Effects of interventions for enhancing resilience in cancer patients: A systematic review and network meta-analysis.

BACKGROUND: Various interventions appear to enhance cancer patients' resilience. However, the best intervention options are still unknown. This systematic review and network meta-analysis aimed to examine the impact of different interventions on resilience and identify the most effective interventions. METHODS: Nine major English and Chinese databases were systematically retrieved for randomized controlled trials (RCTs) published from inception to 13 November 2023. The outcome was resilience. The analysis was conducted using Software Review Manager 5.4, R 4.2.3, and STATA 14.0. RESULTS: The network meta-analysis included 32 RCTs and evaluated 12 interventions. Regarding effectiveness, compared to routine care, the relative effect sizes of attention and interpretation therapy, cyclic adjustment training, cognitive intervention, expressive therapy, positive psychological intervention, social support intervention, and work-environment therapy had statistically significant enhancing resilience, with the SMD (95%CI) of 1.42 (0.75, 2.07), 1.97 (0.76, 3.18), 1.26 (0.76, 1.77), 0.93 (0.08, 1.78), 1.02 (0.55, 1.50), 1.01 (0.48, 1.56), 1.65 (0.94, 2.37), respectively. Considering the rank probability, statistical power, and efficacy, the most effective interventions for improving resilience were attention and interpretation therapy, cognitive intervention, and positive psychological intervention. With the limited quantity of RCTs, the effectiveness of cyclic adjustment training and work-environment therapy still needs to be explored. CONCLUSIONS: Attention and interpretation therapy was the first best choice for boosting resilience out of the 12 interventions. Cognitive intervention and positive psychological intervention were also better choices for improving cancer patients' resilience. Due to the low quality and quantity of included RCTs, the need for multi-center, higher-quality trials with larger samples should be carried out. PROSPERO ID: CRD42023434223. The study did not receive funding support.

A systematic review and meta-analysis of interventions to reduce perceived stress in breast cancer patients.

PURPOSE: Breast cancer (BC) patients commonly face stress that causes severe psychological and physiological problems. The main objective of the review was to confirm the effect of interventions on breast cancer patients' perceived stress, and the secondary objective was to explore the impact of interventions on anxiety, depression, and inflammatory markers. METHODS: A systematic and comprehensive search for randomized controlled trials (RCTs) that reported interventions' effects on perceived stress in breast cancer patients was performed in nine databases. RESULTS: Twenty-four RCTs, including 1887 participants, met the inclusion criteria, summarizing six categories for the intervention group: mindfulness and yoga, exercise, cognitive-behavioral stress management, self-regulation, relaxation training, and acupuncture. Compared with usual care or other types of care, mindfulness and yoga had excellent effects against perceived stress, anxiety, and depression; self-regulation could reduce perceived stress and anxiety; exercise could reduce perceived stress; acupuncture could reduce the level of depression; mindfulness could improve the TNF-α level, and yoga can reduce the level of salivary cortisol and DNA damage. CONCLUSION: This systematic review indicated that nondrug interventions, such as mindfulness and yoga, effectively reduce perceived stress, anxiety, and depression. Rigorous studies with large sample sizes are needed to address the limitations of small sample sizes and shortcomings in methodology in this area.

Global output on artificial intelligence in the field of nursing: A bibliometric analysis and science mapping.

PURPOSE: To analyze the AI research in the field of nursing, to explore the current situation, hot topics, and prospects of AI research in the field of nursing, and to provide a reference for researchers to carry out related studies. METHODS: We used the VOSviewer 1.6.17, SciMAT, and CiteSpace 5.8.R3 to generate visual cooperation network maps for the country, organizations, authors, citations, and keywords and perform burst detection, theme evolution, and so forth. FINDINGS: A total of 9318 articles were obtained from the Web of Science Core Collection database. Four hundred and thirty-one AI research related to the field of nursing was published by 855 institutions from 54 countries. CIN-Computers Informatics Nursing was the top productive journal. The United States was the dominant country. The transnational cooperation between authors from developed countries was closer than that between authors from developing countries. The main hot topics included nurse rostering, nursing diagnosis, nursing decision support, disease risk factor prediction, nursing big data management, expert system, support vector machine, decision tree, deep learning, natural language processing, and nursing education. Machine learning represented one of the cutting-edge and most applicable branches of artificial intelligence in the field of nursing, and deep learning was the hottest technology among many machine learning methods in recent years. One of the most cited papers was published by Burke in 2004 and cited 500 times, which critically evaluated AI methods to deal with nurse scheduling problems. CONCLUSIONS: Although AI has been paid more and more attention to the field of nursing, there is still a lack of high-yielding authors who have been engaged in this field for a long time. Most of the high contribution authors and institutions came from developed countries; therefore, more transnational and multi-disciplinary cooperation is needed to promote the development of AI in the nursing field. This bibliometric analysis not only provided a comprehensive overview to help researchers to understand the important articles, journals, potential collaborators, and institutions in this field but also analyzed the history, hot spots, and future trends of the research topic to provide inspiration for researchers to choose research directions.