Virtual reality-based simulation learning on geriatric oral health care for nursing students: a pilot study.

BACKGROUND: There is a great need for training and education in the nursing curriculum to improve nurses' knowledge and skills to provide oral health care. METHODS: A pilot study was conducted to evaluate the use of a virtual reality (VR)-based Oral Health Care Learning System to train geriatric oral health care among nursing students. Fifty undergraduate nursing students were randomly assigned to experimental (n = 25) and control (n = 25) groups. The experimental group received the VR-based simulation training on geriatric oral health care and the training was implemented twice at two weeks apart from March to November 2021. The control group did not receive the training intervention. Knowledge, attitude, and self-efficacy of geriatric oral health care as well as the intention to assist oral health care for older adults were assessed at the beginning, second, and fourth weeks. Generalized estimating equations were used to analyze the effectiveness of the VR-based simulation training. RESULTS: After the first round of training, students in the experimental group had significantly greater improvements in knowledge and self-efficacy of geriatric oral health care than in the control group. After the second round of training, students in the experimental group had significantly greater improvements in knowledge, attitude, and self-efficacy of geriatric oral health care as well as the intention to assist oral health care for older adult than in the control group. CONCLUSIONS: The VR-based simulation training was effective to improve undergraduate nursing students' knowledge, attitudes and self-efficacy of geriatric oral health as well as the intention to assist oral health care for older adults. The VR-based simulation learning system is an effective tool to provide practice experiences to build confidence and skills and to bridge the gap of understudied geriatric oral health content in entry-level nursing curricula. TRIAL REGISTRATION: ClinicalTrials.gov (NCT05248542; registration date 21/02/2022).

Data-Fusion-Based Quality Enhancement for HR Measurements Collected by Wearable Sensors.

The advancements of Internet of Things (IoT) technologies have enabled the implementation of smart and wearable sensors, which can be employed to provide older adults with affordable and accessible continuous biophysiological status monitoring. The quality of such monitoring data, however, is unsatisfactory due to excessive noise induced by various disturbances, such as motion artifacts. Existing methods take advantage of summary statistics, such as mean or median values, for denoising, without taking into account the biophysiological patterns embedded in data. In this research, a functional data analysis modeling method was proposed to enhance the data quality by learning individual subjects' diurnal heart rate (HR) patterns from historical data, which were further improved by fusing newly collected data. This proposed data-fusion approach was developed based on a Bayesian inference framework. Its effectiveness was demonstrated in an HR analysis from a prospective study involving older adults residing in assisted living or home settings. The results indicate that it is imperative to conduct personalized healthcare by estimating individualized HR patterns. Furthermore, the proposed calibration method provides a more accurate (smaller mean errors) and more precise (smaller error standard deviations) HR estimation than raw HR and conventional methods, such as the mean.

A transformer-based diffusion probabilistic model for heart rate and blood pressure forecasting in Intensive Care Unit.

BACKGROUND AND OBJECTIVE: Vital sign monitoring in the Intensive Care Unit (ICU) is crucial for enabling prompt interventions for patients. This underscores the need for an accurate predictive system. Therefore, this study proposes a novel deep learning approach for forecasting Heart Rate (HR), Systolic Blood Pressure (SBP), and Diastolic Blood Pressure (DBP) in the ICU. METHODS: We extracted 24,886 ICU stays from the MIMIC-III database which contains data from over 46 thousand patients, to train and test the model. The model proposed in this study, Transformer-based Diffusion Probabilistic Model for Sparse Time Series Forecasting (TDSTF), merges Transformer and diffusion models to forecast vital signs. The TDSTF model showed state-of-the-art performance in predicting vital signs in the ICU, outperforming other models' ability to predict distributions of vital signs and being more computationally efficient. The code is available at https://github.com/PingChang818/TDSTF. RESULTS: The results of the study showed that TDSTF achieved a Standardized Average Continuous Ranked Probability Score (SACRPS) of 0.4438 and a Mean Squared Error (MSE) of 0.4168, an improvement of 18.9% and 34.3% over the best baseline model, respectively. The inference speed of TDSTF is more than 17 times faster than the best baseline model. CONCLUSION: TDSTF is an effective and efficient solution for forecasting vital signs in the ICU, and it shows a significant improvement compared to other models in the field.

Decrease in Mobility during the COVID-19 Pandemic and Its Association with Increase in Depression among Older Adults: A Longitudinal Remote Mobility Monitoring Using a Wearable Sensor.

BACKGROUND: Social isolation during COVID-19 may negatively impact older adults' wellbeing. To assess its impact, we measured changes in physical activity and sleep among community-dwelling older adults, from pre-to post-pandemic declaration. METHOD: Physical activity and sleep in older adults (n = 10, age = 77.3 ± 1.9 years, female = 40%) were remotely assessed within 3-month pre-to 6-month post-pandemic declaration using a pendant-wearable system. Depression was assessed pre-and post-pandemic declaration using the Center for Epidemiologic Studies Depression scale and was compared with 48 h continuous physical activity monitoring data before and during pandemic. RESULTS: Compared to pre-pandemic, post-pandemic time spent in standing declined by 32.7% (Cohen's d = 0.78, p < 0.01), walking by 52.2% (d = 1.1, p < 0.01), step-counts by 55.1% (d = 1.0, p = 0.016), and postural transitions by 44.6% (d = 0.82, p = 0.017) with increase in sitting duration by 20.5% (d = 0.5, p = 0.049). Depression symptoms increased by 150% (d = 0.8, p = 0.046). Interestingly, increase in depression was significantly correlated with unbroken-prolong sitting bout (ρ = 0.677, p = 0.032), cadence (ρ = -0.70, p = 0.024), and sleep duration (ρ = -0.72, p = 0.019). CONCLUSION: This is one of the early longitudinal studies highlighting adverse effect of the pandemic on objectively assessed physical activity and sleep in older adults. Our observations showed need for timely intervention to mitigate hard to reverse consequences of decreased physical activity such as depression.

Precision health: A nursing perspective.

Precision health refers to personalized healthcare based on a person's unique genetic, genomic, or omic composition within the context of lifestyle, social, economic, cultural and environmental influences to help individuals achieve well-being and optimal health. Precision health utilizes big data sets that combine omics (i.e. genomic sequence, protein, metabolite, and microbiome information) with clinical information and health outcomes to optimize disease diagnosis, treatment and prevention specific to each patient. Successful implementation of precision health requires interprofessional collaboration, community outreach efforts, and coordination of care, a mission that nurses are well-positioned to lead. Despite the surge of interest and attention to precision health, most nurses are not well-versed in precision health or its implications for the nursing profession. Based on a critical analysis of literature and expert opinions, this paper provides an overview of precision health and the importance of engaging the nursing profession for its implementation. Other topics reviewed in this paper include big data and omics, information science, integration of family health history in precision health, and nursing omics research in symptom science. The paper concludes with recommendations for nurse leaders in research, education, clinical practice, nursing administration and policy settings for which to develop strategic plans to implement precision health.

Critical care nurses' clinical reasoning about physiologic monitor alarm customisation: An interpretive descriptive study.

AIMS AND OBJECTIVES: To explore clinical reasoning about alarm customisation among nurses in intensive care units. BACKGROUND: Critical care nurses are responsible for detecting and rapidly acting upon changes in patients' clinical condition. Nurses use medical devices including bedside physiologic monitors to assist them in their practice. Customising alarm settings on these devices can help nurses better monitor their patients and reduce the number of clinically irrelevant alarms. As a result, customisation may also help address the problem of alarm fatigue. However, little is known about nurses' clinical reasoning with respect to customising physiologic monitor alarm settings. DESIGN: This article is an in-depth report of the qualitative arm of a mixed methods study conducted using an interpretive descriptive methodological approach. METHODS: Twenty-seven nurses were purposively sampled from three intensive care units in an academic medical centre. Semi-structured interviews were conducted by telephone and were analysed using thematic analysis. Consolidated Criteria for Reporting Qualitative Research (COREQ) reporting guidelines were used. RESULTS: Four themes were identified from the interview data: unit alarm culture and context, nurse attributes, motivation to customise and customisation "know-how." A conceptual model demonstrating the relationship of these themes was developed to portray the factors that affect nurses' customisation of alarms. CONCLUSIONS: In addition to drawing on clinical data, nurses customised physiologic monitor alarms based on their level of clinical expertise and comfort. Nurses were influenced by the alarm culture on their clinical unit and colleagues' and patients' responses to alarms, as well as their own technical understanding of the physiologic monitors. RELEVANCE TO CLINICAL PRACTICE: The results of this study can be used to design strategies to support the application of clinical reasoning to alarm management, which may contribute to more appropriate alarm customisation practices and improvements in safety.

Challenges of customizing electrocardiography alarms in intensive care units: A mixed methods study.

BACKGROUND: Customizing monitor alarm settings to individual patients can reduce alarm fatigue in intensive care units (ICUs), but has not been widely studied. OBJECTIVES: To understand ICU nurses' approaches to customization of electrocardiographic (ECG) monitor alarms. METHODS: A convergent mixed methods study was conducted in 3 ICUs in 1 hospital. Data on the type and frequency of ECG alarm customization were collected from patient monitors (n=298). Nurses' customization clinical reasoning was explored through semi-structured interviews (n=27). RESULTS: Of the 298 patients, 58.7% had ≥1 alarm(s) customized. Heart rate limits, irregular heart rate, and atrial fibrillation were the most commonly customized alarms. Interviews revealed that customization practices varied widely and were influenced by factors including clinical expertise, lack of customization education, and negative experiences. CONCLUSION: Alarm customization is nuanced and requires adequate support to develop safe and effective practices. The challenges identified can inform development of strategies to improve alarm customization.

Geographic diversity in genotype frequencies and meta-analysis of the association between rs1801282 polymorphisms and gestational diabetes mellitus.

AIMS: To derive a better understanding of the association between peroxisome proliferator-activated receptor gamma (PPAR-γ) rs1801282 polymorphisms and gestational diabetes mellitus (GDM) in general and in racial and ethnic subgroups and to illustrate geographic distribution of the protective of G allele of rs1801282 in women with and without GDM. METHODS: ProQuest, PubMed, Medline, Web of Science, and Wanfang Data were systematically searched. Case-control studies on association between rs1801282 polymorphisms and GDM were selected. Comprehensive Meta-Analysis 2.0 statistical software was used to determine the relationship between GDM and rs1801282 polymorphism. Race/ethnicity-based and country-based stratified analysis was conducted. RESULTS: Sixteen studies involving 3129 cases and 7168 controls were included. Significant associations were observed between rs1801282 polymorphisms and GDM under the dominant, heterozygote, and allele models. The G allele of rs1801282 polymorphism was associated with a reduced risk of GDM in Asian, especially Chinese, populations. Data revealed significant geographic diversity in frequency of the protective G allele in women with and without GDM. CONCLUSIONS: The rs1801282 polymorphism may not be associated with genetic susceptibility to GDM in whites. The G allele of rs1801282 polymorphism was associated with reduced risk of GDM in Asians, especially Chinese, but not South Koreans.

Sensory Overload and Technology in Critical Care.

In this focus group study, we identified issues associated with sensory overload from medical technology alarms/alerts for intensive care unit nurses. Participants indicated that alarms from cardiopulmonary monitors, ventilators, and intravenous pumps contributed the most to sensory overload and, yet, these alarms were also deemed the most helpful. Alerts/alarms from electronic health records and medication dispensing systems were rated low in contributing to sensory overload, as well as being the least helpful. Specific device/technology barriers, related to device alerts/alarms, are detailed. Future user-centered and integrated improvements in alarm systems associated with medical devices in the intensive care unit are needed.

Critical Care Nurses' Cognitive Ergonomics Related to Medical Device Alarms.

This study uniquely gained insight into the intricacy of intensive care nurses' decision-making process when responding to and managing device alarms. Difficulty in responding to alarms included low staffing, multiple job responsibilities, and competing priority tasks. Novice nurses are more tolerant of alarms sounding owing to a lower threshold of comfort with resetting or silencing alarms; more experienced nurses are more comfortable resetting alarm limits to the patient's baseline. Understanding the decision-making process used by nurses can guide the development of policies and learning experiences that are crucial clinical support for alarm management.

Bradyarrhythmias: Clinical Presentation, Diagnosis, and Management.

Bradyarrhythmias are common clinical findings consisting of physiologic and pathologic conditions (sinus node dysfunction and atrioventricular [AV] conduction disturbances). Bradyarrhythmias can be benign, requiring no treatment; however, acute unstable bradycardia can lead to cardiac arrest. In patients with confirmed or suspected bradycardia, a thorough history and physical examination should include possible causes of sinoatrial node dysfunction or AV block. Management of bradycardia is based on the severity of symptoms, the underlying causes, presence of potentially reversible causes, presence of adverse signs, and risk of progression to asystole. Pharmacologic therapy and/or pacing are used to manage unstable or symptomatic bradyarrhythmias.

Transcription Factor 7-Like 2 (TCF7L2) rs7903146 Polymorphism as a Risk Factor for Gestational Diabetes Mellitus: A Meta-Analysis.

BACKGROUND: There are racial and ethnic differences in the prevalence of gestational diabetes mellitus (GDM). Prior meta-analyses included small samples and very limited non-Caucasian populations. Studies to determine the relationship between transcription factor 7 like-2 (TCF7L2) rs7903146 polymorphism and risk of GDM in Hispanics/Latinos are recently available. The present meta-analysis was to estimate the impact of allele variants of TCF7L2 rs7903146 polymorphism on GDM susceptibility in overall population and racial/ethnic subgroups. METHODS: Literature was searched in multiple databases including PubMed, Web of Science, EMBASE (Ovid SP), Airiti Library, Medline Complete, and ProQuest up to July 2015. Allelic frequency for TCF7L2 rs7903146 polymorphism in GDM and control subjects was extracted and statistical analysis was performed using Comprehensive Meta-Analysis (CMA) 2.0 statistical software. The association between TCF7L2 rs7903146 polymorphism and GDM risk was assessed by pooled odd ratios (ORs) using five gene models (dominant, recessive, homozygote, heterozygote, and allele). Stratified analysis based on race/ethnicity was also conducted. The between-study heterogeneity and contribution of each single study to the final result was tested by Cochran Q test and sensitivity analyses, respectively. Publication bias was evaluated using Egger's linear regression test. RESULTS: A total of 16 studies involving 4,853 cases and 10,631 controls were included in this meta-analysis. Significant association between the T-allele of rs7903146 and GDM risk was observed under all genetic models, dominant model (OR = 1.44, 95% CI = 1.19-1.74), recessive model (OR = 1.35, 95% CI = 1.08-1.70), heterozygous model (OR = 1.31, 95% CI = 1.12-1.53), homozygous model (OR = 1.67, 95% CI = 1.31-2.12), and allele model (OR = 1.31, 95% CI = 1.12-1.53). Stratified analysis by race/ethnicity showed a statistically significant association between rs7903146 polymorphism and susceptibility to GDM under homozygous genetic model (TT versus CC) among whites, Hispanics/Latinos and Asians. Sensitivity analysis showed that the overall findings were robust to potentially influential decisions of the 16 studies included. No significant evidence for publication bias was observed in this meta-analysis for overall studies and subgroup studies. CONCLUSIONS: This meta-analysis showed that the T allele of TCF7L2 rs7903146 polymorphism was associated with susceptibility of GDM in overall population in white, Hispanic/Latino and Asian sub-groups. Asians with homozygous TT allele of rs7903146 polymorphism have highest risk of GDM (OR = 2.08) followed by Hispanics/Latinos (OR = 1.80) and whites (OR = 1.51). The highest and lowest frequency of T allele of rs7903146 was found in Malaysia and South Korea, respectively. Future studies are needed to profile genetic risk for GDM among high risk Asian and Pacific Islander subgroups.

Cardiovascular Disease Risk and Breast Cancer Outcomes: A Pilot Study.

PURPOSE/OBJECTIVES: To assess feasibility of using electronic health records for profiling multiple cardiovascular disease (CVD) risk factors in women with breast cancer at diagnosis and five years post-treatment, and to explore relationships among CVD risk factors and breast cancer outcomes
. DESIGN: Retrospective, descriptive
. SETTING: A comprehensive cancer center in the southwestern United States
. SAMPLE: 200 women with stage 0-III breast cancer.
. METHODS: A record review using an instrument to profile multiple CVD risk factors and breast cancer outcomes
. MAIN RESEARCH VARIABLES: CVD risk factors, such as blood pressure (BP) and hemoglobin A1C (HbA1C), and breast cancer outcomes, such as metastasis
. FINDINGS: Most data on CVD risk factors were undocumented. Even BP values to assess hypertension were missing in 35% of women at breast cancer diagnosis. Women with poor outcomes had trends toward higher blood glucose and HbA1C than women with good outcomes
. CONCLUSIONS: The study failed to comprehensively capture CVD risk factors in women with breast cancer because of missing data. Glucose control may be associated with breast cancer outcomes
. IMPLICATIONS FOR NURSING: Better documentation of shared risk factors for CVD and breast cancer is needed. Prospective studies are needed to evaluate shared CVD risk factors and breast cancer outcomes because of missing health record information
.

Hyperglycemia after cardiac surgery: improving a quality measure.

BACKGROUND: The Surgical Care Improvement Project #4 (SCIP#4) performance measure is used to evaluate achievement of target blood glucose control after cardiac surgery. OBJECTIVES: The purpose of this study was to identify patient characteristics and outcomes in patients undergoing cardiac surgery who met the SCIP#4 performance measure versus those who did not. METHODS: A retrospective case-control design was used. RESULTS: Preoperative hemoglobin A1C (HbA1C) level and history of diabetes were 2 major risk factors for failing to meet the SCIP#4 measure. A trend toward a longer length of stay was observed, mortality was 3 times more prevalent, and renal failure was 4 times more frequent in patients who did not meet the SCIP#4 quality measure. CONCLUSIONS: Not meeting the SCIP#4 measure is associated with adverse outcomes. History of diabetes and preoperative HbAIC level should be considered when evaluating strategies for managing postsurgical hyperglycemia.

Cardiovascular genomics.

PURPOSE: This article provides an update on cardiovascular genomics using three clinically relevant exemplars, including myocardial infarction (MI) and coronary artery disease (CAD), stroke, and sudden cardiac death (SCD). ORGANIZATIONAL CONSTRUCT: Recent advances in cardiovascular genomic research, testing, and clinical implications are presented. METHODS: Genomic nurse experts reviewed and summarized recent salient literature to provide updates on three selected cardiovascular genomic conditions. FINDINGS: Research is ongoing to discover comprehensive genetic markers contributing to many common forms of cardiovascular disease (CVD), including MI and stroke. However, genomic technologies are increasingly being used clinically, particularly in patients with long QT syndrome (LQTS) or hypertrophic cardiomyopathy (HCM) who are at risk for SCD. CONCLUSIONS: Currently, there are no clinically recommended genetic tests for many common forms of CVD even though direct-to-consumer genetic tests are being marketed to healthcare providers and the general public. On the other hand, genetic testing for patients with certain single gene conditions, including channelopathies (e.g., LQTS) and cardiomyopathies (e.g., HCM), is recommended clinically. CLINICAL RELEVANCE: Nurses play a pivotal role in cardiogenetics and are actively engaged in direct clinical care of patients and families with a wide variety of heritable conditions. It is important for nurses to understand current development of cardiovascular genomics and be prepared to translate the new genomic knowledge into practice.

Obstructive sleep apnea hypopnea syndrome and metabolic syndrome: a synergistic cardiovascular risk factor.

PURPOSE: Cardiovascular disease (CVD) is the leading cause of morbidity and mortality for adults in the United States. One risk factor for CVD is metabolic syndrome, which encompasses obesity, hypertension, insulin resistance, proinflammatory state, and prothrombotic state. A lesser-understood risk factor is obstructive sleep apnea hypopnea syndrome (OSAHS). This article explores the physiological consequences of the interaction between OSAHS and metabolic syndrome on the cardiovascular system. DATA SOURCES: Search terms "metabolic syndrome,""obstructive sleep apnea,""cardiovascular disease,""diabetes,""obesity," and "atherosclerosis," were used. Studies involving children were excluded. CONCLUSIONS: Both metabolic syndrome and OSAHS have significant impact on the cardiovascular system; however, when both conditions are present together, the impact is synergistic and CVD risk is multiplied. Treatment with continuous positive airway pressure (CPAP) reduces the global burden of CVD risk. IMPLICATIONS FOR PRACTICE: Providers need to screen patients routinely for both metabolic syndrome and OSAHS. Treatment should include CPAP, weight reduction, oral appliances, and/or upper airway surgeries with concurrent management for metabolic syndrome. Future research should further elucidate the mechanisms of action by which OSAHA and metabolic syndrome contribute to CVD. This understanding can lead to more stringent guidelines on the management of metabolic syndrome and OSAHS.

Acquired long QT syndrome: frequency, onset, and risk factors in intensive care patients.

BACKGROUND: Acquired long QT syndrome is a reversible condition that can lead to torsades de pointes and sudden cardiac death. OBJECTIVE: To determine the frequency, onset, frequency of medications, and risk factors for the syndrome in intensive care patients. METHODS: In a retrospective chart review of 88 subjects, hourly corrected QT intervals calculated by using the Bazett formula were collected. Acquired long QT syndrome was defined as a corrected QT of 500 milliseconds or longer or an increase in corrected QT of 60 milliseconds or greater from baseline level. Risk factors and medications administered were collected from patients' medical records. RESULTS: The syndrome occurred in 46 patients (52%); mean time of onset was 7.4 hours (SD, 9.4) from time of admission. Among the 88 patients, 52 (59%) received a known QTc-prolonging medication. Among the 46 with the syndrome, 23 (50%) received a known QT-prolonging medication. No other risk factor studied was significantly predictive of the syndrome. CONCLUSIONS: Acquired long QT syndrome occurs in patients not treated with a known QT-prolonging medication, indicating the importance of frequent QT monitoring of all intensive care patients.

Shared genomics of type 2 and gestational diabetes mellitus.

Gestational diabetes mellitus (GDM) is one of the most common complications of pregnancy and the prevalence of GDM is increasing worldwide. Short- and long-term complications of GDM on mothers and fetuses are well-recognized. These include more than seven-fold higher risk for type 2 diabetes mellitus (T2DM) later in life in women with GDM than those without. Evidence supports that GDM shares several risk factors with T2DM, including genetic risks. This chapter reviewed studies on candidate genes shared by T2DM and GDM published from 1990 to 2011. At least 20 susceptible genes of T2DM have been studied in women with GDM in various races. Results from current association studies on T2DM susceptible genes in GDM have shown significant heterogeneity There may be primary evidence that polymorphisms of susceptible genes of T2DM such as transcription factor 7-like 2 (TCF7L2) gene, potassium channel voltage-gate KQT-like subfamily member 1 (KCNQ1) gene, and cyclin-dependent kinase 5 regulatory subunit-associated protein 1-like 1 (CDKAL1) gene, may increase risk of GDM. Associations between GDM and many genetic variants have led to different findings across populations. Many genetic polymorphisms related to GDM were investigated in a single study or a single population. Replication studies to verify contributions of both common and rare genetic variants for GDM and T2DM in specific racial/ethnic groups are needed.