# Application of emerging information technologies in the prevention and control of chronic diseases

## TL;DR

Emerging digital health technologies can transform chronic disease management by enabling proactive, personalized care through wearables, AI, and voice-based tools.

## Contribution

This paper reviews how integrating Internet Plus, wearables, AI, and voice-based systems can improve chronic disease prevention and control.

## Key findings

- Wearable devices enable real-time monitoring for timely interventions in chronic conditions.
- AI and machine learning improve predictive diagnostics and personalized treatments.
- Voice-based technologies offer scalable solutions for medication adherence and health education.

## Abstract

Chronic non-communicable diseases (NCDs)—including cardiovascular disease, diabetes, chronic obstructive pulmonary disease (COPD), and chronic kidney disease—pose a major 21st-century global public health challenge. They drive high morbidity, mortality, and escalating healthcare costs. Traditional reactive, clinic-centered care models are ill-equipped to meet the ongoing, complex needs of chronic disease patients. This has prompted a shift toward proactive, personalized, and patient-centered approaches. This narrative review examines the transformative potential of emerging digital health technologies (DHTs) in chronic disease prevention and control. It emphasizes the synergistic integration of four key domains: Internet Plus ecosystems, wearable devices and sensors, artificial intelligence (AI) and machine learning, and interactive voice-based follow-up or conversational agents. Internet Plus serves as the foundational infrastructure. It enables seamless data integration, care coordination, telemedicine, and patient empowerment across stakeholders. Wearable devices facilitate continuous, real-time monitoring of physiological and behavioral data, yielding valuable insights for timely interventions in cardiovascular, metabolic, respiratory, and musculoskeletal disorders. AI and machine learning drive predictive diagnostics, risk stratification, and personalized digital therapeutics, demonstrating superior efficacy and cost-effectiveness in areas like pulmonary rehabilitation and orthopedic care. Voice-based technologies provide scalable, low-cost solutions for medication adherence, symptom monitoring, and health education. They particularly benefit older adults and rural populations. Despite these advances, significant challenges remain. These include data security and privacy risks, health inequities amplified by the digital divide and device biases, and AI limitations (e.g., reproducibility, opacity or “black-box” issues, and unclear legal accountability). In conclusion, the convergence of these technologies promises a more precise, proactive, and inclusive paradigm for chronic disease management. Future success hinges on robust privacy protections, inclusive design, diverse real-world validation, and refined regulatory frameworks to ensure equitable and sustainable implementation.