Analysis of the current status and influencing factors of cardiovascular health in children and adolescents
DOI:
https://doi.org/10.63313/hmt.9021Keywords:
Children and adolescents, Cardiovascular health, Obesity, Risk factors, Health managementAbstract
The pathological origin of cardiovascular disease (CVD) can be traced back to children and adolescents. The younger age and prevalence of its risk factors have become a global public health challenge. This paper systematically analyzes the current severe situation of cardiovascular health in children and adolescents, and points out that obesity, unhealthy lifestyle and health inequality are increasingly prominent, while the traditional prevention and control system has obvious limitations in dynamic risk assessment, multi-source data integration and cross sectoral collaboration. In order to meet these challenges, this paper proposes an innovative path based on artificial intelligence (AI) and multimodal data fusion. By constructing a dynamic risk profile, developing interpretable AI decision support, establishing a "school family community medical" collaborative intervention network, and improving the ethics and privacy protection framework, this paper promotes the transformation of health management to intelligent, accurate and systematic. However, the system still faces multiple challenges such as data barriers, technology transformation, collaborative mechanism and ethical regulation. In the future, it needs to rely on multidisciplinary collaboration and cross sectoral linkage to provide theoretical basis and practical direction for the early prevention and control of cardiovascular health in children and adolescents.
References
[1] Zhengzheng Huang ,Xiuping Li ,Xia Liu ,Yayun Xu ,Haixing Feng and Lijie Ren. (2024) Exercise blood pressure, cardiorespiratory fitness, fatness and cardiovascular risk in children and adolescents. Frontiers in Public Health, 12, 1298612.
[2] Saunders, T.J. (2014) The health impact of sedentary behaviour in children and youth. Applied Physiology, Nutrition, and Metabolism, 39(3), 402-402.
[3] Wang Wenlei ,Zhang Jun ,Hou lie ,Zhang Rong ,Shamusye Muyiduli ,Dong Yan ,Zhe Wei ,Shawulasi Rejiafu ,Fang ping ,Adilah Sulidan. (2024) Association of overweight and obesity with blood pressure in children and adolescents aged 7–17 years in Xinjiang. Chinese Journal of Child Health Care, 32(9), 953-957.
[4] Qing Li , Peng Luting ,Liu Qiang ,Jiang Zhiying ,Wu Su ,Huang Rong ,Chen Mengying ,Li Rong ,Mo Baoqing ,Li Xiaonan. (2020) Clinical characteristics of insulin resistance and its relationship with metabolic complications in obese children and adolescents. Chinese Journal of Applied Clinical Pediatrics, 34(23), 1766-1770.
[5] Zejun Cheng. (2025) A comprehensive framework for cardiovascular disease risk prediction utilizing artificial intelligence-enhanced multimodal data fusion techniques. Journal of Sustainability, Policy, and Practice, 1(2), 101-112.
[6] Palak, Monika Devi, Kapil Kumar Verma, Prikshit Kumar. (2025). Artificial intelligence in cardiovascular risk stratification and early detection of myocardial infarction: Hype or hope. Ippr.Human, *31*(9), 115–127.
[7] World Obesity Federation. (2025). World Obesity Atlas 2025. London: World Obesity Federation. https://data.worldobesity.org/publications/?cat=23
[8] National Health Commission of the People’s Republic of China. (2015). Report on Chinese Residents’ Nutrition and Chronic Disease Status. Official website of the NationalHealthCommission.https://www.nhc.gov.cn/jkj/c100062/201506/a89ed910e9074813951871790c822434.shtml
[9] Paul Garwood, Christian Lindmeier.(2019).New WHO-led study says majority of adolescents worldwide are not sufficiently physically active, putting their current and future health at risk.WTO.2019.11.22
[10] Tan, Jian, Haoyi Fan, Jiawei Luo, Yanjie Zhou, Ning Wang, Xizheng Wang, Guizhi Liu, Chengyu Liu, and Zongmin Wang. (2025). A pediatric ECG database with disease diagnosis covering 11643 children. Scientific Data, *12*, 867.
[11] SCORE2 working group and ESC Cardiovascular risk collaboration. (2021). SCORE2 risk prediction algorithms: New models to estimate 10-year risk of cardiovascular disease in Europe. European Heart Journal, *42*(24), 2439–2454.
[12] Hassija, Vikas, Vinay Chamola, Atmesh Mahapatra, Abhinandan Singal, Divyansh Goel, Kaizhu Huang, Simone Scardapane, Indro Spinelli, Mufti Mahmud, and Amir Hussain. (2024). Interpreting black-box models: A review on explainable artificial intelligence. Cognitive Computation, *16*, 45–74.
[13] Gkika, S. (2013). Cardiovascular risk factors – Subjective public perceptions of their risk and treatment [Master’s thesis, National and Kapodistrian University of Athens].
[14] Wang, Yue, Yaxin Song, Yifei Wang, Translate, Lian Yu, & Jing Wang, Review. (2024). Ethics and governance of artificial intelligence for health: Guidance on large multi-modal models. Chinese Medical Ethics, *37*(9), 1001–1022.
[15] Adadi, A., Berrada, M. (2020). Peeking inside the black-box: A survey on explainable artificial intelligence (XAI). IEEE Access, *8*, 61838–61860.
[16] Cavoukian, A. (2011). Privacy by design: The 7 foundational principles [Policy paper]. Information and Privacy Commissioner of Ontario, Canada.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 by author(s) and Erytis Publishing Limited.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.







