Research Trends on Metabolic Syndrome in Digital Health Care Using Topic Modeling: Systematic Search of Abstracts.

Area of Science:

• Digital Health
• Metabolic Syndrome Research
• Health Informatics

Background:

• Metabolic syndrome (MetS) affects 20%-40% of the global population, necessitating lifestyle modifications for management.
• Digital health care, including wearables and telemedicine, is increasingly vital in healthcare systems.
• Understanding digital health trends for MetS is crucial for effective intervention and prevention strategies.

Purpose of the Study:

• To identify core keywords, topics, and research trends in digital health care for metabolic syndrome (MetS) management.
• To analyze existing literature and pinpoint knowledge gaps in this rapidly evolving field.
• To provide a foundation for future research and development in digital health interventions for MetS.

Main Methods:

• Systematic search of 162 peer-reviewed paper abstracts across 6 academic databases.
• Application of text preprocessing, text network analysis, and BERTopic modeling.
• Analysis of keyword networks and topic modeling to identify research patterns.

Main Results:

• A keyword network of 1047 nodes and 34,377 edges was generated, with top keywords including 'MetS,' 'use,' 'patient,' 'health,' and 'intervention.'
• Twelve distinct topics were identified, highlighting telehealth for diabetes self-management, EHRs for MetS complications, and wearables for metabolic monitoring.
• Research trends indicate a growing focus on precision medicine and the impact of the COVID-19 pandemic on digital health adoption.

Conclusions:

• Analysis of scholarly databases provides valuable insights into digital health care for MetS.
• Identified trends and topics can guide future research and development in this area.
• This study offers a roadmap for advancing digital health strategies in metabolic syndrome management.