RESUMO
Objectives: This study aimed to determine patients' perceived benefits of a WeChat applet for self-management of patients with neurogenic bladder (NGB) and identify the key factors hindering their adoption. Methods: In the qualitative study, 19 NGB patients were invited for semi-structured interviews. They were hospitalized in the rehabilitation departments of two tertiary hospitals in Shenzhen and tried out the self-management applet for two weeks. Data were analyzed using the content analysis method. Results: The results indicated that the WeChat applet of self-management was helpful and embraced by the NGB patients. Three perceived benefits were identified 1) being accessible, flexible, and intuitive to users, 2) driving bladder self-management, and 3) directing the way for care partners and family members. Challenges hindering the adoption of the applet included 1) negative attitudes of patients towards bladder self-management and patient characteristics, 2) concerns about the risks of mHealth, and 3) the necessity of applet upgrading. Conclusion: This study showed feasibility of the WeChat applet for self-management among NGB patients to meet their needs for access to information during hospitalization and after discharge. The study also identified facilitators and barriers to patient use, providing valuable information for healthcare providers to implement mHealth interventions to promote self-management among NGB patients.
RESUMO
Background: This study was performed to construct and validate an early risk warning model of urinary tract infection in patients with neurogenic lower urinary tract dysfunction (NLUTD). Methods: Eligible patients with NLUTD admitted to Shenzhen Longcheng hospital from January 2017 to June 2021 were recruited for model construction, internal validation and external validation. The first time point of data collection was within half a month of patients first diagnosed with NLUTD. The second time point was at the 6-month follow-up. The early warning model was constructed by logistic regression. The model prediction effects were validated using the area under the Receiver Operating Characteristic curve, the Boostrap experiment and the calibration plot of the combined data. The model was externally validated using sensitivity, specificity and accuracy. Results: Six predictors were identified in the model, namely patients ≥65 years old (OR = 2.478, 95%CI [1.215- 5.050]), female (OR = 2.552, 95%CI [1.286-5.065]), diabetes (OR = 2.364, 95%CI) [1.182-4.731]), combined with urinary calculi (OR = 2.948, 95%CI [1.387-6.265]), indwelling catheterization (OR = 1.988, 95%CI [1.003 -3.940]) and bladder behavior training intervention time ≥2 weeks (OR = 2.489, 95%CI [1.233-5.022]); and the early warning model formula was Y = 0.907 × age+ 0.937 × sex + 0.860 × diabetes +1.081 × combined with urinary calculi+ 0.687 × indwelling catheterization+ 0.912 × bladder behavior training intervention time-2.570. The results show that the area under the ROC curve is 0.832, which is close to that of 1,000 Bootstrap internal validation (0.828). The calibration plot shows that the early warning model has good discrimination ability and consistency. The external validation shows the sensitivity is 62.5%, the specificity is 100%, and the accuracy is 90%. Conclusion: The early warning model for urinary tract infection in patients with NLUTD is suitable for clinical practice, which can provide targeted guidance for the evaluation of urinary tract infection in patients with NLUTD.
