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.

Effectiveness of school-based mental well-being interventions among adolescents: A systematic review.

AIMS: This systematic review aimed to identify school-based interventions for ensuring mental health and well-being of adolescents, synthesize existing interventions, and summarize the quality of identified studies. DESIGN: A systematic review, analysis, and synthesis were performed. DATA SOURCES: Search was performed in Cochrane Library, PsychARTICLES, Web of Science, CINAHL, and Medline. REVIEW METHODS: Literature search was performed in March 2019 using inclusion and exclusion criteria. PRISMA guidelines were followed. Identified records were reviewed by title, abstract, and by the full text by two independent researchers. Three authors independently made a quality assessment of the included studies. Included studies were extracted and synthesized. A systematic review was registered in PROSPERO (CRD42019128919). RESULTS: The initial search yielded 1,199 articles. Of them, 57 articles were included in the final analysis and synthesis. Only four studies were assessed as high quality. Identified themes were mental health and well-being, positive psychology, problem-solving and stress reduction, mindfulness, and physical activity. More than half (N = 32, 56.14%) interventions showed a positive outcome after implementation. Most of those interventions focused on positive psychology and mindfulness. CONCLUSION: Mental well-being is important for the healthy development of adolescents. Countries are aware that healthy adolescents will become healthy adults who will contribute to his/her community and will lower costs of the absence of work and treatments. Thus, they support and invest in interventions that prevent mental disorders. There is a need for developing multidimensional mental well-being interventions that are effective in low- and secondary-income countries. IMPACT: This study ensured rigorous methodology, followed PRISMA recommendations and evaluated quality of identified literature using the GRADE guidelines. A critical synthesis was performed to produce an integrated conceptualization of the evidence. The synthesis represents a list of effective school interventions for the promotion of adolescents' mental well-being.

Validation of the Warwick-Edinburgh Mental Well-being Scale among nursing students in Slovenia.

AIM: The aim of this study was to assess the validity of the Warwick-Edinburgh Mental Well-being Scale used for measuring mental well-being. BACKGROUND: Nursing students' mental well-being is often poor due to various academic and personal stressors. Nursing students are involved in clinical practice and are facing birth, death, health, diseases and other stressful situations. They may be exposed to higher levels of stress than students from other study programmes. METHODS: A cross-sectional study was conducted among nursing students in Slovenia. We performed a 6-step analysis of the psychometric properties of the Warwick-Edinburgh Mental Well-being Scale. Moreover, content validity of the scale was assessed. RESULTS: The scale formed a unidimensional scale with good homogeneity (H < 0.40) and reliability (α = 0.91; ß = 0.87; λ = 0.92; ω = 0.91). The confirmatory factor analysis suggested that the WEMWBS was suitable for use as a single scale (RMSEA = 0.085, CFI = 0.907; TLI = 0.891) and measures one construct, mental well-being. I-CVI is acceptable for all 14 items, kappa coefficient was excellent, and S-CVI was assessed as acceptable. CONCLUSIONS: The Slovenian version of the scale achieved good validity and reliability in a sample of nursing students and is recommended for future usage. IMPLICATIONS FOR NURSING MANAGEMENT: The validated questionnaire can be used by nurse managers to assess nursing students' mental well-being during their clinical placement.

R you ready? Using the R programme for statistical analysis and graphics.

For conducting research, nurses typically use commercial statistical packages. R software is a free, powerful, and flexible alternative, but is less familiar and used less frequently in nursing research. In this paper, we use data from a previous study to demonstrate a few typical steps in exploratory data analysis using R. A step-by-step description of some basic analyses in R is provided here, including examples of specific functions to read and manipulate the data, calculate scores from individual questionnaire items, and prepare a correlation plot and summary table.

Development and validation of the type 2 diabetes mellitus 10-year risk score prediction models from survey data.

AIMS: In this paper, we demonstrate the development and validation of the 10-years type 2 diabetes mellitus (T2DM) risk prediction models based on large survey data. METHODS: The Survey of Health, Ageing and Retirement in Europe (SHARE) data collected in 12 European countries using 53 variables representing behavioural as well as physical and mental health characteristics of the participants aged 50 or older was used to build and validate prediction models. To account for strongly unbalanced outcome variables, each instance was assigned a weight according to the inverse proportion of the outcome label when the regularized logistic regression model was built. RESULTS: A pooled sample of 16,363 individuals was used to build and validate a global regularized logistic regression model that achieved an area under the receiver operating characteristic curve of 0.702 (95% CI: 0.698-0.706). Additionally, we measured performance of local country-specific models where AUROC ranged from 0.578 (0.565-0.592) to 0.768 (0.749-0.787). CONCLUSIONS: We have developed and validated a survey-based 10-year T2DM risk prediction model for use across 12 European countries. Our results demonstrate the importance of re-calibration of the models as well as strengths of pooling the data from multiple countries to reduce the variance and consequently increase the precision of the results.

Early detection of type 2 diabetes mellitus using machine learning-based prediction models.

Most screening tests for T2DM in use today were developed using multivariate regression methods that are often further simplified to allow transformation into a scoring formula. The increasing volume of electronically collected data opened the opportunity to develop more complex, accurate prediction models that can be continuously updated using machine learning approaches. This study compares machine learning-based prediction models (i.e. Glmnet, RF, XGBoost, LightGBM) to commonly used regression models for prediction of undiagnosed T2DM. The performance in prediction of fasting plasma glucose level was measured using 100 bootstrap iterations in different subsets of data simulating new incoming data in 6-month batches. With 6 months of data available, simple regression model performed with the lowest average RMSE of 0.838, followed by RF (0.842), LightGBM (0.846), Glmnet (0.859) and XGBoost (0.881). When more data were added, Glmnet improved with the highest rate (+ 3.4%). The highest level of variable selection stability over time was observed with LightGBM models. Our results show no clinically relevant improvement when more sophisticated prediction models were used. Since higher stability of selected variables over time contributes to simpler interpretation of the models, interpretability and model calibration should also be considered in development of clinical prediction models.

What motivates students to enter nursing? Findings from a cross-sectional study.

INTRODUCTION: It is expected that future nurses have high levels of emotional intelligence and empathy, because of their everyday interaction with people in a clinical environment. Thus, nursing students must show interest in nursing studies and in work with people. Moreover, it is desired that they have good communication skills. On the other hand, students who choose nursing as their future career may have high expectations from nursing education. The aim of this study was to explore nursing students' reasons for pursuing nursing studies. METHODS: A cross-sectional study was conducted among undergraduate nursing students in Slovenia and Croatia. RESULTS: A total of 314 students participated in the study. General satisfaction with their studies is higher among those students who are satisfied with their chosen study programme. The most important reasons to enter nursing studies were interest in the subject and good employment possibilities. As the biggest disadvantage of studying nursing, Slovenian students listed crowded schedules, while Croatian students mentioned faculty organization. Students from both countries agreed that the biggest advantage is the ease of finding a job after graduation. DISCUSSION: As there is a lack of workforce in the healthcare sector, particularly a lack of nurses, universities must adjust their demands and improve study conditions to gain students' attention. Therefore, it is important to notice that nursing students perceive more advantages of studying nursing than disadvantages. CONCLUSION: There are many different reasons to enter nursing studies. Students perceive many advantages in studying nursing, such as ease of employment, getting many hours of clinical practice and the possibility for promotion. On the other hand, there are some challenges in studying nursing, such as the schedule and organization of lectures, seminars and clinical placement. There is a need for further research in the field of nursing student's motivation, especially due to their decreasing motivation during the studies.

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.

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.

Mental well-being among nursing students in Slovenia and Northern Ireland: A survey.

Mental well-being is a key for successful and productive living of each individual. An imbalance can occur due to various stressors and environmental factors. Due to academic pressures, distance from home and financial burden, nursing students often meet with mental health problems. The objective of this study was to determine the mental well-being of nursing students in Slovenia and Northern Ireland, and to compare the results obtained. A descriptive cross-sectional study design was used. The survey was carried out in 2017 among nursing students in Slovenia and Northern Ireland using the Warwick-Edinburgh Mental Wellbeing Scale. The study included 90 students from Slovenia and 109 from Northern Ireland. Nursing students in both countries reported average level of mental well-being. Nursing students in Slovenia have significantly higher (p < 0.001) level of mental well-being than nursing students in Northern Ireland. There are some areas that demand special attention by nurse educators to support the mental well-being of students and the impact of this on their education. Further research needs to be undertaken to find out how to improve students' mental well-being and identify factors that are influencing mental well-being of nursing students.

Emotional intelligence among nursing students: Findings from a cross-sectional study.

BACKGROUND: Emotional intelligence in nursing is of global interest. International studies identify that emotional intelligence influences nurses' work and relationships with patients. It is associated with compassion and care. Nursing students scored higher on measures of emotional intelligence compared to students of other study programmes. The level of emotional intelligence increases with age and tends to be higher in women. OBJECTIVES: This study aims to measure the differences in emotional intelligence between nursing students with previous caring experience and those without; to examine the effects of gender on emotional intelligence scores; and to test whether nursing students score higher than engineering colleagues on emotional intelligence measures. DESIGN: A cross-sectional descriptive study design was used. SETTINGS AND PARTICIPANTS: The study included 113 nursing and 104 engineering students at the beginning of their first year of study at a university in Slovenia. DATA: Emotional intelligence was measured using the Trait Emotional Intelligence Questionnaire (TEIQue) and Schutte Self Report Emotional Intelligence Test (SSEIT). METHODS: Shapiro-Wilk's test of normality was used to test the sample distribution, while the differences in mean values were tested using Student t-test of independent samples. RESULTS: Emotional intelligence was higher in nursing students (n = 113) than engineering students (n = 104) in both measures [TEIQue t = 3.972; p < 0.001; SSEIT t = 8.288; p < 0.001]. Although nursing female students achieved higher emotional intelligence scores than male students on both measures, the difference was not statistically significant [TEIQue t = -0.839; p = 0.403; SSEIT t = -1.159; p = 0.249]. EI scores in nursing students with previous caring experience were not higher compared to students without such experience for any measure [TEIQue t = -1.633; p = 0.105; SSEIT t = -0.595; p = 0.553]. CONCLUSIONS: Emotional intelligence was higher in nursing than engineering students, and slightly higher in women than men. It was not associated with previous caring experience.