Background: This study will appraise the effect and safety of advanced nursing care (ANC) on psychological condition (PC) in patients with chronic heart failure (CHF).
Methods: The following databases will be sought from the beginning up to the February 29, 2020: MEDLINE, EMBASE, Cochrane Library, Web of Science, Scopus, the Cumulative Index to Nursing and Allied Health Literature, the Allied and Complementary Medicine Database, the Chinese Scientific Journal Database, and China National Knowledge Infrastructure. There are not language and publication status limitations related to any electronic databases. In addition, we will also identify conference proceedings, reference lists of included studies, and websites of clinical trials registry. Two reviewers will separately carry out study selection, data extraction, and study quality evaluation. Any inconsistencies will be solved by a third reviewer through discussion. RevMan 5.3 software will be utilized to carry out statistical analysis.
Results: This study will comprehensively summarize all potential evidence to systematically address the effects and safety of ANC on PC in patients with CHF.
Conclusion: The findings of the present study will help to determine whether ANC is effective or not on PC in patients with CHF.
Study Registration Number: INPLASY202040077.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7306357 | PMC |
http://dx.doi.org/10.1097/MD.0000000000020355 | DOI Listing |
JMIR Res Protoc
January 2025
National Radiotherapy, Oncology and Nuclear Medicine Centre, Korle-bu Teaching Hospital, Accra, Ghana.
Background: Cancer is a leading cause of global mortality, accounting for nearly 10 million deaths in 2020. This is projected to increase by more than 60% by 2040, particularly in low- and middle-income countries. Yet, palliative and psychosocial oncology care is very limited in these countries.
View Article and Find Full Text PDFJMIR Res Protoc
January 2025
Department of Women's and Children's Health, Participatory eHealth and Health Data Research Group, Uppsala University, Uppsala, Sweden.
Background: Digital health interventions have become increasingly popular in recent years, expanding the possibilities for treatment for various patient groups. In clinical research, while the design of the intervention receives close attention, challenges with research participant engagement and retention persist. This may be partially due to the use of digital health platforms, which may lack adequacy for participants.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Cancer Rehabilitation and Survivorship, Department of Supportive Care, Princess Margaret Cancer Centre, Toronto, ON, Canada.
Background: Virtual follow-up (VFU) has the potential to enhance cancer survivorship care. However, a greater understanding is needed of how VFU can be optimized.
Objective: This study aims to examine how, for whom, and in what contexts VFU works for cancer survivorship care.
PLoS One
January 2025
Nuffield Department of Surgical Sciences, University of Oxford, Oxford, United Kingdom.
Epithelial cancers are typically heterogeneous with primary prostate cancer being a typical example of histological and genomic variation. Prior studies of primary prostate cancer tumour genetics revealed extensive inter and intra-patient genomic tumour heterogeneity. Recent advances in machine learning have enabled the inference of ground-truth genomic single-nucleotide and copy number variant status from transcript data.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Medicine Epidemiology and Population Sciences, Baylor College of Medicine, Houston, Texas, United States of America.
Objectives: It is significant to know how much early detection and screening could reduce the proportion of occult metastases and benefit NSCLC patients.
Methods: We used previously designed and validated mathematical models to obtain the characteristics of LC in the population including undetectable metastases at the time of diagnosis. The survival was simulated using the survival functions from Surveillance, Epidemiology and End Results (SEER) data stratified by stage.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!