This study aimed to explore the relationship between musculoskeletal disorders and the work performance of nursing staff. This cross-sectional study used the Checklist for Musculoskeletal Disorders (MSDs), the Nordic Musculoskeletal Questionnaire (NMQ), and the Work Ability Index to survey the prevalence of MSDs, as well as to determine the impact of MSDs on 117 nursing staff working in an emergency department, an intensive care unit, and general wards. The findings indicate that the nursing staff were exposed to a workplace environment with a high risk of MSDs. Nursing staff in the emergency department were at a particularly high risk of MSDs in their upper and lower limbs, while those working in the intensive care unit had a particularly high risk of injuries caused by manual material handling. Analyzing the relationship between MSD risk factors and NMQ scores showed a 6 times, 3.25 times, and 2.28 times increase in MSD conditions with a high workload compared to a low workload in the hand and wrist, the lower back or waist, and the knee, respectively. Medium and high workloads were found to increase the risk of MSD, which in turn affected the work ability of the nurses.

Background: To determine the impact of combining clinical simulation scenario training and Information Technology Integrated Instruction (ITII) on the teaching of nursing skills.Methods: 120 4th-year students in a nursing program who were enrolled in medical and surgical nursing courses. 61 received innovative instruction (experimental group) and 59 received conventional instruction (control group). The ADDIE model, systematic method of course development that includes analysis, design, development, implementation, and evaluation,was used to build simulation teaching and clinical scenarios and to create and modify objective structure clinical examination (OSCE) scenario checklists for acute myocardial infarction (AMI) care, basic life support and operation of automated external defibrillator (BLS), and subdural hemorrhage (SDH) care. The modified OSCE checklists were assessed for reliability, consistency, and validity. The innovative training included flipped classrooms, clinical simulation scenarios, ITII and blended learning formats.Results: The reliability and validity of the OSCE checklists developed in this study were acceptable and comparable or higher than checklists in past studies and could be utilized as an OSCE performance tool. Students in innovative instruction obtained significantly better OSCE performance, lab scores and improvements from the previous year’s grades. Significant differences were found in situational awareness (SA). No strong correlations were found between OSCE scores and clinical internship scores, and no significant differences were found between the groups in overall clinical internship performance.Conclusions: Innovative instruction showed better performance than conventional methods in summative evaluation of knowledge components, OSCE formative evaluation and clinical nursing internship scores, as well as improved situational awareness in nursing students.

For more than a decade, many countries have been actively developing robotic assistive devices to assist in the rehabilitation of individuals with limb disability to regain function in the extremities. The exoskeleton assistive device in this study has been designed primarily for hemiplegic stroke patients to aid in the extension of fingers to open up the palm to simulate the effects of rehabilitation. This exoskeleton was designed as an anterior-support type to achieve palmar extension and acts as a robotic assistive device for rehabilitation in bilateral upper limb task training. Testing results show that this wearable exoskeleton assistive device with human factor consideration using percentile dimensions can provide comfortable wear on patients as well as adequate torque to pull individual fingers into flexion towards the palm for rehabilitation. We hope this exoskeleton device can help stroke patients with loss of function in the upper extremities to resume motor activities in order to maintain activities of daily living.

This article proposes an intelligent calorie management system, named iBuffet, based on artificial intelligence over the Internet of Things (AIoT) techniques for eating buffet meals with calorie intake control. The proposed iBuffet system consists of intelligent buffet table modules, an artificial intelligence (AI) Wi-Fi base station, a mobile device application (app), and a cloud-based health management platform. The goal of the proposed iBuffet system is to improve consumer dining health through suitable calorie intake control by recording daily meals with calorie intake calculations via the proposed mobile device app. Furthermore, the related information is sent to the cloud-based platform for user health management. The experimental results show that the proposed iBuffet system can recognize 30 kinds of meals within 1 s, which can meet the requirements of actual buffet restaurants.