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.

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.

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.

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.

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.

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.

Utilizing Social Determinants of Health Model to Understand Barriers to Medication Adherence in Patients with Ischemic Stroke: A Systematic Review.

Introduction: Ischemic strokes and their recurrence create an immense disease burden globally. Therefore, preventing recurrent strokes by promoting medication adherence is crucial to reduce morbidity and mortality. In addition, understanding the barriers to medication adherence related to the social determinants of health (SDoH) could promote equity among persons with ischemic stroke. Objective: To explore the barriers to medication adherence among patients with ischemic stroke through the SDoH. Methods: This systematic review included studies published between January 2018 and December 2022 identified through PubMed, MEDLINE, Web of Science, and CINAHL Plus Full Text. The descriptions of the studies were systematically summarized and discussed based on the SDoH from the US Healthy People 2030 initiative. Results: Eight studies met the inclusion criteria and were included in this review. The most common barrier to adherence was inappropriate medication beliefs, medication side effects, and patient-physician relationship, which relate to the dimensions of healthcare access and quality. Health literacy and health perception, dependent on education access and quality, frequently influenced adherence. Other social determinants, such as financial strain and social and community context, were found to alter adherence behaviors. No study addressed the neighborhood and built environment domain. We found that cognitive impairment is another factor that impacts adherence outcomes among stroke patients. Conclusion: Multifaceted approaches are needed to address the SDoH to improve medication adherence among patients with ischemic stroke. This review emphasized strategies, including patient education, provider-patient communication, social support, health literacy, technology, and policy advocacy to enhance adherence.

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.

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.

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.

Associations between diagnosis with stroke, comorbidities, and activity of daily living among older adults in the United States.

Background: Stroke is the leading cause of mortality. This study aimed to investigate the association between stroke, comorbidities, and activity of daily living (ADL) among older adults in the United States. Methods: Participants were 1165 older adults aged 60 and older from two waves (2016 and 2018) of the Health and Retirement Study who had a stroke. Descriptive statistics were used to describe demographic information and comorbidities. Logistic regressions and multiple regression analyses were used to determine associations between stroke, comorbidities, and ADL. Results: The mean age was 75.32 ± 9.5 years, and 55.6% were female. An adjusted analysis shows that older stroke adults living with diabetes as comorbidity are significantly associated with difficulty in dressing, walking, bedding, and toileting. Moreover, depression was significantly associated with difficulty in dressing, walking, bathing, eating, and bedding. At the same time, heart conditions and hypertension as comorbidity were rarely associated with difficulty in ADL. After adjusting for age and sex, heart condition and depression are significantly associated with seeing a doctor for stroke (odds ratio [OR]: 0.66; 95% confidence interval [CI]: 0.49-0.91; p = 0.01) and stroke therapy (OR: 0.46; 95% CI: 0.25-0.84; p = 0.01). Finally, stroke problem (unstandardized ß [B] = 0.58, p = 0.017) and stroke therapy (B = 1.42, p < 0.001) significantly predict a lower level of independence. Conclusion: This study could benefit healthcare professionals in developing further interventions to improve older stroke adults' lives, especially those with a high level of dependence.

Machine learning-based patient classification system for adults with stroke: A systematic review.

OBJECTIVE: To evaluate the existing evidence of a machine learning-based classification system that stratifies patients with stroke. METHODS: The authors carried out a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) recommendations for a review article. PubMed, MEDLINE, Web of Science, and CINAHL Plus Full Text were searched from January 2015 to February 2021. RESULTS: There are twelve studies included in this systematic review. Fifteen algorithms were used in the included studies. The most common forms of machine learning (ML) used to classify stroke patients were the support vector machine (SVM) (n = 8 studies), followed by random forest (RF) (n = 7 studies), decision tree (DT) (n = 4 studies), gradient boosting (GB) (n = 4 studies), neural networks (NNs) (n = 3 studies), deep learning (n = 2 studies), and k-nearest neighbor (k-NN) (n = 2 studies), respectively. Forty-four features of inputs were used in the included studies, and age and gender are the most common features in the ML model. DISCUSSION: There is no single algorithm that performed better or worse than all others at classifying patients with stroke, in part because different input data require different algorithms to achieve optimal outcomes.

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.

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.

The experience of caregivers of chronically ill patients during the COVID-19: A Systematic Review.

OBJECTIVES: This study aimed to identify the difficulties that caregivers of chronically ill patients experienced during the COVID-19 pandemic and to provide directions for future studies. METHODS: Five electronic databases, including PubMed, Web of Science, CINAHL Plus Full Text, EMBASE, and Scopus, were systematically searched from January 2019 to February 2021. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses were employed for the literature screening, inclusion, and exclusion. The Mixed Methods Appraisal Tool was adopted for qualifying appraisal. RESULTS: Six studies met the study criteria, including three quantitative studies, two qualitative studies, and one mixed-method study. Mental health, personal experience, financial problems, physical health, and improvement approaches were the major five themes that participants reported regarding the impact of COVID-19 they encountered during the pandemic. DISCUSSION: The results could heighten healthcare providers, stakeholders, and policy leaders' awareness of providing appropriate support for caregivers. Future research incorporating programs that support caregivers' needs is recommended.

Self-management interventions for adults with stroke: A scoping review.

BACKGROUND: Stroke is a principal cause of mortality and disability globally. Numerous studies have contributed to the knowledge base regarding self-management interventions among chronic disease patients, but there are few such studies for patients with stroke. Therefore, it is necessary to analyze self-management interventions among stroke patients. This scoping review aimed to systematically identify and describe randomized controlled trials (RCTs) of self-management interventions for adults with stroke. METHODS: A review team carried out a scoping review on stroke and self-management interventions based on the methodology of Arksey and O'Malley, following the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). PubMed, Embase, Web of Science, CINAHL Plus Full Text, Medline Plus Full Text, and Cochrane Central Register of Controlled Trials were searched from inception to July 2020. RESULTS: Fifty-four RCTs were included. The most popular study design is comparing a self-management intervention to usual care or waitlist control condition. Physical activity is the most common intervention topic, and interventions were mainly delivered face to face. The majority of interventions were located in inpatient and multiple settings. Interventions were conducted by various providers, with nurses the most common provider group. Symptom management was the most frequently reported outcome domain that improved. CONCLUSIONS: Self-management interventions benefit the symptom management of stroke patients a lot. The reasonable time for intervention is at least 6-12 months. Multifarious intervention topics, delivery formats, and providers are adopted mostly to meet the multiple needs of this population. Physical activity was the most popular topic currently. Studies comparing the effect of different types of self-management interventions are required in the future.

Nutritional Interventions on Physical Functioning for Critically Ill Patients: An Integrative Review.

BACKGROUND: Poor physical functioning (PF) is a common issue among critically ill patients. It was suggested that reasonable nutrition accelerates PF recovery. However, the details and types of nutritional interventions on the PF of different intensive care unit (ICU) patients at present have not been well analyzed yet. This study aimed to systematically synthesize nutritional interventions on PF in different ICU populations. METHODS: Whittemore and Knafl's framework was employed. PubMed, EMBASE, Web of Science, CINAHL Plus with Full Text, and Cochrane Library were searched to obtain studies from January 2010 to September 2020, with a manual search of the included studies' references. Record screening, data extraction, and quality appraisal were conducted independently by each reviewer before reaching an agreement after discussion. RESULTS: Twelve studies were included reporting the effects of early parenteral nutrition, early enteral nutrition, early goal-directed nutrition, early adequate nutrition, higher protein delivery, higher energy delivery, low energy delivery, energy and protein delivery, intermittent enteral feeding on PF like muscle mass, muscle strength, and function. Function was the most common outcome but showed little improvements. Muscle strength outcomes improved the most. The mechanically ventilated were the most popular target ICU population. The commenced time of the interventions is usually within 24 to 48 hours after ICU admission. CONCLUSION: Research on nutritional interventions on critically ill patients' PF is limited, but most are of a high level of evidence. Few intervention studies specified their evidence basis. Qualitative studies investigating timeframe of initiating feeding, perspectives of the patients' perspectives and caregivers are warranted to advance research and further discuss this topic.

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 Association Among Medication Beliefs, Perception of Illness and Medication Adherence in Ischemic Stroke Patients: A Cross-Sectional Study in China.

PURPOSE: To examine the association and the mediating effect among medication beliefs, perception of illness, and medication adherence in ischemic stroke patients. PATIENTS AND METHODS: This is a cross-sectional study, 306 ischemic stroke patients recruited from The Second Affiliated Hospital of Harbin Medical University, China between June 2018 and October 2018. The Beliefs about Medications Questionnaire (BMQ) was used to assess a patient's beliefs about medication. The Brief Illness Perceptions Questionnaire (BIPQ) was used to rapidly determine the cognitive and emotional representation of ischemic stroke. Self-reported adherence was assessed using the Medication Adherence Report Scale (MARS). Logistic regression analysis, Pearson correlations, and mediation analysis were used to evaluate the association and mediating effects among medication beliefs, perception of illness, and medication adherence. RESULTS: Overall, 220 (65.48%) participants were non-adherent to their ischemic stroke medications. Non-adherent patients had greater stroke severity (p = 0.031) compared to adherent patients. After adjusting for demographic characteristics, specific concern (odds ratio [OR]: 0.652, 95% confidence interval [CI]: 0.431 to 0.987, p-value [P] = 0.043), and the perception of illness (overall score) (OR: 0.964, 95% CI: 0.944 to 0.985, P = 0.001) were significantly associated with medication adherence in ischemic stroke patients. The mediation analysis showed the significant indirect effects of specific concern, general overuse, and general harm. It suggested that some impacts of medication beliefs have been mediated on medication adherence. CONCLUSION: Perceived concern about adverse effects of medicines and perception of illness have an influential impact on self-reported medication adherence in ischemic stroke patients. To enhance adherence, patients' beliefs about medication and perceptions of their disease should be reconsidered. Future work should investigate interventions to influence patient adherence by addressing concerns about their ischemic stroke medications and the perception of the disease.