lodhi, S. (2025). Beyond BMI: Exploring obesity trends in the south Asian region. Obesity Pillars , 13 , 100156. https://doi.org/https://doi.org/10.1016/j.obpill.2024.100156 Rogers, E. M., Banks, N. F., & Jenkins, N. D. M. (2024). The effects of sleep disruption on metabolism, hunger, and satiety, and the influence of psychosocial stress and exercise: A narrative review. Diabetes/metabolism research and reviews , 40 (2), e3667. https://doi.org/https://doi.org/10.1002/dmrr.3667 Sa, J., Choe, S., Cho, B.-y., Chaput, J.-P., Kim, G., Park, C.-H., Chung, J., Choi, Y., Nelson, B., & Kim, Y. (2020). Relationship between sleep and obesity among U.S. and South Korean college students. BMC Public Health , 20 (1), 96. https://doi.org/10.1186/s12889-020-8182-2 Saintila, J., Javier-Aliaga, D., Del Carmen Gálvez-Díaz, N., Barreto-Espinoza, L. A., Buenaño-Cervera, N. A., & Calizaya-Milla, Y. E. (2025). Association of sleep hygiene knowledge and physical activity with sleep quality in nursing and medical students: a cross-sectional study. Front Sports Act Living , 7 , 1453404. https://doi.org/10.3389/fspor.2025.1453404 Sebastião, E., Bobitt, J., Papini, C. B., Nakamura, P. M., Kokubun, E., & Gobbi, S. (2021). Sedentary Behavior Is Associated With Low Leisure-Time Physical Activity and High Body Fatness in Older Brazilian Adults. American Journal of Lifestyle Medicine , 15 (3), 286-292. https://doi.org/10.1177/1559827617753355 Sedek, M. B. H., Omar, H., & Ayman, H. (2023). Investigating the relationship of sleep quality and psychological factors among Health Professions students. International Journal of Africa Nursing Sciences , 19 , 100581. https://doi.org/https://doi.org/10.1016/j.ijans.2023.100581 Shechter, A., Grandner, M. A., & St-Onge, M.-P. (2014). The Role of Sleep in the Control of Food Intake. American Journal of Lifestyle Medicine , 8 (6), 371-374. https://doi.org/10.1177/1559827614545315 Silva-Costa, A., Toivanen, S., Rotenberg, L., Viana, M. C., da Fonseca, M. J. M., & Griep, R. H. (2021). Impact of Work-Family Conflict on Sleep Complaints: Results From the Longitudinal Study of Adult Health (ELSA-Brasil). Front Public Health , 9 , 649974. https://doi.org/10.3389/fpubh.2021.649974 Upoma, T. F., Moonajilin, M. S., Rahman, M. E., & Ferdous, M. Z. (2020). Mothers Initial Challenges Having Children with Autism Spectrum Disorders in Bangladesh. Bangladesh Journal of Medical Science , 19 (2). https://doi.org/10.3329/bjms.v19i2.45006 Vestergaard, C. L., Skogen, J. C., Hysing, M., Harvey, A. G., Vedaa, Ø., & Sivertsen, B. (2024). Sleep duration and mental health in young adults. Sleep Medicine , 115 , 30-38. https://doi.org/https://doi.org/10.1016/j.sleep.2024.01.021 Wang, W., Chen, Z., Zhang, W., Yuan, R., Sun, Y., Yao, Q., Lu, J., & Zheng, J. (2024). Association between obesity and sleep disorder in the elderly: evidence from NHANES 2005–2018 [Original Research]. Frontiers in Nutrition , Volume 11 - 2024 . https://doi.org/10.3389/fnut.2024.1401477 Wang, Y., Dai, X., Zhu, J., Xu, Z., Lou, J., & Chen, K. (2023). What complex factors influence sleep quality in college students? PLS-SEM vs. fsQCA [Original Research]. Frontiers in Psychology , Volume 14 - 2023 . https://doi.org/10.3389/fpsyg.2023.1185896 World Health Organization. (2024). Obesity and overweight . https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight World Obesity. (2023). World Obesity Atlas 2023 . Xu, J., Liu, N., Polemiti, E., Garcia-Mondragon, L., Tang, J., Liu, X., Lett, T., Yu, L., Nöthen, M. M., Feng, J., Yu, C., Marquand, A., & Schumann, G. (2023). Effects of urban living environments on mental health in adults. Nat Med , 29 (6), 1456-1467. https://doi.org/10.1038/s41591-023-02365-w Additional Declarations No competing interests reported. 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As individuals navigate busy lifestyles filled with academic pressures and social obligations, it has become clear that these two issues are closely intertwined (Sa et al., \\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Shechter et al., \\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e; Wang et al., \\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). Alarmingly, more than a billion people worldwide are classified as overweight/obese, with this problem becoming increasingly prevalent in low- and middle-income countries (LMICs) like Bangladesh (Edwards et al., \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; World Health Organization, \\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). Rapid socio-economic changes in these regions can disrupt traditional lifestyles and contribute to unhealthy habits (Hossain et al., \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Islam et al., \\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). At the same time, poor sleep quality affects the amount and effectiveness of health, has been recognized as a significant risk factor for numerous health issues, including heart disease, diabetes, and even early death. Research from high-income countries (HICs) suggests a complicated, two-way relationship between excess weight and sleep disturbances (Bove et al., \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Figorilli et al., \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e). Factors like hormones, disrupted body clocks, and lifestyle choices all play a role (Ahmed \\u0026amp; Mohammed, \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e; Sebastião et al., \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). Overweight/obesity accounted for 3.69% of all healthcare expenditures and around 0.13% of Bangladesh’s GDP (Hoque et al., \\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; World Obesity, \\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). The growing number of non-communicable diseases (NCDs) linked to overweight/obesity in Bangladesh puts a heavy load on the healthcare system. This includes not only the cost of medical care but also indirect costs like lost work and lower productivity (Ali et al., \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Das et al., \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Paul et al., \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). However, less is known about how these dynamics play out in South Asian countries, where different cultural and economic factors could influence both how well people sleep and how much they weigh.\\u003c/p\\u003e\\u003cp\\u003eYoung adults, especially, are at a heightened risk for issues with sleep. They often juggle demanding academic schedules, extensive screen time, and the stress of transitioning into adulthood, all of which can lead to insufficient sleep (Bruce et al., \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; Chaput \\u0026amp; Dutil, \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; Islam et al., \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). These difficulties are made worse by infrastructure and socioeconomic limitations. Many young adults in LMICs live in crowded apartments with little privacy, noisy surroundings, erratic power supplies, and no climate control, all of which can make it difficult to get a good night's sleep (Vestergaard et al., \\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e; Xu et al., \\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Competitive tests for admission and a lack of job opportunities frequently increase academic pressure, resulting in long-term stress and unstable study habits (Deng et al., \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Dudo et al., \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Mofatteh, \\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). Expectations from family and culture, especially for women, can also restrict leisure and personal time, which worsens the quality of sleep (Muhammad et al., \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e; Silva-Costa et al., \\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Upoma et al., \\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). Additionally, in LMICs contexts, late-night screen use has increased due to rapid urbanisation and the widespread use of low-cost mobile devices, frequently without corresponding public awareness of healthy sleep practices (Hale et al., \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Maurya et al., \\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). These interrelated factors increase the risk of downstream health issues in addition to affecting sleep. Studies consistently show that poor sleep quality is linked to a higher BMI, a common measure of obesity (Appuhamy et al., \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). These connections raise concerns, not just for physical health, but also for mental well-being and academic success.\\u003c/p\\u003e\\u003cp\\u003eWhile research from HICs has confirmed these links, there is still a significant gap in data from regions like Bangladesh. Understanding how sleep and weight relate in this context is critical for developing effective health interventions. As urbanization increases and traditional lifestyles evolve in Bangladesh, more young adults are struggling with obesity (Riaz \\u0026amp; Lodhi, \\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e). In Bangladesh, urbanisation, changes in eating habits, and less physical activity have led to a quick epidemiological transition, and obesity is now affecting more young adults. There is a rapid change in the country's diet, and more people in cities and with higher education are becoming overweight/obese (Barth-Jaeggi et al., \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; Kabir et al., \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). At the same time, young adults in school are dealing with more stressors that could affect their sleep quality. Young adults are at risk for unique health problems (Cacciatore et al., \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e; Keramat et al., \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). For example, high academic demands, irregular schedules, long periods of screen time, and eating more energy-dense foods can all make it harder to sleep and cause weight gain (Bruce et al., \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; Mofatteh, \\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). Cultural norms and gender roles may also affect how people behave when it comes to their health. For example, women often have more trouble getting enough exercise and sleep (Al-Mamun et al., \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; Muhammad et al., \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e; Silva-Costa et al., \\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). Even though these new worries are coming up, sleep health is still mostly left out of national NCDs prevention plans, and there is not much research on how it relates to obesity in Bangladeshi populations.\\u003c/p\\u003e\\u003cp\\u003eIt is crucial to identify at-risk groups and modifiable behaviours by looking at both the quantity and quality of sleep with BMI. The Social Ecological Model (SEM) serves as the foundation for this study because it acknowledges that a variety of interrelated levels affect health behaviours like sleep and weight control: societal (e.g., cultural norms, public health infrastructure), institutional (e.g., academic stress, campus resources), interpersonal (e.g., peer and family influences), and individual (e.g., biological and behavioural factors) (Chery \\u0026amp; Moise, \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e; Grandner, \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). To address both inadequate sleep and unhealthy weight, the SEM offers a thorough framework for determining modifiable determinants and creating multi-level interventions. This study can help pave the way for healthier futures for young people in this rapidly changing landscape by better understanding the factors that influence sleep and its relationship with body weight.\\u003c/p\\u003e\"},{\"header\":\"Methods\",\"content\":\"\\u003cp\\u003eStudy Design and Participants\\u003c/p\\u003e\\u003cp\\u003eA cross-sectional study was conducted to examine the relationship between sleep quality and body weight among young adults in Bangladesh. This study opted for an online recruitment method, which allowed us to reach a diverse group of young adults, a critical population that often faces unique challenges related to both sleep and weight. Young adults (18 to 25 years old) and a willingness to complete an online survey in English were prerequisites for inclusion. Participants with known sleep disorders or chronic illnesses were excluded. A target sample size of at least 384 was calculated using Cochran’s formula for proportions (with 5% margin of error and a 95% confidence level), assuming a prevalence of poor sleep quality of approximately 50% based on previous literature. To account for potential non-responses or incomplete submissions, the sample was increased by 20%, resulting in a final sample of 445 participants.\\u003c/p\\u003e\\u003cp\\u003eData Collection Tools\\u003c/p\\u003e\\u003cp\\u003eA semi-structured online questionnaire was distributed through digital platforms (such as Facebook Messenger and WhatsApp) to collect data between April and June 2022 (supplementary file). To assess sleep quality, this study used the PSQI. This tool is well-regarded for its ability to cover multiple facets of sleep, making it perfect for this study (Carpi, \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e). The PSQI helped to evaluate not just how long participants slept, but also the quality of that sleep and how rested they felt upon waking. The BMI of participants was calculated based on their self-reported height and weight. the World Health Organization (WHO) classifications were followed to categorize them as underweight, normal weight, overweight, or obese, providing a picture of how their BMI aligned with their sleep quality (World Health Organization, \\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e).\\u003c/p\\u003e\\u003ch2\\u003eStatistical Analysis\\u003c/h2\\u003e\\u003cp\\u003eData were analysed using SPSS version 25. To dive deeper into the relationships between BMI, various sleep parameters, and demographic factors, this study used several statistical analyses. Independent t-tests were used to compare the average BMI between those who reported good sleep quality and those who did not. Pearson's correlation was used to explore the relationships between sleep duration, sleep disturbances, and BMI scores. Finally, binary logistic regression was applied to identify which predictors, like gender, sleep duration, and BMI, were most significantly linked to poor sleep quality. This study aimed to identify the complex interplay between sleep and body weight in this specific group of young adults, ultimately shedding light on the challenges they face and the factors that influence their health. A \\u003cem\\u003ep\\u003c/em\\u003e-value \\u0026lt; .05 was considered statistically significant.\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003eThis study revealed some findings about the sleep quality and body weight of young adults in Bangladesh. The analysis included 445 participants, of whom 45.8% were female and 54.2% were male (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). Most participants (63.2%) were undergraduates, while a smaller percentage had graduate (27.6%) or postgraduate (2.7%) degrees. Most reported a family income of between 11,000 and 40,000 BDT per month (47.4%), followed by \\u0026gt;\\u0026thinsp;40,000 BDT (30.1%) and less than 10,000 BDT (13.5%). Of those underweight, 14.6% were underweight, 54.2% were normal weight, 22.5% were overweight, and 8.8% were obese. Poor sleep quality was experienced by more than half of the sample (56.4%) (PSQI\\u0026thinsp;\\u0026gt;\\u0026thinsp;5). This suggests that sleep issues are quite prevalent in this group, raising important questions about their overall health and well-being.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eDescriptive Statistics of the Study Sample (N\\u0026thinsp;=\\u0026thinsp;445)\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"4\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eVariable\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eCategory\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003en\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e%\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eGender\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eMale\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e241\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e54.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eFemale\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e204\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e45.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eEducation\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eSSC\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eHSC\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e24\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e5.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eUndergrad\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e282\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e63.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eGraduated\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e123\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e27.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003ePostgraduate\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e12\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eOccupation\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eStudent\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e385\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e87.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eHousewife\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eSelf-employed\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e16\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e3.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eService\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e32\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e7.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMonthly family income\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;10000\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e60\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e13.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e11000\\u0026ndash;40000\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e211\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e47.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u0026gt;\\u0026thinsp;40000\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e174\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e39.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eBMI Status\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eUnderweight (\\u0026lt;\\u0026thinsp;18.5)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e65\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e14.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eNormal (18.5\\u0026ndash;24.9)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e241\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e54.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eOverweight (25\\u0026ndash;29.9)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e100\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e22.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eObese (\\u0026ge;\\u0026thinsp;30)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e39\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e8.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSleep Quality\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eGood (PSQI\\u0026thinsp;\\u0026le;\\u0026thinsp;5)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e194\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e43.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003ePoor (PSQI\\u0026thinsp;\\u0026gt;\\u0026thinsp;5)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e251\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e56.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eThis study found that those who reported poor sleep had a significantly higher average BMI. Specifically, the mean BMI for participants with poor sleep quality was 24.11 kg/m\\u0026sup2;, while those who had better sleep averaged at 22.36 kg/m\\u0026sup2;. This difference was statistically significant (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;.001) (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). Further examination revealed relationships between sleep and weight. This study found an inverse correlation between BMI and sleep duration; participants with higher BMIs tended to sleep less (\\u003cem\\u003er\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;\\u0026minus;\\u0026thinsp;.28). Conversely, this study observed positive correlations between BMI and factors such as sleep latency (the time it takes to fall asleep, \\u003cem\\u003er\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;.22) and sleep disturbances (\\u003cem\\u003er\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;.31), along with a strong positive correlation with the total PSQI score (\\u003cem\\u003er\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;.38). These correlations suggest that as BMI increases, sleep quality tends to worsen.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eMean BMI Across Sleep Quality Categories\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"4\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSleep Quality\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eMean BMI (kg/m\\u0026sup2;)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eSD\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003en\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eGood Sleep\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e22.36\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e3.27\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e194\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePoor Sleep\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e24.11\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e3.96\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e251\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003ctfoot\\u003e\\u003ctr\\u003e\\u003ctd colspan=\\\"4\\\"\\u003eIndependent Samples t-test: t (443)\\u0026thinsp;\\u0026asymp;\\u0026thinsp;\\u0026minus;\\u0026thinsp;4.98; \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;.001.\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tfoot\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eThe logistic regression analysis shed light on significant predictors of poor sleep quality. Participants who were overweight (OR\\u0026thinsp;=\\u0026thinsp;1.80, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;.004) and obese (OR\\u0026thinsp;=\\u0026thinsp;2.29, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;.001) had significantly higher odds of reporting poor sleep quality than participants with a normal BMI. Poor sleep quality was more common in female participants than in male participants (OR\\u0026thinsp;=\\u0026thinsp;1.46, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;.01). The data show a significant correlation between short sleep duration, poor overall sleep quality, and higher BMI at the individual level when mapped onto the SEM (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). These results emphasize the importance of understanding how factors like gender and sleep duration can affect sleep quality and BMI among young adults. The high prevalence of poor sleep quality and its connection to higher body weight underscore the pressing need for targeted health interventions in this population.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eBinary Logistic Regression Predicting Poor Sleep Quality\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"6\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePredictor\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eB\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eSE (Adj)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eOR\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e95% CI (Adj)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003ep (Approx)\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eOverweight vs Normal\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.59\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.17\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.80\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1.30\\u0026ndash;2.50\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e.001\\u0026ndash;.004\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eObese vs Normal\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.83\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.24\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2.29\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1.44\\u0026ndash;3.66\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eFemale vs Male\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.38\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.15\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.46\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1.09\\u0026ndash;1.96\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e.01\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSleep duration\\u0026thinsp;\\u0026lt;\\u0026thinsp;6h\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.71\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.20\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2.04\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1.38\\u0026ndash;3.00\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e.001\\u0026ndash;.003\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eKey Findings on Sleep and BMI Categorized by the Social Ecological Model (N\\u0026thinsp;=\\u0026thinsp;445)\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"4\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSEM Level\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eVariable/Construct\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eFinding\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eStatistical Evidence\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eIndividual\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eBMI category (overweight, obese)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eHigher BMI is associated with poor sleep quality\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eOR\\u0026thinsp;=\\u0026thinsp;1.80 [1.30\\u0026ndash;2.50] (overweight), OR\\u0026thinsp;=\\u0026thinsp;2.29 [1.44\\u0026ndash;3.66] (obese); \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;.01\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eSleep duration\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eShort sleep (\\u0026lt;\\u0026thinsp;6 hrs) is linked to poor sleep and higher BMI\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003er\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;\\u0026minus;\\u0026thinsp;.28, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;.01; OR\\u0026thinsp;=\\u0026thinsp;2.04 [1.38\\u0026ndash;3.00]\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eSleep latency and disturbance\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eLonger latency and higher disturbances associated with higher BMI\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003er\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;.22 and .31, respectively; \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;.05 and \\u0026lt;\\u0026thinsp;.01\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eOverall sleep quality (PSQI global score)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003ePoorer sleep associated with higher BMI\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003er\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;.38, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;.01\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eInterpersonal\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eGender\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eFemale students are more likely to report poor sleep quality\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eOR\\u0026thinsp;=\\u0026thinsp;1.46 [1.09\\u0026ndash;1.96], \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;.01\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003ePeer/family stress (inferred from gender/study context)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003ePsychosocial stressors may disproportionately affect female sleep and eating patterns\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eContextual, supported by gendered differences and literature\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eInstitutional\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eAcademic workload (proxied via sleep deprivation)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003ePoor sleep patterns associated with academic pressures, especially sleep duration\\u0026thinsp;\\u0026lt;\\u0026thinsp;6 hours\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eOR\\u0026thinsp;=\\u0026thinsp;2.04 [1.38\\u0026ndash;3.00], \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;.002\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eSocietal\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eCultural norms and public health neglect (inferred)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eSleep health is under-prioritized in health promotion among young adults in Bangladesh\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eContextual analysis based on regional health strategy gaps\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab5\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eKey Findings on Sleep and BMI Categorized by the Social Ecological Model (N\\u0026thinsp;=\\u0026thinsp;445)\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"4\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSEM Level\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eVariable/Construct\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eFinding\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eStatistical Evidence\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eIndividual\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eBMI category (overweight, obese)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eHigher BMI is associated with poor sleep quality\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eOR\\u0026thinsp;=\\u0026thinsp;1.80 [1.30\\u0026ndash;2.50] (overweight), OR\\u0026thinsp;=\\u0026thinsp;2.29 [1.44\\u0026ndash;3.66] (obese); \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;.01\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eSleep duration\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eShort sleep (\\u0026lt;\\u0026thinsp;6 hrs) is linked to poor sleep and higher BMI\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003er\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;\\u0026minus;\\u0026thinsp;.28, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;.01; OR\\u0026thinsp;=\\u0026thinsp;2.04 [1.38\\u0026ndash;3.00]\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eSleep latency and disturbance\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eLonger latency and higher disturbances associated with higher BMI\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003er\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;.22 and .31, respectively; \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;.05 and \\u0026lt;\\u0026thinsp;.01\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eOverall sleep quality (PSQI global score)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003ePoorer sleep associated with higher BMI\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003er\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;.38, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;.01\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eInterpersonal\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eGender\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eFemale students are more likely to report poor sleep quality\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eOR\\u0026thinsp;=\\u0026thinsp;1.46 [1.09\\u0026ndash;1.96], \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;.01\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003ePeer/family stress (inferred from gender/study context)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003ePsychosocial stressors may disproportionately affect female sleep and eating patterns\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eContextual, supported by gendered differences and literature\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eInstitutional\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eAcademic workload (proxied via sleep deprivation)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003ePoor sleep patterns associated with academic pressures, especially sleep duration\\u0026thinsp;\\u0026lt;\\u0026thinsp;6 hours\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eOR\\u0026thinsp;=\\u0026thinsp;2.04 [1.38\\u0026ndash;3.00], \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;.002\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eSocietal\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eCultural norms and public health neglect (inferred)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eSleep health is under-prioritized in health promotion among young adults in Bangladesh\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eContextual analysis based on regional health strategy gaps\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eThe results of this study offer crucial insights into the relationship between sleep quality and body weight among young adults in Bangladesh. This connection is particularly concerning because, as previous research indicates, poor sleep can lead to hormonal changes that affect appetite and metabolism, potentially creating a cycle of weight gain and worsening sleep issues (Chaput et al., \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; Figorilli et al., \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e; Rogers et al., \\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). A significant number of participants are grappling with poor sleep, which aligns with growing global concerns around sleep health. This high prevalence is particularly alarming, given that quality sleep is essential for overall physical and mental well-being. One of the standout findings is the strong link between poor sleep quality and elevated BMI. Participants who reported inadequate sleep consistently had higher BMI values, which suggests that sleep disturbances might be a contributing factor to weight gain in this population.\\u003c/p\\u003e\\u003cp\\u003eStudies revealed that gender plays an important role in sleep quality, with female participants more likely to report poor sleep. This may reflect the additional societal pressures and stressors that young women face, such as balancing academic demands and family responsibilities. Enhancing awareness about these gender differences can help tailor interventions to meet the specific needs of young women, helping them improve both their sleep and overall health (Alosta et al., \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e; Fatima et al., \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; Madrid-Valero et al., \\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Participants who reported sleeping less than six hours a night had notably higher odds of experiencing poor sleep quality. This finding reiterates the importance of promoting healthy sleep habits among young adults, who often prioritize academics, work, and social commitments at the expense of sufficient rest. Encouraging students to make sleep a priority could yield significant benefits for their health and academic performance.\\u003c/p\\u003e\\u003cp\\u003eStudies revealed the potential interaction of institutional factors (e.g., academic workload, dorm environments), interpersonal influences (e.g., peer stress), and individual behaviours (e.g., sleep hygiene) to affect both sleep and weight outcomes (Gallego-G\\u0026oacute;mez et al., \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Lisnyj et al., \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Wang et al., \\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). The findings of the study are also consistent with previous findings. These findings highlight the necessity of multifaceted, integrated approaches to health promotion that address both more general structural and cultural factors as well as modifiable behaviours. Under the direction of the SEM, this study shows a relationship between young adults' BMI and sleep quality, especially in the South Asian context. Understanding the intricate relationship between sleep and obesity requires an understanding of the SEM's premise that individual behaviours are influenced by interacting factors at several levels, including personal, interpersonal, institutional, and societal.\\u003c/p\\u003e\\u003cp\\u003eHigher BMI is significantly correlated with poorer sleep quality, shorter sleep duration, and more disturbances. This relationship may be influenced by biological processes like altered circadian rhythms, impaired glucose metabolism, and dysregulation of leptin and ghrelin (Chaput et al., \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; Figorilli et al., \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e; Rogers et al., \\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). The findings at the individual level support the global evidence. Additionally, participants who do not get enough sleep might feel more exhausted, which could make them less active and more likely to eat foods high in energy. Sleep and eating patterns may be impacted at the interpersonal level by peer pressure and family expectations. Participants, particularly women, may experience additional psychological stress in cultures like Bangladesh, where family obligations and social expectations regarding academic achievement are high. This can lead to sleep disturbances and unhealthy coping mechanisms like emotional eating. The literature on gender differences in stress reactivity and sleep disorders is consistent with our finding that female participants were more likely to report poor sleep quality.\\u003c/p\\u003e\\u003cp\\u003eAcademic workload, strict class schedules, and a dearth of facilities for student wellness can all have a detrimental effect on sleep hygiene at the institutional level. Policies and resources to support mental health, control screen time, and inform students about the importance of sleep for metabolic health are frequently lacking in university settings (Kumar et al., \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e; Saintila et al., \\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e; Sedek et al., \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). The bidirectional relationship between sleep deprivation and obesity, where weight gain results from inadequate sleep and vice versa, may be exacerbated by these structural limitations. Participants' health behaviours are also influenced by public health priorities and wider cultural norms at the societal level. Obesity among young adults is frequently underrecognized as a public health concern, and sleep health is not commonly incorporated into preventive health strategies in many LMICs, such as Bangladesh.\\u003c/p\\u003e\\u003cp\\u003eThis study fills a gap in the existing literature by highlighting the interplay between sleep quality and body weight in a South Asian context. Most research to date has largely focused on HICs, which means that insights from LMICs like Bangladesh are crucial for developing effective public health strategies. Overall, these findings underscore the necessity of integrating sleep health into obesity prevention efforts. By addressing sleep quality as a fundamental aspect of weight management, multifaceted health promotion programs might be created that tackle the issue from various angles. Empowering young adults with education about the importance of sleep could lead to healthier lifestyles and improved health outcomes, laying a stronger foundation for their futures.\\u003c/p\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eIn summary, this study sheds light on a significant public health concern facing young adults in Bangladesh: the worrying relationship between poor sleep quality and higher body weight. The fact that over half of the participants reported inadequate sleep highlights an urgent need for awareness and intervention. These findings indicate that not only is sleep quality crucial for physical health, but it also plays a vital role in managing body weight. This knowledge is instrumental in shaping targeted interventions that address both sleep and obesity, promoting healthier lifestyles. Integrating sleep health into obesity prevention strategies could be a game-changer, especially in LMICs contexts where these issues are becoming increasingly prevalent. These insights will encourage both policymakers and health professionals to consider the broader implications of sleep quality in health promotion. By prioritizing sleep health, these findings can contribute to the overall well-being of young adults, empowering them to lead healthier, more fulfilled lives. It is essential to foster a supportive environment that emphasizes the significance of quality sleep, aiding in the fight against the obesity epidemic and enhancing the health of future generations.\\u003c/p\\u003e\\u003cp\\u003eLimitations\\u003c/p\\u003e\\u003cp\\u003eSelf-reported measures may be prone to social desirability bias or recall, and causal inference is limited by the cross-sectional design. The sample's generalisability to rural populations or young adults who are not students may be limited because it was selected from urban academic settings. Future studies should examine mediators like screen time, stress, and physical activity, as well as include objective sleep measures, longitudinal designs, and a wider range of sociodemographic groups.\\u003c/p\\u003e\\u003cp\\u003eImplications for Practice and Policy\\u003c/p\\u003e\\u003cp\\u003e\\u003cul\\u003e\\u003cli\\u003e\\u003cp\\u003eInclude sleep hygiene in health programs.\\u003c/p\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cp\\u003eIn clinical settings, check for sleep issues in young adults who are at risk of obesity.\\u003c/p\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cp\\u003eIn Bangladesh, national NCDs strategies and frameworks for preventing obesity should incorporate sleep health promotion.\\u003c/p\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cp\\u003eIn addition to weight management programs, primary care physicians and university health programs should screen for poor sleep quality.\\u003c/p\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cp\\u003eTo address young adults' dual burden of inadequate sleep and excess body weight, gender-sensitive, culturally appropriate interventions are required.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/ul\\u003e\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003eEthics approval and consent to participate\\u003c/p\\u003e\\n\\u003cp\\u003eThe study was conducted in accordance with the ethical principles outlined in the \\u003cem\\u003eDeclaration of Helsinki\\u003c/em\\u003e. All participants were provided with detailed information about the study\\u0026rsquo;s purpose, procedures, potential risks, and benefits. All responses were anonymous, ensuring data confidentiality. All participants provided their informed consent to participate in the study after being informed about the purpose of the study. \\u0026nbsp; The ethical aspects of this study were also reviewed and approved by the Biosafety, Biosecurity, and Ethical Review Board of Jahangirnagar University, Savar, Dhaka-1342, Bangladesh [Ref No: BBEC, JU/M 2022/ 19 (6)].\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eConsent for publication\\u003c/p\\u003e\\n\\u003cp\\u003eAll authors\\u003c/p\\u003e\\n\\u003cp\\u003eAvailability of data and material\\u003c/p\\u003e\\n\\u003cp\\u003eThe data can be obtained from the corresponding author upon request.\\u003c/p\\u003e\\n\\u003cp\\u003eCompeting interests\\u003c/p\\u003e\\n\\u003cp\\u003eNone\\u003c/p\\u003e\\n\\u003cp\\u003eFunding\\u003c/p\\u003e\\n\\u003cp\\u003eThis research received no specific grants from public, commercial, or non-profit funding agencies.\\u003c/p\\u003e\\n\\u003cp\\u003eAuthors\\u0026apos; contributions\\u003c/p\\u003e\\n\\u003cp\\u003eFatema Rahaman: Conceptualization; Methodology; Data collection; Writing: review \\u0026amp; editing. Sabkat Kamal: Data curation; Writing: review \\u0026amp; editing. Rajib Ul Islam: Data curation; Writing: review \\u0026amp; editing. Labina Taher: Data curation; Writing: review \\u0026amp; editing. Fariha Noshin: Data curation; Writing: review \\u0026amp; editing. Tabeen Taher: Data curation; Writing: review \\u0026amp; editing. Mst Sabrina Moonajilin: Conceptualization; Methodology; Writing: original draft.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eAcknowledgements\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors acknowledge all the participants, without whom the study would not have been possible.\\u0026nbsp;\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n \\u003cli\\u003eAhmed, S. K., \\u0026amp; Mohammed, R. A. (2025). 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Sleep duration and mental health in young adults. \\u003cem\\u003eSleep Medicine\\u003c/em\\u003e,\\u003cem\\u003e\\u0026nbsp;115\\u003c/em\\u003e, 30-38. https://doi.org/https://doi.org/10.1016/j.sleep.2024.01.021\\u003c/li\\u003e\\n \\u003cli\\u003eWang, W., Chen, Z., Zhang, W., Yuan, R., Sun, Y., Yao, Q., Lu, J., \\u0026amp; Zheng, J. (2024). Association between obesity and sleep disorder in the elderly: evidence from NHANES 2005\\u0026ndash;2018 [Original Research]. \\u003cem\\u003eFrontiers in Nutrition\\u003c/em\\u003e,\\u003cem\\u003e\\u0026nbsp;Volume 11 - 2024\\u003c/em\\u003e. https://doi.org/10.3389/fnut.2024.1401477\\u003c/li\\u003e\\n \\u003cli\\u003eWang, Y., Dai, X., Zhu, J., Xu, Z., Lou, J., \\u0026amp; Chen, K. (2023). What complex factors influence sleep quality in college students? PLS-SEM vs. fsQCA [Original Research]. \\u003cem\\u003eFrontiers in Psychology\\u003c/em\\u003e,\\u003cem\\u003e\\u0026nbsp;Volume 14 - 2023\\u003c/em\\u003e. https://doi.org/10.3389/fpsyg.2023.1185896\\u003c/li\\u003e\\n \\u003cli\\u003eWorld Health Organization. (2024). \\u003cem\\u003eObesity and overweight\\u003c/em\\u003e. https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight\\u003c/li\\u003e\\n \\u003cli\\u003eWorld Obesity. (2023). \\u003cem\\u003eWorld Obesity Atlas 2023\\u003c/em\\u003e.\\u003c/li\\u003e\\n \\u003cli\\u003eXu, J., Liu, N., Polemiti, E., Garcia-Mondragon, L., Tang, J., Liu, X., Lett, T., Yu, L., N\\u0026ouml;then, M. M., Feng, J., Yu, C., Marquand, A., \\u0026amp; Schumann, G. (2023). Effects of urban living environments on mental health in adults. \\u003cem\\u003eNat Med\\u003c/em\\u003e,\\u003cem\\u003e\\u0026nbsp;29\\u003c/em\\u003e(6), 1456-1467. https://doi.org/10.1038/s41591-023-02365-w\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-research-notes\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"resn\",\"sideBox\":\"Learn more about [BMC Research Notes](http://bmcresnotes.biomedcentral.com)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/resn/default.aspx\",\"title\":\"BMC Research Notes\",\"twitterHandle\":\"@BMC_series\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"sleep quality, overweight/obesity, BMI, young adults, health promotion\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7448615/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7448615/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eObjective: Obesity and poor sleep quality are rising concerns in health discussions today, especially among young adults. Young adults may be more vulnerable to weight gain due to academic pressures and lifestyle choices, making this research particularly relevant. The goal of this study is to examine how body mass index (BMI) relates to various aspects of sleep quality in young adults in Bangladesh. A cross-sectional study was conducted involving 445 participants aged 18 to 25, who were recruited online. The Pittsburgh Sleep Quality Index (PSQI) was used to assess sleep quality.\\u003c/p\\u003e\\n\\u003cp\\u003eResults: More than half of our participants (56.4%) reported having poor sleep quality. Those with poor sleep had a higher average BMI of 24.11 kg/m² compared to 22.36 kg/m² for participants with good sleep. There was a notable inverse relationship between BMI and sleep duration, while correlations were positive with sleep latency, sleep disturbances, and the overall PSQI score. Individuals who were overweight and obese faced significantly greater odds of poor sleep quality. Additionally, being female and sleeping less than 6 hours a night were also significant predictors. This study reveals a troubling prevalence of poor sleep quality among young adults in Bangladesh and its strong link to higher BMI, which has important implications for public health. Integrating sleep health promotion within obesity prevention initiatives could foster healthier futures for young adults.\\u003c/p\\u003e\",\"manuscriptTitle\":\"The impact of sleep quality on body weight among young adults: A cross-sectional study\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-09-12 09:29:10\",\"doi\":\"10.21203/rs.3.rs-7448615/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2025-09-29T08:46:27+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-09-26T18:24:12+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-09-26T14:39:00+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"214003004698376928412279968635552698176\",\"date\":\"2025-09-20T12:31:51+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-09-19T19:33:18+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"128942305266979622133150676040178465189\",\"date\":\"2025-09-12T04:20:35+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"220973819642642163243464786063312039254\",\"date\":\"2025-09-09T03:36:56+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"41623384198502345460319592446616475458\",\"date\":\"2025-09-08T02:38:21+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"39843553074730263885336185188786470760\",\"date\":\"2025-09-07T06:54:52+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2025-09-07T02:53:05+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2025-09-07T02:48:06+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvited\",\"content\":\"\",\"date\":\"2025-09-05T11:39:34+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2025-09-05T11:26:33+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"BMC Research Notes\",\"date\":\"2025-09-05T11:22:50+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-research-notes\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"resn\",\"sideBox\":\"Learn more about [BMC Research Notes](http://bmcresnotes.biomedcentral.com)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/resn/default.aspx\",\"title\":\"BMC Research Notes\",\"twitterHandle\":\"@BMC_series\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"865bae31-d1e2-4e10-9284-94d343e570d6\",\"owner\":[],\"postedDate\":\"September 12th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-11-24T16:08:58+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-7448615\",\"link\":\"https://doi.org/10.1186/s13104-025-07544-1\",\"journal\":{\"identity\":\"bmc-research-notes\",\"isVorOnly\":false,\"title\":\"BMC Research Notes\"},\"publishedOn\":\"2025-11-19 15:58:17\",\"publishedOnDateReadable\":\"November 19th, 2025\"},\"versionCreatedAt\":\"2025-09-12 09:29:10\",\"video\":\"\",\"vorDoi\":\"10.1186/s13104-025-07544-1\",\"vorDoiUrl\":\"https://doi.org/10.1186/s13104-025-07544-1\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-7448615\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-7448615\",\"identity\":\"rs-7448615\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}