Sleep Quality and Its Associated Factors among Malaysian Adults: Cross-Sectional Findings from the May Measurement Month 2025 Participants

Sleep Quality and Its Associated Factors among Malaysian Adults: Cross-Sectional Findings from the May Measurement Month 2025 Participants | 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 Research Article Sleep Quality and Its Associated Factors among Malaysian Adults: Cross-Sectional Findings from the May Measurement Month 2025 Participants Yee-How Say, Hooi Chin Beh, Karleen Chong, Maong Hui Cheng, Jazlan Jamaluddin, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9014341/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background Poor sleep quality is increasingly recognised as an important determinant of cardiometabolic health. However, community-based data integrating sleep assessment with blood pressure and body composition measures in Malaysia remains limited. This study aimed to determine the prevalence of poor sleep quality and its associated factors among participants of the May Measurement Month (MMM) 2025 blood pressure screening campaign in Malaysia. Methods This cross-sectional study included 907 adults (median age ± interquartile range: 40 ± 35; 67.4% females), recruited from community-based screening sites in the greater Klang Valley. Sleep quality was assessed using the Brief Pittsburgh Sleep Quality Index (B-PSQI) and categorised as good or poor based on a validated cut-off score. Data on sociodemographic characteristics, lifestyle behaviours (smoking, vaping, alcohol intake, caffeine consumption, and vigorous physical activity), hypertension status (including awareness and blood pressure control), and anthropometric/body composition measures were collected. Bivariate analyses were conducted using appropriate statistical tests, followed by multivariable logistic regression to identify factors independently associated with poor sleep quality after adjusting for potential confounders. Results Poor sleep quality was observed in 31.8% of participants. Ethnicity, vaping, and vigorous exercise were significantly associated with sleep quality. After adjustment, participants from other minority ethnic groups had higher odds of poor sleep compared with Malays (OR 3.44, 95% CI 1.39–8.52; p = 0.008). Past vapers (OR 0.23, 95% CI 0.08–0.66; p = 0.006) and never vapers (OR 0.29, 95% CI 0.10–0.83; p = 0.021) had lower odds of poor sleep compared with current vapers. Lack of vigorous exercise was associated with higher odds of poor sleep (OR 1.67, 95% CI 1.25–2.23; p = 0.001). Hypertension status and body composition indicators were not independently associated. Conclusion Nearly one in three MMM 2025 participants reported poor sleep quality. Minority ethnicity, current vaping, and physical inactivity were independently associated with poor sleep. Integrating sleep assessment and lifestyle counselling into cardiovascular screening initiatives may enhance preventive strategies in Malaysia. Sleep quality Hypertension Vaping Physical activity May Measurement Month Background Sleep is a fundamental biological process essential for cardiovascular, metabolic, cognitive, and psychological health. Inadequate or poor-quality sleep has been associated with increased risks of hypertension, coronary heart disease, stroke, obesity, diabetes, and all-cause mortality [ 1 , 2 ]. In recognition of its broad health implications, sleep duration and quality are increasingly considered integral components of cardiovascular risk assessment and preventive health strategies [ 3 ]. Globally, sleep disturbances are highly prevalent, with approximately 10–30% of the general adult population affected by sleep problems and poor sleep quality [ 4 , 5 ]. In Asia, rapid urbanisation, longer working hours, shift work [ 6 ], and digital device use [ 7 ] have contributed to growing concerns about insufficient and disrupted sleep. In Malaysia, the National Health and Morbidity Survey 2023 found that the prevalence of insufficient sleep (< 7 hours) among adults was 37.7% [ 8 ], and available data suggest that sleep problems are common among working adults [ 9 ] and university students [ 10 ], with associations reported between poor sleep, psychological distress [ 10 ], and cardiometabolic risk factors [ 11 ]. However, large-scale community-based data examining sleep quality in conjunction with cardiovascular screening parameters remain limited. Poor sleep is closely associated with hypertension and cardiovascular disease through multiple biological pathways, including sympathetic nervous system activation, hypothalamic–pituitary–adrenal axis dysregulation, endothelial dysfunction, and systemic inflammation [ 12 , 13 ]. Short sleep duration and sleep fragmentation have been shown to predict incident hypertension and adverse cardiovascular outcomes in prospective studies [ 14 , 15 ]. Conversely, individuals with established hypertension may experience impaired sleep due to nocturnal blood pressure variability, medication effects, or coexisting sleep disorders such as obstructive sleep apnoea [ 16 ]. Despite this bidirectional relationship, evidence on the association between subjective sleep quality and blood pressure (BP) control in community settings remains inconsistent. Lifestyle behaviours also play a critical role in sleep health. Physical activity has been consistently associated with improved sleep quality and efficiency [ 17 ]. In contrast, nicotine exposure—through conventional cigarettes or electronic cigarettes—can delay sleep onset and alter sleep architecture due to its stimulant properties [ 18 ]. Emerging research suggests that e-cigarette use may be associated with sleep disturbances, particularly among younger adults [ 19 ]. Alcohol [ 20 ] and caffeine [ 21 ] intake are also known to influence sleep patterns, although their effects vary according to dose, timing, and individual susceptibility. Anthropometric and body composition indicators, including obesity and central adiposity, are strongly associated with sleep disorders and sleep-disordered breathing [ 22 ]. However, the relationship between general adiposity measures—such as body mass index (BMI), waist circumference (WC), and visceral fat level (VFL)—and subjective sleep quality is less clear in community-based populations without formal sleep disorder assessment. May Measurement Month (MMM) is a global blood pressure screening initiative aimed at raising awareness of hypertension and improving cardiovascular risk detection [ 23 ]. The inclusion of sleep-related assessments within such a large-scale community screening context provides a valuable opportunity to explore the interplay between sleep quality, lifestyle behaviours, hypertension status, and body composition in a real-world Malaysian population. Therefore, the present study aimed to: 1. Determine the prevalence of poor sleep quality among participants of the May Measurement Month 2025 campaign in the Greater Klang Valley, Malaysia, and 2. Examine the associations between sleep quality and demographic characteristics, lifestyle behaviours, hypertension status, and anthropometric/body composition indicators. Understanding these associations may inform integrated strategies for sleep health promotion within cardiovascular risk reduction programs. Methods Study Design and Setting This study was a cross-sectional analysis conducted within the framework of the May Measurement Month (MMM) 2025 blood pressure (BP) screening campaign in the greater Klang Valley, Malaysia. MMM is a global annual initiative led by the International Society of Hypertension aimed at enhancing awareness, detection, and management of hypertension worldwide [23]. Screening was carried out between April and October 2025 across various community-based settings in Malaysia, including public venues, workplaces, universities, and healthcare facilities. Participating sites included the Universiti Malaya Medical Centre (UMMC) outpatient clinic (Kuala Lumpur), Universiti Putra Malaysia (UPM; Serdang), Sunway University (Subang Jaya), AEON Mall Nilai Health Campaign (Nilai), and the Shangri-La Hotel (Kuala Lumpur). Beyond the standard MMM 2025 protocol—which included collection of basic sociodemographic information, lifestyle factors, dietary habits, and BP measurements [24]—all participating centres implemented additional health assessments. These comprised evaluation of sleep quality using the brief Pittsburgh Sleep Quality Index (B-PSQI) and comprehensive anthropometric and body composition measurements to further explore cardiometabolic risk profiles. The primary objective of this extended assessment was to examine the associations between sleep quality, anthropometric and body composition indicators, and both the presence of hypertension and BP control among individuals with hypertension. Study Population Individuals aged 18 years and above were eligible for inclusion. Participants were recruited through community outreach initiatives and voluntary participation at the respective screening sites. Those with missing data on sex, ethnicity, BP measurements, sleep, or anthropometric/body composition variables were excluded. Only complete cases were retained for analysis, yielding a final analytical sample of 907 participants (87.7%) out of 1,034 individuals screened. Hypertension status was defined based on on-site BP measurements obtained during a single visit and/or self-reported prior diagnosis by a healthcare professional, as captured in the MMM2025 questionnaire (“Have you ever been diagnosed with high BP by a health professional [except in pregnancy]?”) [24]. Participants were classified as hypertensive if they had a measured systolic BP (SBP) ≥ 140 mmHg and/or diastolic BP (DBP) ≥ 90 mmHg, or if they reported a previous medical diagnosis of hypertension. Those with measured SBP < 140 mmHg and DBP < 90 mmHg and without a prior diagnosis were considered normotensive. Among individuals identified as hypertensive, awareness was defined by self-report of a previous physician diagnosis. BP control was determined using the measured values at the time of screening: controlled BP was defined as SBP < 140 mmHg and DBP <90 mmHg, whereas uncontrolled BP was defined as SBP ≥140 mmHg and DBP ≥90 mmHg. Ethical approval for the study was obtained from the relevant institutional research ethics committee before data collection, i.e., UMMC Medical Research Ethics Committee (Approval number: MREC: 202535-14824), UPM Ethics Committee for Research Involving Human Subjects (Approval number: JKEUPM-2024-277), Sunway University Research Ethics Committee (Approval number: 2025/REC0104). The study was conducted in accordance with the principles of the Declaration of Helsinki. All participants received information regarding the study objectives, procedures, potential risks and benefits, and their right to withdraw without consequence. Written informed consent was obtained before participation. Data were anonymised and securely stored to maintain confidentiality. Data Collection Procedures Sociodemographic and Lifestyle Assessment Participants completed a structured self-administered questionnaire, with trained research staff available to clarify any questions when needed. The questionnaire captured information on sociodemographic characteristics (age, sex, ethnicity, and years of education) as well as lifestyle behaviours, including smoking status, vaping use, alcohol intake, caffeine consumption, and participation in vigorous physical activity. These variables were treated as potential confounding factors and were adjusted for in the multivariable analyses. BP Measurement BP was assessed using validated automated devices (Omron HEM-7120, Omron HEM-7121, or Rossmax MJ701f) that were regularly calibrated. Measurements were performed by trained personnel in accordance with standardized protocols. Participants were instructed to sit quietly for a minimum of five minutes with their back supported and feet resting flat on the floor prior to the measurement. An appropriately sized cuff was applied to the upper arm at the level of the heart. At least two readings were obtained, and the mean of the measurements was used in the analysis to enhance accuracy and reliability. Brief Pittsburgh Sleep Quality Index (B-PSQI) The Pittsburgh Sleep Quality Index (PSQI) is a widely used self-administered instrument designed to assess sleep quality and disturbances over the preceding month. In the present study, a shortened six-item version, known as the Brief Pittsburgh Sleep Quality Index (B-PSQI), was utilised to measure key dimensions of sleep quality [25]. The B-PSQI includes items that capture essential components of sleep, namely sleep duration, sleep latency, frequency of sleep disturbances, and overall subjective sleep quality. As bedtime and wake-up time are incorporated in the calculation of sleep efficiency, the six questions generate five scored components. These components are summed to produce a global score ranging from 0 to 15, with higher scores reflecting poorer sleep quality. The B-PSQI has demonstrated satisfactory psychometric properties, with reported polychoric ordinal alpha of 0.79 and ordinal omega of 0.91, indicating good internal consistency. In accordance with recommended cut-off criteria, a global score greater than 5 was classified as poor sleep quality, while scores of 5 or below were categorised as good sleep quality [25]. Anthropometric and Body Composition Measurements Anthropometric and body composition assessments were performed by trained staff in accordance with standardized procedures. Standing height was measured using a portable stadiometer (Seca 213). Waist circumference (WC) was measured at the midpoint between the lower border of the least palpable rib and the iliac crest, while hip circumference was measured at the level of the maximum protrusion of the buttocks, using a non-elastic measuring tape calibrated to maintain a constant 100 g tension [26]. Waist-to-hip ratio (WHR) and waist-to-height ratio (WHtR) were subsequently calculated by dividing WC by hip circumference and height, respectively. Body composition was evaluated using a bioelectrical impedance analysis (BIA) device (Omron HBF-375), which provided measurements of body weight, body mass index (BMI; kg/m²), total body fat (TBF; %), visceral fat level (VFL; %), subcutaneous fat (SF; %), skeletal muscle percentage (SM; %), and resting metabolic rate (RM; kcal). In addition to whole-body estimates, segmental analyses of subcutaneous fat and skeletal muscle mass for the trunk, arms, and legs were obtained. Standardised cut-off values were applied to categorise adiposity and related indicators. Overweight and obesity were defined as BMI ≥ 23 kg/m² and ≥ 27.5 kg/m², respectively [27]. High TBF was defined as ≥ 20% in men and ≥ 30% in women, while high VFL was defined as ≥10% [28]. High SM was defined as ≥ 35.8% in men and ≥ 28% in women [28]. Central obesity thresholds were defined as WC ≥ 90 cm for men and ≥ 80 cm for women (WHO/IOTF/IASO, 2000), WHR ≥ 0.90 for men and ≥ 0.85 for women (WHO, 2011), and WHtR ≥ 0.50 [29]. Statistical Analysis All statistical analyses were performed using IBM SPSS Statistics for Windows version 26.0 (IBM Corp., Armonk, NY, USA). The normality of continuous variables was assessed using the Kolmogorov–Smirnov test, with p < 0.05 indicating deviation from normal distribution. Variables that were not normally distributed were summarised as medians with interquartile ranges (IQR), while normally distributed variables were presented as means with 95% confidence intervals (CI), where appropriate. Comparisons between participants with good and poor sleep quality were conducted using the Mann–Whitney U test for non-normally distributed continuous variables and Pearson’s chi-square test for categorical variables. Binary logistic regression analyses were performed to examine factors independently associated with poor sleep quality. Variables entered into the multivariable models included demographic characteristics (age, sex, ethnicity, years of education), lifestyle behaviours (smoking status, vaping, alcohol consumption, caffeine intake, and vigorous physical activity), hypertension-related variables (hypertension status, awareness, and BP control), and anthropometric/body composition categories. Adjusted odds ratios (ORs) with 95% confidence intervals (CIs) and corresponding p -values were reported. All multivariable models were adjusted for potential confounders identified a priori based on clinical relevance and existing literature. Statistical tests were two-sided, and a p -value < 0.05 was considered statistically significant. Results Association of Sleep Quality with Demographic and Lifestyle Factors Age and sex were not significantly associated with sleep quality in both bivariate and multivariable analyses (all p > 0.05) (Tables 1 and 2 ). Ethnicity was significantly associated with sleep quality at the bivariate level (χ² = 16.131, p = 0.001). Poor sleepers comprised a lower proportion of Chinese participants and a higher proportion of Indians and those from other ethnic groups compared with good sleepers. In the adjusted model, participants from other minority ethnic groups had significantly higher odds of poor sleep quality compared with Malays (OR = 3.44, 95% CI: 1.39–8.52, p = 0.008), whereas Chinese and Indian ethnicities were not independently associated (Table 2 ) Table 1 Demographic, lifestyle habits, hypertension, and anthropometric/body composition categories among good and poor quality sleepers Categories Sleep Quality Total ( N = 907) Good ( n = 619) Poor ( n = 288) Median age ± Interquartile Range 40 ± 33 39 ± 33 40 ± 35 p 0.335 Ethnicity Malay 297 (48.0) 139 (48.3) 436 (48.1) Chinese 249 (40.2) 92 (31.9) 341 (37.6) Indian 65 (10.5) 44 (15.3) 109 (12.0) Others α 8 (1.3) 13 (4.5) 21 (2.3) χ 2 ; p 16.131; 0.001** Sex Male 200 (32.3) 96 (33.3) 296 (32.6) Female 419 (67.7) 192 (66.7) 611 (67.4) χ 2 ; p 0.094; 0.760 Tobacco smoking Yes 22 (3.6) 9 (3.1) 31 (3.4) No (but did in past) 247 (40.0) 101 (35.1) 348 (38.4) Never 349 (56.5) 178 (61.8) 527 (58.2) χ 2 ; p 2.296; 0.317 Vaping Yes 6 (1.0) 10 (3.5) 16 (1.8) No (but did in past) 236 (38.2) 91 (31.8) 327 (36.2) Never 376 (60.8) 185 (64.7) 561 (62.1) χ 2 ; p 9.705; 0.008** Alcohol drinking Never/rarely 573 (92.7) 260 (90.9) 833 (92.1) 1–3 times/month 34 (5.5) 22 (7.7) 56 (6.2) 1–6 times per week/Daily 11 (1.8) 4 (1.4) 15 (1.7) χ 2 ; p 1.756; 0.416 Caffeine intake Never/<4 per month 57 (10.1) 28 (10.4) 85 (10.2) 1–6 times/week 312 (55.3) 149 (55.6) 461 (55.4) 1–3 times/day 183 (32.4) 84 (31.3) 267 (32.1) 4+/day 12 (2.1) 7 (2.6) 19 (2.3) χ 2 ; p 0.279; 0.964 Vigorous exercise Yes 398 (64.4) 152 (53.3) 550 (60.9) No 220 (35.6) 133 (46.7) 353 (39.1) χ 2 ; p 10.035; 0.002** Hypertension status Hypertensive 204 (33.0) 106 (36.8) 310 (34.2) Normotensive 415 (67.0) 182 (63.2) 597 (65.8) χ 2 ; p 1.294; 0.255 Awareness among hypertensives Aware 112 (55.7) 70 (66.7) 182 (59.5) Unaware 89 (44.3) 35 (33.3) 124 (40.5) χ 2 ; p 3.428; 0.064 BP control among hypertensives Controlled 66 (32.8) 38 (36.2) 104 (34.0) Uncontrolled 135 (67.2) 67 (63.8) 202 (66.0) χ 2 ; p 0.346; 0.556 WC Class Normal 281 (45.4) 135 (46.9) 416 (45.9) High 338 (54.6) 153 (53.1) 491 (54.1) χ 2 ; p 0.173; 0.677 WHtR Class Normal 249 (40.2) 110 (38.2) 359 (39.6) High 370 (59.8) 178 (61.8) 548 (60.4) χ 2 ; p 0.339; 0.560 WHR Class Normal 331 (53.5) 164 (56.9) 495 (54.6) High 288 (46.5) 124 (43.1) 412 (45.4) χ 2 ; p 0.955; 0.328 TBF Class Normal 185 (31.0) 81 (29.5) 266 (30.5) High 412 (69.0) 194 (70.5) 606 (69.5) χ 2 ; p 0.209; 0.648 VFL Class Normal 392 (64.2) 177 (63.2) 569 (63.9) High 219 (35.8) 103 (36.8) 322 (36.1) χ 2 ; p 0.074; 0.786 BMI Class Normal 234 (39.1) 103 (37.3) 337 (38.5) Overweight 189 (31.6) 91 (33.0) 280 (32.0) Obese 176 (29.4) 82 (29.7) 258 (29.5) χ 2 ; p 0.275; 0.871 SM Class Normal 480 (79.9) 216 (78.5) 696 (79.5) High 121 (20.1) 59 (21.5) 180 (20.5) χ 2 ; p 0.202; 0.653 α Other ethnicities include indigenous groups of Peninsular Malaysia, Sabah, and Sarawak like Peninsular Orang Asli, Kadazan-Dusun-Murut Sabah, and Dayak Sarawak. WC: Waist circumference; WHtR: Waist-Height Ratio; WHR: Waist-Hip Ratio; TBF: Total Body Fat; VFL: Visceral Fat Level; BMI: Body Mass Index; SM: Skeletal Muscle. Continuous variables analyzed by Mann-Whitney U test; Categorical variables analyzed by Pearson’s Chi-square test; * p -value significant at < 0.05; ** p -value significant at < 0.01 Table 2 Binary logistic regression of the association of demographic, lifestyle habits, hypertension, and anthropometric/body composition categories with poor sleep quality Classes B S.E. Wald df p Adjusted Odds Ratio 95.0% C.I. Lower Upper Ethnicity α Chinese -0.227 0.161 1.993 1.000 0.158 0.797 0.582 1.092 Indian 0.380 0.224 2.879 1.000 0.090 1.462 0.943 2.266 Others 1.235 0.463 7.110 1.000 0.008* 3.437 1.387 8.519 Sex β Female sex -0.040 0.152 0.068 1.000 0.795 0.961 0.713 1.295 Tobacco smoking ϒ No (but did in past) 0.036 0.422 0.007 1.000 0.932 1.036 0.453 2.370 Never 0.269 0.418 0.413 1.000 0.520 1.308 0.576 2.969 Vaping ϒ No (but did in past) -1.475 0.539 7.486 1.000 0.006** 0.229 0.080 0.658 Never -1.225 0.532 5.299 1.000 0.021* 0.294 0.103 0.834 Alcohol drinking ϒ 1–3 times/month 0.219 0.293 0.558 1.000 0.455 1.245 0.701 2.212 1–6 times/week/Daily -0.269 0.595 0.204 1.000 0.652 0.765 0.238 2.453 Caffeine intake ϒ 1–6 times/week 0.025 0.256 0.009 1.000 0.922 1.025 0.621 1.692 1–3 times/day -0.063 0.273 0.053 1.000 0.817 0.939 0.549 1.604 4+/day 0.171 0.537 0.102 1.000 0.750 1.187 0.414 3.398 Vigorous exercise ϒ No 0.511 0.149 11.809 1.000 0.001** 1.667 1.246 2.232 Hypertension ¥ Hypertension Presence -0.209 0.173 1.446 1.000 0.229 0.812 0.578 1.140 Hypertensives Who Are Unaware -0.558 0.284 3.866 1.000 0.049* 0.572 0.328 0.998 Hypertensives with BP Uncontrolled -0.403 0.295 1.862 1.000 0.172 0.668 0.375 1.192 WC Class ¥ High 0.035 0.161 0.048 1.000 0.826 1.036 0.755 1.422 WHtR Class ¥ High 0.241 0.162 2.213 1.000 0.137 1.273 0.926 1.750 WHR Class ¥ High -0.149 0.158 0.891 1.000 0.345 0.861 0.632 1.174 TBF Class ¥ High 0.183 0.177 1.067 1.000 0.302 1.200 0.849 1.698 VFL Class ¥ High 0.111 0.168 0.441 1.000 0.507 1.118 0.805 1.553 BMI Class ¥ Overweight 0.139 0.192 0.524 1.000 0.469 1.149 0.788 1.675 Obese 0.153 0.197 0.605 1.000 0.437 1.165 0.792 1.714 SM Class ¥ High 0.042 0.200 0.044 1.000 0.833 1.043 0.705 1.542 α Reference category = Malay; controlled for age and sex β Reference category = Male sex; controlled for age and ethnicity ϒ Reference category = Smoker/vaper/non alcohol-drinker/non- or < 4 times/month caffeine drinker/vigorous exerciser; controlled for age, sex, and ethnicity ¥ Reference category = Normotensive/hypertensive aware/hypertensive with BP controlled/normal WC, WHR, WHtR, WHR, TBF, BMI, SM; controlled for age, years of education, ethnicity, sex, tobacco usage, vaping, alcohol drinking, caffeine usage, and vigorous exercise. Among lifestyle behaviours, tobacco smoking, alcohol intake, and caffeine consumption were not significantly associated with sleep quality in either analysis. Vaping was significantly associated at the bivariate level (χ² = 9.705, p = 0.008), with a higher proportion of poor sleepers reporting current vaping. This association persisted after adjustment, as past vapers (OR = 0.23, 95% CI: 0.08–0.66, p = 0.006) and never vapers (OR = 0.29, 95% CI: 0.10–0.83, p = 0.021) had lower odds of poor sleep quality compared with current vapers (Table 2 ). Engagement in vigorous exercise was also significant in both analyses. Poor sleepers were less likely to report vigorous exercise (53.3% vs. 64.4%; p = 0.002), and those not engaging in vigorous exercise had 1.67 times higher odds of poor sleep quality after adjustment (OR = 1.67, 95% CI: 1.25–2.23, p = 0.001) Association of Sleep Quality with Hypertension and Anthropometrics/Body Composition Hypertension prevalence did not differ significantly across sleep groups ( p = 0.255; Table 1 ), and hypertension status was not independently associated with poor sleep quality (OR = 0.81, 95% CI: 0.58–1.14, p = 0.229; Table 2 ). Among hypertensive participants, awareness showed a borderline bivariate association ( p = 0.064). In the adjusted model, hypertensive individuals who were unaware of their condition had marginally-significant lower odds of poor sleep quality compared with those aware (OR = 0.57, 95% CI: 0.33–0.998, p = 0.049). BP control was not significantly associated in either analysis (Tables 1 and 2 ). . No significant associations were observed between sleep quality and anthropometric or body composition measures—including WC, WHtR, WHR, TBF, VFL, BMI, or SM class—in both bivariate and multivariable analyses (all p > 0.05)(Tables 1 and 2 ). Discussion In this sample of May Measurement Month (MMM) 2025 participants in the greater Klang Valley, Malaysia, approximately one-third (31.8%) were classified as having poor sleep quality. Poor sleep was independently associated with minority ethnicity, current vaping, and lack of vigorous exercise, whereas hypertension status and adiposity-related measures were not significantly associated after multivariable adjustment. The observed prevalence of poor sleep quality in this study is broadly consistent with international estimates, indicating that approximately 10–30% of adults experience clinically significant sleep disturbances or insufficient sleep [ 4 , 5 ]. Although prevalence varies depending on the population studied and the assessment tools used, sleep complaints remain highly common across both high- and middle-income countries. Rapid urbanisation [ 30 ], longer working hours [ 6 ], increased screen exposure [ 7 ], and psychosocial stress [ 31 ] have all been implicated in the growing burden of sleep problems worldwide. Poor sleep is increasingly recognised as a major public health issue because of its wide-ranging health consequences. Evidence from epidemiological and experimental studies has demonstrated strong associations between inadequate sleep and adverse cardiometabolic outcomes, including hypertension, obesity, type 2 diabetes, and cardiovascular disease [ 1 , 2 ]. Sleep disruption has also been associated with impaired cognitive performance, reduced concentration and productivity, mood disturbances, and diminished overall quality of life [ 32 ]. Furthermore, accumulating data suggest that poor sleep may contribute to increased healthcare utilisation and economic costs at the societal level [ 33 , 34 ]. Collectively, these findings underscore the importance of incorporating sleep health into broader non-communicable disease prevention and health promotion strategies. Demographic Factors Ethnicity was significantly associated with sleep quality. Participants from minority ethnic groups, consisting of indigenous groups of Peninsular Malaysia, Sabah, and Sarawak, had more than threefold higher odds of poor sleep compared with Malays. However, the association between minority ethnicity and poor sleep should be interpreted cautiously. The “Others” group included only 21 participants and had a wide confidence interval, suggesting statistical instability. The elevated odds may be driven by the small sample size and the heterogeneous nature of this category. Larger studies with better representation of minority groups are needed to confirm this finding. Nevertheless, ethnic disparities in sleep have been reported internationally, often attributed to socioeconomic differences, occupational demands, psychosocial stress, and environmental exposures [ 35 , 36 ]. In the Malaysian context, cultural practices, shift work patterns, and urban living conditions may contribute to differential sleep patterns across ethnic groups, similar to the multi-ethnic Singaporean population [ 37 ]. Although Chinese and Indian ethnicity were not independently associated after adjustment, the elevated odds observed among other minority groups warrant further exploration, particularly with regard to social determinants of health and access to healthcare. Age and sex were not significantly associated with sleep quality in our cohort. While global studies report poorer sleep quality among women [ 38 , 39 ] and older adults [ 40 , 41 ], the findings are not universal and may depend on population structure and measurement tools. The relatively wide age distribution and predominance of female participants in this opportunistic screening sample may partly explain the absence of significant associations. Lifestyle Behaviours Vaping emerged as a significant independent correlate of poor sleep quality. Current vapers had higher odds of poor sleep compared with past or never users. Nicotine is a central nervous system stimulant that increases sleep latency, reduces total sleep time, and alters sleep architecture [ 18 ]. Emerging evidence suggests that e-cigarette use is associated with sleep disturbances, potentially due to nicotine exposure and behavioural patterns associated with device use [ 19 ]. Our findings add to the growing literature highlighting the potential adverse sleep-related consequences of vaping in adult populations. Engagement in vigorous exercise was protective against poor sleep. Participants who did not perform vigorous exercise had 67% higher odds of poor sleep quality. Regular physical activity has been consistently associated with improved sleep quality, shorter sleep latency, and greater sleep efficiency [ 17 , 42 ]. Biological mechanisms may include thermoregulatory effects, circadian phase shifts, and reductions in anxiety and depressive symptoms [ 17 ]. From a public health perspective, promoting physical activity may confer dual benefits for cardiometabolic health and sleep quality. In contrast, tobacco smoking, alcohol consumption, and caffeine intake were not independently associated with sleep quality in our adjusted models. Although these factors are known to influence sleep physiology [ 18 , 20 , 21 ], their effects may depend on dose, timing, and chronicity of use, which were not captured in detail in this study. Hypertension and Body Composition Contrary to expectations, hypertension status and BP control were not independently associated with poor sleep quality. Short sleep duration and poor sleep have been associated with incident hypertension and adverse cardiovascular outcomes in longitudinal studies [ 14 – 16 ]. However, cross-sectional associations are less consistent, and sleep quality may be influenced by factors other than BP per se, such as obstructive sleep apnoea, stress, or medication use. Interestingly, hypertensive individuals who were unaware of their condition had marginally significantly lower odds of poor sleep compared with those aware, possibly reflecting heightened symptom perception, anxiety, or health-related concerns among those diagnosed. Similarly, no significant associations were observed between sleep quality and anthropometric or body composition indicators, including BMI, overall and central adiposity measures, and SM mass, in contrast with a similar multi-ethnic large population study in Singapore [ 37 ]. Although obesity is strongly associated with sleep disorders such as obstructive sleep apnoea [ 43 ], subjective sleep quality does not always correlate directly with adiposity measures, particularly in community-based samples without formal sleep disorder assessment. Study Strengths, Implications, and Limitations This study leverages data from a relatively large community-based MMM campaign, providing contemporary insights into sleep health within the context of cardiovascular screening in Malaysia. The integration of detailed BP, anthropometric, body composition, and lifestyle data allowed for comprehensive adjustment of potential confounders. Our findings highlight the need to address modifiable behaviours—particularly vaping and physical inactivity—in efforts to improve sleep health. These behaviours are amenable to intervention through counselling and community-based programmes and improving them may yield dual benefits for both sleep and cardiometabolic health. Given the bidirectional relationship between sleep and cardiovascular risk, incorporating brief sleep assessments into hypertension and cardiovascular screening initiatives could support early identification of at-risk individuals and strengthen integrated non-communicable disease prevention strategies. This study has several limitations. Its cross-sectional design precludes causal inference, and sleep quality was assessed using a self-reported measure without objective verification. Hypertension status was based on single-visit BP measurements and/or self-report, which may lead to misclassification. Residual confounding from unmeasured factors such as socioeconomic status, mental health, and shift work cannot be excluded. The opportunistic sampling strategy and use of bioelectrical impedance analysis may limit generalisability and measurement precision. Potential selection bias may have also occurred, as health-conscious individuals were more likely to participate. Females were overrepresented in the sample, which may affect generalisability. Additionally, the study was conducted in the urban greater Klang Valley, limiting applicability to rural or other Malaysian populations. Conclusions In summary, approximately one in three MMM 2025 participants reported poor sleep quality. Minority ethnicity, current vaping, and lack of vigorous exercise were independently associated with poor sleep, whereas hypertension and anthropometric/body composition measures were not. These findings highlight the need to incorporate sleep health promotion—particularly targeting modifiable lifestyle behaviours—into cardiovascular risk reduction strategies in Malaysia. Abbreviations B PSQI—Brief Pittsburgh Sleep Quality Index BMI Body Mass Index BP Blood Pressure BIA Bioelectrical Impedance Analysis CI Confidence Interval DBP Diastolic Blood Pressure IQR Interquartile Range MMM May Measurement Month OR Odds Ratio PSQI Pittsburgh Sleep Quality Index RM Resting Metabolic Rate SBP Systolic Blood Pressure SF Subcutaneous Fat SM Skeletal Muscle TBF Total Body Fat UMMC Universiti Malaya Medical Centre UPM Universiti Putra Malaysia VFL Visceral Fat Level WC Waist Circumference WHR Waist—to—Hip Ratio WHtR Waist—to—Height Ratio Declarations Ethics approval and consent to participate Ethical approval was obtained from the relevant institutional research ethics committee before data collection, i.e., UMMC Medical Research Ethics Committee (Approval number: MREC: 202535-14824), UPM Ethics Committee for Research Involving Human Subjects (Approval number: JKEUPM-2024-277), Sunway University Research Ethics Committee (Approval number: 2025/REC0104). Written informed consent was obtained from all participants before enrolment. Consent for publication Not applicable Availability of data and materials The datasets used and analysed during the current study are available from the corresponding author on reasonable requests. Competing interests All authors declare that they have no competing interests. Funding The Malaysian Society of Hypertension (MSH) provided funding support for the screening campaigns, while the Malaysian Society for World Action on Salt, Sugar and Health (MyWASSH) contributed materials such as posters as part of their corporate social responsibility initiatives. The funders did not play a role in the design of this study nor was involved in the writing of this manuscript. Authors' contributions Conceptualization: YCC; Methodology: YHS and YCC; Data collection: YHS, HCB, KC, MHC, JJ, and SMC; Formal analysis: YHS and YCC; Writing - original draft preparation: YHS and YCC; Writing - review and editing: YHS, HCB, KC, MHC, JJ, SMC, and YCC; Supervision: YCC. All authors have read and agreed to the published version of the manuscript. Acknowledgements The authors would also like to extend their gratitude to all participants who participated in this study. We would also like to thank all student helpers who helped with the recruitment of participants and measurements. References Amin KD, Thakkar A, Budampati T, Matai S, Akkaya E, Shah NP. A good night’s rest: A contemporary review of sleep and cardiovascular health. American Journal of Preventive Cardiology. 2025;21:100924. https://doi.org/10.1016/j.ajpc.2024.100924. Cappuccio FP, Cooper D, D’Elia L, Strazzullo P, Miller MA. Sleep duration predicts cardiovascular outcomes: a systematic review and meta-analysis of prospective studies. European Heart Journal. 2011;32:1484–92. https://doi.org/10.1093/eurheartj/ehr007. Hong S, Lee D-B, Yoon D-W, Yoo S-L, Kim J. The Effect of Sleep Disruption on Cardiometabolic Health. Life. 2025;15:60. https://doi.org/10.3390/life15010060. Hinz A, Glaesmer H, Brähler E, Löffler M, Engel C, Enzenbach C, et al. Sleep quality in the general population: psychometric properties of the Pittsburgh Sleep Quality Index, derived from a German community sample of 9284 people. Sleep Medicine. 2017;30:57–63. https://doi.org/10.1016/j.sleep.2016.03.008. Kocevska D, Lysen TS, Dotinga A, Koopman-Verhoeff ME, Luijk MPCM, Antypa N, et al. Sleep characteristics across the lifespan in 1.1 million people from the Netherlands, United Kingdom and United States: a systematic review and meta-analysis. Nat Hum Behav. 2020;5:113–22. https://doi.org/10.1038/s41562-020-00965-x. Thach T-Q, Mahirah D, Dunleavy G, Zhang Y, Nazeha N, Rykov Y, et al. Association between shift work and poor sleep quality in an Asian multi-ethnic working population: A cross-sectional study. PLoS One. 2020;15:e0229693. https://doi.org/10.1371/journal.pone.0229693. Zhang J, Wang Z, Xu W, Jiang R, Zheng W, Kang W, et al. Bedtime electronic device use and sleep quality in Chinese young adults: a longitudinal study. BMC Public Health. 2026;26:601. https://doi.org/10.1186/s12889-025-26013-9. Institute for Public Health. National Health and Morbidity Survey (NHMS) 2023 Non-Communicable Diseases and Healthcare Demand: Key Findings. 2024. Chan CMH, Siau CS, Wong JE, Wee LH, Jamil NA, Hoe VCW. Prevalence of Insufficient Sleep and Its Associated Factors Among Working Adults in Malaysia. Nat Sci Sleep. 2021;13:1109–16. https://doi.org/10.2147/NSS.S295537. Ong SC, Azman N, Ho HT. Factors Associated With Sleeping Quality and Behaviors Among University Students in Malaysia. Sleep Med Res. 2023;14:43–9. https://doi.org/10.17241/smr.2022.01536. Wan KS, Mohd Yusoff MF, Mat Rifin H, Chan WK, Alias N, Tham SW, et al. Prevalence of high 10-year cardiovascular risk among the general population in Malaysia and the associated factors: a nationwide community-based study in 2023. BMC Public Health. 2025;25:2706. https://doi.org/10.1186/s12889-025-23748-3. Jaspan VN, Greenberg GS, Parihar S, Park CM, Somers VK, Shapiro MD, et al. The Role of Sleep in Cardiovascular Disease. Curr Atheroscler Rep. 2024;26:249–62. https://doi.org/10.1007/s11883-024-01207-5. Miller MA, Howarth NE. Sleep and cardiovascular disease. Emerg Top Life Sci. 2023;7:457–66. https://doi.org/10.1042/ETLS20230111. Yadav D, Hyun DS, Ahn SV, Koh S-B, Kim JY. A prospective study of the association between total sleep duration and incident hypertension. J Clin Hypertens (Greenwich). 2017;19:550–7. https://doi.org/10.1111/jch.12960. Fernandez-Mendoza J, He F, Vgontzas AN, Liao D, Bixler EO. Objective short sleep duration modifies the relationship between hypertension and all-cause mortality. J Hypertens. 2017;35:830–6. https://doi.org/10.1097/HJH.0000000000001253. Shiina K. Obstructive sleep apnea -related hypertension: a review of the literature and clinical management strategy. Hypertens Res. 2024;47:3085–98. https://doi.org/10.1038/s41440-024-01852-y. Korkutata A, Korkutata M, Lazarus M. The impact of exercise on sleep and sleep disorders. npj Biol Timing Sleep. 2025;2:5. https://doi.org/10.1038/s44323-024-00018-w. Grigoriou I, Kotoulas S-C, Porpodis K, Spyratos D, Papagiouvanni I, Tsantos A, et al. The Interactions between Smoking and Sleep. Biomedicines. 2024;12:1765. https://doi.org/10.3390/biomedicines12081765. Sulthana H, Jan A, Verma A, Sah R, Mehta R, Ullah A, et al. Impact of electronic cigarette use and sleep duration, sleep issues and insomnia: a systematic review and meta-analysis. Front Public Health. 2025;13:1662234. https://doi.org/10.3389/fpubh.2025.1662234. Gardiner C, Weakley J, Burke LM, Roach GD, Sargent C, Maniar N, et al. The effect of alcohol on subsequent sleep in healthy adults: A systematic review and meta-analysis. Sleep Medicine Reviews. 2025;80:102030. https://doi.org/10.1016/j.smrv.2024.102030. Gardiner C, Weakley J, Burke LM, Roach GD, Sargent C, Maniar N, et al. The effect of caffeine on subsequent sleep: A systematic review and meta-analysis. Sleep Medicine Reviews. 2023;69:101764. https://doi.org/10.1016/j.smrv.2023.101764. Kawasaki Y, Kitamura E, Kasai T. Impact of Body Composition on Sleep and Its Relationship with Sleep Disorders: Current Insights. Nat Sci Sleep. 2023;15:375–88. https://doi.org/10.2147/NSS.S340946. Beaney T, Burrell LM, Castillo RR, Charchar FJ, Cro S, Damasceno A, et al. May Measurement Month 2018: a pragmatic global screening campaign to raise awareness of blood pressure by the International Society of Hypertension. European Heart Journal. 2019;40:2006–17. https://doi.org/10.1093/eurheartj/ehz300. May Measurement Month. May Measurement Month 2025 (MMM2025) Clinical Study Protocol. 2025. Sancho-Domingo C, Carballo JL, Coloma-Carmona A, Buysse DJ. Brief version of the Pittsburgh Sleep Quality Index (B-PSQI) and measurement invariance across gender and age in a population-based sample. Psychological Assessment. 2021;33:111–21. https://doi.org/10.1037/pas0000959. World Health Organisation (WHO). Waist Circumference and Waist–Hip Ratio: Report of a WHO Expert Consultation. 2011. WHO/IOTF/IASO. The Asia-Pacific perspective: Redefining obesity and its treatment. 2000. Omron. Instruction Manual—Body Composition Monitor Model HBF-375 KaradaScan (TM). Ashwell M, Hsieh SD. Six reasons why the waist-to-height ratio is a rapid and effective global indicator for health risks of obesity and how its use could simplify the international public health message on obesity. International Journal of Food Sciences and Nutrition. 2005;56:303–7. https://doi.org/10.1080/09637480500195066. Hunter JC, Hayden KM. The association of sleep with neighborhood physical and social environment. Public Health. 2018;162:126–34. https://doi.org/10.1016/j.puhe.2018.05.003. Kim E-J, Dimsdale JE. The effect of psychosocial stress on sleep: a review of polysomnographic evidence. Behav Sleep Med. 2007;5:256–78. https://doi.org/10.1080/15402000701557383. Medic G, Wille M, Hemels M. Short- and long-term health consequences of sleep disruption. NSS. 2017;Volume 9:151–61. https://doi.org/10.2147/NSS.S134864. Huyett P, Bhattacharyya N. Incremental health care utilization and expenditures for sleep disorders in the United States. J Clin Sleep Med. 2021;17:1981–6. https://doi.org/10.5664/jcsm.9392. Hafner M, Stepanek M, Taylor J, Troxel WM, van Stolk C. Why Sleep Matters-The Economic Costs of Insufficient Sleep: A Cross-Country Comparative Analysis. Rand Health Q. 2017;6:11. Batool-Anwar S, Quan SF. Sleep Health Disparity and Race/Ethnicity, Socioeconomic Status, and Gender: A Systematic Review. Sleep Med Res. 2024;15:139–50. https://doi.org/10.17241/smr.2024.02152. Jehan S, Myers AK, Zizi F, Pandi-Perumal SR, Jean-Louis G, Singh N, et al. Sleep health disparity: the putative role of race, ethnicity and socioeconomic status. Sleep Med Disord. 2018;2:127–33. Lam CCB, Mina T, Xie W, Low YD, Yew YW, Wang X, et al. The relationships between sleep and adiposity amongst multi-ethnic Asian populations: a cross-sectional analysis of the Health for Life in Singapore (HELIOS) study. Int J Obes (Lond). 2025;49:596–604. https://doi.org/10.1038/s41366-024-01666-5. Hasen AA, Seid AA, Asgedom DK, Hussen NM, Shibeshi AH, Anbesu EW, et al. Magnitude and level of association between poor sleep quality and common mental disorders among reproductive age women in Ethiopia: systematic review and meta-analysis. BMC Psychiatry. 2025;25:840. https://doi.org/10.1186/s12888-025-07314-0. Madrid-Valero JJ, Martínez-Selva JM, Ribeiro Do Couto B, Sánchez-Romera JF, Ordoñana JR. Age and gender effects on the prevalence of poor sleep quality in the adult population. Gaceta Sanitaria. 2017;31:18–22. https://doi.org/10.1016/j.gaceta.2016.05.013. Du M, Liu M, Wang Y, Qin C, Liu J. Global burden of sleep disturbances among older adults and the disparities by geographical regions and pandemic periods. SSM - Population Health. 2024;25:101588. https://doi.org/10.1016/j.ssmph.2023.101588. Kavousi P, Mali E, Seifhashemi N, Souri M, Pakravan L, Khalili F. Worldwide Prevalence of Poor Sleep Quality in Older Adults: A Systematic Review and Meta-Analysis. Iran J Psychiatry. 2025;20:265–80. https://doi.org/10.18502/ijps.v20i2.18207. Xie Y, Liu S, Chen X-J, Yu H-H, Yang Y, Wang W. Effects of Exercise on Sleep Quality and Insomnia in Adults: A Systematic Review and Meta-Analysis of Randomized Controlled Trials. Front Psychiatry. 2021;12:664499. https://doi.org/10.3389/fpsyt.2021.664499. Jehan S, Zizi F, Pandi-Perumal SR, Wall S, Auguste E, Myers AK, et al. Obstructive Sleep Apnea and Obesity: Implications for Public Health. Sleep Med Disord. 2017;1:00019. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 16 May, 2026 Reviewers agreed at journal 16 May, 2026 Reviews received at journal 15 May, 2026 Reviewers agreed at journal 15 May, 2026 Reviewers agreed at journal 14 May, 2026 Reviewers agreed at journal 13 May, 2026 Reviewers invited by journal 06 Mar, 2026 Editor assigned by journal 04 Mar, 2026 Submission checks completed at journal 04 Mar, 2026 First submitted to journal 02 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9014341","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":599766649,"identity":"7c379968-830d-42ce-8fae-4fefb4401a60","order_by":0,"name":"Yee-How Say","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYHACNhAhh+AfIFKLMelaEhuI1mJwvPnYg49tdenzZ+Qek/jZxiDHdyOBdTMPPi1njqUbzmw7nLvhRl6aZG8bg7HkjQS223i13Mgxk+bddiB3g0SO2Q3eNobEDQS13H//DailLl1+Ro7Zzb9tDPWEtdzgYQNqYU5gAFp3G2hLggEhLZJn0swkZ/47bLjhzBvz3zLnJAxnnnnYdnMOHi18xw8/k/hwpk5evj3H2PBNmY083/HkYzfe4NGicACZx8gmASIbmPA5TL4BhfsHqvUHHi2jYBSMglEw4gAAMFFUfZhSuBsAAAAASUVORK5CYII=","orcid":"","institution":"Sunway University","correspondingAuthor":true,"prefix":"","firstName":"Yee-How","middleName":"","lastName":"Say","suffix":""},{"id":599766650,"identity":"223fec1a-5aea-445d-8d02-f9ccfa31b962","order_by":1,"name":"Hooi Chin Beh","email":"","orcid":"","institution":"University of Malaya","correspondingAuthor":false,"prefix":"","firstName":"Hooi","middleName":"Chin","lastName":"Beh","suffix":""},{"id":599766654,"identity":"4314b43f-8f36-4110-97b5-fced27b1954d","order_by":2,"name":"Karleen Chong","email":"","orcid":"","institution":"University of Malaya","correspondingAuthor":false,"prefix":"","firstName":"Karleen","middleName":"","lastName":"Chong","suffix":""},{"id":599766657,"identity":"4d66d789-f70d-4ca1-bdab-e47078d3dbf3","order_by":3,"name":"Maong Hui Cheng","email":"","orcid":"","institution":"Sunway University","correspondingAuthor":false,"prefix":"","firstName":"Maong","middleName":"Hui","lastName":"Cheng","suffix":""},{"id":599766659,"identity":"2a60c40b-e0bc-4c86-87b8-03d2de51ccb8","order_by":4,"name":"Jazlan Jamaluddin","email":"","orcid":"","institution":"University of Malaya","correspondingAuthor":false,"prefix":"","firstName":"Jazlan","middleName":"","lastName":"Jamaluddin","suffix":""},{"id":599766660,"identity":"1c086f1d-5a19-485c-9734-8f00b5099f60","order_by":5,"name":"Siew Mooi Ching","email":"","orcid":"","institution":"Universiti Putra Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Siew","middleName":"Mooi","lastName":"Ching","suffix":""},{"id":599766661,"identity":"e37bf0ce-d184-4fb6-ba80-1ebc2a02c1ab","order_by":6,"name":"Yook Chin Chia","email":"","orcid":"","institution":"Sunway University","correspondingAuthor":false,"prefix":"","firstName":"Yook","middleName":"Chin","lastName":"Chia","suffix":""}],"badges":[],"createdAt":"2026-03-03 00:08:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9014341/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9014341/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103886093,"identity":"80ecc776-2328-4dbd-b770-edb3d124dfee","added_by":"auto","created_at":"2026-03-04 06:56:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1562507,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9014341/v1/094a9303-6356-4b55-90b4-a084b8765bee.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Sleep Quality and Its Associated Factors among Malaysian Adults: Cross-Sectional Findings from the May Measurement Month 2025 Participants","fulltext":[{"header":"Background","content":"\u003cp\u003eSleep is a fundamental biological process essential for cardiovascular, metabolic, cognitive, and psychological health. Inadequate or poor-quality sleep has been associated with increased risks of hypertension, coronary heart disease, stroke, obesity, diabetes, and all-cause mortality [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In recognition of its broad health implications, sleep duration and quality are increasingly considered integral components of cardiovascular risk assessment and preventive health strategies [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGlobally, sleep disturbances are highly prevalent, with approximately 10\u0026ndash;30% of the general adult population affected by sleep problems and poor sleep quality [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In Asia, rapid urbanisation, longer working hours, shift work [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], and digital device use [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] have contributed to growing concerns about insufficient and disrupted sleep. In Malaysia, the National Health and Morbidity Survey 2023 found that the prevalence of insufficient sleep (\u0026lt;\u0026thinsp;7 hours) among adults was 37.7% [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], and available data suggest that sleep problems are common among working adults [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] and university students [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], with associations reported between poor sleep, psychological distress [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], and cardiometabolic risk factors [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, large-scale community-based data examining sleep quality in conjunction with cardiovascular screening parameters remain limited.\u003c/p\u003e \u003cp\u003ePoor sleep is closely associated with hypertension and cardiovascular disease through multiple biological pathways, including sympathetic nervous system activation, hypothalamic\u0026ndash;pituitary\u0026ndash;adrenal axis dysregulation, endothelial dysfunction, and systemic inflammation [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Short sleep duration and sleep fragmentation have been shown to predict incident hypertension and adverse cardiovascular outcomes in prospective studies [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Conversely, individuals with established hypertension may experience impaired sleep due to nocturnal blood pressure variability, medication effects, or coexisting sleep disorders such as obstructive sleep apnoea [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Despite this bidirectional relationship, evidence on the association between subjective sleep quality and blood pressure (BP) control in community settings remains inconsistent.\u003c/p\u003e \u003cp\u003eLifestyle behaviours also play a critical role in sleep health. Physical activity has been consistently associated with improved sleep quality and efficiency [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In contrast, nicotine exposure\u0026mdash;through conventional cigarettes or electronic cigarettes\u0026mdash;can delay sleep onset and alter sleep architecture due to its stimulant properties [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Emerging research suggests that e-cigarette use may be associated with sleep disturbances, particularly among younger adults [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Alcohol [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] and caffeine [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] intake are also known to influence sleep patterns, although their effects vary according to dose, timing, and individual susceptibility.\u003c/p\u003e \u003cp\u003eAnthropometric and body composition indicators, including obesity and central adiposity, are strongly associated with sleep disorders and sleep-disordered breathing [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. However, the relationship between general adiposity measures\u0026mdash;such as body mass index (BMI), waist circumference (WC), and visceral fat level (VFL)\u0026mdash;and subjective sleep quality is less clear in community-based populations without formal sleep disorder assessment.\u003c/p\u003e \u003cp\u003eMay Measurement Month (MMM) is a global blood pressure screening initiative aimed at raising awareness of hypertension and improving cardiovascular risk detection [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The inclusion of sleep-related assessments within such a large-scale community screening context provides a valuable opportunity to explore the interplay between sleep quality, lifestyle behaviours, hypertension status, and body composition in a real-world Malaysian population.\u003c/p\u003e \u003cp\u003eTherefore, the present study aimed to: 1. Determine the prevalence of poor sleep quality among participants of the May Measurement Month 2025 campaign in the Greater Klang Valley, Malaysia, and 2. Examine the associations between sleep quality and demographic characteristics, lifestyle behaviours, hypertension status, and anthropometric/body composition indicators. Understanding these associations may inform integrated strategies for sleep health promotion within cardiovascular risk reduction programs.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Design and Setting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was a cross-sectional analysis conducted within the framework of the May Measurement Month (MMM) 2025 blood pressure (BP) screening campaign in the greater Klang Valley, Malaysia. MMM is a global annual initiative led by the International Society of Hypertension aimed at enhancing awareness, detection, and management of hypertension worldwide [23]. Screening was carried out between April and October 2025 across various community-based settings in Malaysia, including public venues, workplaces, universities, and healthcare facilities. Participating sites included the Universiti Malaya Medical Centre (UMMC) outpatient clinic (Kuala Lumpur), Universiti Putra Malaysia (UPM; Serdang), Sunway University (Subang Jaya), AEON Mall Nilai Health Campaign (Nilai), and the Shangri-La Hotel (Kuala Lumpur).\u003c/p\u003e\n\u003cp\u003eBeyond the standard MMM 2025 protocol—which included collection of basic sociodemographic information, lifestyle factors, dietary habits, and BP measurements [24]—all participating centres implemented additional health assessments. These comprised evaluation of sleep quality using the brief Pittsburgh Sleep Quality Index (B-PSQI) and comprehensive anthropometric and body composition measurements to further explore cardiometabolic risk profiles. The primary objective of this extended assessment was to examine the associations between sleep quality, anthropometric and body composition indicators, and both the presence of hypertension and BP control among individuals with hypertension.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIndividuals aged 18 years and above were eligible for inclusion. Participants were recruited through community outreach initiatives and voluntary participation at the respective screening sites. Those with missing data on sex, ethnicity, BP measurements, sleep, or anthropometric/body composition variables were excluded. Only complete cases were retained for analysis, yielding a final analytical sample of 907 participants (87.7%) out of 1,034 individuals screened.\u003c/p\u003e\n\u003cp\u003eHypertension status was defined based on on-site BP measurements obtained during a single visit and/or self-reported prior diagnosis by a healthcare professional, as captured in the MMM2025 questionnaire (“Have you ever been diagnosed with high BP by a health professional [except in pregnancy]?”) [24]. Participants were classified as hypertensive if they had a measured systolic BP (SBP) ≥ 140 mmHg and/or diastolic BP (DBP) ≥ 90 mmHg, or if they reported a previous medical diagnosis of hypertension. Those with measured SBP \u0026lt; 140 mmHg and DBP \u0026lt; 90 mmHg and without a prior diagnosis were considered normotensive. Among individuals identified as hypertensive, awareness was defined by self-report of a previous physician diagnosis. BP control was determined using the measured values at the time of screening: controlled BP was defined as SBP \u0026lt; 140 mmHg and DBP \u0026lt;90 mmHg, whereas uncontrolled BP was defined as SBP ≥140 mmHg and DBP ≥90 mmHg.\u003c/p\u003e\n\u003cp\u003eEthical approval for the study was obtained from the relevant institutional research ethics committee before data collection, i.e., UMMC Medical Research Ethics Committee (Approval number: MREC: 202535-14824), UPM Ethics Committee for Research Involving Human Subjects (Approval number: JKEUPM-2024-277), Sunway University Research Ethics Committee (Approval number: 2025/REC0104). The study was conducted in accordance with the principles of the Declaration of Helsinki. All participants received information regarding the study objectives, procedures, potential risks and benefits, and their right to withdraw without consequence. Written informed consent was obtained before participation. Data were anonymised and securely stored to maintain confidentiality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Collection Procedures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSociodemographic and Lifestyle Assessment\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants completed a structured self-administered questionnaire, with trained research staff available to clarify any questions when needed. The questionnaire captured information on sociodemographic characteristics (age, sex, ethnicity, and years of education) as well as lifestyle behaviours, including smoking status, vaping use, alcohol intake, caffeine consumption, and participation in vigorous physical activity. These variables were treated as potential confounding factors and were adjusted for in the multivariable analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eBP Measurement\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBP was assessed using validated automated devices (Omron HEM-7120, Omron HEM-7121, or Rossmax MJ701f) that were regularly calibrated. Measurements were performed by trained personnel in accordance with standardized protocols. Participants were instructed to sit quietly for a minimum of five minutes with their back supported and feet resting flat on the floor prior to the measurement. An appropriately sized cuff was applied to the upper arm at the level of the heart. At least two readings were obtained, and the mean of the measurements was used in the analysis to enhance accuracy and reliability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eBrief Pittsburgh Sleep Quality Index (B-PSQI)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Pittsburgh Sleep Quality Index (PSQI) is a widely used self-administered instrument designed to assess sleep quality and disturbances over the preceding month. In the present study, a shortened six-item version, known as the Brief Pittsburgh Sleep Quality Index (B-PSQI), was utilised to measure key dimensions of sleep quality [25]. The B-PSQI includes items that capture essential components of sleep, namely sleep duration, sleep latency, frequency of sleep disturbances, and overall subjective sleep quality. As bedtime and wake-up time are incorporated in the calculation of sleep efficiency, the six questions generate five scored components. These components are summed to produce a global score ranging from 0 to 15, with higher scores reflecting poorer sleep quality. The B-PSQI has demonstrated satisfactory psychometric properties, with reported polychoric ordinal alpha of 0.79 and ordinal omega of 0.91, indicating good internal consistency. In accordance with recommended cut-off criteria, a global score greater than 5 was classified as poor sleep quality, while scores of 5 or below were categorised as good sleep quality [25].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAnthropometric and Body Composition Measurements\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnthropometric and body composition assessments were performed by trained staff in accordance with standardized procedures. Standing height was measured using a portable stadiometer (Seca 213). Waist circumference (WC) was measured at the midpoint between the lower border of the least palpable rib and the iliac crest, while hip circumference was measured at the level of the maximum protrusion of the buttocks, using a non-elastic measuring tape calibrated to maintain a constant 100 g tension [26]. Waist-to-hip ratio (WHR) and waist-to-height ratio (WHtR) were subsequently calculated by dividing WC by hip circumference and height, respectively. Body composition was evaluated using a bioelectrical impedance analysis (BIA) device (Omron HBF-375), which provided measurements of body weight, body mass index (BMI; kg/m²), total body fat (TBF; %), visceral fat level (VFL; %), subcutaneous fat (SF; %), skeletal muscle percentage (SM; %), and resting metabolic rate (RM; kcal). In addition to whole-body estimates, segmental analyses of subcutaneous fat and skeletal muscle mass for the trunk, arms, and legs were obtained. Standardised cut-off values were applied to categorise adiposity and related indicators. Overweight and obesity were defined as BMI ≥ 23 kg/m² and ≥ 27.5 kg/m², respectively [27]. High TBF was defined as ≥ 20% in men and ≥ 30% in women, while high VFL was defined as ≥10% [28]. High SM was defined as ≥ 35.8% in men and ≥ 28% in women [28]. Central obesity thresholds were defined as WC ≥ 90 cm for men and ≥ 80 cm for women (WHO/IOTF/IASO, 2000), WHR ≥ 0.90 for men and ≥ 0.85 for women (WHO, 2011), and WHtR ≥ 0.50 [29].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStatistical Analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were performed using IBM SPSS Statistics for Windows version 26.0 (IBM Corp., Armonk, NY, USA). The normality of continuous variables was assessed using the Kolmogorov–Smirnov test, with \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 indicating deviation from normal distribution. Variables that were not normally distributed were summarised as medians with interquartile ranges (IQR), while normally distributed variables were presented as means with 95% confidence intervals (CI), where appropriate. Comparisons between participants with good and poor sleep quality were conducted using the Mann–Whitney \u003cem\u003eU\u003c/em\u003e test for non-normally distributed continuous variables and Pearson’s chi-square test for categorical variables. Binary logistic regression analyses were performed to examine factors independently associated with poor sleep quality. Variables entered into the multivariable models included demographic characteristics (age, sex, ethnicity, years of education), lifestyle behaviours (smoking status, vaping, alcohol consumption, caffeine intake, and vigorous physical activity), hypertension-related variables (hypertension status, awareness, and BP control), and anthropometric/body composition categories. Adjusted odds ratios (ORs) with 95% confidence intervals (CIs) and corresponding \u003cem\u003ep\u003c/em\u003e-values were reported. All multivariable models were adjusted for potential confounders identified a priori based on clinical relevance and existing literature. Statistical tests were two-sided, and a \u003cem\u003ep\u003c/em\u003e-value \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eAssociation of Sleep Quality with Demographic and Lifestyle Factors\u003c/h2\u003e \u003cp\u003eAge and sex were not significantly associated with sleep quality in both bivariate and multivariable analyses (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Ethnicity was significantly associated with sleep quality at the bivariate level (χ\u0026sup2; = 16.131, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001). Poor sleepers comprised a lower proportion of Chinese participants and a higher proportion of Indians and those from other ethnic groups compared with good sleepers. In the adjusted model, participants from other minority ethnic groups had significantly higher odds of poor sleep quality compared with Malays (OR\u0026thinsp;=\u0026thinsp;3.44, 95% CI: 1.39\u0026ndash;8.52, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008), whereas Chinese and Indian ethnicities were not independently associated (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\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\u003eDemographic, lifestyle habits, hypertension, and anthropometric/body composition categories among good and poor quality sleepers\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\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCategories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eSleep Quality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;907)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;619)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;288)\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\u003eMedian age\u0026thinsp;\u0026plusmn;\u0026thinsp;Interquartile Range\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40\u0026thinsp;\u0026plusmn;\u0026thinsp;33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39\u0026nbsp;\u0026plusmn; 33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40\u0026thinsp;\u0026plusmn;\u0026thinsp;35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEthnicity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e297 (48.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e139 (48.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e436 (48.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChinese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e249 (40.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92 (31.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e341 (37.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65 (10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44 (15.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e109 (12.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003csup\u003eα\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (2.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e16.131; 0.001**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e200 (32.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96 (33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e296 (32.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e419 (67.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e192 (66.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e611 (67.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.094; 0.760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTobacco smoking\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31 (3.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo (but did in past)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e247 (40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101 (35.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e348 (38.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e349 (56.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e178 (61.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e527 (58.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2.296; 0.317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVaping\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (1.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo (but did in past)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e236 (38.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91 (31.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e327 (36.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e376 (60.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e185 (64.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e561 (62.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e9.705; 0.008**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlcohol drinking\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever/rarely\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e573 (92.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e260 (90.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e833 (92.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;3 times/month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56 (6.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;6 times per week/Daily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (1.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.756; 0.416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCaffeine intake\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever/\u0026lt;4 per month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57 (10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (10.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85 (10.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;6 times/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e312 (55.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e149 (55.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e461 (55.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;3 times/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e183 (32.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84 (31.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e267 (32.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4+/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (2.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.279; 0.964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVigorous exercise\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e398 (64.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e152 (53.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e550 (60.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e220 (35.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e133 (46.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e353 (39.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e10.035; 0.002**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertensive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e204 (33.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e106 (36.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e310 (34.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormotensive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e415 (67.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e182 (63.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e597 (65.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.294; 0.255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAwareness among hypertensives\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAware\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e112 (55.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70 (66.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e182 (59.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnaware\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89 (44.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35 (33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e124 (40.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e3.428; 0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBP control among hypertensives\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControlled\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66 (32.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (36.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e104 (34.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUncontrolled\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e135 (67.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67 (63.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e202 (66.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.346; 0.556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWC Class\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e281 (45.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e135 (46.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e416 (45.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e338 (54.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e153 (53.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e491 (54.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.173; 0.677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWHtR Class\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e249 (40.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e110 (38.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e359 (39.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e370 (59.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e178 (61.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e548 (60.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.339; 0.560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWHR Class\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e331 (53.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e164 (56.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e495 (54.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e288 (46.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e124 (43.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e412 (45.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.955; 0.328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTBF Class\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e185 (31.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81 (29.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e266 (30.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e412 (69.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e194 (70.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e606 (69.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.209; 0.648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVFL Class\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e392 (64.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e177 (63.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e569 (63.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e219 (35.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103 (36.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e322 (36.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.074; 0.786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI Class\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e234 (39.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103 (37.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e337 (38.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e189 (31.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91 (33.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e280 (32.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e176 (29.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82 (29.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e258 (29.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.275; 0.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSM Class\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e480 (79.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e216 (78.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e696 (79.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e121 (20.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59 (21.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e180 (20.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.202; 0.653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003eα\u003c/sup\u003eOther ethnicities include indigenous groups of Peninsular Malaysia, Sabah, and Sarawak like Peninsular Orang Asli, Kadazan-Dusun-Murut Sabah, and Dayak Sarawak.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eWC: Waist circumference; WHtR: Waist-Height Ratio; WHR: Waist-Hip Ratio; TBF: Total Body Fat; VFL: Visceral Fat Level; BMI: Body Mass Index; SM: Skeletal Muscle.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eContinuous variables analyzed by Mann-Whitney U test; Categorical variables analyzed by Pearson\u0026rsquo;s Chi-square test; *\u003cem\u003ep\u003c/em\u003e-value significant at \u0026lt;\u0026thinsp;0.05; **\u003cem\u003ep\u003c/em\u003e-value significant at \u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \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\u003eBinary logistic regression of the association of demographic, lifestyle habits, hypertension, and anthropometric/body composition categories with poor sleep quality\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eClasses\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eS.E.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWald\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAdjusted Odds Ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e95.0% C.I.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnicity\u003csup\u003eα\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChinese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.092\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.266\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.008*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8.519\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003csup\u003e\u003cb\u003eβ\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.295\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTobacco smoking\u003c/b\u003e\u003csup\u003e\u003cb\u003eϒ\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo (but did in past)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.370\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.969\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVaping\u003c/b\u003e\u003csup\u003e\u003cb\u003eϒ\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo (but did in past)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.006**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.658\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.021*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.834\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlcohol drinking\u003c/b\u003e\u003csup\u003e\u003cb\u003eϒ\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;3 times/month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.212\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;6 times/week/Daily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.453\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCaffeine intake\u003c/b\u003e\u003csup\u003e\u003cb\u003eϒ\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;6 times/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.692\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;3 times/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.604\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4+/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.537\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.398\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVigorous exercise\u003c/b\u003e\u003csup\u003e\u003cb\u003eϒ\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.232\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026yen;\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension Presence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.140\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertensives Who Are Unaware\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.049*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertensives with BP Uncontrolled\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.192\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWC Class\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026yen;\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.422\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWHtR Class\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026yen;\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.750\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWHR Class\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026yen;\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.174\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTBF Class\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026yen;\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.698\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVFL Class\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026yen;\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.553\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI Class\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026yen;\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.675\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.714\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSM Class\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026yen;\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.542\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003eα\u003c/sup\u003eReference category\u0026thinsp;=\u0026thinsp;Malay; controlled for age and sex\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003eβ\u003c/sup\u003eReference category\u0026thinsp;=\u0026thinsp;Male sex; controlled for age and ethnicity\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003eϒ\u003c/sup\u003eReference category\u0026thinsp;=\u0026thinsp;Smoker/vaper/non alcohol-drinker/non- or \u0026lt;\u0026thinsp;4 times/month caffeine drinker/vigorous exerciser; controlled for age, sex, and ethnicity\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003e\u0026yen;\u003c/sup\u003eReference category\u0026thinsp;=\u0026thinsp;Normotensive/hypertensive aware/hypertensive with BP controlled/normal WC, WHR, WHtR, WHR, TBF, BMI, SM; controlled for age, years of education, ethnicity, sex, tobacco usage, vaping, alcohol drinking, caffeine usage, and vigorous exercise.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAmong lifestyle behaviours, tobacco smoking, alcohol intake, and caffeine consumption were not significantly associated with sleep quality in either analysis. Vaping was significantly associated at the bivariate level (χ\u0026sup2; = 9.705, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008), with a higher proportion of poor sleepers reporting current vaping. This association persisted after adjustment, as past vapers (OR\u0026thinsp;=\u0026thinsp;0.23, 95% CI: 0.08\u0026ndash;0.66, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006) and never vapers (OR\u0026thinsp;=\u0026thinsp;0.29, 95% CI: 0.10\u0026ndash;0.83, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021) had lower odds of poor sleep quality compared with current vapers (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEngagement in vigorous exercise was also significant in both analyses. Poor sleepers were less likely to report vigorous exercise (53.3% vs. 64.4%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002), and those not engaging in vigorous exercise had 1.67 times higher odds of poor sleep quality after adjustment (OR\u0026thinsp;=\u0026thinsp;1.67, 95% CI: 1.25\u0026ndash;2.23, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAssociation of Sleep Quality with Hypertension and Anthropometrics/Body Composition\u003c/h2\u003e \u003cp\u003eHypertension prevalence did not differ significantly across sleep groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.255; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), and hypertension status was not independently associated with poor sleep quality (OR\u0026thinsp;=\u0026thinsp;0.81, 95% CI: 0.58\u0026ndash;1.14, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.229; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAmong hypertensive participants, awareness showed a borderline bivariate association (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.064). In the adjusted model, hypertensive individuals who were unaware of their condition had marginally-significant lower odds of poor sleep quality compared with those aware (OR\u0026thinsp;=\u0026thinsp;0.57, 95% CI: 0.33\u0026ndash;0.998, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.049). BP control was not significantly associated in either analysis (Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e. No significant associations were observed between sleep quality and anthropometric or body composition measures\u0026mdash;including WC, WHtR, WHR, TBF, VFL, BMI, or SM class\u0026mdash;in both bivariate and multivariable analyses (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05)(Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this sample of May Measurement Month (MMM) 2025 participants in the greater Klang Valley, Malaysia, approximately one-third (31.8%) were classified as having poor sleep quality. Poor sleep was independently associated with minority ethnicity, current vaping, and lack of vigorous exercise, whereas hypertension status and adiposity-related measures were not significantly associated after multivariable adjustment.\u003c/p\u003e \u003cp\u003eThe observed prevalence of poor sleep quality in this study is broadly consistent with international estimates, indicating that approximately 10\u0026ndash;30% of adults experience clinically significant sleep disturbances or insufficient sleep [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Although prevalence varies depending on the population studied and the assessment tools used, sleep complaints remain highly common across both high- and middle-income countries. Rapid urbanisation [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], longer working hours [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], increased screen exposure [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], and psychosocial stress [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] have all been implicated in the growing burden of sleep problems worldwide. Poor sleep is increasingly recognised as a major public health issue because of its wide-ranging health consequences. Evidence from epidemiological and experimental studies has demonstrated strong associations between inadequate sleep and adverse cardiometabolic outcomes, including hypertension, obesity, type 2 diabetes, and cardiovascular disease [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Sleep disruption has also been associated with impaired cognitive performance, reduced concentration and productivity, mood disturbances, and diminished overall quality of life [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Furthermore, accumulating data suggest that poor sleep may contribute to increased healthcare utilisation and economic costs at the societal level [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Collectively, these findings underscore the importance of incorporating sleep health into broader non-communicable disease prevention and health promotion strategies.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eDemographic Factors\u003c/h2\u003e \u003cp\u003eEthnicity was significantly associated with sleep quality. Participants from minority ethnic groups, consisting of indigenous groups of Peninsular Malaysia, Sabah, and Sarawak, had more than threefold higher odds of poor sleep compared with Malays. However, the association between minority ethnicity and poor sleep should be interpreted cautiously. The \u0026ldquo;Others\u0026rdquo; group included only 21 participants and had a wide confidence interval, suggesting statistical instability. The elevated odds may be driven by the small sample size and the heterogeneous nature of this category. Larger studies with better representation of minority groups are needed to confirm this finding. Nevertheless, ethnic disparities in sleep have been reported internationally, often attributed to socioeconomic differences, occupational demands, psychosocial stress, and environmental exposures [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In the Malaysian context, cultural practices, shift work patterns, and urban living conditions may contribute to differential sleep patterns across ethnic groups, similar to the multi-ethnic Singaporean population [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Although Chinese and Indian ethnicity were not independently associated after adjustment, the elevated odds observed among other minority groups warrant further exploration, particularly with regard to social determinants of health and access to healthcare.\u003c/p\u003e \u003cp\u003eAge and sex were not significantly associated with sleep quality in our cohort. While global studies report poorer sleep quality among women [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] and older adults [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], the findings are not universal and may depend on population structure and measurement tools. The relatively wide age distribution and predominance of female participants in this opportunistic screening sample may partly explain the absence of significant associations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eLifestyle Behaviours\u003c/h2\u003e \u003cp\u003eVaping emerged as a significant independent correlate of poor sleep quality. Current vapers had higher odds of poor sleep compared with past or never users. Nicotine is a central nervous system stimulant that increases sleep latency, reduces total sleep time, and alters sleep architecture [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Emerging evidence suggests that e-cigarette use is associated with sleep disturbances, potentially due to nicotine exposure and behavioural patterns associated with device use [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Our findings add to the growing literature highlighting the potential adverse sleep-related consequences of vaping in adult populations.\u003c/p\u003e \u003cp\u003eEngagement in vigorous exercise was protective against poor sleep. Participants who did not perform vigorous exercise had 67% higher odds of poor sleep quality. Regular physical activity has been consistently associated with improved sleep quality, shorter sleep latency, and greater sleep efficiency [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Biological mechanisms may include thermoregulatory effects, circadian phase shifts, and reductions in anxiety and depressive symptoms [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. From a public health perspective, promoting physical activity may confer dual benefits for cardiometabolic health and sleep quality.\u003c/p\u003e \u003cp\u003eIn contrast, tobacco smoking, alcohol consumption, and caffeine intake were not independently associated with sleep quality in our adjusted models. Although these factors are known to influence sleep physiology [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], their effects may depend on dose, timing, and chronicity of use, which were not captured in detail in this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eHypertension and Body Composition\u003c/h2\u003e \u003cp\u003eContrary to expectations, hypertension status and BP control were not independently associated with poor sleep quality. Short sleep duration and poor sleep have been associated with incident hypertension and adverse cardiovascular outcomes in longitudinal studies [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, cross-sectional associations are less consistent, and sleep quality may be influenced by factors other than BP per se, such as obstructive sleep apnoea, stress, or medication use. Interestingly, hypertensive individuals who were unaware of their condition had marginally significantly lower odds of poor sleep compared with those aware, possibly reflecting heightened symptom perception, anxiety, or health-related concerns among those diagnosed.\u003c/p\u003e \u003cp\u003eSimilarly, no significant associations were observed between sleep quality and anthropometric or body composition indicators, including BMI, overall and central adiposity measures, and SM mass, in contrast with a similar multi-ethnic large population study in Singapore [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Although obesity is strongly associated with sleep disorders such as obstructive sleep apnoea [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], subjective sleep quality does not always correlate directly with adiposity measures, particularly in community-based samples without formal sleep disorder assessment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eStudy Strengths, Implications, and Limitations\u003c/h2\u003e \u003cp\u003eThis study leverages data from a relatively large community-based MMM campaign, providing contemporary insights into sleep health within the context of cardiovascular screening in Malaysia. The integration of detailed BP, anthropometric, body composition, and lifestyle data allowed for comprehensive adjustment of potential confounders.\u003c/p\u003e \u003cp\u003eOur findings highlight the need to address modifiable behaviours\u0026mdash;particularly vaping and physical inactivity\u0026mdash;in efforts to improve sleep health. These behaviours are amenable to intervention through counselling and community-based programmes and improving them may yield dual benefits for both sleep and cardiometabolic health. Given the bidirectional relationship between sleep and cardiovascular risk, incorporating brief sleep assessments into hypertension and cardiovascular screening initiatives could support early identification of at-risk individuals and strengthen integrated non-communicable disease prevention strategies.\u003c/p\u003e \u003cp\u003eThis study has several limitations. Its cross-sectional design precludes causal inference, and sleep quality was assessed using a self-reported measure without objective verification. Hypertension status was based on single-visit BP measurements and/or self-report, which may lead to misclassification. Residual confounding from unmeasured factors such as socioeconomic status, mental health, and shift work cannot be excluded. The opportunistic sampling strategy and use of bioelectrical impedance analysis may limit generalisability and measurement precision. Potential selection bias may have also occurred, as health-conscious individuals were more likely to participate. Females were overrepresented in the sample, which may affect generalisability. Additionally, the study was conducted in the urban greater Klang Valley, limiting applicability to rural or other Malaysian populations.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, approximately one in three MMM 2025 participants reported poor sleep quality. Minority ethnicity, current vaping, and lack of vigorous exercise were independently associated with poor sleep, whereas hypertension and anthropometric/body composition measures were not. These findings highlight the need to incorporate sleep health promotion\u0026mdash;particularly targeting modifiable lifestyle behaviours\u0026mdash;into cardiovascular risk reduction strategies in Malaysia.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePSQI\u0026mdash;Brief Pittsburgh Sleep Quality Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody Mass Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBlood Pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBIA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBioelectrical Impedance Analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence Interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDBP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDiastolic Blood Pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIQR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInterquartile Range\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMMM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMay Measurement Month\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePSQI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePittsburgh Sleep Quality Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eResting Metabolic Rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSBP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSystolic Blood Pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSubcutaneous Fat\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSkeletal Muscle\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTBF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTotal Body Fat\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUMMC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUniversiti Malaya Medical Centre\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUPM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUniversiti Putra Malaysia\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVFL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVisceral Fat Level\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWaist Circumference\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWHR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWaist\u0026mdash;to\u0026mdash;Hip Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWHtR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWaist\u0026mdash;to\u0026mdash;Height Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was obtained from the relevant institutional research ethics committee before data collection, i.e., UMMC Medical Research Ethics Committee (Approval number: MREC: 202535-14824), UPM Ethics Committee for Research Involving Human Subjects (Approval number: JKEUPM-2024-277), Sunway University Research Ethics Committee (Approval number: 2025/REC0104). Written informed consent was obtained from all participants before enrolment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analysed during the current study are available from the corresponding author on reasonable requests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll\u0026nbsp;authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Malaysian Society of Hypertension (MSH) provided funding support for the screening campaigns, while the Malaysian Society for World Action on Salt, Sugar and Health (MyWASSH) contributed materials such as posters as part of their corporate social responsibility initiatives. The funders did not play a role in the design of this study nor was involved in the writing of this manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: YCC; Methodology: YHS and YCC; Data collection: YHS, HCB, KC, MHC, JJ, and SMC; Formal analysis: YHS and YCC; Writing - original draft preparation: YHS and YCC; Writing - review and editing: YHS, HCB, KC, MHC, JJ, SMC, and YCC; Supervision: YCC. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would also like to extend their gratitude to all participants who participated in this study. We would also like to thank all student helpers who helped with the recruitment of participants and measurements.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAmin KD, Thakkar A, Budampati T, Matai S, Akkaya E, Shah NP. A good night\u0026rsquo;s rest: A contemporary review of sleep and cardiovascular health. American Journal of Preventive Cardiology. 2025;21:100924. https://doi.org/10.1016/j.ajpc.2024.100924.\u003c/li\u003e\n \u003cli\u003eCappuccio FP, Cooper D, D\u0026rsquo;Elia L, Strazzullo P, Miller MA. Sleep duration predicts cardiovascular outcomes: a systematic review and meta-analysis of prospective studies. European Heart Journal. 2011;32:1484\u0026ndash;92. https://doi.org/10.1093/eurheartj/ehr007.\u003c/li\u003e\n \u003cli\u003eHong S, Lee D-B, Yoon D-W, Yoo S-L, Kim J. The Effect of Sleep Disruption on Cardiometabolic Health. Life. 2025;15:60. https://doi.org/10.3390/life15010060.\u003c/li\u003e\n \u003cli\u003eHinz A, Glaesmer H, Br\u0026auml;hler E, L\u0026ouml;ffler M, Engel C, Enzenbach C, et al. Sleep quality in the general population: psychometric properties of the Pittsburgh Sleep Quality Index, derived from a German community sample of 9284 people. Sleep Medicine. 2017;30:57\u0026ndash;63. https://doi.org/10.1016/j.sleep.2016.03.008.\u003c/li\u003e\n \u003cli\u003eKocevska D, Lysen TS, Dotinga A, Koopman-Verhoeff ME, Luijk MPCM, Antypa N, et al. Sleep characteristics across the lifespan in 1.1 million people from the Netherlands, United Kingdom and United States: a systematic review and meta-analysis. Nat Hum Behav. 2020;5:113\u0026ndash;22. https://doi.org/10.1038/s41562-020-00965-x.\u003c/li\u003e\n \u003cli\u003eThach T-Q, Mahirah D, Dunleavy G, Zhang Y, Nazeha N, Rykov Y, et al. Association between shift work and poor sleep quality in an Asian multi-ethnic working population: A cross-sectional study. PLoS One. 2020;15:e0229693. https://doi.org/10.1371/journal.pone.0229693.\u003c/li\u003e\n \u003cli\u003eZhang J, Wang Z, Xu W, Jiang R, Zheng W, Kang W, et al. Bedtime electronic device use and sleep quality in Chinese young adults: a longitudinal study. BMC Public Health. 2026;26:601. https://doi.org/10.1186/s12889-025-26013-9.\u003c/li\u003e\n \u003cli\u003eInstitute for Public Health. National Health and Morbidity Survey (NHMS) 2023 Non-Communicable Diseases and Healthcare Demand: Key Findings. 2024.\u003c/li\u003e\n \u003cli\u003eChan CMH, Siau CS, Wong JE, Wee LH, Jamil NA, Hoe VCW. Prevalence of Insufficient Sleep and Its Associated Factors Among Working Adults in Malaysia. Nat Sci Sleep. 2021;13:1109\u0026ndash;16. https://doi.org/10.2147/NSS.S295537.\u003c/li\u003e\n \u003cli\u003eOng SC, Azman N, Ho HT. Factors Associated With Sleeping Quality and Behaviors Among University Students in Malaysia. Sleep Med Res. 2023;14:43\u0026ndash;9. https://doi.org/10.17241/smr.2022.01536.\u003c/li\u003e\n \u003cli\u003eWan KS, Mohd Yusoff MF, Mat Rifin H, Chan WK, Alias N, Tham SW, et al. Prevalence of high 10-year cardiovascular risk among the general population in Malaysia and the associated factors: a nationwide community-based study in 2023. BMC Public Health. 2025;25:2706. https://doi.org/10.1186/s12889-025-23748-3.\u003c/li\u003e\n \u003cli\u003eJaspan VN, Greenberg GS, Parihar S, Park CM, Somers VK, Shapiro MD, et al. The Role of Sleep in Cardiovascular Disease. Curr Atheroscler Rep. 2024;26:249\u0026ndash;62. https://doi.org/10.1007/s11883-024-01207-5.\u003c/li\u003e\n \u003cli\u003eMiller MA, Howarth NE. Sleep and cardiovascular disease. Emerg Top Life Sci. 2023;7:457\u0026ndash;66. https://doi.org/10.1042/ETLS20230111.\u003c/li\u003e\n \u003cli\u003eYadav D, Hyun DS, Ahn SV, Koh S-B, Kim JY. A prospective study of the association between total sleep duration and incident hypertension. J Clin Hypertens (Greenwich). 2017;19:550\u0026ndash;7. https://doi.org/10.1111/jch.12960.\u003c/li\u003e\n \u003cli\u003eFernandez-Mendoza J, He F, Vgontzas AN, Liao D, Bixler EO. Objective short sleep duration modifies the relationship between hypertension and all-cause mortality. J Hypertens. 2017;35:830\u0026ndash;6. https://doi.org/10.1097/HJH.0000000000001253.\u003c/li\u003e\n \u003cli\u003eShiina K. Obstructive sleep apnea -related hypertension: a review of the literature and clinical management strategy. Hypertens Res. 2024;47:3085\u0026ndash;98. https://doi.org/10.1038/s41440-024-01852-y.\u003c/li\u003e\n \u003cli\u003eKorkutata A, Korkutata M, Lazarus M. The impact of exercise on sleep and sleep disorders. npj Biol Timing Sleep. 2025;2:5. https://doi.org/10.1038/s44323-024-00018-w.\u003c/li\u003e\n \u003cli\u003eGrigoriou I, Kotoulas S-C, Porpodis K, Spyratos D, Papagiouvanni I, Tsantos A, et al. The Interactions between Smoking and Sleep. Biomedicines. 2024;12:1765. https://doi.org/10.3390/biomedicines12081765.\u003c/li\u003e\n \u003cli\u003eSulthana H, Jan A, Verma A, Sah R, Mehta R, Ullah A, et al. Impact of electronic cigarette use and sleep duration, sleep issues and insomnia: a systematic review and meta-analysis. Front Public Health. 2025;13:1662234. https://doi.org/10.3389/fpubh.2025.1662234.\u003c/li\u003e\n \u003cli\u003eGardiner C, Weakley J, Burke LM, Roach GD, Sargent C, Maniar N, et al. The effect of alcohol on subsequent sleep in healthy adults: A systematic review and meta-analysis. Sleep Medicine Reviews. 2025;80:102030. https://doi.org/10.1016/j.smrv.2024.102030.\u003c/li\u003e\n \u003cli\u003eGardiner C, Weakley J, Burke LM, Roach GD, Sargent C, Maniar N, et al. The effect of caffeine on subsequent sleep: A systematic review and meta-analysis. Sleep Medicine Reviews. 2023;69:101764. https://doi.org/10.1016/j.smrv.2023.101764.\u003c/li\u003e\n \u003cli\u003eKawasaki Y, Kitamura E, Kasai T. Impact of Body Composition on Sleep and Its Relationship with Sleep Disorders: Current Insights. Nat Sci Sleep. 2023;15:375\u0026ndash;88. https://doi.org/10.2147/NSS.S340946.\u003c/li\u003e\n \u003cli\u003eBeaney T, Burrell LM, Castillo RR, Charchar FJ, Cro S, Damasceno A, et al. May Measurement Month 2018: a pragmatic global screening campaign to raise awareness of blood pressure by the International Society of Hypertension. European Heart Journal. 2019;40:2006\u0026ndash;17. https://doi.org/10.1093/eurheartj/ehz300.\u003c/li\u003e\n \u003cli\u003eMay Measurement Month. May Measurement Month 2025 (MMM2025) Clinical Study Protocol. 2025.\u003c/li\u003e\n \u003cli\u003eSancho-Domingo C, Carballo JL, Coloma-Carmona A, Buysse DJ. Brief version of the Pittsburgh Sleep Quality Index (B-PSQI) and measurement invariance across gender and age in a population-based sample. Psychological Assessment. 2021;33:111\u0026ndash;21. https://doi.org/10.1037/pas0000959.\u003c/li\u003e\n \u003cli\u003eWorld Health Organisation (WHO). Waist Circumference and Waist\u0026ndash;Hip Ratio: Report of a WHO Expert Consultation. 2011.\u003c/li\u003e\n \u003cli\u003eWHO/IOTF/IASO. The Asia-Pacific perspective: Redefining obesity and its treatment. 2000.\u003c/li\u003e\n \u003cli\u003eOmron. Instruction Manual\u0026mdash;Body Composition Monitor Model HBF-375 KaradaScan (TM).\u003c/li\u003e\n \u003cli\u003eAshwell M, Hsieh SD. Six reasons why the waist-to-height ratio is a rapid and effective global indicator for health risks of obesity and how its use could simplify the international public health message on obesity. International Journal of Food Sciences and Nutrition. 2005;56:303\u0026ndash;7. https://doi.org/10.1080/09637480500195066.\u003c/li\u003e\n \u003cli\u003eHunter JC, Hayden KM. The association of sleep with neighborhood physical and social environment. Public Health. 2018;162:126\u0026ndash;34. https://doi.org/10.1016/j.puhe.2018.05.003.\u003c/li\u003e\n \u003cli\u003eKim E-J, Dimsdale JE. The effect of psychosocial stress on sleep: a review of polysomnographic evidence. Behav Sleep Med. 2007;5:256\u0026ndash;78. https://doi.org/10.1080/15402000701557383.\u003c/li\u003e\n \u003cli\u003eMedic G, Wille M, Hemels M. Short- and long-term health consequences of sleep disruption. NSS. 2017;Volume 9:151\u0026ndash;61. https://doi.org/10.2147/NSS.S134864.\u003c/li\u003e\n \u003cli\u003eHuyett P, Bhattacharyya N. Incremental health care utilization and expenditures for sleep disorders in the United States. J Clin Sleep Med. 2021;17:1981\u0026ndash;6. https://doi.org/10.5664/jcsm.9392.\u003c/li\u003e\n \u003cli\u003eHafner M, Stepanek M, Taylor J, Troxel WM, van Stolk C. Why Sleep Matters-The Economic Costs of Insufficient Sleep: A Cross-Country Comparative Analysis. Rand Health Q. 2017;6:11.\u003c/li\u003e\n \u003cli\u003eBatool-Anwar S, Quan SF. Sleep Health Disparity and Race/Ethnicity, Socioeconomic Status, and Gender: A Systematic Review. Sleep Med Res. 2024;15:139\u0026ndash;50. https://doi.org/10.17241/smr.2024.02152.\u003c/li\u003e\n \u003cli\u003eJehan S, Myers AK, Zizi F, Pandi-Perumal SR, Jean-Louis G, Singh N, et al. Sleep health disparity: the putative role of race, ethnicity and socioeconomic status. Sleep Med Disord. 2018;2:127\u0026ndash;33.\u003c/li\u003e\n \u003cli\u003eLam CCB, Mina T, Xie W, Low YD, Yew YW, Wang X, et al. The relationships between sleep and adiposity amongst multi-ethnic Asian populations: a cross-sectional analysis of the Health for Life in Singapore (HELIOS) study. Int J Obes (Lond). 2025;49:596\u0026ndash;604. https://doi.org/10.1038/s41366-024-01666-5.\u003c/li\u003e\n \u003cli\u003eHasen AA, Seid AA, Asgedom DK, Hussen NM, Shibeshi AH, Anbesu EW, et al. Magnitude and level of association between poor sleep quality and common mental disorders among reproductive age women in Ethiopia: systematic review and meta-analysis. BMC Psychiatry. 2025;25:840. https://doi.org/10.1186/s12888-025-07314-0.\u003c/li\u003e\n \u003cli\u003eMadrid-Valero JJ, Mart\u0026iacute;nez-Selva JM, Ribeiro Do Couto B, S\u0026aacute;nchez-Romera JF, Ordo\u0026ntilde;ana JR. Age and gender effects on the prevalence of poor sleep quality in the adult population. Gaceta Sanitaria. 2017;31:18\u0026ndash;22. https://doi.org/10.1016/j.gaceta.2016.05.013.\u003c/li\u003e\n \u003cli\u003eDu M, Liu M, Wang Y, Qin C, Liu J. Global burden of sleep disturbances among older adults and the disparities by geographical regions and pandemic periods. SSM - Population Health. 2024;25:101588. https://doi.org/10.1016/j.ssmph.2023.101588.\u003c/li\u003e\n \u003cli\u003eKavousi P, Mali E, Seifhashemi N, Souri M, Pakravan L, Khalili F. Worldwide Prevalence of Poor Sleep Quality in Older Adults: A Systematic Review and Meta-Analysis. Iran J Psychiatry. 2025;20:265\u0026ndash;80. https://doi.org/10.18502/ijps.v20i2.18207.\u003c/li\u003e\n \u003cli\u003eXie Y, Liu S, Chen X-J, Yu H-H, Yang Y, Wang W. Effects of Exercise on Sleep Quality and Insomnia in Adults: A Systematic Review and Meta-Analysis of Randomized Controlled Trials. Front Psychiatry. 2021;12:664499. https://doi.org/10.3389/fpsyt.2021.664499.\u003c/li\u003e\n \u003cli\u003eJehan S, Zizi F, Pandi-Perumal SR, Wall S, Auguste E, Myers AK, et al. Obstructive Sleep Apnea and Obesity: Implications for Public Health. Sleep Med Disord. 2017;1:00019.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Sleep quality, Hypertension, Vaping, Physical activity, May Measurement Month","lastPublishedDoi":"10.21203/rs.3.rs-9014341/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9014341/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePoor sleep quality is increasingly recognised as an important determinant of cardiometabolic health. However, community-based data integrating sleep assessment with blood pressure and body composition measures in Malaysia remains limited. This study aimed to determine the prevalence of poor sleep quality and its associated factors among participants of the May Measurement Month (MMM) 2025 blood pressure screening campaign in Malaysia.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis cross-sectional study included 907 adults (median age\u0026thinsp;\u0026plusmn;\u0026thinsp;interquartile range: 40\u0026thinsp;\u0026plusmn;\u0026thinsp;35; 67.4% females), recruited from community-based screening sites in the greater Klang Valley. Sleep quality was assessed using the Brief Pittsburgh Sleep Quality Index (B-PSQI) and categorised as good or poor based on a validated cut-off score. Data on sociodemographic characteristics, lifestyle behaviours (smoking, vaping, alcohol intake, caffeine consumption, and vigorous physical activity), hypertension status (including awareness and blood pressure control), and anthropometric/body composition measures were collected. Bivariate analyses were conducted using appropriate statistical tests, followed by multivariable logistic regression to identify factors independently associated with poor sleep quality after adjusting for potential confounders.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003ePoor sleep quality was observed in 31.8% of participants. Ethnicity, vaping, and vigorous exercise were significantly associated with sleep quality. After adjustment, participants from other minority ethnic groups had higher odds of poor sleep compared with Malays (OR 3.44, 95% CI 1.39\u0026ndash;8.52; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008). Past vapers (OR 0.23, 95% CI 0.08\u0026ndash;0.66; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006) and never vapers (OR 0.29, 95% CI 0.10\u0026ndash;0.83; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021) had lower odds of poor sleep compared with current vapers. Lack of vigorous exercise was associated with higher odds of poor sleep (OR 1.67, 95% CI 1.25\u0026ndash;2.23; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001). Hypertension status and body composition indicators were not independently associated.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eNearly one in three MMM 2025 participants reported poor sleep quality. Minority ethnicity, current vaping, and physical inactivity were independently associated with poor sleep. Integrating sleep assessment and lifestyle counselling into cardiovascular screening initiatives may enhance preventive strategies in Malaysia.\u003c/p\u003e","manuscriptTitle":"Sleep Quality and Its Associated Factors among Malaysian Adults: Cross-Sectional Findings from the May Measurement Month 2025 Participants","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-04 06:54:40","doi":"10.21203/rs.3.rs-9014341/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-16T11:24:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"271564511490495302738899434934398994213","date":"2026-05-16T09:21:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-15T08:00:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"241724818926988564809035410354654269326","date":"2026-05-15T07:41:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"311593123682661137063156859501553345813","date":"2026-05-15T02:54:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"304767964576020019160947123112702181313","date":"2026-05-14T01:10:39+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-06T12:23:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-04T12:30:06+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-04T12:28:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2026-03-02T23:57:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1d28107b-81e0-46a1-b6ef-ed6d421b4b20","owner":[],"postedDate":"March 4th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-16T11:24:20+00:00","index":64,"fulltext":""},{"type":"reviewerAgreed","content":"271564511490495302738899434934398994213","date":"2026-05-16T09:21:52+00:00","index":63,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-15T08:00:06+00:00","index":61,"fulltext":""},{"type":"reviewerAgreed","content":"241724818926988564809035410354654269326","date":"2026-05-15T07:41:32+00:00","index":60,"fulltext":""},{"type":"reviewerAgreed","content":"311593123682661137063156859501553345813","date":"2026-05-15T02:54:32+00:00","index":59,"fulltext":""},{"type":"reviewerAgreed","content":"304767964576020019160947123112702181313","date":"2026-05-14T01:10:39+00:00","index":58,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-06T12:53:05+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-04 06:54:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9014341","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9014341","identity":"rs-9014341","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}