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Background: The anaesthesia ventilator represents the key equipment for intraoperative respiratory care. Improper operation of this device may threaten a patient's health. A self-explanatory interface facilitates handling and decreases the risk of operating errors. This study systematically evaluates the usability of user interfaces in four modern anaesthesia ventilators., Methods: Twenty naïve operators were asked to execute 20 tasks on each of four different anaesthesia ventilators (Avance CS2™, GE Healthcare; Flow-i™, Maquet; and Perseus™ and Primus™, Dräger) in a randomized order. The success of task execution, frequency of requests for assistance, and processing times were recorded. During the tasks, the operators' visual focus was measured via eye-tracking. Additionally, subjective assessments of usability were evaluated by a standardized questionnaire. For comparison, six experienced operators undertook the same protocol., Results: The overall rate of falsely executed tasks was low. Naïve operators requested assistance least when using the Perseus (26). Pooled processing times were shortest for the Perseus (222 s), followed by the Primus (223 s), the Avance (238 s), and the Flow-i (353 s). Task-specific processing times differed considerably between the devices. Eye-tracking analyses revealed associated interface issues that impeded the operators' performance. Operators rated usability best for the Perseus [mean (sd): 67 (17) arbitrary units] and worst for the Flow-i [50 (16) arbitrary units]. Results from experienced operators support these findings by trend., Conclusions: The usability of modern anaesthesia ventilators differs considerably. Interface issues of specific tasks impair the operator's efficiency. Eliminating the specific usability issues might improve the operator's performance and, as a consequence, the patient's safety., (© The Author 2017. Published by Oxford University Press on behalf of the British Journal of Anaesthesia. All rights reserved. For Permissions, please email: journals.permissions@oup.com)

Introduction: Despite the increasingly widespread popularity of electronic devices, there are limited comprehensive studies on the effects of usage and exposure to multiple electronic devices over extended periods of time. Therefore, this study explored the cumulative musculoskeletal implications of exposure to various electronic devices among university students., Methods: A self-reported questionnaire was administered in the university in Hong Kong and students provided information about the frequency and duration of electronic devices use, including computers, mobile phones and game consoles, and reported on any musculoskeletal pain or discomfort that may relate to electronic devices usage in the immediate 12 months prior to the survey date., Results: A total of 503 university students (59% males and 41% females) aged 18-25 years completed the questionnaire. The results showed that 251 (49.9%) respondents reported upper limb musculoskeletal symptoms, particularly in the neck and shoulder regions. Among these, 155 (61.8%) indicated that their discomfort was related to electronic device usage. Statistically significant differences in exposure to electronic devices and musculoskeletal outcomes between genders were found (p < 0.05)., Conclusion: The use of electronic devices and habitual postures were associated with musculoskeletal problems among university students in Hong Kong. This phenomenon highlights the urgent need for ergonomics education and recommendations to increase students' awareness of musculoskeletal wellbeing., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Purpose: To determine the prevalence of repetitive stress symptoms among radiologists working in a archiving and communication systems (PACS) environment and to assess their responses to ergonomic interventions., Methods: A survey instrument was designed in conjunction with occupational health specialists and administered to 107 faculty members, fellows, and residents working in a PACS-based radiology department. Data gathered included the number of hours per day working at a personal computer or PACS monitor, the presence of repetitive stress symptoms, and prior diagnosis of repetitive stress syndrome. Additionally, respondents who had received ergonomic chairs, ergonomic workstations, or ergonomic training were asked to rank the impact of these interventions on the severity of repetitive stress symptoms using a 7-point, Likert-type scale ranging from -3 ("markedly worse") to 3 ("markedly better")., Results: A total of 73 responses were received (a 68% response rate) from 33 faculty members and 40 trainees (residents and fellows). A majority of respondents (68%) reported working more than 8 hours per day at a personal computer or PACS monitor (55% of faculty members, 80% of trainees). Repetitive stress symptoms were reported by 58% of respondents (52% of faculty members, 63% of trainees), and prior diagnoses of repetitive stress syndrome were reported by 38% (39% of faculty members, 38% of trainees). Improvements in repetitive stress symptoms (scale ratings of 1 to 3) were reported by 70% of respondents who received ergonomic chairs (n = 54), 80% who received ergonomic workstations (n = 55), and 80% who underwent ergonomic training (n = 20)., Conclusion: Repetitive stress symptoms are highly prevalent among radiologists working in a PACS-based environment but are responsive to ergonomic interventions. Radiology departments should implement ergonomic initiatives to reduce the risk for repetitive stress injuries.

Twenty ergonomic tasks were evaluated in which human operators performed mixed static work and dynamic work. Steady-state physiological data are the input into a model as regressor variables, which are then multiplied by the respective regressor coefficients. The resultant physiological state model output is a single response variable that represents the workload. Mixed stepping regression techniques were utilized to calculate the regressor coefficients. Ten physiological state model equations resulted. A lower order equation (with three regressor variables) accounted for 80% of the observed variance. The highest order equation (with ten regressor variables) accounted for 89% of the variance.