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Related Concept Videos

Longitudinal Studies01:26

Longitudinal Studies

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Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
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Pulse rhythm01:30

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Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
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Peripheral Artery Disease (PAD) is characterized by narrowed arteries that diminish blood flow to the extremities. Effective management of PAD requires an interprofessional approach involving various healthcare professionals. The critical aspects of interprofessional care for PAD patients focus on risk factor modification, drug therapy, exercise therapy, nutrition therapy, critical limb ischemia care, and interventional radiology and surgical procedures.The primary treatment goal for PAD...
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Related Experiment Video

Updated: Aug 26, 2025

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
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A Precision Health Service for Chronic Diseases: Development and Cohort Study Using Wearable Device, Machine

Chia-Tung Wu1, Ssu-Ming Wang2, Yi-En Su1

  • 1Department of Computer Science and Information EngineeringNational Taiwan University Taipei 10617 Taiwan.

IEEE Journal of Translational Engineering in Health and Medicine
|October 6, 2022
PubMed
Summary
This summary is machine-generated.

This study developed an integrated precision health service using AI to monitor lifestyle and environmental factors for chronic disease prevention. The system accurately predicts health events, outperforming traditional methods.

Keywords:
Precision healthartificial intelligencechronic obstructive pulmonary diseasepanic disorderwearable device

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Area of Science:

  • Digital Health
  • Precision Medicine
  • Artificial Intelligence in Healthcare

Background:

  • Chronic diseases pose a significant global health burden.
  • Existing health monitoring often relies on subjective data and lacks real-time environmental context.
  • There is a need for integrated, scalable solutions for proactive health management.

Purpose of the Study:

  • To develop and validate an integrated precision health service for health promotion and chronic disease prevention.
  • To assess the utility of continuous real-time monitoring of lifestyle and environmental factors.
  • To evaluate the performance of AI-driven prediction models for acute exacerbation events.

Main Methods:

  • Integrated wearable devices, environmental sensors, and a smartphone app for data collection.
  • Utilized an AI-assisted telecare platform for data analysis and insight generation.
  • Employed machine learning and deep learning algorithms to train modular chronic disease prediction models.
  • Collected prospective data from 1,667 patients over 24 months.

Main Results:

  • The AI platform provided comprehensive patient insights and predicted future acute exacerbation events.
  • Validated prediction models for obesity, panic disorder, and COPD achieved high performance metrics (e.g., 88.46% average accuracy).
  • Demonstrated that lifestyle and environmental factors significantly improve prediction accuracy compared to questionnaire data alone.

Conclusions:

  • The integrated precision health service offers a scalable and effective approach to chronic disease prevention.
  • Continuous monitoring of objective lifestyle and environmental data enhances health event prediction.
  • A cost-effective, feature-efficient model facilitates real-world deployment of AI-driven health predictions.