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This scoping review of studies on patients' mental workload (MWL) in health education via e‐learning was conducted to characterize the state of research and to identify barriers to and facilitators of e‐learning. The Cochrane Library, PubMed, Embase, Web of Science, and PsycINFO electronic databases were searched through September 10, 2023. A descriptive analysis of study characteristics and a content analysis of barriers to and facilitators of MWL were conducted. Among the 2837 studies screened for eligibility, 38 were included. A relatively small number of articles used a comprehensive framework to explore barriers to and facilitators of e‐learning or used indicators of MWL to assess application usability and user experience. Analyses of factors influencing the effectiveness of e‐learning in health education identified key predictors across five domains: individual, task, technical, environmental, and interactive aspects. The incorporation of multifaceted influencing factors that contribute to optimizing the MWL should be a priority when using e‐learning in health education. Future research should give more attention to investigating the generative mechanism of e‐learning in health education to enhance targeted program design. [ABSTRACT FROM AUTHOR]

• Some patients with total knee arthroplasty are in a state of self-control fatigue, which negatively influences their rehabilitation behaviour. • Latent profile analysis was employed to define the subgroups of self-regulation fatigue. • Three heterogeneous self-regulation fatigue classes of patients with end-stage knee pathologies were identified with similar patterns in different intercepts. • Lower levels of hope, social support, self-efficacy and education were predictors of a high level of self-regulation fatigue. Patients with total knee arthroplasty encounter stressful events that consume their coping resources, resulting in self-control fatigue. Few studies have focused on this phenomenon. To identify subgroups of patients before total knee arthroplasty according to the heterogeneous patterns of self-regulation fatigue and to analyse the predictors of subtypes. A total of 210 patients awaiting total knee arthroplasty were enrolled. Data of demographic characteristics, clinical characteristics, psychological and social factors were collected. Latent profile analysis was employed to define the subgroups. Predictors of patterns were identified using multinomial logistic regression. Three latent classes were identified: the low, medium, and high self-regulation fatigue classes. For the high self-regulation fatigue class, lower levels of hope, social support, self-efficacy and education were major predictors. These predictors of patients with different levels of self-regulation fatigue provide evidence for the identification of vulnerable populations and lay a foundation for targeted interventions. [ABSTRACT FROM AUTHOR]

Aims: To identify the different classes of total knee arthroplasty patients according to the heterogenous trajectories of psychological resilience and investigate the predictors for different patterns of resilience. Design: A prospective cohort study. Methods: A total of 210 patients with total knee arthroplasty from March to December 2021 were included. Baseline assessment (T0) data were collected before surgery and included demographic, biological (clinical characteristics), psychological (psychological resilience, self‐efficacy, psychological distress, hope, medical coping mode) and social (social support) factors. Resilience measurements were repeated at 3 days after surgery (T1), the date of discharge (T2), and 1 month (T3) and 3 months (T4) after discharge. Latent growth mixture modelling was employed to define different resilience trajectories. Predictors of class membership were identified using multinomial logistic regression. Results: Data from 198 patients were analysed. Three latent classes were identified with similar patterns in different intercepts, showing a significant decrease in resilience from admission (T0) to 3 days after surgery (T1) followed by an increase from T1 to T4. The three trajectories of psychological resilience were named the stable‐resilience class (65.66%), high‐resilience class (17.68%), and low‐resilience class (16.66%). Multinomial logistic regression showed that compared with the stable‐resilience class, the high‐resilience class was predicted by having a higher level of hope, having higher education, living in urban areas and having more children, while the low‐resilience class was predicted by having lower levels of self‐efficacy and hope, living in semirural areas, and having more children. Conclusions: The three trajectories indicated that surgery was the major stressor influencing patients' psychological resilience and that patients in the low‐resilience class needed to be intervened. Impact Predictors of patients in different classes provide evidence for the identification of vulnerable populations and lay a foundation for future research contributing to the development of targeted interventions for improving patients' psychological resilience. No patient or public contribution but the time points of investigation were decided based on our interviews with 12 total knee arthroplasty patients. [ABSTRACT FROM AUTHOR]

Background: Workflow interruptions are common in modern work systems. Electronic health record (EHR) tasks are typical tasks involving human–machine interactions in nursing care, but few studies have examined interruptions and nurses' mental workload in the tasks. Therefore, this study aims to investigate how frequent interruptions and multilevel factors affect nurses' mental workload and performance in EHR tasks. Methods: A prospective observational study was conducted in a tertiary hospital providing specialist and sub-specialist care from June 1st to October 31st, 2021. An observer documented nurses' EHR task interruptions, reactions and performance (errors and near errors) during one-shift observation sessions. Questionnaires were administered at the end of the electronic health record task observation to measure nurses' mental workload for the electronic health record tasks, task difficulty, system usability, professional experience, professional competency, and self-efficacy. Path analysis was used to test a hypothetical model. Results: In 145 shift observations, 2871 interruptions occurred, and the mean task duration was 84.69 (SD 56.68) minutes per shift. The incidence of error or near error was 158, while 68.35% of errors were self-corrected. The total mean mental workload level was 44.57 (SD 14.08). A path analysis model with adequate fit indices is presented. There was a relationship among concurrent multitasking, task switching and task time. Task time, task difficulty and system usability had direct effects on mental workload. Task performance was influenced by mental workload and professional title. Negative affect mediated the path from task performance to mental workload. Conclusions: Nursing interruptions occur frequently in EHR tasks, come from different sources and may lead to elevated mental workload and negative outcomes. By exploring the variables related to mental workload and performance, we offer a new perspective on quality improvement strategies. Reducing harmful interruptions to decrease task time can avoid negative outcomes. Training nurses to cope with interruptions and improve competency in EHR implementation and task operation has the potential to decrease nurses' mental workload and improve task performance. Moreover, improving system usability is beneficial to nurses to mitigate mental workload. [ABSTRACT FROM AUTHOR]