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Physical frailty (PF) is highly prevalent and associated with undesirable outcomes in stroke survivors aged 65 years or older. However, the long-term trajectories of PF are understudied in those older stroke survivors.
To identify PF trajectories and relative predictors associated with the PF trajectories in older stroke survivors.
This is a secondary analysis of a population-based cohort study in the United States.
Six hundred and sixty-three older stroke survivors from the National Health and Ageing Trends Study from 2015 to 2021 were included. PF was operationally assessed based on the Fried Frailty Phenotype. Trajectories were identified by group-based trajectory modelling. The associations between sociodemographic characteristics, clinical factors, symptoms, cognitive factors and PF trajectories were examined using the design-based logistic regression method.
Most older stroke survivors were 75 and older (63.32%), female (53.99%), white (80.54%) and partnered (50.64%). Two PF trajectory groups were identified (Group 1: low risk, robust; 49.47%; Group 2: high risk, deteriorating; 50.53%). Individuals were at a higher risk to be assigned to Group 2 if they were 75–84 years (adjusted odds ratio [aOR]: 2.16, 95% CI: 1.23–3.80) or 85+ years (aOR: 2.77, 95% CI: 1.52–5.04), had fair self-reported health (aOR: 2.78, 95% CI: 1.53–5.07) or poor self-reported health (aOR: 3.37, 95% CI: 1.51–7.52), had comorbidities (aOR: 8.44, 95% CI: 1.31–54.42), had breathing problems (aOR: 2.18, 95% CI: 1.18–4.02) and had balance problems (aOR: 1.70, 95% CI: 1.06–2.73).
PF trajectories in older stroke survivors were heterogeneous and were associated with age, self-rated health status, comorbidities, breathing problems and balance problems.
Early, routine, dynamic screening for stroke-related physical frailty (PF) and relative predictors might be beneficial for identifying the most vulnerable individuals. Our findings might help develop strategies to manage PF progression.
The reporting followed the STROBE guideline.
To identify and appraise the quality of evidence of transitional care interventions on quality of life in lung cancer patients.
Quality of life is a strong predictor of survival. The transition from hospital to home is a high-risk period for patients' readmission and death, which seriously affect their quality of life.
Systematic review and meta-analysis.
The PubMed, Embase, Cochrane Library, Web of Science and CINAHL databases were searched from inception to 22 October 2022. The primary outcome was quality of life. Statistical analysis was conducted using Review Manager 5.4, results were expressed as standard mean difference (SMD) with a 95% confidence interval (CI). The risk of bias of the included studies was assessed using the Cochrane risk of bias assessment tool. This study was complied with PRISMA guidelines and previously registered in PROSPERO (CRD42023429464).
Fourteen randomized controlled trials were included consisting of a total of 1700 participants, and 12 studies were included in the meta-analysis. It was found that transitional care interventions significantly improved quality of life (SMD = 0.21, 95% CI: 0.02 to 0.40, p = .03) and helped reduce symptoms (SMD = −0.65, 95% CI: −1.13 to −0.18, p = .007) in lung cancer patients, but did not significantly reduce anxiety and depression, and the effect on self-efficacy was unclear.
This study shows that transitional care interventions can improve quality of life and reduce symptoms in patients, and that primarily educational interventions based on symptom management theory appeared to be more effective. But, there was no statistically significant effect on anxiety and depression.
This study provides references for the application of transitional care interventions in the field of lung cancer care, and encourages nurses and physicians to apply transitional care plans to facilitate patients' safe transition from hospital to home.
No Patient or Public Contribution.
To explore women's and health professionals' views on the development of a midwifery-led mHealth app intervention in antenatal care and their demands for app functionality.
Descriptive qualitative research was utilized.
In total, 15 pregnant or postpartum women were interviewed via in-depth interviews and 10 health professionals including obstetricians, midwives and obstetric nurses were invited to participate in a focus group discussion (FGD). All interviews and the FGD were analysed using qualitative content analysis.
Four key themes emerged from the data, including (1) limitations of current maternity care services; (2) potential benefits for mHealth app-based midwifery care; (3) possible challenges for providing midwifery care through mHealth apps and (4) suggestions and needs for developing a midwifery-led mHealth app. Participants agreed on the potential need of developing a midwifery-led mHealth app in antenatal care to increase access to midwifery care services and to meet women's diverse needs. Participants preferred to develop professional, reliable, full-featured and interactive mobile applications. The main functions of midwifery-led mHealth apps included personalized assessment and health education, self-monitoring and feedback, data sharing and interactive functions. Women mentioned that online communication and consultation with midwives could help them receive continuous support outside facilities. Health professionals expressed it would be of great convenience and timeliness to send personalized messages to women and to inform them of healthy lifestyles during pregnancy. The challenges included a shortage of human resources, medico-legal risks associated with mHealth and data security risks.
This study explores the individual views and functional needs of target users and healthcare providers for developing a midwifery-led mHealth app in antenatal care, which will serve as a reference for future application development.
Our study has important and practical implications for guiding the development of future midwifery-led mHealth app interventions.
No patient or public contribution.
This study aims to propose a self-management clusters classification method to determine the self-management ability of elderly patients with mild cognitive impairment (MCI) associated with diabetes mellitus (DM).
MCI associated with DM is a common chronic disease in old adults. Self-management affects the disease progression of patients to a large extent. However, the comorbidity and patients' self-management ability are heterogeneous.
A cross-sectional study based on cluster analysis is designed in this paper.
The study included 235 participants. The diabetes self-management scale is used to evaluate the self-management ability of patients. SPSS 21.0 was used to analyse the data, including descriptive statistics, agglomerative hierarchical clustering with Ward's method before k-means clustering, k-means clustering analysis, analysis of variance and chi-square test.
Three clusters of self-management styles were classified as follows: Disease neglect type, life oriented type and medical dependence type. Among all participants, the percentages of the three clusters above are 9.78%, 32.77% and 57.45%, respectively. The difference between the six dimensions of each cluster is statistically significant.
This study classified three groups of self-management styles, and each group has its own self-management characteristics. The characteristics of the three clusters may help to provide personalized self-management strategies and delay the disease progression of MCI associated with DM patients.
Typological methods can be used to discover the characteristics of patient clusters and provide personalized care to improve the efficiency of patient self-management to delay the progress of the disease.
In our study, we invited patients and members of the public to participate in the research survey and conducted data collection.
To generate pre-hospital symptom networks, explore core, bridge and sentinel symptoms, identify pre-hospital symptom clusters and analyse relationship between influencing factors and symptom clusters in decompensated cirrhosis patients.
A cross-sectional study design using the Strengthening the Reporting of Observational Studies in Epidemiology checklist.
Demographical, physiological, psychological and sociological characteristics and the pre-hospital symptoms of 292 decompensated cirrhotic patients were collected from October 2021 to March 2023 in China. Frequencies, percentages, means, standard deviations, independent samples t-tests, one-way analysis of variance, exploratory factor analysis, multiple stepwise regression analysis and network analysis were used for data analysis.
‘I don't look like myself’ and itching were core and bridge symptoms, while bloating and lack of energy were sentinel symptoms in decompensated cirrhotic patients. Monthly family income, anxiety, depression, social support and disease duration influenced the neuropsychological symptom cluster, with worrying as the strongest predictor symptom. Influential factors for cirrhosis-specific symptom cluster included Child-Pugh class, monthly family income, disease duration, anxiety and depression, with itching being the strongest predictor symptom. Monthly family income, disease duration and depression were influential factors for gastrointestinal symptom cluster, with loss of appetite as the strongest predictor symptom.
Neuropsychological, cirrhosis-specific and gastrointestinal symptom clusters were formed in decompensated cirrhotic patients. Through network analysis, direct connections between symptoms, symptom clusters and their influencing factors were revealed, thereby offering clinicians a foundation for effectively managing patients' pre-hospital symptoms.
Decompensated cirrhosis patients commonly have multiple symptoms, while the management of pre-hospital symptoms is often suboptimal. This study identified neuropsychological, cirrhosis-specific, gastrointestinal symptom clusters and recognized core, bridge and sentinel symptoms in these patients. It also revealed the most prominent symptoms within each cluster. This provides insight into the hierarchy of symptoms, improving symptom management in decompensated cirrhosis.
There was no patient or public involvement.
The first step in preventing pressure injuries (PIs), which represent a significant burden on intensive care unit (ICU) patients and the health care system, is to assess the risk for developing PIs. A valid risk assessment scale is essential to evaluate the risk and avoid PIs.
To compare the predictive validity of the Braden scale and Waterlow scale in ICUs.
A multicentre, prospective and cross-sectional study.
We conducted this study among 6416 patients admitted to ICUs in Gansu province of China from April 2021 to October 2022. The incidence and characteristics of PIs were collected. The risk assessment of PIs was determined using the Braden and Waterlow scale. The sensitivity, specificity, positive and negative predictive values, and the area under the receiver operating characteristic curve of the two scales were compared.
Out of 5903 patients, 72 (1.2%) developed PIs. The sensitivity, specificity, positive and negative predictive, and the area under the curve of the Braden scale were 77.8%, 50.9%, 0.014 and 0.996, and 0.689, respectively. These values for the Waterlow scale were 54.2%, 71.1%, 0.017, 0.994 and 0.651.
Both scales could be used for risk assessment of PIs in ICU patients. However, the accuracy of visual inspection for assessment of skin colour, nursing preventive measures for patients and scales inter-rater inconsistency may limited the predictive validity statistics.
Both scales could be used for PIs risk assessment. The low specificity of the Braden scale and low sensitivity of the Waterlow scale remind medical staff to use them in combination with clinical judgement and other objective indicators.
This study was designed to enhance the management of PIs. Patients and the general public were not involved in the study design, analysis, and interpretation of the data or manuscript preparation.
The integration of generative artificial intelligence (AI) into academic research writing has revolutionized the field, offering powerful tools like ChatGPT and Bard to aid researchers in content generation and idea enhancement. We explore the current state of transparency regarding generative AI use in nursing academic research journals, emphasizing the need for explicitly declaring the use of generative AI by authors in the manuscript. Out of 125 nursing studies journals, 37.6% required explicit statements about generative AI use in their authors' guidelines. No significant differences in impact factors or journal categories were found between journals with and without such requirement. A similar evaluation of medicine, general and internal journals showed a lower percentage (14.5%) including the information about generative AI usage. Declaring generative AI tool usage is crucial for maintaining the transparency and credibility in academic writing. Additionally, extending the requirement for AI usage declarations to journal reviewers can enhance the quality of peer review and combat predatory journals in the academic publishing landscape. Our study highlights the need for active participation from nursing researchers in discussions surrounding standardization of generative AI declaration in academic research writing.
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