Background: Serious games have emerged as an innovative educational strategy with the potential to significantly enhance the quality and effectiveness of cardiopulmonary resuscitation (CPR) training. Despite their promise, there remains a degree of controversy when comparing the advantages of serious games with traditional CPR training methods. This study seeks to provide a comprehensive assessment of the impact of serious games on CPR training and education by systematically analyzing the results of previous research. Objective: This study aimed to assess the effect of serious games on CPR training and education by summarizing and pooling the results of previous studies. Methods: We conducted a thorough and systematic search across 9 prominent web-based databases, encompassing the period from the inception of these databases until April 1, 2023. The databases included in our search were PubMed, Cochrane Library, Wiley Online Library, EBSCO (PsycInfo), SpringerLink, Chinese Biology Medicine Disc, Vip Journal Integration Platform, Wanfang Database, and Chinese National Knowledge Infrastructure. The studies selected adhered to the following criteria: (1) being a randomized controlled trial comparing serious games and traditional methods for CPR training; (2) having participants aged 12 years or older in CPR; (3) having an experimental group using serious games and a control group using nongame methods for CPR instruction; and (4) having outcomes including theoretical and skill assessments, compression depth, and rate. The Cochrane risk of bias assessment tool was used to evaluate the risk of bias. Data analysis was performed using RevMan (version 5.3; Cochrane Training), and mean differences (MDs) and standardized mean differences (SMDs) with 95% CIs were used to calculate continuous variables. Results: A total of 9 articles were included, involving 791 study participants, of whom 395 in the experimental group taught CPR training using serious games and 396 in the control group taught CPR training using traditional methods. The results of our meta-analysis indicate that the use of serious games in CPR training yields outcomes that are comparable in effectiveness to traditional training methods across several key areas. Specifically, serious games demonstrated equivalence to traditional formats in theory assessment (SMD –0.22, 95% CI – 0.96 to 0.51; P=.55), skill assessment (SMD –0.49, 95% CI –1.52 to 0.55; P=.36), compression depth (MD –3.17, 95% CI –0.18 to 6.53; P=.06), and compression rate (MD –0.20, 95% CI –7.29 to 6.89; P=.96). Conclusions: In summary, serious games offer a viable and effective CPR education approach, yielding results comparable to traditional formats. This modality is a valuable addition to CPR training methodologies. However, caution is warranted in interpreting these findings due to limited controlled trials, small sample sizes, and low-quality meta-analyzed evidence.

Introduction: Rescuing individuals at sea is a pressing global public health issue, garnering substantial attention from emergency medicine researchers with a focus on improving prevention and control strategies. This study aims to develop a Dynamic Bayesian Networks (DBN) model utilizing maritime emergency incident data and compare its forecasting accuracy to Auto-regressive Integrated Moving Average (ARIMA) and Seasonal Auto-regressive Integrated Moving Average (SARIMA) models.Methods: In this research, we analyzed the count of cases managed by five hospitals in Hainan Province from January 2016 to December 2020 in the context of maritime emergency care. We employed diverse approaches to construct and calibrate ARIMA, SARIMA, and DBN models. These models were subsequently utilized to forecast the number of emergency responders from January 2021 to December 2021. The study indicated that the ARIMA, SARIMA, and DBN models effectively modeled and forecasted Maritime Emergency Medical Service (EMS) patient data, accounting for seasonal variations. The predictive accuracy was evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R²) as performance metrics.Results: In this study, the ARIMA, SARIMA, and DBN models reported RMSE of 5.75, 4.43, and 5.45; MAE of 4.13, 2.81, and 3.85; and R² values of 0.21, 0.54, and 0.44, respectively. MAE and RMSE assess the level of difference between the actual and predicted values. A smaller value indicates a more accurate model prediction. R² can compare the performance of models across different aspects, with a range of values from 0 to 1. A value closer to 1 signifies better model quality. As errors increase, R² moves further from the maximum value. The SARIMA model outperformed the others, demonstrating the lowest RMSE and MAE, alongside the highest R², during both modeling and forecasting. Analysis of predicted values and fitting plots reveals that, in most instances, SARIMA’s predictions closely align with the actual number of rescues. Thus, SARIMA is superior in both fitting and forecasting, followed by the DBN model, with ARIMA showing the least accurate predictions.Discussion: While the DBN model adeptly captures variable correlations, the SARIMA model excels in forecasting maritime emergency cases. By comparing these models, we glean valuable insights into maritime emergency trends, facilitating the development of effective prevention and control strategies.

Background: Patients with diabetes mellitus often suffer from diabetes distress. Social support and certain psychological factors potentially influence diabetes distress, but studies exploring the mechanisms underlying these relationships are scarce.Objectives: To reveal the associations between social support, diabetes stigma, diabetes self-efficacy, and diabetes distress among patients with type 2 diabetes and the underlying mechanisms linking these variables.Design and methods: A multicenter cross-sectional study was adopted and a sample of 431 patients with type 2 diabetes was investigated. Social support, diabetes stigma, diabetes self-efficacy, and diabetes distress were surveyed with the Perceived Social Support Scale, Type 2 Diabetes Stigma Assessment Scale, Self-Efficacy for Diabetes Scale, and Diabetes Distress Scale, respectively. The hypothesized model was verified using structural equation modeling.Results: Social support and diabetes stigma had direct associations with diabetes distress. Diabetes stigma mediated the association between social support and diabetes distress, and the association between diabetes self-efficacy and diabetes distress. Diabetes stigma and self-efficacy exerted a chain mediation effect on the association between social support and diabetes distress.Conclusion: Social support and diabetes stigma were significant predictors of diabetes distress. Diabetes stigma and self-efficacy play essential mediating roles in relieving diabetes distress. This can provide guidance for the development of evidence- and theory-based interventions. Culturally sensitive interventions that aim to provide ongoing social support, decrease diabetes stigma, and enhance self-efficacy have the potential to relieve diabetes distress.

Background: The prevalence of suicidal ideation (SI) among stroke survivors has been the subject of several studies, but systematic review data regarding the prevalence of SI among stroke patients are still scarce. Objectives: To estimate the pooled prevalence of SI among stroke survivors. Methods: A comprehensive search strategy was established across the following electronic databases; Web of Science, PubMed, the Cochrane Library, Chinese Wanfang database and Chinese National Knowledge Infrastructure (CNKI). The 95% confidence interval (CI) given in each identified study was used to assess the outcomes. Cochran’s Q test and I² statistic were used to evaluate heterogeneity. The quality of the final evidence was evaluated using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. We calculated the pooled prevalence of SI among stroke survivors before subgroup analysis and meta-regression was used to find the partial source of heterogeneity. Results: Twenty-one studies with a total of 17,189 participants were included in this study and the pooled prevalence of SI among stroke survivors was 12.25% (95%CI: 9.91–15.07%). Substantial heterogeneity was detected in the estimation of prevalence. The evidence was graded as very low for the overall outcome and the single item subgroup, while it was graded as low for the stable scale subgroup. Conclusions: This meta-analysis suggests a high prevalence of SI among stroke survivors. Routine screening of SI after stroke is needed to plan interventions aimed at reducing suicide risk.

Objectives. Prediction models for the return of spontaneous circulation (ROSC) in patients with cardiac arrest play an important role in helping physicians evaluate the survival probability and providing medical decision-making reference. Although relevant models have been developed, their methodological rigor and model applicability are still unclear. Therefore, this study aims to summarize the evidence for ROSC prediction models and provide a reference for the development, validation, and application of ROSC prediction models. Methods. PubMed, Cochrane Library, Embase, Elsevier, Web of Science, SpringerLink, Ovid, CNKI, Wanfang, and SinoMed were systematically searched for studies on ROSC prediction models. The search time limit was from the establishment of the database to August 30, 2022. Two reviewers independently screened the literature and extracted the data. The PROBAST was used to evaluate the quality of the included literature. Results. A total of 8 relevant prediction models were included, and 6 models reported the AUC of 0.6620.830 in the modeling population, which showed good overall applicability but high risk of bias. The main reasons were improper handling of missing values and variable screening, lack of external validation of the model, and insufficient information of overfitting. Age, gender, etiology, initial heart rhythm, EMS arrival time/BLS intervention time, location, bystander CPR, witnessed during sudden arrest, and ACLS duration/compression duration were the most commonly included predictors. Obvious chest injury, body temperature below 33C, and possible etiologies were predictive factors for ROSC failure in patients with TOHCA. Age, gender, initial heart rhythm, reason for the hospital visit, length of hospital stay, and the location of occurrence in hospital were the predictors of ROSC in IHCA patients. Conclusion. The performance of current ROSC prediction models varies greatly and has a high risk of bias, which should be selected with caution. Future studies can further optimize and externally validate the existing models.