To Evaluate the Cardiovascular Risk Factors Among Teenagers Residing in Rural Areas of Vijayapura District, Karnataka: A Cross-Sectional Study

To Evaluate the Cardiovascular Risk Factors Among Teenagers Residing in Rural Areas of Vijayapura District, Karnataka: A Cross-Sectional Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article To Evaluate the Cardiovascular Risk Factors Among Teenagers Residing in Rural Areas of Vijayapura District, Karnataka: A Cross-Sectional Study Subhajit Giri, Praveen Ganganahalli, M. C. Yadavannavar, Rekha Udgiri This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9333640/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Background The teenage years (ages 13–19) are crucial for development. In India, 253 million teenagers shaping the nation's future, but rising cardiovascular disease (CVD) is a concern. Risk factors like poor diet, inactivity, and obesity begin in adolescence, leading to future heart issues. Although CVD manifests in adulthood, its roots often start young. In India, CVD causes over 28% of annual deaths, with a DALY rate 1.3 times the global average, highlighting need for early intervention. Objectives Identify prevalent cardiovascular risk factors & investigate socio-economic, environmental, and lifestyle factors influencing it among teenagers in rural setting. Methods A cross-sectional study was conducted among 106 teenagers (13–19 years) in rural areas. Data were collected through interviews using structured questionnaires to gather socio-demographic profiles and assess cardiovascular risk factors such as physical activity, dietary habits, and family history. Diet Diversity Score (DDS) evaluated the variety in food consumption, while Standardized Physical Activity Questionnaire (PAQ) assessed physical-activity levels among participants. Results The mean age was 15.41 ± 1.98years (51.9% boys, 48.1% girls). Half belonged to Class IV or V of the modified BG Prasad socio-economic scale. Mean cardiovascular knowledge score was poor (34.91%) to fair (65.09%). The mean IDDS was 5.53 ± 1.25, with 56.6% showing low dietary-diversity. Physical activity was inadequate, with minimal hours spent fitness activities. Conclusion Inadequate physical activity, high salt intake, high animal food consumption, and moderate intake of legumes, fruits, and vegetables can independently increase CVD, regardless of BMI. Most teenagers had poor to fair knowledge of CVD risk factors. Effective interventions are needed to improve cardiovascular health knowledge and promote diverse, healthy diets among rural adolescents. Cardiovascular Risk Factors Teenagers Physical Activity Questionnaire Diet Diversity Score Lifestyle Factors Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION The teenage stage, which includes physical, cognitive, and psychosocial development, is the time between puberty and maturity [ 1 ]. WHO encompasses the ages 13 to 19 years as the crucial phase of teenage [ 2 ]. With nearly 120 million young females and 133 million males, India's teenage population reaches a staggering 253 million, shaping the nation's future demographic [ 3 ]. Cardiovascular disease (CVD) is the most prevalent cause of mortality globally and its risk factors can develop as early as teenage. A concerning trend is the rise of unhealthy lifestyles among teenagers, potentially setting the stage for future heart problems. Even though cardiovascular diseases typically show symptoms in adults, the seeds of these problems can be sown much earlier, during childhood and adolescence. The presence of other risk factors seems to play a role in how these early signs (atherosclerotic changes) develop and worsen [ 4 ]. Furthermore, unhealthy habits and risk factors present during teenage years can predict a higher risk of poor cardiovascular health later in life. Therefore, to create effective policies for preventing and controlling CVD, it is vital to understand how these behavioural risk factors are distributed among teenagers, particularly in low and middle-income countries [ 5 ]. The rise of coronary heart disease (CHD) is a major concern, fuelled by factors like high blood pressure, unhealthy cholesterol levels, diabetes, obesity, lack of physical activity, and smoking. The World Health Organization (WHO) aptly calls CHD a "modern epidemic," highlighting its widespread impact that transcends individual aging [ 6 ]. Critically, the groundwork for these problems, atherosclerosis, begins in childhood and manifests later in life. Research shows that risk factors identified in childhood tend to persist through adolescence and into adulthood [ 6 ]. Furthermore, habits related to diet and physical activity, which significantly influence heart health, are often established during childhood. This means unhealthy dietary choices or a lack of physical activity in childhood can lead to high cholesterol and an increased risk of heart disease even in young adulthood. Several factors influence the risk of cardiovascular disease (CVD). Modifiable risk factors in teenage include obesity, overweight, physical inactivity and unhealthy eating habits [ 7 ]. Family history of CVD is an example of a non-modifiable risk factor. Beyond these individual factors, broader trends like industrialization, urbanization, and economic development are also linked to a higher risk of CVD in young people [ 8 ]. In India, CVD is a major concern, responsible for an estimated 28.1% of deaths annually. Studies show that India's age-standardized DALY rate for CVD is 1.3 times higher than the global average. [9] Furthermore, in 2016, India accounted for a staggering 23.1% and 14% of the world's DALYs due to ischemic heart disease (IHD) and stroke, respectively [ 9 ]. MATERIALS AND METHODS Study design A community-based cross-sectional study was conducted in the rural field practice area of Ukkali village under the Department of Community Medicine, located in the northern part of Karnataka state. The study population consisted of adolescents in the teenage age group of 13 to 19 years. Individuals were included in the study if they belonged to the specified age group, were permanent residents of the selected village, and were willing to participate in the study with informed consent. Adolescents who were suffering from any chronic illness or had physical disabilities that could interfere with exercise were excluded from the study. Study setting The study was conducted in Ukkali village, which falls under the rural field practice area of the Department of Community Medicine. Houses in the village were selected using a simple random sampling technique without replacement, and eligible adolescents were selected from these households. The selection process was continued until the required sample size was attained. The study was done for a duration of six months, from July 2024 to December 2024. Sampling A study conducted by Harshal Patil et al. involving school children aged 9–18 found that only 25.4% demonstrated adequate knowledge of cardiovascular disease (CVD) risk factors [ 12 ]. After considering the confidence limit to 95%, 5% significance level, and 0.10 margin of error. The sample size was determined by use the subsequent formula: Sample size (n) = [Z 2 p (1-p)] /d 2 where, z = 1.96 for 95% confidence (value from the standard normal distribution reflecting the confidence level), d = 0.1 (margin of error), n = population size, p = 0.254 (population proportion) To ensure sufficient data, we projected a 10% drop-out rate (i.e. 7). So, the final sample size was 80. Data Collection Data were collected using the interview technique with the help of a structured proforma. A structured questionnaire was used to obtain information on socio-demographic, economic, cultural, and behavioural factors, as well as knowledge regarding cardiovascular disease risk factors. Dietary patterns were assessed by evaluating dietary behaviours, food preferences, and the diversity of consumption of specific food groups using the Dietary Diversity Score (DDS). In addition, the level of physical activity was assessed using a standardized Physical Activity Questionnaire (PAQ). Knowledge Structured questionnaire on CVD covering general information, risk factors, symptoms, diagnosis, and prevention. Correct answers scored '1' and incorrect '0', with a total score of 18. The composite score was scaled to 100 points and categorized as poor (≤ 50%), fair (> 50%). Anthropometrics Participants were weighed (to the nearest 0.1 kg) and measured (to the nearest 0.1 cm) using an Apollo digital scale and Secastadiometer, respectively, in an upright position without shoes or bulky clothing. BMI and WHR were used to assess nutritional status, based on WHO age- and sex-specific standards. Blood pressure : Participants were asked to relax and sit for 10 minutes before blood pressure measurements. The left arm was positioned at heart level, and three readings were taken at three-minute intervals, with the average of the last two used for analysis. Blood pressure categories were: normal (< 120/80 mmHg, below 90th percentile), prehypertension (120–129/<80 mmHg, 90th to < 95th percentile), stage I hypertension (130–139/80–89 mmHg, 95th to 99th percentile), and stage II hypertension (≥ 140/≥90 mmHg, above 99th percentile) based on age, gender, and height. Physical activity A standardized self-reported Physical activity questionnaire (GPAQ) assessed teenagers' physical activity over the past 7 days. Dietary pattern assessment Dietary behaviours, food preferences, and diversity of consumption of specific food groups by using the Individual Diet Diversity score (IDDS) Statistical analysis Data were entered in Excel and analyzed using SPSS version 26. Categorical variables were shown as frequencies and percentages, and quantitative data as means and standard deviations. The chi-square test, t-tests, and ANOVA were used for analysis, with a p-value of < 0.05 considered statistically significant. Ethical consideration This study was approved by the Institutional review board [BLDE (DU)/IEC/1106/2024-25 dt-13/07/2024] by BLDE (DU) Shri B M Patil Medical College, Hospital & Research Centre, Vijayapura, Karnataka. Written informed consent for participation in the study was obtained from the participants' legal guardian or next of kin. RESULTS The study involved 106 teenagers, with 51.9% boys and 48.1% girls. Participants were classified by age into early (43.4%), mid (17%), and late (39.6%) teens. The mean age of study population was 15.41 ± 1.98 years. Most were Hindu (72.6%) and from joint families (62.3%). A large majority were students (85.8%),37.7% were in high school, 37.7% in pre-university, 21.7% with primary education and 2.8% without formal schooling (Table 1 ). Table 1 Distribution of participants Socio-demographic profile (n = 106) Variables Frequency Percent Age Group (years) 13–15 46 43.4 15–17 18 17.0 17–19 42 39.6 Sex FEMALE 51 48.1 MALE 55 51.9 Region Christian 6 5.7 Hindu 77 72.6 Muslim 23 21.7 Education High school (7th to 10th std) 40 37.7 Illiterate/No formal schooling 03 2.8 Pre-university (> 10th to ≤PUC2) 40 37.7 Primary school (< 7th std) 23 21.7 Occupation Student 91 85.8 Working 15 14.2 Father of most of the teenagers were either graduates (37.7%) or had pre-university education (41.5%), with a predominant occupation as self-employed (45.3%). Mothers were mainly housewives (82.1%) with pre-university education (39.6%), and the majority of participants came from CLASS-IV (44.3%) or CLASS-III (38.7%) socio-economic backgrounds, living primarily in joint families (62.3%) (Table 2 ). Table 2 Distribution of parents according to socio-demographic profile Variables Frequency Percent Father’s Education Illiterate/No formal schooling 6 5.7 Primary school 7 6.6 High school 9 8.5 Pre-university 44 41.5 Graduation 40 37.7 Father’s Occupation Government employee 12 11.3 Private employee 31 29.2 Retired 2 1.9 Self-employed 48 45.3 Unemployed 13 12.3 Mother’s Education Illiterate/No formal schooling 24 22.6 Primary school 12 11.3 High school 17 16.0 Pre-university 42 39.6 Graduation 11 10.4 Mother’s Occupation Housewife 87 82.1 Private employee 6 5.7 Self-employed 13 12.3 Socio-economic status CLASS-I 2 1.9 CLASS-II 10 9.4 CLASS-III 41 38.7 CLASS-IV 47 44.3 CLASS-V 6 5.7 Type of family Extended 8 7.5 Joint 66 62.3 Nuclear 32 30.2 A larger percentage of males (55.8%) have a normal BMI, while more females (65.2%) are underweight. However, 63.6% of females are at risk of metabolic complications based on their WHR, whereas only 36.4% of males fall into this risk category (Table 3 ). Table 3 Distribution of body mass index (BMI) and waist-hip ratio (WHR) among teenagers Variables FEMALE MALE BMI Under weight 15(65.2%) 8(34.8%) Normal weight 34(44.2%) 43(55.8%) Overweight 2(33.3%) 4(66.7%) WHR Normal 44(46.3%) 51(53.7%) Risk of metabolic complication 7(63.6%) 4(36.4%) The Individual Dietary Diversity Score (IDDS) had a mean value of 5.53 (SD = 1.25), with scores ranging from 3 to 8. The mean score for knowledge regarding cardiovascular disease (CVD) risk factors was 9.94 (SD = 1.82), with a range of 4 to 15. The Global Physical Activity Questionnaire (GPAQ) score showed a mean of 3.38 (SD = 1.08), with a maximum score of 5.0 (Table 4 ). Table 4 Mean level of score obtained by the participants Mean Total Individual Diet Diversity score (IDDS) CVS risk factor: knowledge level SCORE Global Physical activity questionnaire (GPAQ) 5.53 9.94 3.3774 Std. Deviation 1.251 1.820 1.08191 Minimum 3 4 00 Maximum 8 15 5.00 Cereals (96.2%) and fats (62.3%) were the most consumed. The lowest consumption was observed for organ meats (1.9%) and flesh meats (6.6%), indicating a potential deficiency in protein sources. There was a high consumption of cereals but a low intake of nutrient-dense foods such as dark green leafy vegetables (28.4%) suggesting a gap in the diversity of nutrient intake, which might contribute to nutritional deficiencies (Fig. 1). [ Fig. 1 The prevalence of IDD food groups consumed by the teenagers in rural areas] About 88.7% of teenagers participate in moderate-intensity activities like brisk walking, while only 36.8% engage in vigorous sports. Additionally, 92.5% walk or cycle for at least 10 minutes continuously, but just 24.5% were involved in vigorous-intensity activities (Fig. 2). [ Fig. 2 Physical activity of teenagers based on Global Physical activity Questionnaire] The highest level of knowledge was observed for lifestyle habits (71.93%) and diabetes mellitus (52.83%). However, comparatively lower levels of understanding were observed for lifestyle recommendations (46.7%), control of cholesterol and blood pressure (51.65%), and general cardiovascular risks (50%) (Fig. 3). [ Fig. 3 Cardiovascular risk factors knowledge level among teenagers] The most prevalent risk factors observed were low Individual Dietary Diversity Score (IDDS) (56.6%) and low physical activity (57.5%). Other identified factors included poor cardiovascular knowledge (34.9%), family history of cardiovascular disease (20.7%), high body mass index (5.6%), and an elevated waist-to-hip ratio (10.4%) (Fig. 4). [ Fig. 4 Prevalence of different cardiovascular risk factors among teenagers] Students were found to have a more balanced dietary diversity compared to working teenagers (p = 0.001). Lower maternal education was significantly associated with lower dietary diversity (p = 0.035), and teenagers belonging to lower socio-economic classes had significantly poorer dietary diversity (p = 0.0001). Other variables did not show statistically significant associations, as presented in Table 5 . Table 5 Association bet. Individual Dietary Diversity Score and demographic variables (n = 106) Variables Individual Dietary Diversity Score χ2 (p value) Low [n (%)] High [n (%)] Age group (years) 13–15 27(58.6%) 19(41.3%) 2.862 (0.239) 16–17 7(38.9%) 11(61.1%) 18–19 26(61.9%) 16(38.1%) Sex Male 32(62.7%) 19(37.3%) 1.509 (0.219) Female 28(50.9%) 27(49.1%) Religion Hindu 37(48.1%) 40(51.9%) 11.025 (0.004) Muslim 20(87%) 3(13.0%) Christian 3(50%) 3(50%) Education Primary school ( 10th to ≤PUC2) 20(50%) 20(50%) Illiterate/No formal schooling 2(66.7%) 1(33.3%) Occupation Student 46(50.6%) 45(49.4%) 9.596 (0.001) Working 14(93.3%) 1(6.7%) Father’s education Primary school 6(85.7%) 1(14.3%) 3.819 (0.431) High school 6(66.7%) 3(33.3%) Pre-university 24(54.5%) 20(45.5%) Graduation 20(50%) 20(50%) Illiterate/No formal schooling 4(66.7%) 2(33.3%) Father’s occupation Government employee 2(16.7%) 10(83.3%) 21.497 (0.0002) Private employee 12(38.7%) 19(61.3%) Self-employed 33(68.8%) 15(31.3%) Retired 1(50%) 1(50%) Unemployed 12(92.3%) 1(7.7%) Mother’s education Primary school 10(83.3%) 2(16.7%) 10.339 (0.035) High school 10(58.8%) 7(41.2%) Pre-university 25(59.5%) 17(40.5%) Graduation 2(18.2%) 9(81.8%) Illiterate/No formal schooling 13(54.2%) 11(45.8%) Mother’s Occupation Housewife 48(55.2%) 39(44.8%) 1.022 (0.599) Private employee 3(50%) 3(50%) Self-employed 9(69.2%) 4(30.8%) Socio-economic status Class-I 1(50%) 1(50%) 25.142 (0.0001) Class-II 1(10%) 9(90%) Class-III 16(39%) 25(61%) Class-IV 37(78.7%) 10(21.3%) Class-V 5(83.3%) 1(16.7%) Type of family Extended 6(75%) 2(25%) 2.501 (0.286) Joint 39(59.1%) 27(40.9%) Nuclear 15(46.9%) 17(53.1%) Table 6 Association between level of CV knowledge and demographic variables (n = 106) Variables Level of CV knowledge score χ2 (p value) Poor [n (%)] Fair [n (%)] Age group (years) 13–15 20(43.5%) 26(56.5%) 3.889 (0.143) 16–17 7(38.9%) 11(61.1%) 18–19 10(23.8%0 32(76.2%) Sex Male 16(29.1%) 39(70.9%) 1.701 (0.192) Female 21(41.2%) 30(58.8%) Religion Hindu 23(29.9%) 54(70.1%) 3.884 (0.143) Muslim 12(52.2%) 11(47.8%) Christian 2(33.3%) 4(66.7%) Education Primary school ( 10th to ≤PUC2) 9(22.5%) 31(77.5%) Illiterate/No formal schooling 2(66.7%) 1(33.3%) Occupation Student 30(33%) 61(67%) 1.064 (0.302) Working 7(46.7%) 8(53.3%) Father’s education Primary school 5(71.4%) 2(28.6%) 7.7 (0.103) High school 3(33.3%) 6(66.7%) Pre-university 17(38.6%) 27(61.4%) Graduation 9(22.5%) 31(77.5%) Illiterate/No formal schooling 3(50%) 3(50%) Father’s occupation Government employee 3(25%) 9(75%) 12.208 (0.015) Private employee 10(32.3%) 21(67.7%) Self-employed 13(27.1%) 35(72.9%) Retired 1(50%) 1(50%) Unemployed 10(76.9%) 3(23.1%) Mother’s education Primary school 8(66.7%) 4(33.3%) 6.248 (0.181) High school 6(35.3%) 11(64.7%) Pre-university 13(31%) 29(69%) Graduation 3(27.3%) 8(72.7%) Illiterate/No formal schooling 7(29.2%) 17(70.8%) Mother’s Occupation Housewife 31(35.6%) 56(64.4%) 1.422 (0.491) Private employee 3(50%) 3(50%) Self-employed 3(23.1%) 10(76.9%) Socio-economic status Class-I 1(50%) 1(50%) 9.758 (0.044) Class-II 2(20%) 8(80%) Class-III 12(29.3%) 29(70.7%) Class-IV 16(34%) 31(66%) Class-V 6(85.7%) 1(14.3%) Type of family Extended 4(50%) 4(50%) 1.511 (0.47) Joint 24(36.4%) 42(63.6%) Nuclear 9(28.1%) 23(71.9%) BMI Under weight 18(78.3%) 5(21.7%) 26.391 (0.0001) Normal weight 16(20.8%) 61(79.2%) Over weight 3(50%) 3(50%) Table 6 presents the association between cardiovascular (CV) knowledge and demographic variables among adolescents (n = 106). A greater proportion of teenagers aged 18–19 years demonstrated fair knowledge compared to younger age groups, although the association was not statistically significant (p = 0.143). Similarly, gender and religion showed no significant association with CV knowledge. Educational status was significantly associated with knowledge levels (p = 0.0001), with adolescents having higher education demonstrating better knowledge. Father’s occupation (p = 0.015), socio-economic status (p = 0.044), and BMI (p = 0.0001) also showed significant associations with CV knowledge. However, occupation of the adolescent, father’s education, mother’s education, mother’s occupation, and type of family were not significantly associated with the level of CV knowledge. DISCUSSION Our study found that 34.91% of adolescents had poor knowledge, while 65.09% had fair knowledge of CVD risk factors. Previous studies reported inadequate knowledge among 20.21%, 41%; moderate knowledge in 54.4%, 36.5%; adequate knowledge in 25.4%, 22.5% of school children aged 9–18 years [ 10 ] and 14–16 years [ 11 ] respectively, it shows that still intervention needed to improve knowledge among adolescents. In the current study, 5.66% of participants were overweight, and 9.43% were at risk of metabolic complications based on increased WHR. Compared to the previous studies conducted by George GM et al. [ 13 ] and Munusamy G et al. [ 14 ] in India that 9.5% and 15% were overweight respectively. The difference in this percentage may be due to rural areas of our study. The prevalence of low dietary diversity (56.6%) and insufficient physical activity (57.5%) in this study is consistent with findings from other rural settings in India. For instance, a study in rural Maharashtra by Patil R et al. [ 15 ] reported that around 60% of adolescents had inadequate physical activity, and 54% had poor dietary diversity, indicating that these risk factors are common in rural adolescent populations across different regions of India. The dietary habits observed, such as the heavy reliance on cereals and limited intake of protein-rich foods, align with findings from rural communities in India where carbohydrate-centric diets prevail due to affordability and availability. A study in rural Tamil Nadu by Bose et al. [ 16 ] also reported a high intake of cereals (over 90%) and low consumption of fruits, vegetables, and animal-based proteins, mirroring the dietary trends seen in our study. Internationally, studies on adolescents in low-income rural settings in countries like Nigeria and Brazil also show a similar pattern of high cereal consumption with low dietary diversity, suggesting that rural adolescents globally face comparable nutritional challenges [ 17 , 18 ]. Addressing these gaps requires policies that improve access to diverse food groups and nutrition education tailored to the local context [ 19 ]. The finding that a larger proportion of females were underweight (65.2%) and at higher risk of metabolic complications based on WHR (63.6%) reflects similar trends observed in other rural Indian studies. Research in rural Gujarat by Pradhan et al. [ 20 ] reported that adolescent girls were more likely to be underweight and have a higher waist-to-hip ratio than boys, indicating consistent gender disparities across different rural regions. These gender differences can be attributed to cultural factors, dietary restrictions, and possibly greater physical workload among rural girls, who may engage in more household chores. In contrast, studies from urban areas often report a higher prevalence of obesity among girls, highlighting the urban-rural divide in risk factor profiles. While 88.7% of participants in this study reported engaging in moderate-intensity activities, a smaller fraction (36.8%) participated in vigorous sports. The levels of physical activity observed in our study are comparable to those in other rural regions of India. For instance, a study conducted in rural Maharashtra found that approximately 85% of adolescents engaged in some form of physical activity, primarily moderate activities such as walking or helping with household chores, while less than 30% participated in organized sports or vigorous exercises [ 15 ]. This indicates that adolescents in rural areas commonly engage in physical activities that are integrated into their daily routines rather than structured exercise programs. Similarly, a study in rural Tamil Nadu by Rajaraman et al. [ 21 ] reported that while most adolescents were involved in moderate activities, less than 40% engaged in high-intensity physical activities. The limited participation in sports or structured exercise may be attributed to a lack of facilities, cultural norms, or prioritization of household or farming tasks over recreational activities. This trend is in line with findings from rural Ethiopia [ 22 ], where adolescents engaged more in daily moderate activities like walking and farming than structured sports. In contrast, studies from urban environments, such as urban schools in the Philippines [ 23 ], report higher participation in organized sports, likely due to better infrastructure and awareness. The disparity in physical activity intensity emphasizes the need for community-based interventions that promote sports and recreational activities in rural settings. Schools in rural areas could play a key role in providing structured physical activity programs. The study revealed moderate knowledge of cardiovascular risk factors, particularly lifestyle habits (71.93%) and diabetes (52.83%), but lower awareness of specific risk control measures (46.7%). These findings are consistent with similar research in rural Bangladesh by Alam et al. [ 24 ], where adolescents demonstrated limited knowledge about cardiovascular disease prevention, reflecting a broader trend in South Asian rural communities. Compared to adolescents in urban areas, rural teenagers often have less access to health information, which could explain the lower levels of knowledge. Urban studies, such as those conducted in Mumbai [ 25 ], frequently report higher health literacy levels among adolescents, linked to better educational resources and awareness programs. The association between dietary diversity and socio-economic status, as well as maternal education, echoes findings from rural Andhra Pradesh by Reddy et al. [ 26 ], where lower socio-economic classes had poorer dietary quality. This relationship suggests that poverty and lower education levels significantly hinder access to diverse, nutrient-rich foods. Globally, similar associations are seen in studies from rural China and sub-Saharan Africa [ 27 – 29 ], where adolescents from lower-income families consume less diverse diets due to economic constraints. Addressing these socio-economic disparities through social support programs, school feeding initiatives, and targeted nutritional interventions is essential for improving adolescent health outcomes. LIMITATION: The data on physical activity, dietary habits, and cardiovascular knowledge were self-reported by the participants, which could introduce recall bias or social desirability bias. The study was conducted in a specific rural area of Vijayapura District, Karnataka. As such, the findings may not be generalizable to adolescents in other rural areas or different regions of India, where cultural, social, and environmental factors might differ. While dietary diversity was assessed, micronutrient analysis and bio-physiological measures like blood glucose and lipid profiles were not included, which could be considered in future research. CONCLUSION Addressing CVD risk factors in teenagers requires promoting diverse diets, increasing awareness about cardiovascular health and encouraging physical activity. Early interventions, particularly in low socio-economic groups, can significantly reduce future CVD risks. Community-based education programs, improved dietary habits, and promoting physical activity should be emphasized. Declarations AUTHORS CONTRIBUTION All authors have contributed equally. FINANCIAL SUPPORT AND SPONSORSHIP Nil CONFLICT OF INTEREST There are no conflicts of interest DECLARATION OF GENERATIVE AI AND AIASSISTED TECHNOLOGIES IN THE WRITING PROCESS The authors haven’t used any generative AI/AI assisted technologies in the writing process. References Voelker DK, Reel JJ, Greenleaf C. Weight status and body image perceptions in adolescents: current perspectives. Adolesc Health Med Ther. 2015;6:149–58. Available from: http://dx.doi.org/10.2147/AHMT.S68344 World Health Organization South-. East Asia [Internet]. Who.int. [cited 2026 Mar 07]. Available from: http://www.searo.who.int/ Population enumeration data (final population). Single year age data 2011. [Internet] Office of the Registrar General & Census Commissioner, Government of India. (2011). [cited 2026 Mar 07]. Available from: https://censusindia.gov.in/nada/index.php/catalog/1436 Alwan A, MacLean DR, Riley LM, d’Espaignet ET, Mathers CD, Stevens GA, Bettcher D. 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Afr J Biomed Res. 2018;21(1):69–77. Souza AM, Pereira RA, Yokoo EM. Dietary patterns and associated factors among adolescents in a low-income rural area in Brazil. Cad Saude Publica. 2013;29(8):1567–78. 10.1590/0102-311X00106012 . Ojo G, Olumide F. Dietary diversity and nutritional status among adolescents in rural Nigeria. Afr J Food Agric Nutr Dev. 2019;19(2):14465–82. 10.18697/ajfand.85.17200 . Pradhan A, Rao KR. Gender differences in nutritional status and dietary diversity among adolescents in rural Gujarat, India. Public Health Nutr. 2018;21(3):485–93. 10.1017/S1368980017002874 . Rajaraman V, Manjula V. Physical activity patterns among adolescents in rural Tamil Nadu. J Clin Diagn Res. 2016;10(4):LC20–4. 10.7860/JCDR/2016/17641.7603 . Regassa N, Stoecker BJ. Physical activity patterns and its determinants among Ethiopian youth. J Phys Act Health. 2012;9(1):73–81. 10.1123/jpah.9.1.73 . Dumith SC, Gigante DP, Domingues MR, Kohl HW. Physical activity change during adolescence: A systematic review and a pooled analysis. Int J Epidemiol. 2011;40(3):685–98. 10.1093/ije/dyq272 . Alam DS, Chowdhury MA, Siddique TA, Ahmed T. Cardiovascular health knowledge and preventive practices among adolescents in rural Bangladesh. Glob Health Action. 2016;9(1):32003. 10.3402/gha.v9.32003 . Patel V, Chauhan A, Kumar A. Health literacy among urban adolescents: A study from Mumbai, India. J Family Med Prim Care. 2019;8(6):1870–5. 10.4103/jfmpc.jfmpc_263_19 . Reddy KS, Shah B. Socio-economic disparities in dietary patterns among rural adolescents in Andhra Pradesh. J Nutr Health Sci. 2013;4(2):95–102. Wang Z, Zhai F, Zhang B. Socioeconomic disparities in dietary diversity in rural China: The role of household income and education. Public Health Nutr. 2014;17(4):927–36. 10.1017/S1368980013000256 . Afoakwa EO, Ephraim SY. Socio-economic and dietary determinants of dietary diversity among rural adolescents in Ghana. Afr J Food Agric Nutr Dev. 2017;17(3):12343–56. 10.18697/ajfand.79.16246 . Steyn NP, Nel JH, Casey A. Dietary diversity and socio-economic status in rural adolescents in South Africa. Nutr Res. 2014;34(1):57–65. 10.1016/j.nutres.2013.11.002 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 14 May, 2026 Reviewers agreed at journal 14 May, 2026 Reviews received at journal 12 May, 2026 Reviewers agreed at journal 12 May, 2026 Reviewers agreed at journal 10 May, 2026 Reviewers agreed at journal 07 May, 2026 Reviewers invited by journal 07 May, 2026 Editor invited by journal 17 Apr, 2026 Editor assigned by journal 17 Apr, 2026 Submission checks completed at journal 17 Apr, 2026 First submitted to journal 17 Apr, 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-9333640","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":640249381,"identity":"d01f9e61-5163-4073-8c19-dd0ecb22be5c","order_by":0,"name":"Subhajit Giri","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIie3PMWuDQBTA8SeCtxj34JCvIBRuiZwfpIvhIF1SSLZCArkSMJ/BD5G1dLxwUJcj0i3gEilkbvcOeSc4Gh0Lvf/iKe/HOwFstr+YBHAdAa7XnKMYH86rHEa8hswNEYMIgNe8qvZjd0FVXMPVOyPBZP+pXpYlO+wVbtnEj11kfFrQMNccL+Yvjzqq+JueIfmYP4sOEmnfC0eZa0h6FEioROIIdYeQK5JtS06clnUfAYpEISESiWT03LNlrP2HaZ4VSBaAhKf0jFvSO/8SaFJXq2zNJ7vi60f8soSWT/XlexN3kjYO4EfmMGsm055xEwMgF3NIBgzbbDbbP+sGkSJhlInucP8AAAAASUVORK5CYII=","orcid":"","institution":"BLDE (DU)","correspondingAuthor":true,"prefix":"","firstName":"Subhajit","middleName":"","lastName":"Giri","suffix":""},{"id":640249386,"identity":"fa0aaefd-dc24-4683-a904-6b1d9ee7ea43","order_by":1,"name":"Praveen Ganganahalli","email":"","orcid":"","institution":"BLDE (DU)","correspondingAuthor":false,"prefix":"","firstName":"Praveen","middleName":"","lastName":"Ganganahalli","suffix":""},{"id":640249390,"identity":"662b5ebb-50e4-4e2a-abd0-b1e25bdb44d7","order_by":2,"name":"M. C. Yadavannavar","email":"","orcid":"","institution":"BLDE (DU)","correspondingAuthor":false,"prefix":"","firstName":"M.","middleName":"C.","lastName":"Yadavannavar","suffix":""},{"id":640249392,"identity":"1ff3d2f3-2229-4ac1-80eb-fdfbc115570d","order_by":3,"name":"Rekha Udgiri","email":"","orcid":"","institution":"BLDE (DU)","correspondingAuthor":false,"prefix":"","firstName":"Rekha","middleName":"","lastName":"Udgiri","suffix":""}],"badges":[],"createdAt":"2026-04-06 11:55:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9333640/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9333640/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109405621,"identity":"0d04a8a1-aeda-48d4-82cb-0470430551a6","added_by":"auto","created_at":"2026-05-17 13:19:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":204301,"visible":true,"origin":"","legend":"\u003cp\u003eThe prevalence of IDD food groups consumed by the teenagers in rural areas\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9333640/v1/03926e2aadbfb95282d73c43.png"},{"id":109405493,"identity":"839de38c-3ae6-4a6a-9c34-06b61e7f3ebd","added_by":"auto","created_at":"2026-05-17 13:18:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":156894,"visible":true,"origin":"","legend":"\u003cp\u003ePhysical activity of teenagers based on Global Physical activity Questionnaire\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9333640/v1/2ecea3454c38293b3b348b5f.png"},{"id":109337997,"identity":"c62de13e-0760-4b59-bbad-5ce15fa06648","added_by":"auto","created_at":"2026-05-15 17:48:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":70568,"visible":true,"origin":"","legend":"\u003cp\u003eCardiovascular risk factors knowledge level among teenagers\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9333640/v1/b5af4917384845d7876c514c.png"},{"id":109405768,"identity":"e8d59222-1689-4bf6-a79c-91b390711b36","added_by":"auto","created_at":"2026-05-17 13:20:09","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":87026,"visible":true,"origin":"","legend":"\u003cp\u003ePrevalence of different cardiovascular risk factors among teenagers\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9333640/v1/26ae2d62096ff8afe65b9a52.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eTo Evaluate the Cardiovascular Risk Factors Among Teenagers Residing in Rural Areas of Vijayapura District, Karnataka: A Cross-Sectional Study\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eThe teenage stage, which includes physical, cognitive, and psychosocial development, is the time between puberty and maturity [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. WHO encompasses the ages 13 to 19 years as the crucial phase of teenage [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. With nearly 120\u0026nbsp;million young females and 133\u0026nbsp;million males, India's teenage population reaches a staggering 253\u0026nbsp;million, shaping the nation's future demographic [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Cardiovascular disease (CVD) is the most prevalent cause of mortality globally and its risk factors can develop as early as teenage. A concerning trend is the rise of unhealthy lifestyles among teenagers, potentially setting the stage for future heart problems.\u003c/p\u003e \u003cp\u003eEven though cardiovascular diseases typically show symptoms in adults, the seeds of these problems can be sown much earlier, during childhood and adolescence. The presence of other risk factors seems to play a role in how these early signs (atherosclerotic changes) develop and worsen [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Furthermore, unhealthy habits and risk factors present during teenage years can predict a higher risk of poor cardiovascular health later in life. Therefore, to create effective policies for preventing and controlling CVD, it is vital to understand how these behavioural risk factors are distributed among teenagers, particularly in low and middle-income countries [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe rise of coronary heart disease (CHD) is a major concern, fuelled by factors like high blood pressure, unhealthy cholesterol levels, diabetes, obesity, lack of physical activity, and smoking. The World Health Organization (WHO) aptly calls CHD a \"modern epidemic,\" highlighting its widespread impact that transcends individual aging [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Critically, the groundwork for these problems, atherosclerosis, begins in childhood and manifests later in life. Research shows that risk factors identified in childhood tend to persist through adolescence and into adulthood [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Furthermore, habits related to diet and physical activity, which significantly influence heart health, are often established during childhood. This means unhealthy dietary choices or a lack of physical activity in childhood can lead to high cholesterol and an increased risk of heart disease even in young adulthood.\u003c/p\u003e \u003cp\u003eSeveral factors influence the risk of cardiovascular disease (CVD). Modifiable risk factors in teenage include obesity, overweight, physical inactivity and unhealthy eating habits [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Family history of CVD is an example of a non-modifiable risk factor. Beyond these individual factors, broader trends like industrialization, urbanization, and economic development are also linked to a higher risk of CVD in young people [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In India, CVD is a major concern, responsible for an estimated 28.1% of deaths annually. Studies show that India's age-standardized DALY rate for CVD is 1.3 times higher than the global average.\u003csup\u003e[9]\u003c/sup\u003e Furthermore, in 2016, India accounted for a staggering 23.1% and 14% of the world's DALYs due to ischemic heart disease (IHD) and stroke, respectively [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eA community-based cross-sectional study was conducted in the rural field practice area of Ukkali village under the Department of Community Medicine, located in the northern part of Karnataka state. The study population consisted of adolescents in the teenage age group of 13 to 19 years. Individuals were included in the study if they belonged to the specified age group, were permanent residents of the selected village, and were willing to participate in the study with informed consent. Adolescents who were suffering from any chronic illness or had physical disabilities that could interfere with exercise were excluded from the study.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy setting\u003c/h3\u003e\n\u003cp\u003eThe study was conducted in Ukkali village, which falls under the rural field practice area of the Department of Community Medicine. Houses in the village were selected using a simple random sampling technique without replacement, and eligible adolescents were selected from these households. The selection process was continued until the required sample size was attained. The study was done for a duration of six months, from July 2024 to December 2024.\u003c/p\u003e\n\u003ch3\u003eSampling\u003c/h3\u003e\n\u003cp\u003eA study conducted by Harshal Patil et al. involving school children aged 9\u0026ndash;18 found that only 25.4% demonstrated adequate knowledge of cardiovascular disease (CVD) risk factors [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. After considering the confidence limit to 95%, 5% significance level, and 0.10 margin of error. The sample size was determined by use the subsequent formula: Sample size (n) = [Z\u003csup\u003e2\u003c/sup\u003e p (1-p)] /d\u003csup\u003e2\u003c/sup\u003e where,\u003c/p\u003e \u003cp\u003e \u003cb\u003ez\u003c/b\u003e\u0026thinsp;=\u0026thinsp;1.96 for 95% confidence (value from the standard normal distribution reflecting the confidence level), \u003cb\u003ed\u003c/b\u003e\u0026thinsp;=\u0026thinsp;0.1 (margin of error), \u003cb\u003en\u003c/b\u003e\u0026thinsp;=\u0026thinsp;population size, \u003cb\u003ep\u003c/b\u003e\u0026thinsp;=\u0026thinsp;0.254 (population proportion)\u003c/p\u003e \u003cp\u003eTo ensure sufficient data, we projected a 10% drop-out rate (i.e. 7). So, the final sample size was 80.\u003c/p\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eData were collected using the interview technique with the help of a structured proforma. A structured questionnaire was used to obtain information on socio-demographic, economic, cultural, and behavioural factors, as well as knowledge regarding cardiovascular disease risk factors. Dietary patterns were assessed by evaluating dietary behaviours, food preferences, and the diversity of consumption of specific food groups using the Dietary Diversity Score (DDS). In addition, the level of physical activity was assessed using a standardized Physical Activity Questionnaire (PAQ).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eKnowledge\u003c/strong\u003e \u003cp\u003eStructured questionnaire on CVD covering general information, risk factors, symptoms, diagnosis, and prevention. Correct answers scored '1' and incorrect '0', with a total score of 18. The composite score was scaled to 100 points and categorized as poor (\u0026le;\u0026thinsp;50%), fair (\u0026gt;\u0026thinsp;50%).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eAnthropometrics\u003c/strong\u003e \u003cp\u003eParticipants were weighed (to the nearest 0.1 kg) and measured (to the nearest 0.1 cm) using an Apollo digital scale and Secastadiometer, respectively, in an upright position without shoes or bulky clothing. BMI and WHR were used to assess nutritional status, based on WHO age- and sex-specific standards.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e\u003cem\u003eBlood pressure\u003c/em\u003e: Participants were asked to relax and sit for 10 minutes before blood pressure measurements. The left arm was positioned at heart level, and three readings were taken at three-minute intervals, with the average of the last two used for analysis. Blood pressure categories were: normal (\u0026lt;\u0026thinsp;120/80 mmHg, below 90th percentile), prehypertension (120\u0026ndash;129/\u0026lt;80 mmHg, 90th to \u0026lt;\u0026thinsp;95th percentile), stage I hypertension (130\u0026ndash;139/80\u0026ndash;89 mmHg, 95th to 99th percentile), and stage II hypertension (\u0026ge;\u0026thinsp;140/\u0026ge;90 mmHg, above 99th percentile) based on age, gender, and height.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePhysical activity\u003c/strong\u003e \u003cp\u003eA standardized self-reported Physical activity questionnaire (GPAQ) assessed teenagers' physical activity over the past 7 days.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eDietary pattern assessment\u003c/strong\u003e \u003cp\u003eDietary behaviours, food preferences, and diversity of consumption of specific food groups by using the Individual Diet Diversity score (IDDS)\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eStatistical analysis\u003c/strong\u003e \u003cp\u003eData were entered in Excel and analyzed using SPSS version 26. Categorical variables were shown as frequencies and percentages, and quantitative data as means and standard deviations. The chi-square test, t-tests, and ANOVA were used for analysis, with a p-value of \u0026lt;\u0026thinsp;0.05 considered statistically significant.\u003c/p\u003e \u003c/p\u003e\n\u003ch3\u003eEthical consideration\u003c/h3\u003e\n\u003cp\u003eThis study was approved by the Institutional review board [BLDE (DU)/IEC/1106/2024-25 dt-13/07/2024] by BLDE (DU) Shri B M Patil Medical College, Hospital \u0026amp; Research Centre, Vijayapura, Karnataka. Written informed consent for participation in the study was obtained from the participants' legal guardian or next of kin.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eThe study involved 106 teenagers, with 51.9% boys and 48.1% girls. Participants were classified by age into early (43.4%), mid (17%), and late (39.6%) teens. The mean age of study population was 15.41\u0026thinsp;\u0026plusmn;\u0026thinsp;1.98 years. Most were Hindu (72.6%) and from joint families (62.3%). A large majority were students (85.8%),37.7% were in high school, 37.7% in pre-university, 21.7% with primary education and 2.8% without formal schooling (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\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\u003eDistribution of participants Socio-demographic profile (n\u0026thinsp;=\u0026thinsp;106)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercent\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eAge Group\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u0026ndash;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u0026ndash;17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17\u0026ndash;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFEMALE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMALE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e51.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eRegion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChristian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHindu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMuslim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh school (7th to 10th std)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIlliterate/No formal schooling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-university (\u0026gt;\u0026thinsp;10th to \u0026le;PUC2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary school (\u0026lt;\u0026thinsp;7th std)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eOccupation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStudent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e85.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWorking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFather of most of the teenagers were either graduates (37.7%) or had pre-university education (41.5%), with a predominant occupation as self-employed (45.3%). Mothers were mainly housewives (82.1%) with pre-university education (39.6%), and the majority of participants came from CLASS-IV (44.3%) or CLASS-III (38.7%) socio-economic backgrounds, living primarily in joint families (62.3%) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\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\u003eDistribution of parents according to socio-demographic profile\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercent\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eFather\u0026rsquo;s Education\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIlliterate/No formal schooling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-university\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGraduation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eFather\u0026rsquo;s Occupation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGovernment employee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrivate employee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRetired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelf-employed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eMother\u0026rsquo;s Education\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIlliterate/No formal schooling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-university\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGraduation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eMother\u0026rsquo;s Occupation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHousewife\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e82.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrivate employee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelf-employed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eSocio-economic status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCLASS-I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCLASS-II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCLASS-III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCLASS-IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCLASS-V\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eType of family\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtended\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJoint\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e62.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNuclear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eA larger percentage of males (55.8%) have a normal BMI, while more females (65.2%) are underweight. However, 63.6% of females are at risk of metabolic complications based on their WHR, whereas only 36.4% of males fall into this risk category (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of body mass index (BMI) and waist-hip ratio (WHR) among teenagers\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFEMALE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMALE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnder weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15(65.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8(34.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34(44.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43(55.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2(33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4(66.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44(46.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e51(53.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRisk of metabolic complication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7(63.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4(36.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe Individual Dietary Diversity Score (IDDS) had a mean value of 5.53 (SD\u0026thinsp;=\u0026thinsp;1.25), with scores ranging from 3 to 8. The mean score for knowledge regarding cardiovascular disease (CVD) risk factors was 9.94 (SD\u0026thinsp;=\u0026thinsp;1.82), with a range of 4 to 15. The Global Physical Activity Questionnaire (GPAQ) score showed a mean of 3.38 (SD\u0026thinsp;=\u0026thinsp;1.08), with a maximum score of 5.0 (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMean level of score obtained by the participants\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\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal Individual Diet Diversity score (IDDS)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCVS risk factor: knowledge level SCORE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGlobal Physical activity questionnaire (GPAQ)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.53\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.94\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.3774\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStd. Deviation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.08191\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCereals (96.2%) and fats (62.3%) were the most consumed. The lowest consumption was observed for organ meats (1.9%) and flesh meats (6.6%), indicating a potential deficiency in protein sources. There was a high consumption of cereals but a low intake of nutrient-dense foods such as dark green leafy vegetables (28.4%) suggesting a gap in the diversity of nutrient intake, which might contribute to nutritional deficiencies (Fig.\u0026nbsp;1).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e[\u003cb\u003eFig.\u0026nbsp;1\u003c/b\u003e The prevalence of IDD food groups consumed by the teenagers in rural areas]\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAbout 88.7% of teenagers participate in moderate-intensity activities like brisk walking, while only 36.8% engage in vigorous sports. Additionally, 92.5% walk or cycle for at least 10 minutes continuously, but just 24.5% were involved in vigorous-intensity activities (Fig.\u0026nbsp;2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e[\u003cb\u003eFig.\u0026nbsp;2\u003c/b\u003e Physical activity of teenagers based on Global Physical activity Questionnaire]\u003c/p\u003e \u003cp\u003eThe highest level of knowledge was observed for lifestyle habits (71.93%) and diabetes mellitus (52.83%). However, comparatively lower levels of understanding were observed for lifestyle recommendations (46.7%), control of cholesterol and blood pressure (51.65%), and general cardiovascular risks (50%) (Fig.\u0026nbsp;3).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e[\u003cb\u003eFig.\u0026nbsp;3\u003c/b\u003e Cardiovascular risk factors knowledge level among teenagers]\u003c/p\u003e \u003cp\u003eThe most prevalent risk factors observed were low Individual Dietary Diversity Score (IDDS) (56.6%) and low physical activity (57.5%). Other identified factors included poor cardiovascular knowledge (34.9%), family history of cardiovascular disease (20.7%), high body mass index (5.6%), and an elevated waist-to-hip ratio (10.4%) (Fig.\u0026nbsp;4).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e[\u003cb\u003eFig.\u0026nbsp;4\u003c/b\u003e Prevalence of different cardiovascular risk factors among teenagers]\u003c/p\u003e \u003cp\u003eStudents were found to have a more balanced dietary diversity compared to working teenagers (p\u0026thinsp;=\u0026thinsp;0.001). Lower maternal education was significantly associated with lower dietary diversity (p\u0026thinsp;=\u0026thinsp;0.035), and teenagers belonging to lower socio-economic classes had significantly poorer dietary diversity (p\u0026thinsp;=\u0026thinsp;0.0001). Other variables did not show statistically significant associations, as presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation bet. Individual Dietary Diversity Score and demographic variables (n\u0026thinsp;=\u0026thinsp;106)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eIndividual Dietary Diversity Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eχ2\u003c/p\u003e \u003cp\u003e(p value)\u003c/p\u003e\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow [n (%)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh [n (%)]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eAge group\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u0026ndash;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27(58.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19(41.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e2.862\u003c/p\u003e \u003cp\u003e(0.239)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u0026ndash;17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(38.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11(61.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u0026ndash;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26(61.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16(38.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32(62.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19(37.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.509\u003c/p\u003e \u003cp\u003e(0.219)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28(50.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27(49.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eReligion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHindu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37(48.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40(51.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e11.025\u003c/p\u003e \u003cp\u003e(0.004)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMuslim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20(87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(13.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChristian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(50%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary school\u003c/p\u003e \u003cp\u003e(\u0026lt;\u0026thinsp;7th std)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17(73.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6(26.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e4.291\u003c/p\u003e \u003cp\u003e(0.231)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003cp\u003e(7th to 10th std)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20(50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20(50%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-university\u003c/p\u003e \u003cp\u003e(\u0026gt;\u0026thinsp;10th to \u0026le;PUC2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20(50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20(50%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIlliterate/No formal schooling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(33.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eOccupation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStudent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46(50.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45(49.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e9.596\u003c/p\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWorking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14(93.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(6.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eFather\u0026rsquo;s education\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(85.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(14.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e3.819\u003c/p\u003e \u003cp\u003e(0.431)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(33.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-university\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24(54.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20(45.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGraduation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20(50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20(50%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIlliterate/No formal schooling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(33.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eFather\u0026rsquo;s occupation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGovernment employee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10(83.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e21.497\u003c/p\u003e \u003cp\u003e(0.0002)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrivate employee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12(38.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19(61.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelf-employed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33(68.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15(31.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRetired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(50%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12(92.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(7.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eMother\u0026rsquo;s education\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10(83.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e10.339\u003c/p\u003e \u003cp\u003e(0.035)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10(58.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7(41.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-university\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25(59.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17(40.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGraduation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(18.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9(81.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIlliterate/No formal schooling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13(54.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11(45.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eMother\u0026rsquo;s Occupation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHousewife\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48(55.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39(44.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e1.022\u003c/p\u003e \u003cp\u003e(0.599)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrivate employee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(50%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelf-employed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9(69.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4(30.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eSocio-economic status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClass-I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e25.142\u003c/p\u003e \u003cp\u003e(0.0001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClass-II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9(90%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClass-III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16(39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25(61%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClass-IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37(78.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10(21.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClass-V\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(83.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(16.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eType of family\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtended\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e2.501\u003c/p\u003e \u003cp\u003e(0.286)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJoint\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39(59.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27(40.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNuclear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15(46.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17(53.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between level of CV knowledge and demographic variables (n\u0026thinsp;=\u0026thinsp;106)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eLevel of CV knowledge score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eχ2\u003c/p\u003e \u003cp\u003e(p value)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePoor [n (%)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFair [n (%)]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAge group\u003c/p\u003e \u003cp\u003e(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u0026ndash;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20(43.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26(56.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e3.889\u003c/p\u003e \u003cp\u003e(0.143)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u0026ndash;17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(38.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11(61.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u0026ndash;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10(23.8%0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32(76.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16(29.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39(70.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.701\u003c/p\u003e \u003cp\u003e(0.192)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21(41.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30(58.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eReligion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHindu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23(29.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54(70.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e3.884\u003c/p\u003e \u003cp\u003e(0.143)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMuslim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12(52.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11(47.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChristian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4(66.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary school\u003c/p\u003e \u003cp\u003e(\u0026lt;\u0026thinsp;7th std)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16(69.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7(30.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e17.929\u003c/p\u003e \u003cp\u003e(0.0001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003cp\u003e(7th to 10th std)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10(25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30(75%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-university\u003c/p\u003e \u003cp\u003e(\u0026gt;\u0026thinsp;10th to \u0026le;PUC2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9(22.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31(77.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIlliterate/No formal schooling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(33.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOccupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStudent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30(33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61(67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.064\u003c/p\u003e \u003cp\u003e(0.302)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWorking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(46.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8(53.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eFather\u0026rsquo;s education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(71.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(28.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e7.7\u003c/p\u003e \u003cp\u003e(0.103)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6(66.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-university\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17(38.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27(61.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGraduation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9(22.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31(77.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIlliterate/No formal schooling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(50%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eFather\u0026rsquo;s occupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGovernment employee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9(75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e12.208\u003c/p\u003e \u003cp\u003e(0.015)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrivate employee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10(32.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21(67.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelf-employed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13(27.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35(72.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRetired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(50%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10(76.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(23.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eMother\u0026rsquo;s education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8(66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4(33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e6.248\u003c/p\u003e \u003cp\u003e(0.181)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(35.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11(64.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-university\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13(31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29(69%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGraduation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(27.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8(72.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIlliterate/No formal schooling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(29.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17(70.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMother\u0026rsquo;s Occupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHousewife\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31(35.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56(64.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e1.422\u003c/p\u003e \u003cp\u003e(0.491)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrivate employee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(50%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelf-employed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(23.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10(76.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eSocio-economic status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClass-I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e9.758\u003c/p\u003e \u003cp\u003e(0.044)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClass-II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8(80%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClass-III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12(29.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29(70.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClass-IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16(34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31(66%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClass-V\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(85.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(14.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eType of family\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtended\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4(50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e1.511\u003c/p\u003e \u003cp\u003e(0.47)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJoint\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24(36.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42(63.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNuclear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9(28.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23(71.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnder weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18(78.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5(21.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e26.391\u003c/p\u003e \u003cp\u003e(0.0001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16(20.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61(79.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOver weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(50%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e presents the association between cardiovascular (CV) knowledge and demographic variables among adolescents (n\u0026thinsp;=\u0026thinsp;106). A greater proportion of teenagers aged 18\u0026ndash;19 years demonstrated fair knowledge compared to younger age groups, although the association was not statistically significant (p\u0026thinsp;=\u0026thinsp;0.143). Similarly, gender and religion showed no significant association with CV knowledge. Educational status was significantly associated with knowledge levels (p\u0026thinsp;=\u0026thinsp;0.0001), with adolescents having higher education demonstrating better knowledge. Father\u0026rsquo;s occupation (p\u0026thinsp;=\u0026thinsp;0.015), socio-economic status (p\u0026thinsp;=\u0026thinsp;0.044), and BMI (p\u0026thinsp;=\u0026thinsp;0.0001) also showed significant associations with CV knowledge. However, occupation of the adolescent, father\u0026rsquo;s education, mother\u0026rsquo;s education, mother\u0026rsquo;s occupation, and type of family were not significantly associated with the level of CV knowledge.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eOur study found that 34.91% of adolescents had poor knowledge, while 65.09% had fair knowledge of CVD risk factors. Previous studies reported inadequate knowledge among 20.21%, 41%; moderate knowledge in 54.4%, 36.5%; adequate knowledge in 25.4%, 22.5% of school children aged 9\u0026ndash;18 years [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and 14\u0026ndash;16 years [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] respectively, it shows that still intervention needed to improve knowledge among adolescents.\u003c/p\u003e \u003cp\u003eIn the current study, 5.66% of participants were overweight, and 9.43% were at risk of metabolic complications based on increased WHR. Compared to the previous studies conducted by George GM et al. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] and Munusamy G et al. [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] in India that 9.5% and 15% were overweight respectively. The difference in this percentage may be due to rural areas of our study.\u003c/p\u003e \u003cp\u003eThe prevalence of low dietary diversity (56.6%) and insufficient physical activity (57.5%) in this study is consistent with findings from other rural settings in India. For instance, a study in rural Maharashtra by Patil R et al. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] reported that around 60% of adolescents had inadequate physical activity, and 54% had poor dietary diversity, indicating that these risk factors are common in rural adolescent populations across different regions of India.\u003c/p\u003e \u003cp\u003eThe dietary habits observed, such as the heavy reliance on cereals and limited intake of protein-rich foods, align with findings from rural communities in India where carbohydrate-centric diets prevail due to affordability and availability. A study in rural Tamil Nadu by Bose et al. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] also reported a high intake of cereals (over 90%) and low consumption of fruits, vegetables, and animal-based proteins, mirroring the dietary trends seen in our study. Internationally, studies on adolescents in low-income rural settings in countries like Nigeria and Brazil also show a similar pattern of high cereal consumption with low dietary diversity, suggesting that rural adolescents globally face comparable nutritional challenges [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Addressing these gaps requires policies that improve access to diverse food groups and nutrition education tailored to the local context [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe finding that a larger proportion of females were underweight (65.2%) and at higher risk of metabolic complications based on WHR (63.6%) reflects similar trends observed in other rural Indian studies. Research in rural Gujarat by Pradhan et al. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] reported that adolescent girls were more likely to be underweight and have a higher waist-to-hip ratio than boys, indicating consistent gender disparities across different rural regions. These gender differences can be attributed to cultural factors, dietary restrictions, and possibly greater physical workload among rural girls, who may engage in more household chores. In contrast, studies from urban areas often report a higher prevalence of obesity among girls, highlighting the urban-rural divide in risk factor profiles.\u003c/p\u003e \u003cp\u003eWhile 88.7% of participants in this study reported engaging in moderate-intensity activities, a smaller fraction (36.8%) participated in vigorous sports. The levels of physical activity observed in our study are comparable to those in other rural regions of India. For instance, a study conducted in rural Maharashtra found that approximately 85% of adolescents engaged in some form of physical activity, primarily moderate activities such as walking or helping with household chores, while less than 30% participated in organized sports or vigorous exercises [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. This indicates that adolescents in rural areas commonly engage in physical activities that are integrated into their daily routines rather than structured exercise programs. Similarly, a study in rural Tamil Nadu by Rajaraman et al. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] reported that while most adolescents were involved in moderate activities, less than 40% engaged in high-intensity physical activities. The limited participation in sports or structured exercise may be attributed to a lack of facilities, cultural norms, or prioritization of household or farming tasks over recreational activities. This trend is in line with findings from rural Ethiopia [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], where adolescents engaged more in daily moderate activities like walking and farming than structured sports. In contrast, studies from urban environments, such as urban schools in the Philippines [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], report higher participation in organized sports, likely due to better infrastructure and awareness. The disparity in physical activity intensity emphasizes the need for community-based interventions that promote sports and recreational activities in rural settings. Schools in rural areas could play a key role in providing structured physical activity programs.\u003c/p\u003e \u003cp\u003eThe study revealed moderate knowledge of cardiovascular risk factors, particularly lifestyle habits (71.93%) and diabetes (52.83%), but lower awareness of specific risk control measures (46.7%). These findings are consistent with similar research in rural Bangladesh by Alam et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], where adolescents demonstrated limited knowledge about cardiovascular disease prevention, reflecting a broader trend in South Asian rural communities. Compared to adolescents in urban areas, rural teenagers often have less access to health information, which could explain the lower levels of knowledge. Urban studies, such as those conducted in Mumbai [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], frequently report higher health literacy levels among adolescents, linked to better educational resources and awareness programs.\u003c/p\u003e \u003cp\u003eThe association between dietary diversity and socio-economic status, as well as maternal education, echoes findings from rural Andhra Pradesh by Reddy et al. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], where lower socio-economic classes had poorer dietary quality. This relationship suggests that poverty and lower education levels significantly hinder access to diverse, nutrient-rich foods. Globally, similar associations are seen in studies from rural China and sub-Saharan Africa [\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], where adolescents from lower-income families consume less diverse diets due to economic constraints. Addressing these socio-economic disparities through social support programs, school feeding initiatives, and targeted nutritional interventions is essential for improving adolescent health outcomes.\u003c/p\u003e\n\u003ch3\u003eLIMITATION:\u003c/h3\u003e\n\u003cp\u003eThe data on physical activity, dietary habits, and cardiovascular knowledge were self-reported by the participants, which could introduce recall bias or social desirability bias. The study was conducted in a specific rural area of Vijayapura District, Karnataka. As such, the findings may not be generalizable to adolescents in other rural areas or different regions of India, where cultural, social, and environmental factors might differ. While dietary diversity was assessed, micronutrient analysis and bio-physiological measures like blood glucose and lipid profiles were not included, which could be considered in future research.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eAddressing CVD risk factors in teenagers requires promoting diverse diets, increasing awareness about cardiovascular health and encouraging physical activity. Early interventions, particularly in low socio-economic groups, can significantly reduce future CVD risks. Community-based education programs, improved dietary habits, and promoting physical activity should be emphasized.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAUTHORS CONTRIBUTION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have contributed equally.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFINANCIAL SUPPORT AND SPONSORSHIP\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNil\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONFLICT OF INTEREST\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere are no conflicts of interest\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDECLARATION OF GENERATIVE AI AND AIASSISTED TECHNOLOGIES IN THE WRITING PROCESS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors haven’t used any generative AI/AI assisted technologies in the writing process.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eVoelker DK, Reel JJ, Greenleaf C. 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Nutr Res. 2014;34(1):57\u0026ndash;65. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.nutres.2013.11.002\u003c/span\u003e\u003cspan address=\"10.1016/j.nutres.2013.11.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":false,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cardiovascular Risk Factors, Teenagers, Physical Activity Questionnaire, Diet Diversity Score, Lifestyle Factors","lastPublishedDoi":"10.21203/rs.3.rs-9333640/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9333640/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe teenage years (ages 13\u0026ndash;19) are crucial for development. In India, 253\u0026nbsp;million teenagers shaping the nation's future, but rising cardiovascular disease (CVD) is a concern. Risk factors like poor diet, inactivity, and obesity begin in adolescence, leading to future heart issues. Although CVD manifests in adulthood, its roots often start young. In India, CVD causes over 28% of annual deaths, with a DALY rate 1.3 times the global average, highlighting need for early intervention.\u003c/p\u003e\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eIdentify prevalent cardiovascular risk factors \u0026amp; investigate socio-economic, environmental, and lifestyle factors influencing it among teenagers in rural setting.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA cross-sectional study was conducted among 106 teenagers (13\u0026ndash;19 years) in rural areas. Data were collected through interviews using structured questionnaires to gather socio-demographic profiles and assess cardiovascular risk factors such as physical activity, dietary habits, and family history. Diet Diversity Score (DDS) evaluated the variety in food consumption, while Standardized Physical Activity Questionnaire (PAQ) assessed physical-activity levels among participants.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe mean age was 15.41\u0026thinsp;\u0026plusmn;\u0026thinsp;1.98years (51.9% boys, 48.1% girls). Half belonged to Class IV or V of the modified BG Prasad socio-economic scale. Mean cardiovascular knowledge score was poor (34.91%) to fair (65.09%). The mean IDDS was 5.53\u0026thinsp;\u0026plusmn;\u0026thinsp;1.25, with 56.6% showing low dietary-diversity. Physical activity was inadequate, with minimal hours spent fitness activities.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eInadequate physical activity, high salt intake, high animal food consumption, and moderate intake of legumes, fruits, and vegetables can independently increase CVD, regardless of BMI. Most teenagers had poor to fair knowledge of CVD risk factors. Effective interventions are needed to improve cardiovascular health knowledge and promote diverse, healthy diets among rural adolescents.\u003c/p\u003e","manuscriptTitle":"To Evaluate the Cardiovascular Risk Factors Among Teenagers Residing in Rural Areas of Vijayapura District, Karnataka: A Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-15 17:48:14","doi":"10.21203/rs.3.rs-9333640/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-14T10:38:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"310841566704300738475468228888934138368","date":"2026-05-14T10:35:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-12T10:13:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"100732356076598184245301352539325853105","date":"2026-05-12T09:55:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"101607905930210911747099084312528197119","date":"2026-05-10T18:34:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"278776596777355732772865079156837442640","date":"2026-05-07T09:58:38+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-07T08:07:55+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-17T14:27:06+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-17T10:27:22+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-17T09:57:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Public Health","date":"2026-04-17T08:36:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e9020ee3-3e7a-4704-b86a-a946de8a2501","owner":[],"postedDate":"May 15th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-14T10:38:30+00:00","index":69,"fulltext":""},{"type":"reviewerAgreed","content":"310841566704300738475468228888934138368","date":"2026-05-14T10:35:52+00:00","index":67,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-12T10:13:09+00:00","index":66,"fulltext":""},{"type":"reviewerAgreed","content":"100732356076598184245301352539325853105","date":"2026-05-12T09:55:27+00:00","index":65,"fulltext":""},{"type":"reviewerAgreed","content":"101607905930210911747099084312528197119","date":"2026-05-10T18:34:18+00:00","index":60,"fulltext":""},{"type":"reviewerAgreed","content":"278776596777355732772865079156837442640","date":"2026-05-07T09:58:38+00:00","index":41,"fulltext":""},{"type":"reviewersInvited","content":"30","date":"2026-05-07T08:07:55+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-15T17:48:14+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-15 17:48:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9333640","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9333640","identity":"rs-9333640","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}