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Heart failure is a leading cause of hospitalisation and often coexists with seven comorbid conditions on average. This study aimed to examine the gender differences in disease burden, symptom burden, and quality of life among older adults with heart failure and multimorbidity.
Cross-sectional study.
This study utilised a baseline survey from an ongoing cohort study in 2022–2023. Adults aged ≥ 50 years with heart failure and more than one chronic condition were recruited from a university-affiliated hospital using an electronic patient portal. Disease burden was measured using a modified Disease Burden Impact Scale. The Edmonton Symptom Assessment Scale and EuroQoL-5D-5L assessed symptom burden and quality of life. Gender differences in baseline outcomes were examined using Pearson's Chi-square tests, Welch's t-tests, and multiple linear regressions.
Among 353 participants who completed the baseline survey, the mean (±SD) age was 70 (±9.5) years, and 50.1% were women (mean age: 67 ± 9 vs. men: 72 ± 10). In adjusted models, women had 4.9 points higher disease burden (p = 0.003) and reported higher symptom scores of pain (p = 0.018), tiredness (p = 0.021), nausea (p = 0.007), and loss of appetite compared to men (p = 0.036). Women had significantly more moderate/severe problems in usual activities and pain/discomfort and 0.07 points lower EuroQoL index than men (p = 0.010).
There were gender differences in disease/symptom burdens and quality of life. Women living with heart failure and multimorbidity had higher burdens but lower quality of life.
Identifying gender differences among people with heart failure and multimorbidity can be the first step to explaining health disparities. Research should take more inclusive and equitable approaches to address these differences. Healthcare providers, including nurses, should implement targeted strategies for effective multimorbidity management by considering these differences and disparities in clinical settings.
STROBE checklist, cross-sectional.
No patient or public contribution.
The incidence of cancer continues to increase, and cancer patients still suffer from a range of burdens, leading to decreased quality of life. AI has been increasingly studied in the field of cancer care, demonstrating its enormous potential. However, most AI applications in cancer care are still in the developmental stage, and the strength of evidence from randomized controlled trials is not yet sufficient.
To evaluate the effects of AI-enhanced interventions in randomized controlled trials conducted in clinical settings and the impact of AI-enhanced interventions on the health outcomes of adult cancer patients.
Meta-analysis of randomized controlled trials.
Nine databases (MEDLINE, Embase, the Cochrane Central Register of Controlled Trials, CINAHL, PsycINFO, Web of Science, CNKI, VIP, and Sinomed) were systematically searched, and metadata analysis was performed using R software and R Studio. The quality of the included studies was evaluated using the Cochrane Risk of Bias tool (RoB2) and the GRADE approach. The process was independently completed by two authors. The intervention effect was estimated by calculating the standardized mean difference (SMD) and 95% confidence interval (CI) using a random-effects model.
A total of ten articles were included. Meta-analysis results showed that AI-enhanced interventions can significantly improve the quality of life (SMD 0.89, 95% CI 0.06–1.73), symptom burden (SMD −0.81, 95% CI −1.44 to −0.18), anxiety (SMD −0.20, 95% CI −0.32 to −0.07), and self-efficacy (SMD 0.55, 95% CI 0.06 to 1.03) of cancer patients. The type of AI application and the duration of the intervention had an impact on the quality of life of cancer patients: the effect of algorithm recommendations (SMD 1.49, 95% CI 0.04–2.93) was better than that of risk alerts (SMD 0.33, 95% CI 0.03–0.63), and the effect of short-term interventions (< 3 months) (SMD 1.49, 95% CI 0.04–2.93) was better than that of long-term interventions (≥ 3 months) (SMD 0.19, 95% CI −0.04 to 0.43). Sensitivity analysis showed that the results of this study were stable and reliable.
AI-enhanced interventions are effective tools for improving patient outcomes. When integrating AI into clinical practice for cancer patients, priority should be given to the type of technology involved, ensuring its acceptability by enhancing perceived usefulness. AI technology should be adopted to relieve clinical nurses from documentation and low-complexity tasks, thereby addressing concerns about the loss of “humanistic care.” We recommend the formal integration of AI literacy frameworks, such as N.U.R.S.E.S., into nursing education and practice.
Trial Registration: PROSPERO (registration number: CRD420251040938).
The aim of this study was to explore the follow-up needs of patients with diabetic foot ulcers.
A qualitative descriptive study was conducted, guided by Engel's biopsychosocial model.
Purposive sampling was used to recruit 17 patients with diabetic foot ulcers of Wagner grade ≥ 1. These patients had received care in the Endocrinology and Metabolism Department or the Wound Clinic of a tertiary hospital in Xi'an, China. Guided by the biopsychosocial model, individual face-to-face semi-structured interviews were conducted between April and July 2024. Data were analysed using thematic analysis.
Nine themes were identified. In the biological domain, patients reported needs for disease care and lifestyle modification. In the psychological domain, they highlighted the importance of emotional coping support and building trust with healthcare providers. In the social domain, patients expressed needs for personalised follow-up, equitable access to healthcare, financial support, community assistance, and management of family caregiving role conflicts.
This study identified biological, psychological, and social follow-up needs among patients with diabetic foot ulcers, highlighting the importance of holistic follow-up to promote recovery and improve quality of life after discharge.
Nurses should assess and address the biological, psychological, and social follow-up needs of patients with diabetic foot ulcers after discharge, providing tailored care to promote ulcer healing and prevent recurrence.
This study was reported in accordance with the Consolidated Criteria for Reporting Qualitative Research checklist.
None.
To understand the current situation of nurses' compassion competence and analyse the characteristics and influencing factors of different categories of nurses' compassion competence based on latent profile analysis, to provide a theoretical basis for formulating targeted compassion training programmes.
A cross-sectional study.
From June to October 2023, 550 nurses from tertiary grade A hospitals in Shandong province were selected by convenience sampling and investigated by utilising a demographic characteristics questionnaire, the Compassion Competence Scale for the Nurses, the Mindful Attention Awareness Scale and the Maslach Burnout Inventory-Human Service Survey. Latent profile analysis was performed to explore the potential categories of nurses' compassion competence, and single-factor analysis and logistic regression analysis were used to explore the related influencing factors.
A total of 513 nurses were included. The compassion competence of nurses could be divided into four categories: the compassion competence deficient group (7.56%), the compassion competence low-imbalanced group (15.35%), the compassion competence high-balanced group (50.38%) and the compassion competence excellent group (26.70%). Department, years of working, humanistic care training experience, whether work is supported by colleagues and leaders, mindfulness and job burnout were the influencing factors of different potential categories (all p < 0.05).
There are four categories into which nurses' compassion competency can be categorised. Nursing managers and medical institutions can formulate precise training methods that enhance nurses' compassion competency based on the traits of various nurse categories in order to improve the quality of nursing service.
The results of this study help to understand the categories and heterogeneity of nurses' compassion competence and provide a basis for nursing managers and medical institutions to improve the compassion competence of different categories of nurses.
All participants were nurses who completed an electronic questionnaire related to this study.
FreshRSS 