Use of augmented and virtual reality in resuscitation training: A systematic review.

Objectives: To evaluate the effectiveness of augmented reality (AR) and virtual reality (VR), compared with other instructional methods, for basic and advanced life support training. Methods: This systematic review was part of the continuous evidence evaluation process of the International Liaison Committee on Resuscitation (ILCOR) and reported based on the Preferred Reporting Items for Systematic review and Meta-Analysis (PRISMA) guidelines and registered with PROSPERO (CRD42023376751). MEDLINE, EMBASE, and SCOPUS were searched from inception to January 16, 2024. We included all published studies comparing virtual or augmented reality to other methods of resuscitation training evaluating knowledge acquisition and retention, skills acquisition and retention, skill performance in real resuscitation, willingness to help, bystander CPR rate, and patients' survival. Results: Our initial literature search identified 1807 citations. After removing duplicates, reviewing the titles and abstracts of the remaining 1301 articles, full text review of 74 articles and searching references lists of relevant articles, 19 studies were identified for analysis. AR was used in 4 studies to provide real-time feedback during CPR, demonstrating improved CPR performance compared to groups trained with no feedback, but no difference when compared to other sources of CPR feedback. VR use in resuscitation training was explored in 15 studies, with the majority of studies that assessed CPR skills favoring other interventions over VR, or showing no difference between groups. Conclusion: Augmented and virtual reality can be used to support resuscitation training of lay people and healthcare professionals, however current evidence does not clearly demonstrate a consistent benefit when compared to other methods of training.

Nurses' occupational physical activity and workload in a perioperative intensive care unit in Slovenia.

The field of nursing includes heavy occupational physical demands, including walking and standing for longer periods of time, in addition to moving and lifting. As such, in the context of a typical work shift, many nurses generally achieve the World Health Organization's recommended 10,000 steps per day. This study aimed at estimating the daily physical activity and workload of nurses in a perioperative intensive care unit. The data sources for this study included data from the hospital information system on various procedures and interventions, and the Silva Ex3 Plus pedometers for measuring steps, kilometers, calories, and activity time across various shifts in a perioperative intensive care unit. Twenty nurses from Slovenia volunteered to participate in this observational study. Over 13 weeks, a nurse working an 8-hour shift walked an average of 5,938 steps (4.4 km). However, nurses who worked a 12-hour weekend day shift came very close to the World Health Organization's recommendation with an average of 9,003 steps (6.5 km). A total of 227 patients were admitted and an average of 80 nursing interventions were performed per day and there was a positive relationship between physical activity, workload, and patient admissions in the perioperative intensive care unit (p = 0.001). Results of this study could help managers better understand nurses' physical activity and workload during various shifts in the perioperative intensive care unit.

Cognitive and Emotional Perceptions of Illness in Patients Diagnosed with Type 2 Diabetes Mellitus.

Type 2 diabetes mellitus (T2DM) affects a patient's physical, social, and mental well-being. Perceptions of the illness are linked to quality of life. The aim of this study was to assess illness perception in patients diagnosed with T2DM and to validate the Brief Illness Perception Questionnaire in the Slovenian language. A cross-sectional study involved 141 patients diagnosed with T2DM. We performed a content analysis of the questionnaire and estimated the S-CVI, I-CVI, kappa coefficient. We also used Cronbach's alpha to assess the reliability. Participants did not have a very threatening perception of T2DM, but being overweight and having cardiovascular disease were significant contributors to a more threatening perception. The most frequently indicated factors influencing the onset and development of T2DM were heredity and genetics, stress and other psychological distress, and poor and inadequate nutrition. I-CVI ranged from 0.833 to 1.00, while the kappa is greater than 0.74, confirming the excellent validity of the questions. The content validity assessment of the questionnaire further confirms that the questionnaire is suitable for use with the target population in Slovenia. The questionnaire proved to be a valid and reliable tool that can be used to assess the relationship between illness perception and self-management of T2DM.

The ChatGPT effect and transforming nursing education with generative AI: Discussion paper.

AIM: The aim of this study is to present the possibilities of nurse education in the use of the Chat Generative Pre-training Transformer (ChatGPT) tool to support the documentation process. BACKGROUND: The success of the nursing process is based on the accuracy of nursing diagnoses, which also determine nursing interventions and nursing outcomes. Educating nurses in the use of artificial intelligence in the nursing process can significantly reduce the time nurses spend on documentation. DESIGN: Discussion paper. METHODS: We used a case study from Train4Health in the field of preventive care to demonstrate the potential of using Generative Pre-training Transformer (ChatGPT) to educate nurses in documenting the nursing process using generative artificial intelligence. Based on the case study, we entered a description of the patient's condition into Generative Pre-training Transformer (ChatGPT) and asked questions about nursing diagnoses, nursing interventions and nursing outcomes. We further synthesized these results. RESULTS: In the process of educating nurses about the nursing process and nursing diagnosis, Generative Pre-training Transformer (ChatGPT) can present potential patient problems to nurses and guide them through the process from taking a medical history, setting nursing diagnoses and planning goals and interventions. Generative Pre-training Transformer (ChatGPT) returned appropriate nursing diagnoses, but these were not in line with the North American Nursing Diagnosis Association - International (NANDA-I) classification as requested. Of all the nursing diagnoses provided, only one was consistent with the most recent version of the North American Nursing Diagnosis Association - International (NANDA-I). Generative Pre-training Transformer (ChatGPT) is still not specific enough for nursing diagnoses, resulting in incorrect answers in several cases. CONCLUSIONS: Using Generative Pre-training Transformer (ChatGPT) to educate nurses and support the documentation process is time-efficient, but it still requires a certain level of human critical-thinking and fact-checking.

Artificial intelligence based prediction models for individuals at risk of multiple diabetic complications: A systematic review of the literature.

AIM: The aim of this review is to examine the effectiveness of artificial intelligence in predicting multimorbid diabetes-related complications. BACKGROUND: In diabetic patients, several complications are often present, which have a significant impact on the quality of life; therefore, it is crucial to predict the level of risk for diabetes and its complications. EVALUATION: International databases PubMed, CINAHL, MEDLINE and Scopus were searched using the terms artificial intelligence, diabetes mellitus and prediction of complications to identify studies on the effectiveness of artificial intelligence for predicting multimorbid diabetes-related complications. The results were organized by outcomes to allow more efficient comparison. KEY ISSUES: Based on the inclusion/exclusion criteria, 11 articles were included in the final analysis. The most frequently predicted complications were diabetic neuropathy (n = 7). Authors included from two to a maximum of 14 complications. The most commonly used prediction models were penalized regression, random forest and Naïve Bayes model neural network. CONCLUSION: The use of artificial intelligence can predict the risks of diabetes complications with greater precision based on available multidimensional datasets and provides an important tool for nurses working in preventive health care. IMPLICATIONS FOR NURSING MANAGEMENT: Using artificial intelligence contributes to a better quality of care, better autonomy of patients in diabetes management and reduction of complications, costs of medical care and mortality.

Emotional Intelligence among Nursing Students: Findings from a Longitudinal Study.

Emotional intelligence is an important factor for nursing students' success and work performance. Although the level of emotional intelligence increases with age and tends to be higher in women, results of different studies on emotional intelligence in nursing students vary regarding age, study year, and gender. A longitudinal study was conducted in 2016 and 2019 among undergraduate nursing students to explore whether emotional intelligence changes over time. A total of 111 undergraduate nursing students participated in the study in the first year of their study, and 101 in the third year. Data were collected using the Trait Emotional Intelligence Questionnaire Short Form (TEIQue-SF) and Schutte Self Report Emotional Intelligence Test (SSEIT). There was a significant difference in emotional intelligence between students in their first (M = 154.40; 95% CI: 101.85-193.05) and third year (M = 162.01; 95% CI: 118.65-196.00) of study using TEIQue-SF questionnaire. There was a weak correlation (r = 0.170) between emotional intelligence and age measuring using the TEIQue-SF questionnaire, and no significant correlation when measured using SSEIT (r = 0.34). We found that nursing students' emotional intelligence changes over time with years of education and age, suggesting that emotional intelligence skills can be improved. Further research is needed to determine the gendered nature of emotional intelligence in nursing students.

The Impact of Mobile Health Use on the Self-care of Patients With Type 2 Diabetes: Protocol for a Randomized Controlled Trial.

BACKGROUND: Chronic diseases are a substantial public health issue worldwide and affect an individual's quality of life. Due to the alarming rise in type 2 diabetes, health care that was primarily focused on diagnosis and treatment of the disease is increasingly focused on prevention and self-care. Patients who adhere to a constant and strict treatment regimen (physical activity, diet, medication) and regularly monitor their health are more likely to maintain self-care and health, prevent the exacerbation of the disease, and avoid the complications of diabetes (retinopathy, diabetic feet, etc). In recent years, many innovative devices that have become increasingly present in inpatient health care, such as mobile apps, are available to help patients maintain consistency in monitoring their health status. Mobile apps make it easier for individuals to monitor their self-care and illness and follow instructions regarding disease control. OBJECTIVE: This study aims to determine the impact of mobile app use on self-care in patients with type 2 diabetes. We will evaluate and test the usefulness of the forDiabetes app as a tool to improve the self-care of individuals with type 2 diabetes. METHODS: We will perform a double-blind randomized controlled trial. The study will include individuals aged over 18 years diagnosed with or have regulated type 2 diabetes who are treated in family medicine practices. Additionally, the individuals included in the study should not have any acute complications due to the consequences of type 2 diabetes. They will use an Android or iOS mobile phone and a blood glucose meter during the investigation. With the help of simple randomization, individuals will be divided into the intervention and control groups. Individuals in the intervention group will use the forDiabetes mobile app to monitor their self-care for type 2 diabetes. Individuals in the control group will not receive a particular intervention. Data will be collected using the Self-care of Diabetes Inventory questionnaire and Brief Illness Perception Questionnaire. Blood sugar, blood pressure, glycated hemoglobin (HbA1c), and weight measurements will be monitored using calibrated instruments during the study by the nurses employed at the family medicine practice. Data will be collected at the beginning of the study and after a patient visits the family medicine practice. RESULTS: In the first half of 2020, we have prepared a translation of the mobile app that will be used by the participants of the intervention group, as well as more detailed instructions for using the app. We have also prepared a translation of the questionnaires in Slovene. The research results will be published in 2023. CONCLUSIONS: This research contributes to greater visibility and usability of mobile apps for the self-care of patients with type 2 diabetes and raises awareness of the possible use of innovative methods. TRIAL REGISTRATION: Clinicaltrials.gov NCT04999189; https://clinicaltrials.gov/ct2/show/NCT04999189. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/31652.

Extracting New Temporal Features to Improve the Interpretability of Undiagnosed Type 2 Diabetes Mellitus Prediction Models.

Type 2 diabetes mellitus (T2DM) often results in high morbidity and mortality. In addition, T2DM presents a substantial financial burden for individuals and their families, health systems, and societies. According to studies and reports, globally, the incidence and prevalence of T2DM are increasing rapidly. Several models have been built to predict T2DM onset in the future or detect undiagnosed T2DM in patients. Additional to the performance of such models, their interpretability is crucial for health experts, especially in personalized clinical prediction models. Data collected over 42 months from health check-up examinations and prescribed drugs data repositories of four primary healthcare providers were used in this study. We propose a framework consisting of LogicRegression based feature extraction and Least Absolute Shrinkage and Selection operator based prediction modeling for undiagnosed T2DM prediction. Performance of the models was measured using Area under the ROC curve (AUC) with corresponding confidence intervals. Results show that using LogicRegression based feature extraction resulted in simpler models, which are easier for healthcare experts to interpret, especially in cases with many binary features. Models developed using the proposed framework resulted in an AUC of 0.818 (95% Confidence Interval (CI): 0.812-0.823) that was comparable to more complex models (i.e., models with a larger number of features), where all features were included in prediction model development with the AUC of 0.816 (95% CI: 0.810-0.822). However, the difference in the number of used features was significant. This study proposes a framework for building interpretable models in healthcare that can contribute to higher trust in prediction models from healthcare experts.

A Review of Mortality Risk Prediction Models in Smartphone Applications.

Healthcare professionals in healthcare systems need access to freely available, real-time, evidence-based mortality risk prediction smartphone applications to facilitate resource allocation. The objective of this study is to evaluate the quality of smartphone mobile health applications that include mortality prediction models, and corresponding information quality. We conducted a systematic review of commercially available smartphone applications in Google Play for Android, and iTunes for iOS smartphone applications. We performed initial screening, data extraction, and rated smartphone application quality using the Mobile Application Rating Scale: user version (uMARS). The information quality of smartphone applications was evaluated using two patient vignettes, representing low and high risk of mortality, based on critical care data from the Medical Information Mart for Intensive Care (MIMIC) III database. Out of 3051 evaluated smartphone applications, 33 met our final inclusion criteria. We identified 21 discrete mortality risk prediction models in smartphone applications. The most common mortality predicting models were Sequential Organ Failure Assessment (SOFA) (n = 15) and Acute Physiology and Clinical Health Assessment II (n = 13). The smartphone applications with the highest quality uMARS scores were Observation-NEWS 2 (4.64) for iOS smartphones, and MDCalc Medical Calculator (4.75) for Android smartphones. All SOFA-based smartphone applications provided consistent information quality with the original SOFA model for both the low and high-risk patient vignettes. We identified freely available, high-quality mortality risk prediction smartphone applications that can be used by healthcare professionals to make evidence-based decisions in critical care environments.

Perception of the Online Learning Environment of Nursing Students in Slovenia: Validation of the DREEM Questionnaire.

At the time of the outbreak of the coronavirus pandemic, several measures were in place to limit the spread of the virus, such as lockdown and restriction of social contacts. Many colleges thus had to shift their education from personal to online form overnight. The educational environment itself has a significant influence on students' learning outcomes, knowledge, and satisfaction. This study aims to validate the tool for assessing the educational environment in the Slovenian nursing student population. To assess the educational environment, we used the DREEM tool distributed among nursing students using an online platform. First, we translated the survey questionnaire from English into Slovenian using the reverse translation technique. We also validated the DREEM survey questionnaire. We performed psychometric testing and content validation. I-CVI and S-CVI are at an acceptable level. A high degree of internal consistency was present, as Cronbach's alpha was 0.951. The questionnaire was completed by 174 participants, of whom 30 were men and 143 were women. One person did not define gender. The mean age of students was 21.1 years (SD = 3.96). The mean DREEM score was 122.2. The mean grade of student perception of learning was 58.54%, student perception of teachers was 65.68%, student academic self-perception was 61.88%, student perception of the atmosphere was 60.63%, and social self-perception of students was 58.93%. Although coronavirus has affected the educational process, students still perceive the educational environment as positive. Nevertheless, there is still room for improvement in all assessed areas.

Evaluating Quality, Usability, Evidence-Based Content, and Gamification Features in Mobile Learning Apps Designed to Teach Children Basic Life Support: Systematic Search in App Stores and Content Analysis.

BACKGROUND: Globally, 3.7 million people die of sudden cardiac death annually. Following the World Health Organization endorsement of the Kids Save Lives statements, initiatives to train school-age children in basic life support (BLS) have been widespread. Mobile phone apps, combined with gamification, represent an opportunity for including mobile learning (m-learning) in teaching schoolchildren BLS as an additional teaching method; however, the quality of these apps is questionable. OBJECTIVE: This study aims to systematically evaluate the quality, usability, evidence-based content, and gamification features (GFs) of commercially available m-learning apps for teaching guideline-directed BLS knowledge and skills to school-aged children. METHODS: We searched the Google Play Store and Apple iOS App Store using multiple terms (eg, cardiopulmonary resuscitation [CPR] or BLS). Apps meeting the inclusion criteria were evaluated by 15 emergency health care professionals using the user version of the Mobile Application Rating Scale and System Usability Scale. We modified a five-finger mnemonic for teaching schoolchildren BLS and reviewed the apps' BLS content using standardized criteria based on three CPR guidelines. GFs in the apps were evaluated using a gamification taxonomy. RESULTS: Of the 1207 potentially relevant apps, only 6 (0.49%) met the inclusion criteria. Most apps were excluded because the content was not related to teaching schoolchildren BLS. The mean total scores for the user version of the Mobile Application Rating Scale and System Usability Scale score were 3.2/5 points (95% CI 3.0-3.4) and 47.1/100 points (95% CI 42.1-52.1), respectively. Half of the apps taught hands-only CPR, whereas the other half also included ventilation. All the apps indicated when to start chest compressions, and only 1 app taught BLS using an automated external defibrillator. Gamification was well integrated into the m-learning apps for teaching schoolchildren BLS, whereas the personal and fictional, educational, and performance gamification groups represented most GFs. CONCLUSIONS: Improving the quality and usability of BLS content in apps and combining them with GFs can offer educators novel m-learning tools to teach schoolchildren BLS skills.

Digital Tools in Behavior Change Support Education in Health and Other Students: A Systematic Review.

Due to the increased prevalence of chronic diseases, behavior changes are integral to self-management. Healthcare and other professionals are expected to support these behavior changes, and therefore, undergraduate students should receive up-to-date and evidence-based training in this respect. Our work aims to review the outcomes of digital tools in behavior change support education. A secondary aim was to examine existing instruments to assess the effectiveness of these tools. A PIO (population/problem, intervention, outcome) research question led our literature search. The population was limited to students in nursing, sports sciences, and pharmacy; the interventions were limited to digital teaching tools; and the outcomes consisted of knowledge, motivation, and competencies. A systematic literature review was performed in the PubMed, CINAHL, MEDLINE, Web of Science, SAGE, Scopus, and Cochrane Library databases and by backward citation searching. We used PRISMA guidelines 2020 to depict the search process for relevant literature. Two authors evaluated included studies using the Mixed Methods Appraisal Tool (MMAT) independently. Using inclusion and exclusion criteria, we included 15 studies in the final analysis: six quantitative descriptive studies, two randomized studies, six mixed methods studies, and one qualitative study. According to the MMAT, all studies were suitable for further analysis in terms of quality. The studies resorted to various digital tools to improve students' knowledge of behavior change techniques in individuals with chronic disease, leading to greater self-confidence, better cooperation, and practical experience and skills. The most common limitations that have been perceived for using these tools are time and space constraints.

The Effects of Gamification and Oral Self-Care on Oral Hygiene in Children: Systematic Search in App Stores and Evaluation of Apps.

BACKGROUND: Poor oral hygiene is a great public health problem worldwide. Oral health care education is a public health priority as the maintenance of oral hygiene is integral to overall health. Maintaining optimal oral hygiene among children is challenging and can be supported by using relevant motivational approaches. OBJECTIVE: The primary aim of this study was to identify mobile smartphone apps that include gamification features focused on motivating children to learn, perform, and maintain optimal oral hygiene. METHODS: We searched six online app stores using four search terms ("oral hygiene game," "oral hygiene gamification," "oral hygiene brush game," and "oral hygiene brush gamification"). We identified gamification features, identified whether apps were consistent with evidence-based dentistry, performed a quality appraisal with the Mobile App Rating Scale user version (uMARS), and quantified behavior scores (Behavior Change score, uMARS score, and Coventry, Aberdeen, and London-Refined [CALO-RE] score) using three different instruments that measure behavior change. RESULTS: Of 612 potentially relevant apps included in the analysis, 17 met the inclusion criteria. On average, apps included 6.87 (SD 4.18) out of 31 possible gamification features. The most frequently used gamification features were time pressure (16/17, 94%), virtual characters (14/17, 82%), and fantasy (13/17, 76%). The most common oral hygiene evidence-based recommendation was brushing time (2-3 minutes), which was identified in 94% (16/17) of apps. The overall mean uMARS score for app quality was high (4.30, SD 0.36), with good mean subjective quality (3.79, SD 0.71) and perceived impact (3.58, SD 0.44). Sufficient behavior change techniques based on three taxonomies were detected in each app. CONCLUSIONS: The majority of the analyzed oral hygiene apps included gamification features and behavior change techniques to perform and maintain oral hygiene in children. Overall, the apps contained some educational content consistent with evidence-based dentistry and high-quality background for oral self-care in children; however, there is scope for improvement.

Evaluation of Mobile Phone Mortality Risk Score Applications Using Data from the Electronic Medical Records.

Various mobile phone apps in the form of medical calculators are available for different prognostic assessments, especially for patients in intensive care units. We performed a systematic review of mobile phone apps in online mobile phone stores to identify apps for mortality risk prediction in intensive care units. Out of 2737 potential mobile phone apps, we included 20 of them in the final content analysis. The most frequently used mortality risk model was Sequential Organ Failure Assessment also known as SOFA. The mobile phone apps were compared based on realistic electronic medical record data. The discrepancies were shown in patients with lower mortality rate. Our results show that this kind of mobile phone apps can be helpful to healthcare professionals and are appropriate for use in clinical practices in most cases.