Hybrid visualization-based framework for depressive state detection and characterization of atypical patients.

INTRODUCTION: Depression is a global concern, with a significant number of people affected worldwide, particularly in low- and middle-income countries. The rising prevalence of depression emphasizes the importance of early detection and understanding the origins of such conditions. OBJECTIVE: This paper proposes a framework for detecting depression using a hybrid visualization approach that combines local and global interpretation. This approach aims to assist in model adaptation, provide insights into patient characteristics, and evaluate prediction model suitability in a different environment. METHODS: This study utilizes R programming language with the Caret, ggplot2, Plotly, and Dalex libraries for model training, visualization, and interpretation. Data from the NHANES repository was used for secondary data analysis. The NHANES repository is a comprehensive source for examining health and nutrition of individuals in the United States, and covers demographic, dietary, medication use, lifestyle choices, reproductive and mental health data. Penalized logistic regression models were built using NHANES 2015-2018 data, while NHANES 2019-March 2020 data was used for evaluation at the global-specific and local level interpretation. RESULTS: The prediction model that supports this framework achieved an average AUC score of 0.748 (95% CI: 0.743-0.752), with minimal variability in sensitivity and specificity. CONCLUSION: The built-in prediction model highlights chest pain, the ratio of family income to poverty, and smoking status as crucial features for predicting depressive states in both the original and local environments.

The KIDSCREEN-27 scale: translation and validation study of the Slovenian version.

BACKGROUND: There are many methods available for measuring social support and quality of life (QoL) of adolescents, of these, the KIDSCREEN tools are most widely used. Thus, we aimed to translate and validate the KIDSCREEN-27 scale for the usage among adolescents aged between 10 and 19 years old in Slovenia. METHODS: A cross-sectional study was conducted among 2852 adolescents in primary and secondary school from November 2019 to January 2020 in Slovenia. 6-steps method of validation was used to test psychometric properties of the KIDSCREEN-27 scale. We checked descriptive statistics, performed a Mokken scale analysis, parametric item response theory, factor analysis, classical test theory and total (sub)scale scores. RESULTS: All five subscales of the KIDSCREEN-27 formed a unidimensional scale with good homogeneity and reliability. The confirmatory factor analysis showed poor fit in user model versus baseline model metrics (CFI = 0.847; TLI = 0.862) and good fit in root mean square error (RMSEA = 0.072; p(χ2) < 0.001). A scale reliability was calculated using Cronbach's α (0.93), beta (0.86), G6 (0.95) and omega (0.93). CONCLUSIONS: The questionnaire showed average psychometric properties and can be used among adolescents in Slovenia to find out about their quality of life. Further research is needed to explore why fit in user model metrics is poor.

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.

Health Recommender Systems: Systematic Review.

BACKGROUND: Health recommender systems (HRSs) offer the potential to motivate and engage users to change their behavior by sharing better choices and actionable knowledge based on observed user behavior. OBJECTIVE: We aim to review HRSs targeting nonmedical professionals (laypersons) to better understand the current state of the art and identify both the main trends and the gaps with respect to current implementations. METHODS: We conducted a systematic literature review according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and synthesized the results. A total of 73 published studies that reported both an implementation and evaluation of an HRS targeted to laypersons were included and analyzed in this review. RESULTS: Recommended items were classified into four major categories: lifestyle, nutrition, general health care information, and specific health conditions. The majority of HRSs use hybrid recommendation algorithms. Evaluations of HRSs vary greatly; half of the studies only evaluated the algorithm with various metrics, whereas others performed full-scale randomized controlled trials or conducted in-the-wild studies to evaluate the impact of HRSs, thereby showing that the field is slowly maturing. On the basis of our review, we derived five reporting guidelines that can serve as a reference frame for future HRS studies. HRS studies should clarify who the target user is and to whom the recommendations apply, what is recommended and how the recommendations are presented to the user, where the data set can be found, what algorithms were used to calculate the recommendations, and what evaluation protocol was used. CONCLUSIONS: There is significant opportunity for an HRS to inform and guide health actions. Through this review, we promote the discussion of ways to augment HRS research by recommending a reference frame with five design guidelines.

Impact of Person-Centered Interventions on Patient Outcomes in Acute Care Settings: A Systematic Review.

BACKGROUND: Preventing adverse events is one of the most important issues in health care. PURPOSE: The purpose of this systematic review was to determine the impact of person-centered interventions on patient outcomes in an acute care setting. METHODS: The review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. Eligible interventions included person-centered interventions that address at least one of these outcomes: pressure ulcer, accidental falls, medication errors, and/or cross infection. RESULTS: The review showed that there is a paucity of evidence supporting the use of person-centered interventions in reducing patient falls. For the other outcomes, existing research provides an insufficient evidence base on which to draw conclusions. CONCLUSIONS: Theory of person-centeredness is still in its ascendency. Poor evidence may also be the result of quantitative research designs that are insufficient in studying the impact of a person-centered approach. We postulate that use of mixed-methods designs is beneficial and would give a clearer picture of the impact of person-centered interventions.

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.

Nurses' perceptions of early person-centred palliative care: a cross-sectional descriptive study.

BACKGROUND: Palliative care is aimed at improving the quality of life of an individual with chronic noncommunicable disease and their care partners. Limitations in the provision of palliative care are mainly lack of knowledge and experience by nurses, fear of treating palliative persons, loss of control over treatment and fear of providing poor-quality palliative care to persons and care partners. AIM: The aim of this study was to investigate the perception, knowledge and attitudes of palliative care by nurses who use palliative care approaches in practice, as well as the difference in perception, knowledge and attitudes of palliative care between nurses in Slovenia and Finland. METHODS: We conducted a cross-sectional descriptive study. The survey included 440 nurses in clinical environments in Slovenia and Finland with a completed bachelor, master or doctoral level of education. RESULTS: We found statistically significant differences between both countries in the perception of palliative care. Differences between the two countries in the knowledge of palliative care were not confirmed. We confirmed statistically significant differences between both countries in the attitudes of palliative nursing care. CONCLUSION: Early person-centred palliative care is an important part of the holistic and integrative treatment of a person who has a disease with disturbing symptoms. For such an approach, it is important to educate nurses about knowledge, expectations, values and beliefs in developing a concept of person-centred palliative care to improve quality of life. The better perception, knowledge and attitudes of palliative care by nurses may help persons to improve and raise their quality of life, as well as diminish stress in their care partners and improve quality of life.

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.

Recognizing the intensity of strength training exercises with wearable sensors.

In this paper we propose a system based on a network of wearable accelerometers and an off-the-shelf smartphone to recognize the intensity of stationary activities, such as strength training exercises. The system uses a hierarchical algorithm, consisting of two layers of Support Vector Machines (SVMs), to first recognize the type of exercise being performed, followed by recognition of exercise intensity. The first layer uses a single SVM to recognize the type of the performed exercise. Based on the recognized type a corresponding intensity prediction SVM is selected on the second layer, specializing in intensity prediction for the recognized type of exercise. We evaluate the system for a set of upper-body exercises using different weight loads. Additionally, we compare the most important features for exercise and intensity recognition tasks and investigate how different sliding window combinations, sensor configurations and number of training subjects impact the algorithm performance. We perform all of the experiments for two different types of features to evaluate the feasibility of implementation on resource constrained hardware. The results show the algorithm is able to recognize exercise types with approximately 85% accuracy and 6% intensity prediction error. Furthermore, due to similar performance using different types of features, the algorithm offers potential for implementation on resource constrained hardware.

Analyzing Information Seeking and Drug-Safety Alert Response by Health Care Professionals as New Methods for Surveillance.

BACKGROUND: Patterns in general consumer online search logs have been used to monitor health conditions and to predict health-related activities, but the multiple contexts within which consumers perform online searches make significant associations difficult to interpret. Physician information-seeking behavior has typically been analyzed through survey-based approaches and literature reviews. Activity logs from health care professionals using online medical information resources are thus a valuable yet relatively untapped resource for large-scale medical surveillance. OBJECTIVE: To analyze health care professionals' information-seeking behavior and assess the feasibility of measuring drug-safety alert response from the usage logs of an online medical information resource. METHODS: Using two years (2011-2012) of usage logs from UpToDate, we measured the volume of searches related to medical conditions with significant burden in the United States, as well as the seasonal distribution of those searches. We quantified the relationship between searches and resulting page views. Using a large collection of online mainstream media articles and Web log posts we also characterized the uptake of a Food and Drug Administration (FDA) alert via changes in UpToDate search activity compared with general online media activity related to the subject of the alert. RESULTS: Diseases and symptoms dominate UpToDate searches. Some searches result in page views of only short duration, while others consistently result in longer-than-average page views. The response to an FDA alert for Celexa, characterized by a change in UpToDate search activity, differed considerably from general online media activity. Changes in search activity appeared later and persisted longer in UpToDate logs. The volume of searches and page view durations related to Celexa before the alert also differed from those after the alert. CONCLUSIONS: Understanding the information-seeking behavior associated with online evidence sources can offer insight into the information needs of health professionals and enable large-scale medical surveillance. Our Web log mining approach has the potential to monitor responses to FDA alerts at a national level. Our findings can also inform the design and content of evidence-based medical information resources such as UpToDate.

Mobile Applications for Type 2 Diabetes Risk Estimation: a Systematic Review.

Screening for chronical diseases like type 2 diabetes can be done using different methods and various risk tests. This study present a review of type 2 diabetes risk estimation mobile applications focusing on their functionality and availability of information on the underlying risk calculators. Only 9 out of 31 reviewed mobile applications, featured in three major mobile application stores, disclosed the name of risk calculator used for assessing the risk of type 2 diabetes. Even more concerning, none of the reviewed applications mentioned that they are collecting the data from users to improve the performance of their risk estimation calculators or offer users the descriptive statistics of the results from users that already used the application. For that purpose the questionnaires used for calculation of risk should be upgraded by including the information on the most recent blood sugar level measurements from users. Although mobile applications represent a great future potential for health applications, developers still do not put enough emphasis on informing the user of the underlying methods used to estimate the risk for a specific clinical condition.

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.

Psychometric Testing of the Slovene Version of the Perceived Inventory of Technological Competency as Caring in Nursing.

The Perceived Inventory of Technological Competency as Caring in Nursing (PITCCN) questionnaire has been designed to measure technological competency as caring in nursing practice. It incorporates the use of technology with the fundamental principles of caring that are central to nursing. As there were no psychometrically sound instruments to quantify the concept of technological competency as caring in the Slovene language, we adapted the English version of the questionnaire to the local environment. The goal was to assess the level of psychometric properties of the PITCCN investigated in Slovene hospitals. METHODS: Content validity was conducted with eight experts and quantified by the content validity index (CVI) and the modified Cohen's kappa index. Face validity was assessed through discussions with participants from the target culture in the pilot study. To assess construct validity and internal consistency, a cross-sectional research methodology was used on a convenience sample of 121 nursing personnel from four hospitals. Principal component analysis (PCA) was used to examine construct validity, while Cronbach's alpha and adjusted item-total correlations were used to measure internal consistency. RESULTS: The content and face validity of PITCCN were adequate. The scale validity index (S-CVI) was 0.97. Cronbach's α was 0.92, and subscale reliabilities ranged from 0.810 to 0.925. PCA showed four components, which explained more than 73.49% of the variance. CONCLUSIONS: The Slovenian version of PITCCN (PITCCN_SI) has good psychometric properties.

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.

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.

Person-Centred Care: A Support Strategy for Managing Non-Communicable Diseases.

BACKGROUND: Over the last decade, the inadequacy and unsustainability of current healthcare services for managing long-term co-morbid and multi-morbid diseases have become evident. METHODS: This study, involving 426 adults with at least one non-communicable disease in Slovenia, aimed to explore the link between quality of life, life satisfaction, person-centred care, and non-communicable disease management. RESULTS: Results indicated generally positive perceptions of quality of life, general health, and life satisfaction of individuals with non-communicable diseases. Participants assessed their physical health as the highest of the four quality of life domains, followed by the environment, social relations, and psychological health. Significant differences occurred in life satisfaction, general health, quality of life, and person-centred care for managing non-communicable diseases. But, there were no significant differences in person-centred care according to the living environment. The study revealed a positive association between person-centred care and effective non-communicable disease management, which is also positively associated with quality of life, general health, and life satisfaction. CONCLUSIONS: Person-centred care is currently the most compassionate and scientific practice conceived, representing a high ethical standard. However, implementing this approach in healthcare systems requires a cohesive national strategy led by capable individuals to foster stakeholder collaboration. Such an approach is crucial to address the deficiencies of existing healthcare services and ensure person-centred care sustainability in non-communicable disease management.

Using generative artificial intelligence in bibliometric analysis: 10 years of research trends from the European Resuscitation Congresses.

Aims: The aim of this study is to use generative artificial intelligence to perform bibliometric analysis on abstracts published at European Resuscitation Council (ERC) annual scientific congress and define trends in ERC guidelines topics over the last decade. Methods: In this bibliometric analysis, the WebHarvy software (SysNucleus, India) was used to download data from the Resuscitation journal's website through the technique of web scraping. Next, the Chat Generative Pre-trained Transformer 4 (ChatGPT-4) application programming interface (Open AI, USA) was used to implement the multinomial classification of abstract titles following the ERC 2021 guidelines topics. Results: From 2012 to 2022 a total of 2491 abstracts have been published at ERC congresses. Published abstracts ranged from 88 (in 2020) to 368 (in 2015). On average, the most common ERC guidelines topics were Adult basic life support (50.1%), followed by Adult advanced life support (41.5%), while Newborn resuscitation and support of transition of infants at birth (2.1%) was the least common topic. The findings also highlight that the Basic Life Support and Adult Advanced Life Support ERC guidelines topics have the strongest co-occurrence to all ERC guidelines topics, where the Newborn resuscitation and support of transition of infants at birth (2.1%; 52/2491) ERC guidelines topic has the weakest co-occurrence. Conclusion: This study demonstrates the capabilities of generative artificial intelligence in the bibliometric analysis of abstract titles using the example of resuscitation medicine research over the last decade at ERC conferences using large language models.