Behavioural risk factors for cardiovascular diseases among adolescents of secondary school in Tulsipur Sub-Metropolitan City, Nepal: A cross-sectional study

Sita Bista; Bishow Puri; Sanju Maharjan; Poshan Thapa; Buna Bhandari

doi:10.1371/journal.pone.0313943

. 2025 Sep 11;20(9):e0313943. doi: 10.1371/journal.pone.0313943

Behavioural risk factors for cardiovascular diseases among adolescents of secondary school in Tulsipur Sub-Metropolitan City, Nepal: A cross-sectional study

Sita Bista ^1,^*, Bishow Puri ¹, Sanju Maharjan ¹, Poshan Thapa ^2,^‡ ,^✉, Buna Bhandari ^3,^4,^‡,^✉

Editor: Chiranjivi Adhikari⁵

PMCID: PMC12425188 PMID: 40934233

Abstract

Background

Cardiovascular diseases (CVDs) are a leading cause of global death and disability, affecting one-third of adult population. Often overlooked in school-going adolescents, behavioural risk factors are crucial contributors to CVD risk which begin early and accelerate during adolescent period. This study aims to assess the behavioural risk factors and their associated determinants among adolescents of Tulsipur Sub-Metropolitan City, Nepal.

Methods

A school-based cross-sectional study was conducted among 361 adolescents aged 16–19 years studying in grade 11 and 12 from public and private schools. Schools were selected using a stratified proportionate sampling method. Data were collected through a self-administered, structured, and validated questionnaire covering socio-demographic characteristics, behavioural risk factors of CVDs, and parental information. Descriptive and analytical statistics were used to analyse the data.

Results

The most prevalent behavioural risk factor was the consumption of calorie drinks (99%), followed by sedentary behaviour (60%), insufficient fruit and vegetable intake (57%), physical inactivity (35%), and consumption of processed food high in salt (33%). The prevalence of current smoking, alcohol consumption, and smokeless tobacco use was 12%, 10%, and 9% respectively. Key factors associated with the behavioural risk include maternal education, ethnicity, and education system. Parental tobacco and alcohol use were also associated with adolescent smoking and alcohol consumption.

Conclusions

The high prevalence of CVD risk factors among adolescents in Nepal highlights the urgent need for targeted interventions in both household and school settings. These interventions should aim to reduce behavioural risk factors to prevent the future burden of CVDs in resource-limited areas like Nepal.

Introduction

Cardiovascular disease (CVD) is the leading cause of preventable death worldwide, responsible for 17.9 million deaths in 2019, which represented 32% of all global deaths [1]. If current trend continues, it is estimated that approximately 23 million individuals will die from CVD by 2030 [2]. CVD affects one-third of adult population globally, making it a growing epidemic and one of the most significant public health challenges [3]. This is particularly concerning for adolescents aged 10–24 years, who represent over 25% of the global population and are increasingly at risk of developing CVD in adulthood [3]. The burden of CVD is particularly high in low- and middle-income countries (LMICs) including in South Asia, where three-quarters of these deaths occur [1].

In Nepal, CVD contributed to 26.9% of all deaths and 12.8% of total Disability-Adjusted Life Years (DALYs) in 2017 [4]. Of particular concern is the mortality rate among adolescents, with CVD-related deaths in the 15–19 age group reported at 3.9 per 100,000, accounting for 4.8% of total CVD deaths in the country [4]. This early onset of CVD risk in adolescence not only has direct health implications but can also place a heavy burden on Nepal’s macroeconomic conditions, as CVD typically affects individuals in their most productive years [1].

Evidence shows that physical inactivity, unhealthy diets, smoking, and alcohol consumption are major CVD risk factors among adolescents [3]. These behaviours often begin in childhood and become more pronounced during adolescence, though the clinical manifestations of CVD generally appear in adulthood [2]. Many LMICs, including Nepal, are experiencing rapid, unplanned urbanization, which has led to reduced physical activity spaces for children. Simultaneously, increased access to junk food and fast food has made adolescents more vulnerable to the development of CVD risk factors [5].

Despite these growing concerns, limited research exists on the behavioural risk factors for CVD in semi-urban areas of Nepal especially among understudied adolescent population. This study seeks to address that gap by assessing the prevalence of CVD risk and its associated factors. The findings aim to inform the development of adolescent-focused public health interventions that are both practical and effective in resource-limited settings like Nepal, with a focus on preventing the escalating burden of CVD.

Methods

Study design and setting

This cross-sectional study was conducted among secondary-level school-going adolescents aged 16–19 in Tulsipur Sub-Metropolitan City, Nepal. Tulsipur is located in the mid-western region of Nepal, a rapidly urbanizing area where people from neighbouring districts relocate for better education and opportunities. The city’s recent growth, including the emergence of fast-food chains and changing lifestyles, poses an increased risk of CVDs (6).

Study period

The study was conducted in 6 months from November 2021 to May 2022, from inception of research through literature review and problem identification to final data analysis and report preparation. The data was collected from 25^th March to 8^th April 2022.

Study population

The population consisted of students in grades 11 and 12 from public and private schools. Eligible participants were students between the ages of 16 and 19 years. Students with a history of CVDs or other chronic diseases were excluded from the study.

Operational Definition

Operational definitions used in the study are provided in S1 Appendix.

Sample size

The total calculated sample size was 367 students, based on the following assumptions: the total number of secondary-level school adolescents (16–19 aged) in Tulsipur Sub-Metropolitan City (N) = 5,262, a confidence level of α = 0.05, Z = 1.96 (for 95% confidence interval), p = 0.366 (prevalence of tobacco use according to the STEPs survey in Lumbini Province, 2019 (7)), and a 10% non-response rate.

Sampling

A stratified proportionate sampling method was employed using the sampling frame obtained from municipal education section. The two strata were defined based on schools’ affiliation (Public/Private), with the sample size proportionally distributed based on the student population in each stratum. Two public and two private secondary schools were randomly selected. Within each school, students were selected through a simple random lottery method using a random number generator. The students were divided into grades 11 and 12, with a proportional distribution according to age. The student’s records and details of each selected school were obtained from the school administration. Each student was assigned a unique identification number. Based on proportionate sample of selected schools, Microsoft excel was used to generate a predefined and non-repeating number. The randomly generated numbers were cross-verified with the student details. Thereafter, selected students were invited to participate in the study. If selected students did not provide consent or were absent, alternative students were taken through additional random numbers generated. The sampling method is illustrated in Fig 1.

Data collection tools and techniques

Data were collected using a self-administered questionnaire developed in Nepali after obtaining written consent from parents and verbal consent from students following ethical guidelines. The structured questionnaire was adapted from a study by Islam et al., conducted among school-going children in Bangladesh, with permission from the original authors [6]. The questionnaire contained 18 screening questions covering the five primary CVD risk behaviours. It was pretested with 10% of the sample size, i.e., among 37 students from Ghorahi Sub-Metropolitan City, a location with similar characteristics to the study area and revised as necessary. The pre-testing data was not included in final analysis. Data was collected by first and second authors (SB and BP) and regular supervision was maintained to minimize information bias, ensuring no more than 15 participants were in a room at any time. Any questions or concerns were addressed before the data collection process began.

Data processing and analysis

All data were systematically compiled, coded, checked, and edited on the same collection day. Data were entered into EpiData version 4.6, rechecked, and cleaned to ensure data quality. Statistical analysis was conducted using SPSS version 22.0. Univariate analysis was used to calculate frequencies, percentages, means, medians and standard deviation for the socio-demographic variables and parents’ information. Chi-square analysis was used to assess the association between socio-demographic characteristics, parental information, and the behavioural risk factors of CVDs. Variables with p < 0.25 from chi-square were considered for univariate and multivariate logistic regression analysis. Confounding was assessed using a threshold of 20% change, and all variables were retained in the final adjusted model to control for confounding.

Ethical considerations: Ethical approval was obtained from the Institutional Review Committee of Tribhuvan University Institute of Medicine, Nepal [IRC no: 355(6–11) E2 078/079]. Both written parental consent and the student’s consent were obtained for students under 18. On the first day, Parents received a parental consent form through students. Only after obtaining parental consent, students were given a written consent form. Students aged 18 or above provided their written informed consent. Anonymity and confidentiality were strictly maintained by not disclosing any personally identifiable information.

Results

The self-administered questionnaire was completed by 367 participants, but six responses were incomplete. Thus, the final analysis included 361 responses.

Characteristics of the participants and parents

The mean age of the participants was 17.5 ± 0.92 years. More than half (54%) were female, and 70% belonged to the Brahmin/Chhetri ethnicity. Around half (50.4%) were in grades 11, and 61% were enrolled in the management faculty. Most participants (66.8%) attended private schools, and 76% lived with family members.

About one-quarter (24%) of the respondents’ mothers and 10.5% of their fathers had no formal education. The majority of fathers (29%) were involved in business, while 63% of mothers were homemakers. In total, 20% of respondents reported that their parents had chronic illnesses, 15% responded their parents smoked, 24% indicated smokeless tobacco use, and 27% reported alcohol consumption. Additional information can be found in Table 1.

Table 1. Social and demographic characteristics of participants and Parents (n = 361).

SOCIO-DEMOGRAPHIC CHARACTERISTICS OF PARTICIPANTS	NUMBER	PERCENTAGE
AGE
Mean ± SD	17.5 ± 0.92
GENDER
Male	164	45.4
Female	195	54.0
Do nott want to disclose	2	0.6
ETHNICITY
Brahmin/Chhetri	255	70.6
Janajati	68	18.8
Dalit/Muslim/others	68	10.6
RELIGION
Hindu	354	98.1
Others	7	1.9
GRADE
11	182	50.4
12	179	49.6
EDUCATION SYSTEM
Private	241	66.8
Public	120	33.2
CURRENT LIVING CONDITION
Living with family members	274	76
Renting a room with friend	61	16.9
Alone/others	26	7.2
CHARACTERISTICS OF PARENTS
Mother education	n = 357 ^*
No formal schooling	90	25.2
Primary school and below	146	40.9
Secondary school	60	16.8
High School or above	41	16.9
Mother occupation	n = 357 ^*
Unemployed	233	65.3
Agriculture	47	13.2
Business	39	10.9
Others^¶	38	10.6
Father education	n = 350 ^#
No formal education	37	10.6
Primary School or below	106	30.3
Secondary school	101	28.9
High School or above	106	30.3
Father occupation	n = 350 ^#
Unemployed	27	7.7
Agriculture	101	28.9
Business	105	30
Others^¶	117	33.4
Chronic illness of father or mother or both	n = 358 ^§	^* multiple response
Hypertension	48	13.4
Heart Disease	10	2.8
Diabetes mellitus	11	3
Cancer	1	0.3
Chronic respiratory	4	1.1
Behavioral risks of father or mother or both	n = 358 ^§	^* multiple response
Smoking habit	53	14.8
Smokeless tobacco	88	24.4
Alcohol use	98	27.4

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*4 children do not have mother

#11 children do not have father

§3 children neither have father nor mother

Others^¶ include foreign employment, government employee, non-government employee and retired from government job.

Distribution of CVD risk factors among the participants

The most prevalent behavioural risk factor was the consumption of calorie-dense drinks (99%), followed by sedentary behaviour (60%) and insufficient fruit and vegetable intake (57%) was the third most common risk factor. These findings are presented in Fig 2.

Association between CVD Risk Factors and Socio-demographic Characteristics

Chi square test revealed processed food high in salt consumption was associated with grade (p = 0.007), ethnicity (p = 0.028), and mother’s education (p = 0.012). Added salt intake was also associated with the mother’s education (p = 0.049). However, calorie drink consumption did not show any significant associations. Detailed results are provided in Table 2.

Table 2. Association of processed food high in salt consumption, calorie drink consumption and added salt intake with socio-demographic characteristics (n = 361).

Characteristics	Categories	Processed food consumption (always or often Yes = 119) n (%)	Calorie drink consumption (yes = 356) n (%)	Added salt intake (always or often n = 56) n (%)
Age	16–17	63(52.9%)	170(47.8%)	26(46.4%)
	18–19	56(47.1%)	186(52.2%)	30(53.6%)
	p-value	0.181	0.925	0.808
Gender⁺	Male	47(39.5%)	164(46.3%)	25(44.6%)
	Female	72(60.5%)	190(53.7%)	31(55.4%)
	p-value	0.098	0.107	0.865
Ethnicity	Brahmin/Chhetri	93(78.1%)	250(70.2%)	40(71.4%)
	Others	26(21.9%)	106(29.8%)	16(28.6%)
	p-value	0.028 ^*	0.327	0.887
Religion	Hindu	116(97.5%)	349(98%)	54(96.4%)
	Others	3(2.5%)	7(2%)	2(3.6%)
	p-value	0.876	1	0.662
Grade	11	72(60.5%)	177(49.7%)	33(58.9%)
	12	47(39.5%)	179(50.3%)	23(41.1%)
	p-value	0.007 ^*	0.075	0.166
Education System	Public	43(36.1%)	119(33.4%)	21(37.5%)
	Private	76(63.9%)	237(66.6%)	35(62.5%)
	p-value	0.413	1	0.462
Current living condition	Living with family members	87 (73.1%)	271(76.1%)	43(76.8%)
	Others	32(26.9%)	85(23.9%)	13(23.2%)
	p-value	0.385	0.403	0.866
Mother’s^* education	No formal schooling	20(16.9%)	89(25.2%)	20(35.7%)
	Formal schooling	98(83.1%)	264(74.8%)	36(64.3%)
	p-value	0.012 ^*	1	0.049 ^*
Mother’s* occupation	Employed	41(34.7%)	123(34.8%)	21(37.5%)
	Unemployed	77(65.3%)	230(65.2%)	35(62.5%)
	p-value	0.935	0.353	0.601
Father’s^#education	No formal schooling	13(11%)	38(11%)	7(12.7%)
	Formal schooling	105(89%)	308(89%)	48(82.3%)
	p-value	0.945	1	0.627
Father’s^# occupation	Employed	114(96.6%)	330(95.4%)	51(92.7%)
	Unemployed	4(3.4%)	16(4.6%)	4(7.3%)
	p-value	0.450	1	0.488
Parents’^§ chronic illness	Yes	22(18.6%)	69(19.5%)	8(14.3%)
	No	96(81.4%)	285(80.5%)	48(85.7%)
	p-value	0.761	0.583	0.279
Parent’s^§ smoking habit	Yes	15(12.8%)	51(14.4%)	11(19.6%)
	No	102(87.2%)	303(85.6%)	45(80.4%)
	p-value	0.461	0.106	0.267
Parent’s^§ smokeless tobacco use	Yes	31(26.5%)	86(24.3%)	16(28.6%)
	No	86(73.5%)	268(75.7%)	40(71.4%)
	p-value	0.558	0.254	0.450
Parent’s^§ drinking habit	Yes	34(29.1%)	96(27.1%)	16(28.6%)
	No	83(70.9%)	258(72.9%)	40(71.4%)
	p-value	0.618	0.302	0.827

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+n = 359 (2 children did not disclose their gender) ^*n = 357 (four children did not have mother)

#n = 350 (eleven children did not have father) ^§n = 358(three children did not have both of parents)

*Statistically significant(p < 0.05)

Percentages represent column% among those who reported the behaviour.

Further analysis showed that sedentary behaviour was associated with the education system (p = 0.003), and physical inactivity was associated with the education system (p < 0.001), mother’s education (p = 0.025), and father’s education (p = 0.046). Insufficient fruit and vegetable intake did not show significant associations with any variables. These findings are presented in Table 3.

Table 3. Association of sedentary behaviour, insufficient fruit and vegetable intake, and physical inactivity (n = 361).

Characteristics	Categories	Sedentary behaviour (yes = 217)	Insufficient fruit and vegetable intake(yes = 206)	Physical inactivity(yes = 126)
Age	16-17	104(49.7%)	96(46.6%)	54(42.9%)
	18-19	113(52.1%)	110(53.4%)	72(57.1%)
	p-value	0.99	0.563	0.158
Gender⁺	Male	98(45.6%)	95(46.3%)	62(50%)
	Female	117(54.4%)	110(53.7%)	62(50%)
	p-value	0.720	0.928	0.066
Ethnicity	Brahmin/Chhetri	156(71.9%)	144(69.9%)	92(73%)
	Others	61(28.1%)	62(30.1%)	34(27%)
	p-value	0.512	0.240	0.7
Religion	Hindu	212(97.7%)	203(98.5%)	123(97.6%)
	Others	5(2.3%)	3(1.5%)	3(2.4%)
	p-value	0.820	0.703	0.964
Grade	Eleven	104(47.9%)	98(47.6%)	58(46%)
	Twelve	113(52.1%)	108(52.4%)	68(54%)
	p-value	0.246	0.213	0.223
Education system	Public	85(39.2%)	76(36.9%)	24(19%)
	Private	132(60.8%)	130(63.1%)	102(81%)
	p-value	0.003 ^*	0.089	<0.001 ^*
Current living condition	Living with family members	163(75.1%)	155(75.2%)	98(77.8%)
	Others	54(24.9%)	51(24.8%)	28(22.2%)
	p-value	0.669	0.736	0.541
Mother^* education	No formal schooling	51(23.7%)	44(21.7%)	23(18.3%)
	Formal schooling	164(76.3%)	159(78.3%)	103(81.7%)
	p-value	0.425	0.077	0.025 ^*
Mother^* occupation	Employed	78(36.3%)	75(36.9%)	46(36.5%)
	Unemployed	137(63.7%)	128(63.1%)	80(63.5%)
	p-value	0.372	0.255	0.546
Father^# education	No formal schooling	18(8.5%)	18(8.9%)	8(6.4%)
	Formal schooling	194(91.5%)	184(91.1%)	117(93.6%)
	p-value	0.078	0.172	0.046 ^*
Father^# occupation	Employed	203(95.8%)	196(97%)	118(94.4%)
	Unemployed	9(4.2%)	6(3%)	7(5.6%)
	p-value	0.717	0.094	0.492
Parent’s^§ chronic illness	Yes	43(20%)	37(18%)	24(19%)
	No	172(80%)	168(82%)	102(81%)
	p-value	0.794	0.406	0.859
Parent’s^§ smoking habit	Yes	28(13%)	26(12.7%)	18(14.4%)
	No	188(87%)	179(87.3%)	107(85.6%)
	p-value	0.226	0.191	0.875
Parent’s^§ smokeless tobacco use	Yes	46(21.3%)	54(26.3%)	34(27.2%)
	No	170(78.7%)	151(73.7%)	91(72.8%)
	p-value	0.075	0.371	0.399
Parent’s^§ drinking habit	Yes	64(29.6%)	55(26.8%)	41(32.8%)
	No	152(70.4%)	150(73.2%)	84(67.2%)
	p-value	0.238	0.789	0.092

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+n = 359 (two children did not disclose their gender) *n = 357 (four children do not have mother), ^#n = 350 (eleven children do not have father), ^§n = 358(three children do not have both of parents)

*Statistically significant(p < 0.05)

Percentages represent column% among those who reported the behaviour.

Similarly, the use of refined vegetable oil was significantly associated with ethnicity (p = 0.014), education system (p = 0.001), current living conditions (p = 0.026), parental chronic illness (p = 0.026), and parental alcohol use (p = 0.003). Current smoking was associated with gender (p = < 0.001), ethnicity (p = 0.012), mother’s education (p = 0.029), parents’ smoking habits (p = 0.002), and parents’ smokeless tobacco use (p = < 0.001). Additionally, current alcohol use was significantly associated with gender (p = < 0.001), parents’ smokeless tobacco use (p = 0.017), and parents’ alcohol use (p = 0.022). Current smokeless tobacco use was significantly associated with gender (p = < 0.001). These results are shown in Table 4.

Table 4. Association of refined vegetable oil use, current tobacco smoking, current alcohol use and current smokeless tobacco use (n = 361).

Characteristics	Categories	Refined vegetable oil user (yes = 70) n (%)	Current smoker(yes = 44) n (%)	Current alcohol user(yes = 37) n(%)	Current smokeless tobacco use(yes = 31) n(%)
Age	16-17	34(48.6%)	17(38.6%)	11(29.7%)	12(38.7%)
	18-19	36(51.4%)	27(61.4%)	26(70.3%)	19(61.3%)
	p-value	0.904	0.188	0.019	0.283
Gender⁺	Male	28(41.2%)	38(86.4%)	34(91.9%)	28(90.3%)
	Female	40(58.8.6%)	6(13.6%)	3(8.1%)	3(9.7%)
	p-value	0.061	<0.001 ^*	<0.001 ^*	<0.001 ^*
Ethnicity	Brahmin/Chhetri	41(58.6%)	24(54.5%)	21(56.8%)	22(71%)
	Others	29(41.4%)	20(45.5%)	16(43.2%)	9(29%)
	p-value	0.014 ^*	0.012 ^*	0.05	0.966
Religion	Hindu	68(97.1%)	42(95.5%)	36(97.3%)	30(96.8%)
	Others	2(2.9%)	2(4.5%)	1(2.7%)	1(3.2%)
	p-value	0.890	0.450	1	1
Grade	11	38(54.3%)	20(45.5%)	14(37.8%)	14(45.2%)
	12	32(45.7%)	24(54.5%)	23(62.2%)	17(54.8%)
	p-value	0.659	0.482	0.106	0.541
Education system	Public	37(52.9%)	18(40.9%)	14(37.8%)	10(32.3%)
	Private	33(47.1%)	26(59.1%)	23(62.2%)	21(67.7%)
	p-value	0.001 ^*	0.249	0.531	0.903
Current living condition	Living with family members	46(65.7%)	32(72.7%)	31(83.8%)	24(77.4%)
	Others	24(34.3%)	12(27.3%)	6(16.2%)	7(22.6%)
	p-value	0.026 ^*	0.599	0.237	0.836
Mother^* education	No formal schooling	21(30.4%)	5(11.6%)	6(16.2%)	5(16.7%)
	Formal schooling	48(69.6%)	38(88.4%)	31(83.8%)	25(83.3%)
	p-value	0.266	0.029 ^*	0.183	0.260
Mother^* occupation	Employed	26(37.7%)	13(30.2%)	10(27%)	9(30%)
	Unemployed	43(62.3%)	30(69.8%)	27(73%)	21(70%)
	p-value	0.530	0.535	0.315	0.592
Father^# Education	No formal schooling	8(11.8%)	5(11.6%)	2(5.7%)	2(6.7%)
	Formal schooling	60(88.2%)	38(88.4%)	33(94.3%)	28(93.3%)
	p-value	0.789	1	0.457	0.642
Father^# Occupation	Employed	65(95.6%)	42(97.7%)	34(97.1%)	29(96.7%)
	Unemployed	3(4.4%)	1(2.3%)	1(2.9%)	1(3.3%)
	p-value	1	0.717	0.932	1
Parent’s^§ chronic illness	Yes	20(29%)	6(14%)	8(21.6%)	6(20%)
	No	49(71%)	37(86%)	29(78.4%)	24(80%)
	p-value	0.026 ^*	0.324	0.738	0.949
Parent’s^§ smoking habit	Yes	15(21.7%)	13(30.2%)	9(24.3%)	5(16.7%)
	No	54(78.3%)	30(69.8%)	28(75.7%)	25(83.3%)
	p-value	0.121	0.002 ^*	0.085	0.764
Parent’s^§ smokeless tobacco use	Yes	21(30.4%)	20(46.5%)	15(40.5%)	9(30%)
	No	48(69.6%)	23(53.5%)	22(59.5%)	21(70%)
	p-value	0.217	<0.001 ^*	0.017 ^*	0.471
Parent’s^§ alcohol use	Yes	28(40.6%)	16(37.2%)	16(43.2%)	12(40%)
	No	41(59.4%)	27(62.8%)	21(56.8%)	18(60%)
	p-value	0.003 ^*	0.123	0.022 ^*	0.105

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+n = 359 (two children did not disclose their gender)

*n = 357 (four children do not have mother),

#n = 350 (eleven children do not have father)

§n = 358(three children do not have both of parents)

*Statistically significant(p < 0.05)

Percentages represent column% among those who reported the behaviour.

The logistic regression model findings showed that processed food high in salt consumption was significantly associated with sex, ethnicity, grade and mother’s education. However, calorie drink consumption and added salt intake did not show any significant associations. Sedentary behaviour was significantly associated with education system, parents’ tobacco use and parent’s alcohol use. Similarly, insufficient fruit and vegetable intake and physical activity was associated with education system only. Additional findings are presented in Table 5 and refer to S1 Table for further details of regression analysis.

Table 5. Association and odds ratio of different predictors and independent variables.

Category	Predictor	B (SE)	p-value	UOR (95% CI)	AOR (95% CI)
Processed Food Consumption	Gender (Male vs. Female)	−0.516 (0.256)	0.044 ^*	0.686 [0.439, 1.072]	0.597 [0.362, 0.986]
	Ethnicity (Brahmin/Chhetri vs. Others)	0.775 (0.288)	0.007 ^*	—	2.171 [1.234, 3.821]
	Grade (12 vs. 11)	−0.602 (0.255)	0.018	0.544 [0.348, 0.850]	0.548 [0.332, 0.903]
	Mother’s Education (Formal vs. No Formal Schooling)	1.063 (0.354)	0.003 ^*	2.030 [1.164, 3.538]	2.895 [1.447, 5.793]
	Age (19–18 vs. 16–17)	−0.046 (0.259)	0.860	0.741 [0.477, 1.150]	0.955 [0.575, 1.586]
Added Salt Intake	Grade (12 vs. 11)	−0.436 (0.324)	0.178	0.666 [0.374, 1.186]	0.646 [0.343, 1.219]
Added Salt Intake	Mother’s Education (Formal vs. No Formal Schooling)	−0.585 (0.366)	0.110	0.545 [0.297, 1.002]	0.557 [0.272, 1.141]
Sedentary Behaviour	Education System (Private vs Public)	−0.696 (0.239)	0.004 ^*	0.499 [0.312, 0.796]	0.351 [0.202, 0.609]
	Parent’s Smokeless Tobacco Use (Yes vs. No)	−0.440 (0.248)	0.076	0.644 [0.396, 1.047]	0.510 [0.280, 0.930]
	Parent’s Drinking Habit (Yes vs. No)	0.291 (0.247)	0.239	1.337 [0.825, 2.169]	2.380 [1.275, 4.442]
Insufficient Fruit & Vegetable Intake	Grade (12 vs. 11)	0.221 (0.239)	0.355	1.304 [0.859, 1.980]	1.247 [0.781, 1.991]
	Education System (Private vs Public)	–0.656 (0.265)	0.013 ^*	0.678 [0.433, 1.063]	0.519 [0.309, 0.873]
	Mother’s Education (Formal vs. No Formal Schooling)	0.355 (0.295)	0.229	1.539 [0.952, 2.488]	1.426 [0.800, 2.544]
	Father’s Education (Formal vs. No Formal Schooling)	0.307 (0.394)	0.436	1.597 [0.813, 3.139]	1.359 [0.628, 2.942]
	Father’s Occupation (Employed vs. Unemployed)	0.895 (0.546)	0.101	2.367 [0.841, 6.666]	2.447 [0.840, 7.129]
	Parent’s Smoking Habit (Yes vs. No)	–0.542 (0.374)	0.147	0.678 [0.378, 1.216]	0.582 [0.279, 1.211]
Physical Inactivity	Age (19–18 vs. 16–17)	0.200 (0.259)	0.439	1.368 [0.885, 2.115]	1.222 [0.736, 2.029]
	Gender (Male vs. Female)	0.114 (0.248)	0.646	1.304 [0.843, 2.017]	1.121 [0.690, 1.822]
	Grade (12 vs. 11)	0.045 (0.249)	0.856	1.310 [0.849, 2.021]	1.046 [0.642, 1.704]
	Education System (Private vs Public)	1.033 (0.295)	<0.001 ^*	2.935 [1.754, 4.913]	2.810 [1.574, 5.013]
	Mother’s Education (Formal vs. No Formal Schooling)	0.247 (0.320)	0.441	1.830 [1.073, 3.120]	1.280 [0.683, 2.397]
	Father’s Education (Formal vs. No Formal Schooling)	0.467 (0.461)	0.311	2.250 [0.998, 5.072]	1.596 [0.647, 3.938]
	Parent’s Drinking Habit (Yes vs. No)	0.371 (0.302)	0.219	1.507 [0.934, 2.431]	1.449 [0.802, 2.619]
Refined Vegetable Oil Use	Sex (Male vs. Female)	–0.277 (0.311)	0.374	0.798 [0.467, 1.362]	0.758 [0.412, 1.395]
	Ethnicity (Brahmin/Chhetri vs. Others)	–0.320 (0.313)	0.307	0.509 [0.296, 0.875]	0.726 [0.394, 1.341]
	Education System (Private vs Public)	–1.124 (0.319)	<0.001 ^*	0.356 [0.209, 0.607]	0.325 [0.174, 0.608]
	Parent’s Chronic Illness (Yes vs. No)	0.751 (0.341)	0.028 ^*	1.951 [1.068, 3.565]	2.119 [1.086, 4.133]
	Parent’s Alcohol Use (Yes vs. No)	1.004 (0.365)	0.006 ^*	2.137 [1.232, 3.706]	2.728 [1.333, 5.584]
Current Smoker	Sex (Male vs. Female)	2.428 (0.507)	<0.001 ^*	9.500 [3.901, 23.134]	11.336 [4.196, 30.623]
	Ethnicity (Brahmin/Chhetri vs. Others)	–0.732 (0.425)	0.085	0.447 [0.235, 0.850]	0.481 [0.209, 1.105]
	Mother’s Education (Formal vs. No Formal Schooling)	1.542 (0.612)	0.012 ^*	2.821 [1.075, 7.405]	4.675 [1.408, 15.523]
	Parent’s Smoking Habit (Yes vs. No)	1.117 (0.551)	0.043 ^*	2.979 [1.435, 6.185]	3.055 [1.037, 8.997]
	Parent’s Smokeless Tobacco Use (Yes vs. No)	1.010 (0.438)	0.021 ^*	3.159 [1.638, 6.090]	2.745 [1.163, 6.479]
	Parent’s Drinking Habit (Yes vs. No)	–0.415 (0.478)	0.385	1.684 [0.864, 3.283]	0.661 [0.259, 1.684]
Alcohol Use	Age (19–18 vs. 16–17)	0.557 (0.469)	0.235	2.364 [1.130, 4.943]	1.745 [0.696, 4.377]
	Sex (Male vs. Female)	2.647 (0.641)	<0.001 ^*	16.738 [5.035, 55.644]	14.112 [4.015, 49.600]
	Ethnicity (Brahmin/Chhetri vs. Others)	–0.781 (0.453)	0.085	0.505 [0.252, 1.011]	0.458 [0.188, 1.113]
	Parent’s Smokeless Tobacco Use (Yes vs. No)	0.528 (0.465)	0.256	2.316 [1.143, 4.694]	1.695 [0.682, 4.212]
	Parent’s Alcohol Use (Yes vs. No)	0.237 (0.461)	0.607	2.221 [1.106, 4.459]	1.268 [0.513, 3.133]
Smokeless Tobacco Use	Sex (Male vs. Female)	2.557 (0.646)	<0.001 ^*	13.176 [3.926, 44.221]	12.896 [3.636, 45.740]
	Parent’s Smoking Habit (Yes vs. No)	–0.132 (0.661)	0.842	1.167 [0.426, 3.196]	0.877 [0.240, 3.202]
	Parent’s Smokeless Tobacco use (Yes vs. No)	0.103 (0.509)	0.840	1.351 [0.594, 3.070]	1.108 [0.409, 3.005]
	Parent’s Alcohol Use (Yes vs. No)	0.415 (0.472)	0.379	1.876 [0.868, 4.055]	1.515 [0.600, 3.825]

Open in a new tab

*Statistically significant (p < 0.05)

Discussion

The findings of this study highlight a significant burden of behavioural risk factors for CVD among school-going adolescents in Nepal. The study found a high prevalence of consumption of calorie-dense drinks (99%), sedentary behaviour (60%), low fruit and vegetable intake (57%), and physical inactivity (35%). Key socio-demographic factors, such as gender, ethnicity, religion and parental behaviours, were associated with these risk factors. Additionally, processed food consumption, smoking, and alcohol use were closely linked with parental habits, highlighting the strong influence of family environment on adolescent health behaviours. Our study’s findings on physical inactivity (34.9%) significantly associated with education system (OR=2.810) were notably higher than those reported in the 2019 STEPS survey (10.8%) and a study from Bangladesh (8%) [6–8]. However, they were comparable to findings from Western Nepal (38%) [9] and lower than the 85% reported in the Global School-Based Student Health Survey (GSHS) [10]. These differences may reflect Tulsipur’s recent urbanization and variations in socio-cultural contexts. Greater involvement in household chores and walking to school may also explain the relatively higher activity levels among our respondents.

Sedentary behaviour, reported by 60% of adolescents, was significantly associated with the education system (OR=0.351), a prevalence similar to that found in a Brazilian study (58.1%) [11]. Public schooling and lack of formal parental education often reflect lower socio-economic status, which may limit opportunities for physical activity. In aadition, urbanization, lack of open spaces for exercise, and increased screen time also contribute to sedentary behaviour among adolescents, as observed in other urban settings [12–14].

Low fruit and vegetable intake was reported by 57% of adolescents and was significantly associated with education system (OR=0.519). This figure is consistent with findings from Western Nepal (58%) [9] but lower than the 98% reported in Bangladesh [6]. These variations may be explained by differences in study populations, as our study focused primarily on urban adolescents.

Consumption of calorie-dense drinks was very high (99%), comparable to findings from Western Nepal (83.1%) [9] but considerably higher than those reported by the GSHS (33.3%) and in India (44.8%) [10,15]. This pattern may reflect the rapid urbanization of Tulsipur, where healthier food options have been increasingly replaced by fast food and sugary drinks. Despite the high prevalence, no significant associations were observed between calorie drink consumption and socio-demographic factors in this study.

The prevalence of added salt intake (15.5%) was lower than that reported in Bangladesh (66.9%) [6], likely due to differences in food practices. Processed food consumption reported by 33% of adolescents was significantly associated with gender (OR=0.597), mother’s education (OR=2.895) and ethnicity (OR=2.171). This prevalence is lower than the 75% reported in the GSHS and the 49.2% observed in India [7,10,13–15]. The variation may be explained by differences in measurement, as our study focused on daily or frequent consumption. Adolescents from Brahmin/Chhetri, a comparatively advantaged group may have greater access to income and urban food systems, leading to increased processed food consumption. In addition, female adolescents may be more likely to prefer or use processed foods during food preparation. Furthermore, adolescents whose mother had no formal education may be less exposed to healthy dietary practices and thus rely more on processed foods as a part of their school snacks. Smoking rates (12%) were higher than those reported in the GSHS and in a study from Bangladesh [6,10,14]. Smoking was significantly associated with gender (OR=11.336), mother education (OR=4.675), parent’s smoking (OR=3.055) and smokeless tobacco use (OR=2.745). Adolescents may imitate their parents’ behaviours, as suggested by the strong association between parental and adolescent smoking. The link between smoking and lower socio-economic status, indicated by mothers’ lack of formal education, is consistent with findings from previous research [15–17].

Similarly smokeless tobacco use (OR=12.896) and current alcohol use (OR=14.112) were higher among males, reflecting societal norms in Nepal that accept tobacco and alcohol consumption among men. The lack of effective enforcement of tobacco control measures likely contributes to easy access to tobacco products for adolescents [18]. The widespread availability of alcohol in the local market may also account for the higher rates observed in this study. Gender and cultural norms likely also play a significant role in shaping these behaviours in the context of Nepal.

Strengths and limitations

Our study used a standardized questionnaire designed for adolescents to estimate and examine the relationship between key factors associated with CVD risk. To our knowledge, these factors have not been systematically explored in previous studies in Nepal.

Several factors may have influenced the findings of this study. First, the results represent adolescents aged 16–19 years from Tulsipur Sub-Metropolitan City and may not be generalizable to adolescents in other regions of Nepal, particularly rural areas. Although stratification was conducted by public and private schools, it was not extended to rural versus urban schools. However, Tulsipur, located in the inner terai region, includes both terai and hilly characteristics, and its rapid urbanization reflects a mix of urban and rural settings. Given the adequate sample size, the findings may may still provide some insights applicable to similar diverse (urban, rural, hilly, and plain) landscapes in other settings.

The use of a self-administered questionnaire may have introduced self-report and recall bias Students might misinterpret information due to less knowledge, personal perception and inaccurately recalling past events. To minimize these risks, we used standardized questions with clear wording and provided clarification before data collection. We also reduced the possibility of information bias in group settings by limiting the number of participants to 15 per group and ensuring adequate spacing to minimize peer influence.

Conclusion

This study provides evidence that behavioural risk factors for CVD are disproportionately distributed among specific subgroups of adolescents, including males, and those with a parental history of CVD or lower parental education. The high prevalence of these risk factors during adolescence may lead to an increased incidence of CVD in adulthood, potentially resulting in significant health and economic burdens. To address this, targeted interventions may include age-appropriate school health education on cardiovascular risk factors integrated into the curriculum; promotion of daily physical activity; restrictions on processed fast foods and sugary beverages in school cafeterias; regular health screenings (e.g., blood pressure, BMI, blood glucose); mental health workshops and counseling sessions; and referral of high-risk children to health professionals.

Additionally, there must be improved monitoring and enforcement of existing policies, such as restrictions on smoking and alcohol use among adolescents and regulations discouraging the marketing of fast food, processed foods, and high-calorie drinks at the local government level.

Supporting information

S1 Appendix. Operational definitions.

(DOCX)

pone.0313943.s001.docx^{(14.3KB, docx)}

S2 Appendix. Global research questionnaire.

(DOCX)

pone.0313943.s002.docx^{(66.9KB, docx)}

S1 Table. Regression analysis.

(DOCX)

pone.0313943.s003.docx^{(88.2KB, docx)}

Data Availability

All relevant data are within the manuscript and its Supporting Information files.

Funding Statement

The author(s) received no specific funding for this work.

References

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PLoS One. doi: 10.1371/journal.pone.0313943.r001

Decision Letter 0

Shalik Ram Dhital

8 Dec 2024

PONE-D-24-49797Behavioural risk factors for cardiovascular disease among adolescents of secondary level school in a sub-metropolitan setting in Nepal: A cross-sectional studyPLOS ONE

Dear Dr. Bista,

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Reviewer #2: No

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Reviewer #2: No

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Reviewer #1: Review of "Behavioural risk factors for cardiovascular disease among adolescents in Nepal: A cross-sectional study"

Introduction and Objectives: The manuscript aims to assess the prevalence of cardiovascular disease (CVD) risk factors among adolescents in a semi-urban setting in Nepal. This objective is significant, as early exposure to risk factors can exacerbate the burden of CVD later in life, especially in resource-limited settings.

Methodology: This study utilizes a cross-sectional design with a sample of 361 adolescents aged 16 to 19, selected via stratified proportionate sampling. The methods are appropriate for descriptive epidemiology; however, several issues limit the rigor of the analysis:

- Sampling Bias: Stratification only by school type (public/private) may inadequately represent other socio-economic or demographic factors relevant to CVD risk, such as family income and geographic region.

- Self-Reporting: Data were gathered through self-administered questionnaires, which can introduce recall and reporting bias, particularly for socially influenced behaviors (e.g., smoking and alcohol consumption). Mitigating steps like supervision were attempted, yet objective measures (such as biomarkers for smoking) could strengthen validity.

- Statistical Analysis: The study employs both univariate and bivariate analyses to investigate associations between CVD risk factors and demographic variables. While adequate, the study lacks multivariate analysis, which could better control for potential confounding factors, improving the robustness of findings on associations between behaviors and socio-demographic variables.

Results and Data Presentation: Results are comprehensive, with clear prevalence statistics for risk factors such as calorie-dense drink consumption, sedentary behavior, and smoking. However, the presentation would benefit from:

- Further Detailing: Additional tables or visual aids summarizing complex associations (e.g., those affected by parental influence) would enhance clarity.

- Statistical Interpretation: The manuscript lacks sufficient emphasis on confidence intervals or effect sizes, making it challenging to gauge the strength of associations. This omission reduces interpretive depth, particularly in relation to public health interventions.

Utility and Novelty of Findings: The study reveals high prevalence rates of CVD risk behaviors, highlighting critical areas for intervention, including physical inactivity and poor dietary habits. While the study addresses a vital public health issue, the lack of innovative or highly detailed insights may limit its impact for top-tier journals. Most findings, such as associations between adolescent smoking and parental behaviors, align closely with existing literature from similar settings. Nonetheless, the study’s context (Nepal) may be valuable for policymakers within the region.

Recommendations for Revision:

1. Enhance Methodological Rigor: Addressing self-reporting limitations with more objective measures, if feasible, or by discussing potential biases in-depth, could enhance the study's reliability.

2. Statistical Refinement: Adding multivariate analyses would help substantiate causal inferences and control for confounding variables, lending greater credibility to findings.

3. Data Visualization: Incorporate more visual data representations, particularly for associations impacted by socio-demographic factors, to improve readability and impact.

Conclusion: The manuscript provides a well-structured but relatively standard analysis of behavioral CVD risk factors in a semi-urban Nepali adolescent population. Although the findings reinforce global public health recommendations, methodological limitations (e.g., reliance on self-report, lack of multivariate analysis) and the narrow focus on descriptive statistics reduce the study’s potential impact.

Reviewer #2: Comments

Thank you for providing me opportunity to review the paper. I've some comments listed below:

1. In the title sub-metropolitan setting is misleading as this study is conducted in Tulsipur sub-metropolitan city only. Abstract doesn't says what is the study population is?

2. Does your study really used simple random lottery method to select students from the school. IF so how have you reach out the specific students to collect information? If your study is stratified proportionate sampling, you should present the results stratified by rural and urban schools and further stratified by 11 and 12th grade. You need to be cautious while merging two groups and estimate the proportion for all population.

Please add details on how you reach out to the students.

3. Line 79: Missing inline citation

3. Line number 94-96: Who or how many people collected data and who supervised whom? It needs to be clarified.

4. Line 103: what are you calculating confidence interval of ??

5. For those with age less than 18 what have you done.

5. Statistical analysis part misses about SD that you calculated in results

6. Line 117: Please keep all details of participants characteristics within manuscript.

7. Calculate % and its 95% CI rather than just point estimation (use Wilson method or other appropriate method to calculate 95% CI)

9. Results from supplementary tables are in the discussion. Please keep whatever you discuss in the main text

10. Include number of missing data each variables if missing.

11. Reference should be formatted as per journal requirement

12. requires extensive grammar correction

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Reviewer #1: No

Reviewer #2: No

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PLoS One. 2025 Sep 11;20(9):e0313943. doi: 10.1371/journal.pone.0313943.r002

Author response to Decision Letter 1

2 Mar 2025

Dear Dr. Dhital,

Academic editor, PLOS One

We thank you and the reviewers for the constructive comments on our manuscript, “Behavioural risk factors for cardiovascular disease among adolescents of secondary level school in a sub-metropolitan setting in Nepal: A cross-sectional study (PONE-D-24-49797).” We have carefully addressed all comments and revised the manuscript accordingly. Below is a point-by-point response to each comment.

Recommendations for Revision from Reviewer 1 :

1. Enhance Methodological Rigor: Addressing self-reporting limitations with more objective measures, if feasible, or by discussing potential biases in-depth, could enhance the study's reliability.

Response: Thank you for your insightful comments. We have revised the limitation section accordingly and added the potential bais in the revised manuscript. Changes can be seen in page no 20, line no 243-249.

2. Statistical Refinement: Adding multivariate analyses would help substantiate causal inferences and control for confounding variables, lending greater credibility to findings.

Response: We have incorporated the reviewer’s suggestion and conducted the multivariate analysis (Binary logistic regression) along with the Chi square test to ensure the robustness of association. The multivariate analysis data analysis is presented in table 5and findings are updated throughout the manuscript in the result ( page 16-17 and Line no 170-178) and discussion as well.

3. Data Visualization: Incorporate more visual data representations, particularly for associations impacted by socio-demographic factors, to improve readability and impact

Response: Thanks you for the suggestion. We have substituted table 1 with the bar chart displaying the prevalence of behavioral risk factors in adolescents and binary logistic regression table 5) was added to results section displaying significance of association and Odds ratio.

Reviewer #2: Comments

1. In the title sub-metropolitan setting is misleading as this study is conducted in Tulsipur sub-metropolitan city only. Abstract doesn't says what is the study population is?

Response: Thank you for your valuable remarks. We have revised the title to specifically mention "Tulsipur Sub-Metropolitan City" instead of the general term "sub-metropolitan setting" for clarity. We have also clarified the study population in the abstract ( line 17-18)

2. Does your study really used simple random lottery method to select students from the school. If so how have you reach out the specific students to collect information? If your study is stratified proportionate sampling, you should present the results stratified by rural and urban schools and further stratified by 11 and 12th grade. You need to be cautious while merging two groups and estimate the proportion for all population.

Please add details on how you reach out to the students.

Response: Thank you for providing opportuntiy to clarify our sampling approach. A stratified proportionate sampling method was employed using sampling frame obtained from municipal education section. The two strata were defined based on schools’ affiliation (Public/Private), with the sample size proportionally distributed based on the student population in each stratum. Two public and two private secondary schools were randomly selected. The students were divided into grades 11 and 12, with a proportional distribution according to age. Within each school, students were selected through a simple random lottery method using a random number generator. The student’s records and details of each selected school were obtained from the school administration. Each student were assigned a unique identification number. Based on proportionate sample of selected schools, Microsoft excel was used to generate a predefined and non-repeating number. The randomly generated numbers were cross-verified with the student details. Then after, selected students were invited to participate in study. If selected students didn’t provide consent or were absent, alternative students were taken through additional random numbers generated. These information are clarified in the revised manuscript (page no 4-5, line no 86-98)

There was stratification by public and private schools, then proportionate sampling was taken from 11th and 12th grade but not stratified by rural and urban schools and which is included as a limitation of the study in the limitation section.

3. Line 79: Missing inline citation

Response: We have inserted citation.

4. Line number 94-96: Who or how many people collected data and who supervised whom? It needs to be clarified.

Respone: The data was collected by the first and second author (SB and BP) who were supervised by the senior author (BB and PT). We have calrified our approach in the revised manuscript.

5. Line 103: what are you calculating confidence interval of ??

Response: The 95% confidence interval was calculated to measure the precision of the estimated associations in the chi-square test.

6. For those with age less than 18 what have you done.

Response: Both written parental consent and student’s consent were taken for age less than 18 and those students with aged 18 or above provided their written consent. On the first day, Parents were provided with parental consent form through students, then only if agreed by parents for involvement in study, students were provided with written consent form. This has been clarified in the revised manuscript ( page 6, line 117-121)

7. Statistical analysis part misses about SD that you calculated in results

Response: Thank you. This issue has been addressed at data processing and analysis section. ( page no 7, line no 127)

8. Line 117: Please keep all details of participants characteristics within manuscript.

Response: We have moved the table of characteristics of participants from the supplementary file to the manuscript (Table 1page 7, line 136).

9. Calculate % and its 95% CI rather than just point estimation (use Wilson method or other appropriate method to calculate 95% CI)

10. Response: Adjusted Odds ratio has been calculated by using binary logistic regression analysis.

Results from supplementary tables are in the discussion. Please keep whatever you discuss in the main text

Response: Data discussed in the manuscript are presented within the manuscript; only further information is provided at the supplementary file section.

11. Include number of missing data each variables if missing.

Response: Thank you for your suggestion to include number of missing data each variables if missing. We didn’t find any missing values of variables after carefully reviewing the dataset, except for the parent’s characteristics that has been clarified in the footnote of Table 1.

12. Reference should be formatted as per journal requirement

Response: Thank you, we have formatted as per journal requirement

13. requires extensive grammar correction

Response: Thank you. We have revised and proof read the manuscript for the language and grammatical errors.

We sincerely appreciate the opportunity to revise and resubmit our manuscript. We are confident that the quality of the revised manuscript has significantly improved and now meets the expectations of the reviewers and the journal.

Thank you for your time and consideration.

Sincerely,

Sita Bista

Attachment

Submitted filename: Rebuttal letter_BB edits_17th Feb_2025.docx

pone.0313943.s005.docx^{(22.7KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0313943.r003

Decision Letter 1

Chiranjivi Adhikari

28 Apr 2025

PONE-D-24-49797R1Behavioural risk factors for cardiovascular disease among adolescents of secondary school in Tulsipur sub-metropolitan city, Nepal: A cross-sectional studyPLOS ONE

Dear Dr. Bista,

Please submit your revised manuscript by Jun 12 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Chiranjivi Adhikari, MPH, MHEd., PhD Candidate

Academic Editor

PLOS ONE

Additional Editor Comments:

Dear previous Editors, and all the reviewers,

I would also take an opportunity to thank our esteemed reviewers, and previous editors, for their scientific comments.

Dear Authors,

Thank you for submitting the scientific work "Behavioral risk factors for cardiovascular disease among adolescents of secondary school in Tulsipur sub-metropolitan city, Nepal: A cross-sectional study". The manuscript is well documented in language and coherence, clarification and revisions for the following comments, along with those submitted by the previous editors, and reviewers, are requested though:

1.In line 82-85, N=5262, is this total of school going adolsescent of Tulsipur, or of only 16-19? pls clarify, and adjust for sampling accordingly.

2. Line 104-105, What was the pretested sample population, and place, pls specify.

3. Table 2, 3, 4; percent total was observed, but columwise p-value does not comply with, so columwise % total may be better interpretable?!

4. Table 5 shows the UOR (?), and the list of regressors are too many and poorly interpretable, such as mother's education and ethnicity may be just the confounders for processed food consumption. So better to reduce the risk factors with further adjustment with modelling and so, better interpretatble to discuss more contextually. And then, re-discuss as of the new results.

With regards,

Chiranjivi

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #3: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #3: (No Response)

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #3: (No Response)

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: (No Response)

Reviewer #3: (No Response)

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: (No Response)

Reviewer #3: (No Response)

**********

6. Review Comments to the Author

Reviewer #1: All comments have been addressed. I do think the manuscript has been properly improved and can be accepted.

Reviewer #3: Title and Abstract

Comment:

1. The phrase “affecting one-third of adolescents” in the abstract may be misleading. Please clarify whether this refers to actual/clinical CVD or its risk factors. (Same comment for introduction part; Line no 43)

2. “Schoolswere” has a typo

3. Spacing and formatting inconsistent on Key words section (It applies to the whole manuscript).

Introduction

Comment:

1. The paragraph shifts abruptly from global data to adolescent.

Suggestion: It would be easier to understand if the data flowed from global to regional to national and local.

Method

1. What was the mechanism of identifying students with a history of CVDs or other chronic diseases (any students found during data collection if yes how)? Please mentioned it.

2. The manuscript mentions the assessment of cardiovascular disease (CVD) risk behaviors among adolescents. The authors need to clarify whether any procedures were in place to ethically manage participants who were identified as being at heightened risk during the study.

Result

1. author used asterisks (*) at multiples places, used different indicators (eg #) with clear footnotes and explanations (it applies to the other sections of results eg table no 3, 4)

2. Spelling error (line no 146 )

3. In the “Association Between CVD Risk Factors and Socio-demographic Characteristics” section, the author needs to indicate the significant result within the table by using an indicator (e.g., 0.028*) and highlighting the result; it could help the reader.

Discussion

1. In the first paragraph of the discussion, the author had summarized the findings, which can be more concise, and if the author put “what is going to be discussed,” it would be easier for readers. Then only start the discussion in another paragraph.

2. The author discussed it well, but it would be better to discuss separately to risks. For eg discuss physical activity in one single, calorie intake in another.

Conclusion/Recommendation

1. The word used by author “targeted intervention” sounds vague, what types of interventions and to whom/who are the high risk group? Could be mentioned, and it will be more specific.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean? ). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy .

Reviewer #1: No

Reviewer #3: No

**********

PLoS One. 2025 Sep 11;20(9):e0313943. doi: 10.1371/journal.pone.0313943.r004

Author response to Decision Letter 2

7 Aug 2025

12th July, 2025

Dear Dr. Adhikari,

Academic editor, PLOS One

Recommendations for Revision

Academic Editor

1.In line 82-85, N=5262, is this total of school going adolescent of Tulsipur, or of only 16-19? Response: Thank you for your comment. This is the total population of secondary level school adolescents of age between 16-19 studying in only grade 11 and 12, which is clarified in the manuscript( page 5, line 81)

2. Line 104-105, What was the pretested sample population, and place, pls specify.

Response: Thank. We have provided details of the pretesting in the revised manuscript as follows

Pretesting was conducted among 37 students from Ghorahi sub-metropolitan city, a location with similar characteristics to the study area.(page 6 , line 103-105). The pre-testing data was not included in final analysis.

3. Table 2, 3, 4; percent total was observed, but columwise p-value does not comply with, so columwise % total may be better interpretable?!

Response: Thank you for your suggestion. We have updated all tables with the column wise percentage and total as evidenced in revised table Table 2, 3 and 4.

4. Table 5 shows the UOR (?), and the list of regressors are too many and poorly interpretable, such as mother's education and ethnicity may be just the confounders for processed food consumption. So better to reduce the risk factors with further adjustment with modelling and so, better interpretable to discuss more contextually. And then, re-discuss as of the new results.

Response: Thank you for your suggestion. We have conducted further analysis adjusting for potential confounders using a multivariable logistic model and now present both Adjusted Odds Ratios (AORs) and Unadjusted Odds Ratios (UORs) in the revised Table 5 (page 21, line no 181). The discussion section has also been updated to reflect and contextualize these updated findings accordingly.

Reviewer #3:

Title and Abstract

Comment:

Response: Thank you for your valuable remarks. We acknowledge that the original phrasing may have caused confusion. The statement was intended to refer to cardiovascular disease (CVD) affecting one-third of adult population. We have revised the sentence in both the abstract (Line no.13, page no.2) and the introduction (Line no 40, page no. 3) to clarify this confusion.

2. “Schoolswere” has a typo

Response: Thank you. We have edited it. (Line no 18, page no.2)

3. Spacing and formatting inconsistent on Key words section (It applies to the whole manuscript).

Response: We appreciate for highlighting the formatting issue. We have carefully reviewed and formatted the whole manuscript including spacing of the Keywords section.

Introduction

Comment:

1. The paragraph shifts abruptly from global data to adolescent.

Suggestion: It would be easier to understand if the data flowed from global to regional to national and local.

Response: Thank you for the insightful suggestion. We have revised the paragraph to enhance clarity and improve the flow, presenting the data in a logical sequence from global to regional to national levels, while maintaining a focus on the global burden and its relevance to adolescents. Unfortunately, sufficient data on the local burden are not available. (see changes page 3, line 38-46)

Method

1. What was the mechanism of identifying students with a history of CVDs or other chronic diseases (any students found during data collection if yes how)? Please mentioned it.

Response: Thank you for your valuable comment. All data were based on the students’ self-reported responses regarding their history of cardiovascular disease (CVD). None of the participants reported having a history of CVDs or other chronic conditions at the time of data collection.

Response: Thank you for this important observation. While our study did not involve clinical assessment or diagnosis of individual cardiovascular risk, we acknowledge the ethical responsibility when identifying high-risk behaviours among adolescents. To address this, we shared the de-identified and aggregated findings with the relevant municipal and school authorities to raise awareness about the high prevalence of major CVD risk factors and the importance of school-based health education program. As noted in the conclusion, this highlights an area for future research and opportunity for school based intervention. However, due to limited resources and observational nature of the study, we were not able to offer individual counseling or follow-up for participants identified as having potentially high CVD risk behaviors.

Result

1. author used asterisks (*) at multiples places, used different indicators (eg #) with clear footnotes and explanations (it applies to the other sections of results eg table no 3, 4)

Response: We appreciate your suggestions. We have revised the use of footnote indicators across all applicant tables, including Tables 1, 2, 3 and 4 to enhance clarity and maintain consistency throughout the manuscript.

2. Spelling error (line no 146)

Response: Thank you. We have corrected the errors on line 148, page no.12 and proofread the revised manuscript

3. In the “Association between CVD Risk Factors and Socio-demographic Characteristics” section, the author needs to indicate the significant result within the table by using an indicator (e.g., 0.028*) and highlighting the result; it could help the reader.

Response: Thank you for your feedback. We have highlighted the significant result consistently across all tables using an indicator.

Discussion

Response: Thank you. We have revised our discussion accordingly in the revised manuscript including breaking a paragraph in line no 190-194, page no. 25.

2. The author discussed it well, but it would be better to discuss separately to risks. For eg discuss physical activity in one single, calorie intake in another.

Response: Thank you. We have discussed the individual risk factors in individual paragraphs.

Conclusion/Recommendation

1. The word used by author “targeted intervention” sounds vague, what types of interventions and to whom/who are the high risk group? Could it be mentioned, and it will be more specific.

Response: We have clarified the possible targeted intervention in the revised manuscript as follows:

Targeted interventions may include age-appropriate school health education on cardiovascular risk factors integrated into the curriculum; promotion of daily physical activity; restrictions on processed fast foods and sugary beverages in school cafeterias; regular health screenings (e.g., blood pressure, BMI, blood glucose); mental health workshops and counseling sessions; and referral of high-risk children to health professionals. (line 272-277, page 29)

Thank you for your time and consideration.

Sincerely,

Sita Bista

Attachment

Submitted filename: Rebuttal letter _12th July.docx

pone.0313943.s006.docx^{(24.7KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0313943.r005

Decision Letter 2

Chiranjivi Adhikari

17 Aug 2025

Behavioural risk factors for cardiovascular disease among adolescents of secondary school in Tulsipur sub-metropolitan city, Nepal: A cross-sectional study

PONE-D-24-49797R2

Dear Dr Bista,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Chiranjivi Adhikari, MPH, MHEd., PhD Candidate

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0313943.r006

Acceptance letter

Chiranjivi Adhikari

PONE-D-24-49797R2

PLOS ONE

Dear Dr. Bista,

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team.

At this stage, our production department will prepare your paper for publication. This includes ensuring the following:

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Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Mr. Chiranjivi Adhikari

Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Appendix. Operational definitions.

(DOCX)

pone.0313943.s001.docx^{(14.3KB, docx)}

S2 Appendix. Global research questionnaire.

(DOCX)

pone.0313943.s002.docx^{(66.9KB, docx)}

S1 Table. Regression analysis.

(DOCX)

pone.0313943.s003.docx^{(88.2KB, docx)}

Attachment

Submitted filename: Rebuttal letter_BB edits_17th Feb_2025.docx

pone.0313943.s005.docx^{(22.7KB, docx)}

Attachment

Submitted filename: Rebuttal letter _12th July.docx

pone.0313943.s006.docx^{(24.7KB, docx)}

Data Availability Statement

All relevant data are within the manuscript and its Supporting Information files.

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PERMALINK

Behavioural risk factors for cardiovascular diseases among adolescents of secondary school in Tulsipur Sub-Metropolitan City, Nepal: A cross-sectional study

Sita Bista

Bishow Puri

Sanju Maharjan

Poshan Thapa

Buna Bhandari

Roles

Abstract

Background

Methods

Results

Conclusions

Introduction

Methods

Study design and setting

Study period

Study population

Operational Definition

Sample size

Sampling

Fig 1. Sampling method used in recruiting participants.

Data collection tools and techniques

Data processing and analysis

Results

Characteristics of the participants and parents

Table 1. Social and demographic characteristics of participants and Parents (n = 361).

Distribution of CVD risk factors among the participants

Fig 2. Prevalence of behavioral risk factors among secondary school adolescents.

Association between CVD Risk Factors and Socio-demographic Characteristics

Table 2. Association of processed food high in salt consumption, calorie drink consumption and added salt intake with socio-demographic characteristics (n = 361).

Table 3. Association of sedentary behaviour, insufficient fruit and vegetable intake, and physical inactivity (n = 361).

Table 4. Association of refined vegetable oil use, current tobacco smoking, current alcohol use and current smokeless tobacco use (n = 361).

Table 5. Association and odds ratio of different predictors and independent variables.

Discussion

Strengths and limitations

Conclusion

Supporting information

Data Availability

Funding Statement

References

Decision Letter 0

Shalik Ram Dhital

Roles

Author response to Decision Letter 1

Decision Letter 1

Chiranjivi Adhikari

Roles

Author response to Decision Letter 2

Decision Letter 2

Chiranjivi Adhikari

Roles

Acceptance letter

Chiranjivi Adhikari

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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