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BACKGROUND: Nonadherence to medication among patients with heart disease poses serious risks, including worsened heart failure and increased mortality rates. OBJECTIVE: This study aims to explore the complex interplay between comorbidities, medication nonadherence, activities of daily living, and heart condition status in older American adults, using both traditional statistical methods and machine learning. METHODS: Data from 326 older adults with heart conditions, drawn from the Health and Retirement Study, were analyzed. Descriptive statistics characterized demographic profiles and comorbidities, whereas logistic regression, multiple regression analyses, and decision tree models were used to address our research inquiries. In addition, a machine learning approach, specifically decision tree models, was integrated to enhance predictive accuracy. RESULTS: Our analysis showed that factors like age, gender, hypertension, and stroke history were significantly linked to worsening heart conditions. Notably, depression emerged as a robust predictor of medication nonadherence. Further adjusted analyses underscored significant correlations between stroke and challenges in basic activities such as dressing, bathing, and eating. Depression correlated significantly with difficulties in dressing, bed mobility, and toileting, whereas lung disease was associated with bathing hindrances. Intriguingly, our decision tree model revealed that patients experiencing dressing challenges, but not toileting difficulties, were more prone to report no improvement in heart condition status over the preceding 2 years. CONCLUSIONS: Blending traditional statistics with machine learning in this study reveals significant implications for crafting personalized interventions to improve patients' depression, leading to increased activities of daily living, medication adherence, reduced severity of comorbidities, and ultimately better management of heart conditions.
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Background: Integrating Artificial Intelligence (AI) into healthcare has transformed the landscape of patient care and healthcare delivery. Despite this, there remains a notable gap in the existing literature synthesizing the comprehensive understanding of AI's utilization in nursing care. Objective: This systematic review aims to synthesize the available evidence to comprehensively understand the application of AI in nursing care. Methods: Studies published between January 2019 and December 2023, identified through CINAHL Plus with Full Text, Web of Science, PubMed, and Medline, were included in this review. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines guided the identification, screening, exclusion, and inclusion of articles. The convergent integrated analysis framework, as proposed by the Joanna Briggs Institute, was employed to synthesize data from the included studies for theme generation. Results: A total of 337 records were identified from databases. Among them, 35 duplicates were removed, and 302 records underwent eligibility screening. After applying inclusion and exclusion criteria, eleven studies were deemed eligible and included in this review. Through data synthesis of these studies, six themes pertaining to the use of AI in nursing care were identified: 1) Risk Identification, 2) Health Assessment, 3) Patient Classification, 4) Research Development, 5) Improved Care Delivery and Medical Records, and 6) Developing a Nursing Care Plan. Conclusion: This systematic review contributes valuable insights into the multifaceted applications of AI in nursing care. Through the synthesis of data from the included studies, six distinct themes emerged. These findings not only consolidate the current knowledge base but also underscore the diverse ways in which AI is shaping and improving nursing care practices.
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BACKGROUND: A previous systematic review reporting the contributions of informal, unpaid caregivers to patient heart failure (HF) self-care requires updating to better inform research, practice, and policy. OBJECTIVE: The aim of this study was to provide an updated review answering the questions: (1) What specific activities do informal caregivers of adults with HF take part in related to HF self-care? (2) Have the activities that informal caregivers of adults with HF take part in related to HF self-care changed over time? (3) What are the gaps in the science? METHODS: This review followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. PubMed, CINAHL, EMBASE, and Cochrane CENTRAL databases were searched. Eligible studies involved an informal, unpaid caregiver of an adult with HF as a study variable or participant. Caregiving activities were benchmarked using the theory of self-care in chronic illness. RESULTS: Two thousand one hundred fifty-four research reports were identified, of which 64 met criteria. Caregivers' contributions occurred in self-care maintenance (91%), monitoring (54%), and management (46%). Activities performed directly on or to the patient were reported more frequently than activities performed for the patient. Change over time involved the 3 domains differentially. Gaps include ambiguous self-care activity descriptions, inadequate caregiving time quantification, and underrepresented self-care monitoring, supportive, and communication activities. CONCLUSIONS: Newly identified caregiver-reported activities support updating the theory of self-care in chronic illness to include activities currently considered ancillary to HF self-care. Identified gaps highlight the need to define specific caregiving activities, determine task difficulty and burden, and identify caregiver self-care strategy and education needs. Exposing the hidden work of caregiving is essential to inform policy and practice.