Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 2 appraisals
JMIR mHealth and uHealth
mHealth Technologies for the Care of Children With Congenital Heart Disease: Scoping Review
BACKGROUND: Mobile information technology (IT) is increasingly being used in the health care sector, and it can play a critical role in both the care of children with congenital heart disease (CHD) and the quality of life of their families. OBJECTIVE: This study aimed to conduct a scoping review of the application of mobile health (mHealth) technologies in the care of children with CHD. We summarized the forms of mHealth interventions and effects on CHD to provide a reference for future research in this field. METHODS: We searched PubMed; Embase; Web of Science; the Cochrane Library; CINAHL; China National Knowledge Infrastructure; Wanfang Data; the Chinese Biomedical Database; VIP Chinese Science and Technology Journal Database; National Guideline Clearinghouse of the United States; the website of the Registered Nurses' Association of Ontario, Canada; the Guidelines International Network; the American Heart Association; and the American Association of Cardiovascular and Pulmonary Rehabilitation. The search period was from the establishment of the databases to June 12, 2025. The retrieved literature was screened and analyzed. RESULTS: A total of 519 Chinese- and English-language articles were identified, with 44 (8.5%) studies meeting the inclusion criteria. The primary forms of mHealth interventions for patients with CHD included mobile apps, wearable devices, and remote monitoring equipment. The findings indicated that mHealth technologies could improve exercise capacity, nutritional status, psychological well-being, and quality of life in children with CHD. CONCLUSIONS: The application of mHealth in the care of children with CHD is feasible and demonstrates positive effects. Future research should emphasize peer education and patient privacy protection while further exploring remote education and health management based on theoretical frameworks and intelligent ITs to enhance quality of life for both children with CHD and their parents.
19 July 2026
Read appraisal →Journal of pediatric health care : official publication of National Association of Pediatric Nurse Associates & Practitioners
Systematic Review of Studies Investigating Infant Feeding Difficulties: Focus on Machine Learning Applications
INTRODUCTION: This study aims to systematically review studies using machine learning for infant feeding difficulties and identify the clinical applicability and limitations of such approaches. METHODS: A literature search was conducted in accordance with the PRISMA guidelines. Studies on the use of machine learning for infants with feeding difficulties were analyzed. RESULTS: We screened 1,104 studies, and 10 were eligible for inclusion. These studies applied machine learning to measure physiological signals during feeding, such as the number of sucking, swallowing and breathing events, to classify or predict feeding difficulties. The most common algorithms in the analyzed studies included support vector machines (SVMs), k-nearest neighbors (KNNs), and decision trees (DTs), which generally achieved high classification accuracy. DISCUSSION: Although all included studies used sensor-based data, only four directly applied machine learning models. Future research should expand the direct applications of machine learning, standardize measurements, and validate algorithms to improve clinical utility.
2 July 2026
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