Analysis of the current status and correlates of knowledge-attitude-practice (KAP) regarding cardiovascular and cerebrovascular diseases (CCVD) in adolescents and young adults

Abstract

Objective

To investigate knowledge-attitude-practice (KAP) regarding cardiovascular and cerebrovascular diseases (CCVD) among adolescents and young adults, and to identify associated factors to provide evidence for targeted prevention.

Methods

A cross-sectional survey was conducted through digital platforms from August to October 2025, recruiting 636 participants from North, East and Southwest China through convenience sampling. Data were collected via a self-designed CCVD KAP questionnaire, S-CVI was 0.93, and Cronbach's α was 0.92. The data were analyzed using SPSS 27.0.

Results

The overall KAP score was (74.90 ± 20.83), with a scoring rate 63.47%. Among the three dimensions, the attitude subscale scored the highest, followed by knowledge and practice. Multiple linear regression analysis showed that the total score positively correlated with female gender, better self-rated health, and overweight; negatively with East and Southwest China residence, higher-maternal education, and unknown family history. For knowledge and attitude, exercise and sleep were positively correlated, smoking and drinking negatively. Higher education positively correlated with knowledge and attitude but negatively with practice.

Conclusion

Awareness of CCVD was moderate, with positive attitude but suboptimal behavioral adherence. These findings support targeted interventions to address the knowledge-practice gap, particularly among those with low education, unhealthy lifestyles, or uncertain family history.

Highlights

• •Chinese adolescents and young adultsshowed moderate knowledge-attitude-practice levels for cardiovascular and cerebrovascular diseases, with higher attitude scores.
• •Overweight participants showed higher knowledge-attitude-practice scores, while residents of East and Southwest Chinascored lower.
• •Regular exercise and adequate sleep correlated positively with knowledge-attitude-practice scores, while smoking and drinking correlated negatively.
• •Higher maternal education was unexpectedly associated with lower knowledge and attitude scores among offspring.

1. Introduction

Cardiovascular and cerebrovascular diseases (CCVD), including hypertension, coronary heart disease, and stroke, are the leading cause of global mortality (Martin et al., 2025; Palaniappan et al., 2026) and the predominant cause of death in both urban and rural China (National Center for Cardiovascular Diseases, 2024). The rising younger-onset CCVD trend is concerning: 18.37% of adolescents exhibit clustering of cardiovascular metabolic risk factors (Wang et al., 2023), and over 10% of myocardial infarctions in China occur in those aged 20–35 years, corroborated by expert consensus (Chinese Society of Cardiology, 2025). The global prevalence of pediatric hypertension has nearly doubled over the past two decades to 4.28% (Ruan et al., 2025). This trend is clinically significant, as adolescent hypertension tracks into adulthood and promotes early target organ damage, notably left ventricular hypertrophy and arterial stiffening (Khoury and Urbina, 2021). Given the increasing prevalence of subclinical pediatric cardiovascular disease, timely early intervention, screening, and health education are imperative (Wang, 2025).

The Healthy China Initiative (2019–2030) has set a target of reducing CCVD mortality to 190.70 per 100,000 by 2030 (Healthy China Action Promotion Committee, 2019), necessitating the extension of prevention strategies from high-risk adults to adolescents and young adults. Despite growing research attention, the evidence base remains limited, with existing studies being relatively sparse and heterogeneous in geographic and age coverage (Sun et al., 2023), mostly confined to localized or narrow age range samples. Moreover, although young individuals generally hold favorable attitudes toward CCVD prevention, prior studies have revealed persistent gaps in factual knowledge and behavioral adherence. Accordingly, this study applies the KAP framework to systematically characterize CCVD-related knowledge, attitudes, and practices among adolescents and young adults, and to comprehensively analyze their multifaceted associated factors.

2. Methods

2.1. Study design and population

A cross-sectional survey was conducted from August to October 2025 via the Questionnaire Star platform, with QR codes and links distributed through WeChat and QQ. A total of 661 participants were recruited from North, East, and Southwest China using convenience sampling. After excluding 25 invalid responses, 636 valid questionnaires were retained (effective rate: 96.22%). Inclusion criteria were as follows: age ≤ 35 years, voluntary participation, and the ability to complete the survey independently. Exclusion criteria comprised severe physical or mental illness or CCVD-related intervention participation within the past three months.

This study was approved by the Ethics Committee of Tianjin Medical University (approval No. TMUHMEC20250007). Electronic informed consent was obtained from all participants or their legal guardians prior to data collection. For participants under 18 years of age, additional parental consent and adolescent assent were also secured.

2.2. Measures

Demographic data encompassed 16 variables: age, gender, education level, only child status, residential status, residential region, father's education level, mother's education level, father's occupational type, mother's occupational type, household monthly income, household size, family history of CCVD, self-rated health status, height, and weight. Education level was classified as lower (junior high school or below), medium (senior high school, technical secondary school, or junior college diploma), or higher (bachelor's degree or above). Household size was categorized as small (1–2 members), medium (3–4 members), or large (5 members or more). Household monthly income was divided into low (<2600 CNY), medium (2600–5000 CNY), upper-middle (5000–8000 CNY), and high (>8000 CNY) categories. Family history of CCVD was recorded as yes, no, or unknown (participants unable to report whether a family history of CCVD existed). Body mass index (BMI), derived from self-reported height and weight, served as an objective anthropometric measure and was not incorporated into the KAP scale, which is designed to assess subjective, self-reported constructs. BMI was classified as underweight (<18.50 kg/m²), normal weight (18.50–23.90 kg/m²), overweight (24.00–27.90 kg/m²), or obese (≥28.00 kg/m²). Additional lifestyle variables included adequate sleep (≥7 h/day) and regular exercise (≥3 times/week).

The questionnaire was based on the KAP model, comprising 41 items across three dimensions: knowledge (14 items), attitude (15 items), and practice (12 items), using single-choice and multiple-choice questions. For the knowledge dimension one point was assigned for each correct answer and zero otherwise. The attitude dimension was assessed using a Likert scale with predefined scoring criteria. For the practice dimension single-choice items were scored on item-specific ordinal scales with varying ranges while multiple-choice items were scored by summing the number of correct options selected. The maximum scores for the knowledge, attitude, and practice dimensions were 48, 34, and 37, respectively. The scoring rate for each dimension was calculated as the mean actual score divided by the maximum possible score multiplied by 100%.

The questionnaire was developed through Chinese and international literature review based on the KAP framework, expert consultation, and pre-survey. Content validity was evaluated by a panel of six experts (two attending physicians, one epidemiologist, and three senior specialty nurses), all holding senior titles. Using a single-round Delphi process, the experts rated item representativeness of each item on a 4-point scale. The I-CVI ranged from 0.83 to 1.00, and the S-CVI was 0.93, both exceeding recommended thresholds and confirming satisfactory content validity. A pre-survey was conducted among thirty adolescents and young adults.

Exploratory factor analysis (EFA) was performed on the final sample (N = 636) using Principal Axis Factoring (PAF) with Promax rotation. A forced three-factor solution was prespecified per the KAP framework, with scree-plot inspection as supplementary support, accounting for 36.50% of the total variance and aligning with the theoretical dimensions. Although below the 50% threshold, this conservative estimate reflects PAF's extraction of only common variance and our theory-driven retention prioritizing conceptual coherence over statistical optimization. The solution demonstrated clean loadings with no cross-loadings, supported by strong sampling adequacy (KMO = 0.93; Bartlett's P < 0.01). Item loadings ranged from 0.45 to 0.70, confirming structural validity. Of the 41 original items, 13 multiple-choice items were excluded due to violation of the continuous-data assumption, leaving 28 items for EFA with no further eliminations; these 13 items were treated as categorical covariates in subsequent analyses. Cronbach's α was 0.92 overall, with subscale values of 0.86 (knowledge), 0.87 (attitude), and 0.72 (practice), all exceeding 0.70.

2.3. Statistical methods

Qualitative data were described using the number of cases, and comparisons between groups were performed using t-tests, one-way ANOVA. Multiple linear regression analysis was used to explore associated factors, the significance level α = 0.05. Variable selection was guided by the KAP theoretical framework and prior literature (Yang et al., 2024; Li et al., 2021; Jacobs Jr et al., 2022; Zhang et al., 2021; Chen and Wang, 2006; Lim et al., 2021). Variables were selected across the following domains: demographic characteristics (age, gender, only child), socioeconomic factors (education, parental education, parental occupation, household income), family structure (household size), geographic factors (residential region, urban-rural status), health status (BMI, self-rated health), health behaviors (exercise, sleep, smoking, drinking), and family history of CCVD. All variables were included based on their documented associations with health literacy and KAP in previous studies. Multicollinearity was assessed using the variance inflation factor (VIF), and residual independence was examined via the Durbin-Watson statistic. All VIF values were below the recommended threshold of 5, and Durbin-Watson statistics ranged from 1.69 to 1.93, suggesting no significant autocorrelation. Normality, homoscedasticity, influential observations, and linearity were evaluated using P—P plots, scatterplots of standardized residuals versus predicted values, Cook's distance, and partial regression plots, respectively, with all assumptions being satisfactorily met. Given the exploratory design aiming to generate preliminary hypotheses, corrections for multiple comparisons were not implemented. The observed associations in this exploratory study are hypothesis-generating and require confirmation in future research. All statistical analyses were performed using SPSS Statistics for Windows, version 27.0 (IBM Corp., Armonk, NY, USA).

3. Results

3.1. Basic characteristics of the research subjects

Among the respondents, 70.59% were aged 18–35 years, 57.55% were female. Characteristics of the study participants are presented in Table 1, Table 2.

Table 1.

Distribution of demographic characteristics among adolescents and young adults in North China, East China, and Southwest China, August to October 2025.

Variable	Category	Frequency	Proportion (%)
Age	<18 years old	187	29.40
	18–35 years old	449	70.59
Gender	male	270	42.45
	female	366	57.55
Education level	lower educational level	163	25.56
	medium educational level	164	25.78
	higher educational level	309	48.58
Only child	yes	288	45.28
	no	348	54.72
Residential status	urban	378	59.43
	rural	258	40.57
Residential region	North China	201	31.60
	East China	217	34.12
	Southwest China	218	34.28
Father's education level	junior high school and below	155	24.37
	senior high school or technical secondary school	152	23.90
	college diploma	137	21.54
	bachelor's degree and above	192	30.19
Mother's education level	junior high school and below	154	24.21
	senior high school or technical secondary school	186	29.25
	college diploma	106	16.67
	bachelor's degree and above	190	29.87
Father's occupational type	manual labor	345	54.25
	mental labor	261	41.04
	others	30	4.72
Mother's occupational type	manual labor	293	46.07
	mental labor	306	48.11
	others	37	5.82
Household monthly income	low income	30	4.72
	medium income	80	12.58
	upper-middle income	159	25.00
	high income	367	57.70
Household size	small household	65	10.22
	medium household	440	69.18
	large household	131	20.60
Family history	yes	181	28.46
	no	256	40.25
	unknown	199	31.29
Total		636	100.00

Table 2.

Distribution of lifestyle-related behaviors among adolescents and young adults in North China, East China, and Southwest China, August to October 2025.

Variable	Category	Frequency	Proportion (%)
Health status	very healthy	216	33.96
	relatively healthy	197	30.97
	general health	88	13.84
	relatively unhealthy	84	13.21
	poor health	51	8.02
Body mass index	underweight	173	27.20
	normal weight	381	59.91
	overweight	43	6.76
	obese	39	6.13
Exercise behavior	never exercise	58	9.12
	1–2 times/week	176	27.67
	3–4 times/week	176	27.67
	≥5 times/week	226	35.53
Smoking behavior	≥4 times/week	86	13.52
	<4 times/week	121	19.03
	never smoke	429	67.45
Drinking behavior	≥3 times/week	82	12.89
	<3 times/week	191	30.03
	never drink alcohol	363	57.08
Sleep behavior	more than 9 h	166	26.10
	7–8 h	278	43.71
	6–7 h	118	18.55
	less than 6 h	74	11.64
total		636	100.00

3.2. KAP scores for CCVD

The overall KAP score regarding CCVD was (74.90 ± 20.83) points, with a scoring rate of 63.47%. The score for the knowledge dimension was (29.26 ± 9.99) points, with a scoring rate of 60.96%. The score for the attitude dimension was (23.51 ± 8.23) points, with a scoring rate of 69.15%. And the score for the practice dimension was (22.13 ± 5.74) points, with a scoring rate of 59.81%.

3.3. Univariate analysis of KAP regarding CCVD

For the total KAP score, all variables except household size demonstrated statistically significant associations (P < 0.05). Most demographic and lifestyle variables were also significantly associated with dimension-specific scores, though the association patterns varied across dimensions.

In terms of score characteristics, participants from North China, those residing in urban areas, and those with a confirmed family history of CCVD generally exhibited significantly higher scores across all three dimensions. Participants whose parents had a junior high school education or below, or whose parents were employed in “other” occupations, tended to score higher than participants whose parents were engaged in manual or mental labor occupations.

Health status, BMI, and health behaviors were also found to be significantly associated with KAP scores (P < 0.05). Additional information is available in Table 3, Table 4.

Table 3.

Univariate analysis of factors associated with total knowledge-attitude-practice score regarding cardiovascular and cerebrovascular diseases among adolescents and young adults in North China, East China, and Southwest China, August to October 2025.

Variable	score	F/t	P
Age		4.35	<0.01
<18	80.40 ± 21.11
18–35	72.62 ± 20.30
Gender		−3.86	<0.01
Male	71.23 ± 20.80
Female	77.61 ± 20.46
Only child		−2.76	<0.01
Yes	72.41 ± 19.98
No	76.96 ± 21.32
Educational level		15.16	<0.01
Lower educational level	80.80 ± 19.03
Medium educational level	68.42 ± 18.02
Higher educational level	75.24 ± 17.88
Residential status		4.80	<0.01
Urban	78.07 ± 21.16
Rural	70.26 ± 19.47
Residential region		143.94	<0.01
North China	91.42 ± 17.91
East China	70.99 ± 16.08
Southwest China	63.57 ± 17.88
Father's occupational type		19.43	<0.01
Manual labor	77.16 ± 20.07
mental labor	70.04 ± 20.64
others	91.23 ± 18.82
Mother's occupational type		17.21	<0.01
Manual labor	73.78 ± 19.09
Mental labor	73.68 ± 21.54
Others	93.92 ± 19.28
Father's education level		26.69	<0.01
Junior high school and below	84.62 ± 16.56
Senior high school or technical secondary school	78.99 ± 20.52
College diploma	68.94 ± 18.50
Bachelor's degree and above	68.08 ± 21.97
Mother's education level		23.10	<0.01
Junior high school and below	84.97 ± 16.80
Senior high school or technical secondary school	76.07 ± 19.86
College diploma	71.12 ± 18.80
Bachelor's degree and above	67.71 ± 22.42
Health status		71.97	<0.01
Very healthy	84.81 ± 19.41
Relatively healthy	79.91 ± 18.62
General health	71.40 ± 16.92
Relatively unhealthy	55.18 ± 12.56
Poor health	52.14 ± 6.62
Household size		1.96	0.14
Small household	73.60 ± 15.61
Medium household	75.96 ± 21.18
Large household	72.01 ± 21.72
Household monthly income		6.74	<0.01
Low income	76.40 ± 18.57
Medium income	81.23 ± 17.72
Upper-middle income	78.40 ± 19.84
High income	71.89 ± 21.55
Family history		72.71	<0.01
Yes	80.32 ± 18.05
No	81.42 ± 20.23
Unknown	61.59 ± 17.56
Body mass index		10.83	<0.01
Underweight	75.50 ± 18.21
Normal weight	72.42 ± 21.05
Overweight	80.12 ± 23.38
Obese	90.82 ± 18.99

Table 4.

Univariate analysis of factors associated with knowledge-attitude-practice scores regarding cardiovascular and cerebrovascular diseases among adolescents and young adults in North China, East China, and Southwest China, August to October 2025.

Variable	knowledge			attitude			practice
	score	F/t	P	score	F/t	P	score	F/t	P
Age		1.77	0.78		5.19	<0.01		5.31	<0.01
<18	30.42 ± 11.08			25.94 ± 7.26			24.04 ± 6.02
18–35	28.78 ± 9.47			22.49 ± 8.40			21.34 ± 5.43
Gender		−3.83	<0.01		−4.64	<0.01		−0.71	0.48
Male	27.51 ± 9.65			21.77 ± 8.38			21.94 ± 5.59
Female	30.55 ± 10.05			24.79 ± 7.89			22.27 ± 5.85
only child		−1.77	0.08		−3.42	<0.01		−2.05	0.04
Yes	28.50 ± 9.44			22.29 ± 7.98			21.62 ± 5.47
No	29.89 ± 10.39			24.51 ± 8.31			22.56 ± 5.93
Educational level		10.28	<0.01		18.89	<0.01		22.90	<0.01
Lower educational level	30.92 ± 10.43			25.21 ± 7.07			24.66 ± 5.52
Medium educational level	26.60 ± 8.48			20.49 ± 7.52			21.33 ± 5.10
Higher educational level	29.80 ± 10.84			24.21 ± 8.75			21.23 ± 5.79
Residential status		3.56	<0.01		6.27	<0.01		2.26	0.02
Urban	30.38 ± 10.57			25.15 ± 8.00			22.54 ± 6.07
Rural	27.63 ± 8.83			21.10 ± 7.97			21.53 ± 5.17
Residential region		69.10	<0.01		294.25	<0.01		53.20	<0.01
North China	35.41 ± 10.96			30.76 ± 3.42			25.25 ± 6.58
East China	28.51 ± 7.63			20.75 ± 7.16			21.72 ± 4.85
Southwest China	24.34 ± 7.96			19.56 ± 8.03			19.67 ± 4.21
Father's occupational type		9.44	<0.01		104.42	<0.01		11.10	<0.01
Manual labor	30.29 ± 9.80			24.51 ± 7.82			22.36 ± 5.60
Mental labor	27.41 ± 9.67			21.28 ± 9.40			21.35 ± 5.60
Others	33.63 ± 11.96			31.27 ± 2.68			26.33 ± 6.61
Mother's occupational type		9.42	<0.01		108.98	<0.01		8.38	<0.01
Manual labor	28.66 ± 9.28			23.48 ± 7.89			21.63 ± 5.12
Mental labor	29.02 ± 10.20			22.58 ± 8.50			22.08 ± 5.91
Others	36.05 ± 11.35			31.30 ± 2.73			26.57 ± 7.10
Father's education level		15.73	<0.01		37.78	<0.01		13.60	<0.01
Junior high school and below	32.55 ± 9.41			27.97 ± 6.14			24.10 ± 5.45
Senior high school or technical secondary school	31.30 ± 9.78			24.74 ± 7.76			22.95 ± 5.64
College diploma	26.87 ± 8.92			21.05 ± 7.63			21.02 ± 5.22
Bachelor's degree and above	26.70 ± 10.26			20.68 ± 8.71			20.69 ± 5.86
Mother's education level		15.81	<0.01		32.29	<0.01		8.68	<0.01
Junior high school and below	33.29 ± 9.06			27.95 ± 6.32			23.74 ± 5.87
Senior high school or technical secondary school	29.68 ± 9.81			23.82 ± 7.64			22.57 ± 5.31
College diploma	27.82 ± 8.85			21.86 ± 7.68			21.44 ± 5.37
Bachelor's degree and above	26.40 ± 10.40			20.52 ± 8.87			20.79 ± 5.89
Health status		104.69	<0.01		136.19	<0.01		59.18	<0.01
Very healthy	32.55 ± 10.46			27.57 ± 6.76			24.68 ± 5.99
Relatively healthy	31.18 ± 9.89			25.78 ± 6.99			22.68 ± 5.38
General health	28.74 ± 8.71			21.98 ± 7.32			20.68 ± 4.67
Relatively unhealthy	22.10 ± 5.66			15.00 ± 5.23			18.08 ± 3.90
Poor health	20.63 ± 2.01			13.78 ± 4.23			17.73 ± 2.47
Household size		1.76	0.18		5.75	<0.01		0.45	0.64
Small household	29.46 ± 8.47			21.42 ± 6.73			22.72 ± 5.01
Medium household	29.67 ± 10.12			24.17 ± 8.17			22.11 ± 5.85
Large household	27.79 ± 10.15			22.31 ± 8.82			21.91 ± 5.70
Household monthly income		5.05	<0.01		7.40	<0.01		4.57	<0.01
Low income	29.70 ± 10.00			23.97 ± 7.14			22.73 ± 6.22
Medium income	32.40 ± 8.87			25.19 ± 7.47			23.64 ± 5.33
Upper-middle income	30.11 ± 9.86			25.45 ± 7.33			22.84 ± 5.77
High income	28.18 ± 10.12			22.26 ± 8.63			21.45 ± 5.68
Family history		53.71	<0.01		66.87	<0.01		47.94	<0.01
Yes	31.98 ± 9.14			25.08 ± 6.62			23.26 ± 5.44
No	31.40 ± 10.41			26.47 ± 7.42			23.55 ± 6.01
Unknown	24.04 ± 8.02			18.26 ± 8.07			19.29 ± 4.50
Body mass index		6.80	<0.01		40.40	<0.01		2.96	0.04
Underweight	29.33 ± 9.27			23.95 ± 7.38			22.32 ± 5.22
Normal weight	28.35 ± 10.00			22.34 ± 8.49			21.72 ± 5.65
Overweight	31.63 ± 10.49			25.58 ± 8.33			22.91 ± 6.68
Obese	35.23 ± 10.23			30.67 ± 3.91			24.92 ± 6.86
Exercise behavior		4.34	<0.01		18.78	<0.01
Never exercise	26.47 ± 8.82			21.50 ± 6.00
1–2 times/week	29.11 ± 8.48			26.09 ± 6.55
3–4 times/week	31.16 ± 9.88			24.98 ± 7.90
≥5 times/week	28.62 ± 11.17			20.86 ± 9.22
Smoking behavior		76.82	<0.01		227.50	<0.01
≥4 times/week	22.11 ± 5.31			15.99 ± 5.76
< 4 times/week	23.29 ± 7.15			16.27 ± 6.44
Never smoke	32.58 ± 9.86			27.29 ± 6.40
Drinking behavior		99.69	<0.01		135.77	<0.01
≥3 times/week	22.21 ± 5.07			16.12 ± 5.53
< 3 times/week	24.31 ± 6.68			19.43 ± 8.19
Never drink alcohol	33.46 ± 10.19			27.32 ± 6.41
Sleep behavior		74.37	<0.01		78.57	<0.01
More than 9 h	29.69 ± 10.65			23.18 ± 7.77
7–8 h	32.70 ± 9.89			27.21 ± 6.51
6–7 h	25.39 ± 7.60			20.31 ± 8.64
Less than 6 h	21.58 ± 4.28			15.42 ± 6.08

3.4. Multiple linear regression analysis of KAP regarding CCVD

Multiple linear regression analyses were performed to identify factors associated with KAP scores. To ensure robustness, two modeling strategies were conducted: Model 1, based on univariate significance screening, and Model 2, which incorporated all relevant variables guided by the KAP theoretical framework and prior literature (Yang et al., 2024; Chen et al., 2006). The results derived from the two approaches were generally consistent. For the attitude dimension, all candidate variables met the univariate screening threshold, rendering the results from Models 1 and 2 essentially consistent; therefore, only one model was fitted. Model 2 was designated as the primary inferential model, as it avoids the potential exclusion of important confounders that may arise from univariate significance screening. Unstandardized coefficients (B) with 95% confidence intervals are reported to emphasize the magnitude and precision of the associations.

For total score, female gender, good health, and overweight were positive correlates; medium education, rural residence, East or Southwest China, paternal college education, maternal education of senior high school or above, poor health, and unknown family history were negative.

For knowledge, higher education, paternal senior high or technical secondary education, medium income, overweight, regular exercise, and adequate sleep were positive; East or Southwest China, higher maternal education, unknown family history, smoking, and drinking were negative.

For attitude, higher education, overweight, very good health, regular exercise, and adequate sleep were positive; rural residence, East or Southwest China, higher maternal education, large household size, poor health, unknown family history, smoking, and drinking were negative.

For practice, good health was positive; Southwest China and unknown family history were negative. Regular exercise, smoking, drinking, and sleep were excluded as components of the practice score. Regression analysis results are presented in Table 5, Table 6.

Table 5.

Multiple linear regression analysis of factors associated with total knowledge-attitude-practice score regarding cardiovascular and cerebrovascular diseases among adolescents and young adults in North China, East China, and Southwest China, August to October 2025.

Variable		M1		M2
		B	95%CI	B	95%CI	ΔB
		90.18	83.68, 96.69	90.41	83.89, 96.93	0.22
Age	<18
	18–35	−2.69	−6.00, 0.61	−2.63	−5.94, 0.68	0.06
Gender	male
	female	3.36	1.01, 5.71	3.49	1.14, 5.85	0.13
Only child	yes
	no	0.66	−1.75, 3.07	0.90	−1.53, 3.32	0.24
Educational level	low educational level
	medium educational level	−4.01	−7.51, −0.51	−3.91	−7.41, −0.41	0.10
	high educational level	1.56	−2.07, 5.20	1.63	−2.02, 5.27	0.07
Residential status	urban
	rural	−3.04	−5.48, −0.60	−2.79	−5.24, −0.34	0.25
Residential region	North China
	East China	−10.70	−13.95, −7.46	−10.61	−13.89, −7.34	0.09
	Southwest China	−15.03	−18.31, −11.74	−14.94	−18.23, −11.65	0.08
Father's occupational type	manual labor
	mental labor	−0.72	−3.27, 1.82	−0.66	−3.20, 1.89	0.07
	others	−2.75	−9.17, 3.67	−2.84	−9.25, 3.57	−0.09
Mother's occupational type	manual labor
	mental labor	0.47	−2.05, 2.98	0.40	−2.12, 2.93	−0.07
	others	4.52	−1.24, 10.29	4.45	−1.31, 10.21	−0.07
Father's education level	junior high school and below
	senior high school or technical secondary school	−0.96	−4.55, 2.63	−1.15	−4.75, 2.45	−0.19
	college diploma	−4.78	−8.58, −0.98	−4.95	−8.75, −1.15	−0.17
	bachelor's degree and above	−2.53	−6.59, 1.53	−2.62	−6.68, 1.45	−0.09
Mother's education level	junior high school and below
	senior high school or technical secondary school	−3.96	−7.41, −0.51	−3.93	−7.38, −0.48	0.03
	college diploma	−5.79	−9.85, −1.72	−5.87	−9.93, −1.80	−0.08
	bachelor's degree and above	−4.18	−8.24, −0.11	−4.17	−8.24, −0.11	<0.01
Health status	general health
	very healthy	6.84	3.01, 10.67	6.82	2.99, 10.65	−0.02
	relatively healthy	3.86	0.13, 7.59	3.98	0.25, 7.71	0.12
	relatively unhealthy	−8.39	−12.91, −3.86	−8.33	−12.85, −3.81	0.06
	poor health	−10.03	−15.21, −4.84	−9.99	−15.17, −4.80	0.04
Household size	medium household
	small household			−0.23	−4.14, 3.67	−0.23
	large household			−2.56	−5.46, 0.34	−2.56
Household monthly income	upper-middle income
	low income	−0.69	−6.47, 5.09	−0.95	−6.74, 4.84	−0.26
	medium income	2.57	−1.38, 6.52	2.50	−1.45, 6.45	−0.07
	high income	−0.07	−2.94, 2.80	−0.22	−3.09, 2.65	−0.15
Family history	no
	yes	1.55	−1.39, 4.49	1.40	−1.54, 4.34	−0.15
	unknown	−8.90	−11.95, −5.85	−8.88	−11.93, −5.83	0.03
Body mass index	normal weight
	underweight	−0.78	−3.50, 1.94	−0.61	−3.34, 2.12	0.17
	overweight	6.94	2.28, 11.60	7.19	2.52, 11.85	0.25
	obese	3.27	−1.75, 8.29	3.52	−1.51, 8.55	0.25

Notes: For the total score outcome in Table 5, Model 1 had an overall F-statistic of 23.11 with an adjusted R² of 0.53; Model 2 presented an F-statistic of 24.51 and an adjusted R² of 0.53, revealing that the selected predictors collectively explained 53.00% of the variance in total score. Notably, global F-tests indicated that all constructed regression models reached statistical significance at the level of P < 0.01.

Abbreviations: M1, Model 1 (variables selected based on univariate screening, P < 0.05); M2, Model 2 (variables selected based on the knowledge-attitude-practice theoretical framework and prior literature); ΔB, change in unstandardized regression coefficient from Model 1 to Model 2 (i.e., B2–B1).

Table 6.

Multiple linear regression analysis of factors associated with knowledge-attitude-practice scores regarding cardiovascular and cerebrovascular diseases among adolescents and young adults in North China, East China, and Southwest China, August to October 2025.

Variable		knowledge					attitude		practice
		M1		M2			M		M1		M2
		B	95%CI	B	95%CI	ΔB	B	95%CI	B	95%CI	B	95%CI	ΔB
	constant	28.10	23.78, 32.42	27.89	23.55, 32.22	−0.22	26.65	23.95, 29.35	25.36	23.23, 27.50	25.34	23.15, 27.54	−0.02
Age	<18
	18–35			1.09	−0.66, 2.83	1.09	−0.05	−2.02, 0.08	−0.48	−1.59, 0.64	−0.48	−1.60, 0.64	<0.01
Gender	male
	female	0.03	0.56, 1.94	0.63	−0.62, 1.88	0.60	0.04	−0.02, 1.49			0.03	−0.77, 0.82	0.03
Only child	yes
	no						0.03	−0.26, 1.26	0.29	−0.52, 1.10	0.29	−0.52, 1.10	<0.01
Educational level	low educational level
	medium educational level	0.03	−1.22, 2.36	0.33	−1.51, 2.16	0.30	−0.01	−1.32, 0.91	−1.44	−2.62, −0.27	−1.45	−2.63, −0.27	<0.01
	high educational level	0.14	1.15, 4.41	2.17	0.27, 4.07	2.03	0.18	1.74, 4.03	−1.93	−3.15, −0.71	−1.93	−3.16, −0.71	<0.01
Residential status	urban
	rural	−0.04	−2.00, 0.55	−0.75	−2.02, 0.52	−0.71	−0.09	−2.29, −0.76	−0.18	−1.00, 0.64	−0.18	−1.00, 0.64	<0.01
Residential region	North China
	East China	−0.09	−3.62, −0.27	−2.15	−3.85, −0.44	−2.06	−0.25	−5.30, −3.23	−1.56	−2.66, −0.47	−1.56	−2.66, −0.46	<0.01
	Southwest China	−0.20	−5.98, −2.33	−4.21	−6.04, −2.38	−4.01	−0.20	−4.51, −2.32	−2.93	−4.03, −1.82	−2.93	−4.04, −1.82	<0.01
Father's occupational type	manual labor
	mental labor	−0.03	−1.96, 0.69	−0.60	−1.92, 0.73	−0.56	−0.01	−1.02, 0.57	−0.01	−0.87, 0.85	−0.01	−0.87, 0.85	<0.01
	others	−0.05	−5.72, 0.94	−2.25	−5.59, 1.09	−2.20	0.03	−0.74, 3.27	−0.33	−2.49, 1.84	−0.33	−2.49, 1.84	<0.01
Mother's occupational type	manual labor
	mental labor	0.05	−0.35, 2.28	0.94	−0.38, 2.25	0.89	−0.01	−0.92, 0.67	0.38	−0.47, 1.23	0.38	−0.47, 1.23	<0.01
	others	0.04	−1.14, 4.85	1.89	−1.10, 4.89	1.85	0.01	−1.40, 2.20	1.88	−0.07, 3.82	1.88	−0.07, 3.82	<0.01
Father's education level	junior high school and below
	senior high school or technical secondary school	0.09	0.21, 3.95	2.09	0.21, 3.96	2.00	−0.03	−1.76, 0.50	−0.47	−1.68, 0.74	−0.47	−1.69, 0.74	<0.01
	college diploma	0.01	−1.85, 2.16	0.18	−1.83, 2.18	0.17	−0.07	−2.63, −0.22	−1.18	−2.46, 0.10	−1.18	−2.46, 0.10	<0.01
	bachelor's degree and above	0.04	−1.27, 3.00	0.86	−1.27, 2.99	0.82	−0.02	−1.57, 1.01	−0.79	−2.15, 0.58	−0.79	−2.16, 0.58
Mother's educational level	junior high school and below
	senior high school or technical secondary school	−0.12	−4.39, −0.81	−2.59	−4.38, −0.79	−2.47	−0.10	−2.82, −0.66	−0.32	−1.48, 0.84	−0.31	−1.48, 0.85	0.01
	college diploma	−0.08	−4.29, −0.10	−2.29	−4.39, −0.19	−2.21	−0.08	−3.08, −0.54	−0.86	−2.23, 0.51	−0.86	−2.23, 0.52	<0.01
	bachelor's degree and above	−0.12	−4.78, −0.56	−2.58	−4.70, −0.47	−2.46	−0.09	−2.88, −0.32	0.02	−1.35, 1.38	0.02	−1.35, 1.39	<0.01
Health status	general health
	very healthy	−0.01	−2.28, 1.74	−0.21	−2.22, 1.80	−0.20	0.12	0.93, 3.35	2.34	1.05, 3.63	2.35	1.05, 3.64	<0.01
	relatively healthy	−0.05	−2.96, 0.96	−0.95	−2.91, 1.01	−0.90	0.07	−0.01, 2.35	1.35	0.10, 2.61	1.35	0.09, 2.61
	relatively unhealthy	−0.07	−4.46, 0.37	−2.05	−4.47, 0.36	−1.98	−0.08	−3.37, −0.47	−1.04	−2.57, 0.49	−1.04	−2.57, 0.49	<0.01
	poor health	−0.06	−4.84, 0.64	−2.22	−4.97, 0.53	−2.16	−0.09	−4.39, −1.09	−1.27	−3.01, 0.48	−1.27	−3.02, 0.49	<0.01
Household size	medium household
	small household						−0.04	−2.35, 0.10
	large household						−0.06	−2.13, −0.31
Household monthly income	upper-middle income
	low income	−0.04	−4.72, 1.31	−1.78	−4.80, 1.24	−1.75	−0.05	−3.59, 0.04	0.41	−1.54, 2.36	0.41	−1.54, 2.37	<0.01
	medium income	0.07	0.10, 4.21	2.11	0.05, 4.16	2.04	0.01	−1.07, 1.41	0.82	−0.51, 2.15	0.82	−0.52, 2.15	<0.01
	high income	0.01	−1.32, 1.70	0.19	−1.31, 1.70	0.18	−0.02	−1.25, 0.56	−0.10	−1.06, 0.87	−0.10	−1.06, 0.87	<0.01
Family history	no
	yes	0.05	−0.35, 2.72	1.06	−0.48, 2.61	1.01	−0.01	−1.08, 0.79	0.34	−0.65, 1.33	0.34	−0.65, 1.33	<0.01
	unknown	−0.10	−3.83, −0.60	−2.15	−3.76, −0.53	−2.05	−0.15	−3.55, −1.61	−1.84	−2.87, −0.82	−1.84	−2.87, −0.82	<0.01
Exercise behavior	never exercise
	1–2 times/week	0.03	−1.56, 3.06	0.79	−1.52, 3.10	0.75	0.11	0.66, 3.43
	3–4 times/week	0.19	1.90, 6.59	4.34	1.99, 6.69	4.15	0.11	0.62, 3.44
	≥5 times/week	0.23	2.46, 7.24	4.90	2.51, 7.29	4.67	0.04	−0.72, 2.16
Smoking behavior	never smoke
	≥4 times/week	−0.16	−6.89, −2.51	−4.28	−6.97, −2.58	−4.12	−0.15	−4.91, −2.28
	< 4 times/week	−0.17	−6.28, −2.11	−4.77	−6.37, −2.19	−4.61	−0.17	−4.86, −2.34
Drinking behavior	never drink alcohol
	≥3 times/week	−0.15	−6.71, −2.36	−4.61	−6.79, −2.44	−4.46	−0.12	−4.18, −1.56
	< 3 times/week	−0.17	−5.35, −2.10	−3.86	−5.50, −2.22	−3.69	−0.09	−2.52, −0.54
Sleep behavior	less than 6 h
	6–7 h	0.06	−0.64, 3.91	1.54	−0.74, 3.82	1.48	0.05	−0.42, 2.32
	7–8 h	0.21	2.04, 6.57	4.23	1.97, 6.50	4.02	0.17	1.45, 4.18
	more than 9 h	0.19	1.87, 6.62	4.19	1.81, 6.57	4.00	0.08	0.13, 3.00
Body mass index	normal weight
	underweight	−0.01	−1.69, 1.15	−0.22	−1.64, 1.20	−0.21	0.01	−0.63, 1.08	−0.82	−1.74, 0.09	−0.83	−1.75, 0.09	<0.01
	overweight	0.07	0.34, 5.17	2.64	0.23, 5.06	2.58	0.07	0.92, 3.83	1.12	−0.45, 2.68	1.12	−0.45, 2.69	<0.01
	obese	0.04	−1.12, 4.09	1.44	−1.16, 4.04	1.40	0.04	−0.18, 2.96	0.34	−1.36, 2.03	0.34	−1.36, 2.03	<0.01

Notes: Separate regression analyses for the three KAP dimensions are presented in Table 6. For the knowledge dimension, Model 1 yielded an F-statistic of 14.63 and an adjusted R² of 0.45, while Model 2 had an F-statistic of 14.31 and an adjusted R² of 0.45, explaining 45.00% of the variance in knowledge scores. The hierarchical regression model for the attitude dimension obtained an overall F-statistic of 37.67 and an adjusted R² of 0.71, which represented the optimal explanatory performance across all dimensions. For the practice dimension, Model 1 had an F-statistic of 9.96 and an adjusted R² of 0.29, and Model 2 showed an F-statistic of 9.61 and an adjusted R² of 0.29, with predictors explaining 29.00% of the variance in practice scores. Notably, global F-tests indicated that all constructed regression models reached statistical significance at the level of P < 0.01.

Abbreviations: M1, Model 1 (univariate screening); M2, Model 2 (theory-driven selection); M, single model for attitude dimension (all variables entered); ΔB, change in B coefficient from M1 to M2.

4. Discussion

The overall KAP score fell within the moderate range, indicating that recent health education efforts in China have yielded preliminary positive outcomes among adolescents and young adults. The dimension scores ranked in descending order as attitude, knowledge, and practice, a pattern consistent with the findings of Yang (Yang et al., 2024). This hierarchy reflects a generally proactive orientation toward disease prevention, while concurrently revealing a discernible gap between health awareness and actual behavioral engagement.

Knowledge scores were lower than attitude scores, suggesting that participants possessed limited CCVD-specific knowledge. This pattern may reflect the broad and non-specific nature of current health education, which often lack sufficient depth. Moreover, health information delivered through fragmented media channels may increase awareness, but tends to match adolescents' cognitive inclination toward immediate and tangible content rather than systematic reasoning, which may explain why attitudes were more favorable than knowledge in this population (Yang et al., 2024).

The practice dimension scored the lowest, underscoring a critical concern. Despite adequate recognition of healthy behaviors, the persistence of unhealthy habits may stem from developmental psychological characteristics, environmental constraints, inadequate health services, and peer influence compounded by emotional lability (Gui et al., 2025).

Higher educational attainment was positively associated with elevated scores in both health knowledge and attitude domains, a relationship associated with enhanced access to health information and greater proactive awareness of disease prevention within this demographic. This observation aligns with the established consensus identifying educational level as a primary determinant of health literacy. Consistent with prior investigations, data from surveys encompassing 25 provinces in China indicate that the overall health literacy among Chinese residents remains lower than that of developed nations, with significant disparities observed across geographic regions, urban-rural divides, and socioeconomic strata (Li et al., 2021).

Within this sample, participants from East and Southwest China had significantly lower KAP scores than those from North China. Given the convenience sampling design, these regional variations warrant cautious interpretation. The observed differences may relate to cross-regional variations in medical resources, health education coverage, and public health awareness, though digital health education may help address such gaps in resource-limited settings (Shakya et al., 2025). However, as none of these factors were directly measured, these explanations remain speculative. Consistent with this observation, inadequate resource allocation and limited health education have been linked to lower health awareness in some regions (Lin, 2022). Furthermore, the observed regional patterns may reflect sampling-related factors, including recruitment approaches, socioeconomic composition, and online platform usage, rather than true geographic disparities. Our data support only the presence of regional differences within this specific sample, and region-specific recommendations require confirmation in population-based studies.

Higher maternal education was unexpectedly associated with lower knowledge and attitude scores among offspring, though this should not be interpreted as causal (Xu and Xu, 2024). One possible explanation is that demanding careers may reduce parent-child health communication and prioritize academic achievement over health discussions (Wang and Zhu, 2024). However, as this pathway was not assessed, the interpretation remains speculative. Residual confounding from unmeasured variables such as parenting styles and family health culture, as well as selection bias, may also contribute.

Participants from larger households had lower attitude scores, which may partly reflect the widespread perception that CCVD predominantly affects older adults. Middle-income groups demonstrated superior CCVD knowledge scores, plausibly attributable to greater financial stability and more effective use of public health resources (Gui et al., 2025), whereas upper-middle-income individuals tended to forgo public health education in favor of private services.

A positive CCVD family history was associated with higher practice scores, whereas uncertain family history correlated with lower knowledge and attitude scores, confirming that risk perception drives health management. The high proportion of “unknown” responses suggests insufficient awareness of familial CCVD susceptibility rather than actual absence of affected relatives.

Excellent self-rated health was associated with higher attitude and practice scores, whereas fair or poor ratings corresponded to lower scores consistent with self-efficacy theory (Leung et al., 2025), which posits that better health is associated with greater self-management confidence and positive health behaviors.

Regular exercise and sufficient sleep were positively associated with KAP scores, whereas smoking and excessive drinking were negatively associated. This suggests that healthier lifestyles correspond to stronger health awareness and greater engagement with CCVD-related knowledge (Lim et al., 2021).

Overweight status was positively associated with CCVD knowledge and attitude, possibly reflecting health-information seeking motivated by weight concerns rather than superior health literacy. However, practice scores did not increase correspondingly, indicating a persistent knowledge-practice gap (Upadhayay and Bhaumik, 2025). As health anxiety and information-seeking behavior were not directly assessed, this interpretation warrants caution. These findings highlight the need for prevention strategies that target modifiable lifestyle factors and bridge the knowledge-practice gap.

This study has several strengths and limitations. A key strength is its multi-regional design covering North, East, and Southwest China, with a relatively balanced sample that enables preliminary geographic comparisons. Additionally, our findings yield valuable practical implications for health promotion initiatives targeting adolescents and young adults.

Several limitations warrant consideration. First, the use of convenience sampling through online platforms may constrain the generalizability of the findings, as individuals with higher educational attainment and greater health related interest are likely overrepresented, thereby introducing potential educational and self selection biases. To mitigate this concern, neutral recruitment language was adopted, and statistical adjustments for educational level and socioeconomic status were applied across all regression models. Second, the geographic coverage restricted to three regions limits the external validity of the results when extrapolated to the broader Chinese population. Furthermore, the online survey modality may inherently exclude individuals without internet access. Third, the cross sectional design precludes any causal inferences. Fourth, self-reported health behavior data are prone to recall bias and social desirability bias. To mitigate these effects, anonymous questionnaire administration, reverse-scored items, and a minimum completion time threshold were implemented. Finally, no correction for multiple comparisons was performed. Consequently, isolated significant findings should be interpreted with prudence and await replication in independent samples.

5. Conclusions

This cross-sectional survey reveals a moderate level of CCVD-related KAP among adolescents and young adults. Key associated factors include educational attainment, health behaviors, family history of CCVD, and geographic region. Based on these observed associations, several targeted interventions merit consideration: community- and school-based health education for lower-educated groups; interventions targeting the cognition-behavior gap for higher-educated groups; and enhanced resource allocation for regions with lower scores. Broader strategies should include smoking and alcohol cessation programs, promotion of regular exercise and adequate sleep, and family-based health management. However, given the cross-sectional design, these findings require cautious interpretation and verification in future prospective cohort studies with nationally representative samples.

Declaration of generative AI and AI-assisted technologies in the manuscript preparation process

During the preparation of this work, the author used deepseek to improve language, grammar, and readability. After using this tool, the author reviewed and edited the content as needed and takes full responsibility for the content of the published article.

CRediT authorship contribution statement

Tongyan Sun: Writing – original draft, Visualization, Validation, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Hua Zhang: Writing – review & editing, Supervision, Resources, Project administration, Funding acquisition, Conceptualization, Methodology.

Funding

This work was supported by the Tianjin Municipal Education Commission Social Science Project (2022SK206).

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data availability

Data will be made available on request.