Pressure ulcers (PUs) pose a significant challenge in the care of bedridden patients, to which automated tilt nursing beds have emerged as a promising solution. However, the lack of effective models to elucidate the mechanical responses of deep tissue during assisted repositioning and identify the optimal tilt angle has hindered the implementation of effective automatic assisted repositioning systems for long-term care patients. Therefore, this study developed a novel computational model that integrates the buttocks with a support mattress to simulate automatic assisted repositioning, thereby analyzing deep tissue responses and optimizing tilt angles for effective load offloading. Inverse modeling was used to reconstruct the 3D shape of the buttocks, nodal equivalence techniques were employed to simplify the mesh and accurately represent internal tissue contacts, and soft tissue parameters were optimized using Response Surface Methodology (RSM). Finally, finite element (FE) analysis was conducted to evaluate the biomechanical responses and optimize the repositioning strategies. Model validation demonstrated a deformation error of 6.93 +/- 7.41 mm (mean +/- standard deviation) and interface pressure differences within 22.4%, demonstrating the efficacy and bio-fidelity of the system. Repositioning simulations at angles from 0 degrees to 30 degrees showed a 20% reduction in total soft tissue strain, with peak equivalent stress decreasing by 22.27% at the mattress- to-buttock interface and by 20.43% at the muscle-to-adipose tissue interface. These simulations suggest that a 30 degrees turning angle is beneficial for alleviating pressure concentration, which may inspire the design and optimization of automatic assisted repositioning strategies in rehabilitation practices.

Research and practice in patient safety education have garnered widespread attention; however, a comprehensive bibliometric analysis is lacking. This study aimed to provide a comprehensive understanding of the research focus and research trends in the globalization of the field of patient safety education and to describe the general characteristics of publications. Data on articles and reviews about student safety education were extracted from Web of Science. Microsoft Excel 2019, CiteSpace 6.1.R3, VOSviewer 1.6.18, SATI 3.2, Scimago Graphica, and Pajek were used for quantitative analysis. Collaboration networks of countries, institutions, journals, authors, and keywords were visualized based on publications from January 2000 to September 2022. A total of 573 papers were published between 2000 to 2022, showing an overall increasing trend. The USA, England, and Australia are the top three most prolific countries; Johns Hopkins University, the University of Technology Sydney, and the University of Toronto are the top three most productive institutions; Nurse Education Today, Journal of Nursing Education, and BMC Medical Education are the most productive journals; Based on content analysis five research hotspots focused on: (1) Quality Improvement of Patient safety Teaching and Learning; (2) Patient safety Teaching Content; (3)Specialized Teaching in Patient Safety; (4) Integrating Patient Safety and Clinical Teaching; (5)Patient Safety Teaching Assessment Content. Through keyword clustering analysis, five research hotspots and relevant contents were identified. According to this study, simulation, communication, collaboration, and medication may attract more attention from researchers and educators, and could be the major trend for future study.

In the formulation of strategies for walking rehabilitation, achieving precise identification of the current state and making rational predictions about the future state are crucial but often unrealized. To tackle this challenge, our study introduces a unified framework that integrates a novel 3D walking motion capture method using multi-source image fusion and a walking rehabilitation simulation approach based on multi-agent reinforcement learning. We found that, (i) the proposal achieved an accurate 3D walking motion capture and outperforms other advanced methods. Experimental evidence indicates that, compared to similar visual skeleton tracking methods, the proposed approach yields results with higher Pearson correlation (r=0.93), intra-class correlation coefficient (ICC(2,1)=0.91), and narrower confidence intervals ([0.90,0.95] for r, [0.88,0.94] for ICC(2,1)) when compared to standard results. The outcomes of the proposed approach also exhibit commendable correlation and concurrence with those obtained through the IMU-based skeleton tracking method in the assessment of gait parameters ([0.85,0.89] for r, [0.75,0.81] for ICC(2,1)); (ii) multi-agent reinforcement learning has the potential to be used to solve the simulation task of gait rehabilitation. In mimicry experiment, our proposed simulation method for gait rehabilitation not only enables the intelligent agent to converge from the initial state to the target state, but also observes evolutionary patterns similar to those observed in clinical practice through motor state resolution. This study offers valuable contributions to walking rehabilitation, enabling precise assessment and simulation-based interventions, with potential implications for clinical practice and patient outcomes. © 2024 Elsevier B.V.

Background and Objective. Sleep-disordered breathing (SDB) poses health risks linked to hypertension, cardiovascular disease, and diabetes. However, the time-consuming and costly standard diagnostic method, polysomnography (PSG), limits its wide adoption and leads to underdiagnosis. To tackle this, cost-effective algorithms using single-lead signals (like respiratory, blood oxygen, and electrocardiogram) have emerged. Despite respiratory signals being preferred for SDB assessment, a lack of comprehensive reviews addressing their algorithmic scope and performance persists. This paper systematically reviews 2012-2022 literature, covering signal sources, processing, feature extraction, classification, and application, aiming to bridge this gap and provide future research references. Methods. This systematic review followed the registered PROSPERO protocol (CRD42022385130), initially screening 342 papers, with 32 studies meeting data extraction criteria. Results. Respiratory signal sources include nasal airflow (NAF), oronasal airflow (OAF), and respiratory movement-related signals such as thoracic respiratory effort (TRE) and abdominal respiratory effort (ARE). Classification techniques include threshold rule-based methods (8), machine learning models (13), and deep learning models (11). The NAF-based algorithm achieved the highest average accuracy at 94.11%, surpassing 78.19% for other signals. Hypopnea detection sensitivity with single-source respiratory signals remained modest, peaking at 73.34%. The TRE and ARE signals proved to be reliable in identifying different types of SDB because distinct respiratory disorders exhibited different patterns of chest and abdominal motion. Conclusions. Multiple detection algorithms have been widely applied for SDB detection, and their accuracy is closely related to factors such as signal source, signal processing, feature selection, and model selection.

BACKGROUND: Adolescent idiopathic scoliosis (AIS), three-dimensional spine deformation, affects body motion. Previous research had indicated pathological gait patterns of AIS. However, the impact of the curve number on the walking mechanism has not been established. Therefore, this study aimed to compare the gait symmetry and kinematics in AIS patients with different curve numbers to healthy control.; RESULTS: In the spinal region, double curves AIS patients demonstrated a smaller sagittal symmetry angle (SA) and larger sagittal convex ROM of the trunk and lower spine than the control group. In the lower extremities, the single curve patients showed a significantly reduced SA of the knee joint in the frontal plane, while the double curves patients showed a significantly reduced SA of the hip in the transverse plane.; CONCLUSION: The curve number indeed affects gait symmetry and kinematics in AIS patients. The double curves patients seemed to adopt a more "careful walking" strategy to compensate for the effect of spinal deformation on sensory integration deficits. This compensation mainly occurred in the sagittal plane. Compared to double curves patients, single curve patients unitized a similar walking strategy with healthy subjects. © 2024. The Author(s).

Background: Gait model consists of a marker set and a segment pose estimation algorithm. Plugin marker set and inverse kinematic algorithm (IK.) are prevalent in gait analysis, especially musculoskeletal motion analysis. Adding extra markers for the plugin marker set could increase the robustness to marker misplacement, motion artifacts, and even markers occlusion. However, how the different marker sets affect the gait analysis's kinematic output is unclear. Therefore, this study aims to investigate the effect of marker sets on the kinematic output during level walking in different populations. Results: In all three planes, there are significant differences (P © 2023, The Author(s).

Background: Owing to the outbreak of the Omicron variant of SARS-CoV-2 in Shanghai, China, partitioned dynamic closure and control management plans were implemented on March 28, 2022. This created huge emergency pressure on Shanghai's medical and healthcare systems. However, the perceptions of job demands of healthcare workers (HCWs) and classification of frontline HCWs in mobile cabin hospitals are unknown.Methods: In this study, we investigated the job demands of 1223 frontline HCWs working in mobile cabin hospitals during the COVID-19 pandemic April 2022 to May 2022. We performed latent class analysis to identify classification features of job demands. A binary multivariate logistic regression model was used to explore the influencing factors of latent class.Results: The total mean job demand score was 132.26 (SD = 9.53), indicating a high level of job demand. A two class model provided the best fit. The two classes were titled "middle-demand group" (17.66%) and "high -demand group" (82.34%). A regression analysis suggested that female HCWs, HCWs satisfied with the doctor/ nurse-patient relationship, HCWs who believed that the risk of working in mobile cabin hospitals was high, and HCWs without physical discomfort during the pandemic were more likely to be in the "high-demand group".Conclusion: Characteristics of the "high-demand group" subtype suggest that attention should be paid to the physical condition of frontline HCWs and the job demands of female HCWs. Managers should strengthen the training of HCWs in terms of their communication skills as well as their knowledge and technical skills to aid epidemic prevention and control.

本研究通过提高康复辅具的社会认知度、普及康复辅具配置服务，将会促进辅具资源整合优化，产生较大的经济效益，同时本项目通过对老年及残疾群体进行辅具配置服务及科普宣教，将很大程度上降低老年失能风险、提高失能和半失能老人及残疾群体的生活自理能力，减少照护成本，减轻医疗卫生机构和社会的压力，同时促进老年及残疾群体回归家庭、回归工作、回归社会，又将继续贡献其社会价值。本课题通过应用示范将获得大量康复辅具应用、康复辅具配置服务、服务效果评价等海量相关数据，这为国家制定相关政策、指导辅具服务体系建设、高效推进辅具产业发展均有重要的意义。_x000D_本研究目前共完成24个重点试验区的应用示范，通过建档以及辅具科普措施，对试验区功能障碍患者实现全覆盖，试验区内老人每年新增失能率降低22%,培训165名辅具配置服务专业人员。通过课题实施，形成了西部地区辅具服务模式研究报告，建立了西部地区康复辅具服务推广方案，并完成康复辅具社会认知度调查报告。本课题通过提高康复辅具的社会认知度、普及康复辅具配置服务，通过对老年及残疾群体进行辅具配置服务及科普宣教，有效降低了示范区内老年群体失能风险、有望降低失能老人照护成本，减轻医疗卫生机构和社会的压力。This study aims to improve the social awareness of rehabilitation aids and popularize rehabilitation aid allocation services,which will promote the integration and optimization of aid resources and generate significant economic benefits.At the same time,this project will greatly reduce the risk of elderly disability,improve the self-care ability of disabled and semi disabled elderly and disabled groups,and reduce care costs by providing aid allocation services and promoting science education for the elderly and disabled,Reducing the pressure on healthcare institutions and society,while promoting the return of elderly and disabled groups to their families,work,and society,will continue to contribute to their social value.This project will obtain a large amount of relevant data on the application,configuration services,and service effectiveness evaluation of rehabilitation assistive devices through application demonstration.This is of great significance for the country to formulate relevant policies,guide the construction of assistive device service system,and efficiently promote the development of assistive device industry._x000D__x000D_At present,this study has completed the application demonstration of 24 key experimental areas.Through the establishment of archives and the popularization of assistive technology measures,full coverage has been achieved for patients with functional impairments in the experimental area.The annual increase in disability rate among the elderly in the experimental area has been reduced by 22%,and 165 assistive configuration service professionals have been trained.Through the implementation of the project,a research report on the service model of assistive devices in the western region has been formed,a promotion plan for rehabilitation assistive device services in the western region has been established,and a survey report on the social awareness of rehabilitation assistive devices has been completed.This project effectively reduces the risk of disability for the elderly population in the demonstration area,is expected to reduce the cost of care for disabled elderly people,and alleviate the pressure on medical and health institutions and society by improving the social awareness of rehabilitation aids,popularizing rehabilitation aid configuration services,and providing assistance and science education to the elderly and disabled groups.

Objectives: A large body of literature addresses experiences of spouse and adult-children caregiver of individuals with dementia (IWDs) but has not examined the role and strength of social networks in associations between spouses and adult-children caregivers' experience. Based on the stress process model, we aimed to explore the strength levels of social networks and their association with spouses/ adult-children caregivers for IWDs.Design: A cross-sectional study. Setting and Participants: A questionnaire-based survey was conducted with a total of 146 family caregivers of IWDs (78 adult-child, and 68 spouses) in China.Methods: Data collection comprised 4 sections: (1) care-related stressors (dementia stage, neuropsychiatric symptoms); (2) caregiver context; (3) social network, using the Lubben Social Network Scale; and (4) caregiving experience, using the short-form Zarit Burden Interview and 9-item Positive Aspects of Caregiving Scale. Linear regression, mediation model analysis, and interactive analysis were performed to explore the mechanisms of associations between variables.Results: Spouses had weaker social network strength (beta = -0.294, P =.001) and reported greater positive aspects of caregiving ( beta = 0.234, P =.003) than adult-children caregivers; no significant difference was found between them for caregiver burden. Mediation analysis suggests that associations between caregiver type and caregiver burden are indirect-only mediation effects of social networks ( beta = 0.140, 95% CI = 0.066-0.228). The social network strength suppressed the association between caregiver type and positive aspects of caregiving. The caregiver type/social network interaction statistically significantly (P =.025) affected the "positive aspects": a stronger social network was associated with more positive aspects of caregiving among the spouse subgroup (beta = 0.341, P =.003).Conclusions and Implications: Social networks mediate responses to caregiving experiences among different care provider types and are vital intervention targets, especially for spousal caregivers. Our results can serve as references for identifying caregivers for clinical intervention.(c) 2023 AMDA - The Society for Post-Acute and Long-Term Care Medicine.

随着社会老龄化的加速，失能人群逐年递增，康复管理需求日趋多元化，然而，传统辅具功能碎片化、医疗资源相对匮乏等诸多因素作用下，现有辅具配置及康复护理服务难以满足广大老年和残疾人群体不同层次的需求，呈现出老龄多、慢病多、失能多、康复辅具服务少的特征。本课题立足于西部地区，聚焦行动不便、长期卧床和认知障碍三种典型功能障患者，深入调研并分析西部地区辅具使用现状及需求的特征实情。课题设计面向不同典型功能障碍患者的三种康复辅具技术解决方案，融合传感检测与通信，借助智能辅具与医疗网络，构建医—患—机协同的功能障碍人群康养机构智能照护管理平台。面向医院、社区、家庭等不同场景，从患者、护理人员两个群体不同角度考虑，以需求为导向展开模块化功能设计和关键技术的研发，构建应用性广的共性智能康复辅具模块与多层次个性化组配策略，进而形成适配患者健康特点的个性化辅具。通过连续、动态感知患者包括生理、行为、环境等多源健康信息互联与分析控制，为患者提供连续、高效的全过程化监护。同时，以信息互联为核心，通过数据整合与分析，协助护理人员迅速获取管辖范围内分散患者康复护理相关动态，优化资源分配与响应，实现互联式、集群化智能照护管理模式，提升护理效率。进一步通过应用场景下的方案部署及测试，对方案可行性进行评价与优化，旨在通过建立可推广的功能障碍患者康复辅具技术方案，更好地服务于广大老年与残疾人群体。The population of disabled people increases as the acceleration of aging procedure,results in an innegligible diversified demand expansion in rehabilitation management.However,due to various factors such as the functional fragmentation of traditional assistive devices and the general lack of medical resources,etc,the existing assistive devices configuration and rehabilitation nursing services are difficult to meet the needs of the elderly and disabled groups at different levels.Based on the western region,this project focuses on patients with three typical functional disabilities(patients with mobility difficulties,long-term bedridden or cognitive impairment),and performed investigations on the corresponding assistive devices demands in this area.Three technical solutions were designed for rehabilitation AIDS for dysfunctional patients,which integrate sensing and communication,as well as take advantage of intelligent AIDS and medical network,an intelligent care management platform for dysfunctional patients of rehabilitation institutions is constructed.Considering different perspectives of patients and nursing staffs at different scenes such as hospitals,communities and families,demand-oriented intelligent and wide-adaptable functional rehabilitation modules were design with relative key technologies development carried out,combined with the multi-level personalized demand-matching strategy,the personalized AIDS adapted to patients'identical health characteristics can be constructed.Through the continuous and dynamic multi-source health information including physiological,behavior and environmental parameters collected and analyzed via these intelligent AIDs,whole-process nursing services can be provided to patients more efficiently.Meanwhile,through data integration and analysis,the nursing staffs are able to remotely obtain real-time information from multiple patients scattered around,which may enable the clustered intelligent care management mode and help optimize medical resources distribution with faster response and higher nursing efficiency.The project also investigated the feasibility of the solutions under clinical scenarios for further optimizations,aiming to establish popularizable technical schemes of rehabilitation assistive devices for dysfunctional patients,so as to better serve the elderly and disabled groups.