SynthesisJournal of medical Internet research2024
Research Trends on Metabolic Syndrome in Digital Health Care Using Topic Modeling: Systematic Search of Abstracts.
Synthesis in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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.
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.
Who cites it
4 citing papers in PubMed.
- Cross-Cultural Perspectives on Metabolic Syndrome Management and Attitudes Toward Using Digital Health Tools in Two Distinct Populations: Protocol for a Qualitative Descriptive Study.JMIR research protocols · 2026Article
- Implementation and User Evaluation of the SANGYAN Digital Health Platform to Enhance Knowledge About COVID-19 and Other Health Conditions: Quasi-Experimental Study.JMIR infodemiology · 2026Article
- Shifts in dental trauma research (2000-2025): a literature-based thematic analysis using topic modeling.BMC oral health · 2026Article
- Mapping the mHealth Nexus: A Semantic Analysis of mHealth Scholars' Research Propensities Following an Interdisciplinary Training Institute.Applied sciences (Basel, Switzerland) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMetabolic syndrome (MetS) is a prevalent health condition that affects 20%-40% of the global population. Lifestyle modification is essential for the prevention and management of MetS. Digital health care, which incorporates technologies like wearable devices, mobile apps, and telemedicine, is increasingly becoming integral to health care systems. By analyzing existing research trends in the application of digital health care for MetS management, this study identifies gaps in current knowledge and suggests avenues for future research.
objectiveThis study aimed to identify core keywords, topics, and research trends concerning the use of digital health care in the management of MetS.
methodsA systematic search of abstracts from peer-reviewed papers was conducted across 6 academic databases. 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.
resultsAnalysis of the 162 selected abstracts yielded a keyword network comprising 1047 nodes and 34,377 edges. The top 5 core keywords were identified as "MetS," "use," "patient," "health," and "intervention." We identified 12 unique topics, with topic 1 focusing on the use of telehealth for self-management of diabetes. The diversity of the 12 topics reflected various aspects of digital health care, including telehealth for diabetes management, electronic health records for MetS complications, and wearable devices for monitoring metabolic status. Research trends showed an expanding field of precision medicine driven by the demand for tailored interventions and the significant impact of the COVID-19 pandemic.
conclusionsBy analyzing past research trends and extracting data from scholarly databases, this study has provided valuable insights that can guide future investigations in the field of digital health care and MetS management.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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 OpenQuestion graph.