Sue & Bill Gross School of Nursing

AIM: This paper describes the trajectory during 1 year of four patient-reported outcomes (PROs), namely, sleep, depressive symptoms, health-related quality of life (HrQoL), and well-being, in patients with heart failure (HF), their relationship and the patient characteristics associated with changes in these PROs. METHODS AND RESULTS: Data analyses of PROs from 603 patients (mean age 67 years; 29% female, 60% NYHA II) enrolled in the HF-Wii study. On short term, between baseline and 3 months, 16% of the patients experienced continuing poor sleep, 11% had sustained depressive symptoms, 13% had consistent poor HrQoL, and 13% consistent poor well-being. Across the entire 1-year period only 21% of the patients had good PRO scores at all timepoints (baseline, 3, 6, and 12 months). All others had at least one low score in any of the PROs at some timepoint during the study. Over the 12 months, 17% had consistently poor sleep, 17% had sustained symptoms of depression, 15% consistently rated a poor HrQoL, and 13% poor well-being. Different patient characteristics per PRO were associated with a poor outcomes across the 12 months. Age, education, New York Heart Association, and length of disease were related to two PRO domains and submaximal exercise capacity (6 min test), co-morbidity, and poor physical activity to one. CONCLUSION: In total, 79% of the patients with HF encountered problems related to sleep, depressive symptoms, HrQoL, and well-being at least once during a 1-year period. This underscores the need for continuous monitoring and follow-up of patients with HF and the need for dynamic adjustments in treatment and care regularly throughout the HF trajectory.

Generative Artificial Intelligence is set to revolutionize healthcare delivery by transforming traditional patient care into a more personalized, efficient, and proactive process. Chatbots, serving as interactive conversational models, will probably drive this patient-centered transformation in healthcare. Through the provision of various services, including diagnosis, personalized lifestyle recommendations, dynamic scheduling of follow-ups, and mental health support, the objective is to substantially augment patient health outcomes, all the while mitigating the workload burden on healthcare providers. The life-critical nature of healthcare applications necessitates establishing a unified and comprehensive set of evaluation metrics for conversational models. Existing evaluation metrics proposed for various generic large language models (LLMs) demonstrate a lack of comprehension regarding medical and health concepts and their significance in promoting patients well-being. Moreover, these metrics neglect pivotal user-centered aspects, including trust-building, ethics, personalization, empathy, user comprehension, and emotional support. The purpose of this paper is to explore state-of-the-art LLM-based evaluation metrics that are specifically applicable to the assessment of interactive conversational models in healthcare. Subsequently, we present a comprehensive set of evaluation metrics designed to thoroughly assess the performance of healthcare chatbots from an end-user perspective. These metrics encompass an evaluation of language processing abilities, impact on real-world clinical tasks, and effectiveness in user-interactive conversations. Finally, we engage in a discussion concerning the challenges associated with defining and implementing these metrics, with particular emphasis on confounding factors such as the target audience, evaluation methods, and prompt techniques involved in the evaluation process.

BACKGROUND: Approximately 70-80% of people experiencing homelessness in the United States use tobacco. Smoking cessation programs specifically for this population have been found to be less effective for African American participants. The purpose of this study was to explore discrimination experiences and their impact on smoking habits and readiness to quit cigarette smoking while experiencing homelessness. METHODS: In the qualitative phase of this mixed methods study, five focus groups were conducted for African Americans residing in a homeless shelter in Skid Row, Los Angeles, CA. Using a semi-structured interview guide, we asked participants about discrimination experiences, how smoking habits were impacted by these experiences, and tools needed to successfully abstain from cigarette smoking. Qualitative descriptive content analysis was used to explore discrimination experiences and its association with readiness to quit cigarette smoking. RESULTS: Of the 17 participants, 14 (82.4%) were male, and the average age was 46.8 years. Using a qualitative In Vivo coding method, three themes were revealed: Experiencing Discrimination while Black, The Psychosocial Fabric-Why Quitting Cigarette Smoking is a Challenge, and The Lesser of Two Evils-Choosing to Smoke over More Harmful Options. Participants discussed working in the blue-collar workforce while Black, identifying as a double minority, smoking to cope with stress, early exposure to cigarettes, smoking being a central part of ones belonging to a group, and the legality of cigarette smoking. DISCUSSION: Our findings show that African Americans experiencing homelessness (1) may experience discrimination in multiple settings, regardless of housing status, (2) could have grown up around cigarette smoking and remain surrounded by it while experiencing homelessness, and (3) may experience a calming effect with smoking, which slows some from reacting negatively to adverse situations. CONCLUSION: Barriers to successfully abstaining from smoking are multifactorial among African Americans experiencing homelessness and should be addressed individually. Future research should explore the cultural tailoring of interventions that support cessation efforts unique to minoritized populations to improve smoking cessation programs offered to this population.

BACKGROUND: Stress and anxiety during pregnancy are extremely prevalent and are associated with numerous poor outcomes, among the most serious of which are increased rates of preterm birth and low birth weight infants. Research supports that while in-person mindfulness training is effective in reducing pregnancy stress and anxiety, there are barriers limiting accessibility. OBJECTIVE: The aim of this paper is to determine if mindfulness meditation training with the Headspace app is effective for stress and anxiety reduction during pregnancy. METHODS: A longitudinal, single-arm trial was implemented with 20 pregnant women who were instructed to practice meditation via the Headspace app twice per day during the month-long trial. Validated scales were used to measure participants levels of stress and anxiety pre- and postintervention. Physiological measures reflective of stress (heart rate variability and sleep) were collected via the Oura Ring. RESULTS: Statistically significant reductions were found in self-reported levels of stress (P=.005), anxiety (P=.01), and pregnancy anxiety (P<.0001). Hierarchical linear modeling revealed a statistically significant reduction in the physiological data reflective of stress in 1 of 6 heart rate variability metrics, the low-frequency power band, which decreased by 13% (P=.006). A total of 65% of study participants (n=13) reported their sleep improved during the trial, and 95% (n=19) stated that learning mindfulness helped with other aspects of their lives. Participant retention was 100%, with 65% of participants (n=13) completing about two-thirds of the intervention, and 50% of participants (n=10) completing ≥95%. CONCLUSIONS: This study found evidence to support the Headspace app as an effective intervention to aid in stress and anxiety reduction during pregnancy.

We conducted a cross-sectional study, examining the mediation effects of depression and anxiety on the association between discrimination and readiness to quit cigarette smoking among African American adult cigarette smokers experiencing homelessness. Using a convenience sample, participants were recruited from a homeless shelter in Southern California. Scores of discrimination, depressive, and anxiety symptoms, and readiness to quit smoking were analyzed using linear regression modeling. We enrolled 100 participants; 58 participants were male. In the final model, discrimination had no association with readiness to quit (b = 0.02; 95% CI [-0.04, 0.08]; p = 0.47). The indirect effects of depression (b = 0.04, [0.01, 0.07]; p = 0.02) and anxiety (b = 0.03; [0.01, 0.05]; p = 0.04) reached statistical significance; the direct effects of depression (b = -0.01; [-0.09, 0.04]; p = 0.70) and anxiety (b = -0.00; [-0.09, 0.06]; p = 0.86) did not. Future studies should explore these associations to enhance smoking cessation programs for this population.

GeneXpert MTB/RIF, a tool widely used for diagnosing tuberculosis, has limitations for detecting rifampin resistance in certain variants. We report transmission of a pre-extensively drug-resistant variant in Botswana that went undetected by GeneXpert. The public health impact of misdiagnosis emphasizes the need for comprehensive molecular testing to identify resistance and guide treatment.

The adverse effects of loneliness on both physical and mental well-being are profound. Although previous research has utilized mobile sensing techniques to detect mental health issues, few studies have utilized state-of-the-art wearable devices to forecast loneliness and comprehend the physiological manifestations of loneliness and its predictive nature. The primary objective of this study is to examine the feasibility of forecasting loneliness by employing wearable devices, such as smart rings and watches, to monitor early physiological indicators of loneliness. Furthermore, smartphones are employed to capture initial behavioral signs of loneliness. To accomplish this, we employed personalized machine learning techniques, leveraging a comprehensive dataset comprising physiological and behavioral information obtained during our study involving the monitoring of college students. Through the development of personalized models, we achieved a notable accuracy of 0.82 and an F-1 score of 0.82 in forecasting loneliness levels seven days in advance. Additionally, the application of Shapley values facilitated model explainability. The wealth of data provided by this study, coupled with the forecasting methodology employed, possesses the potential to augment interventions and facilitate the early identification of loneliness within populations at risk.

Sleep quality is crucial to both mental and physical well-being. The COVID-19 pandemic, which has notably affected the population's health worldwide, has been shown to deteriorate people's sleep quality. Numerous studies have been conducted to evaluate the impact of the COVID-19 pandemic on sleep efficiency, investigating their relationships using correlation-based methods. These methods merely rely on learning spurious correlation rather than the causal relations among variables. Furthermore, they fail to pinpoint potential sources of bias and mediators and envision counterfactual scenarios, leading to a poor estimation. In this paper, we develop a Causal Machine Learning method, which encompasses causal discovery and causal inference components, to extract the causal relations between the COVID-19 pandemic (treatment variable) and sleep quality (outcome) and estimate the causal treatment effect, respectively. We conducted a wearable-based health monitoring study to collect data, including sleep quality, physical activity, and Heart Rate Variability (HRV) from college students before and after the COVID-19 lockdown in March 2020. Our causal discovery component generates a causal graph and pinpoints mediators in the causal model. We incorporate the strongly contributing mediators (i.e., HRV and physical activity) into our causal inference component to estimate the robust, accurate, and explainable causal effect of the pandemic on sleep quality. Finally, we validate our estimation via three refutation analysis techniques. Our experimental results indicate that the pandemic exacerbates college students' sleep scores by 8%. Our validation results show significant p-values confirming our estimation.

BACKGROUND: The clinical nurse leader (CNL) care model has existed since 2007. However, there is limited understanding how the model can best be implemented. PURPOSE: A validated CNL Practice Survey measuring domains theorized to influence CNL implementation was used to examine the link between CNL domains and CNL implementation success. METHODS: Mixed methods were used to analyze data from a nationwide 2015 survey administered to clinicians and administrators involved in CNL initiatives. RESULTS: Of total respondents (n = 920), 543 (59%) provided success scores, with 349 (38%) providing comments. Respondents with negative comments gave significantly lower average CNL success scores. The majority of negative comments mapped onto Readiness and Structuring domains, providing details of barriers to CNL implementation success. CONCLUSIONS: Findings provide information about structural domains that can be strategically targeted to better prepare settings for CNL implementation and success.

Irritability is a term used to describe feelings of anger, annoyance and impatience, and is commonly experienced by individuals in daily life. However, there are diverse conceptualizations of irritability in public and clinical research, which often result in confusing irritability with anger and other overlapping concepts. This, in turn, leads to a lack of conceptual clarity. Accordingly, the purpose of this concept analysis was to explore the irritability concept, including its definitions, defining characteristics, antecedents, consequences and empirical referents. The findings showed that irritability is predominantly conceptualized as a psychophysiological concept in the literature. We demonstrated that irritability can be differentiated from overlapping concepts like anger by qualities, such as 'unpredictability and lowered emotion control', 'lowered threshold for negative emotional stimuli', 'being manifested in response to frustrative situations or physiological needs' and 'experience of disproportionate and unjustified emotional irritation'. Importantly, severe irritability prospectively predicts psychiatric disorders and greater impairments in health, financial, educational and social functioning in individuals. Taken together, our analysis showed that one should take into account the context, duration, intensity and importantly outcomes, when assessing irritability in an individual. Considering these findings and the presence of irritability in nursing practice, it is crucial for nurses to recognize and successfully identify this concept in the nursing care they provide within the diverse settings and patient populations.