The relationship between alarm fatigue and burnout among critical care nurses: A cross-sectional study.

BACKGROUND: Alarm fatigue has significant negative impacts on nurses and patient safety. However, the relationship between alarm fatigue and burnout is still unclear. AIMS: This study aimed to explore the relationship between alarm fatigue and burnout among critical care nurses. STUDY DESIGN: A descriptive-analytical cross-sectional study design was used. Data were collected from five hospitals in mainland China between January 2022 and March 2022. A general information questionnaire, the Chinese version of the Intensive Care Unit Nurse Alarm Fatigue Questionnaire, and the Chinese version of the Maslach Burnout Inventory were used. RESULTS: A total of 236 critical care nurses were enrolled in this study. The mean score of alarm fatigue among critical care nurses was 21.11 ± 6.83. The results showed that critical care nurses experienced moderate alarm fatigue levels, and most nurses had moderate to high levels of burnout. The multiple linear regression analyses showed that alarm fatigue was independently associated with emotional exhaustion, depersonalization dimensions, and reduced personal accomplishment dimension. CONCLUSIONS: Alarm fatigue was associated with burnout among critical care nurses. Reducing critical care nurses' alarm fatigue may help to alleviate burnout. RELEVANCE TO CLINICAL PRACTICE: Managers should provide comprehensive training for nurses and promote the application of artificial intelligence technology in alarm management to reduce alarm fatigue and improve burnout among critical care nurses.

Development and validation of two aspiration prediction models in patients receiving nasogastric feeding.

AIM: To develop and validate two aspiration prediction models in patients receiving nasogastric feeding. BACKGROUND: Aspiration is one of the most serious complications of nasogastric feeding. However, there is a lack of aspiration prediction models for nasogastric feeding. METHODS: A total of 515 patients receiving nasogastric feeding were randomly selected for this unmatched case-control study, with 103 patients in the case group and 412 patients in the control group. Logistic regression was used to develop nomogram and Classification And Regression Tree (CART) models. The performances of the models were internally validated using 1,000 bootstrapped samples. RESULTS: The predictive accuracy of the CART model (94.5%) was higher than that of the nomogram model (89.1%). The area under the receiver operating characteristic curve of the CART model (0.96) was slightly higher than that of the nomogram model (0.93). CONCLUSIONS: The intubation depth, number of comorbidities, aspiration history, indwelling days, food type and the use of sedative-hypnotics may be used to identify aspiration risk. IMPLICATIONS FOR NURSING MANAGEMENT: Two aspiration prediction models are provided for nurses to evaluate aspiration risk and increase the quality of nursing management.

RETRACTED: Development and evaluation of a second victim training course for nursing managers.

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/policies/article-withdrawal). This article has been retracted at the request of the Editor-in-Chief. The Journal was alerted to legal and ethical concerns regarding the publication of this paper. Neither the use of the copyrighted Jefferson Scale of Empathy© nor the adaptation was authorized by the copyright holders at Thomas Jefferson University. The Editor-in-Chief has therefore determined that the paper should be retracted. The corresponding author acknowledged the notice reporting the outcome of this retraction.

Effectiveness of Mobile Applications on Medication Adherence in Adults with Chronic Diseases: A Systematic Review and Meta-Analysis.

BACKGROUND: Medication adherence is frequently suboptimal in adults with chronic diseases, resulting in negative consequences. Traditional interventions to improve adherence are complex and not widely effective. Mobile applications may be a scalable means to support medication adherence. OBJECTIVE: To investigate the effect of mobile apps on medication adherence in adults with chronic diseases. METHODS: MEDLINE, EMBASE, CINAHL Plus, Cochrane Central Register of Controlled Trials, and Web of Science were searched for randomized controlled trials evaluating the effectiveness of any mobile application (app) intervention directed at patients with chronic disease to improve medication adherence in comparison with usual care. A random-effects model was used to pool the outcome data. Risk of bias and quality of study were assessed per Cochrane guidelines. RESULTS: Fourteen studies were included in this systematic review involving 1,785 participants, 940 of whom were randomized to a mobile app intervention group and 845 to the usual care group. The meta-analysis showed that the use of mobile apps was associated with a significant improvement in patient adherence to medication (Cohen's d = 0.40, 95% CI = 0.27-0.52; P < 0.001), with a low quality of GRADE evidence. There was no evidence of publication bias (Egger's test; P = 0.81) or substantial heterogeneity (I2 = 29%). In the sensitivity analysis, our findings remained robust to change in inclusion criteria based on study quality (Cohen's d = 0.43, 95% CI = 0.33-0.54; P < 0.001). The included apps incorporated 9 features, sorted from high to low based on relative weights (RW): documentation (RW = 0.254), medication reminder (RW = 0.204), data sharing (RW = 0.148), feedback message (RW = 0.104), clinical decision support (RW = 0.097), education (RW = 0.081), customization (RW = 0.049), data statistics (RW = 0.041), and appointment reminder (RW = 0.041). In the subgroup analysis, the effect was not sensitive to study characteristics or app features (0.37 ≤ P ≤ 0.95). App acceptability was reported by participants in the intervention group in 8 studies: 144 of 156 participants (91.7%) were satisfied with all aspects of the apps. CONCLUSIONS: Compared with conventional care, mobile apps are effective interventions to help improve medication adherence in adults with chronic diseases. Although promising, these results should be interpreted with caution given the low level of evidence and short intervention duration. Future research will not only need to identify ideal app features and the costs to providers but also need to improve the apps to make them user friendly, secure, and effective based on patient-centered theory. DISCLOSURES: Funding for this study was provided by Chongqing Science and Technology Bureau (No. cstc2017shmsA130115). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors have no conflicts of interest to disclose.