Clinical Nursing Research

Clinical Nursing Research, Ahead of Print. <br/>In Brazil, research indicates that primary family members are the main source of support for individuals with chronic conditions such as hypertension (HTN). The burden of caregiving not only hinders effective HTN management but can also cause stress and anxiety, potentially leading to HTN in caregivers. Despite this, few studies have explored the impact of caregiving on these family members. Aims of the study were to: (1) Describe the prevalence of blood pressure (BP) control in family members of individuals with HTN; (2) identify family member perspectives on facilitators and barriers to HTN management; and (3) identify influences that help or interfere with family member functioning (levels of stress, quality of life [QOL], and caregiver burden). This descriptive, cross-sectional study included 213 family members randomly selected from 3 Family Health Strategy units. Family members were largely female (n = 143; 67.1%); the mean age was 60.1 years (SD ± 17.02) and 42.6% (n = 96) had less than a high school education. The three most important facilitators and barriers were related to medication, medical visits, healthy eating, physical activity, and stress. The mean systolic BP was 132.7 (SD ± 21.9) mmHg and a diastolic BP of 85.9 (SD ± 18.1) mmHg with 120 (56.3%) of family members classified as having normal BP. In regard to family member contributions to the self-care of the individual with HTN, family members displayed low levels of self-care maintenance (n = 148; 69.4%) and management (n = 47; 71.2%) support, while a slight majority (n = 114; 53.5%) had adequate levels of self-care confidence in supporting the individual with HTN. Family members (n = 189; 88.8%) showed moderate-to-high levels of perceived stress, but good physical (n = 189; 88.7%) and mental QOL (n = 196; 92%) and low levels of caregiver burden (n = 113; 53.1%). A variety of contextual sociocultural influences were associated with the outcomes under study. Family-based interventions are urgently needed to address the inadequate management of HTN.

Clinical Nursing Research, Ahead of Print. <br/>African American patient populations are disproportionately diagnosed with severe obstructive sleep apnea (OSA) compared to non-Hispanic white adults. Research suggests a link between OSA and anxiety. However, OSA and anxiety symptoms may present differently across minority groups. Research examining the reliability and validity of measures used to assess anxiety symptom severity in African American patient populations living with OSA is needed. This pilot study evaluated the reliability and validity of the Patient Reported Outcomes Measurement Information System (PROMIS) Anxiety short form and the longer State-Trait Anxiety Inventory-State (STAI-S) in a sample of African American adults recently diagnosed with OSA. In this cross-sectional pilot study, 32 African American patients newly diagnosed with OSA were recruited from an academic sleep medicine clinic in the Southeastern United States to complete survey measures. Participants completed the 6-item PROMIS Anxiety short form and 20-item STAI-S. Cronbach’s alphas assessed the internal reliability of measures, and concurrent validity between measures was assessed using correlation coefficients (rs). Both PROMIS Anxiety and STAI-S showed excellent internal consistency with Cronbach’s α ≥.90. The PROMIS Anxiety T-scores and STAI-S scores were positively moderately correlated with each other (rs = .68; p < .0001), indicating concurrent validity. These results illustrate that both the PROMIS Anxiety and STAI-S scales have strong internal reliability in this sample of African American adults with co-occurring OSA and prediabetes. The brief PROMIS Anxiety short-form measure is validated to the longer STAI measure in this sample. An advantage of the PROMIS Anxiety scale is that it has a lower participant burden when completing questionnaires, contributing to a more refined approach to diagnosis and management when both OSA and anxiety coexist. These findings also underscore the importance of validating psychometric measures in marginalized populations, such as African Americans, who have historically been underrepresented in psychometric research.

Clinical Nursing Research, Ahead of Print. <br/>In the midst of a pandemic like COVID-19, migrant workers are highly vulnerable to the risk of mental health challenges and disorders. This study aimed to examine the variations in mental health and associated factors among migrant workers in Korea during and after the COVID-19 pandemic. A total of 830 migrant workers were included in this quantitative population-based study across two distinct COVID-19 periods: 415 from August 15, 2020 to January 20, 2021 (termed “during COVID-19” post-WHO pandemic declaration) and 415 from June 10 to September 18, 2023 (termed “after COVID-19” post-WHO and Korean Government announcement of the end COVID-19 as a global health emergency). The mental health of migrants was assessed using Patient Health Questionnaire-9 for depression and Generalized Anxiety Disorder-7 for anxiety, and compared between the two defined COVID-19 periods. Migrant workers exhibited higher means of depression (15.99 ± 5.16 vs. 8.78 ± 5.77) and anxiety (11.02 ± 4.45 vs. 6.97 ± 4.42) during the COVID-19 compared to after the COVID-19. The prevalence of severe depression (30.4% vs. 8.2%, p = .038) and severe anxiety (24.1% vs. 5.3%, p = .047) was significantly higher during the COVID-19 than after the pandemic. Changes in the mean depression scores among migrants were significantly influenced by living status (0.12 ± 11.10, p = .030) and employment status (0.44 ± 10.33, p = .043), while changes in mean anxiety scores were influenced by social relief funds (2.88 ± 8.61, p = .046) and employment status (1.13 ± 8.39, p = .012). This study highlights the increased susceptibility of migrant workers to depression and anxiety during COVID-19 compared to after the pandemic. Thus, it is imperative to prioritize the expansion of social support funds, ensure employment stability, and implement regular health check-up services to reduce mental health disorders among migrants.

Clinical Nursing Research, Ahead of Print. <br/>Although there are many sleep-related complaints in chronic obstructive pulmonary disease (COPD) patients, nocturnal leg cramps have not been adequately and extensively studied. This study fills a significant gap in the literature by determining the prevalence and influencing factors of nocturnal leg cramps in COPD patients. However, our findings also underscore the need for further research, inspiring future studies and interventions in this area. This study was conducted with a rigorous methodology, employing a comprehensive approach to evaluate the probability of experiencing nocturnal leg cramps in 215 COPD and 215 control group patients matched for age and gender. Logistic regression analysis was used, supplemented by artificial neural networks, to identify the influencing factors. This robust methodology ensures the reliability and validity of our findings. The findings of this study are not only significant but also enlightening, shedding light on the prevalence and influencing factors of nocturnal leg cramps in COPD patients. The frequency of experiencing these cramps was found to be 40.9% in chronic obstructive pulmonary patients and 21.9% in the control group (p < .05). In COPD patients, factors such as milk group food consumption, blood erythrocyte level, the cover used while sleeping, blood creatinine level, the presence of coronary artery disease, the diagnosis of diabetes mellitus, the upper mid-arm muscle area, and use of drugs with methylxanthine active ingredient methylxanthine were found to affect the occurrence of these cramps. Our findings not only call for further research but also have immediate practical implications. They highlight the crucial role of nurses in managing nocturnal leg cramps in COPD patients. By controlling patients’ cold stress, the bed covers they use, and their dairy product consumption, nurses can significantly contribute to managing these cramps, thereby improving the quality of life for these patients. This underscores the importance of their role in patient care and management.

Clinical Nursing Research, Ahead of Print. <br/>Alzheimer’s disease (AD) patients admitted to intensive care units (ICUs) exhibit varying survival outcomes due to the unique challenges in managing AD patients. Stratifying patient mortality risk and understanding the criticality of nursing care are important to improve the clinical outcomes of AD patients. This study aimed to leverage machine learning (ML) and electronic health records (EHRs) only consisting of demographics, disease history, and routine lab tests, with a focus on nursing care, to facilitate the optimization of nursing practices for AD patients. We utilized Medical Information Mart for Intensive Care III, an open-source EHR dataset, and AD patients were identified based on the International Classification of Diseases, Ninth Revision codes. From a cohort of 453 patients, a total of 60 features, encompassing demographics, laboratory tests, disease history, and number of nursing events, were extracted. ML models, including XGBoost, random forest, logistic regression, and multi-layer perceptron, were trained to predict the 30-day mortality risk. In addition, the influence of nursing care was analyzed in terms of feature importance using values calculated from both the inherent XGBoost module and the SHapley Additive exPlanations (SHAP) library. XGBoost emerged as the lead model with a high accuracy of 0.730, area under the curve (AUC) of 0.750, sensitivity of 0.688, and specificity of 0.740. Feature importance analyses using inherent XGBoost module or SHAP both indicated the number of nursing care within 14 days post-admission as an important denominator for 30-day mortality risk. When nursing care events were excluded as a feature, stratifying patient mortality risk was also possible but the model’s AUC of receiver operating characteristic curve was reduced to 0.68. Nursing care plays a pivotal role in the survival outcomes of AD patients in ICUs. ML models can be effectively employed to predict mortality risks and underscore the importance of specific features, including nursing care, in patient outcomes. Early identification of high-risk AD patients can aid in prioritizing intensive nursing care, potentially improving survival rates.