Resilience and Self-Management of Hypertension in African American Adults Using a Conceptualized Resilience Framework: An Exploratory Analysis.

BACKGROUND: Hypertension rates have increased worldwide, with the most significant increase in morbidity and mortality observed among African Americans. Resilience is a potential factor influencing how individuals manage health-related challenges or self-management tasks for hypertension. Research is scarce related to resilience and self-management frameworks in African Americans with hypertension. OBJECTIVES: We aimed to describe a conceptualized resilience framework and preliminary findings of the association among resilience precursors, stress response, hypertension self-management behaviors, and health outcomes in African Americans with hypertension. METHODS: This cross-sectional, descriptive-correlational study included African American adults with hypertension, aged 25 years and older, recruited from an academic university and surrounding urban communities in the Midwest. Participants completed standardized, validated questionnaires to examine the association among resilience precursors, stress response, hypertension self-management behaviors, health-related quality of life (HRQOL), and blood pressure at baseline. Descriptive statistics were used to describe the sample demographic characteristics, whereas Pearson's correlational and multiple regression analyses were conducted to determine the associations among the variables. RESULTS: African Americans with hypertension (N = 30) were included in this preliminary study, with a mean age of 59.17 years; 66.7% were female. The mean systolic blood pressure was 136 (SD = 16.8) mmHg; the mean diastolic blood pressure was 78.1 (SD = 13) mmHg. Pearson's correlation analysis revealed significant relationships between resilience precursors, stress response, hypertension self-management behaviors and capability, and health outcome components. Multiple regression analysis showed that poor perceived resilience significantly predicted depression. Low dispositional optimism and low perceived resilience were significant predictors of stress. Higher perceived resilience significantly predicted self-efficacy. Perceived stress was negatively and significantly associated with HRQOL. Finally, higher self-efficacy significantly predicted better HRQOL. DISCUSSION: This study underscores the significant association between resilience, stress, self-management behaviors, and health outcomes in African Americans with hypertension. Further research with larger sample sizes and longitudinal designs is warranted to confirm and expand upon these findings.

The effects of residential segregation on cognition among U.S. older adults: a systematic review based on the social determinants of health model.

OBJECTIVES: This systematic review aims to advance the understanding of the complicated effects of segregation on older adults' cognition and provide guidance for future research. METHOD: A systematic review using the Social Determinants of Health framework to examine the relationship between segregation and cognition across the selected literature. RESULTS: Eight papers met the criteria for inclusion. All selected studies examined the influence of living in a segregated area on older adults' cognition, covering older adults from different racial/ethnic groups. The association between segregation and cognition was found in different directions across different racial/ethnic groups. The effects can be varied depending on race/ethnicity, level of education, neighborhood socioeconomic status, or social context. CONCLUSION: This review identified existing gaps in understanding the relationship between segregation and cognition. Future studies should carefully adopt the segregation measures, acknowledge the varying segregation experience among different racial/ethnic groups, and consider more social determinant factors in research.

Understanding the Relationship Between Comorbidities, Medication Nonadherence, Activities of Daily Living, and Heart Condition Status Among Older Adults in the United States: A Regression Analysis and Machine Learning Approach.

BACKGROUND: Nonadherence to medication among patients with heart disease poses serious risks, including worsened heart failure and increased mortality rates. OBJECTIVE: This study aims to explore the complex interplay between comorbidities, medication nonadherence, activities of daily living, and heart condition status in older American adults, using both traditional statistical methods and machine learning. METHODS: Data from 326 older adults with heart conditions, drawn from the Health and Retirement Study, were analyzed. Descriptive statistics characterized demographic profiles and comorbidities, whereas logistic regression, multiple regression analyses, and decision tree models were used to address our research inquiries. In addition, a machine learning approach, specifically decision tree models, was integrated to enhance predictive accuracy. RESULTS: Our analysis showed that factors like age, gender, hypertension, and stroke history were significantly linked to worsening heart conditions. Notably, depression emerged as a robust predictor of medication nonadherence. Further adjusted analyses underscored significant correlations between stroke and challenges in basic activities such as dressing, bathing, and eating. Depression correlated significantly with difficulties in dressing, bed mobility, and toileting, whereas lung disease was associated with bathing hindrances. Intriguingly, our decision tree model revealed that patients experiencing dressing challenges, but not toileting difficulties, were more prone to report no improvement in heart condition status over the preceding 2 years. CONCLUSIONS: Blending traditional statistics with machine learning in this study reveals significant implications for crafting personalized interventions to improve patients' depression, leading to increased activities of daily living, medication adherence, reduced severity of comorbidities, and ultimately better management of heart conditions.

Use of deep artificial neural networks to identify stroke during triage via subtle changes in circulating cell counts.

BACKGROUND: The development of tools that could help emergency department clinicians recognize stroke during triage could reduce treatment delays and improve patient outcomes. Growing evidence suggests that stroke is associated with several changes in circulating cell counts. The aim of this study was to determine whether machine-learning can be used to identify stroke in the emergency department using data available from a routine complete blood count with differential. METHODS: Red blood cell, platelet, neutrophil, lymphocyte, monocyte, eosinophil, and basophil counts were assessed in admission blood samples collected from 160 stroke patients and 116 stroke mimics recruited from three geographically distinct clinical sites, and an ensemble artificial neural network model was developed and tested for its ability to discriminate between groups. RESULTS: Several modest but statistically significant differences were observed in cell counts between stroke patients and stroke mimics. The counts of no single cell population alone were adequate to discriminate between groups with high levels of accuracy; however, combined classification using the neural network model resulted in a dramatic and statistically significant improvement in diagnostic performance according to receiver-operating characteristic analysis. Furthermore, the neural network model displayed superior performance as a triage decision making tool compared to symptom-based tools such as the Cincinnati Prehospital Stroke Scale (CPSS) and the National Institutes of Health Stroke Scale (NIHSS) when assessed using decision curve analysis. CONCLUSIONS: Our results suggest that algorithmic analysis of commonly collected hematology data using machine-learning could potentially be used to help emergency department clinicians make better-informed triage decisions in situations where advanced imaging techniques or neurological expertise are not immediately available, or even to electronically flag patients in which stroke should be considered as a diagnosis as part of an automated stroke alert system.

Stroke Risk Screening Scales (SRSS): Identification of Domain and Item Generation.

BACKGROUND: Stroke is a principal cause of mortality and disability in Thailand and globally. Early and comprehensive risk identification would be critical to identify people at high risk for stroke. Therefore, a comprehensive stroke risk screening tool is needed to assess all possible stroke risks and potential at-risk populations. In the future, such an instrument would benefit early detection and stroke prevention planning. OBJECTIVE: The objective of the Stroke Risk Screening Scales (SRSS) development is to identify the domains and generating appropriate questions for the new SRSS. METHODS: Using deductive methods suggested by Godfred Boateng and colleagues (2018), the domains were classified based on the existing literature. The questions were generated based on a comprehensive analysis of existing stroke risk screening scales and their representativeness of each domain. Five existing stroke risk screening tools including 1) the Stroke RiskometerTM, 2) the Framingham 10-Year Risk Score, 3) the Stroke Risk Screening Tool (The Department of Disease Control of Thailand), 4) the My Risk Stroke Calculator, and 5) QStroke were included and identified. RESULTS: Overall, 18 domains were included, and each domain was represented with at least one or more questions. Eight domains (44.44 %) are consisting of a dichotomous question alone, another eight domains (44.44 %) consist of multiple questions, which combined between dichotomous, categorical, or fill-in-the-blank questions, one domain (5.55 %) consists of a fill-in-the-blank question, and another one (5.55 %) include only one categorical question. CONCLUSIONS: Developing a comprehensive tool to be used for stroke risk screening by extending the knowledge of stroke, stroke risk factors, and the best practice for tool development is necessitated for further practical stroke prevention planning.

The Evaluation of Content Relevance and Representativeness of the New Stroke Risk Screening Scales.

Stroke is a leading cause of death and disability worldwide. Early and comprehensive risk identification is essential for identifying individuals at high risk for stroke. This study aimed to evaluate each question in the new Stroke Risk Screening Scales (SRSS) and assess the domains for content relevance and representativeness. Initially, six stroke experts were invited to evaluate the SRSS questions. The content validity index (CVI), including the item-CVI (I-CVI) and the average-CVI (Ave-CVI), was then calculated. In our study, the acceptable standards for I-CVI and Ave-CVI were ≥0.78 and ≥0.9, respectively. The results showed that all invited experts accepted the invitation and evaluated the SRSS questions. The previous version of the SRSS consisted of 33 questions. Of these, 30 questions reached an I-CVI of ≥0.78, indicating good content validity. Three questions had an I-CVI of 0.67 and were considered invalid; thus, they were deleted. The overall instrument achieved an Ave-CVI of 0.95. Comprehensive SRSS are essential for effective stroke prevention planning. By facilitating the early identification of individuals at high risk for stroke, these scales help reduce the incidence and impact of stroke. The high content validity found in this study supports the reliability of the SRSS as a screening tool. In the future, implementing such validated scales in clinical practice can improve early intervention strategies, ultimately enhancing health outcomes and optimizing the use of healthcare resources.

Factor Impacting Quality of Life Among Sepsis Survivors During and After Hospitalization: A Systematic Review of Current Empirical Evidence.

Background: There remains a gap in understanding post-sepsis outcomes, particularly regarding the factors that influence the quality of life (QOL) among sepsis survivors during and after hospitalization. Objective: To determine factors impacting QOL among sepsis survivors during and after hospitalization based on the evaluation and synthesis of current evidence. Methods: This review encompassed studies published from January 2020 to December 2024, sourced from Scopus, PubMed, Medline, ScienceDirect, CINAHL Plus with Full Text, and Web of Science. The process of identifying, screening, excluding, and including articles followed the guidelines set by the Preferred Reporting Items for Systematic Reviews (PRISMA). Data synthesis for theme generation was conducted using the convergent integrated analysis framework as recommended by the Joanna Briggs Institute. Results: A total of 1164 records were identified from the databases. After removing 130 duplicates, 1034 articles remained for screening based on their titles and abstracts according to the inclusion and exclusion criteria. At this stage, 1021 articles did not meet the criteria and were excluded, leaving 13 articles eligible for full-text screening. During this phase, 5 articles were excluded for various reasons, resulting in eight studies being included in the systematic review. Data synthesis of these studies revealed seven themes related to factors impacting QOL among sepsis survivors during and after hospitalization: 1) Physical Health Dimension, 2) Mental Health Dimension, 3) Treatment During Hospitalization, 4) Spiritual Dimension, 5) Social Support, 6) Mortality, and 7) Blood Biomarkers. Conclusion: This systematic review provides valuable insights into the factors affecting the quality of life among sepsis survivors during and after hospitalization. These findings enhance the current knowledge base and offer clinicians, researchers, and policymakers actionable insights to improve outcomes and well-being for sepsis survivors.

Educational Interventions for Individuals with Insulin-Treated Type 2 Diabetes Mellitus: A Systematic Review.

Objective: To evaluate and synthesize evidence on the impact of educational interventions for individuals with insulin-treated type 2 diabetes mellitus (T2DM). Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, five electronic databases (Scopus, PubMed, Medline, CINAHL Plus with Full Text, and Web of Science) were systematically searched in February 2024. The search focused on studies published between 2019 and 2024 that investigated the impact of educational interventions on individuals with insulin-treated T2DM. Reference lists of the included studies were also manually searched. Titles and abstracts were screened for eligibility, and relevant full texts were assessed. Results: Out of 1,032 identified records, 11 studies met the inclusion criteria. According to the data synthesized using a convergent integrated analysis framework, five major themes have emerged: 1) Glycemic control (including subthemes improving HbA1C, decreasing postprandial plasma glucose, and decreasing fasting plasma glucose), 2) Insulin-related complications (including subthemes reducing hypoglycemic events and reducing the size of lipohypertrophy), 3) Knowledge, Attitude, and Practice (including subthemes engaging in self-management, improving insulin injection technique, improving knowledge, and improving attitude toward insulin treatment), 4) Optimal dose of insulin, and 5) Improving quality of life. Conclusion: Educational interventions are crucial for improving diabetes-related outcomes and reducing complications in individuals with insulin-treated T2DM. These interventions enhance knowledge, attitudes, and self-management practices, leading to better glycemic control and quality of life. Healthcare settings should develop and provide tailored educational programs for individuals with insulin-treated T2DM to optimize outcomes and minimize complications.

Exploring the Linkages Among Chronic Illness, Substance Use, and COVID-19 Infection in Adults Aged 50 Years and Older: Retrospective Cross-Sectional Analysis of National Representative Data.

BACKGROUND: The co-occurrence of chronic illnesses and substance use presents complex challenges for health care systems. Understanding the interplay between these factors, compounded by the context of the COVID-19 pandemic, is essential for effective intervention strategies. OBJECTIVE: This study aims to investigate the relationships among chronic illness, substance use, and COVID-19 infection in adults aged 50 years and older. METHODS: Participants were 1196 adults aged 50 years and older. Descriptive statistics were used to describe demographic information. Logistic regressions and multiple regression analyses were used to determine associations between chronic illnesses, substance use, and COVID-19 infection. Mediation analysis was used to determine the effect of chronic illness mediators in the association between COVID-19 concerns and substance use. RESULTS: The mean age was 68 (SD 10.3) years, with 58.6% (701/1196) being women. Adjusted analysis revealed that age and sex (women) significantly predicted a lower level of substance use (P<.05). However, marital status (separated or widowed) and chronic illness significantly predicted a higher level of substance use (P<.05). Furthermore, having dementia, arthritis, and high cholesterol significantly predicted a higher level of concern about the COVID-19 pandemic (P<.05). Logistic regression analysis indicated that individuals with hypertension (odds ratio [OR] 1.91, 95% CI 1.37-2.66; P<.001), lung disease (OR 2.42, 95% CI 1.23-4.75; P=.01), heart condition (OR 1.99, 95% CI 1.28-3.10; P=.002), stroke (OR 2.35, 95% CI 1.07-5.16; P=.03), and arthritis (OR 1.72, 95% CI 1.25-2.37; P=.001) were more likely to have their work affected by the COVID-19 pandemic. The mediation analysis showed a significant effect of COVID-19 concern on substance use through the mediation of chronic illness, with a 95% CI of -0.02 to -0.01 and an indirect effect of -0.01. CONCLUSIONS: Our study reveals complex associations among chronic illnesses, substance use, and COVID-19 infection among adults aged 50 years and older. It underscores the impact of demographics and specific chronic conditions on substance use behaviors and COVID-19 concerns. In addition, certain chronic illnesses were linked to heightened vulnerability in employment status during the pandemic. These findings emphasize the need for targeted interventions addressing physical health and substance use in this population during the COVID-19 pandemic.

An Integrative Review of Social Determinants of Glycemic Targets Achievement in Adults with Type 2 Diabetes in the United States.

Several individual social determinants of health have been identified as significant factors contributing to achieving glycemic targets (glycated hemoglobin < 7). However, it remains unclear how these social variables individually or collectively contribute to glycemic targets among adults with type 2 diabetes (T2D) in the United States (U.S.) The purpose of the current integrative review (IR) was to describe and synthesize findings from studies on social determinants of glycemic target achievement in adults with T2D in the U.S. and integrate them into the United States Department of Health and Human Services Conceptual Framework. The databases searched included PubMed, CINAHL Plus with Full Text, Medline with Full Text [EBSCO], Google Scholar, bibliography, and hand searching. A total of 948 records were identified. After excluding duplicates and irrelevant studies based on inclusion and exclusion criteria through title, abstract, and full-text screening, 13 studies were finally included in this IR. The results revealed that race/ethnicity, economic access and stability, educational access and quality, healthcare access and quality, neighborhood and built environment, and social and community context contribute to glycemic target achievement among adult patients with T2D in the U.S. Integrating findings from key studies on social determinants of glycemic health may contribute to developing interventions aimed at reducing and eventually eradicating health disparities for individuals with and at risk for T2D in the U.S.

Assessing the Efficacy of ChatGPT Versus Human Researchers in Identifying Relevant Studies on mHealth Interventions for Improving Medication Adherence in Patients With Ischemic Stroke When Conducting Systematic Reviews: Comparative Analysis.

BACKGROUND: ChatGPT by OpenAI emerged as a potential tool for researchers, aiding in various aspects of research. One such application was the identification of relevant studies in systematic reviews. However, a comprehensive comparison of the efficacy of relevant study identification between human researchers and ChatGPT has not been conducted. OBJECTIVE: This study aims to compare the efficacy of ChatGPT and human researchers in identifying relevant studies on medication adherence improvement using mobile health interventions in patients with ischemic stroke during systematic reviews. METHODS: This study used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Four electronic databases, including CINAHL Plus with Full Text, Web of Science, PubMed, and MEDLINE, were searched to identify articles published from inception until 2023 using search terms based on MeSH (Medical Subject Headings) terms generated by human researchers versus ChatGPT. The authors independently screened the titles, abstracts, and full text of the studies identified through separate searches conducted by human researchers and ChatGPT. The comparison encompassed several aspects, including the ability to retrieve relevant studies, accuracy, efficiency, limitations, and challenges associated with each method. RESULTS: A total of 6 articles identified through search terms generated by human researchers were included in the final analysis, of which 4 (67%) reported improvements in medication adherence after the intervention. However, 33% (2/6) of the included studies did not clearly state whether medication adherence improved after the intervention. A total of 10 studies were included based on search terms generated by ChatGPT, of which 6 (60%) overlapped with studies identified by human researchers. Regarding the impact of mobile health interventions on medication adherence, most included studies (8/10, 80%) based on search terms generated by ChatGPT reported improvements in medication adherence after the intervention. However, 20% (2/10) of the studies did not clearly state whether medication adherence improved after the intervention. The precision in accurately identifying relevant studies was higher in human researchers (0.86) than in ChatGPT (0.77). This is consistent with the percentage of relevance, where human researchers (9.8%) demonstrated a higher percentage of relevance than ChatGPT (3%). However, when considering the time required for both humans and ChatGPT to identify relevant studies, ChatGPT substantially outperformed human researchers as it took less time to identify relevant studies. CONCLUSIONS: Our comparative analysis highlighted the strengths and limitations of both approaches. Ultimately, the choice between human researchers and ChatGPT depends on the specific requirements and objectives of each review, but the collaborative synergy of both approaches holds the potential to advance evidence-based research and decision-making in the health care field.

Association between post-stroke depression and functional outcomes: A systematic review.

BACKGROUND: Post-stroke depression (PSD) is a frequent problem in stroke patients, affecting their rehabilitation process and functional outcomes. Several studies have investigated the relationship between PSD and functional outcomes, but the results have been inconsistent. OBJECTIVE: This systematic review of non-experimental studies aims to investigate the prevalence of post-stroke depression and the association between post-stroke depression and functional outcomes. METHOD: A search of PubMed, MEDLINE, Web of Science, and CINAHL Plus with Full Text was carried out from inception until January 2024. The literature was screened using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, with relevant papers included. We extracted data from non-experimental studies that examined associations between PSD and functional outcomes. The Joanna Briggs Institute for systematic reviews was used for critical appraisal. RESULTS: Twenty-one studies met the study criteria, including sixteen cohort studies, four cross-sectional studies, and one case-control study. PSD prevalences ranged from 12.2% to 32.2% in the first two weeks, 17.9 to 35.5% in the first month, and 10.4% to 32.0% in the third month following a stroke. Functional outcomes were evaluated in four domains: degree of dependence, basic activity of daily living, instrumental activity of daily living, and physical and cognitive function. Significant associations between PSD and functional outcomes were identified after controlling potential factors such as age, comorbidities, and stroke severity. PSD had negative associations with functional outcomes in all four measure domains from one month to five years after a stroke. Depression treatment showed positive results on functional outcomes in stroke patients. CONCLUSION: PSD prevalence was high in the first three months after stroke. PSD is significantly associated with poor functional outcomes. PSD assessment and management should be performed on a frequent basis in the early stages of stroke to achieve the best possible functional recovery.

Medication Adherence of Older Adults with Hypertension: A Systematic Review.

Objective: Hypertension (HTN) significantly increases the risk of stroke and heart disease, which are the leading causes of death and disability globally, particularly among older adults. Antihypertensive medication is a proven treatment for blood pressure control and preventing complications. However, medication adherence rates in older adults with HTN are low. In this review, we systematically identified factors influencing medication adherence in older adults with HTN. Methods: We applied the PRISMA guidelines and conducted systematic searches on PubMed, MEDLINE, and Google Scholar in July 2022 to identify preliminary studies reporting factors influencing medication adherence among older adults with HTN. The convergent integrated analysis framework suggested by the Joanna Briggs Institute for systematic reviews was adopted for data synthesis. Results: Initially, 448 articles were identified, and after title and abstract screening, 16 articles qualified for full-text review. During this phase, three articles were excluded for reporting on irrelevant populations or focusing on issues beyond the review's aim, leaving thirteen studies in the final review. After data synthesis, fifteen themes were extracted from the key findings of the included studies. The most prevalent themes included the number of medications used (53.9%, n=7 studies), financial status (38.5%, n=5), sex (38.5%, n=5), age (30.1%, n=4), duration of disease (23.1%, n=3), comorbidities (23.1%, n=3), and health compliance (23.1%, n=3). Other themes, such as education, health literacy, health belief, medication belief, perception of illness, patient-physician relationship, self-efficacy, and social support, were also identified. Conclusion: The findings of this review highlight critical areas for developing innovative, evidence-based programs to improve medication adherence in hypertensive older adults. Insights from this review can contribute to improving medication adherence and preventing future health complications.

A Systematic Review of the Application of Artificial Intelligence in Nursing Care: Where are We, and What's Next?

Background: Integrating Artificial Intelligence (AI) into healthcare has transformed the landscape of patient care and healthcare delivery. Despite this, there remains a notable gap in the existing literature synthesizing the comprehensive understanding of AI's utilization in nursing care. Objective: This systematic review aims to synthesize the available evidence to comprehensively understand the application of AI in nursing care. Methods: Studies published between January 2019 and December 2023, identified through CINAHL Plus with Full Text, Web of Science, PubMed, and Medline, were included in this review. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines guided the identification, screening, exclusion, and inclusion of articles. The convergent integrated analysis framework, as proposed by the Joanna Briggs Institute, was employed to synthesize data from the included studies for theme generation. Results: A total of 337 records were identified from databases. Among them, 35 duplicates were removed, and 302 records underwent eligibility screening. After applying inclusion and exclusion criteria, eleven studies were deemed eligible and included in this review. Through data synthesis of these studies, six themes pertaining to the use of AI in nursing care were identified: 1) Risk Identification, 2) Health Assessment, 3) Patient Classification, 4) Research Development, 5) Improved Care Delivery and Medical Records, and 6) Developing a Nursing Care Plan. Conclusion: This systematic review contributes valuable insights into the multifaceted applications of AI in nursing care. Through the synthesis of data from the included studies, six distinct themes emerged. These findings not only consolidate the current knowledge base but also underscore the diverse ways in which AI is shaping and improving nursing care practices.

Brain Expression Levels of Commonly Measured Blood Biomarkers of Neurological Damage Differ with Respect to Sex, Race, and Age.

It is increasingly evident that blood biomarkers have potential to improve the diagnosis and management of both acute and chronic neurological conditions. The most well-studied candidates, and arguably those with the broadest utility, are proteins that are highly enriched in neural tissues and released into circulation upon cellular damage. It is currently unknown how the brain expression levels of these proteins is influenced by demographic factors such as sex, race, and age. Given that source tissue abundance is likely a key determinant of the levels observed in the blood during neurological pathology, understanding such influences is important in terms of identifying potential clinical scenarios that could produce diagnostic bias. In this study, we leveraged existing mRNA sequencing data originating from 2,642 normal brain specimens harvested from 382 human donors to examine potential demographic variability in the expression levels of genes which code for 28 candidate blood biomarkers of neurological damage. Existing mass spectrometry data originating from 26 additional normal brain specimens harvested from 26 separate human donors was subsequently used to tentatively assess whether observed transcriptional variance was likely to produce corresponding variance in terms of protein abundance. Genes associated with several well-studied or emerging candidate biomarkers including neurofilament light chain (NfL), ubiquitin carboxyl-terminal hydrolase isozyme L1 (UCH-L1), neuron-specific enolase (NSE), and synaptosomal-associated protein 25 (SNAP-25) exhibited significant differences in expression with respect to sex, race, and age. In many instances, these differences in brain expression align well with and provide a mechanistic explanation for previously reported differences in blood levels.

Current Status of Barriers to mHealth Access Among Patients With Stroke and Steps Toward the Digital Health Era: Systematic Review.

BACKGROUND: Mobile health (mHealth) offers significant benefits for patients with stroke, facilitating remote monitoring and personalized health care solutions beyond traditional settings. However, there is a dearth of comprehensive data, particularly qualitative insights, on the barriers to mHealth access. Understanding these barriers is crucial for devising strategies to enhance mHealth use among patients with stroke. OBJECTIVE: This study aims to examine the recent literature focusing on barriers to mHealth access among patients with stroke. METHODS: A systematic search of PubMed, MEDLINE, Web of Science, and CINAHL Plus Full Text was conducted for literature published between 2017 and 2023. Abstracts and full texts were independently screened based on predetermined inclusion and exclusion criteria. Data synthesis was performed using the convergent integrated analysis framework recommended by the Joanna Briggs Institute. RESULTS: A total of 12 studies met the inclusion criteria. The majority were qualitative studies (about 42%), followed by mixed methods (25%), pilot studies (about 17%), nonrandomized controlled trials (about 8%), and observational studies (about 8%). Participants included patients with stroke, caregivers, and various health care professionals. The most common mHealth practices were home-based telerehabilitation (30%) and poststroke mHealth and telecare services (20%). Identified barriers were categorized into two primary themes: (1) at the patient level and (2) at the health provider-patient-device interaction level. The first theme includes 2 subthemes: health-related issues and patient acceptability. The second theme encompassed 3 subthemes: infrastructure challenges (including software, networking, and hardware), support system deficiencies, and time constraints. CONCLUSIONS: This systematic review underscores significant barriers to mHealth adoption among patients with stroke. Addressing these barriers in future research is imperative to ensure that mHealth solutions effectively meet patients' needs.

Using ChatGPT in Medical Research: Current Status and Future Directions.

Objective: This review aims to evaluate the current evidence on the use of the Generative Pre-trained Transformer (ChatGPT) in medical research, including but not limited to treatment, diagnosis, or medication provision. Methods: This review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched Google Scholar, Web of Science, PubMed, and Medline to identify studies published between 2022 and 2023 that aimed to utilize ChatGPT in medical research. All identified references were stored in EndNote. Results: We initially identified 114 articles, out of which six studies met the inclusion and exclusion criteria for full-text screening. Among the six studies, two focused on drug development (33.33%), two on literature review writing (33.33%), and one each on medical report improvement, provision of medical information, improving research conduct, data analysis, and personalized medicine (16.67% each). Conclusion: ChatGPT has the potential to revolutionize medical research in various ways. However, its accuracy, originality, academic integrity, and ethical issues must be thoroughly discussed and improved before its widespread implementation in clinical research and medical practice.

Using Neural Networks Algorithm in Ischemic Stroke Diagnosis: A Systematic Review.

Objective: To evaluate the evidence of artificial neural network (NNs) techniques in diagnosing ischemic stroke (IS) in adults. Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was utilized as a guideline for this review. PubMed, MEDLINE, Web of Science, and CINAHL Plus Full Text were searched to identify studies published between 2018 and 2022, reporting using NNs in IS diagnosis. The Critical Appraisal Checklist for Diagnostic Test Accuracy Studies was adopted to evaluate the included studies. Results: Nine studies were included in this systematic review. Non-contrast computed tomography (NCCT) (n = 4 studies, 26.67%) and computed tomography angiography (CTA) (n = 4 studies, 26.67%) are among the most common features. Five algorithms were used in the included studies. Deep Convolutional Neural Networks (DCNNs) were commonly used for IS diagnosis (n = 3 studies, 33.33%). Other algorithms including three-dimensional convolutional neural networks (3D-CNNs) (n = 2 studies, 22.22%), two-stage deep convolutional neural networks (Two-stage DCNNs) (n = 2 studies, 22.22%), the local higher-order singular value decomposition denoising algorithm (GL-HOSVD) (n = 1 study, 11.11%), and a new deconvolution network model based on deep learning (AD-CNNnet) (n = 1 study, 11.11%) were also utilized for the diagnosis of IS. Conclusion: The number of studies ensuring the effectiveness of NNs algorithms in IS diagnosis has increased. Still, more feasibility and cost-effectiveness evaluations are needed to support the implementation of NNs in IS diagnosis in clinical settings.

How does the COVID-19 pandemic impact medication adherence of patients with chronic disease?: A systematic review.

OBJECTIVE: To determine how the COVID-19 pandemic impacts patients with chronic disease medication adherence. METHODS: Four electronic databases, PubMed, MEDLINE, Web of Science, and CINAHL Plus Full Text, were searched for literature between 2019 and 2021. Abstracts and later full texts were independently screened by the authors of this review using inclusion and exclusion criteria to determine relevance to our study. Joanna Briggs Institute (JBI) critical appraisal tools were used to assess the quality of included texts. Relevant information and data from the included texts were extracted into tables for data synthesis and analysis. RESULTS: Ten studies met the study criteria, the most popular study design was cross-sectional design (n = 9, 90.0%), others were case series (n = 1, 10.0%). Barriers to medication adherence and facilitators of medication adherence were the major two themes that participants reported regarding the impact of COVID-19 on medication adherence. Moreover, these two main themes have been organized in sub-themes that are dealt with in-depth. DISCUSSION: Our results could heighten healthcare providers, stakeholders, and policy leaders' awareness of providing appropriate support for chronic disease patients, especially regarding medication adherence. Future research incorporating programs that support patients' needs is recommended.

Management and Characteristics of Embolism and Thrombosis After COVID-19 Vaccination: Scoping Review.

This scoping review aims to 1) identify characteristics of participants who developed embolism and/or thrombotic event(s) after COVID-19 vaccination and 2) review the management during the new vaccine development of the unexpected event(s). This review was conducted following PRISMA for scoping review guidelines. Peer-reviewed articles were searched for studies involving participants with embolism and/or thrombotic event(s) after COVID-19 vaccination with the management described during the early phase after the approval of vaccines. The 12 studies involving 63 participants were included in this review. The majority of participants' ages ranged from 22 to 49 years. The embolism and/or thrombotic event(s) often occur within 30 days post-vaccination. Five of the included studies reported the event after receiving viral vector vaccines and suggested a vaccine-induced immune thrombotic thrombocytopenia as a plausible mechanism. Cerebral venous sinus thrombosis was the most frequently reported post-vaccination thrombosis complication. In summary, the most frequently reported characteristics and management from this review were consistent with international guidelines. Future studies are recommended to further investigate the incidence and additional potential complications to warrant the benefit and safety after receiving COVID-19 vaccine and other newly developed vaccines.