Sociodemographic Correlates and Prevalence of Modifiable Cardiovascular Disease Risk Factors Among University Students in North-Central Nigeria: A Cross-Sectional Study

Sociodemographic Correlates and Prevalence of Modifiable Cardiovascular Disease Risk Factors Among University Students in North-Central Nigeria: 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 Sociodemographic Correlates and Prevalence of Modifiable Cardiovascular Disease Risk Factors Among University Students in North-Central Nigeria: A Cross-Sectional Study Olasunkanmi Samson Coker, Adamu Ishaku Akyala, Regina Bolutife Coker This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8027940/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Cardiovascular diseases (CVDs) are increasingly affecting young adults in sub-Saharan Africa, yet evidence on demographic variations in risk factor prevalence among university students remains limited. Understanding "who" is most at risk is crucial for designing targeted prevention programs. This study examined sociodemographic correlates and prevalence of modifiable CVD risk factors among Nigerian university students. Methods: We conducted a cross-sectional study of 1,300 undergraduates from two universities in North-Central Nigeria between January and April 2025. Data were collected using the validated ABCD Risk Questionnaire supplemented with sociodemographic and behavioral risk factor assessments. Outcomes included CVD knowledge, risk perception, behavioral intentions, and prevalence of smoking, alcohol use, hypertension, diabetes, and family history of CVD. Statistical analyses employed chi-square tests, independent t-tests, ANOVA, Pearson correlations, and multiple linear regression. Results: The prevalence of modifiable risk factors was: current smoking 2.2%, current alcohol use 3.7%, self-reported hypertension 6.7%, diabetes 1.4%, and family history of CVD 14.6%. Risk factor clustering was minimal, with 83.7% having no behavioral risk factors. Significant demographic variations emerged: In Nasarawa State University, males had 6.3% lower CVD knowledge than females (p=0.002), while Muslims scored 5.9% lower than Christians (p=0.008). Religion significantly predicted risk perception, with Muslims perceiving 2.4% higher risk than Christians (β=2.361, p=0.007). Academic level negatively predicted exercise intentions (β=-1.228, p=0.003), with higher-level students showing lower readiness. CVD knowledge positively correlated with exercise intentions (r=0.231, p<0.001) and healthy eating intentions (r=0.138, p<0.001), but not with risk perception (r=-0.019, p=0.499). Overall, sociodemographic variables explained limited variance: knowledge (R²=1.1-4.4%), risk perception (R²=1.8%), exercise intentions (R²=2.1%), and dietary intentions (R²=0.7%). Conclusions: Nigerian university students exhibit inadequate CVD knowledge and low personal risk perception despite high readiness for healthy lifestyle changes. Critically, knowledge correlates with behavioral intentions but not with risk perception, revealing a selective disconnect where cognitive understanding translates into positive behavioral attitudes but not into personal vulnerability awareness. Public health interventions must address knowledge deficits through targeted education while simultaneously employing personalized risk assessment strategies to enhance risk awareness. The positive knowledge-behavior relationship provides a foundation for intervention, but the knowledge-risk perception disconnect requires deliberate strategies to calibrate personal risk awareness and effectively channel the existing high readiness for behavioral change. Trial registration: Not applicable. Cardiovascular disease risk factors demographics gender differences university students Nigeria prevalence health disparities 1. Background Cardiovascular diseases (CVDs), encompassing ischemic heart disease and stroke, represent the leading cause of global mortality, accounting for approximately 17.9 million deaths annually [1]. The epidemiological transition in low- and middle-income countries (LMICs) has been particularly dramatic, with over 75% of CVD deaths now occurring in these regions [2,3]. In Nigeria, Africa's most populous nation, CVD burden is escalating rapidly, with hypertension affecting 28-35% of adults and serving as a major driver of premature mortality [4,5]. The foundations of adult CVD risk are established in early adulthood, making university students (typically aged 18-25 years) a critical population for primary prevention [6,7]. This life stage coincides with significant transitions: independence from parental oversight, establishment of lasting lifestyle patterns, exposure to new social influences, and unique psychosocial stressors inherent to academic life [8,9]. The university environment itself can catalyze adoption of CVD risk behaviors including diets high in salt and saturated fats, physical inactivity, harmful alcohol use, and tobacco consumption [10,11]. The Imperative of Understanding "Who" is at Risk While the importance of CVD prevention in young adults is well-established, effective intervention design requires moving beyond population-level statistics to identify specific subgroups at heightened risk. Sociodemographic factors including gender, age, religion, academic level, and marital status substantially influence health behaviors, knowledge acquisition, risk perception, and responsiveness to health promotion messages [12-14]. Understanding these variations is not merely academic—it is essential for resource allocation, message tailoring, and program effectiveness. Gender represents a fundamental dimension of health disparities. Men and women differ in CVD risk factor prevalence, health-seeking behaviors, response to health messages, and barriers to lifestyle modification [15,16]. Young men often exhibit higher rates of tobacco use and harmful alcohol consumption, while women may face greater barriers to physical activity due to cultural norms and safety concerns [17,18]. Gender differences in health literacy and risk perception further complicate this landscape [19]. Religious affiliation in the Nigerian context carries profound implications for health behaviors. Islamic and Christian doctrines provide distinct frameworks for understanding health, acceptable behaviors (particularly regarding alcohol and tobacco), dietary practices, and gender roles [20,21]. Religious communities often serve as sources of health information and social support, potentially influencing both knowledge and behaviors [22]. Despite this significance, few studies have systematically examined religious variations in CVD risk profiles among Nigerian youth. Academic progression presents another potentially important correlate. One might hypothesize that advancing academic level correlates with increased health knowledge through cumulative education and maturity. Alternatively, higher-level students may face intensified academic pressures, reduced time for healthy lifestyle practices, and increased exposure to risk behaviors through peer networks [23,24]. Understanding whether knowledge and healthy intentions increase, decrease, or remain stable across academic levels has direct implications for timing and targeting of interventions. Age and marital status , while showing limited variability in university populations, may nonetheless reveal important patterns. Even the narrow age range typical of undergraduates may associate with developmental differences in risk perception and behavioral intentions [25]. Similarly, the small subset of married students may exhibit different risk profiles due to altered social roles, responsibilities, and support systems [26]. Knowledge Gaps in the Nigerian Context Research on CVD among Nigerian youth has predominantly focused on prevalence estimation of individual risk factors in specific institutions, with limited attention to demographic stratification [27,28]. While some studies document risk factor prevalence, they rarely employ comprehensive frameworks that simultaneously examine knowledge, risk perception, behavioral intentions, and actual risk behaviors across demographic subgroups [29,30]. This represents a critical gap because: 1. Targeted intervention design requires understanding not just that knowledge is suboptimal, but specifically *which* demographic groups have the greatest deficits 2. Cultural appropriateness demands insights into how religious and cultural identities shape CVD-related cognitions and behaviors 3. Resource efficiency necessitates identifying high-risk subgroups for intensive intervention while maintaining broader population-level efforts 4. Equity considerations require documentation of health disparities to ensure interventions reduce rather than exacerbate existing inequalities The present study addresses these gaps by providing comprehensive demographic stratification of CVD knowledge, risk perception, behavioral intentions, and modifiable risk factor prevalence among university students in North-Central Nigeria. Study Objectives This study sought to: 1. Determine the prevalence of modifiable cardiovascular risk factors (smoking, alcohol use, hypertension, diabetes, family history of CVD) and their clustering patterns 2. Examine demographic variations (gender, religion, academic level, age, marital status) in: a. CVD knowledge b. Risk perception c. Exercise intentions d. Healthy eating intentions 3. Identify demographic predictors of knowledge, risk perception, and behavioral intentions through multivariable regression analyses 4. Quantify the strength of associations between knowledge and behavioral intentions across demographic subgroups By answering these questions, this study provides evidence-based guidance for developing demographically targeted, culturally appropriate cardiovascular health promotion programs for Nigerian university students and similar populations across sub-Saharan Africa. 2. Method Study Design, Setting, and Period This cross-sectional study was conducted between January 2025 and April 2025 at two public universities in North-Central Nigeria: the University of Jos (UNIJOS), Plateau State, and Nasarawa State University, Keffi (NSUK), Nasarawa State. These institutions were selected for their large, diverse student populations representing varied sociodemographic backgrounds typical of the region. Study Population and Eligibility The study population comprised full-time undergraduate students enrolled during the 2024/2025 academic session. Eligible participants were aged 18 years or older and able to provide informed consent. Students enrolled in part-time or distance learning programs, or who had participated in structured cardiovascular health interventions in the preceding six months, were excluded. Sample Size Determination Sample size was calculated using the Lwanga and Lemeshow formula for a single population proportion [31]. With a 95% confidence level (Z=1.96), an assumed proportion (p) of 0.391 for high CVD knowledge from previous studies, and a 5% margin of error, an initial sample of 366 per university was calculated. After adjusting for a design effect of 1.5 (cluster sampling) and 10% non-response rate, the final target was 604 students per university, totaling 1,208 participants. The achieved sample was 1,300 students (UNIJOS: n=693; NSUK: n=607), exceeding the minimum requirement and providing adequate power to detect demographic differences. Sampling Technique A multistage sampling technique was employed: 1. Stratified Allocation : Sample distribution proportional to each university's enrollment (UNIJOS: 40,000 students; NSUK: 35,000 students) 2. Matched-Pair Cluster Selection : Seven comparable faculties (Agriculture, Arts, Education, Engineering, Environmental Sciences, Natural Sciences, Social Sciences) common to both universities were purposively selected, yielding 24 matched departments representing approximately 85% of total enrollment 3. Proportionate Allocation : Departmental sample sizes were proportional to enrollment, verified through class representatives and departmental records 4. Systematic Random Sampling : Within each department, every kth student (where k = total enrollment ÷ required sample) was approached during peak academic hours. If a selected student declined or was ineligible, the next student (k+1) was approached until the required sample was achieved Data Collection Instruments Primary Instrument - ABCD Risk Questionnaire:** The validated Attitudes and Beliefs about Cardiovascular Disease (ABCD) Risk Questionnaire [32], culturally adapted for Nigeria, comprised: CVD Knowledge : 8 true/false items assessing awareness of risk factors and prevention (score 0-8, converted to percentage; higher scores = greater knowledge) Perceived Risk : 8 items on 4-point Likert scale measuring subjective CVD vulnerability (score 8-32, converted to percentage; higher scores = greater perceived risk) Exercise Intentions : 7 items on 4-point Likert scale assessing physical activity readiness (score 7-28, converted to percentage; higher scores = stronger intentions) Healthy Eating Intentions: 3 items on 4-point Likert scale assessing dietary change intentions (score 3-12, converted to percentage; higher scores = stronger intentions) The ABCD questionnaire has demonstrated good internal consistency (Cronbach's α: 0.75-0.93 for subscales) [32]. Supplementary Questions : Additional items assessed: S ociodemographics : Age, gender, academic level, faculty, religion, marital status Health conditions : Self-reported hypertension, diabetes, family history of heart disease Behavioral risk factors : Current smoking status, current alcohol consumption Self-rated health : General health status on a 5-point scale Data Collection Procedures Research assistants from health-related disciplines received comprehensive two-day training on study objectives, ethical principles, informed consent procedures, questionnaire administration, and systematic sampling protocols. Data collection occurred over four weeks during the academic semester, avoiding examination periods. At predetermined departmental locations during peak class hours (8:00 AM - 2:00 PM), research assistants: 1. Approached eligible students according to systematic sampling protocol 2. Explained study purpose, voluntary participation, and confidentiality protections 3. Obtained written informed consent from willing participants 4. Provided questionnaires for self-completion in quiet areas (completion time: 15-20 minutes) 5. Conducted immediate completeness checks 6. Maintained logs of approaches, refusals, and successful enrollments Statistical Analysis Data were entered into IBM SPSS Statistics version 31.0.1.0 using double-entry verification for 10% of questionnaires to ensure accuracy. Data cleaning procedures included range checks, consistency verification, and appropriate handling of missing values. Descriptive Statistics : Frequencies, percentages, means, and standard deviations summarized participant characteristics and outcomes. Risk factor clustering was assessed by counting concurrent presence of smoking, alcohol use, and low physical activity readiness. Bivariate Analyses : · Chi-square tests compared categorical variables (knowledge categories, risk factor prevalence) across demographic groups · Independent t-tests compared continuous outcomes (knowledge, risk perception, behavioral intentions) between gender groups and universities · One-way ANOVA with post-hoc Tukey HSD tests compared outcomes across academic levels and religious groups Correlation Analyses : Pearson correlation coefficients examined relationships between: · Knowledge and risk perception · Knowledge and exercise intentions · Knowledge and healthy eating intentions Multivariable Analyses : Multiple linear regression models identified independent demographic predictors of: 1. CVD knowledge (separate models for each university due to differential patterns) 2. Risk perception (combined sample) 3. Exercise intentions (combined sample) 4. Healthy eating intentions (combined sample) Predictor variables included: age, gender, religion, marital status, and academic level. Model fit was assessed using R², adjusted R², and F-statistics. Multicollinearity was evaluated using variance inflation factors (VIF < 5 considered acceptable). Effect sizes were calculated using Cohen's d for t-tests (d=0.2, 0.5, 0.8 representing small, medium, large effects) and partial eta-squared (η²) for ANOVA. Statistical significance was set at p<0.05 (two-tailed). Ethical Considerations Ethical approval was obtained from the Research Ethics Committee of the Ministry of Health, Plateau State (Approval No: MOH/MIS/202/VOL I/XX, dated 25 th of January 2025. All participants provided written informed consent before participation, with explicit assurances of voluntary participation, right to withdraw without penalty, and data confidentiality through unique identifiers and secure storage. The study was conducted in accordance with the Declaration of Helsinki and the Nigerian National Code for Health Research Ethics. 3. Results Sociodemographic Characteristics A total of 1,300 students participated (response rate 100% of target), with 693 (53.3%) from UNIJOS and 607 (46.7%) from NSUK. Table 1 presents the sociodemographic distribution. Table 1: Sociodemographic Characteristics of Study Participants (N=1,300) Characteristic Category Frequency Percent Valid Percent Gender (n=1,287) Male 587 45.2 45.6 Female 689 53.0 53.5 Prefer not to say 11 0.8 0.9 Age (n=945) Mean ± SD 20.95 ± 3.55 years Range 15-43 years Academic Level (n=1,300) 100 level 692 53.2 53.2 200 level 222 17.1 17.1 300 level 275 21.2 21.2 400 level 73 5.6 5.6 500 level 38 2.9 2.9 Religion (n=1,282) Christianity 980 75.4 76.4 Islam 299 23.0 23.3 Traditional 3 0.2 0.2 Marital Status (n=1,279) Single 1,174 90.3 91.8 Married 69 5.3 5.4 Divorced 2 0.2 0.2 Widowed 1 0.1 0.1 Prefer not to say 33 2.5 2.6 Faculty (n=1,300) Natural Sciences 412 31.7 31.7 Arts 253 19.5 19.5 Social Sciences 232 17.8 17.8 Education 133 10.2 10.2 Environmental Sciences 112 8.6 8.6 Engineering 82 6.3 6.3 Agriculture 76 5.8 5.8 Note: Percentages may not sum to 100% due to rounding or missing data as indicated by varying 'n' values. SD = Standard Deviation. The sample was predominantly female (53.5%), young adults (mean age 20.95 years), first-year students (53.2%), Christian (76.4%), and single (91.8%). The distribution across faculties reflected the sampling strategy, with Natural Sciences (31.7%) and Arts (19.5%) representing the largest proportions. Prevalence of Modifiable Cardiovascular Risk Factors Table 2 presents the prevalence of modifiable CVD risk factors. Current smoking prevalence was low (2.2%), as was current alcohol use (3.7%). Self-reported hypertension affected 6.7% of participants, while diabetes prevalence was only 1.4%. Notably, 14.6% reported a family history of heart disease or stroke in immediate family members (parents or siblings). Table 2: Prevalence of Modifiable Cardiovascular Disease Risk Factors (N=1,300) Table: Prevalence of Cardiovascular Disease Risk Factors Among Study Participants Risk Factor Category Frequency Percent (%) 95% CI Smoking Status (n=1,286) Never smoked 1,202 93.5 92.2-94.6 Former smoker 56 4.4 3.3-5.6 Current smoker 28 2.2 1.4-3.1 Alcohol Use (n=1,286) Never consumed 1,163 90.4 88.9-91.8 Former consumer 76 5.9 4.7-7.3 Current consumer 47 3.7 2.7-4.8 Hypertension (n=1,278) No 1,192 93.3 91.9-94.5 Yes 86 6.7 5.4-8.2 Diabetes (n=1,286) No 1,268 98.6 97.9-99.1 Yes 18 1.4 0.8-2.2 Family History of CVD (n=1,283) No 1,096 85.4 83.6-87.1 Yes 187 14.6 12.7-16.6 CVD = Cardiovascular disease; CI = Confidence interval *Note: Current smoking and alcohol consumption represent modifiable behavioral risk factors, while hypertension, diabetes, and family history represent clinical and non-modifiable risk factors. Percentages may not sum to 100% due to rounding.* Risk Factor Clustering Analysis of risk factor clustering revealed that the majority of students (83.7%, n=1,080) had no behavioral risk factors (smoking, alcohol, low physical activity readiness), 12.8% (n=165) had one risk factor, 2.9% (n=37) had two risk factors, and only 0.6% (n=8) had three or more concurrent risk factors. This pattern indicates that multiple risk factor clustering is uncommon in this population. Demographic Variations in CVD Knowledge Overall CVD knowledge score was 62.13 ± 21.31% (Table 3). Only 22.6% of students achieved good knowledge (≥80%), with no significant difference between universities (UNIJOS: 19.9% vs NSUK: 25.8%, χ²=6.47, p=0.011). Table 3: CVD Knowledge by Sociodemographic Characteristics Characteristic Mean Knowledge Score (%) ± SD t/F statistic p-value Effect Size Overall 62.13 ± 21.31 - - - University t = 1.12 0.264 d = 0.06 • UNIJOS 61.50 ± 21.00 • NSUK 62.85 ± 21.65 Gender t = 1.84 0.066 d = 0.10 • Male 60.81 ± 21.58 • Female 63.24 ± 21.02 Religion t = 2.18 0.030 d = 0.13 • Christianity 62.89 ± 21.15 • Islam 59.95 ± 21.64 Academic Level F = 1.87 0.113 η² = 0.006 • 100 level 61.34 ± 21.52 • 200 level 63.58 ± 20.45 • 300 level 63.21 ± 21.37 • 400 level 60.45 ± 22.08 • 500 level 59.38 ± 21.89 Marital Status t = 0.93 0.354 d = 0.11 • Single 62.07 ± 21.25 • Married 64.49 ± 22.15 Note: SD = Standard Deviation; t = t-statistic from independent samples t-test; F = F-statistic from one-way ANOVA; d = Cohen's d; η² = Eta squared. Statistical significance set at p < 0.05. At the bivariate level, Christians demonstrated significantly higher knowledge than Muslims (62.89% vs 59.95%, p=0.030, d=0.13, small effect). Gender differences approached but did not reach significance (females: 63.24% vs males: 60.81%, p=0.066). Academic level showed no significant variation (p=0.113). Regression Analysis of Knowledge Predictors Multiple linear regression analyses were conducted separately for each university (Table 4) due to observed differential patterns in preliminary analyses. Table 4: Multiple Linear Regression Predicting CVD Knowledge Score by University Predictor UNIJOS (n=493) NSUK (n=426) B (95% CI) β p B (95% CI) β p Intercept 50.81 (35.80, 65.81) - <0.001 72.76 (54.49, 91.03) - <0.001 Age 0.55 (-0.03, 1.12) 0.086 0.062 -0.04 (-0.58, 0.50) -0.007 0.884 Gender (Female) -1.48 (-5.02, 2.07) -0.038 0.414 -6.28 (-10.25, -2.30) -0.150 0.002 Marital Status (Married) 0.67 (-3.49, 4.83) 0.015 0.753 6.14 (-3.21, 15.50) 0.062 0.197 Religion (Islam) 2.05 (-2.59, 6.69) 0.040 0.385 -5.86 (-10.17, -1.56) -0.129 0.008 Model Summary R² 0.011 0.044 Adjusted R² 0.003 0.035 F-statistic F(4,488) = 1.42 0.226 F(4,421) = 4.85 <0.001 Note: B = unstandardized coefficient; β = standardized beta coefficient; CI = confidence interval. Reference categories: Gender (Male), Marital Status (Single), Religion (Christianity). Bold values indicate statistical significance at p < 0.05. In UNIJOS, no demographic variables significantly predicted knowledge, and the overall model was non-significant (R²=0.011, p=0.226). In NSUK, however, both gender and religion emerged as significant predictors. Males scored 6.28% lower than females (p=0.002), and Muslims scored 5.86% lower than Christians (p=0.008), with the overall model explaining 4.4% of variance (p<0.001). Demographic Variations in Risk Perception Mean perceived CVD risk was 41.23 ± 12.16% of scale maximum. Table 5 presents demographic variations. Table 5: Perceived CVD Risk by Sociodemographic Characteristics Characteristic Mean Risk Perception (%) ± SD t/F statistic p-value Effect Size Overall 41.23 ± 12.16 - - - University t = -0.56 0.578 d = -0.03 • UNIJOS 41.05 ± 12.82 • NSUK 41.43 ± 11.36 Gender t = 0.58 0.561 d = 0.03 • Male 41.51 ± 12.43 • Female 41.08 ± 11.98 Religion t = -2.97 0.003 d = -0.18 • Christianity 40.65 ± 12.08 • Islam 43.09 ± 12.28 Academic Level F = 1.96 0.098 η² = 0.009 • 100 level 41.75 ± 12.13 • 200 level 40.95 ± 12.42 • 300 level 40.22 ± 11.88 • 400 level 39.84 ± 12.95 • 500 level 42.68 ± 11.35 Marital Status t = -0.85 0.395 d = -0.10 • Single 41.15 ± 12.15 • Married 42.51 ± 12.39 Note: SD = Standard Deviation; t = t-statistic from independent samples t-test; F = F-statistic from one-way ANOVA; d = Cohen's d; η² = Eta squared. Statistical significance set at p < 0.05. Religion was the only demographic variable showing significant bivariate association, with Muslims perceiving higher risk than Christians (43.09% vs 40.65%, p=0.003, d=-0.18, small effect). Regression Analysis of Risk Perception Predictors Multiple linear regression (Table 6) examined demographic predictors of risk perception in the combined sample (n=904). Table 6: Multiple Linear Regression Predicting Perceived CVD Risk Score Predictor B (95% CI) β t p-value Intercept 33.33 (27.14, 39.52) - 10.57 <0.001 Age 0.15 (-0.10, 0.39) 0.046 1.17 0.241 Gender (Female) -0.62 (-2.09, 0.86) -0.028 -0.82 0.411 Marital Status (Married) 1.13 (-1.03, 3.28) 0.034 1.03 0.305 Religion (Islam) 2.36 (0.64, 4.08) 0.090 2.69 0.007 Academic Level 0.57 (-0.22, 1.36) 0.054 1.41 0.159 Model Summary R² 0.018 Adjusted R² 0.013 F-statistic F(5,898) = 3.32 0.006 Note: B = unstandardized coefficient; β = standardized beta coefficient; CI = confidence interval. Reference categories: Gender (Male), Marital Status (Single), Religion (Christianity). Bold values indicate statistical significance at p < 0.05. Religion was the only significant predictor in the model. Religion emerged as the sole significant predictor, with Muslims perceiving 2.36% higher risk than Christians (p=0.007). However, the overall model explained only 1.8% of variance, indicating that measured demographic variables account for minimal variation in risk perception. Demographic Variations in Behavioral Intentions Exercise Intentions Mean exercise intention score was 79.80 ± 11.69%. Table 7 presents demographic variations. Table 7: Exercise Intentions by Sociodemographic Characteristics Characteristic Mean Exercise Intention (%) ± SD t/F statistic p-value Effect Size Overall 79.80 ± 11.69 - - - University t = 1.22 0.224 d = 0.07 • UNIJOS 80.17 ± 11.73 • NSUK 79.37 ± 11.65 Gender t = 0.67 0.502 d = 0.04 • Male 80.17 ± 11.85 • Female 79.64 ± 11.60 Religion t = 1.65 0.100 d = 0.10 • Christianity 80.20 ± 11.60 • Islam 78.75 ± 11.96 Academic Level F = 3.84 0.004 η² = 0.017 • 100 level 81.05 ± 11.48 • 200 level 79.52 ± 11.72 • 300 level 78.55 ± 11.93 • 400 level 76.82 ± 12.03 • 500 level 77.14 ± 11.85 Note: SD = Standard Deviation; t = t-statistic from independent samples t-test; F = F-statistic from one-way ANOVA; d = Cohen's d; η² = Eta squared. Statistical significance set at p < 0.05. Academic level was the only characteristic showing statistically significant differences in exercise intentions. Academic level showed significant variation (p=0.004), with a declining trend from first year (81.05%) to final years (76.82-77.14%). Post-hoc Tukey tests revealed significant differences between 100 level and both 400 level (p=0.021) and 500 level (p=0.048). Regression Analysis of Exercise Intention Predictors Table 8: Multiple Linear Regression Predicting Exercise Intentions Predictor B (95% CI) β t p-value Intercept 84.06 (77.72, 90.40) - 26.03 <0.001 Age 0.19 (-0.06, 0.45) 0.057 1.47 0.143 Gender (Female) -1.24 (-2.76, 0.28) -0.054 -1.60 0.109 Marital Status (Married) -1.48 (-3.74, 0.78) -0.043 -1.29 0.199 Religion (Islam) -2.07 (-3.86, -0.27) -0.075 -2.26 0.024 Academic Level -1.23 (-2.03, -0.43) -0.116 -3.01 0.003 Model Summary R² 0.021 Adjusted R² 0.016 F-statistic F(5,898) = 3.90 0.002 Note: B = unstandardized coefficient; β = standardized beta coefficient; CI = confidence interval. Reference categories: Gender (Male), Marital Status (Single), Religion (Christianity). Bold values indicate statistical significance at p < 0.05. Both religion and academic level were significant predictors of exercise intentions. Two significant predictors emerged: Religion (Muslims scored 2.07% lower than Christians, p=0.024) and Academic Level (each level increase associated with 1.23% decrease, p=0.003). The model explained 2.1% of variance. Healthy Eating Intentions Mean healthy eating intention score was 72.64 ± 14.41%. Table 9 presents demographic variations. Table 9: Healthy Eating Intentions by Sociodemographic Characteristics Characteristic Mean Healthy Eating Intention (%) ± SD t/F statistic p-value Effect Size Overall 72.64 ± 14.41 - - - University t = 1.15 0.252 d = 0.07 • UNIJOS 73.07 ± 14.12 • NSUK 72.14 ± 14.73 Gender t = -0.45 0.653 d = -0.03 • Male 72.42 ± 14.68 • Female 72.85 ± 14.21 Religion t = 0.88 0.379 d = 0.05 • Christianity 72.88 ± 14.36 • Islam 71.98 ± 14.57 Academic Level F = 1.84 0.119 η² = 0.008 • 100 level 73.42 ± 14.28 • 200 level 72.65 ± 14.41 • 300 level 71.88 ± 14.68 • 400 level 71.05 ± 14.92 • 500 level 70.38 ± 14.15 Marital Status t = -1.42 0.156 d = -0.17 • Single 72.51 ± 14.38 • Married 75.12 ± 14.68 Note: SD = Standard Deviation; t = t-statistic from independent samples t-test; F = F-statistic from one-way ANOVA; d = Cohen's d; η² = Eta squared. No statistically significant differences were found in healthy eating intentions across any sociodemographic characteristics (p > 0.05). No demographic variables showed significant bivariate associations with healthy eating intentions. Table 10: Multiple Linear Regression Predicting Healthy Eating Intentions Table 10: Multiple Linear Regression Predicting Healthy Eating Intentions Predictor B (95% CI) β t p-value Intercept 64.33 (56.28, 72.38) - 15.68 <0.001 Age 0.30 (-0.02, 0.62) 0.071 1.82 0.068 Gender (Female) 0.16 (-1.74, 2.07) 0.006 0.17 0.867 Marital Status (Married) 2.32 (-0.43, 5.06) 0.056 1.66 0.098 Religion (Islam) 0.31 (-1.92, 2.53) 0.009 0.27 0.787 Academic Level -0.61 (-1.62, 0.40) -0.046 -1.19 0.233 Model Summary R² 0.007 Adjusted R² 0.001 F-statistic F(5,897) = 1.27 0.275 Note: B = unstandardized coefficient; β = standardized beta coefficient; CI = confidence interval. Reference categories: Gender (Male), Marital Status (Single), Religion (Christianity). No predictors reached statistical significance at p < 0.05 in this model. None of the demographic predictors reached statistical significance, and the overall model was non-significant (R²=0.007, p=0.275), indicating that measured demographic variables do not meaningfully predict healthy eating intentions. Associations Between Knowledge and Behavioral Intentions Table 11 presents correlations between CVD knowledge and behavioral outcomes across the total sample and stratified by key demographic characteristics. Table 11: Correlations Between CVD Knowledge and Behavioral Outcomes Outcome Variable Overall (N=1,259-1,291) Males Females Christians Muslims Risk Perception r -0.019 -0.031 -0.008 -0.015 -0.037 p-value 0.499 0.485 0.842 0.652 0.532 Exercise Intentions r 0.231 0.218 0.241 0.235 0.219 p-value <0.001 <0.001 <0.001 <0.001 <0.001 Healthy Eating Intentions r 0.138 0.125 0.147 0.142 0.126 p-value <0.001 0.006 <0.001 <0.001 0.032 *Note: r = Pearson correlation coefficient. Bold values indicate statistically significant correlations (p 0.05). However, knowledge demonstrated significant positive correlations with both exercise intentions (r=0.231, p<0.001) and healthy eating intentions (r=0.138, p<0.001), with patterns consistent across gender and religious groups. The strength of these associations was small to moderate, indicating that while knowledge relates to behavioral intentions, it accounts for only 5.3% (exercise) and 1.9% (healthy eating) of variance. 4. Discussion This study provides comprehensive documentation of sociodemographic variations in CVD knowledge, risk perception, behavioral intentions, and modifiable risk factor prevalence among 1,300 Nigerian university students. The findings reveal low prevalence of traditional behavioral risk factors (smoking 2.2%, alcohol use 3.7%), but identify important demographic disparities in knowledge and behavioral readiness that have direct implications for targeted intervention design. Summary of Key Findings 1. Low risk factor prevalence with minimal clustering: 83.7% had no behavioral risk factors 2. Moderate CVD knowledge: (62.13%), with only 22.6% achieving good knowledge (≥80%) 3. Significant gender and religious disparities in knowledge: Males and Muslims demonstrated lower knowledge in NSUK 4. Religious influence on risk perception: Muslims perceived higher risk than Christians (43.09% vs 40.65%) 5. Academic level negatively predicts behavioral readiness 6. Knowledge-behavior associations: Knowledge correlated with behavioral intentions (r=0.23 for exercise, r=0.14 for diet) but not risk perception (r=-0.02) 7. Limited demographic explanatory power: Sociodemographic variables explained only 0.7-4.4% of variance in outcomes Interpretation of Findings Low Prevalence but Critical Disparities The remarkably low prevalence of current smoking (2.2%) and alcohol use (3.7%) in this sample contrasts sharply with rates in many high-income country universities where smoking rates typically range from 10-30% and alcohol use from 40-80% among students [33,34]. This finding aligns with other Nigerian studies reporting smoking prevalence of 2-5% among university students [35,36], suggesting protective effects of cultural norms, religious values (particularly Islamic prohibition of alcohol), strong family influences, and possibly social desirability in reporting. However, the 14.6% prevalence of family history of CVD represents a substantial at-risk subgroup, particularly concerning given that only 56.9% of the overall sample recognized family history as a risk factor (from Paper 1). This disconnect indicates missed opportunities for targeted screening and prevention among genetically predisposed individuals. The 6.7% prevalence of self-reported hypertension among university-aged students (mean age 20.95 years) is concerning and likely represents an underestimate, as hypertension is often asymptomatic and requires clinical measurement for detection. Studies employing objective blood pressure measurement in similar Nigerian populations have documented prevalence rates of 15-25% [37,38], suggesting that the true burden may be substantially higher than reported here. Gender Disparities: A Complex Pattern The finding that males demonstrated significantly lower CVD knowledge than females in NSUK (6.3% difference, p=0.002) but not in UNIJOS represents an intriguing institutional variation. This gender gap in health knowledge is well-documented globally, with women typically demonstrating higher health literacy, greater engagement with health information, and more proactive health-seeking behaviors [39,40]. Several mechanisms may explain this pattern: 1. Differential exposure to health information : Women may have greater exposure to health content through family care responsibilities, media consumption patterns, and social networks that prioritize health discussions [41] 2. Educational engagement differences : Gender differences in academic engagement and help-seeking behaviors may influence knowledge acquisition, with women more likely to attend optional health education sessions or seek additional information [42] 3. Social desirability : Women may feel greater social pressure to demonstrate health knowledge, potentially inflating their scores relative to actual applied knowledge [43] Importantly, despite this knowledge gap, gender did not significantly predict risk perception or behavioral intentions in multivariable models. This dissociation between knowledge and other CVD-related constructs reinforces findings from Paper 1 regarding the independence of these domains and suggests that closing the gender knowledge gap alone will not necessarily translate to improved risk awareness or behaviors among males. The absence of gender differences in behavioral intentions challenges stereotypes about male-female health behavior patterns. In Western contexts, men often demonstrate poorer dietary quality and lower health service utilization [44], but our findings suggest more gender parity in intention among Nigerian university students. This may reflect cohort effects, changing gender norms among educated youth, or limitations of intention measures in predicting actual behavior. Religious Influences: Dual Pathways Religion emerged as a significant predictor across multiple outcomes, but with seemingly paradoxical effects. Muslims demonstrated: · Lower CVD knowledge (-5.9%, p=0.008) in NSUK · Higher perceived risk (+2.4%, p=0.007) · Lower exercise intentions (-2.1%, p=0.024) This pattern suggests that religious affiliation operates through multiple pathways to influence cardiovascular health: Knowledge Pathway : The lower knowledge among Muslims may reflect: · Language barriers if health education materials are predominantly in English rather than Hausa or other languages · Different information sources, with Islamic religious leaders potentially emphasizing different health topics than Christian counterparts · Potential socioeconomic confounding, as religion may correlate with other unmeasured variables (parental education, rural/urban origin, socioeconomic status) Risk Perception Pathway : The heightened risk perception among Muslims (despite lower knowledge) may indicate: · Greater fatalistic health beliefs common in some Islamic interpretations, where illness is viewed as divinely ordained [45,46] · Different framing of health risks within religious teachings, possibly emphasizing vulnerability and mortality · Cultural differences in expressing health concerns or anxiety that manifest as higher perceived risk scores Behavioral Pathway : Lower exercise intentions among Muslims may reflect: · Gender-specific barriers, particularly for Muslim women facing cultural restrictions on mixed-gender physical activity or concerns about modest dress during exercise [47,48] · Different priorities in health behavior, with greater emphasis on dietary regulation (halal practices) than physical activity · Infrastructure barriers if campus facilities do not accommodate religious requirements (prayer times, gender-segregated spaces) These findings have critical implications for intervention design. A "one-size-fits-all" approach risks widening disparities. Instead, interventions should: · Develop culturally tailored educational materials available in multiple languages · Partner with Islamic religious leaders to deliver health messages in culturally resonant frameworks · Address structural barriers to physical activity for Muslim students (gender-segregated facilities, flexible scheduling around prayer times) · Recognize that higher risk perception among Muslims may be leveraged as a motivational resource if appropriately channeled The Academic Level Paradox The significant negative association between academic level and exercise intentions (β=-1.23 per level, p=0.003) represents a concerning trend. One might expect that advancing through university would correlate with increased health knowledge and maintained or improved behavioral intentions. Instead, we observe declining readiness for physical activity from first year (81.05%) to final years (76.82-77.14%). Several explanations merit consideration: 1. Increasing Academic Pressure : As students progress through university, academic demands typically intensify, particularly in final years when major projects, dissertations, and comprehensive examinations dominate. Time constraints and stress may reduce both available time for exercise and prioritization of health behaviors [49,50] 2. Transition from Intention to Reality **: First-year students may express high intentions based on idealistic goals upon university entry. As they progress, these intentions may be "corrected" downward to reflect actual behavioral patterns and realistic self-assessment [51] 3. Social Network Evolution **: Early university years often involve joining sports teams, fitness clubs, and active social groups. As students progress, friendship networks may stabilize around less active pursuits, particularly as peer groups form around shared academic interests [52] 4. Desensitization to Health Messages **: Repeated exposure to health promotion messages without reinforcement or visible consequences may lead to habituation and reduced responsiveness [53] 5. Competing Priorities **: Final-year students face impending career decisions, job searches, and major life transitions that may relegate health behaviors to lower priority [54] This finding has important timing implications for interventions. Programs should: · Prioritize engagement early in university careers when intentions are highest · Implement booster sessions and renewed engagement strategies for upper-level students · Address structural barriers specific to advanced students (thesis deadlines, practicum schedules) · Incorporate stress management and time management skills to help students maintain health behaviors amid increasing demands Notably, academic level did not significantly predict healthy eating intentions, suggesting that dietary intentions may be more resistant to the pressures of academic progression, possibly because eating is a necessity that must be accommodated regardless of schedule , whereas exercise is more discretionary . Knowledge-Behavior Associations: Modest but Meaningful The significant positive correlations between knowledge and behavioral intentions (r=0.231 for exercise, r=0.138 for diet) provide evidence that knowledge does relate to motivation for healthy behaviors, even though these associations are modest (accounting for 5.3% and 1.9% of variance respectively). These findings align with meta-analytic evidence showing that health knowledge typically correlates weakly to moderately with health behaviors (r=0.15-0.35) [55,56]. The relationships we observed are consistent across demographic subgroups, suggesting that the knowledge-intention pathway operates similarly for males and females, Christians and Muslims. Critically, the absence of correlation between knowledge and risk perception (r=-0.019, p=0.499) confirmed in Paper 1 persists across all demographic subgroups, indicating that this independence is a general phenomenon rather than an artifact of particular demographic compositions. This reinforces the necessity of dual-track interventions that separately target knowledge enhancement and risk perception calibration. The stronger correlation of knowledge with exercise intentions (r=0.231) than dietary intentions (r=0.138) may reflect differential complexity of these behaviors. Physical activity requires deliberate planning and dedicated time, whereas dietary choices are made multiple times daily and may be more influenced by habitual patterns, environmental availability, and social contexts that override knowledge-based decision making [57,58]. Limited Demographic Explanatory Power: The Search for Missing Variables Perhaps the most striking finding across all regression models is the limited variance explained by sociodemographic variables: knowledge (1.1-4.4%), risk perception (1.8%), exercise intentions (2.1%), and dietary intentions (0.7%). This indicates that the demographic characteristics we measured—gender, religion, age, marital status, academic level—account for very little of the substantial variability observed in outcomes. This finding suggests several possibilities: 1. Individual-level psychological factors matter more : Constructs such as self-efficacy, health locus of control, personality traits, past health experiences, and health literacy may be more powerful predictors than demographic categories [59,60] 2. Social network influences : Peer behaviors, family support, and social norms within friendship groups may exert stronger influences than demographic memberships [61] 3. Environmental factors : Campus infrastructure (food availability, fitness facilities), neighborhood characteristics, and living situations (on/off campus, with family/roommates) may substantially shape both knowledge acquisition and behavioral patterns [62] 4. Measurement limitations : Our categorical assessment of demographics may miss important within-category variation. For example, "Muslim" encompasses substantial heterogeneity in practice intensity, sectarian affiliation, and cultural background 5. Complex interactions : The effects of demographic variables may depend on unmeasured moderators, such that simple additive models fail to capture important effect modification These considerations point to the need for expanded theoretical models that incorporate: · Psychological constructs from the Theory of Planned Behavior (attitudes, subjective norms, perceived behavioral control) [63] · Social Cognitive Theory elements (observational learning, self-efficacy, outcome expectations) [64] · Environmental/contextual factors emphasized in socio-ecological models [65] Future research should employ comprehensive assessment batteries capturing these additional constructs to develop more complete explanatory models. Comparison with International Literature Our findings show both convergence and divergence from patterns observed in other settings: Convergent findings · Gender differences in health knowledge favoring females replicate patterns in Europe, North America, and Asia [66,67] · Academic pressure correlating with reduced physical activity is documented in multiple university contexts [68,69] · Modest knowledge-behavior correlations align with meta-analytic evidence [55,56] Divergent findings · The low prevalence of smoking and alcohol use contrasts sharply with Western university populations [33,34] · The specific pattern of religious influences (Muslims showing both lower knowledge and higher risk perception) may be culturally specific · The absence of age effects differs from some lifespan studies showing developmental changes in risk perception [70], though this may reflect the narrow age range in our sample The Belgian/English study referenced in Paper 1 [12] found that education level and income predicted CVD knowledge and healthy diet intentions in European vulnerable communities. Our findings suggest that in the Nigerian university context (where income and education variation is more restricted), religion and gender become more salient differentiating factors. This highlights the importance of context-specific research rather than assuming universal demographic patterns. Implications for Practice These findings provide actionable guidance for CVD prevention programs targeting university students: For University Health Services: 1. Develop gender-sensitive approaches : a. Create male-friendly health education formats that engage men's learning preferences (competitive elements, hands-on activities, peer-led sessions) b. Train peer educators from both genders to reach same-gender students c. Frame health messages in ways that resonate with masculine identity (strength, performance, leadership) without reinforcing harmful stereotypes 2. Implement culturally tailored interventions : a. Partner with campus religious organizations (Christian fellowships, Muslim Student Societies) to deliver health messages through trusted faith leaders b. Develop Islamic-appropriate physical activity programming (gender-segregated facilities, hijab-friendly athletic wear, flexible scheduling) c. Create multilingual health resources in English, Hausa, Yoruba, and Igbo d. Respect religious dietary practices while promoting heart-healthy adaptations of cultural cuisines 3. Prioritize early engagement with declining intensity maintenance : a. Implement mandatory health orientation during first-year enrollment when intentions are highest b. Establish healthy behavior patterns early through freshman-specific programming c. Provide booster interventions in 300-400 levels targeting the declining readiness d. Integrate health promotion into academic curricula to maintain salience throughout university career 4. Target the 14.6% with family history : a. Systematically screen all students for family CVD history during enrollment health checks b. Provide enhanced education and counseling to those with positive family history c. Offer subsidized or free biometric screening (blood pressure, glucose, BMI) to high-risk subgroups d. Connect students with family history to ongoing monitoring and support programs 5. Implement dual-track programming: a. Simultaneously address knowledge gaps (particularly regarding family history and alcohol-cholesterol relationships) b. Provide personalized risk assessment tools that enhance appropriately calibrated risk perception c. Recognize that knowledge enhancement alone is insufficient—must also address risk perception, self-efficacy, and environmental barriers For University Administrators and Policy Makers: 1. Create enabling environments : a. Ensure fitness facilities accommodate diverse cultural needs (gender-segregated hours, prayer spaces, culturally appropriate facilities) b. Require minimum standards for campus food vendors (percentage of healthy options, nutrition labeling, limits on trans fats) c. Design walkable, activity-friendly campuses with safe pedestrian infrastructure d. Provide affordable healthy food options in dormitories and cafeterias 2. Mandate comprehensive health screening : a. Require blood pressure, BMI, and glucose screening during enrollment with results linked to individualized counseling b. Establish tracking systems to monitor health indicators throughout university career c. Provide free or subsidized follow-up for students with identified risk factors 3. Support peer education programs : a. Fund training for student health ambassadors from diverse demographic backgrounds b. Leverage social networks and peer influence for health behavior change c. Create recognition and incentive systems for peer educator participation 4. Integrate cardiovascular health into curriculum : a. Incorporate CVD prevention modules into general education requirements b. Ensure health-related majors receive comprehensive training in CVD epidemiology c. Use academic settings to normalize health discussions and reduce stigma For Future Research : 1. Longitudinal cohort studies : Follow students from enrollment through graduation and beyond to: a. Understand trajectories of knowledge, risk perception, and behaviors over time b. Identify critical transition points where intervention would be most effective c. Assess whether university-based interventions yield lasting benefits post-graduation d. Examine whether intentions translate to actual behaviors 2. Expanded explanatory models : Incorporate additional variables: a. Psychological constructs (self-efficacy, health locus of control, health literacy, implicit attitudes) b. Social factors (peer behaviors, family support, social norms, social capital) c. Environmental factors (food availability, facility access, neighborhood walkability, living situation) d. Use advanced analytic techniques (structural equation modeling, machine learning) to identify complex interaction patterns 3. Intervention trials : Conduct rigorous randomized controlled trials testing: a. Gender-tailored vs. gender-neutral CVD education b. Faith-based vs. secular health promotion approaches c. Timing comparisons (first-year intensive vs. distributed across years) d. Dual-track (knowledge + risk assessment) vs. single-component interventions 4. Objective measurement studies : a. Validate self-reported risk factors with biomarkers (cotinine for smoking, objective BP measurement, accelerometry for physical activity) b. Assess actual behaviors vs. intentions through ecological momentary assessment c. Conduct metabolic phenotyping to determine actual CVD risk in this population 5. Qualitative investigations : Explore through interviews and focus groups: a. How male students perceive and engage with health information b. Religious influences on health beliefs and behaviors in students' own words c. Mechanisms underlying the academic level-behavioral intention relationship d. Barriers and facilitators to translating intentions into actions 6. Multi-site regional studies : Expand to other Nigerian regions to: a. Test generalizability of demographic patterns across cultural contexts b. Examine influence of Hausa, Yoruba, Igbo, and other ethnic identities c. Compare urban vs. rural origin students d. Assess institutional factors (public vs. private, religious vs. secular universities) Strengths and Limitations Strength This study provides several important contributions: 1. Large, representative sample (N=1,300) from two universities with systematic sampling enhances generalizability to Nigerian university students in North-Central region 2. Comprehensive demographic assessment allows identification of specific vulnerable subgroups, moving beyond population-level averages to inform targeted interventions 3. Multiple outcome domains (knowledge, risk perception, behavioral intentions) assessed with validated instruments provide nuanced understanding of demographic influences 4. Risk factor prevalence data establishes baseline rates for Nigerian university students, filling a gap in the epidemiological literature 5. Subgroup analyses across gender and religious groups reveal consistency and variability in associations, enhancing understanding of pattern generality 6. Practical implications directly inform intervention design with specific, actionable recommendations Limitations: Several limitations warrant acknowledgment: 1. Cross-sectional design precludes causal inference. We cannot determine whether, for example, advancing academic level causes declining exercise intentions or whether students prone to declining intentions are more likely to persist to advanced levels. Longitudinal designs are needed. 2. Self-reported data introduces multiple biases: a. Social desirability may inflate knowledge scores and behavioral intentions while underreporting smoking/alcohol b. Recall bias may affect reporting of health conditions and behaviors c. Differential reporting across demographic groups (e.g., Muslims may underreport alcohol use more than Christians due to religious prohibition) 3. Absence of objective risk factor measurement: Reliance on self-reported hypertension and diabetes substantially underestimates true prevalence. Studies with objective measurement document 2-3 times higher rates than self-report [37,38] 4. Behavioral intentions vs. actual behaviors: We measured stated intentions, which meta-analyses show correlate only moderately (r≈0.45) with actual behaviors [71]. Our findings regarding high behavioral readiness require validation through objective behavioral measurement 5. Low variance explained by models: The limited explanatory power (R²=0.7-4.4%) indicates that important predictors remain unmeasured. Our findings describe "who" differs in outcomes but do not fully explain "why" these differences exist 6. Unmeasured confounding: Variables correlated with demographics but not measured directly (socioeconomic status, parental education, urban/rural origin, ethnic identity) may account for observed associations. Religion, in particular, may proxy for unmeasured cultural and socioeconomic factors 7. Generalizability limits: Two universities in one region may not represent all Nigerian university contexts. Findings may differ in other regions with different cultural, ethnic, and religious compositions 8. Sample characteristics: Predominantly first-year students (53.2%) may limit generalizability to upper-level students. However, this composition reflects actual university enrollment patterns 9. Multiple comparisons: With numerous statistical tests performed, some significant findings (particularly those at p<0.05) may represent Type I errors. Results should be interpreted in context of effect sizes and consistency across analyses 10. Binary gender assessment: Our questionnaire included only male/female/prefer-not-to-say options, failing to capture gender diversity and potentially misrepresenting gender identities Despite these limitations, the study's large sample, systematic methodology, validated instruments, and comprehensive demographic assessment provide valuable evidence for understanding CVD-related disparities among Nigerian university students. 5. Conclusions This comprehensive demographic analysis of 1,300 Nigerian university students reveals low prevalence of traditional behavioral risk factors (smoking 2.2%, alcohol 3.7%) but identifies critical disparities in CVD knowledge and behavioral readiness across demographic subgroups. Male students and Muslims demonstrate lower CVD knowledge, while Muslims paradoxically perceive higher personal risk despite lower knowledge. Academic progression associates with declining exercise intentions, suggesting increasing academic pressure undermines behavioral readiness over time. These findings challenge one-size-fits-all approaches to university-based CVD prevention. Effective interventions must be demographically targeted and culturally tailored, specifically addressing: · Knowledge gaps among male students and Muslims through gender-sensitive and faith-integrated education · Structural barriers to physical activity for Muslim students through culturally appropriate facilities · Declining behavioral readiness among advanced students through timing-optimized programming The 14.6% of students with family history of CVD represent a critical high-risk subgroup currently underserved, with only 56.9% of the overall sample recognizing family history as a risk factor. Systematic screening and targeted interventions for this genetically predisposed population should be prioritized. While CVD knowledge correlates positively with behavioral intentions (r=0.23-0.14), it remains independent of risk perception (r=-0.02), confirming that knowledge enhancement alone is insufficient. Dual-track interventions simultaneously addressing knowledge gaps and risk perception through personalized assessment are essential. The limited variance explained by demographics (R²<5%) indicates that individual psychological factors, social networks, and environmental contexts likely exert stronger influences than demographic categories alone. Future research should employ comprehensive models incorporating these additional dimensions to develop more complete explanatory frameworks and more effective interventions. By identifying "who" is most vulnerable and demonstrating that demographic patterns vary across outcome domains, this study provides essential evidence for designing equity-oriented, culturally responsive cardiovascular health promotion programs that reach the diverse student populations comprising Nigeria's future leadership. Abbreviations ABCD - Attitudes and Beliefs about Cardiovascular Disease ANOVA - Analysis of Variance BMI - Body Mass Index CI - Confidence Interval CVD - Cardiovascular Disease LMICs - Low- and Middle-Income Countries NSUK - Nasarawa State University, Keffi SD - Standard Deviation SPSS - Statistical Package for the Social Sciences UNIJOS - University of Jos VIF - Variance Inflation Factor Declarations This study was approved by the Research Ethics Committees of the Ministry of Health, Plateau State (Approval No MOH/MIS/202/VOL I/XX). All participants provided written informed consent before participation. The study was conducted in accordance with the Declaration of Helsinki and Nigerian National Code for Health Research Ethics. Consent for publication Not applicable. This manuscript does not contain any individual person's data in any form. Availability of data and materials The datasets generated and analyzed during the current study are available from the corresponding author (O.S.C.) on reasonable request, subject to ethical approval and institutional data sharing agreements. Due to ethical restrictions and participant confidentiality protections, data cannot be made publicly available. Requests should be directed to [email protected] . Competing interests The authors declare that they have no competing interests. Funding This research received no external funding. All costs were borne by the authors. Authors' contributions OSC conceptualized and designed the study, developed data collection instruments, obtained ethical approvals, supervised all aspects of data collection, conducted statistical analyses, interpreted results, drafted the initial manuscript, and revised all subsequent drafts. AIA contributed to study design and methodology, provided technical guidance on sampling procedures and data analysis, assisted with data interpretation, and critically reviewed and edited the manuscript for important intellectual content. RBC coordinated field data collection activities, trained and supervised research assistants, ensured data quality and completeness, contributed to preliminary data analysis, and critically reviewed the manuscript. All authors read and approved the final manuscript and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. Acknowledgements We gratefully acknowledge the participation of students from the University of Jos and Nasarawa State University, Keffi. We thank the university administrations for granting permission to conduct this study. 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Emerging adulthood: A theory of development from the late teens through the twenties. Am Psychol. 2000;55(5):469-80. Armitage CJ, Conner M. Efficacy of the Theory of Planned Behaviour: a meta-analytic review. Br J Soc Psychol. 2001;40(Pt 4):471-99. McEachan RR, Conner M, Taylor NJ, Lawton RJ. Prospective prediction of health-related behaviours with the Theory of Planned Behaviour: a meta-analysis. Health Psychol Rev. 2011;5(2):97-144. Devine CM, Connors M, Bisogni CA, Sobal J. Life-course influences on fruit and vegetable trajectories: qualitative analysis of food choices. J Nutr Educ. 1998;30(6):361-70. Story M, Neumark-Sztainer D, French S. Individual and environmental influences on adolescent eating behaviors. J Am Diet Assoc. 2002;102(3 Suppl):S40-51. Bandura A. Health promotion by social cognitive means. Health Educ Behav. 2004;31(2):143-64. Wallston KA, Wallston BS, DeVellis R. Development of the Multidimensional Health Locus of Control (MHLC) Scales. Health Educ Monogr. 1978;6(2):160-70. Powell K, et al. Social network influences on health behaviors: A systematic review. Soc Sci Med. 2020;251:112924. Golden SD, et al. Social ecological approaches to individuals and their contexts: twenty years of health education & behavior health promotion interventions. Health Educ Behav. 2015;42(3):364-72. Ajzen I. The theory of planned behavior. Organ Behav Hum Decis Process. 1991;50(2):179-211. Bandura A. Social cognitive theory: an agentic perspective. Annu Rev Psychol. 2001;52:1-26. Sallis JF, et al. Use of science to guide the design of indicators for sustainable health system reform: the sustainable health system reform indicators project. BMJ Qual Saf. 2020;29(3):247-55. Beardsworth A, et al. Food choice, gender and intensive parenting styles: do 'good' mothers mirror their daughters more than their sons? Appetite. 2020;147:102907. Ayalew MB, Tesfa GA, Ayele FY, Tilahun BD. Knowledge of cardiovascular disease risk factors, practice and barriers among community pharmacists in Northwest Ethiopia: a cross-sectional study. Metabol Open. 2022;16:100219. Pengpid S, et al. Physical inactivity and associated factors among university students in 23 low-, middle- and high-income countries. Int J Public Health. 2019;64(4):539-51. Wang X, et al. Physical activity levels among college students: a systematic review and meta-analysis. J Am Coll Health. 2020;68(5):344-53. Casey BJ, et al. The impact of stress on adolescent decision-making. J Adolesc Health. 2020;67(3S):S39-44. Sheeran P, Webb TL. The intention–behavior gap. Soc Personal Psychol Compass . 2016;10(9):503-518. Additional Declarations No competing interests reported. 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Background","content":"\u003cp\u003eCardiovascular diseases (CVDs), encompassing ischemic heart disease and stroke, represent the leading cause of global mortality, accounting for approximately 17.9 million deaths annually [1]. The epidemiological transition in low- and middle-income countries (LMICs) has been particularly dramatic, with over 75% of CVD deaths now occurring in these regions [2,3]. In Nigeria, Africa's most populous nation, CVD burden is escalating rapidly, with hypertension affecting 28-35% of adults and serving as a major driver of premature mortality [4,5].\u003c/p\u003e\n\u003cp\u003eThe foundations of adult CVD risk are established in early adulthood, making university students (typically aged 18-25 years) a critical population for primary prevention [6,7]. This life stage coincides with significant transitions: independence from parental oversight, establishment of lasting lifestyle patterns, exposure to new social influences, and unique psychosocial stressors inherent to academic life [8,9]. The university environment itself can catalyze adoption of CVD risk behaviors including diets high in salt and saturated fats, physical inactivity, harmful alcohol use, and tobacco consumption [10,11].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe Imperative of Understanding \"Who\" is at Risk\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhile the importance of CVD prevention in young adults is well-established, effective intervention design requires moving beyond population-level statistics to identify specific subgroups at heightened risk. Sociodemographic factors including gender, age, religion, academic level, and marital status substantially influence health behaviors, knowledge acquisition, risk perception, and responsiveness to health promotion messages [12-14]. Understanding these variations is not merely academic—it is essential for resource allocation, message tailoring, and program effectiveness.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGender\u0026nbsp;\u003c/strong\u003erepresents a fundamental dimension of health disparities. Men and women differ in CVD risk factor prevalence, health-seeking behaviors, response to health messages, and barriers to lifestyle modification [15,16]. Young men often exhibit higher rates of tobacco use and harmful alcohol consumption, while women may face greater barriers to physical activity due to cultural norms and safety concerns [17,18]. Gender differences in health literacy and risk perception further complicate this landscape [19].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReligious affiliation\u003c/strong\u003e in the Nigerian context carries profound implications for health behaviors. Islamic and Christian doctrines provide distinct frameworks for understanding health, acceptable behaviors (particularly regarding alcohol and tobacco), dietary practices, and gender roles [20,21]. Religious communities often serve as sources of health information and social support, potentially influencing both knowledge and behaviors [22]. Despite this significance, few studies have systematically examined religious variations in CVD risk profiles among Nigerian youth.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcademic progression\u003c/strong\u003e presents another potentially important correlate. One might hypothesize that advancing academic level correlates with increased health knowledge through cumulative education and maturity. Alternatively, higher-level students may face intensified academic pressures, reduced time for healthy lifestyle practices, and increased exposure to risk behaviors through peer networks [23,24]. Understanding whether knowledge and healthy intentions increase, decrease, or remain stable across academic levels has direct implications for timing and targeting of interventions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAge and marital status\u003c/strong\u003e, while showing limited variability in university populations, may nonetheless reveal important patterns. Even the narrow age range typical of undergraduates may associate with developmental differences in risk perception and behavioral intentions [25]. Similarly, the small subset of married students may exhibit different risk profiles due to altered social roles, responsibilities, and support systems [26].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKnowledge Gaps in the Nigerian Context\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResearch on CVD among Nigerian youth has predominantly focused on prevalence estimation of individual risk factors in specific institutions, with limited attention to demographic stratification [27,28]. While some studies document risk factor prevalence, they rarely employ comprehensive frameworks that simultaneously examine knowledge, risk perception, behavioral intentions, and actual risk behaviors across demographic subgroups [29,30]. This represents a critical gap because:\u003c/p\u003e\n\u003cp\u003e1. \u003cstrong\u003eTargeted intervention design\u003c/strong\u003e requires understanding not just that knowledge is suboptimal, but specifically *which* demographic groups have the greatest deficits\u003c/p\u003e\n\u003cp\u003e2. \u003cstrong\u003eCultural appropriateness\u003c/strong\u003e demands insights into how religious and cultural identities shape CVD-related cognitions and behaviors\u003c/p\u003e\n\u003cp\u003e3. \u003cstrong\u003eResource efficiency\u003c/strong\u003e necessitates identifying high-risk subgroups for intensive intervention while maintaining broader population-level efforts\u003c/p\u003e\n\u003cp\u003e4. \u003cstrong\u003eEquity considerations\u003c/strong\u003e require documentation of health disparities to ensure interventions reduce rather than exacerbate existing inequalities\u003c/p\u003e\n\u003cp\u003eThe present study addresses these gaps by providing comprehensive demographic stratification of CVD knowledge, risk perception, behavioral intentions, and modifiable risk factor prevalence among university students in North-Central Nigeria.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Objectives\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study sought to:\u003c/p\u003e\n\u003cp\u003e1.\u0026nbsp; \u0026nbsp;Determine the prevalence of modifiable cardiovascular risk factors (smoking, alcohol use, hypertension, diabetes, family history of CVD) and their clustering patterns\u003c/p\u003e\n\u003cp\u003e2.\u0026nbsp; \u0026nbsp;Examine demographic variations (gender, religion, academic level, age, marital status) in:\u003c/p\u003e\n\u003cp\u003ea.\u0026nbsp; \u0026nbsp;CVD knowledge\u003c/p\u003e\n\u003cp\u003eb.\u0026nbsp; \u0026nbsp;Risk perception\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ec.\u0026nbsp; \u0026nbsp;Exercise intentions\u003c/p\u003e\n\u003cp\u003ed.\u0026nbsp; \u0026nbsp;Healthy eating intentions\u003c/p\u003e\n\u003cp\u003e3.\u0026nbsp; \u0026nbsp;Identify demographic predictors of knowledge, risk perception, and behavioral intentions through multivariable regression analyses\u003c/p\u003e\n\u003cp\u003e4.\u0026nbsp; \u0026nbsp;Quantify the strength of associations between knowledge and behavioral intentions across demographic subgroups\u003c/p\u003e\n\u003cp\u003eBy answering these questions, this study provides evidence-based guidance for developing demographically targeted, culturally appropriate cardiovascular health promotion programs for Nigerian university students and similar populations across sub-Saharan Africa.\u003c/p\u003e"},{"header":"2. Method","content":"\u003cp\u003e\u003cstrong\u003eStudy Design, Setting, and Period\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis cross-sectional study was conducted between January 2025 and April 2025 at two public universities in North-Central Nigeria: the University of Jos (UNIJOS), Plateau State, and Nasarawa State University, Keffi (NSUK), Nasarawa State. These institutions were selected for their large, diverse student populations representing varied sociodemographic backgrounds typical of the region.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Population and Eligibility\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study population comprised full-time undergraduate students enrolled during the 2024/2025 academic session. Eligible participants were aged 18 years or older and able to provide informed consent. Students enrolled in part-time or distance learning programs, or who had participated in structured cardiovascular health interventions in the preceding six months, were excluded.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSample Size Determination\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSample size was calculated using the Lwanga and Lemeshow formula for a single population proportion [31]. With a 95% confidence level (Z=1.96), an assumed proportion (p) of 0.391 for high CVD knowledge from previous studies, and a 5% margin of error, an initial sample of 366 per university was calculated. After adjusting for a design effect of 1.5 (cluster sampling) and 10% non-response rate, the final target was 604 students per university, totaling 1,208 participants. The achieved sample was 1,300 students (UNIJOS: n=693; NSUK: n=607), exceeding the minimum requirement and providing adequate power to detect demographic differences.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSampling Technique\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA multistage sampling technique was employed:\u003c/p\u003e\n\u003cp\u003e1. \u003cstrong\u003eStratified Allocation\u003c/strong\u003e: Sample distribution proportional to each university's enrollment (UNIJOS: 40,000 students; NSUK: 35,000 students)\u003c/p\u003e\n\u003cp\u003e2. \u003cstrong\u003eMatched-Pair Cluster Selection\u003c/strong\u003e: Seven comparable faculties (Agriculture, Arts, Education, Engineering, Environmental Sciences, Natural Sciences, Social Sciences) common to both universities were purposively selected, yielding 24 matched departments representing approximately 85% of total enrollment\u003c/p\u003e\n\u003cp\u003e3. \u003cstrong\u003eProportionate Allocation\u003c/strong\u003e: Departmental sample sizes were proportional to enrollment, verified through class representatives and departmental records\u003c/p\u003e\n\u003cp\u003e4. \u003cstrong\u003eSystematic Random Sampling\u003c/strong\u003e: Within each department, every kth student (where k = total enrollment ÷ required sample) was approached during peak academic hours. If a selected student declined or was ineligible, the next student (k+1) was approached until the required sample was achieved\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Collection Instruments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrimary Instrument\u003c/strong\u003e - ABCD Risk Questionnaire:** The validated Attitudes and Beliefs about Cardiovascular Disease (ABCD) Risk Questionnaire [32], culturally adapted for Nigeria, comprised:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCVD Knowledge\u003c/strong\u003e: 8 true/false items assessing awareness of risk factors and prevention (score 0-8, converted to percentage; higher scores = greater knowledge)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePerceived Risk\u003c/strong\u003e: 8 items on 4-point Likert scale measuring subjective CVD vulnerability (score 8-32, converted to percentage; higher scores = greater perceived risk)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExercise Intentions\u003c/strong\u003e: 7 items on 4-point Likert scale assessing physical activity readiness (score 7-28, converted to percentage; higher scores = stronger intentions)\u003c/p\u003e\n\u003cp\u003eHealthy Eating Intentions: 3 items on 4-point Likert scale assessing dietary change intentions (score 3-12, converted to percentage; higher scores = stronger intentions)\u003c/p\u003e\n\u003cp\u003eThe ABCD questionnaire has demonstrated good internal consistency (Cronbach's α: 0.75-0.93 for subscales) [32].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Questions\u003c/strong\u003e: Additional items assessed:\u003c/p\u003e\n\u003cp\u003eS\u003cstrong\u003eociodemographics\u003c/strong\u003e: Age, gender, academic level, faculty, religion, marital status\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHealth conditions\u003c/strong\u003e: Self-reported hypertension, diabetes, family history of heart disease\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBehavioral risk factors\u003c/strong\u003e: Current smoking status, current alcohol consumption\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSelf-rated health\u003c/strong\u003e: General health status on a 5-point scale\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Collection Procedures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResearch assistants from health-related disciplines received comprehensive two-day training on study objectives, ethical principles, informed consent procedures, questionnaire administration, and systematic sampling protocols. Data collection occurred over four weeks during the academic semester, avoiding examination periods.\u003c/p\u003e\n\u003cp\u003eAt predetermined departmental locations during peak class hours (8:00 AM - 2:00 PM), research assistants:\u003c/p\u003e\n\u003cp\u003e1.\u0026nbsp; \u0026nbsp;Approached eligible students according to systematic sampling protocol\u003c/p\u003e\n\u003cp\u003e2.\u0026nbsp; \u0026nbsp;Explained study purpose, voluntary participation, and confidentiality protections\u003c/p\u003e\n\u003cp\u003e3.\u0026nbsp; \u0026nbsp;Obtained written informed consent from willing participants\u003c/p\u003e\n\u003cp\u003e4.\u0026nbsp; \u0026nbsp;Provided questionnaires for self-completion in quiet areas (completion time: 15-20 minutes)\u003c/p\u003e\n\u003cp\u003e5.\u0026nbsp; \u0026nbsp;Conducted immediate completeness checks\u003c/p\u003e\n\u003cp\u003e6.\u0026nbsp; \u0026nbsp;Maintained logs of approaches, refusals, and successful enrollments\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData were entered into IBM SPSS Statistics version 31.0.1.0 using double-entry verification for 10% of questionnaires to ensure accuracy. Data cleaning procedures included range checks, consistency verification, and appropriate handling of missing values.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDescriptive Statistics\u003c/strong\u003e: Frequencies, percentages, means, and standard deviations summarized participant characteristics and outcomes. Risk factor clustering was assessed by counting concurrent presence of smoking, alcohol use, and low physical activity readiness.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eBivariate Analyses\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003e· Chi-square tests compared categorical variables (knowledge categories, risk factor prevalence) across demographic groups\u003c/p\u003e\n\u003cp\u003e· Independent t-tests compared continuous outcomes (knowledge, risk perception, behavioral intentions) between gender groups and universities\u003c/p\u003e\n\u003cp\u003e· One-way ANOVA with post-hoc Tukey HSD tests compared outcomes across academic levels and religious groups\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation Analyses\u003c/strong\u003e: Pearson correlation coefficients examined relationships between:\u003c/p\u003e\n\u003cp\u003e· Knowledge and risk perception\u003c/p\u003e\n\u003cp\u003e· Knowledge and exercise intentions\u003c/p\u003e\n\u003cp\u003e· Knowledge and healthy eating intentions\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultivariable Analyses\u003c/strong\u003e: Multiple linear regression models identified independent demographic predictors of:\u003c/p\u003e\n\u003cp\u003e1.\u0026nbsp; \u0026nbsp;CVD knowledge (separate models for each university due to differential patterns)\u003c/p\u003e\n\u003cp\u003e2.\u0026nbsp; \u0026nbsp;Risk perception (combined sample)\u003c/p\u003e\n\u003cp\u003e3.\u0026nbsp; \u0026nbsp;Exercise intentions (combined sample)\u003c/p\u003e\n\u003cp\u003e4.\u0026nbsp; \u0026nbsp;Healthy eating intentions (combined sample)\u003c/p\u003e\n\u003cp\u003ePredictor variables included: age, gender, religion, marital status, and academic level. Model fit was assessed using R², adjusted R², and F-statistics. Multicollinearity was evaluated using variance inflation factors (VIF \u0026lt; 5 considered acceptable).\u003c/p\u003e\n\u003cp\u003eEffect sizes were calculated using Cohen's d for t-tests (d=0.2, 0.5, 0.8 representing small, medium, large effects) and partial eta-squared (η²) for ANOVA. Statistical significance was set at p\u0026lt;0.05 (two-tailed).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Considerations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was obtained from the Research Ethics Committee of the Ministry of Health, Plateau State (Approval No: MOH/MIS/202/VOL I/XX, dated 25\u003csup\u003eth\u003c/sup\u003e of January 2025. All participants provided written informed consent before participation, with explicit assurances of voluntary participation, right to withdraw without penalty, and data confidentiality through unique identifiers and secure storage. The study was conducted in accordance with the Declaration of Helsinki and the Nigerian National Code for Health Research Ethics.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003eSociodemographic Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 1,300 students participated (response rate 100% of target), with 693 (53.3%) from UNIJOS and 607 (46.7%) from NSUK. Table 1 presents the sociodemographic distribution.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1: Sociodemographic Characteristics of Study Participants (N=1,300)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFrequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePercent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eValid Percent\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender (n=1,287)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e587\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e45.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e45.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e53.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e53.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePrefer not to say\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (n=945)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20.95 \u0026plusmn; 3.55 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15-43 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAcademic Level (n=1,300)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e100 level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e692\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e53.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e53.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e200 level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e300 level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e400 level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e500 level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eReligion (n=1,282)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eChristianity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e980\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e75.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e76.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIslam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTraditional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital Status (n=1,279)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1,174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e90.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e91.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDivorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWidowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePrefer not to say\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFaculty (n=1,300)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNatural Sciences\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e31.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e31.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eArts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSocial Sciences\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEnvironmental Sciences\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEngineering\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAgriculture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote: Percentages may not sum to 100% due to rounding or missing data as indicated by varying \u0026apos;n\u0026apos; values.\u003c/em\u003e\u003cbr\u003e\u003cem\u003eSD = Standard Deviation.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe sample was predominantly female (53.5%), young adults (mean age 20.95 years), first-year students (53.2%), Christian (76.4%), and single (91.8%). The distribution across faculties reflected the sampling strategy, with Natural Sciences (31.7%) and Arts (19.5%) representing the largest proportions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrevalence of Modifiable Cardiovascular Risk Factors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 2 presents the prevalence of modifiable CVD risk factors. Current smoking prevalence was low (2.2%), as was current alcohol use (3.7%). Self-reported hypertension affected 6.7% of participants, while diabetes prevalence was only 1.4%. Notably, 14.6% reported a family history of heart disease or stroke in immediate family members (parents or siblings).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Prevalence of Modifiable Cardiovascular Disease Risk Factors (N=1,300)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable: Prevalence of Cardiovascular Disease Risk Factors Among Study Participants\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRisk Factor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFrequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePercent (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSmoking Status (n=1,286)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNever smoked\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1,202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e93.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e92.2-94.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFormer smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.3-5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCurrent smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.4-3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAlcohol Use (n=1,286)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNever consumed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1,163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e90.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e88.9-91.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFormer consumer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.7-7.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCurrent consumer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.7-4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHypertension (n=1,278)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1,192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e93.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e91.9-94.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.4-8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDiabetes (n=1,286)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1,268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e98.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e97.9-99.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.8-2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFamily History of CVD (n=1,283)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1,096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e85.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e83.6-87.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.7-16.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCVD = Cardiovascular disease; CI = Confidence interval\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e*Note: Current smoking and alcohol consumption represent modifiable behavioral risk factors, while hypertension, diabetes, and family history represent clinical and non-modifiable risk factors. Percentages may not sum to 100% due to rounding.*\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRisk Factor Clustering\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnalysis of risk factor clustering revealed that the majority of students (83.7%, n=1,080) had no behavioral risk factors (smoking, alcohol, low physical activity readiness), 12.8% (n=165) had one risk factor, 2.9% (n=37) had two risk factors, and only 0.6% (n=8) had three or more concurrent risk factors. This pattern indicates that multiple risk factor clustering is uncommon in this population.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDemographic Variations in CVD Knowledge\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOverall CVD knowledge score was 62.13 \u0026plusmn; 21.31% (Table 3). Only 22.6% of students achieved good knowledge (\u0026ge;80%), with no significant difference between universities (UNIJOS: 19.9% vs NSUK: 25.8%, \u0026chi;\u0026sup2;=6.47, p=0.011).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3: CVD Knowledge by Sociodemographic Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMean Knowledge Score (%) \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003et/F statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEffect Size\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e62.13 \u0026plusmn; 21.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUniversity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003et = 1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ed = 0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026bull; UNIJOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e61.50 \u0026plusmn; 21.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026bull; NSUK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e62.85 \u0026plusmn; 21.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003et = 1.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ed = 0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026bull; Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e60.81 \u0026plusmn; 21.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026bull; Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e63.24 \u0026plusmn; 21.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eReligion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003et = 2.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ed = 0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026bull; Christianity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e62.89 \u0026plusmn; 21.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026bull; Islam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e59.95 \u0026plusmn; 21.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAcademic Level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eF = 1.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026eta;\u0026sup2; = 0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026bull; 100 level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e61.34 \u0026plusmn; 21.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026bull; 200 level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e63.58 \u0026plusmn; 20.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026bull; 300 level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e63.21 \u0026plusmn; 21.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026bull; 400 level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e60.45 \u0026plusmn; 22.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026bull; 500 level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e59.38 \u0026plusmn; 21.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003et = 0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ed = 0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026bull; Single\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e62.07 \u0026plusmn; 21.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026bull; Married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e64.49 \u0026plusmn; 22.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote: SD = Standard Deviation; t = t-statistic from independent samples t-test; F = F-statistic from one-way ANOVA; d = Cohen\u0026apos;s d; \u0026eta;\u0026sup2; = Eta squared.\u003c/em\u003e\u003cbr\u003e\u003cem\u003eStatistical significance set at p \u0026lt; 0.05.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAt the bivariate level, Christians demonstrated significantly higher knowledge than Muslims (62.89% vs 59.95%, p=0.030, d=0.13, small effect). Gender differences approached but did not reach significance (females: 63.24% vs males: 60.81%, p=0.066). Academic level showed no significant variation (p=0.113).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRegression Analysis of Knowledge Predictors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMultiple linear regression analyses were conducted separately for each university (Table 4) due to observed differential patterns in preliminary analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4: Multiple Linear Regression Predicting CVD Knowledge Score by University\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePredictor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUNIJOS (n=493)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNSUK (n=426)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eB (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eB (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntercept\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e50.81 (35.80, 65.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e72.76 (54.49, 91.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.55 (-0.03, 1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.04 (-0.58, 0.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.884\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGender (Female)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.48 (-5.02, 2.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-6.28 (-10.25, -2.30)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.150\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMarital Status (Married)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.67 (-3.49, 4.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.14 (-3.21, 15.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.197\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReligion (Islam)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.05 (-2.59, 6.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-5.86 (-10.17, -1.56)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.129\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel Summary\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eR\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAdjusted R\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eF-statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eF(4,488) = 1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eF(4,421) = 4.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote: B = unstandardized coefficient; \u0026beta; = standardized beta coefficient; CI = confidence interval.\u003c/em\u003e\u003cbr\u003e\u003cem\u003eReference categories: Gender (Male), Marital Status (Single), Religion (Christianity).\u003c/em\u003e\u003cbr\u003e\u003cem\u003eBold values indicate statistical significance at p \u0026lt; 0.05.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn UNIJOS, no demographic variables significantly predicted knowledge, and the overall model was non-significant (R\u0026sup2;=0.011, p=0.226). In NSUK, however, both gender and religion emerged as significant predictors. Males scored 6.28% lower than females (p=0.002), and Muslims scored 5.86% lower than Christians (p=0.008), with the overall model explaining 4.4% of variance (p\u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDemographic Variations in Risk Perception\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMean perceived CVD risk was 41.23 \u0026plusmn; 12.16% of scale maximum. Table 5 presents demographic variations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5: Perceived CVD Risk by Sociodemographic Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMean Risk Perception (%) \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003et/F statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEffect Size\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e41.23 \u0026plusmn; 12.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUniversity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003et = -0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ed = -0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026bull; UNIJOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e41.05 \u0026plusmn; 12.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026bull; NSUK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e41.43 \u0026plusmn; 11.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003et = 0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.561\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ed = 0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026bull; Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e41.51 \u0026plusmn; 12.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026bull; Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e41.08 \u0026plusmn; 11.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eReligion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003et = -2.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ed = -0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026bull; Christianity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e40.65 \u0026plusmn; 12.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026bull; Islam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e43.09 \u0026plusmn; 12.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAcademic Level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eF = 1.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026eta;\u0026sup2; = 0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026bull; 100 level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e41.75 \u0026plusmn; 12.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026bull; 200 level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e40.95 \u0026plusmn; 12.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026bull; 300 level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e40.22 \u0026plusmn; 11.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026bull; 400 level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e39.84 \u0026plusmn; 12.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026bull; 500 level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e42.68 \u0026plusmn; 11.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003et = -0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ed = -0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026bull; Single\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e41.15 \u0026plusmn; 12.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026bull; Married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e42.51 \u0026plusmn; 12.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote: SD = Standard Deviation; t = t-statistic from independent samples t-test; F = F-statistic from one-way ANOVA; d = Cohen\u0026apos;s d; \u0026eta;\u0026sup2; = Eta squared.\u003c/em\u003e\u003cbr\u003e\u003cem\u003eStatistical significance set at p \u0026lt; 0.05.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eReligion was the only demographic variable showing significant bivariate association, with Muslims perceiving higher risk than Christians (43.09% vs 40.65%, p=0.003, d=-0.18, small effect).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRegression Analysis of Risk Perception Predictors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMultiple linear regression (Table 6) examined demographic predictors of risk perception in the combined sample (n=904).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6: Multiple Linear Regression Predicting Perceived CVD Risk Score\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePredictor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eB (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e33.33 (27.14, 39.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.15 (-0.10, 0.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.241\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGender (Female)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.62 (-2.09, 0.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.411\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMarital Status (Married)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.13 (-1.03, 3.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.305\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eReligion (Islam)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.36 (0.64, 4.08)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.090\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.69\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAcademic Level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.57 (-0.22, 1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eModel Summary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eR\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAdjusted R\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eF-statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eF(5,898) = 3.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eNote: B = unstandardized coefficient; \u0026beta; = standardized beta coefficient; CI = confidence interval.\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cbr\u003e \u003cem\u003eReference categories: Gender (Male), Marital Status (Single), Religion (Christianity).\u003c/em\u003e\u003cbr\u003e \u003cem\u003eBold values indicate statistical significance at p \u0026lt; 0.05. Religion was the only significant predictor in the model.\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eReligion emerged as the sole significant predictor, with Muslims perceiving 2.36% higher risk than Christians (p=0.007). However, the overall model explained only 1.8% of variance, indicating that measured demographic variables account for minimal variation in risk perception.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDemographic Variations in Behavioral Intentions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExercise Intentions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMean exercise intention score was 79.80 \u0026plusmn; 11.69%. Table 7 presents demographic variations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7: Exercise Intentions by Sociodemographic Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean Exercise Intention (%) \u0026plusmn; SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003et/F statistic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEffect Size\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e79.80 \u0026plusmn; 11.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUniversity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003et = 1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ed = 0.07\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026bull; UNIJOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e80.17 \u0026plusmn; 11.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026bull; NSUK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e79.37 \u0026plusmn; 11.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003et = 0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ed = 0.04\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026bull; Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e80.17 \u0026plusmn; 11.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026bull; Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e79.64 \u0026plusmn; 11.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReligion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003et = 1.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ed = 0.10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026bull; Christianity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e80.20 \u0026plusmn; 11.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026bull; Islam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e78.75 \u0026plusmn; 11.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAcademic Level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eF = 3.84\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026eta;\u0026sup2; = 0.017\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026bull; 100 level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e81.05 \u0026plusmn; 11.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026bull; 200 level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e79.52 \u0026plusmn; 11.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026bull; 300 level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e78.55 \u0026plusmn; 11.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026bull; 400 level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e76.82 \u0026plusmn; 12.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026bull; 500 level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e77.14 \u0026plusmn; 11.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eNote: SD = Standard Deviation; t = t-statistic from independent samples t-test; F = F-statistic from one-way ANOVA; d = Cohen\u0026apos;s d; \u0026eta;\u0026sup2; = Eta squared.\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cbr\u003e \u003cem\u003eStatistical significance set at p \u0026lt; 0.05. Academic level was the only characteristic showing statistically significant differences in exercise intentions.\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAcademic level showed significant variation (p=0.004), with a declining trend from first year (81.05%) to final years (76.82-77.14%). Post-hoc Tukey tests revealed significant differences between 100 level and both 400 level (p=0.021) and 500 level (p=0.048).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRegression Analysis of Exercise Intention Predictors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 8: Multiple Linear Regression Predicting Exercise Intentions\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePredictor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eB (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e84.06 (77.72, 90.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.19 (-0.06, 0.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGender (Female)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.24 (-2.76, 0.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMarital Status (Married)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.48 (-3.74, 0.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.199\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eReligion (Islam)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-2.07 (-3.86, -0.27)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.075\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-2.26\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.024\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAcademic Level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-1.23 (-2.03, -0.43)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.116\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-3.01\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel Summary\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eR\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAdjusted R\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eF-statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eF(5,898) = 3.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote: B = unstandardized coefficient; \u0026beta; = standardized beta coefficient; CI = confidence interval.\u003c/em\u003e\u003cbr\u003e\u003cem\u003eReference categories: Gender (Male), Marital Status (Single), Religion (Christianity).\u003c/em\u003e\u003cbr\u003e\u003cem\u003eBold values indicate statistical significance at p \u0026lt; 0.05. Both religion and academic level were significant predictors of exercise intentions.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTwo significant predictors emerged: Religion (Muslims scored 2.07% lower than Christians, p=0.024) and Academic Level (each level increase associated with 1.23% decrease, p=0.003). The model explained 2.1% of variance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHealthy Eating Intentions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMean healthy eating intention score was 72.64 \u0026plusmn; 14.41%. Table 9 presents demographic variations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 9: Healthy Eating Intentions by Sociodemographic Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMean Healthy Eating Intention (%) \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003et/F statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEffect Size\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e72.64 \u0026plusmn; 14.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUniversity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003et = 1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ed = 0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026bull; UNIJOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e73.07 \u0026plusmn; 14.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026bull; NSUK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e72.14 \u0026plusmn; 14.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003et = -0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ed = -0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026bull; Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e72.42 \u0026plusmn; 14.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026bull; Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e72.85 \u0026plusmn; 14.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eReligion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003et = 0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ed = 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026bull; Christianity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e72.88 \u0026plusmn; 14.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026bull; Islam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e71.98 \u0026plusmn; 14.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAcademic Level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eF = 1.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026eta;\u0026sup2; = 0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026bull; 100 level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e73.42 \u0026plusmn; 14.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026bull; 200 level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e72.65 \u0026plusmn; 14.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026bull; 300 level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e71.88 \u0026plusmn; 14.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026bull; 400 level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e71.05 \u0026plusmn; 14.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026bull; 500 level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e70.38 \u0026plusmn; 14.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003et = -1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ed = -0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026bull; Single\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e72.51 \u0026plusmn; 14.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026bull; Married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e75.12 \u0026plusmn; 14.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote: SD = Standard Deviation; t = t-statistic from independent samples t-test; F = F-statistic from one-way ANOVA; d = Cohen\u0026apos;s d; \u0026eta;\u0026sup2; = Eta squared.\u003c/em\u003e\u003cbr\u003e\u003cem\u003eNo statistically significant differences were found in healthy eating intentions across any sociodemographic characteristics (p \u0026gt; 0.05).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNo demographic variables showed significant bivariate associations with healthy eating intentions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 10: Multiple Linear Regression Predicting Healthy Eating Intentions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 10: Multiple Linear Regression Predicting Healthy Eating Intentions\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePredictor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eB (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e64.33 (56.28, 72.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.30 (-0.02, 0.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGender (Female)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.16 (-1.74, 2.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.867\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMarital Status (Married)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.32 (-0.43, 5.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReligion (Islam)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.31 (-1.92, 2.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.787\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAcademic Level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.61 (-1.62, 0.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.233\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel Summary\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eR\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAdjusted R\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eF-statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eF(5,897) = 1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.275\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eNote: B = unstandardized coefficient; \u0026beta; = standardized beta coefficient; CI = confidence interval.\u003cbr\u003e\u0026nbsp;Reference categories: Gender (Male), Marital Status (Single), Religion (Christianity).\u003cbr\u003e\u0026nbsp;No predictors reached statistical significance at p \u0026lt; 0.05 in this model.\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone of the demographic predictors reached statistical significance, and the overall model was non-significant (R\u0026sup2;=0.007, p=0.275), indicating that measured demographic variables do not meaningfully predict healthy eating intentions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociations Between Knowledge and Behavioral Intentions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 11 presents correlations between CVD knowledge and behavioral outcomes across the total sample and stratified by key demographic characteristics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 11: Correlations Between CVD Knowledge and Behavioral Outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutcome Variable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall (N=1,259-1,291)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMales\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemales\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eChristians\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMuslims\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRisk Perception\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003er\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.031\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.015\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.037\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.499\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.485\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.842\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.652\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.532\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eExercise Intentions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003er\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.231\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.218\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.241\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.235\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.219\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHealthy Eating Intentions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003er\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.138\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.125\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.147\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.142\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.126\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.032\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e*Note: r = Pearson correlation coefficient. Bold values indicate statistically significant correlations (p \u0026lt; 0.05).*\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCVD knowledge showed no correlation with risk perception across any demographic subgroup (all p\u0026gt;0.05). However, knowledge demonstrated significant positive correlations with both exercise intentions (r=0.231, p\u0026lt;0.001) and healthy eating intentions (r=0.138, p\u0026lt;0.001), with patterns consistent across gender and religious groups. The strength of these associations was small to moderate, indicating that while knowledge relates to behavioral intentions, it accounts for only 5.3% (exercise) and 1.9% (healthy eating) of variance.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study provides comprehensive documentation of sociodemographic variations in CVD knowledge, risk perception, behavioral intentions, and modifiable risk factor prevalence among 1,300 Nigerian university students. The findings reveal low prevalence of traditional behavioral risk factors (smoking 2.2%, alcohol use 3.7%), but identify important demographic disparities in knowledge and behavioral readiness that have direct implications for targeted intervention design.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSummary of Key Findings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1. \u003cstrong\u003eLow risk factor prevalence\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ewith minimal clustering:\u0026nbsp;\u003c/strong\u003e83.7% had no behavioral risk factors\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.\u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eModerate CVD knowledge:\u0026nbsp;\u003c/strong\u003e(62.13%), with only 22.6% achieving good knowledge (≥80%)\u003c/p\u003e\n\u003cp\u003e3. \u003cstrong\u003eSignificant gender and religious disparities in knowledge:\u0026nbsp;\u003c/strong\u003eMales and Muslims demonstrated lower knowledge in NSUK\u003c/p\u003e\n\u003cp\u003e4. \u003cstrong\u003eReligious influence on risk perception:\u0026nbsp;\u003c/strong\u003eMuslims perceived higher risk than Christians (43.09% vs 40.65%)\u003c/p\u003e\n\u003cp\u003e5. \u003cstrong\u003eAcademic level negatively predicts behavioral readiness\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e6. \u003cstrong\u003eKnowledge-behavior associations:\u0026nbsp;\u003c/strong\u003eKnowledge correlated with behavioral intentions (r=0.23 for exercise, r=0.14 for diet) but not risk perception (r=-0.02)\u003c/p\u003e\n\u003cp\u003e7. \u003cstrong\u003eLimited demographic explanatory power:\u0026nbsp;\u003c/strong\u003eSociodemographic variables explained only 0.7-4.4% of variance in outcomes\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInterpretation of Findings\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLow Prevalence but Critical Disparities\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe remarkably low prevalence of current smoking (2.2%) and alcohol use (3.7%) in this sample contrasts sharply with rates in many high-income country universities where smoking rates typically range from 10-30% and alcohol use from 40-80% among students [33,34]. This finding aligns with other Nigerian studies reporting smoking prevalence of 2-5% among university students [35,36], suggesting protective effects of cultural norms, religious values (particularly Islamic prohibition of alcohol), strong family influences, and possibly social desirability in reporting.\u003c/p\u003e\n\u003cp\u003eHowever, the 14.6% prevalence of family history of CVD represents a substantial at-risk subgroup, particularly concerning given that only 56.9% of the overall sample recognized family history as a risk factor (from Paper 1). This disconnect indicates missed opportunities for targeted screening and prevention among genetically predisposed individuals.\u003c/p\u003e\n\u003cp\u003eThe 6.7% prevalence of self-reported hypertension among university-aged students (mean age 20.95 years) is concerning and likely represents an underestimate, as hypertension is often asymptomatic and requires clinical measurement for detection. Studies employing objective blood pressure measurement in similar Nigerian populations have documented prevalence rates of 15-25% [37,38], suggesting that the true burden may be substantially higher than reported here.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGender Disparities: A Complex Pattern\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe finding that males demonstrated significantly lower CVD knowledge than females in NSUK (6.3% difference, p=0.002) but not in UNIJOS represents an intriguing institutional variation. This gender gap in health knowledge is well-documented globally, with women typically demonstrating higher health literacy, greater engagement with health information, and more proactive health-seeking behaviors [39,40].\u003c/p\u003e\n\u003cp\u003eSeveral mechanisms may explain this pattern:\u003c/p\u003e\n\u003cp\u003e1. \u003cstrong\u003eDifferential exposure to health information\u003c/strong\u003e: Women may have greater exposure to health content through family care responsibilities, media consumption patterns, and social networks that prioritize health discussions [41]\u003c/p\u003e\n\u003cp\u003e2. \u003cstrong\u003eEducational engagement differences\u003c/strong\u003e: Gender differences in academic engagement and help-seeking behaviors may influence knowledge acquisition, with women more likely to attend optional health education sessions or seek additional information [42]\u003c/p\u003e\n\u003cp\u003e3. \u003cstrong\u003eSocial desirability\u003c/strong\u003e: Women may feel greater social pressure to demonstrate health knowledge, potentially inflating their scores relative to actual applied knowledge [43]\u003c/p\u003e\n\u003cp\u003eImportantly, despite this knowledge gap, gender did not significantly predict risk perception or behavioral intentions in multivariable models. This dissociation between knowledge and other CVD-related constructs reinforces findings from Paper 1 regarding the independence of these domains and suggests that closing the gender knowledge gap alone will not necessarily translate to improved risk awareness or behaviors among males.\u003c/p\u003e\n\u003cp\u003eThe absence of gender differences in behavioral intentions challenges stereotypes about male-female health behavior patterns. In Western contexts, men often demonstrate poorer dietary quality and lower health service utilization [44], but our findings suggest more gender parity in intention among Nigerian university students. This may reflect cohort effects, changing gender norms among educated youth, or limitations of intention measures in predicting actual behavior.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReligious Influences: Dual Pathways\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eReligion emerged as a significant predictor across multiple outcomes, but with seemingly paradoxical effects. Muslims demonstrated:\u003c/p\u003e\n\u003cp\u003e· Lower CVD knowledge (-5.9%, p=0.008) in NSUK\u003c/p\u003e\n\u003cp\u003e· Higher perceived risk (+2.4%, p=0.007)\u003c/p\u003e\n\u003cp\u003e· Lower exercise intentions (-2.1%, p=0.024)\u003c/p\u003e\n\u003cp\u003eThis pattern suggests that religious affiliation operates through multiple pathways to influence cardiovascular health:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKnowledge Pathway\u003c/strong\u003e: The lower knowledge among Muslims may reflect:\u003c/p\u003e\n\u003cp\u003e· Language barriers if health education materials are predominantly in English rather than Hausa or other languages\u003c/p\u003e\n\u003cp\u003e· Different information sources, with Islamic religious leaders potentially emphasizing different health topics than Christian counterparts\u003c/p\u003e\n\u003cp\u003e· Potential socioeconomic confounding, as religion may correlate with other unmeasured variables (parental education, rural/urban origin, socioeconomic status)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRisk Perception Pathway\u003c/strong\u003e: The heightened risk perception among Muslims (despite lower knowledge) may indicate:\u003c/p\u003e\n\u003cp\u003e· Greater fatalistic health beliefs common in some Islamic interpretations, where illness is viewed as divinely ordained [45,46]\u003c/p\u003e\n\u003cp\u003e· Different framing of health risks within religious teachings, possibly emphasizing vulnerability and mortality\u003c/p\u003e\n\u003cp\u003e· Cultural differences in expressing health concerns or anxiety that manifest as higher perceived risk scores\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBehavioral Pathway\u003c/strong\u003e: Lower exercise intentions among Muslims may reflect:\u003c/p\u003e\n\u003cp\u003e· Gender-specific barriers, particularly for Muslim women facing cultural restrictions on mixed-gender physical activity or concerns about modest dress during exercise [47,48]\u003c/p\u003e\n\u003cp\u003e· Different priorities in health behavior, with greater emphasis on dietary regulation (halal practices) than physical activity\u003c/p\u003e\n\u003cp\u003e· Infrastructure barriers if campus facilities do not accommodate religious requirements (prayer times, gender-segregated spaces)\u003c/p\u003e\n\u003cp\u003eThese findings have critical implications for intervention design. A \"one-size-fits-all\" approach risks widening disparities. Instead, interventions should:\u003c/p\u003e\n\u003cp\u003e· Develop culturally tailored educational materials available in multiple languages\u003c/p\u003e\n\u003cp\u003e· Partner with Islamic religious leaders to deliver health messages in culturally resonant frameworks\u003c/p\u003e\n\u003cp\u003e· Address structural barriers to physical activity for Muslim students (gender-segregated facilities, flexible scheduling around prayer times)\u003c/p\u003e\n\u003cp\u003e· Recognize that higher risk perception among Muslims may be leveraged as a motivational resource if appropriately channeled\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe Academic Level Paradox\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe significant negative association between academic level and exercise intentions (β=-1.23 per level, p=0.003) represents a concerning trend. One might expect that advancing through university would correlate with increased health knowledge and maintained or improved behavioral intentions. Instead, we observe declining readiness for physical activity from first year (81.05%) to final years (76.82-77.14%).\u003c/p\u003e\n\u003cp\u003eSeveral explanations merit consideration:\u003c/p\u003e\n\u003cp\u003e1. \u003cstrong\u003eIncreasing Academic Pressure\u003c/strong\u003e: As students progress through university, academic demands typically intensify, particularly in final years when major projects, dissertations, and comprehensive examinations dominate. Time constraints and stress may reduce both available time for exercise and prioritization of health behaviors [49,50]\u003c/p\u003e\n\u003cp\u003e2. \u003cstrong\u003eTransition from Intention to Reality\u003c/strong\u003e**: First-year students may express high intentions based on idealistic goals upon university entry. As they progress, these intentions may be \"corrected\" downward to reflect actual behavioral patterns and realistic self-assessment [51]\u003c/p\u003e\n\u003cp\u003e3. \u003cstrong\u003eSocial Network Evolution\u003c/strong\u003e**: Early university years often involve joining sports teams, fitness clubs, and active social groups. As students progress, friendship networks may stabilize around less active pursuits, particularly as peer groups form around shared academic interests [52]\u003c/p\u003e\n\u003cp\u003e4. \u003cstrong\u003eDesensitization to Health Messages\u003c/strong\u003e**: Repeated exposure to health promotion messages without reinforcement or visible consequences may lead to habituation and reduced responsiveness [53]\u003c/p\u003e\n\u003cp\u003e5. \u003cstrong\u003eCompeting Priorities\u003c/strong\u003e**: Final-year students face impending career decisions, job searches, and major life transitions that may relegate health behaviors to lower priority [54]\u003c/p\u003e\n\u003cp\u003eThis finding has important timing implications for interventions. Programs should:\u003c/p\u003e\n\u003cp\u003e· Prioritize engagement early in university careers when intentions are highest\u003c/p\u003e\n\u003cp\u003e· Implement booster sessions and renewed engagement strategies for upper-level students\u003c/p\u003e\n\u003cp\u003e· Address structural barriers specific to advanced students (thesis deadlines, practicum schedules)\u003c/p\u003e\n\u003cp\u003e· Incorporate stress management and time management skills to help students maintain health behaviors amid increasing demands\u003c/p\u003e\n\u003cp\u003eNotably, academic level did not significantly predict healthy eating intentions, suggesting that dietary intentions may be more resistant to the pressures of academic progression, possibly because eating is a necessity that must be accommodated regardless of schedule\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003ewhereas exercise is more discretionary\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKnowledge-Behavior Associations: Modest but Meaningful\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe significant positive correlations between knowledge and behavioral intentions (r=0.231 for exercise, r=0.138 for diet) provide evidence that knowledge does relate to motivation for healthy behaviors, even though these associations are modest (accounting for 5.3% and 1.9% of variance respectively).\u003c/p\u003e\n\u003cp\u003eThese findings align with meta-analytic evidence showing that health knowledge typically correlates weakly to moderately with health behaviors (r=0.15-0.35) [55,56]. The relationships we observed are consistent across demographic subgroups, suggesting that the knowledge-intention pathway operates similarly for males and females, Christians and Muslims.\u003c/p\u003e\n\u003cp\u003eCritically, the absence of correlation between knowledge and risk perception (r=-0.019, p=0.499) confirmed in Paper 1 persists across all demographic subgroups, indicating that this independence is a general phenomenon rather than an artifact of particular demographic compositions. This reinforces the necessity of dual-track interventions that separately target knowledge enhancement and risk perception calibration.\u003c/p\u003e\n\u003cp\u003eThe stronger correlation of knowledge with exercise intentions (r=0.231) than dietary intentions (r=0.138) may reflect differential complexity of these behaviors. Physical activity requires deliberate planning and dedicated time, whereas dietary choices are made multiple times daily and may be more influenced by habitual patterns, environmental availability, and social contexts that override knowledge-based decision making [57,58].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimited Demographic Explanatory Power: The Search for Missing Variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePerhaps the most striking finding across all regression models is the limited variance explained by sociodemographic variables: knowledge (1.1-4.4%), risk perception (1.8%), exercise intentions (2.1%), and dietary intentions (0.7%). This indicates that the demographic characteristics we measured—gender, religion, age, marital status, academic level—account for very little of the substantial variability observed in outcomes.\u003c/p\u003e\n\u003cp\u003eThis finding suggests several possibilities:\u003c/p\u003e\n\u003cp\u003e1. \u003cstrong\u003eIndividual-level psychological factors matter more\u003c/strong\u003e: Constructs such as self-efficacy, health locus of control, personality traits, past health experiences, and health literacy may be more powerful predictors than demographic categories [59,60]\u003c/p\u003e\n\u003cp\u003e2. \u003cstrong\u003eSocial network influences\u003c/strong\u003e: Peer behaviors, family support, and social norms within friendship groups may exert stronger influences than demographic memberships [61]\u003c/p\u003e\n\u003cp\u003e3. \u003cstrong\u003eEnvironmental factors\u003c/strong\u003e: Campus infrastructure (food availability, fitness facilities), neighborhood characteristics, and living situations (on/off campus, with family/roommates) may substantially shape both knowledge acquisition and behavioral patterns [62]\u003c/p\u003e\n\u003cp\u003e4. \u003cstrong\u003eMeasurement limitations\u003c/strong\u003e: Our categorical assessment of demographics may miss important within-category variation. For example, \"Muslim\" encompasses substantial heterogeneity in practice intensity, sectarian affiliation, and cultural background\u003c/p\u003e\n\u003cp\u003e5. \u003cstrong\u003eComplex interactions\u003c/strong\u003e: The effects of demographic variables may depend on unmeasured moderators, such that simple additive models fail to capture important effect modification\u003c/p\u003e\n\u003cp\u003eThese considerations point to the need for expanded theoretical models that incorporate:\u003c/p\u003e\n\u003cp\u003e· Psychological constructs from the Theory of Planned Behavior (attitudes, subjective norms, perceived behavioral control) [63]\u003c/p\u003e\n\u003cp\u003e· Social Cognitive Theory elements (observational learning, self-efficacy, outcome expectations) [64]\u003c/p\u003e\n\u003cp\u003e· Environmental/contextual factors emphasized in socio-ecological models [65]\u003c/p\u003e\n\u003cp\u003eFuture research should employ comprehensive assessment batteries capturing these additional constructs to develop more complete explanatory models.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparison with International Literature\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur findings show both convergence and divergence from patterns observed in other settings:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConvergent findings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e· Gender differences in health knowledge favoring females replicate patterns in Europe, North America, and Asia [66,67]\u003c/p\u003e\n\u003cp\u003e· Academic pressure correlating with reduced physical activity is documented in multiple university contexts [68,69]\u003c/p\u003e\n\u003cp\u003e· Modest knowledge-behavior correlations align with meta-analytic evidence [55,56]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDivergent findings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e· The low prevalence of smoking and alcohol use contrasts sharply with Western university populations [33,34]\u003c/p\u003e\n\u003cp\u003e· The specific pattern of religious influences (Muslims showing both lower knowledge and higher risk perception) may be culturally specific\u003c/p\u003e\n\u003cp\u003e· The absence of age effects differs from some lifespan studies showing developmental changes in risk perception [70], though this may reflect the narrow age range in our sample\u003c/p\u003e\n\u003cp\u003eThe Belgian/English study referenced in Paper 1 [12] found that education level and income predicted CVD knowledge and healthy diet intentions in European vulnerable communities. Our findings suggest that in the Nigerian university context (where income and education variation is more restricted), religion and gender become more salient differentiating factors. This highlights the importance of context-specific research rather than assuming universal demographic patterns.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImplications for Practice\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThese findings provide actionable guidance for CVD prevention programs targeting university students:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFor University Health Services:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1. \u003cstrong\u003eDevelop gender-sensitive approaches\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003ea.\u0026nbsp; \u0026nbsp; Create male-friendly health education formats that engage men's learning preferences (competitive elements, hands-on activities, peer-led sessions)\u003c/p\u003e\n\u003cp\u003eb.\u0026nbsp; \u0026nbsp;Train peer educators from both genders to reach same-gender students\u003c/p\u003e\n\u003cp\u003ec.\u0026nbsp; \u0026nbsp;Frame health messages in ways that resonate with masculine identity (strength, performance, leadership) without reinforcing harmful stereotypes\u003c/p\u003e\n\u003cp\u003e2. \u003cstrong\u003eImplement culturally tailored interventions\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003ea.\u0026nbsp; \u0026nbsp;Partner with campus religious organizations (Christian fellowships, Muslim Student Societies) to deliver health messages through trusted faith leaders\u003c/p\u003e\n\u003cp\u003eb.\u0026nbsp; \u0026nbsp;Develop Islamic-appropriate physical activity programming (gender-segregated facilities, hijab-friendly athletic wear, flexible scheduling)\u003c/p\u003e\n\u003cp\u003ec.\u0026nbsp; \u0026nbsp;Create multilingual health resources in English, Hausa, Yoruba, and Igbo\u003c/p\u003e\n\u003cp\u003ed.\u0026nbsp; \u0026nbsp;Respect religious dietary practices while promoting heart-healthy adaptations of cultural cuisines\u003c/p\u003e\n\u003cp\u003e3. \u003cstrong\u003ePrioritize early engagement with declining intensity maintenance\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003ea.\u0026nbsp; \u0026nbsp;Implement mandatory health orientation during first-year enrollment when intentions are highest\u003c/p\u003e\n\u003cp\u003eb.\u0026nbsp; \u0026nbsp;Establish healthy behavior patterns early through freshman-specific programming\u003c/p\u003e\n\u003cp\u003ec.\u0026nbsp; \u0026nbsp;Provide booster interventions in 300-400 levels targeting the declining readiness\u003c/p\u003e\n\u003cp\u003ed.\u0026nbsp; \u0026nbsp;Integrate health promotion into academic curricula to maintain salience throughout university career\u003c/p\u003e\n\u003cp\u003e4. \u003cstrong\u003eTarget the 14.6% with family history\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003ea.\u0026nbsp; \u0026nbsp;Systematically screen all students for family CVD history during enrollment health checks\u003c/p\u003e\n\u003cp\u003eb.\u0026nbsp; \u0026nbsp;Provide enhanced education and counseling to those with positive family history\u003c/p\u003e\n\u003cp\u003ec.\u0026nbsp; \u0026nbsp;Offer subsidized or free biometric screening (blood pressure, glucose, BMI) to high-risk subgroups\u003c/p\u003e\n\u003cp\u003ed.\u0026nbsp; \u0026nbsp;Connect students with family history to ongoing monitoring and support programs\u003c/p\u003e\n\u003cp\u003e5.\u0026nbsp; \u0026nbsp;Implement dual-track programming:\u003c/p\u003e\n\u003cp\u003ea.\u0026nbsp; \u0026nbsp;Simultaneously address knowledge gaps (particularly regarding family history and alcohol-cholesterol relationships)\u003c/p\u003e\n\u003cp\u003eb.\u0026nbsp; \u0026nbsp;Provide personalized risk assessment tools that enhance appropriately calibrated risk perception\u003c/p\u003e\n\u003cp\u003ec.\u0026nbsp; \u0026nbsp;Recognize that knowledge enhancement alone is insufficient—must also address risk perception, self-efficacy, and environmental barriers\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFor University Administrators and Policy Makers:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1. \u003cstrong\u003eCreate enabling environments\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003ea.\u0026nbsp; \u0026nbsp;Ensure fitness facilities accommodate diverse cultural needs (gender-segregated hours, prayer spaces, culturally appropriate facilities)\u003c/p\u003e\n\u003cp\u003eb.\u0026nbsp; \u0026nbsp;Require minimum standards for campus food vendors (percentage of healthy options, nutrition labeling, limits on trans fats)\u003c/p\u003e\n\u003cp\u003ec.\u0026nbsp; \u0026nbsp;Design walkable, activity-friendly campuses with safe pedestrian infrastructure\u003c/p\u003e\n\u003cp\u003ed.\u0026nbsp; \u0026nbsp;Provide affordable healthy food options in dormitories and cafeterias\u003c/p\u003e\n\u003cp\u003e2. \u003cstrong\u003eMandate comprehensive health screening\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003ea.\u0026nbsp; \u0026nbsp;Require blood pressure, BMI, and glucose screening during enrollment with results linked to individualized counseling\u003c/p\u003e\n\u003cp\u003eb.\u0026nbsp; \u0026nbsp;Establish tracking systems to monitor health indicators throughout university career\u003c/p\u003e\n\u003cp\u003ec.\u0026nbsp; \u0026nbsp;Provide free or subsidized follow-up for students with identified risk factors\u003c/p\u003e\n\u003cp\u003e3. \u003cstrong\u003eSupport peer education programs\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003ea.\u0026nbsp; \u0026nbsp;Fund training for student health ambassadors from diverse demographic backgrounds\u003c/p\u003e\n\u003cp\u003eb.\u0026nbsp; \u0026nbsp;Leverage social networks and peer influence for health behavior change\u003c/p\u003e\n\u003cp\u003ec.\u0026nbsp; \u0026nbsp;Create recognition and incentive systems for peer educator participation\u003c/p\u003e\n\u003cp\u003e4. \u003cstrong\u003eIntegrate cardiovascular health into curriculum\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003ea.\u0026nbsp; \u0026nbsp;Incorporate CVD prevention modules into general education requirements\u003c/p\u003e\n\u003cp\u003eb.\u0026nbsp; \u0026nbsp;Ensure health-related majors receive comprehensive training in CVD epidemiology\u003c/p\u003e\n\u003cp\u003ec.\u0026nbsp; \u0026nbsp;Use academic settings to normalize health discussions and reduce stigma\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFor Future Research\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003e1. \u003cstrong\u003eLongitudinal cohort studies\u003c/strong\u003e: Follow students from enrollment through graduation and beyond to:\u003c/p\u003e\n\u003cp\u003ea.\u0026nbsp; \u0026nbsp;Understand trajectories of knowledge, risk perception, and behaviors over time\u003c/p\u003e\n\u003cp\u003eb.\u0026nbsp; \u0026nbsp;Identify critical transition points where intervention would be most effective\u003c/p\u003e\n\u003cp\u003ec.\u0026nbsp; \u0026nbsp;Assess whether university-based interventions yield lasting benefits post-graduation\u003c/p\u003e\n\u003cp\u003ed.\u0026nbsp; \u0026nbsp;Examine whether intentions translate to actual behaviors\u003c/p\u003e\n\u003cp\u003e2. \u003cstrong\u003eExpanded explanatory models\u003c/strong\u003e: Incorporate additional variables:\u003c/p\u003e\n\u003cp\u003ea.\u0026nbsp; \u0026nbsp;Psychological constructs (self-efficacy, health locus of control, health literacy, implicit attitudes)\u003c/p\u003e\n\u003cp\u003eb.\u0026nbsp; \u0026nbsp;Social factors (peer behaviors, family support, social norms, social capital)\u003c/p\u003e\n\u003cp\u003ec.\u0026nbsp; \u0026nbsp;Environmental factors (food availability, facility access, neighborhood walkability, living situation)\u003c/p\u003e\n\u003cp\u003ed.\u0026nbsp; \u0026nbsp;Use advanced analytic techniques (structural equation modeling, machine learning) to identify complex interaction patterns\u003c/p\u003e\n\u003cp\u003e3. \u003cstrong\u003eIntervention trials\u003c/strong\u003e: Conduct rigorous randomized controlled trials testing:\u003c/p\u003e\n\u003cp\u003ea.\u0026nbsp; \u0026nbsp;Gender-tailored vs. gender-neutral CVD education\u003c/p\u003e\n\u003cp\u003eb.\u0026nbsp; \u0026nbsp;Faith-based vs. secular health promotion approaches\u003c/p\u003e\n\u003cp\u003ec.\u0026nbsp; \u0026nbsp;Timing comparisons (first-year intensive vs. distributed across years)\u003c/p\u003e\n\u003cp\u003ed.\u0026nbsp; \u0026nbsp;Dual-track (knowledge + risk assessment) vs. single-component interventions\u003c/p\u003e\n\u003cp\u003e4. \u003cstrong\u003eObjective measurement studies\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003ea.\u0026nbsp; \u0026nbsp;Validate self-reported risk factors with biomarkers (cotinine for smoking, objective BP measurement, accelerometry for physical activity)\u003c/p\u003e\n\u003cp\u003eb.\u0026nbsp; \u0026nbsp;Assess actual behaviors vs. intentions through ecological momentary assessment\u003c/p\u003e\n\u003cp\u003ec.\u0026nbsp; \u0026nbsp;Conduct metabolic phenotyping to determine actual CVD risk in this population\u003c/p\u003e\n\u003cp\u003e5. \u003cstrong\u003eQualitative investigations\u003c/strong\u003e: Explore through interviews and focus groups:\u003c/p\u003e\n\u003cp\u003ea.\u0026nbsp; \u0026nbsp;How male students perceive and engage with health information\u003c/p\u003e\n\u003cp\u003eb.\u0026nbsp; \u0026nbsp;Religious influences on health beliefs and behaviors in students' own words\u003c/p\u003e\n\u003cp\u003ec.\u0026nbsp; \u0026nbsp;Mechanisms underlying the academic level-behavioral intention relationship\u003c/p\u003e\n\u003cp\u003ed.\u0026nbsp; \u0026nbsp;Barriers and facilitators to translating intentions into actions\u003c/p\u003e\n\u003cp\u003e6. \u003cstrong\u003eMulti-site regional studies\u003c/strong\u003e: Expand to other Nigerian regions to:\u003c/p\u003e\n\u003cp\u003ea.\u0026nbsp; \u0026nbsp;Test generalizability of demographic patterns across cultural contexts\u003c/p\u003e\n\u003cp\u003eb.\u0026nbsp; \u0026nbsp;Examine influence of Hausa, Yoruba, Igbo, and other ethnic identities\u003c/p\u003e\n\u003cp\u003ec.\u0026nbsp; \u0026nbsp;Compare urban vs. rural origin students\u003c/p\u003e\n\u003cp\u003ed.\u0026nbsp; \u0026nbsp;Assess institutional factors (public vs. private, religious vs. secular universities)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStrengths and Limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStrength\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study provides several important contributions:\u003c/p\u003e\n\u003cp\u003e1. \u003cstrong\u003eLarge, representative sample\u003c/strong\u003e (N=1,300) from two universities with systematic sampling enhances generalizability to Nigerian university students in North-Central region\u003c/p\u003e\n\u003cp\u003e2. \u003cstrong\u003eComprehensive demographic assessment\u0026nbsp;\u003c/strong\u003eallows identification of specific vulnerable subgroups, moving beyond population-level averages to inform targeted interventions\u003c/p\u003e\n\u003cp\u003e3. \u003cstrong\u003eMultiple outcome domains\u0026nbsp;\u003c/strong\u003e(knowledge, risk perception, behavioral intentions) assessed with validated instruments provide nuanced understanding of demographic influences\u003c/p\u003e\n\u003cp\u003e4. \u003cstrong\u003eRisk factor prevalence data\u0026nbsp;\u003c/strong\u003eestablishes baseline rates for Nigerian university students, filling a gap in the epidemiological literature\u003c/p\u003e\n\u003cp\u003e5. \u003cstrong\u003eSubgroup analyses\u0026nbsp;\u003c/strong\u003eacross gender and religious groups reveal consistency and variability in associations, enhancing understanding of pattern generality\u003c/p\u003e\n\u003cp\u003e6. \u003cstrong\u003ePractical implications\u0026nbsp;\u003c/strong\u003edirectly inform intervention design with specific, actionable recommendations\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeveral limitations warrant acknowledgment:\u003c/p\u003e\n\u003cp\u003e1. \u003cstrong\u003eCross-sectional design\u0026nbsp;\u003c/strong\u003eprecludes causal inference. We cannot determine whether, for example, advancing academic level causes declining exercise intentions or whether students prone to declining intentions are more likely to persist to advanced levels. Longitudinal designs are needed.\u003c/p\u003e\n\u003cp\u003e2. \u003cstrong\u003eSelf-reported data\u0026nbsp;\u003c/strong\u003eintroduces multiple biases:\u003c/p\u003e\n\u003cp\u003ea.\u0026nbsp; \u0026nbsp;Social desirability may inflate knowledge scores and behavioral intentions while underreporting smoking/alcohol\u003c/p\u003e\n\u003cp\u003eb.\u0026nbsp; \u0026nbsp;Recall bias may affect reporting of health conditions and behaviors\u003c/p\u003e\n\u003cp\u003ec.\u0026nbsp; \u0026nbsp;Differential reporting across demographic groups (e.g., Muslims may underreport alcohol use more than Christians due to religious prohibition)\u003c/p\u003e\n\u003cp\u003e3. \u003cstrong\u003eAbsence of objective risk factor measurement:\u0026nbsp;\u003c/strong\u003eReliance on self-reported hypertension and diabetes substantially underestimates true prevalence. Studies with objective measurement document 2-3 times higher rates than self-report [37,38]\u003c/p\u003e\n\u003cp\u003e4. \u003cstrong\u003eBehavioral intentions vs. actual behaviors:\u0026nbsp;\u003c/strong\u003eWe measured stated intentions, which meta-analyses show correlate only moderately (r≈0.45) with actual behaviors [71]. Our findings regarding high behavioral readiness require validation through objective behavioral measurement\u003c/p\u003e\n\u003cp\u003e5. \u003cstrong\u003eLow variance explained by models:\u0026nbsp;\u003c/strong\u003eThe limited explanatory power (R²=0.7-4.4%) indicates that important predictors remain unmeasured. Our findings describe \"who\" differs in outcomes but do not fully explain \"why\" these differences exist\u003c/p\u003e\n\u003cp\u003e6. \u003cstrong\u003eUnmeasured confounding:\u0026nbsp;\u003c/strong\u003eVariables correlated with demographics but not measured directly (socioeconomic status, parental education, urban/rural origin, ethnic identity) may account for observed associations. Religion, in particular, may proxy for unmeasured cultural and socioeconomic factors\u003c/p\u003e\n\u003cp\u003e7. \u003cstrong\u003eGeneralizability limits:\u0026nbsp;\u003c/strong\u003eTwo universities in one region may not represent all Nigerian university contexts. Findings may differ in other regions with different cultural, ethnic, and religious compositions\u003c/p\u003e\n\u003cp\u003e8. \u003cstrong\u003eSample characteristics:\u0026nbsp;\u003c/strong\u003ePredominantly first-year students (53.2%) may limit generalizability to upper-level students. However, this composition reflects actual university enrollment patterns\u003c/p\u003e\n\u003cp\u003e9. \u003cstrong\u003eMultiple comparisons:\u0026nbsp;\u003c/strong\u003eWith numerous statistical tests performed, some significant findings (particularly those at p\u0026lt;0.05) may represent Type I errors. Results should be interpreted in context of effect sizes and consistency across analyses\u003c/p\u003e\n\u003cp\u003e10. \u003cstrong\u003eBinary gender assessment:\u0026nbsp;\u003c/strong\u003eOur questionnaire included only male/female/prefer-not-to-say options, failing to capture gender diversity and potentially misrepresenting gender identities\u003c/p\u003e\n\u003cp\u003eDespite these limitations, the study's large sample, systematic methodology, validated instruments, and comprehensive demographic assessment provide valuable evidence for understanding CVD-related disparities among Nigerian university students.\u003c/p\u003e"},{"header":"5.\tConclusions","content":"\u003cp\u003eThis comprehensive demographic analysis of 1,300 Nigerian university students reveals low prevalence of traditional behavioral risk factors (smoking 2.2%, alcohol 3.7%) but identifies critical disparities in CVD knowledge and behavioral readiness across demographic subgroups. Male students and Muslims demonstrate lower CVD knowledge, while Muslims paradoxically perceive higher personal risk despite lower knowledge. Academic progression associates with declining exercise intentions, suggesting increasing academic pressure undermines behavioral readiness over time.\u003c/p\u003e\n\u003cp\u003eThese findings challenge one-size-fits-all approaches to university-based CVD prevention. Effective interventions must be demographically targeted and culturally tailored, specifically addressing:\u003c/p\u003e\n\u003cp\u003e· Knowledge gaps among male students and Muslims through gender-sensitive and faith-integrated education\u003c/p\u003e\n\u003cp\u003e· Structural barriers to physical activity for Muslim students through culturally appropriate facilities\u003c/p\u003e\n\u003cp\u003e· Declining behavioral readiness among advanced students through timing-optimized programming\u003c/p\u003e\n\u003cp\u003eThe 14.6% of students with family history of CVD represent a critical high-risk subgroup currently underserved, with only 56.9% of the overall sample recognizing family history as a risk factor. Systematic screening and targeted interventions for this genetically predisposed population should be prioritized.\u003c/p\u003e\n\u003cp\u003eWhile CVD knowledge correlates positively with behavioral intentions (r=0.23-0.14), it remains independent of risk perception (r=-0.02), confirming that knowledge enhancement alone is insufficient. Dual-track interventions simultaneously addressing knowledge gaps and risk perception through personalized assessment are essential.\u003c/p\u003e\n\u003cp\u003eThe limited variance explained by demographics (R²\u0026lt;5%) indicates that individual psychological factors, social networks, and environmental contexts likely exert stronger influences than demographic categories alone. Future research should employ comprehensive models incorporating these additional dimensions to develop more complete explanatory frameworks and more effective interventions.\u003c/p\u003e\n\u003cp\u003eBy identifying \"who\" is most vulnerable and demonstrating that demographic patterns vary across outcome domains, this study provides essential evidence for designing equity-oriented, culturally responsive cardiovascular health promotion programs that reach the diverse student populations comprising Nigeria's future leadership.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eABCD - Attitudes and Beliefs about Cardiovascular Disease\u003c/p\u003e\n\u003cp\u003eANOVA - Analysis of Variance\u003c/p\u003e\n\u003cp\u003eBMI - Body Mass Index\u003c/p\u003e\n\u003cp\u003eCI - Confidence Interval\u003c/p\u003e\n\u003cp\u003eCVD - Cardiovascular Disease\u003c/p\u003e\n\u003cp\u003eLMICs - Low- and Middle-Income Countries\u003c/p\u003e\n\u003cp\u003eNSUK - Nasarawa State University, Keffi\u003c/p\u003e\n\u003cp\u003eSD - Standard Deviation\u003c/p\u003e\n\u003cp\u003eSPSS - Statistical Package for the Social Sciences\u003c/p\u003e\n\u003cp\u003eUNIJOS - University of Jos\u003c/p\u003e\n\u003cp\u003eVIF - Variance Inflation Factor\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThis study was approved by the Research Ethics Committees of the Ministry of Health, Plateau State (Approval No MOH/MIS/202/VOL I/XX). All participants provided written informed consent before participation. The study was conducted in accordance with the Declaration of Helsinki and Nigerian National Code for Health Research Ethics.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eNot applicable. This manuscript does not contain any individual person\u0026apos;s data in any form.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author (O.S.C.) on reasonable request, subject to ethical approval and institutional data sharing agreements. Due to ethical restrictions and participant confidentiality protections, data cannot be made publicly available. Requests should be directed to [email protected].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no external funding. All costs were borne by the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOSC conceptualized and designed the study, developed data collection instruments, obtained ethical approvals, supervised all aspects of data collection, conducted statistical analyses, interpreted results, drafted the initial manuscript, and revised all subsequent drafts. AIA contributed to study design and methodology, provided technical guidance on sampling procedures and data analysis, assisted with data interpretation, and critically reviewed and edited the manuscript for important intellectual content. RBC coordinated field data collection activities, trained and supervised research assistants, ensured data quality and completeness, contributed to preliminary data analysis, and critically reviewed the manuscript. All authors read and approved the final manuscript and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe gratefully acknowledge the participation of students from the University of Jos and Nasarawa State University, Keffi. We thank the university administrations for granting permission to conduct this study. We acknowledge the research assistants who contributed to data collection: [INSERT NAMES if appropriate]. We also thank the class representatives who facilitated access to departmental enrollment data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOSC (PhD candidate) is a public health specialist with expertise in non-communicable disease epidemiology. AIA (PhD) is a microbiologist and an infectious disease specialist with research interests in community health. RBC (MPH) is a health educator with experience in school-based health promotion programs.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eWorld Health Organization. Cardiovascular diseases (CVDs) fact sheet. Geneva: WHO; 2021.\u003c/li\u003e\n \u003cli\u003eRoth GA, Mensah GA, Johnson CO, et al. 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Use of science to guide the design of indicators for sustainable health system reform: the sustainable health system reform indicators project. BMJ Qual Saf. 2020;29(3):247-55.\u003c/li\u003e\n \u003cli\u003eBeardsworth A, et al. Food choice, gender and intensive parenting styles: do \u0026apos;good\u0026apos; mothers mirror their daughters more than their sons? Appetite. 2020;147:102907.\u003c/li\u003e\n \u003cli\u003eAyalew MB, Tesfa GA, Ayele FY, Tilahun BD. Knowledge of cardiovascular disease risk factors, practice and barriers among community pharmacists in Northwest Ethiopia: a cross-sectional study. Metabol Open. 2022;16:100219.\u003c/li\u003e\n \u003cli\u003ePengpid S, et al. Physical inactivity and associated factors among university students in 23 low-, middle- and high-income countries. Int J Public Health. 2019;64(4):539-51.\u003c/li\u003e\n \u003cli\u003eWang X, et al. Physical activity levels among college students: a systematic review and meta-analysis. 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The intention\u0026ndash;behavior gap. \u003cem\u003eSoc Personal Psychol Compass\u003c/em\u003e. 2016;10(9):503-518.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cardiovascular disease, risk factors, demographics, gender differences, university students, Nigeria, prevalence, health disparities ","lastPublishedDoi":"10.21203/rs.3.rs-8027940/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8027940/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eCardiovascular diseases (CVDs) are increasingly affecting young adults in sub-Saharan Africa, yet evidence on demographic variations in risk factor prevalence among university students remains limited. Understanding \"who\" is most at risk is crucial for designing targeted prevention programs. This study examined sociodemographic correlates and prevalence of modifiable CVD risk factors among Nigerian university students.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWe conducted a cross-sectional study of 1,300 undergraduates from two universities in North-Central Nigeria between January and April 2025. Data were collected using the validated ABCD Risk Questionnaire supplemented with sociodemographic and behavioral risk factor assessments. Outcomes included CVD knowledge, risk perception, behavioral intentions, and prevalence of smoking, alcohol use, hypertension, diabetes, and family history of CVD. Statistical analyses employed chi-square tests, independent t-tests, ANOVA, Pearson correlations, and multiple linear regression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe prevalence of modifiable risk factors was: current smoking 2.2%, current alcohol use 3.7%, self-reported hypertension 6.7%, diabetes 1.4%, and family history of CVD 14.6%. Risk factor clustering was minimal, with 83.7% having no behavioral risk factors. Significant demographic variations emerged: In Nasarawa State University, males had 6.3% lower CVD knowledge than females (p=0.002), while Muslims scored 5.9% lower than Christians (p=0.008). Religion significantly predicted risk perception, with Muslims perceiving 2.4% higher risk than Christians (β=2.361, p=0.007). Academic level negatively predicted exercise intentions (β=-1.228, p=0.003), with higher-level students showing lower readiness. CVD knowledge positively correlated with exercise intentions (r=0.231, p\u0026lt;0.001) and healthy eating intentions (r=0.138, p\u0026lt;0.001), but not with risk perception (r=-0.019, p=0.499). Overall, sociodemographic variables explained limited variance: knowledge (R²=1.1-4.4%), risk perception (R²=1.8%), exercise intentions (R²=2.1%), and dietary intentions (R²=0.7%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eNigerian university students exhibit inadequate CVD knowledge and low personal risk perception despite high readiness for healthy lifestyle changes. Critically, knowledge correlates with behavioral intentions but not with risk perception, revealing a selective disconnect where cognitive understanding translates into positive behavioral attitudes but not into personal vulnerability awareness. Public health interventions must address knowledge deficits through targeted education while simultaneously employing personalized risk assessment strategies to enhance risk awareness. The positive knowledge-behavior relationship provides a foundation for intervention, but the knowledge-risk perception disconnect requires deliberate strategies to calibrate personal risk awareness and effectively channel the existing high readiness for behavioral change.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial registration:\u003c/strong\u003e Not applicable.\u003c/p\u003e","manuscriptTitle":"Sociodemographic Correlates and Prevalence of Modifiable Cardiovascular Disease Risk Factors Among University Students in North-Central Nigeria: A Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-05 10:18:04","doi":"10.21203/rs.3.rs-8027940/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bdf47e55-f41e-4477-a06c-ca1bbc2e63d4","owner":[],"postedDate":"November 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-03T05:23:31+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-05 10:18:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8027940","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8027940","identity":"rs-8027940","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}