ABSTRACT
This systematic review evaluates the efficacy of self-care interventions for atrial fibrillation (AF), focusing on strategies for maintenance, monitoring, and management applied individually or in combination. Adhering to the 2020 PRISMA guidelines, the search strategy spanned literature from 2005 to 2023, utilizing keywords and subject headings for "atrial fibrillation" and "self-care" combined with the Boolean operator AND. The databases searched included Medline, Embase, and CINAHL. The initial search, conducted on February 17, 2021, and updated on May 16, 2023, identified 5160 articles, from which 2864 unique titles and abstracts were screened. After abstract screening, 163 articles were reviewed in full text, resulting in 27 articles being selected for data extraction; these studies comprised both observational and randomized controlled trial designs. A key finding in our analysis reveals that self-care interventions, whether singular, dual, or integrated across all three components, resulted in significant improvements across patient-reported, clinical, and healthcare utilization outcomes compared to usual care. Educational interventions, often supported by in-person sessions or telephone follow-ups, emerged as a crucial element of effective AF self-care. Additionally, the integration of mobile and web-based technologies alongside personalized education showed promise in enhancing outcomes, although their full potential remains underexplored. This review highlights the importance of incorporating comprehensive, theory-informed self-care interventions into routine clinical practice and underscores the need for ongoing innovation and the implementation of evidence-based strategies. The integration of education and technology in AF self-care aligns with the recommendations of leading health organizations, advocating for patient-centered, technology-enhanced approaches to meet the evolving needs of the AF population.
ABSTRACT
This paper reviews the potential use of fuzzy c-means clustering (FCM) and explores modifications to the distance function and centroid initialization methods to enhance image segmentation. The application of interest in the paper is the segmentation of breast tumours in mammograms. Breast cancer is the second leading cause of cancer deaths in Canadian women. Early detection reduces treatment costs and offers a favourable prognosis for patients. Classical methods, like mammograms, rely on radiologists to detect cancerous tumours, which introduces the potential for human error in cancer detection. Classical methods are labour-intensive, and, hence, expensive in terms of healthcare resources. Recent research supplements classical methods with automated mammogram analysis. The basic FCM method relies upon the Euclidean distance, which is not optimal for measuring non-spherical structures. To address these limitations, we review the implementation of a Mahalanobis-distance-based FCM (FCM-M). The three objectives of the paper are: (1) review FCM, FCM-M, and three centroid initialization algorithms in the literature, (2) illustrate the effectiveness of these algorithms in image segmentation, and (3) develop a Python package with the optimized algorithms to upload onto GitHub. Image analysis of the algorithms shows that using one of the three centroid initialization algorithms enhances the performance of FCM. FCM-M produced higher clustering accuracy and outlined the tumour structure better than basic FCM.
