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Introduction: The goal of this systematic review was to examine the effectiveness of e-health interventions for chemotherapy-induced nausea and vomiting (CINV).
Methods: A literature search was conducted across the databases of PubMed, Web of Science, Embase, CINAHL, and Cochrane Library from database establishment to 3 March 2024. We included randomized controlled trials in English where the intervention group was via e-health. Two reviewers independently carried out the screening, data extraction, and quality appraisal of the studies. Using Stata 17.0, meta-analyses were conducted to synthesize the effects of outcomes of interest.
Results: A total of 6663 studies were retrieved, with only 8 RCTs meeting criteria, involving 620 patients. Meta-analysis revealed that e-health interventions significantly reduce CINV severity (MD = - 7.687; 95% CI - 11.903, - 3.326; p < 0.001). However, results regarding CINV incidence reduction and quality of life improvement are inconclusive due to variations in intervention content, modality, and frequency among studies.
Conclusions: e-health interventions may reduce the severity and incidence of CINV, while enhancing quality of life. However, the results should be interpreted cautiously. Higher quality studies are needed in the future to further validate the effectiveness of e-health interventions for CINV.
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http://dx.doi.org/10.1007/s00520-024-08869-6 | DOI Listing |
Glob J Qual Saf Healthc
August 2025
Metropolitan School of Business and Management, London, UK.
Introduction: Telemedicine, also known as e-health, utilizes computer technology to deliver clinical healthcare remotely. Since its inception in the 1960s, telemedicine has evolved significantly, offering several advantages to both patients and healthcare providers, including remote care and monitoring. This study contributes to existing literature by exploring the effectiveness of telemedicine and patient satisfaction in managing health conditions in Canada, with a focus on service delivery, accessibility, efficiency, doctor-patient relationships, and network interconnectivity.
View Article and Find Full Text PDFReprod Health
September 2025
Department of Sexual and Reproductive Health including UNDP/UNFPA/UNICEF/WHO/World Bank Special Programme of Research, Development and Research Training in Human Reproduction, World Health Organization, Avenue Appia 20, 1211, Geneva, Switzerland.
Background: The COVID-19 pandemic disrupted the provision of sexual and reproductive health services, including contraceptive and family planning (FP) services. The World Health Organization conducted a multi-country study in India, Nigeria and Tanzania to assess the impact of the pandemic on the health system's capacity to provide contraceptive and FP services. In this paper, we share the results of a qualitative study aimed at understanding clients' perspectives at the primary healthcare level on accessing contraceptive services in COVID-19-affected areas in the three aforementioned countries.
View Article and Find Full Text PDFAnn Anat
September 2025
Division of Anatomy, Department 1, Faculty of Dentistry, "Carol Davila" University of Medicine and Pharmacy, Bucharest 050474, Romania. Electronic address:
Purpose: This study aimed to investigate the prevalence and anatomical patterns of temporal bone pneumatisation surrounding the internal acoustic meatus (IAM), specifically across its three anatomical regions: the porus acusticus internus (medial opening), the proper IAM (tubular midportion), and the fundus (lateral end). A secondary objective was to evaluate the association between pneumatisation and the thickness of the overlying tegmen in each region.
Methods: A total of 160 IAMs (80 patients, bilateral assessment) were analyzed using cone-beam computed tomography (CBCT).
Neurology
September 2025
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy.
Background And Objectives: Multiple sclerosis (MS) is common in adults while myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) is rare. Our previous machine-learning algorithm, using clinical variables, ≤6 brain lesions, and no Dawson fingers, achieved 79% accuracy, 78% sensitivity, and 80% specificity in distinguishing MOGAD from MS but lacked validation. The aim of this study was to (1) evaluate the clinical/MRI algorithm for distinguishing MS from MOGAD, (2) develop a deep learning (DL) model, (3) assess the benefit of combining both, and (4) identify key differentiators using probability attention maps (PAMs).
View Article and Find Full Text PDFOnline J Public Health Inform
September 2025
Department of Health Psychology, Faculty of Psychology, Open University of the Netherlands, Valkenburgerweg 177, Heerlen, 6419 AT, The Netherlands, 31 455762888.
Background: Tailoring intervention content, such as those designed to improve physical activity (PA) behavior, can enhance effectiveness. Previous Bayesian network research showed that it might be relevant to tailor PA interventions based on demographic factors such as gender, revealing differences in determinants' roles between subpopulations. In order to optimize tailoring, one needs to understand the differences between subpopulations based on different characteristics.
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