Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Oct 23, 2023
Date Accepted: Oct 31, 2024
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Research trends on metabolic syndrome in digital healthcare using topic modeling
ABSTRACT
Background:
Metabolic syndrome (MetS) is a prevalent health condition, affecting 20–40% of the global population. It elevates the risk of cardiovascular disease by aggregating multiple risk factors such as obesity, high cholesterol, elevated triglycerides, hypertension, and elevated blood sugar. Lifestyle modification is essential for both its prevention and management. Digital healthcare, incorporating technologies like wearable devices, mobile applications, and telemedicine, is increasingly becoming integral to healthcare systems. It offers the potential for monitoring physical activity and facilitating behavioral change, thus mitigating the risks associated with MetS. By analyzing the existing research trends in the application of digital healthcare for MetS management, this study aims to identify gaps in current knowledge and suggest avenues for future research.
Objective:
This study aims to identify core keywords, topics, and research trends concerning the use of digital healthcare in the management of MetS.
Methods:
A systematic search of abstracts from peer-reviewed papers was conducted across six academic databases: PubMed, EMBASE, Cochrane, CINAHL, SCOPUS, and Web of Science. Following eligibility screening, 162 abstracts were selected for further analysis. The methodological approach included text preprocessing, text network analysis, and topic modeling using the BERTopic algorithm. A text network was constructed based on the co-occurrence of keywords. Each topic was named after meticulous examination of the associated keywords and papers. Research trends were then identified by plotting the frequency of studies corresponding to each topic over time
Results:
The analysis yielded a keyword network comprising 1047 nodes and 34,377 edges from the 162 selected abstracts. The top five core keywords were identified as “MetS,” “use,” “patient,” “health,” and “intervention.” In total, 12 unique topics were identified. The examination of research trends revealed an expanding field, driven by the demand for tailored interventions and the influence of the ongoing pandemic.
Conclusions:
By analyzing past research trends and extracting data from scholarly databases, this study provides valuable insights that can guide future investigations in the field of digital healthcare and MetS management.
Citation
