Search

Many countries have implemented strict preventive measures and mandatory policies to curb virus transmission during the COVID-19 pandemic. Some have adopted softer approaches, such as nudge-based intervention, to influence public health behavior. This systematic review, conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) 2020 guidelines, aims to determine if the nudge-based intervention can effectively influence people's preventive behavior during the early period of the COVID-19 pandemic. The review indicated an overall positive outcome, but results were mixed as nudge-based interventions substantially depended on the situational context. While the review found that the nudging technique that presents and conveys decision-related information was essential to nudging people, a secondary nudge would often applied to deliver the interventions. In addition, there was no indication of an ideal nudge technique that would be effective in most situations. Conversely, our findings indicate that the nudge would likely suffer from habituation after repeated intervention or backfire due to inappropriate use of nudges. Also, the ceiling effect would inhibit any nudge influences regardless of the technique(s) used. In sum, the results and the applicability of nudge-based interventions were mixed, highlighting the need for further research to advance the theory and practical developments. [ABSTRACT FROM AUTHOR]

Speaking valves enable tracheostomy patients to speak naturally. However, improper use may cause dangerous overpressure, leading to severe complications or even patient's death. We address this life-threatening issue by creating a biomimetic speaking valve, which incorporates an integrated overpressure valve that automatically opens when reaching critical pressure levels. To enhance safety, we integrated a whistle module to provide an audible alert for medical staff. Fundamental research on the Utricularia vulgaris trapdoor inspired our abstracted valve form. Through a comprehensive analysis using generalized linear mixed-effect models, we examined various membrane parameter effects on the function of the biomimetic overpressure valve. This enabled us to adjust the valve's opening pressure to cater to patient's unique requirements, thus potentially saving lives by applying a solution from nature. [ABSTRACT FROM AUTHOR]

We report the case of an acute cerebellitis following COVID-19 in 32-year-old man who presented with a life-threatening critical cerebellar syndrome contrasting with normal paraclinical findings. Despite this fulminant critical presentation, the patient fully recovered in 37 days after early treatment with high-dose steroids and intravenous immunoglobulins. This case highlights the need for clinicians to be aware of acute cerebellitis following COVID-19, despite normal laboratory, imaging and electroencephalography findings and the importance to start appropriate treatment as soon as possible. [ABSTRACT FROM AUTHOR]

Background: Intensive care units are critical environments where various alarm systems play a pivotal role in patient monitoring and safety. Alarm fatigue can lead to slower response times and missed alarms, compromising patient safety and increasing stress and burnout among intensive care unit nurses. Understanding how intensive care unit nurses respond to and manage these alarms is crucial in evaluating their impact on patient care and nursing well-being. Methods: This descriptive qualitative study explored the experiences of intensive care unit nurses in alarm management. Conducted in the medical and surgical intensive care units of a Northern Taiwan medical center, the study involved 15 nurses. Semi-structured interviews were utilized to investigate the working experiences of ICU nurses in alarm management and to identify their coping strategies for dealing with the constant inundation of medical device alarms. The interviews were transcribed, and content analysis was applied to identify key themes in the responses. Results: The study revealed five main themes in intensive care unit nurses' strategies for managing alarms: (1) Mastering alarm signals and acting; (2) Team monitoring for life preservation; (3) Enhancing senses and distinguishing carefully; (4) Learning from the lessons of incidents for vigilant reflection; and (5) Detach alarms' influence on daily life. These coping strategies are effective in alarm management, safeguarding patients' lives, enhancing the serenity of the clinical environment, and mitigating the physical and mental exhaustion caused by alarm fatigue. Conclusions: Intensive Care Unit nurses develop various coping strategies to manage medical device alarms, based on their experience. These strategies are crucial in maintaining patient safety and reducing nurse alarm fatigue. They can also be used for nursing education and clinical training. [ABSTRACT FROM AUTHOR]

Use of Intra-Aortic Balloon Pump (IABP) in combination with Impella has been described as an alternative strategy for mechanical circulatory support (MCS) in patients with cardiogenic shock (CS). We provide a systematic review aimed to explore the effectiveness of this paired MCS approach. We conducted a comprehensive systematic search in MEDLINE, Scopus, and Cochrane databases to identify all studies that investigated dual MCS with IABP and Impella. Our search strategy identified 12 articles, including 1 randomized controlled trial, 1 retrospective study, 1 case series, 7 case report and 2 animal studies. Rationale for this combined MCS strategy stems from an observed reduction in myocardial oxygen demand/supply ratio compared to the use of each device alone, without determining significant variations in left ventricular work. Nonetheless, this combined approach also leads to a 30–40 % decline in Impella flow, increasing the risk of bleeding, Impella displacement, as well as triggering positioning and pressure alarms. Additionally, hemolytic risk data yielded inconclusive results. Importantly, there were no notable disparities in mortality rates when comparing the combined strategy to the use of each device individually. At the current state-of-the-art, there are no conclusive data demonstrating net clinical benefits of combining Impella with IABP. Considering the substantial risks of morbidity associated, we recommend against its use in clinical practice. [Display omitted] • MCS combination is an interesting strategy but need a strong rationale due to side effects. • Impella functioning is severely worsened when combined with IABP. • Hemodynamic benefit of IABP and Impella combination does not justify increased risk. • Expansion of registries on major outcomes is needed to examine dual MCS strategies. [ABSTRACT FROM AUTHOR]

Background: Nuisance and false alarms distract clinicians from urgent alerts, raising patient safety risks. Local Problem: High alarm rates in a pediatric progressive care unit resulted in experiencing 180-250 alarms per day or 1 alarm every 3 to 4 minutes per clinician. Methods: Through Plan-Do-Study-Act cycles, environmental, policy, and technology changes were implemented to decrease the average alarms/day/bed and percentage of time in alarm. Interventions: Alarm settings tailored to patient needs using features embedded within the patient monitoring system were implemented and monitored with the assistance of alarm champions. Results: The average number of alarms/day/bed decreased from 177.69 to 96.94 over the course of 10 years, a 45.45% reduction. The percentage of time in alarm decreased from 7.52% to 2.83%, a 62.37% reduction. Conclusions: Arming clinicians with technology to analyze real-time clinical data made alarms meaningful and actionable, decreasing false alarms without compromising patient safety. [ABSTRACT FROM AUTHOR]

Aim Design Data Sources Methods Results Conclusion Implication for the Profession/Patient Care Reporting Method Patient or Public Contribution To provide a contemporaneous evidentiary overview of neonatal and paediatric studies investigating alarm‐related patient safety and alarm system management. Furthermore, to describe how clinical alarm burden is captured and reported, to identify clinical devices that contribute to alarm burden, to explore alarm‐related and patient safety measures and terminologies and to review alarm management initiatives.Scoping review.A systematic search of PubMed, Embase, Cumulative Index to Nursing and Allied Health Literature, Scopus and EBSCOhost was conducted from 2013 to 2023 using predetermined search terms, index terms, medical subject headings and truncation.Observational and qualitative studies with neonatal and paediatric populations reporting monitoring and alarm practices; and interventional studies reporting the success of alarm safety interventions were included. The quality of the included studies was assessed using the mixed methods appraisal tool.The search yielded 37 studies of acceptable quality. The majority explored alarm burden associated with physiological monitoring (n = 35; 95%). Alarm definitions were reported in 46% (n = 17) of studies, and commonly included what constituted actionable and non‐actionable alarms. While 32% (n = 12) of studies considered alarms in relation to clinical outcomes surrounding patient safety, clinician response to alarms was only reported in 19% (n = 7) of studies. Alarm and monitoring interventions were assessed in 51% (n = 19) of included studies, with categorization into six domains: changing alarm parameters, clinician education, communication and planning, technology, alarm ordering and standardization or guidelines.This review has demonstrated the enormity of alarms in clinical settings, heterogeneity of alarm definitions and outlined interventions associated with alarm burden and patient safety.Strategies to ensure appropriate alarm limits are set and clinicians are empowered through education to recognize and respond appropriately to alarms can maximize patient safety.This review adheres to the preferred reporting items for systematic reviews and meta‐analysis protocols extension for scoping reviews.No patient or public contribution. [ABSTRACT FROM AUTHOR]

In Intensive Care Unit (ICU), the settings of the critical alarms should be sensitive and patient-specific to detect signs of deteriorating health without ringing continuously, but alarm thresholds are not always calibrated to operate this way. An assessment of the connection between critical alarm threshold settings and the patient-specific variables in ICU would deepen our understanding of the issue. The aim of this retrospective descriptive and exploratory study was to assess this relationship using a large cohort of ICU patient stays. A retrospective study was conducted on some 70,000 ICU stays taken from the MIMIC-IV database. Critical alarm threshold values and threshold modification frequencies were examined. The link between these alarm threshold settings and 30 patient variables was then explored by computing the Shapley values of a Random Tree Forest model, fitted with patient variables and alarm settings. The study included 57,667 ICU patient stays. Alarm threshold values and alarm threshold modification frequencies exhibited the same trend: they were influenced by the vital sign monitored, but almost never by the patient's overall health status. This exploratory study also placed patients' vital signs as the most important variables, far ahead of medication. In conclusion, alarm settings were rigid and mechanical and were rarely adapted to the evolution of the patient. The management of alarms in ICU appears to be imperfect, and a different approach could result in better patient care and improved quality of life at work for staff. [ABSTRACT FROM AUTHOR]

This study is aimed at developing an intelligent factory control system for improving environment quality and reducing electricity consumption. By automating intelligent equipment and leveraging internet networks, the system enables the remote monitoring and management of environmental conditions. We combine the fuzzy method and deep reinforcement learning (DRL) to handle complex factory data and optimize decision-making. The fuzzy method uses fuzzy sets and rules to generate accurate outputs from the data. On the other hand, the DRL system learns optimal policies by interacting with the environment using environment quality, central air conditioner (AC), and alarm data. Hardware implementation uses an ESP32-S microcontroller to send data to Google's Firebase cloud for seamless management and monitoring through a mobile app or website. The study involves developing 36 fuzzy rules and creating 10 models with different combinations of hidden layers, epochs, and learning rate values. Among the fuzzy inference system (FIS)-DRL modeling results, the fourth model stands out as the preferred option to proceed with the experiment, as it achieves the highest accuracy of 91.16%. Note that this model also exhibits a loss value of 1.64% and an incredibly short inference time of only 3 ms. The proposed system offers benefits such as enhanced energy efficiency and reduced costs, making it ideal for intelligent factories. By optimizing resource usage, it will contribute to sustainable development in various industries. [ABSTRACT FROM AUTHOR]

Intensive Care Unit (ICU) nurses are burdened by excessive number of false and irrelevant alarms generated by patient monitoring systems. Nurses rely on these patient monitoring systems for timely and relevant medical information concerning patients. However, the systems currently in place are not sensitive to the perceptual and cognitive abilities of nurses and thus fail to communicate information efficiently. An efficient communication and an effective collaboration between patient monitoring systems and ICU nurses is only possible by designing systems sensitive to the abilities and preferences of nurses. In order to design these sensitive systems, we need to gain in-depth understanding of the user group through revealing their latent individual characteristics. To this end, we conducted a survey on individual characteristics involving nurses from two IC units. Our results shed light on the personality and other characteristics of ICU nurses. Subsequently, we performed hierarchical cluster analysis to develop data-driven nurse profiles. We suggest design recommendations tailored to four distinct user profiles to address their unique needs. By optimizing the system interactions to match the natural tendencies of nurses, we aspire to alleviate the cognitive burden induced by system use to ensure that healthcare providers receive relevant information, ultimately improving patient safety. [ABSTRACT FROM AUTHOR]

Background: Retinopathy of prematurity (ROP) is a leading cause of avoidable blindness in children, particularly in Latin America, where hyperoxia is a significant risk factor. This study evaluated resource availability and use for administering and monitoring supplemental oxygen in Mexico. Methods: In 2011, an observational study in which 32 government neonatal intensive care units (NICUs) across Mexico were visited. Data collected included occupancy, staffing levels, and equipment to deliver and monitor supplemental oxygen. Preterm infants receiving oxygen were observed. In 2023, 13 NICUs were revisited, and similar data collected. Staffing levels were benchmarked against Argentinian and US recommendations. Results: In 2011, only 38% of NICUs had adequate medical and staffing levels to meet recommended cot-to-staff ratios for all shifts. Staffing ratios were worse during weekends and at night than during weekdays. Only 25.5% of cots had blenders, and 80.1% had saturation monitors. 153 infants were observed 87% of whom were being monitored. Upper and lower oxygen saturations were ≥ 96% in 53%, and ≤ 89% in 8%, respectively. Alarm settings were inadequate, as 38% and 32% of upper and lower alarms were switched off and 16% and 53% were incorrectly set, respectively. In the 13 NICUs with data from 2011 and 2023, cot-to-staff ratios deteriorated over time, and in 2023 no unit had recommended ratios for all shifts. Equipment provision did not change, with similar proportions of babies in oxygen being monitored (79% 2011; 75% 2023). Rates of hyperoxia decreased slightly from 54% in 2011 to 49% in 2023. More upper alarms were set (46% 2011; 75% 2023), but a higher proportion were incorrectly set (52% 2011; 68% 2023). Conclusions: Between 2011 and 2023, cot-to-staff ratios worsened, and equipment for safe oxygen delivery and monitoring remained insufficient. Despite available monitoring equipment, oxygen saturations often exceeded recommended levels, and alarms were frequently not set or incorrectly configured. Urgent improvements are needed in healthcare workforce numbers and practices, along with ensuring adequate equipment for safe oxygen delivery. [ABSTRACT FROM AUTHOR]

Aim: This research study aims to determine nurses' alarm fatigue (AF) levels in paediatric critical care units in two governmental hospitals and to examine the significant differences in the mean between nurses' attributes, nurses' working environment, and nurses' alarm management with the level of fatigue caused by the alarm. Background: In recent years, AF has become a significant and growing concern among nurses. However, in the Saudi Arabian paediatrics context, the impact of AF on nurses working in intensive care units remains unexplored. Method: A descriptive cross-sectional survey was conducted using a non-probability purposive sampling method. Data were collected from 216 nurses in two governmental hospitals through self-administered questionnaires comprised of four sections: individual attributes, work environment, alarm management, and AF scale. Data analysis: The Statistical Package of Social Science (SPSS) was used to analyse the data, and ANOVA was utilised to describe the sample's demographic characteristics and determine any differences. Results: Most participants were female, held a bachelor's degree, and were aged 31 to 35. Of the participants, 62.5% reported experiencing a medium level of AF, 29.2% reported a low level, and 8.3% reported a high level. Participants expressed that recurrent false alarms disrupt patient care and decrease trust in alarm systems. Significant differences in AF levels were observed based on marital status and the percentage of non-actionable alarms. Conclusions: Nurses working in paediatric critical units with high rates of false alarms, the frequent de-activation of alarms, and decreased trust in alarm systems are more likely to experience AF. Addressing AF is crucial for patient safety; nurse training on alarm management, the collaboration between biomedical and nursing staff, and technological advancements can help mitigate this issue. Implications for Practice: To minimise the adverse effects of AF, policymakers, biomedical experts, and nursing administrators must establish comprehensive policies and protocols concerning alarms. These measures aim to ensure secure and efficient care for the well-being of patients and nurses. [ABSTRACT FROM AUTHOR]

Objective: This study was undertaken to develop and evaluate a machine learning‐based algorithm for the detection of focal to bilateral tonic–clonic seizures (FBTCS) using a novel multimodal connected shirt. Methods: We prospectively recruited patients with epilepsy admitted to our epilepsy monitoring unit and asked them to wear the connected shirt while under simultaneous video‐electroencephalographic monitoring. Electrocardiographic (ECG) and accelerometric (ACC) signals recorded with the connected shirt were used for the development of the seizure detection algorithm. First, we used a sliding window to extract linear and nonlinear features from both ECG and ACC signals. Then, we trained an extreme gradient boosting algorithm (XGBoost) to detect FBTCS according to seizure onset and offset annotated by three board‐certified epileptologists. Finally, we applied a postprocessing step to regularize the classification output. A patientwise nested cross‐validation was implemented to evaluate the performances in terms of sensitivity, false alarm rate (FAR), time in false warning (TiW), detection latency, and receiver operating characteristic area under the curve (ROC‐AUC). Results: We recorded 66 FBTCS from 42 patients who wore the connected shirt for a total of 8067 continuous hours. The XGBoost algorithm reached a sensitivity of 84.8% (56/66 seizures), with a median FAR of.55/24 h and a median TiW of 10 s/alarm. ROC‐AUC was.90 (95% confidence interval =.88–.91). Median detection latency from the time of progression to the bilateral tonic–clonic phase was 25.5 s. Significance: The novel connected shirt allowed accurate detection of FBTCS with a low false alarm rate in a hospital setting. Prospective studies in a residential setting with a real‐time and online seizure detection algorithm are required to validate the performance and usability of this device. [ABSTRACT FROM AUTHOR]

Background: Nurses work in complex environments with high numbers of alarms, including nuisance alarms and other factors. Alarm-related adverse events are common. Aim: Evaluate the effect of implementing safety bundle regarding alarm fatigue on nurses' perception and practices in cardiac care units. Design: A quasi- experimental design used in this study. Setting: The study carried out at the Cardiac Care Units (CCU) at Tanta University Hospital. Subjects and Method: Subjects: All nurses (50) of both sex working at CCU at Tanta University Hospital. Tools: Three tools were utilized. Tool I: Critical Care Nurses' Questionnaire. Tool II: Nurse s' Alarm Fatigue Level Questionnaire. Tool III: Nurses' Alarm Fatigue Management Observational Checklist. Results: There was statistically significant positive correlation between perception of clinical alarms, alarm fatigue level and alarm fatigue management pre and post implementation of alarm safety bundle. Conclusion: It was concluded that there were statistically significant differences between nurses' perception and practices categories indicating good knowledge and practice after the nurses implementing safety bundle regarding alarm fatigue, compared with poor perception and practice pre- implementing safety bundle regarding alarm fatigue p value (0.005*). Recommendation: It is recommended to develop a standardized tool that assesses nurses' perception of clinical alarms and alarm management guidelines. In service training implementing safety bundle regarding alarm fatigue should be conducted be conducted for nurses regarding alarm management and increase nurses' awareness of the presence of alarm fatigue. Future study should be made to implement fundamental universal alarm management guidelines all across the world. [ABSTRACT FROM AUTHOR]

A ventilator central monitoring system (VCMS) that can efficiently respond to and treat patients' respiratory issues in intensive care units (ICUs) is critical. Using Internet of Things (IoT) technology without loss or delay in patient monitoring data, clinical staff can overcome spatial constraints in patient respiratory management by integrated monitoring of multiple ventilators and providing real-time information through remote mobile applications. This study aimed to establish a VCMS and assess its effectiveness in an ICU setting. A VCMS comprises central monitoring and mobile applications, with significant real-time information from multiple patient monitors and ventilator devices stored and managed through the VCMS server, establishing an integrated monitoring environment on a web-based platform. The developed VCMS was analyzed in terms of real-time display and data transmission. Twenty-one respiratory physicians and staff members participated in usability and satisfaction surveys on the developed VCMS. The data transfer capacity derived an error of approximately 10 − 7 , and the difference in data transmission capacity was approximately 1.99 × 10 − 7 ± 9.97 × 10 − 6 with a 95% confidence interval of − 1.16 × 10 − 7 to 5.13 × 10 − 7 among 18 ventilators and patient monitors. The proposed VCMS could transmit data from various devices without loss of information within the ICU. The medical software validation, consisting of 37 tasks and 9 scenarios, showed a task completion rate of approximately 92%, with a 95% confidence interval of 88.81–90.43. The satisfaction survey consisted of 23 items and showed results of approximately 4.66 points out of 5. These results demonstrated that the VCMS can be readily used by clinical ICU staff, confirming its clinical utility and applicability. The proposed VCMS can help clinical staff quickly respond to the alarm of abnormal events and diagnose and treat based on longitudinal patient data. The mobile applications overcame space constraints, such as isolation to prevent respiratory infection transmission of clinical staff for continuous monitoring of respiratory patients and enabled rapid consultation, ensuring consistent care. [ABSTRACT FROM AUTHOR]

This paper reviews alarm processing methods in electrical power systems, focusing on evolving strategies beyond traditional fault analysis to accommodate modern grid complexities. Historically, alarm processing has predominantly aimed at fault analysis, increasingly merging with technological advances in communication and computing. However, it still needs to fully meet the challenges posed by the dynamic characteristics of modern power systems. This review points out certain inadequacies in current practices, notably their limited adaptation to new grid conditions. The authors propose a novel generation of alarm processing methodologies designed for future grids, emphasizing managing rare events and enhancing operator decision-making through advanced anomaly detection and explainable artificial intelligence. This synthesis presents a prospective direction for future research and applications in alarm processing, advocating for methodologies better suited to supporting system operators amidst technological advancements. [ABSTRACT FROM AUTHOR]

Infrared (IR) small target detection in sky scenes is crucial for aerospace, border security, and atmospheric monitoring. Most current works are typically designed for generalized IR scenes, which may not be optimal for the specific scenario of sky backgrounds, particularly for detecting small and dim targets at long ranges. In these scenarios, the presence of heavy clouds usually causes significant false alarms due to factors such as strong edges, streaks, large undulations, and isolated floating clouds. To address these challenges, we propose an infrared dim and small target detection algorithm based on morphological filtering with dual-structure elements. First, we design directional dual-structure element morphological filters, which enhance the grayscale difference between the target and the background in various directions, thus highlighting the region of interest. The grayscale difference is then normalized in each direction to mitigate the interference of false alarms in complex cloud backgrounds. Second, we employ a dynamic scale awareness strategy, effectively preventing the loss of small targets near cloud edges. We enhance the target features by multiplying and fusing the local response values in all directions, which is followed by threshold segmentation to achieve target detection results. Experimental results demonstrate that our method achieves strong detection performance across various complex cloud backgrounds. Notably, it outperforms other state-of-the-art methods in detecting targets with a low signal-to-clutter ratio (MSCR ≤ 2). Furthermore, the algorithm does not rely on specific parameter settings and is suitable for parallel processing in real-time systems. [ABSTRACT FROM AUTHOR]

Multiple demands on a clinician's time and attention may contribute to burnout and medical errors. This scoping review explores the breadth of studies on physician attention, characterizing the studies by how they relate to clinician attention and clinical outcomes. The authors categorize and characterize studies along different characteristics such as the study methods, metrics used to measure attention, clinician studied, setting, and study intent to inform future research and review. Background: The concept of attention can provide insight into the needs of clinicians and how health systems design can impact patient care quality and medical errors. Purpose: To conduct a scoping review to 1) identify and characterize literature relevant to clinician attention; 2) compile metrics used to measure attention; and 3) create a framework of key concepts. Data Sources: Cumulated Index to Nursing and Allied Health Literature (CINAHL), Medline (PubMed), and Embase (Ovid) from 2001 to 26 February 2024. Study Selection: English-language studies addressing health care worker attention in patient care. At least dual review and data abstraction. Data Extraction: Article information, health care professional studied, practice environment, study design and intent, factor type related to attention, and metrics of attention used. Data Synthesis: Of 6448 screened articles, 585 met inclusion criteria. Most studies were descriptive (n = 469) versus investigational (n = 116). More studies focused on barriers to attention (n = 387; 342 descriptive and 45 investigational) versus facilitators to improving attention (n = 198; 112 descriptive and 86 investigational). We developed a framework, grouping studies into 6 categories: 1) definitions of attention, 2) the clinical environment and its effect on attention, 3) personal factors affecting attention, 4) relationships between interventions or factors that affect attention and patient outcomes, 5) the effect of clinical alarms and alarm fatigue on attention, and 6) health information technology's effect on attention. Eighty-two metrics were used to measure attention. Limitations: Does not synthesize answers to specific questions. Quality of studies was not assessed. Conclusion: This overview may be a resource for researchers, quality improvement experts, and health system leaders to improve clinical environments. Future systematic reviews may synthesize evidence on metrics to measure attention and on the effectiveness of barriers or facilitators related to attention. Primary Funding Source: None. [ABSTRACT FROM AUTHOR]

The article explores the use of machine learning (ML) models in critical care to predict patient deterioration and improve outcomes. It acknowledges that while there is evidence supporting the effectiveness of ML models in predicting outcomes, there is a lack of robust evidence on how these models impact patient care and outcomes in real-world clinical settings. The article highlights a study that implemented an ML model in a hospital, which resulted in faster administration of medications and fluids and a significant reduction in mortality. However, the study had limitations, including an early termination due to the COVID-19 pandemic and a nonrandomized design. The article concludes that more research is needed to fully understand the benefits and challenges of implementing ML models in clinical settings. [Extracted from the article]

This systematic review and meta-analysis aimed to research on the effect of the interventions developed to reduce alarm fatigue in intensive care units. Three researchers independently searched 14 databases. The databases were scanned to access articles published between 01.01.2010 and 31.12.2022. The Joanna Briggs Institute Meta-Analysis of Statistics Assessment and Review Instruments (JBI-MAStARI) were used to assess methodological quality. The study is preregistered on PROSPERO (CRD42023387649). Of the 11632 studies, 19 articles that met the criteria were included in the systematic review, and seven articles were included in the meta-analysis study. While the effect of the strategies applied in reducing alarm fatigue was found to be significant in five articles (p < 0.05). The meta-analysis results showed that strategies implemented to reduce alarm fatigue may have a significant effect (p > 0.05) in reducing nurses' alarm fatigue. Seven of the articles included in the systematic review showed that the implemented interventions indicated that decline in the perception of the existence of non-actionable/false alarms. After the intervention, the proportion of nurses who agreed with the statement that non-actionable/false alarms interrupted patient care increased, while in another study, the number of nurses who believed that alarms were missed and/or responses were delayed increased. In this study, the effect size obtained from all articles was not significant and showed that the interventions may not have an effect on reducing alarm fatigue. [ABSTRACT FROM AUTHOR]

The 7th National Audit Project of the Royal College of Anaesthetists (NAP7) recommended that an emergency call system be immediately accessible in all anaesthesia locations. It is essential that all theatre team members can rapidly call for help to reduce the risk of patient harm. However, the ability of staff to activate this system in a timely manner can be affected by cluttered or unfamiliar environments and cognitive overload. One proposed strategy to enable rapid identification and activation of emergency call systems is to install a red vertical painted stripe on the wall from the ceiling to the activation button. We investigated the effect of introducing this vertical red line on activation times in operating theatres in the UK and Australia. Operating theatre team members, including anaesthetists, surgeons, anaesthetic nurses, surgical and theatre nurses, operating theatre practitioners, and technicians, were approached without prior warning and asked to simulate activation of an emergency call. Vertical red lines were installed, and data collection repeated in the same operating theatres 4–12 months later. After installation of vertical red lines, the proportion of activations taking >10 s decreased from 31.9% (30/94) to 13.6% (17/125, P =0.001), and >20 s decreased from 19.1% (18/94) to 4.8% (6/125, P <0.001). The longest duration pre-installation was 120 s, and post-installation 35 s. This simple, safe, and inexpensive design intervention should be considered as a design standard in all operating theatres to minimise delays in calling for help. [ABSTRACT FROM AUTHOR]

While surveillance technology, such as GPS and motion sensors, is increasingly used in dementia care to heighten safety, it also poses new risks and ethical concerns. This paper explores the development of ethical practices around technology and presents a case study of infrastructuring the ethical use of surveillance technology in dementia care. Our ethnographic fieldwork at a Danish nursing home identified surveillance practices based on continuous tinkering and distributed negotiations to manage risks. However, new legislation and subsequent increased use of surveillance technology challenged care workers’ tinkering, resulting in both false, redundant, and failing alarms. The misleading alarms potentially jeopardise the safety of residents and result in a breakdown of the ethical commitment of care workers to care for residents. Drawing on the notion of infrastructuring, we demonstrate how new ethical surveillance practices were developed through a codesign process. The codesign process creates an organisational framework and space for reflection, which supports care workers in creative tinkering and safe implementation of large-scale strategies in a local context. Through our case, we argue for the need to more broadly address the infrastructuring of ethics and suggest that codesign lends itself as a promising approach to this endeavour. [ABSTRACT FROM AUTHOR]

Aim Design Methods Results Conclusion Implications for the Profession and/or Patient Care Impact Reporting Method Patient or Public Contribution Trial Registration To evaluate the effectiveness of utilizing the integrated pulmonary index for capnography implementation during sedation administered by nurses.Cluster‐randomized trial.Participants were enrolled from the interventional radiology department at an academic hospital in Canada. Nurses were randomized to either enable or disable the Integrated Pulmonary Index feature of the capnography monitor. Procedures were observed by a research assistant to collect information about alarm performance characteristics. The primary outcome was the number of seconds in an alert condition state without an intervention being applied.The number of seconds in an alarm state without intervention was higher in the group that enabled the integrated pulmonary index compared to the group that disabled this feature, but this difference did not reach statistical significance. Likewise, the difference between groups for the total alarm duration, total number of alarms and the total number of appropriate alarms was not statistically significant. The number of inappropriate alarms was higher in the group that enabled the Integrated Pulmonary Index, but this estimate was highly imprecise. There was no difference in the odds of an adverse event (measured by the Tracking and Reporting Outcomes of Procedural Sedation tool) occurring between groups. Desaturation events were uncommon and brief in both groups but the area under the SpO2 90% desaturation curve scores were lower for the group that enabled the integrated pulmonary index.Enabling the integrated pulmonary index during nurse‐administered procedural sedation did not reduce nurses' response times to alarms. Therefore, integrating multiple physiological parameters related to respiratory assessment into a single index did not lower the threshold for intervention by nurses.The time it takes to respond to capnography monitor alarms will not be reduced if the integrated pulmonary Iindex feature of capnography monitors is enabled during nurse‐administered procedural sedation.Results do not support the routine enabling of the integrated pulmonary index when nurses use capnography to monitor patients during procedural sedation as a strategy to reduce the time it takes to initiate responses to alarms.CONSORT.There was no patient or public contribution.This study was prospectively registered at ClinicalTrials.gov (ID: NCT05068700). [ABSTRACT FROM AUTHOR]

This concise analysis examines the application of AI, particularly chatbots and natural language processing (NLP), to enhance maternal-infant healthcare in Spain and Brazil, where maternal mortality rates are significantly different, as well as birth rate and reproductive health planning or birth control. The application will be preliminary tested in complex situations regarding these two use cases. It highlights the integration of disruptive technologies into prenatal care, emphasizing the role of AI in facilitating personalized healthcare and self-management through data acquisition on health parameters, such as weight gain, blood pressure, incontinence, mental health, nutrition and physical activity. The study reflects on the impact of digital tools in optimizing patient engagement and early diagnosis of alarm symptoms, showcasing the potential of m-Health applications in transforming care practices and contributing to attaining Global Health Objectives for 2030. [ABSTRACT FROM AUTHOR]

Several auditory factors can affect healthcare provider performance, well-being, and patient outcomes. Music volume in the OR is a key factor that can be controlled during time-sensitive situations. Therefore, the research questions for this paper are: does the CanaryBox increase the proportion of alarms that clinicians respond to during surgery, and does it reduce the time clinicians take to respond to alarms? Furthermore, how can we convert data from patient monitoring into usable formats to study how the surgical environment changes affect patient physiology and clinician behavior?. [ABSTRACT FROM AUTHOR]

Objective: The aim of the study was to evaluate the feasibility and potential effects of a workplace intervention to reduce and break up sitting. Methods: Office workers were randomized in clusters to intervention (n = 22) or control (n = 22). The intervention included a height-adjustable workstation, education, computer prompt software, and line manager support. Outcomes included device-measured workplace sitting and ecological momentary assessed workplace productivity. Recruitment, retention, and data completion rates were assessed. Results: Recruitment (N = 44), retention (91%), and workplace sitting measurement rates demonstrated study feasibility. At 8 weeks, workplace sitting was 11% lower (95% CI: -20.71, -1.30) in the intervention group compared with control participants. Intervention participants were also more engaged, motivated, and productive while sitting (P ≥ 0.016). Conclusions: It was feasible to implement and evaluate this office workplace intervention, with potential benefits on workplace sitting and ecological momentary assessed productivity. [ABSTRACT FROM AUTHOR]

While network attacks play a critical role in many advanced persistent threat (APT) campaigns, an arms race exists between the network defenders and the adversary: to make APT campaigns stealthy, the adversary is strongly motivated to evade the detection system. However, new studies have shown that neural network is likely a game-changer in the arms race: neural network could be applied to achieve accurate, signature-free, and low-false-alarm-rate detection. In this work, we investigate whether the adversary could fight back during the next phase of the arms race. In particular, noticing that none of the existing adversarial example generation methods could generate malicious packets (and sessions) that can simultaneously compromise the target machine and evade the neural network detection model, we propose a novel attack method to achieve this goal. We have designed and implemented the new attack. We have also used Address Resolution Protocol (ARP) Poisoning and Domain Name System (DNS) Cache Poisoning as the case study to demonstrate the effectiveness of the proposed attack. [ABSTRACT FROM AUTHOR]

Background Postoperative acute kidney injury (AKI) is a common condition after surgery, however, the available data about nationwide epidemiology of postoperative AKI in China from large and high-quality studies are limited. This study aimed to determine the incidence, risk factors and outcomes of postoperative AKI among patients undergoing surgery in China. Methods This was a large, multicentre, retrospective study performed in 16 tertiary medical centres in China. Adult patients (≥18 years of age) who underwent surgical procedures from 1 January 2013 to 31 December 2019 were included. Postoperative AKI was defined by the Kidney Disease: Improving Global Outcomes creatinine criteria. The associations of AKI and in-hospital outcomes were investigated using logistic regression models adjusted for potential confounders. Results Among 520 707 patients included in our study, 25 830 (5.0%) patients developed postoperative AKI. The incidence of postoperative AKI varied by surgery type, which was highest in cardiac (34.6%), urologic (8.7%) and general (4.2%) surgeries. A total of 89.2% of postoperative AKI cases were detected in the first 2 postoperative days. However, only 584 (2.3%) patients with postoperative AKI were diagnosed with AKI on discharge. Risk factors for postoperative AKI included older age, male sex, lower baseline kidney function, pre-surgery hospital stay ≤3 days or >7 days, hypertension, diabetes mellitus and use of proton pump inhibitors or diuretics. The risk of in-hospital death increased with the stage of AKI. In addition, patients with postoperative AKI had longer lengths of hospital stay (12 versus 19 days) and were more likely to require intensive care unit care (13.1% versus 45.0%) and renal replacement therapy (0.4% versus 7.7%). Conclusions Postoperative AKI was common across surgery type in China, particularly for patients undergoing cardiac surgery. Implementation and evaluation of an alarm system is important for the battle against postoperative AKI. [ABSTRACT FROM AUTHOR]

Pyroptosis, a non-apoptotic programmed cellular inflammatory death mechanism characterized by gasdermin (GSDM) family proteins, has gathered significant attention in the cancer treatment. However, the alarming clinical trial data indicates that pyroptosis-mediated cancer therapeutic efficiency is still unsatisfactory. It is essential to integrate the burgeoning biomedical findings and innovations with potent technology to hasten the development of pyroptosis-based antitumor drugs. Considering the rapid development of pyroptosis-driven cancer nanotherapeutics, here we aim to summarize the recent advances in this field at the intersection of pyroptosis and nanotechnology. First, the foundation of pyroptosis-based nanomedicines (NMs) is outlined to illustrate the reliability and effectiveness for the treatment of tumor. Next, the emerging nanotherapeutics designed to induce pyroptosis are overviewed. Moreover, the cross-talk between pyroptosis and other cell death modalities are discussed, aiming to explore the mechanistic level relationships to provide guidance strategies for the combination of different types of antitumor drugs. Last but not least, the opportunities and challenges of employing pyroptosis-based NMs in potential clinical cancer therapy are highlighted. This abstract graphic takes concerted action with abstract section and illustrates the development status of pyroptosis-based nanomedicines for cancer treatment. [Display omitted] [ABSTRACT FROM AUTHOR]

Nowadays, The Universal Serial Bus (USB) is one of the most adopted communication standards. However, the ubiquity of this technology has attracted the interest of attackers. This situation is alarming, considering that the USB protocol has penetrated even into critical infrastructures. Unfortunately, the majority of the contemporary security detection and prevention mechanisms against USB-specific attacks work at the application layer of the USB protocol stack and, therefore, can only provide partial protection, assuming that the host is not itself compromised. Toward this end, we propose a USB authentication system designed to identify (and possibly block) heterogeneous USB-based attacks directly from the physical layer. Empirical observations demonstrate that any extraneous/malicious activity initiated by malicious/compromised USB peripherals tends to consume additional electrical power. Driven by this observation, our proposed solution is based on the analysis of the USB power consumption patterns. Valuable power readings can easily be obtained directly by the power lines of the USB connector with low-cost, off-the-shelf equipment. Our experiments demonstrate the ability to effectively distinguish benign from malicious USB devices, as well as USB peripherals from each other, relying on the power side channel. At the core of our analysis lies an Autoencoder model that handles the feature extraction process; this process is paired with a long short-term memory (LSTM) and a convolutional neural network (CNN) model for detecting malicious peripherals. We meticulously evaluated the effectiveness of our approach and compared its effectiveness against various other shallow machine learning (ML) methods. The results indicate that the proposed scheme can identify USB devices as benign or malicious/counterfeit with a perfect F1-score. [ABSTRACT FROM AUTHOR]

Background: Noise pollution in intensive care units is a relevant problem, associated with psychological and physiological consequences for patients and healthcare staff. Sources of noise pollution include medical equipment, alarms, communication tools, staff activities, and conversations. Aims: To explore the cumulative effects of noise caused by an increasing number and type of medical devices in an intensive care setting on simulated patients with increasing clinical complexity. Secondly, to measure medical device alarms and nursing activities' sound levels, evaluating their role as potentially disruptive noises. Study Design: Observational simulation study (reported according to the STROBE checklist). Using an electronic sound meter, the sound levels of an intensive care room in seven simulated clinical scenarios were measured on a single day (09 March 2022), each featuring increasing numbers of devices, hypothetically corresponding to augmented patients' clinical complexity. Secondly, noise levels of medical device alarms and specific nursing activities performed at a distance of three meters from the sound meter were analysed. Results: The empty room's mean baseline noise level was 37.8 (±0.7) dBA; among the simulated scenarios, noise ranged between 45.3 (±1.0) and 53.5 (±1.5) dBA. Alarms ranged between 76.4 and 81.3 dBA, while nursing tasks (closing a drawer, opening a saline bag overwrap, or sterile packages) and speaking were all over 80 dBA. The noisiest activity was opening a sterile package (98 dBA). Conclusion: An increased number of medical devices, an expression of patients' higher clinical complexity, is not a significant cause of increased noise. Some specific nursing activities and conversations produce higher noise levels than medical devices and alarms. This study's findings suggest further research to assess the relationships between these factors and to encourage adequate noise reduction strategies. Relevance to Clinical Practice: Excessive noise level in the intensive care unit is a clinical issue that negatively affects patients' and healthcare providers' well‐being. The increase in baseline room noise from medical devices is generally limited. Typical nursing tasks and conversations produce higher noise levels than medical devices and alarms. These findings could be helpful to raise awareness among healthcare professionals to recognize noise sources. The noisiest components of the environment can be modified by staff behaviour, promoting noise reduction strategies and improving the critical care environment. [ABSTRACT FROM AUTHOR]

Background: Meeting glycemic recommendations is challenging for youth with type 1 diabetes. Diabetes technology, including continuous glucose monitoring (CGM) and hybrid closed-loop (HCL) automated insulin delivery systems, significantly increase achievement of glycemic targets; however, many youth struggle to sustain use of early HCL systems. Nocturnal alarm fatigue contributes to disrupted sleep and device discontinuation. Methods: We examined the frequency and causes of nocturnal (10:00 p.m. to 6:00 a.m.) alarms in pediatric patients (N = 76, median age 14.5 years [interquartile range 11.8–17.0 years, range 7–24 years]) starting on a first-generation HCL system in a prospective observational study. Device data were analyzed with linear mixed-effects models to examine change across time at 3-month intervals for 12 months. Results: At baseline (HCL system in nonautomated mode), participants averaged 3.3 ± 0.6 alarms per night. In the 2 weeks after starting HCL (automated) mode, alarm frequency significantly increased to 5.4 ± 0.5 times per night (P <0.001). Alarm frequency decreased through the remainder of the observational period; however, CGM sensor and HCL system use also declined. The types of alarms were evenly distributed among sensor maintenance, sensor threshold, pump, and HCL-specific alarms. Conclusion: These data show that HCL system nocturnal alarms are frequent and may be barriers to sleep quality and device use. Further research is needed to assess the impact of diabetes technology on sleep and to determine method to improve sleep quality with technology use. [ABSTRACT FROM AUTHOR]

Background: Alarm fatigue among nurses working in the intensive care unit has garnered considerable attention as a national patient safety priority. A viable solution for reducing the frequency of alarms and unnecessary noise is intensive care unit alarm monitor customization. Local Problem: A 24-bed cardiovascular and thoracic surgery intensive care unit in a large academic medical center identified a high rate of alarms and associated noise as a problem contributing to nurse alarm fatigue. Methods: An alarm monitor quality improvement project used both alarm frequency and nurse surveys before and after implementation to determine the effectiveness of interventions. Multimodal interventions included nurse training sessions, informational flyers, organizational policies, and an alarm monitor training video. Unexpected results inspired an extensive investigation and secondary analysis, which included examining the data-capturing capabilities of the alarm monitors and the impact of context factors. Results: Alarm frequencies unexpectedly increased after the intervention. The software data-capturing features of the alarm monitors for determining frequency did not accurately measure nurse interactions with monitors. Measured increases in patient census, nurse staffing, and data input from medical devices from before to after the intervention substantially affected project results. Conclusions: Alarm frequencies proved an unreliable measure of nurse skills and practices in alarm customization. Documented changes in context factors provided strong anecdotal evidence of changed circumstances that clarified project results and underscored the critical importance of contemporaneous collection of context data. Designs and methods used in quality improvement projects must include reliable outcome measures to achieve meaningful results. [ABSTRACT FROM AUTHOR]

The technical provisions for protecting built cultural heritage originate from the interdisciplinary disciplines of architecture and engineering, which mutually operate despite encountering specific challenges. Therefore, the lack of adaptable applications capable of integrating the management of complex data originating from various sources has prompted the development of an Internet-of- Things-based application that enables "on-the-fly" data acquisition and information processing from distant locations, records the timeline on a remote desktop, and sends email and brief early warning text messages of alarm when a programmed threshold is exceeded. Furthermore, a dedicated platform is designed to administer and grant users access to a database containing data gathered through the analysis of diagnostic non-destructive methods (i.e., archival documentation, image processing for infrared thermography, acoustic and ultrasonic tomography, and so forth). The system architecture is distinguished by a distributed intelligence consisting of multiple nodes, which enables the remote processing of locally acquired information. The arrangement was strategically planned to optimize the utilization of the wireless digital bus connecting sensors and data storage units. This enables the examination of the case study to be conducted remotely, with all collected data accessible in a suitable semantic web environment. The data can then be analysed and interpreted following the investigation's context. Hence, the present paper aims to provide a detailed vision of the tests conducted on the case study, showcasing the prototype demonstrator's stress test, the analysis layout, and the project's architecture. [ABSTRACT FROM AUTHOR]

The accurate interpretation of CRRT machine alarms is crucial in the intensive care setting. ChatGPT, with its advanced natural language processing capabilities, has emerged as a tool that is evolving and advancing in its ability to assist with healthcare information. This study is designed to evaluate the accuracy of the ChatGPT-3.5 and ChatGPT-4 models in addressing queries related to CRRT alarm troubleshooting. This study consisted of two rounds of ChatGPT-3.5 and ChatGPT-4 responses to address 50 CRRT machine alarm questions that were carefully selected by two nephrologists in intensive care. Accuracy was determined by comparing the model responses to predetermined answer keys provided by critical care nephrologists, and consistency was determined by comparing outcomes across the two rounds. The accuracy rate of ChatGPT-3.5 was 86% and 84%, while the accuracy rate of ChatGPT-4 was 90% and 94% in the first and second rounds, respectively. The agreement between the first and second rounds of ChatGPT-3.5 was 84% with a Kappa statistic of 0.78, while the agreement of ChatGPT-4 was 92% with a Kappa statistic of 0.88. Although ChatGPT-4 tended to provide more accurate and consistent responses than ChatGPT-3.5, there was no statistically significant difference between the accuracy and agreement rate between ChatGPT-3.5 and -4. ChatGPT-4 had higher accuracy and consistency but did not achieve statistical significance. While these findings are encouraging, there is still potential for further development to achieve even greater reliability. This advancement is essential for ensuring the highest-quality patient care and safety standards in managing CRRT machine-related issues. [ABSTRACT FROM AUTHOR]

This study aimed to assess levels of alarm and compassion fatigue among critical care nurses, and investigate whether compassion fatigue is linked to alarm fatigue and can predict its occurrence. Descriptive correlational design was employed in this research. A total of 413 critical care nurses working in the health care network in Istanbul were selected through convenience sampling. Surveys were utilized to collect data regarding alarm fatigue, compassion fatigue, and personal characteristics of critical care nurses. Study findings revealed that participating critical care nurses experienced alarm fatigue and were at risk of compassion fatigue. A positive and statistically significant relationship was found between alarm fatigue and compassion fatigue, indicating alarm fatigue influences compassion fatigue. This study illuminated significant relationships among alarm fatigue and compassion fatigue in critical care nurses. Awareness of the existence of these phenomena may encourage clinical care nurses to seek help in coping with alarm and compassion fatigue, allowing them to provide quality care. [ABSTRACT FROM AUTHOR]

Introduction: Portable oxygen concentrators (POCs) are medical devices that provide supplemental oxygen to patients requiring long-term oxygen therapy. However, little information is available on day-to-day patterns of how or even whether patients actively switch between their POC mobility features and flow setting options. Methods: A retrospective analysis was conducted to assess POC usage among patients who used an Inogen One G5 POC in the USA. This study aimed (1) to describe the patterns of use of POCs, (2) to analyze their compatibility with the prescribed oxygen therapy settings, and (3) to demonstrate the contribution of POC usage to get a standardized long-term oxygen therapy (LTOT). Data were directly downloaded from the devices returned for service or at the end of the Medicare Durable Medical Equipment rental period and streamed via a mobile application from 2018 to 2022. Daily usage, disconnections from the device, use of prescribed pulse delivery settings, breaths per minute, power sources, and movement with the POC were assessed. Device alert histories were also examined. Results: Data revealed a mean daily usage of 4.29 ± 3.23 h/day, ranging from 0.35 to 15.52 h/day. The prescribed pulse delivery setting was used by 31.34% of patients for at least 80% of their POC use time. When the POC was on battery power, patients were moving/mobile 41.99 ± 33.33% of the time. On the basis of the device-generated alerts, some patients continued to use their POC very close to or even beyond the lifetime of the column/sieve bed. Alerts or alarms potentially requiring repair occurred at a rate of 1.63 events per 100 years of use, indicating that device reliability did not significantly influence the use patterns. Conclusion: Patients used their POCs when mobile and at rest. A large proportion of patients adjust their POC settings during the day, which potentially indicates the need for the dynamic individualization of oxygen dose delivery to match activities of daily living or sleep. Patients require follow-up to ensure timely replacement of POC columns. Plain Language Summary: This study aimed to (1) describe the patterns of use of portable oxygen concentrators (POCs), (2) analyze their compatibility with the prescribed oxygen therapy settings, and (3) demonstrate the contribution of POC usage to get a standardized long-term oxygen therapy (LTOT). A retrospective analysis was conducted on data downloaded directly from devices and streamed via a mobile application. Daily usage, disconnections from the device, use of prescribed pulse delivery settings, breaths per minute, power sources, and movement with the device were assessed. Device alert histories were also examined. Patients used their portable oxygen concentrators when mobile and at rest, and large proportion of patients adjust their settings during the day. There was a low incidence of alarms or alerts requiring repairs, indicating device reliability. Patients require follow-up to ensure timely replacement of columns. [ABSTRACT FROM AUTHOR]

Remote Monitoring (RM) has been shown to provide useful information about arrhythmic events in patients with implantable loop recorders (ILRs), however there is few and conflicting data about the false positive (FP) alarms burden and characteristics among ILR recipients. The aim of the present systematic review was to evaluate incidence and characteristics of FP alarms among ILR patients followed by RM. We developed a systematic research in Embase, MEDLINE and PubMed databases and selected all papers focused on false positive ILR transmissions published from June 1, 2013 to June 1, 2023. Case reports, meeting summaries, posters and simple reviews were excluded. Twelve reports were finally selected, including five prospective and seven retrospective studies. Information about population characteristics, device type and setting, overall transmissions and FP alarms and any adopted strategies to reduce them were extracted from an overall population of 3.305 patients. FP alarms were 59.7% of the overall remote transmissions and were found in 1/5 of the analyzed population. FP alarms for atrial fibrillation were the most common cause of false transmissions and were mainly due to premature atrial and ventricular complexes. No clinical predictors of FP alarms were identified, except for nonparasternal ILR implantation site. Since the overload work due to FP alarms might reduce the benefit of remote monitoring of ILR patients, the device optimization is an important step until an help from machine‐learning algorithms is available. [ABSTRACT FROM AUTHOR]

Nocturnal enuresis is a common childhood problem impacting the quality of life of children and families. Treatment with an enuresis alarm is recommended for 8–12 weeks by the International Children's Continence Society as first line management of monosymptomatic nocturnal enuresis. However, the effectiveness of alarm therapy varies between 80% and 45.9%. There is minimal evidence within the literature exploring the factors impacting this varying response to alarm therapy. Therefore, this literature review aims to explore factors that impact the effectiveness of the enuresis alarm as a treatment for nocturnal enuresis, in children aged 5–17 years. Literature searches were conducted on MEDLINE (Ovid), SCOPUS and CINAHL Databases. The PRISMA tool was used to report the data in the search strategy. The inclusion criteria of children aged 5 to 17 years was chosen based on International Children's Continence Society Guidelines. English language, academic journals and studies in the past 10 years were selected as additional inclusion criterion to identify the most recent, robust literature for the review. All 13 primary research articles were critiqued using the Caldwell Framework. Data were extracted and presented in table format highlighting study methodology, sample, duration of treatment, relevance to review topic and key findings. The findings highlight factors influencing the effectiveness of alarm therapy related to the impact on the child and family, heighten arousal to the alarm, the duration of therapy, age of child and the impact of overlearning. This review provides health professionals with an insight into strategies that may help children and their family to respond successfully to enuresis alarm treatment. [ABSTRACT FROM AUTHOR]

This paper presents an original wireless DYU Air Box of an environment-monitoring IoT (EMIoT) system on a campus to offer information on environmental conditions through the public ThingSpeak IoT platform for stakeholders including all the students and employees on the Da-Yeh University (DYU) campus in Taiwan. Firstly, the proposed wireless heterogeneous multi-sensor module aggregates BME680, SCD30, PMS7003, and BH1750 sensors with a TTGO ESP32 Wi-Fi device based on the I2C and UART interface standards of series communication. Through the DYU-802.1X Wi-Fi network with the WPA2 Enterprise security directly, the wireless multi-sensor monitoring module further forwards the observation data of environmental conditions on campus via the DYU-802.1X Wi-Fi network to the public ThingSpeak IoT platform, which is a cloud service platform to aggregate, visualize, and analyze live sensing data of air quality index (AQI), concentrations of PM1.0/2.5 and CO2, brightness, ambient temperature, and relative humidity (RH). The results illustrate the proposed DYU Air Box for monitoring the indoor environmental conditions on campus and validate them with sufficient accuracy and confidence with commercialized measurement instruments. In this work, the wireless smart environment-monitoring IoT system features monitoring and automatic alarm functions for monitoring AQI, CO2, and PM concentrations, as well as ambient illumination, temperature, and RH parameters and collaboration and interoperability through the Enterprise Intranet. All the organizational stakeholders interested in the environmental conditions of the DYU campus can openly access the information according to their interests. In the upcoming future, the information of the environmental conditions in the DYU campus will be developed to be simultaneously accessed by all the stakeholders through both the public ThingSpeak IoT platform and the private EMIoT system. [ABSTRACT FROM AUTHOR]

A model was developed to assess how elevated absolute thresholds and enlarged auditory filters can impede the ability to detect alarms in a noisy background, such alarms being of paramount importance to ensure the safety of workers. Based on previously measured masked thresholds of 80 listeners in five groups (normal hearing to strongly impaired), the model was derived from signal detection theory (SDT) applied to Glasberg and Moore's excitation pattern model. The model can describe the influence of absolute thresholds and enlarged auditory filters together or separately on the detection ability for normal hearing and hearing-impaired listeners with various hearing profiles. Furthermore, it suggests that enlarged auditory filters alone can explain all of the impairment in this specific alarm detection task. Finally, the possibility of further development of the model into an alarm detection model is discussed. [ABSTRACT FROM AUTHOR]

The article discusses the importance of measuring the workload and activity levels in intensive care units (ICUs) and how it can impact patient outcomes. The authors emphasize that the capability of ICU nurses, frequency of alarm responses, and other factors such as delirium, mental status, frailty, and physical capability of patients should also be considered when evaluating the strain of the ICU. They also highlight the need to evaluate the characteristics of each region and country using their own databases. Additionally, the article mentions a model called PROSPECT that predicts long-term physical, mental, and cognitive problems after critical illness using variables collected early in an ICU admission. The model suggests the need for resource allocation and early interventions, such as rehabilitation services and palliative/supportive care, for patients with high PROSPECT scores. Interestingly, illness severity assessed via the Acute Physiology and Chronic Health Evaluation IV score was not found to be an independent predictor of post-ICU disability in the PROSPECT model. [Extracted from the article]

Pulmonary and systemic congestion as a consequence of heart failure are clinically recognized as alarm signals for clinical outcome and mortality. Although signs and symptoms of congestion are well detectable in patients, monitoring of congestion in small animals with heart failure lacks adequate noninvasive methodology yet. Here, we developed a novel ultrasonography-based scoring system to assess pulmonary and systemic congestion in experimental heart failure, by using lung ultrasound (LUS) and imaging of the inferior vena cava (Cava), termed CavaLUS. CavaLUS was established and tested in a rat model of supracoronary aortic banding and a mouse model of myocardial infarction, providing high sensitivity and specificity while correlating to numerous parameters of cardiac performance and disease severity. CavaLUS, therefore, provides a novel comprehensive tool for experimental heart failure in small animals to noninvasively assess congestion. [ABSTRACT FROM AUTHOR]

Aim: In 2017, the Leiden University Medical Centre implemented a secondary alarm system using handheld devices to ensure accurate patient monitoring on the single room NICU. Initially, alarms remained active on the handheld devices until one of the caregivers in the alarm chain accepted the alarm. In 2020, a bidirectional communication protocol (BCP) was implemented, enabling automated withdrawal of resolved alarms. The aim of this study was to evaluate the effect of this implementation on the alarm duration and pressure. Methods: Data of all alarms of the secondary alarm chain in the 90 days before and after the implementation were analysed and compared between both periods. Results: Following the implementation of the BCP, 60% of the alarms were withdrawn before the designated nurse responded. Despite a significant higher total number of alarms, the median alarm duration decreased from 9 (7–14) to 6 (4–10) s, the acceptance rate of the designated nurse increased from 93% to 95% and the median time of alarm sounding per phone per hour significantly decreased from 71 (51–101) to 51 (35–69) s following implementation of the BCP. Conclusion: This study showed that automated withdrawal of resolved alarms significantly reduces alarm duration and pressure on a NICU. [ABSTRACT FROM AUTHOR]

For implementing earthquake early warning (EEW), real‐time parameters of ground motion (GM) which can be obtained at the beginning of earthquake wave arrival (i.e., P‐wave arrival) are usually used to estimate the intensity measure (IM) of the following GM for supporting alarm decision. However, prior studies have proved that the estimated IM is hard to relate structural damage effectively, thus affecting the decision‐making behavior and resulting in more economic losses and casualties. Although the real‐time parameters may be adopted to directly predict structural damage, it is difficult to construct the accurate mathematical model between the parameters and structural damage index. To solve the above problems, a machine learning‐based real‐time damage prediction framework that can directly predict the structural maximum inter‐story drift ratio (MIDR) after P‐wave arrival is proposed for use in EEW. Within the framework of the proposed method, the database for training the prediction model come from the accurate nonlinear time‐history analyses of 32 typical building types under the real GM records, where the real‐time characteristic parameters of P‐wave of the records are taken as the inputs. To achieve accurate and efficient prediction, a total of 32 alternative characteristic parameters of P‐wave are selected and a parameter optimization approach is developed to remove the redundant and useless parameters for various type of building structure. Moreover, an interpretable approach is adopted to explain the effects of different input parameters on the prediction results and demonstrate the reliability of the proposed method. Finally, the framework is thoroughly assessed and investigated through two seismic damage cases and an occurred seismic event, which has an accuracy rate above 96.4% on average for risk‐consistent early‐warning trigger classification. These results show that the proposed method have the ability of continuously stable and excellent prediction power when using it for EEW. [ABSTRACT FROM AUTHOR]

Continuous monitoring of vital signs using a wireless wearable device was implemented in 2018 at a surgical care unit of an academic hospital. This study aimed at gaining insight into nurses' and patients' perspectives regarding the use and innovation of a continuous vital signs monitoring system, three years after its introduction. This qualitative study was performed in a surgical, non-intensive care unit of an academic hospital in 2021. Key-user nurses (nurses with additional training and expertise with the device) and patients were selected for semi-structured interviews, and nurses from the ward were selected for a focus group interview using a topic list. Transcripts of the audio tapes were deductively analysed using four dimensions for adoptions of information and communication technologies (ICT) devices in healthcare. The device provided feelings of safety for nurses and patients. Nurses and patients had a few issues with the device, including the size and the battery life. Nurses gained knowledge and skills in using the system for measurement and interpretations. They perceived the system as a tool to improve the recognition of clinical decline. The use of the system could be further developed regarding the technical device's characteristics, nurses' interpretation of the data and the of type of alarms, the information needs of patients, and clarification of the definition and standardization of continuous monitoring. Three years after the introduction, wireless continuous vital signs monitoring is the new standard of care according to the end-users at the general surgical ward. [ABSTRACT FROM AUTHOR]