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To explore the network characteristics of symptom clusters in people with type 2 diabetes mellitus through network analysis, identify the core and bridging symptoms within the symptom network, and provide a foundation for targeted interventions and symptom management in people with T2DM.
A cross-sectional survey.
A total of 360 people with T2DM who were hospitalised in the endocrinology departments of two hospitals with Grade A in Daqing City between August 2024 and February 2025 were selected using a convenience sampling method. The symptoms of people with T2DM were measured using the Chinese version of the Diabetes Symptom Checklist-Revised (DSC-R). Symptom clusters were identified through factor analysis, and network analysis was used to identify core and bridging symptoms. This research adhered to the STROBE guidelines.
Six symptom clusters were obtained from factor analysis, which were psychological-behavioural symptom cluster, ophthalmological-neuropathy symptom cluster, cardiovascular symptom cluster, metabolic symptom cluster, body symptom cluster and nephrotic symptom cluster. Symptom network analysis revealed that ‘Deteriorating vision’ exhibited the highest strength centrality and expected influence. The top three symptoms with the highest bridge strength and bridge expected influence were ‘Aching calves when walking’, ‘Queer feeling in the legs or feet’ and ‘Sleepiness or drowsiness’.
People with T2DM commonly exhibit a range of symptoms. ‘Deteriorating vision’ is the most core symptom in people with T2DM. ‘Aching calves when walking’, ‘Queer feeling in the legs or feet’ and ‘Sleepiness or drowsiness’ are identified as the bridging symptoms in the network analysis. Healthcare professionals can design targeted interventions based on symptom clusters, core symptoms and bridging symptoms, thereby improving the efficiency of symptom management and optimising outcomes for people with T2DM.
No patient or public contribution.
To compare and rank the efficacy of different non-pharmacological interventions on anxiety, depression, sleep disorders, and the quality of life in liver transplantation patients.
In recent years, numerous non-pharmacological interventions have been developed to address anxiety, depression, sleep disorders, and the quality of life in liver transplantation patients. However, it remains unclear which non-pharmacological intervention serves as the most effective and preferred approach.
A systematic review and network meta-analysis in accordance with the PRISMA guidelines.
Relevant randomised controlled trials were extracted from eight electronic databases. A network meta-analysis was then performed to evaluate the relative efficacy of the non-pharmacological interventions for liver transplantation patients. The quality of the data was assessed using the Cochrane Risk of Bias tool. We registered this study in PROSPERO, number CRD42023450346.
A total of 25 randomised controlled trials were included. Spouse support education combined with mindfulness training, individualised psychological intervention, and cognitive behavioural therapy were found to be significantly effective for both anxiety and depression. The top three interventions against anxiety were spouse support education combined with mindfulness training, individualised psychological intervention, and exercise rehabilitation training. Meanwhile, individualised psychological intervention, spouse support education combined with mindfulness training, and cognitive behavioural therapy were the top-ranked three interventions for reducing depression. Sleep hygiene education was the most effective to improve sleep disorders. Continuous care based on a mobile medical platform emerged as the most effective intervention in improving the quality of life.
Several non-pharmacological interventions appeared to be effective in treating anxiety, depression, sleep disorders, and improving the quality of life among liver transplantation patients. More high-quality clinical trials should be incorporated in the future to investigate the reliability of existing findings.
Healthcare professionals should be encouraged to apply these promising non-pharmacological interventions during clinical care.
This study did not directly involve patients or public contributions to the manuscript.
To develop a predictive model for high-burnout of nurses.
A cross-sectional study.
This study was conducted using an online survey. Data were collected by the Chinese Maslach Burnout Inventory-General Survey (CMBI-GS) and self-administered questionnaires that included demographic, behavioural, health-related, and occupational variables. Participants were randomly divided into a development set and a validation set. In the development set, multivariate logistic regression analysis was conducted to identify factors associated with high-burnout risk, and a nomogram was constructed based on significant contributing factors. The discrimination, calibration, and clinical practicability of the nomogram were evaluated in both the development and validation sets using receiver operating characteristic (ROC) curve analysis, Hosmer–Lemeshow test, and decision curve analysis, respectively. Data analysis was performed using Stata 16.0 software.
A total of 2750 nurses from 23 provinces of mainland China responded, with 1925 participants (70%) in a development set and 825 participants (30%) in a validation set. Workplace violence, shift work, working time per week, depression, stress, self-reported health, and drinking were significant contributors to high-burnout risk and a nomogram was developed using these factors. The ROC curve analysis demonstrated that the area under the curve of the model was 0.808 in the development set and 0.790 in the validation set. The nomogram demonstrated a high net benefit in the clinical decision curve in both sets.
This study has developed and validated a predictive nomogram for identifying high-burnout in nurses.
The nomogram conducted by our study will assist nursing managers in identifying at-high-risk nurses and understanding related factors, helping them implement interventions early and purposefully.
The study adhered to the relevant EQUATOR reporting guidelines: TRIPOD Checklist for Prediction Model Development and Validation.
No patient or public contribution.
FreshRSS 