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Background: Up to 85% of pregnant women experience nausea and vomiting in pregnancy (NVP), which can impact both the pregnant woman and developing fetus. Traditional East Asian Medicine (TEAM) including acupuncture and herbal medicine has been used to treat NVP; however, its effectiveness remains under debate. This study aimed to systematically review the existing evidence from systematic reviews on the effectiveness of TEAM for NVP and to critically evaluate the quality of these reviews.
Methods: Nine databases were searched from their inception until January 2024. Search terms included, "Hyperemesis gravidarum", "Nausea", "Vomiting", "acupuncture" and "herbal medicine". Systematic reviews (SRs) that evaluated the effect of TEAM treatment for NVP were included. We evaluated methodological quality, reporting quality, and risk of bias using the AMSTAR-2, ROBIS tool, and PRISMA guidelines.
Results: In total, 20,121 publications were retrieved from the databases. Twenty-five SRs met the inclusion criteria, indicating that acupuncture and related techniques, and herbal medicines are effective in alleviating NVP. Various methods including traditional acupuncture, acupressure, acupoint injection, electroacupuncture, herbal acupoint patching, and herbal decoctions were used. Herbs like ginger and additional aromatherapies such as lemon and cardamom have also shown beneficial effects. However, there are controversies regarding the consistency of results and the quality of methodologies. Despite low risk of bias across reviews, all were deemed low or critically low in methodological quality, with none fully adhering to PRISMA guidelines.
Conclusion: This comprehensive review underscores the potential of TEAM in managing NVP but highlights significant gaps in research quality and reporting. Future studies of higher methodological quality are essential to validate these findings and guide clinical practice.
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http://dx.doi.org/10.2147/IJWH.S512247 | DOI Listing |
J Med Internet Res
September 2025
Department of Information Systems and Cybersecurity, The University of Texas at San Antonio, 1 UTSA Circle, San Antonio, TX, 78249, United States, 1 (210) 458-6300.
Background: Adverse drug reactions (ADR) present significant challenges in health care, where early prevention is vital for effective treatment and patient safety. Traditional supervised learning methods struggle to address heterogeneous health care data due to their unstructured nature, regulatory constraints, and restricted access to sensitive personal identifiable information.
Objective: This review aims to explore the potential of federated learning (FL) combined with natural language processing and large language models (LLMs) to enhance ADR prediction.
J Med Internet Res
September 2025
Institute for Health Care Management and Research, University of Duisburg-Essen, Essen, Germany.
Background: Mental and behavioral disorders affect approximately 28% of the adult population in Germany per year, with treatment being provided through a diverse health care system. Yet there are access and capacity problems in outpatient mental health care. One innovation that could help reduce these barriers and improve the current state of care is the use of mobile health (mHealth) apps, known in Germany as Digitale Gesundheitsanwendungen (DiGA).
View Article and Find Full Text PDFJMIR Med Inform
September 2025
Department of Hepatobiliary and Vascular Surgery, First Affiliated Hospital of Chengdu Medical College, Chengdu, China.
Background: Primary liver cancer, particularly hepatocellular carcinoma (HCC), poses significant clinical challenges due to late-stage diagnosis, tumor heterogeneity, and rapidly evolving therapeutic strategies. While systematic reviews and meta-analyses are essential for updating clinical guidelines, their labor-intensive nature limits timely evidence synthesis.
Objective: This study proposes an automated literature screening workflow powered by large language models (LLMs) to accelerate evidence synthesis for HCC treatment guidelines.
JMIR Hum Factors
September 2025
Department of Music, Arts and Culture Studies, Centre of Excellence in Music, Mind, Body and Brain, University of Jyväskylä, Seminaarinkatu 15, Jyväskylän yliopisto, Jyväskylä, 40014, Finland, 358 6643034.
Background: Personalized Interactive Music Systems (PIMSs) are emerging as promising devices for enhancing physical activity and exercise outcomes. By leveraging real-time data and adaptive technologies, PIMSs align musical features, such as tempo and genre, with users' physical activity patterns, including frequency and intensity, enhancing their overall experience.
Objective: This exploratory systematic review and meta-analysis evaluates the effectiveness of PIMSs across physical, psychophysical, and affective domains.
JBJS Rev
September 2025
Joondalup Health Campus, Joondalup, Australia.
Background: Postoperative swelling is a common complication after total knee arthroplasty (TKA), associated with pain, limited mobility, and delayed recovery. This study aimed to systematically review the literature on interventions that reduce postoperative swelling, categorized into preoperative, intraoperative, and postoperative phases.
Methods: A Preferred Reporting Items for Systematic Reviews and Meta-Analyses-compliant search of PubMed, Medline, Embase, and Cochrane databases was performed for clinical studies evaluating interventions to reduce swelling after primary TKA.