Skip to contents Skip to Global Navigation Menu
  • KDCA
  • Contact us
  • E-Submission

PHRP : Osong Public Health and Research Perspectives

OPEN ACCESS. pISSN: 2210-9099. eISSN: 2233-6052
Search
Advanced Search

Articles

Page Path

Original Article

Correlates and co-occurrence of risk factors for non-communicable diseases among adolescents in schools in Karnataka, India: a cross-sectional study

Tejaswini Bangalore Darukaradhya1orcid, Krishnamurthy Jayanna1,2orcid, Shivaraj Nallur Somanna3orcid, Sony Sequeira1orcid, Shalini Chandrashekar Nooyi3orcid
Osong Public Health and Research Perspectives 2026;17(1):83-93.
Published online: January 30, 2026

1Division of Public Health, Faculty of Life and Allied Health Sciences, M.S. Ramaiah University of Applied Sciences, Bengaluru, India

2Center for Integrative Health and Wellbeing, Bengaluru, India

3Department of Community Medicine, Ramaiah Medical College, M.S. Ramaiah University of Applied Sciences, Bengaluru, India

Corresponding author: Tejaswini Bangalore Darukaradhya Division of Public Health, Department of Allied Health Sciences, Faculty of Life and Allied Health Sciences, M.S. Ramaiah University of Applied Sciences, Bengaluru, Karnataka 560054, India E-mail: drtejaswini02@gmail.com
• Received: June 10, 2025   • Revised: December 10, 2025   • Accepted: January 11, 2026

© 2026 Korea Disease Control and Prevention Agency.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

  • 1,189 Views
  • 80 Download
prev

Full Article

Download PDF Download PDF
  • Objectives
    Non-communicable disease (NCD) risk among adolescents represents a growing concern due to modifiable, lifestyle-related behavioral risk factors. Early identification and control of these factors are essential for prevention. This study assessed the correlates and co-occurrence of NCD-related lifestyle risk factors among school-going adolescents in Karnataka, India, aiming to inform intervention development.
  • Methods
    Screening was conducted among 1,100 school adolescents aged 13–16 years from 8 randomly selected urban and rural schools in Karnataka. Data were collected using a validated self-administered questionnaire covering sociodemographic characteristics and social cognitive theory predictors of lifestyle practices. Descriptive statistics, chi-square tests, and logistic regression were employed.
  • Results
    Of the 1,100 adolescents surveyed, 552 and 548 were from urban and rural areas, respectively. Both groups reported high rates of insufficient fruit and vegetable (FV) intake (96.7% and 67.7%, respectively), inadequate physical activity (96.7% and 68.6%), tobacco use (5.6% and 11.5%), and alcohol consumption (5.6% and 10.8%). On logistic regression, urban adolescents were significantly more likely than rural peers to exhibit multiple behavioral risk factors, with 19-fold higher odds of having ≥1 factor (adjusted odds ratio [AOR], 19.04; p<0.001) and 4-fold higher odds of having ≥2 (AOR, 4.06; p<0.001). Parental (particularly maternal) education was associated with NCD risk (AOR, 1.82; p=0.001). Physical inactivity significantly co-occurred with low FV intake (71.7%) and junk food consumption (72.8%).
  • Conclusion
    Unhealthy lifestyle behaviors among adolescents displayed significant co-occurrence, underscoring the critical need for comprehensive, theory-based school interventions to address multiple interconnected risk factors and mitigate the burden of NCDs.
  • Keywords: Adolescent; Health risk behaviors; Lifestyle; Noncommunicable diseases
Non-communicable diseases (NCDs) impose a growing public health burden, accounting for approximately 74% of deaths worldwide, with over 85% of premature NCD deaths occurring in low- and middle-income countries (LMICs) [1]. Major NCDs include cardiovascular diseases, chronic respiratory diseases, type 2 diabetes, and cancer. These conditions are primarily driven by behavioral risk factors that often emerge during childhood and adolescence and are shaped by social determinants such as socioeconomic status and education [2]. Behavioral risk factors, also known as modifiable lifestyle factors, include unhealthy diet, inadequate physical activity, tobacco use, and harmful alcohol consumption [1].
School-going adolescents are particularly susceptible to unhealthy lifestyles that may persist into adulthood and adversely affect health outcomes, increasing the risk of premature morbidity and mortality [3–5]. Adolescence is a critical developmental period characterized by rapid physical, psychological, and social transitions that can shape health behaviors and attitudes, making it an opportune window for preventive interventions and health promotion [3,5]. The approximately 1.2 billion adolescents (10–19 years old) worldwide—90% of whom live in LMICs—often face structural and contextual challenges that compound health risks [6]. In India, evidence indicates substantial variation in behavioral risk profiles across urban and rural settings and between private and public school adolescents, impacted by differences in access, exposure, and socioeconomic context [7–9].
Alongside behavioral risk factors, socialization, technology, and environmental influences can substantially affect adolescent decision-making, shaping health behaviors and well-being [10]. One Indian study reported that excessive social media use was associated with poor sleep, stress, and unhealthy lifestyle choices [11], while another study suggested that school climate may negatively influence adolescent health [12]. These factors can have lasting effects, highlighting the need for multifactorial interventions.
Schools represent a key setting for promoting healthy lifestyle behaviors among adolescents, providing a platform for comprehensive health education and interventions [13]. Implementing health-promoting initiatives within schools can enable outreach to large numbers of adolescents and support positive behavior change, ultimately reducing the burden of NCDs and promoting sustainable health outcomes [13].
Surveys in India and elsewhere have reported a high prevalence of risk behaviors and unhealthy lifestyles among adolescents, supporting the need for interventions within educational settings. A recent national cross-sectional survey among 15–17-year-olds reported 3.1% tobacco use, 25.2% inadequate physical activity, 6.2% overweight, and 1.8% obesity [13]. A survey in Tanzania similarly reported a high prevalence of combined risk factors (17.6% unhealthy diet and inadequate physical activity), emphasizing the importance of adopting a strategic, multifactorial approach rather than focusing on single risk factors [14].
A review published in India reported that young people (10–24 years old) are vulnerable to a range of intrinsic and extrinsic factors that affect health and safety. Nearly 10%–30% of adolescents experience behaviors and conditions that adversely affect health, warranting urgent attention from policymakers and public health professionals. If not addressed early, NCDs can impose a substantial burden on Indian society in terms of mortality, morbidity, disability, and socioeconomic losses [15]. The present study was conducted as part of a program to inform the development and implementation of interventions for adolescents in urban (Bengaluru) and rural (Chikkaballapura district) areas of South Karnataka. The specific objectives were to assess the correlates and co-occurrence of lifestyle risk factors among school-going adolescents in Karnataka that predispose them to NCDs.
Study Design
A cross-sectional design was adopted to investigate NCD-related lifestyle risk factors among school-going adolescents.
Setting
The study was conducted in 2 districts of Karnataka, India: Bengaluru (urban) and Chikkaballapura (rural). Both public and private schools in urban and rural areas were selected to ensure diversity and contextual representation of the region’s cultural and socioeconomic contexts.
Participants
School-going adolescents aged 13–16 years were randomly selected using a multistage sampling strategy. First, Bengaluru and Chikkaballapura districts were chosen as field practice areas of a private institution (M.S. Ramaiah University of Applied Sciences) in Karnataka. Bengaluru, the urban state capital, has a diverse metropolitan population, whereas Chikkaballapura provides rural demographic representation. Second, within each district, 4 schools were randomly selected using probability proportional to enrollment size. Third, classes from grades 8 to 10 were included. Fourth, all eligible adolescents aged 13–16 years were invited to participate. Informed consent was obtained from both students and parents/guardians. A total of 1,100 adolescents participated in the study (a 100% response rate).
Sample Size
The sample size was calculated based on Dorle et al. [16] using prevalence estimates for physical activity (68.9% with intervention vs. 35.1% without). Assuming 95% confidence, 90% power, a 10% risk difference, and 5% loss to follow-up, the required minimum sample size was 1,100 participants. Physical activity was selected for the sample size estimation as part of a broader intervention program because of its variability, statistical strength, and relevance to adolescent risk profiling.
Variables
The outcome variables included unhealthy diet (low fruit and vegetable [FV] intake, junk food consumption, and sugar-sweetened beverage [SSB] consumption), physical inactivity, tobacco use, and alcohol consumption. Exposure variables included sociodemographic characteristics (gender, age, residence, parental education, and occupation) and environmental influences. All variables were dichotomized into favorable and unfavorable categories based on World Health Organization (WHO) standards or daily guidelines (Table 1).
Data Collection and Measurements
Data were collected using a pre-validated, pilot-tested questionnaire developed through a 5-step approach. The tool covered sociodemographic characteristics, NCD knowledge, lifestyle behaviors (dietary habits, physical activity, tobacco use, and alcohol consumption), and environmental influences (peer pressure, media exposure, and cultural norms). Standardized items were incorporated from the Food Frequency Questionnaire [17], the Global School-based Student Health Survey [18], the Global Youth Tobacco Survey [19], the WHO STEPwise approach to NCD risk factor surveillance instrument [20], and prior research. Pilot testing among 180 adolescents informed refinements; the Cronbach α internal consistency coefficient was 0.85. Trained teachers administered the questionnaire in classrooms.
Statistical Analysis
Descriptive statistics were used to summarize participant characteristics and the prevalence of lifestyle-related risk factors. Chi-square tests were used to assess associations between risk behaviors and sociodemographic variables. Univariate logistic regression was used to estimate crude odds ratios (ORs) and 95% confidence intervals (CIs); variables displaying significance at p<0.05 were entered into multivariable models using backward stepwise elimination. Diagnostic checks (assumptions of a binary outcome and independence of observations, multicollinearity assessed using variance inflation factors, outliers assessed using the Cook distance, and model fit assessed using the Hosmer-Lemeshow test) were performed systematically and are reported in the Supplementary Material 1. For the co-occurrence analysis, ≥2 behavioral risk factors were operationally defined as the presence of 2 or more unfavorable lifestyle behaviors (diet, physical inactivity, tobacco use, and alcohol consumption). Pairwise co-occurrence was assessed descriptively, and overall clustering was analyzed using logistic regression with a binary outcome (≥2 vs. <2 risk factors). All analyses were conducted using IBM SPSS ver. 20.0 (IBM Corp.), with p-values of less than 0.05 considered to indicate statistical significance.
Ethical Consideration
This study was approved by the institutional ethics committee of Ramaiah Medical College prior to study initiation (Ref no: MSRMC/EC/AP-02/11-2022). Permission was obtained from the schools, and written informed consent from parents/guardians and assent from the adolescents were secured. The study was prospectively registered in the Clinical Trials Registry (CTRI) of India (registration number: CTRI/2023/01 /048977). Data were kept confidential and anonymized before analysis.
Participant Characteristics
The study included 1,100 school-going adolescents aged 13–16 years (Table 2). Of these, 46.0% (n=506) were ≤14 years old and 54.0% (n=594) were >14 years old. The sample comprised 50.2% urban and 49.8% rural adolescents, with 54.6% girls and 45.4% boys. Overall, 63.2% of participants’ fathers and 34.0% of mothers had primary- or secondary-level education. Regarding occupation, 54.5% of fathers were unskilled workers and 77.5% of mothers were homemakers. Clustering of lifestyle risk factors was common: 95% reported ≥1 risk factor, 86% reported ≥2, 70% had ≥3, 13% had ≥4, and 3% had 5 or 6. This pattern reflects the increasing burden of risk as the number of co-occurring factors rises.
Associations between Sociodemographic Variables and Lifestyle Behaviors
Table 3 displays significant associations between sociodemographic variables and lifestyle practices among adolescents. Chi-square analysis indicated that school location (urban vs. rural) was significantly associated with FV intake (χ2=159, p<0.001), junk food intake (χ2=71.5, p<0.001), SSB intake (χ2=44.07, p<0.001), physical activity (χ2=152, p<0.001), tobacco use (χ2=12.17, p<0.001), and alcohol consumption (χ2=9.71, p<0.001). Additionally, age (≤14 years vs. >14 years) was associated with FV intake (χ2=12.5, p=0.001) and alcohol consumption (χ2=4.3, p<0.001). Parental education was significantly associated with FV intake (father’s education: χ2=10.7, p=0.013; mother’s education: χ2=37.1, p<0.001), physical activity (father’s education: χ2=10.1, p=0.018; mother’s education: χ2=17.9, p<0.001), and tobacco use (father’s education: χ2=15.9, p=0.001; mother’s education: χ2=15.0, p<0.001).
Predictors of Lifestyle Behaviors
Logistic regression analysis revealed that urban school location was a strong predictor of several unhealthy lifestyle behaviors, including junk food intake (adjusted OR [AOR], 5.20; p<0.001), SSB consumption (AOR, 2.39; p<0.001), and physical inactivity (AOR, 13.39; p<0.001). Younger age (≤14 years) predicted inadequate FV intake (AOR, 1.63; p=0.007). A maternal educational attainment of secondary school was also associated with increased risk relative to no formal education, particularly for inadequate FV intake (AOR, 1.59; p=0.023), and junk food intake (AOR, 1.71; p=0.011) (Table 4, Table S1).
Clustering of Risk Behaviors
Table 5 highlights significant clustering of unhealthy lifestyle behaviors among adolescents. Physical inactivity frequently co-occurred with low FV intake (72%) and junk food consumption (73%). Low FV intake also co-occurred with tobacco use (5.8%) and alcohol consumption (5.6%). Co-occurrence of tobacco and alcohol use was also significant (1.82%). Adjusted analyses confirmed these associations, including those of low FV intake with physical inactivity (AOR, 0.24; 95% CI, 0.17–0.34), tobacco use (AOR, 2.01; 95% CI, 1.22–3.30), and alcohol consumption (AOR, 2.36; 95% CI, 1.43–3.89). Figure 1 complements Table 5 by illustrating the most prevalent co-occurrence patterns of lifestyle risk behaviors, with physical inactivity and junk food consumption showing the highest clustering among adolescents.
Urban adolescents were significantly more likely to exhibit clustered risk behaviors, with 19-fold higher odds of having ≥1 risk factor (AOR, 19.04) and 4-fold higher odds of having ≥2 risk factors (AOR, 4.06) compared with rural peers. Higher parental education, particularly father’s education, was associated with a lower likelihood of risk factor clustering. Boys showed slightly more risk factors than girls, although the difference was not statistically significant (Table 6).
Key Results
This study assessed the co-occurrence of NCD risk factors and their correlates among urban and rural school-going adolescents in Karnataka. Data from 1,100 adolescents aged 13–16 years showed a high prevalence of lifestyle-related risk behaviors, with significant urban–rural differences. Insufficient FV intake (96.7% and 67.7%, respectively) and physical inactivity (96.7% and 68.6%) were particularly common, whereas tobacco use (5.6% and 11.5%) and alcohol consumption (5.6% and 10.8%) were more prevalent among rural adolescents. These findings are consistent with other research reporting high rates of lifestyle-related risk factors among adolescents in LMIC contexts, reflecting greater exposure to unhealthy environments, with implications for early onset of NCDs and the urgent need for targeted preventive interventions [21,22]. Evidence also links these risk behaviors to psychological distress and reduced quality of life among adolescents [23], reinforcing the importance of early identification and intervention to mitigate the future burden of NCDs [23]. These patterns may be influenced by increased access to fast food outlets, limited integration of health education into school curricula, and greater digital exposure that promotes sedentary behavior and unhealthy dietary choices [23].
Differences in lifestyle-related risk factors between urban and rural adolescents were significant. Urban adolescents reported higher rates of insufficient FV intake, junk food and SSB consumption, and physical inactivity, whereas rural adolescents reported higher tobacco and alcohol use. These patterns are shaped by socioeconomic status, food accessibility, environmental factors, media exposure, and cultural norms. Evidence from India indicates that urban adolescents are more likely to have multiple risk factors than their rural counterparts [24], highlighting the need for targeted interventions in both settings. The association between urban school location and higher risk behaviors is consistent with findings from other studies [24,25].
Logistic regression analysis indicated that urban school location and younger age (≤14 years) significantly predicted unhealthy diet and lifestyle behaviors. Lower parental education, particularly maternal education, was also associated with higher risk. These findings are consistent with evidence identifying urban residence and lower parental education as strong predictors of multiple lifestyle-related risk factors. Boys more often exhibited multiple risk factors than girls, although the difference was not statistically significant. This pattern may reflect differences in pocket money, parental investment, and resilience and vulnerability profiles [26–28]. Urban adolescents had 19-fold higher odds of having ≥1 risk factor and 4-fold higher odds of having ≥2 risk factors than rural adolescents. This aligns with previous research highlighting the co-occurrence of unhealthy behaviors and the influence of demographic factors such as school location and parental education [29].
The study contributes to the growing evidence on clustering of unhealthy lifestyle risk factors among adolescents and the role of demographic characteristics, underscoring the need for multifaceted public health strategies [30]. Educational institutions provide an ideal setting for promoting healthy behaviors during formative years [31,32]. Policy-level measures such as regulating food advertising to adolescents, mandating daily physical activity in schools, and implementing school-based screening programs are recommended. Emphasizing school-based interventions, leveraging parental influence, and addressing socioeconomic disparities may promote healthier choices and reduce NCD risk [31,32].
The current findings are consistent with global studies reporting co-occurring behavioral risk factors among adolescents. Studies in developed nations have similarly documented clustering of physical inactivity, poor dietary intake, tobacco use, and alcohol consumption [33,34]. In rural contexts, such co-occurrence may be driven by limited health awareness and the societal normalization of tobacco and alcohol use, where these behaviors are often culturally accepted and less regulated [35].
Parental education emerged as a key determinant of adolescent habits, consistent with the literature on parental influence in health education [36–38]. Parents with higher educational attainment may have greater health literacy and more resources to support healthier choices. Interventions that engage both parents and adolescents may therefore be pivotal in reducing risk behaviors [39–42].
Overall, our findings emphasize the need for tailored public health approaches that consider the socioeconomic and cultural contexts of urban and rural adolescents. While urban adolescents face greater challenges related to diet and physical inactivity, rural adolescents are more likely to engage in tobacco and alcohol use. These differences support the need for context-specific implementation strategies. Integrating behavior change communication within schools and communities may enhance the effectiveness of interventions [39–43].
Strengths
This study has multiple strengths. It employed a robust design and a large sample size and provides a comprehensive analysis of lifestyle risk factors and their co-occurrence in Karnataka. The focus on adolescents and the potential to inform school-based interventions align with the United Nations Sustainable Development Goals, particularly efforts to reduce premature NCD mortality.
Limitations
The cross-sectional design provides only a snapshot of behaviors, limiting causal inference. Self-reported data may have introduced bias, as participants could have underreported or overreported behaviors due to recall errors. Social desirability bias may also have influenced responses, particularly regarding tobacco and alcohol use. In addition, the data did not fully capture parental influences such as parenting styles and family dynamics.
This study highlights the prevalence and clustering of NCD-related lifestyle risk factors among school-going adolescents in Karnataka, with urban school location and low parental education emerging as strong predictors. Addressing these multiple interrelated behaviors early is critical to reducing premature NCD morbidity and mortality.
Based on the findings, recommended actionable strategies include integrating school-based physical activity modules into daily timetables, embedding health education within curricula, strengthening parental engagement—particularly among mothers—through structured involvement programs, and leveraging digital behavior-change tools tailored for adolescents. Teacher-led health promotion initiatives grounded in social cognitive theory may further enhance self-efficacy, model healthy behaviors, and promote supportive environments. These strategies can be implemented within schools and reinforced by families, providing a practical foundation for sustainable behavior change. Future research should consider using a longitudinal design to assess the long-term impact of such interventions, thereby informing evidence-based policies and programs for effective NCD prevention and control among adolescents.
Adolescents in Karnataka, India, display a high prevalence of lifestyle-related risk factors for non-communicable diseases (NCDs). Urban adolescents have a significantly higher likelihood of multiple risk factors, highlighting geographic health disparities. The findings reinforce the need for comprehensive, theory-driven school-based interventions to promote sustainable health behaviors and reduce the long-term burden of NCDs.

Ethics Approval

This study was approved by the institutional ethics committee of Ramaiah Medical College prior to study initiation (Ref no: MSRMC/EC/AP-02/11-2022). The study was conducted in accordance with the principles of the Declaration of Helsinki. Written informed consent was obtained from participants and their parents/guardians.

Conflicts of Interest

The authors have no conflicts of interest to declare.

Funding

None.

Availability of Data

The datasets generated and analyzed during the present study are not publicly available but are available from the corresponding author upon reasonable request. Due to ethical considerations and institutional policies, access to the raw data is restricted to authorized researchers who meet applicable confidentiality and regulatory requirements.

Authors’ Contributions

Conceptualization: TBD; Methodology: TBD, SNS, KJ; Data curation: TBD; Formal analysis: TBD, SNS, KJ; Investigation: TBD; Project administration: SS; Visualization: SS; Supervision: KJ, SCN; Writing–original draft: TBD; Writing–review & editing: all authors. All authors read and approved the final manuscript.

Acknowledgements

Artificial intelligence was not used for concept development or analysis for this manuscript.

Supplementary Material 1. Detailed data analysis methods; Table S1. Predictors of non-communicable disease behavioral risk factors among adolescents: unadjusted ORs (additional information for Table 4). Supplementary data are available at https://doi.org/10.24171/j.phrp.2025.0204.
Supplementary Material 1.
Title
j-phrp-2025-0204-Supplementary-Material-1.pdf
Table S1.
Predictors of non-communicable disease behavioral risk factors among adolescents: unadjusted ORs (additional information for Table 4)
j-phrp-2025-0204-Supplementary-Table-S1.pdf
Figure 1.
Co-occurrence patterns of NCD behavioral risk factors among school-going adolescents.
NCD, non-communicable disease; FV, fruit and vegetable.
Figure 1. Co-occurrence patterns of NCD behavioral risk factors among school-going adolescents.  
Correlates and co-occurrence of risk factors for non-communicable diseases among adolescents in schools in Karnataka, India: a cross-sectional study
Table 1.
Outcome variables and operational definitions
Table 1.
Variable Definition Classification criteria
1. Unhealthy diet (composite) Includes 3 dietary components reflecting poor nutritional habits: FV consumption, junk food, and SSBs See subcomponents below
 FV consumption Less than 5 servings per day (approximately 400 g); indicates inadequate dietary consumption Never or less than once a day versus more than once a day
 Junk/processed food consumption Foods high in fat, salt, and sugar with low nutritive value Daily or more than once a day versus less than once a day
 SSB consumption Consumption exceeding 50 g of free sugars per day Daily or more than once a day versus less than once a day
2. Physical inactivity Insufficient bodily movement requiring energy expenditure (at least 60 min/d of moderate-to-vigorous physical activity in the past 7 days) Never or insufficient versus daily or sufficient
3. Tobacco use Use of any smoked (cigarette, cigar, pipe) or smokeless (gutkha, zarda, paan masala, khaini) tobacco in the past 30 days Yes versus no
4. Alcohol consumption Consumption of any form of alcohol in the past 30 days Yes versus no

FV, fruit and vegetable; SSB, sugar-sweetened beverage.

Table 2.
Sociodemographic characteristics of school-going adolescents (n=1,100)
Table 2.
Variable Category Frequency (%)
Age (y) ≤14 506 (46.0)
>14 594 (54.0)
School location Urban area 552 (50.2)
Rural area 548 (49.8)
Gender Boys 499 (45.4)
Girls 601 (54.6)
Father’s education Primary 237 (21.5)
Secondary 695 (63.2)
Diploma/degree 45 (4.1)
No formal education 123 (11.2)
Mother’s education Primary 240 (21.8)
Secondary 374 (34.0)
Diploma/degree 10 (0.9)
No formal education 476 (43.3)
Father’s occupationa) Skilled 66 (6.0)
Semiskilled 428 (38.9)
Unskilled 600 (54.5)
No employment 6 (0.5)
Mother’s occupation Homemaker 853 (77.5)
Working 247 (22.5)
Behavioral risk factors One risk factor 1,047 (95.2)
Two risk factors 943 (85.7)
Three risk factors 765 (69.5)
Four risk factors 143 (13.0)
Five or 6 risk factors 32 (2.9)

Adolescents’ characteristics across demographic factors are presented as numbers and percentages.

a)Classification is based on the Indian National Classification of Occupations.

Table 3.
Associations between non-communicable disease risk factors and demographic variables among adolescents
Table 3.
Variable Proportion of adolescents with inadequate FV intake Proportion of adolescents with unhealthy diet (junk) Proportion of adolescents consuming SSBs Proportion of adolescents with physical inactivity Proportion of adolescents using tobacco Proportion of adolescents consuming alcohol
Never or <once a day χ2/p Daily >once a day χ2/p Daily >once a day χ2/p Never or insufficient PA χ2/p Daily >once a day χ2/p Daily >once a day χ2/p
Gender 0.5/0.479 0.006/0.939 0.136/0.712 0. 261/0.609 0.529/0.467 0.067/0.796
 Boy (n=499) 415 (83.2) 416 (83.4) 326 (65.3) 416 (83.4) 46 (9.2) 42 (8.4)
 Girl (n=601) 490 (81.5) 500 (83.2) 399 (66.4) 494 (82.2) 48 (8.0) 48 (8.0)
School location 159/0.001 71.5/0.001 44.07/0.001 152.24/0.001 12.17/0.001 9.71/0.002
 Urban area (n=552) 534 (96.7) 512 (92.8) 416 (75.4) 534 (96.7) 31 (5.6) 31 (5.6)
 Rural area (n=548) 371 (67.7) 404 (73.7) 309 (56.4) 376 (68.6) 63 (11.5) 59 (10.8)
Age 12.5/0.001 0.049/0.826 0.102/0.75 2.82/0.093 4.305/0.038
 ≤14 y (n=506) 394 (77.9) 420 (83.0) 331 (65.4) 413 (81.6) 0.803/0.37 51 (10.1) 32 (6.3)
 >14 y (n=594) 511 (86.0) 496 (83.5) 394 (66.3) 497 (83.7) 43 (7.2) 58 (9.8)
Father’s education 10.7/0.013 12.28/0.006 9.92/0.019 10.09/0.018 15.862/0.001 2.65/0.448
 Primary (n=237) 211 (89.0) 205 (86.5) 167 (70.5) 209 (88.2) 24 (10.1) 15 (6.3)
 Secondary (n=695) 554 (79.7) 586 (84.3) 464 (66.8) 566 (81.4) 44 (6.3) 61 (8.8)
 Diploma/degree (n=45) 38 (84.4) 32 (71.1) 26 (57.8) 32 (71.1) 8 (17.8) 2 (4.4)
 No formal education (n=123) 102 (82.9) 93 (75.6) 68 (55.3) 103 (83.7) 18 (14.6) 1 2 (9.8)
Mother’s education 37.1/0.001 4.078/0.253 12.97/0.005 17.88/0.001 14.97/0.002 0.161/0.984
 Primary (n=240) 215 (89.6) 200 (83.3) 180 (75.0) 209 (87.1) 18 (7.5) 19 (7.9)
 Secondary (n=374) 277 (74.1) 321 (85.8) 244 (65.2) 292 (78.1) 26 (7.1) 32 (8.6)
 Diploma/degree (n=10) 5 (50.0) 7 (70.0) 5 (50.0) 5 (50.0) 4 (40.0) 1 (10.0)
 No formal education (n=476) 408 (85.7) 388 (81.5) 296 (62.2) 404 (84.9) 46 (9.7) 38 (8.0)
Father’s occupation 37.6/0.001 8.763/0.03 7.055/0.07 18.88/0.001 6.266/0.099 9.19/0.027
 Skilled (n=66) 51 (77.3) 52 (78.8) 47 (71.2) 52 (78.8) 7 (10.6) 3 (4.5)
 Semiskilled (n=428) 318 (74.3) 343 (80.1) 262 (61.2) 334 (78.0) 40 (9.3) 48 (11.2)
 Unskilled (n=600) 532 (88.7) 517 (86.2) 412 (68.7) 521 (86.8) 45 (7.5) 39 (6.5)
 No employment (n=6) 4 (66.7) 4 (66.7) 4 (66.7) 3 (50.0) 2 (33.3) 4 (66.7)
Mother’s occupation 0.002/0.968 0.018/0.895 2.917/0.08 1.04/0.308 0.53/0.82 0.003/0.96
 Non-working (n=853) 702 (82.3) 711 (83.4) 551 (64.6) 711 (83.4) 72 (8.4) 70 (8.2)
 Working (n =247) 203 (82.2) 205 (83.0) 174 (70.4) 199 (80.6) 22 (8.9) 20 (8.1)

Data are presented as n (%). Behavioral patterns: The table presents data on unhealthy dietary behaviors (inadequate FV intake, junk food intake, and SSB intake), physical inactivity, tobacco use, and alcohol consumption among adolescents. Statistical measures: The table includes numbers (n) and percentages of adolescents engaging in unhealthy behaviors, along with chi-square (χ2) values and corresponding p-values indicating the statistical significance of observed differences across demographic categories (gender, school location, age, parental education, and parental occupation). A p-value less than 0.05 was considered to indicate statistical significance.

FV, fruit and vegetable; SSB, sugar-sweetened beverage; PA, physical activity.

Table 4.
Predictors of non-communicable disease behavioral risk factors among adolescents: AORs
Table 4.
Variable Inadequate FV intake Unhealthy diet (junk) SSBs Physical inactivity Tobacco Alcohol
AOR (95% CI) p AOR (95% CI) p AOR (95% CI) p AOR (95% CI) p AOR (95% CI) p AOR (95% CI) p
Gender
 Boy 0.97 (0.69–1.38) 0.878 0.95 (0.68–1.33) 0.761 0.91 (0.70–1.19) 0.496 0.99 (0.70–1.40) 0.963 1.21 (0.78–1.88) 0.398 1.10 (0.71–1.71) 0.674
 Girl (ref.) 1 1 1 1 1 1
School location
 Urban area 0.08 (0.05–0.14) 0.001 5.20 (3.46–7.84) 0.001 2.39 (1.89–3.19) 0.001 13.39 (7.92–22.7) 0.001 0.43 (0.26–0.71) 0.001 0.55 (0.33–0.91) 0.019
 Rural area (ref.) 1 1 1 1 1 1
Age (y)
 ≤14 1.63 (1.14–2.32) 0.007 0.99 (0.70–1.39) 0.954 0.97 (0.74–1.26) 0.810 1.00 (0.70–1.41) 0.977 1.44 (0.92–2.26) 0.110 0.58 (0.36–0.92) 0.021
 >14 (ref.) 1 1 1 1 1 1
Father’s education
 Primary 1.05 (0.52–2.13) 0.886 1.67 (0.91–3.06) 0.100 1.48 (0.92–2.39) 0.108 0.96 (0.48–1.92) 0.903 0.85 (0.42–1.72) 0.656 0.65 (0.28–1.55) 0.309
 Secondary 1.09 (0.61–1.95) 0.768 1.65 (0.99–2.76) 0.055 1.44 (0.94–2.19) 0.091 0.91 (0.51–1.63) 0.746 0.41 (0.22–0.78) 0.007 0.85 (0.42–1.71) 0.647
 Diploma/degree 0.41 (0.13–1.29) 0.126 0.93 (0.39–2.22) 0.862 1.08 (0.51–2.29) 0.849 0.74 (0.29–1.92) 0.539 0.98 (0.34–2.85) 0.967 0.35 (0.07–1.80) 0.208
 No formal education (ref.) 1 1 1 1 1 1
Mother’s education
 Primary 0.86 (0.58–1.48) 0.579 0.91 (0.58–1.44) 0.695 1.62 (1.13–2.34) 0.009 1.05 (0.63–1.75) 0.854 0.96 (0.52–1.75) 0.881 1.12 (0.61–2.05) 0.714
 Secondary 1.59 (1.07–2.37) 0.023 1.71 (1.13–2.58) 0.011 1.23 (0. 98–1.68) 0.199 0.90 (0.61–1.35) 0.617 0.77 (0.44–1.33) 0.347 1.00 (0.59–1.71) 0.995
 Diploma/degree 4.42 (0.95–20.5) 0.058 1.44 (0.31–6.76) 0.648 0.78 (0.28–3.05) 0.717 0.48 (0.11–2.05) 0.318 2.79 (0.61–12.70) 0.184 3.00 (0.36–30.4) 0.353
 No formal education (ref.) 1 1 1 1 1 1
Father’s occupation
 Skilled 1.64 (0.24–11.4) 0.619 1.03 (0.16–6.85) 0.972 0.92 (0.15–5.72) 0.927 1.75 (0.29–10.50) 0.541 0.32 (0.04–2.41) 0.270 0.0
 Semiskilled 1.28 (0.20–8.08) 0.797 1.39 (0.23–8.40) 0.720 0.65 (0.11–3.76) 0.628 2.17 (0.40–11.70) 0.366 0.33 (0.05–2.15) 0.248 0.0
 Unskilled 0.96 (0.15–6.12) 0.963 1.27 (0.21–7.71) 0.799 0.64 (0.11–3.74) 0.621 1.92 (0.35–10.40) 0.451 0.33 (0.05–2.14) 0.244 0.0
 No employment (ref.) 1 1 1 1 1 1
Mother’s occupation
 Non-working 1.37 (0.90–2.09) 0.142 1.01 (0.67–1.52) 0.964 0.73 (0.53–1.01) 0.058 1.00 (0.66–1.59) 0.983 1.04 (0.60–1.77) 0.894 0.95 (0.56–1.62) 0.851
 Working (ref.) 1 1 1 1 1 1

A p-value less than 0.05 was considered to indicate statistical significance. The table presents behavioral patterns among school-going adolescents and includes AORs, 95% CIs, and p-values across demographic categories (gender, school location, age, parental education, and parental occupation).

AOR, adjusted odds ratio; FV, fruit and vegetable; CI, confidence interval; SSB, sugar-sweetened beverage; ref., reference category.

Table 5.
Prevalence of co-occurrence of behavioral non-communicable disease risk factors among school-going adolescents
Table 5.
Combination of risk factors Co-occurrence count (n) Co-occurrence (%)=(co-occurrence/total)×100 OR (95% CI) AOR (95% CI)
Low FV intake and junk eating 758 68.9 0.83 (0.56–1.23) 1.35 (0.87–2.11)
Low FV intake and physical inactivity 789 71.7 0.24 (0.17–0.34)a) 0.24 (0.17–0.34)a)
Low FV intake and tobacco intake 64 5.8 2.39 (1.50–3.80)a) 2.01 (1.22–3.30)a)
Low FV intake and alcohol intake 61 5.6 2.42 (1.56–3.88)a) 2.36 (1.43–3.89)a)
Physical inactivity and junk eating 801 72.8 0.21 (3.47–6.61)a) 0.21 (0.15–0.30)a)
Physical inactivity and tobacco intake 72 6.5 1.52 (0.92–2.53) 1.52 (0.92–2.53)
Physical inactivity and alcohol intake 75 6.8 0.95 (0.54–1.70) 0.89 (0.49–1.66)
Tobacco use and junk eating 76 6.9 1.20 (0.78–2.06) 1.08 (0.61–1.91)
Alcohol intake and junk eating 76 6.9 0.91 (0.50–1.65) 0.92 (0.49–1.77)
Tobacco use and alcohol intake 20 1.8 0.28 (0.16–0.48)a) 0.31 (0.18–0.55)a)

OThe table shows the prevalence of co-occurrence of behavioral risk factors among school-going adolescents. It includes co-occurrence counts (n) and percentages, as well as ORs, AORs, 95% CIs, and p-values. A p-value less than 0.05 was considered to indicate statistical significance.

OR, odds ratio; CI, confidence interval; AOR, adjusted odds ratio; FV, fruit and vegetable.

a)Statistically significant associations.

Table 6.
Association between co-occurrence of behavioral risk factors and demographic variables
Table 6.
Variable Reference group ≥1 Risk factor (n=1,047; 95%) ≥2 Risk factors (n=943; 86%) ≥3 Risk factors (n=765; 70%) ≥4 Risk factors (n=143, 13%) Five or 6 risk factors (n=32; 3%)
Gender Boy (ref.: girl) 0.59 (0.34–1.04) 1.07 (0.75–1.53) 1.05 (0.81–1.373) 0.91 (0.63–1.29) 1.46 (0.70–3.06)
School location Urban area (ref.: rural area) 19.04 (5.95–61.51)a) 4.06 (2.74–6.02)a) 1.99 (1.52–2.61)a) 0.41 (0.28–0.59)a) 0.35 (0.16–0.87)a)
Age (y) ≤14 (ref.: >14) 0.97 (0.54–1.77) 1.33 (0.93–1.89) 0.96 (0.74–1.26) 1.15 (0.80–1.65) 1.17 (0.56–2.46)
Father’s education (ref: no formal education)
Primary 0.67 (0.21–2.18) 2.02 (1.08–3.84) 1.46 (0.91–2.33) 1.05 (0.54–2.06) 0.59 (0.17–2.05)
Secondary 0.72 (0.26–1.99) 1.72 (1.05–2.84) 1.55 (1.03–2.32) 1.01 (0.57–1.79) 0.44 (0.16–1.18)
Diploma/degree 0.37 (0.09–1.50) 1.34 (0.56–3.21) 0.87 (0.43–1.75) 0.61 (0.19–1.97) 1.10 (0.20–5.99)
Mother’s education (Ref.: no formal education)
Primary 0.83 (0.49–1.74) 1.16 (0.71–1.91) 1.35 (0.94–1.94) 1.19 (0.72–1.98) 0.66 (0.21–2.12)
Secondary 1.82 (0.93–3.56) 1.48 (0.97–2.26) 1.27 (0.92–1.75) 1.25 (0.81–1.93) 0.81 (0.34–1.93)
Diploma/degree 1.22 (0.15–10.27) 3.04 (0.34–26.91) 2.10 (0.49–9.02) 1.98 (0.34–11.59) 0.00
Father’s occupation (ref: no employment)
Skilled 2.31 (0.19–28.7) 2.11 (0.39–14.83) 1.28 (0.20–8.08) 0.32 (0.05–2.10) -
Semiskilled 2.78 (0.27–28.25) 2.18 (0.35–13.58) 0.92 (0.16–5.39) 0.52 (0.09–2.87) -
Unskilled 2.39 (0.23–24.73) 1.84 (0.29–11.53) 0.93 (0.16–5.46) 0.33 (0.06–1.87) -
Mother’s occupation Homeworker (ref.: no employment) 1.04 (0.52–2.09) 0.91 (0.59–1.41) 0.79 (0.57–1.09) 1.21 (0.77–1.89) 1.14 (0.45–2.88)

The table presents the co-occurrence of combinations of behavioral risk factors and their associations with demographic factors among school-going adolescents. It includes adjusted odds ratios, 95% confidence intervals, and p-values across demographic categories. A p-value less than 0.05 was considered to indicate statistical significance.

Ref., reference; -, not estimable due to zero cell count or insufficient sample size.

a)Statistically significant associations.

  • 1. World Health Organization (WHO) (CH). Non-communicable diseases key facts by WHO [Internet]. WHO; 2025 [cited 2023 Jun 1]. Available from: https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases.
  • 2. United Nations International Children's Emergency Fund (UNICEF) (US). Non-communicable diseases [Internet]. UNICEF; 2021 [cited 2023 Aug 14]. Available from: https://data.unicef.org/topic/child-health/noncommunicable-diseases/.
  • 3. Akseer N, Mehta S, Wigle J, et al. Non-communicable diseases among adolescents: current status, determinants, interventions and policies. BMC Public Health 2020;20:1908.
  • 4. Marathe A, Mane S, Agarkhedkar S. Risk-factors of non-communicable diseases in urban adolescents in Western India. World J Adv Res Rev 2020;8:064−71.
  • 5. Darukaradhya TB. An alarming non-communicable diseases burden: time for adolescent health investment. Int J Community Med Public Health 2024;11:590−3.
  • 6. Shinde S, Harling G, Assefa N, et al. Counting adolescents in: the development of an adolescent health indicator framework for population-based settings. EClinicalMedicine 2023;61:102067.
  • 7. Ali D, Shah S, Nazir M, et al. Assessment of the magnitude of behavioural risk factors among school going adolescents of Kashmir Valley: a cross sectional study. Int J Community Med Public Health 2023;10:1156−60.
  • 8. Patton GC, Sawyer SM, Santelli JS, et al. Our future: a Lancet commission on adolescent health and wellbeing. Lancet 2016;387:2423−78.
  • 9. Jangid AK. Healthcare inequality: bridging the gap in rural and urban areas in India. IOSR J Nurs Heal Sci 2025;14:5−6.
  • 10. Viner RM, Ozer EM, Denny S, et al. Adolescence and the social determinants of health. Lancet 2012;379:1641−52.
  • 11. Abinayaa M. Social media addiction and its impact on mental health among adolescents. Int J Psychȯl 2023;11:2484−90.
  • 12. Podiya JK, Navaneetham J, Bhola P. Influences of school climate on emotional health and academic achievement of school-going adolescents in India: a systematic review. BMC Public Health 2025;25:54.
  • 13. Mathur P, Kulothungan V, Leburu S, et al. Baseline risk factor prevalence among adolescents aged 15-17 years old: findings from National Non-communicable Disease Monitoring Survey (NNMS) of India. BMJ Open 2021;11:e044066.
  • 14. Shayo FK. Co-occurrence of risk factors for non-communicable diseases among in-school adolescents in Tanzania: an example of a low-income setting of sub-Saharan Africa for adolescence health policy actions. BMC Public Health 2019;19:972.
  • 15. Sunitha S, Gururaj G. Health behaviours & problems among young people in India: cause for concern & call for action. Indian J Med Res 2014;140:185−208.
  • 16. Dorle AS, Mallapur AA, Mannapur BS, et al. A school based intervention model to promote healthy life style among school children for prevention of risk factors for cardiovascular disease in Bagalkot, Karnataka: a quasi experimental study. Med Innov 2017;6:24−30.
  • 17. Shaikh NI, Frediani JK, Ramakrishnan U, et al. Development and evaluation of a nutrition transition-FFQ for adolescents in South India. Public Health Nutr 2017;20:1162−72.
  • 18. World Health Organization (WHO) (CH). Noncommunicable disease surveillance, monitoring and reporting [Internet]. WHO; 2025 [cited 2025 Sep 22]. Available from: https://www.who.int/teams/noncommunicable-diseases/surveillance/systems-tools/global-school-based-student-health-survey.
  • 19. World Health Organization (WHO) (CH). Noncommunicable disease surveillance, monitoring and reporting [Internet]. WHO; 2024 [cited 2024 Sep 21]. Available from: https://www.who.int/teams/noncommunicable-diseases/surveillance/systems-tools/global-youth-tobacco-survey/questionnaire.
  • 20. World Health Organization (WHO) (CH). Noncommunicable disease surveillance, monitoring and reporting [Internet]. WHO; 2024 [cited 2024 May 21]. Available from: https://www.who.int/teams/noncommunicable-diseases/surveillance/systems-tools/steps/instrument.
  • 21. Pati MK, Swaroop N, Kar A, et al. A narrative review of gaps in the provision of integrated care for noncommunicable diseases in India. Public Health Rev 2020;41:8.
  • 22. Li X, Dessie Y, Mwanyika-Sando M, et al. Co-occurrence of and factors associated with health risk behaviors among adolescents: a multi-center study in sub-Saharan Africa, China, and India. EClinicalMedicine 2024;70:102525.
  • 23. Champion KE, Chapman C, Gardner LA, et al. Lifestyle risks for chronic disease among Australian adolescents: a cross-sectional survey. Med J Aust 2022;216:156−7.
  • 24. Panda A, Parida J, Jena S, et al. Prevalence and associated risk factors of overweight and obesity among adolescent population of India: a scoping review. BMC Nutr 2025;11:110.
  • 25. Wattelez G, Frayon S, Caillaud C, et al. Physical activity in adolescents living in rural and urban New Caledonia: the role of socioenvironmental factors and the association with weight status. Front Public Health 2021;9:623685.
  • 26. Lozza E, Jarach CM, Sesini G, et al. Should I give kids money?: the role of pocket money on at-risk behaviors in Italian adolescents. Ann Ist Super Sanita 2023;59:37−42.
  • 27. Barcellos SH, Carvalho LS, Lleras-Muney A. Child gender and parental investments in India: are boys and girls treated differently? Am Econ J Appl Econ 2014;6:157−89.
  • 28. Mawarni DP, Safira L, Aprilia CA. Association between pocket money availability and frequency of fast-food consumption toward overnutrition case among junior high school student, South Jakarta. In: ICPH 2020: Proceedings of the 7th International Conference on Public Health; 2020 Nov 18-19; Surakarta, ID. Masters Program in Public Health, Universitas Sebelas Maret; 2020. p. 128−34.
  • 29. Song Y, Liu J, Zhao Y, et al. Unhealthy lifestyles and clusters status among 3637 adolescents aged 11-23 years: a school-based cross-sectional study in China. BMC Public Health 2023;23:1279.
  • 30. Darukaradhya TB, Krishnamurthy J. Modifying non-communicable disease behaviours through effective health communication and behaviour change: a systematic review. Prev Med Res Rev 2025;2:24−39.
  • 31. Jayanna K. Integrative approach to lifestyle management: implications for public health research & practice in the context of SDG-3. J Ayurveda Integr Med 2022;14:100796.
  • 32. Kar A, Jayanna K. Using mixed-methods approach in an implementation research project to design a comprehensive urban primary health care intervention for management of diabetes and hypertension. SAGE Res Methods Cases 2021 Jan 11 [Epub]. https://doi.org/10.4135/9781529764239.
  • 33. Lioret S, Touvier M, Lafay L, et al. Dietary and physical activity patterns in French children are related to overweight and socioeconomic status. J Nutr 2008;138:101−7.
  • 34. Jago R, Fox KR, Page AS, et al. Physical activity and sedentary behaviour typologies of 10-11 year olds. Int J Behav Nutr Phys Act 2010;7:59.
  • 35. Mudgal SK, Patidar V, Sharma SK, et al. Tobacco and alcohol use among adolescents and young adults in aspirational districts in India: NFHS-5 based secondary analysis. Pan Afr Med J 2025;51:17.
  • 36. Munoz-Galiano IM, Connor JD, Gomez-Ruano MA, et al. Influence of the parental educational level on physical activity in schoolchildren. Sustainability 2020;12:3920.
  • 37. Yanez AM, Bennasar-Veny M, Leiva A, et al. Implications of personality and parental education on healthy lifestyles among adolescents. Sci Rep 2020;10:7911.
  • 38. Moral-Garcia JE, Urchaga-Litago JD, Ramos-Morcillo AJ, et al. Relationship of parental support on healthy habits, school motivations and academic performance in adolescents. Int J Environ Res Public Health 2020;17:882.
  • 39. Decker MJ, Gutmann-Gonzalez A, Saphir M, et al. Integrated theory-based health and development interventions for young people: a global scoping review. Health Educ Behav 2024;51:82−93.
  • 40. Champion KE, Hunter E, Gardner LA, et al. Parental information needs and intervention preferences for preventing multiple lifestyle risk behaviors among adolescents: cross-sectional survey among parents. JMIR Pediatr Parent 2023;6:e42272.
  • 41. Ho YL, Mahirah D, Ho CZ, et al. The role of the family in health promotion: a scoping review of models and mechanisms. Health Promot Int 2022;37:daac119.
  • 42. Sampaio F, Nystrand C, Feldman I, et al. Evidence for investing in parenting interventions aiming to improve child health: a systematic review of economic evaluations. Eur Child Adolesc Psychiatry 2024;33:323−55.
  • 43. Darukaradhya T, Jayanna K. Development of a health promotion intervention in managing behavioral risk factors for non-communicable diseases in adolescents: an intervention mapping approach. J Epidemiol Found India 2024;2:56−65.

Download Citation

Download a citation file in RIS format that can be imported by all major citation management software, including EndNote, ProCite, RefWorks, and Reference Manager.

Format:

Include:

Correlates and co-occurrence of risk factors for non-communicable diseases among adolescents in schools in Karnataka, India: a cross-sectional study
Osong Public Health Res Perspect. 2026;17(1):83-93.   Published online February 10, 2026
DOI: https://doi.org/10.24171/j.phrp.2025.0204
Download Citation

Download a citation file in RIS format that can be imported by all major citation management software, including EndNote, ProCite, RefWorks, and Reference Manager.
Format:
Include:
Correlates and co-occurrence of risk factors for non-communicable diseases among adolescents in schools in Karnataka, India: a cross-sectional study
Osong Public Health Res Perspect. 2026;17(1):83-93.   Published online February 10, 2026
DOI: https://doi.org/10.24171/j.phrp.2025.0204
Close

Figure

Related articles

PHRP : Osong Public Health and Research Perspectives

About

Browse articles 

Editorial policies

For authors  

phrp 사이트 QRCODE(URL : https://ophrp.org)
© Korea Disease Control and Prevention Agency.