Digital intelligence for cardiometabolic health management in older adults: protocol for a community-based prospective cohort study

Cardiovascular diseases (CVDs) represent a significant cause of morbidity and mortality among older adults in China, exerting a substantial influence on their quality of life and life expectancy. The country’s ageing population is predicted to intensify this burden, underscoring the need for the development of innovative and effective management strategies. The prospective cohort study will recruit older patients with cardiometabolic diseases from multiple community health service centres in Shanghai. Participants will be comprehensively assessed for CVD risk factors, medications, functional status, and multi-omics data, along with continuous sign monitoring data from wearable bracelets and their electronic health records. The primary objective of the study is twofold: firstly, to explore the independent and joint predictive power of digital biomarkers and clinical indicators for cardiovascular events (new incident/recurrent), dementia, death and other adverse outcomes in older adults; and secondly, to establish a digital twin platform for precision medicine. The study proposes a novel integration of digital intelligence into cardiometabolic health management for older adults. The utilisation of comprehensive data collection and advanced analytical methods is anticipated to generate valuable insights into the intricate interplay of risk factors and outcomes. The development of personalised risk assessment models and intelligent alert systems has the potential to enhance the prevention and treatment of CVDs, thereby improving health outcomes and quality of life for older adults. ChiCTR2500096478 (Chinese Clinical Trial Registry). Registered 24 January 2025 - Prospectively registered.

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Publication Details

Journal
BMC Geriatrics
Published
2025-10-31
DOI
https://doi.org/10.1186/s12877-025-06548-7
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00

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article

Digital intelligence for cardiometabolic health management in older adults: protocol for a community-based prospective cohort study

Shasha Geng, Xin Chen, Yingqian Zhu, H. Jiang et al.
BMC Geriatrics
Artificial Intelligence in Healthcare and Education
article

Digital intelligence for cardiometabolic health management in older adults: protocol for a community-based prospective cohort study

Shasha Geng, Xin Chen, Yingqian Zhu, H. Jiang, Ning Xu, Fengyi Lu, Liying Huang, Yue Liu, Li Wang, Qi Wang, Qingqing Li, Li Yang, Hong Jiang, Huang Qian, Xiaotong Guo, Liang Zheng, Jing Fang, Meng Chao, P. X. Shen, Ningyuan Fang, S.M. Ling, Jianli Ge
article en

Abstract

Cardiovascular diseases (CVDs) represent a significant cause of morbidity and mortality among older adults in China, exerting a substantial influence on their quality of life and life expectancy. The country’s ageing population is predicted to intensify this burden, underscoring the need for the development of innovative and effective management strategies. The prospective cohort study will recruit older patients with cardiometabolic diseases from multiple community health service centres in Shanghai. Participants will be comprehensively assessed for CVD risk factors, medications, functional status, and multi-omics data, along with continuous sign monitoring data from wearable bracelets and their electronic health records. The primary objective of the study is twofold: firstly, to explore the independent and joint predictive power of digital biomarkers and clinical indicators for cardiovascular events (new incident/recurrent), dementia, death and other adverse outcomes in older adults; and secondly, to establish a digital twin platform for precision medicine. The study proposes a novel integration of digital intelligence into cardiometabolic health management for older adults. The utilisation of comprehensive data collection and advanced analytical methods is anticipated to generate valuable insights into the intricate interplay of risk factors and outcomes. The development of personalised risk assessment models and intelligent alert systems has the potential to enhance the prevention and treatment of CVDs, thereby improving health outcomes and quality of life for older adults. ChiCTR2500096478 (Chinese Clinical Trial Registry). Registered 24 January 2025 - Prospectively registered.

BMC GeriatricsVol. 25(1)
Tongji University (CN), Renji Hospital (CN), Shanghai East Hospital (CN)
Shanghai Municipal Health Commission, Shanghai Pudong New Area Health Commission, National Key Research and Development Program of China
Openalex Percentile: Top 76%
Artificial Intelligence in Healthcare and Education
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