Evidence mapPaperPMID 41647744Full record

ReviewFrontiers in public health2026

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

Tong Feng, Yi Li, Yinhe Feng, Chunfang Zeng

Abstract readReview
In one paragraph

Review in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Tong Feng *Department of Respiratory and Critical Care Medicine, Deyang People's Hospital, Deyang, China.
Yi Li *Department of Respiratory and Critical Care Medicine, Deyang People's Hospital, Deyang, China.
Yinhe FengDepartment of Respiratory and Critical Care Medicine, Deyang People's Hospital, Deyang, China.
Chunfang ZengDepartment of Respiratory and Critical Care Medicine, Deyang People's Hospital, Deyang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Chronic DiseaseDigital HealthArtificial IntelligenceHumansInternetTelemedicineWearable Electronic Devicesartificial intelligencechronic non-communicable diseasesdigital health technologiesinternet pluswearable devices

Identifiers

PMID41647744
PMCPMC12868171

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.