Conectar
To conduct a comprehensive assessment of nursing interventions for patients with hypertension, diabetes, and dyslipidemia and analyse the components, delivery methods and outcomes of intervention programmes.
Scoping review.
Systematic searches were performed in four Chinese databases (WanFang, CNKI, Chinese Biomedical Literature Database, and the VIP database) and six English databases (CINAHL, MEDLINE, Web of Science, PubMed, Embase, The Cochrane Library) from their inception until October 2023. An updated search was performed on 6 August 2024.
Two reviewers independently retrieved full-text studies and conducted the initial screening of titles and abstracts, followed by full-text analysis and data extraction.
A total of 49 articles were included in this review. The nursing interventions consisted of various components, including fitness exercise, a balanced diet, mental health support, medication administration and others. The most commonly used delivery method was health education, with an increasing trend towards online interventions. However, the included studies did not provide details on delivery methods, including the team qualifications, subject areas or intervention duration and frequency. The nursing interventions achieved their research aims to varying degrees, as measured by subjective and/or objective indicators.
The nursing interventions for the three highs are diverse, including offline, online and combined methods, covering exercise, diet, and mental health. Future efforts can draw on these intervention components and methods and establish a nurse-led multidisciplinary team. The measurement of objective indicators, including blood lipids, should be taken seriously. Developing more diverse subjective measurement indicators can comprehensively assess patients' health.
This review offers clear guidance for the subsequent prevention and management of the three highs and consolidates evidence for healthcare professionals to devise targeted intervention strategies.
We followed Arksey's five-step framework and the PRISMA extension for scoping reviews (PRISMA-ScR).
No.
To identify the barriers and enablers in the implementation of evidence-based physical activity (PA) programmes for the improvement of health outcomes among pregnant women with gestational diabetes mellitus (GDM), and to develop strategies for implementing this evidence in clinical practice.
A convergent mixed-methods study was conducted, integrating a descriptive qualitative research design with a cross-sectional survey. In-depth interview was used to collect the views and cognitions about physical activity from medical staff, leaders and pregnant women. The qualitative data was analysed using directed content analysis, guided by the Ottaw Model of Research Use (OMRU). A self-designed questionnaire, which was based on the current best evidence for physical activity during pregnancy, was administered to gather data regarding nurse’ knowledge of physical activity (PA safety, managing blood glucose with PA, etc.), their management practice (timing of assessments, provision of information, etc.), as well as the knowledge levels of physical activity among pregnant women with GDM (principles of exercise, PA precautions, etc.).
A total of 12 medical staff members and 14 pregnant women were interviewed. Ten nurses and 102 pregnant women with GDM completed the questionnaire. We generated 12 subthemes organised within three themes of the OMRU from the data, including insufficient professional autonomy, positive attitudes towards evidence implementation, shortage of nursing staff, implementation climate, etc. The average knowledge score of physical activity among nurses and pregnant women was 5 (SD 2.36) points and 5.2 (SD 1.70) points, respectively. Ten strategies for overcoming barriers and amplifying enablers for the implementation of the physical activity improvement programme for pregnant women with GDM, under the guidance of the OMRU were constructed.
An accumulation of evidence, adopters and practice environment factors across the OMRU domains explains why physical activity improvement initiatives for pregnant women with GDM are hard to implement.
This study helps to recognise barriers and facilitators to physical activity improvement particularly at the evidence, potential adopter and practical environment level.
Healthcare workers (doctors, nurses, etc.) and pregnant women with GDM in a university hospital located in Sichuan Province.
To explore the factors influencing the intention of patients with coronary heart disease to undergo cardiac rehabilitation.
This is a qualitative content analysis study.
Semi structured, face-to-face interviews were conducted in the Department of Cardiology at a tertiary Grade-A hospital in Baoding, China, from January to March 2025. To ensure sample diversity, purposeful sampling was employed. The interview guide was based on the Reasoned Action Approach theory, literature review, and team deliberations. Data were analysed using deductive content analysis.
Twenty patients with coronary heart disease participated in the interviews (average age 57.9 years; 10 males, 10 females; 0–360 months disease course). Nine themes were identified from the three dimensions of RAA attitudes, perceived norms, and perceived behavioural control, reflecting patients' attitudes regarding cardiac rehabilitation (rehabilitation is beneficial, safety concerns, and non-essential treatment strategy); the impact of external factors on cardiac rehabilitation in patients (lack of professional recommendations, lack of awareness among friends and family); and barriers and facilitators in the implementation of cardiac rehabilitation (limited resources, insufficient self-efficacy, responsibility-driven, and individualised needs are challenging to fulfil).
To enhance the cardiac rehabilitation intentions of patients with coronary heart disease, healthcare providers should comprehensively assess influencing factors from the patient's perspective. Tailored interventions should focus on cognitive restructuring, support system enhancement, and patient empowerment.
This study highlights factors influencing patients' cardiac rehabilitation intentions. Nurses, equipped with relevant knowledge and skills, can provide systematic cardiac rehabilitation education during hospitalisation, thereby enhancing intentions and improving participation in cardiac rehabilitation.
This study adheres to the Consolidated Criteria for Reporting Qualitative Research (COREQ) checklist guidelines.
Patients with coronary heart disease participated in the interviews and provided essential insights for this study.
To investigate the physical activity levels of lung cancer survivors, analyse the influencing factors, and construct a predictive model for the physical activity levels of lung cancer survivors based on machine learning algorithms.
This was a cross-sectional study.
Convenience sampling was used to survey lung cancer survivors across 14 hospitals in eastern, central, and western China. Data on demographic, disease-related, health-related, physical, and psychosocial factors were also collected. Descriptive analyses were performed using SPSS 25.0, and predictors were identified through multiple logistic regression analyses. Four machine learning models—random forest, gradient boosting tree, support vector machine, and logistic regression—were developed and evaluated based on the Area Under the Curve of the Receiver Operating Characteristic (AUC-ROC), accuracy, precision, recall, and F1 score. The best model was used to create an online computational tool using Python 3.11 and Flask 3.0.3. This study was conducted and reported in accordance with the TRIPOD guidelines and checklist.
Among the 2231 participants, 670 (30%), 1185 (53.1%), and 376 (16.9%) exhibited low, moderate, and high physical activity levels, respectively. Multivariate logistic regression identified 15 independent influencing factors: residential location, geographical region, religious beliefs, histological type, treatment modality, regional lymph node stage, grip strength, 6-min walking distance, globulin, white blood cells, aspartate aminotransferase, blood urea, MDASI score, depression score, and SRAHP score. The random forest model performed best among the four algorithms, achieving AUC-ROC values of 0.86, 0.70, 0.72, and 0.67, respectively, and was used to develop an online predictive tool (URL: http://10.60.32.178:5000).
This study developed a machine learning model to predict physical activity levels in lung cancer survivors, with the random forest model demonstrating the highest accuracy and clinical utility. This tool enables the early identification of low-activity survivors, facilitating timely, personalised rehabilitation and health management.
The development of a predictive model for physical activity levels in lung cancer survivors can help clinical medical staff identify survivors with relatively low physical activity levels as early as possible. Thus, personalised rehabilitation plans can be formulated to optimise quality of life during their survival period.
Physical activity has been used as a nonpharmacological intervention in cancer patient rehabilitation plans. However, a review of past studies has shown that lung cancer survivors generally have low physical activity levels. In this study, we identified the key factors influencing physical activity among lung cancer survivors through a literature review. We constructed a prediction model for their physical activity levels using machine learning algorithms. Clinical medical staff can use this model to identify patients with low physical activity levels early and to develop personalised intervention plans to improve their quality of life during survival.
The study adhered to the relevant EQUATOR reporting guidelines, the TRIPOD Checklist for Prediction Model Development and Validation.
During the data collection phase, participants were recruited to complete the questionnaires.
FreshRSS 