Cross-cultural validation of the stroke riskometer using generalizability theory

Evaluating the influence of a 3-min online video on the community knowledge of stroke in four Arab countries

Introduction

Studies from developed and developing countries showed that the knowledge levels of stroke need improvement. Educational campaigns varied and were of limited influence predominantly because of their short duration and the need for financial support. The study aims to test the impact of a 3-min online video on the knowledge of stroke and factors influencing the knowledge score in four Arab countries.

Methods

A cross-sectional web-based pre-post study was conducted in Egypt, Jordan, Lebanon, and the United Arab Emirates. The data were collected using the snowball technique. Participants were adults aged 18 years and above. The questionnaire sequence was conducting a pretest, followed by the educational video explaining stroke occurrence, types, risks, warning signs, preventive measures, and treatment, and finally, a posttest to evaluate the differences in knowledge from baseline. Statistical analysis included paired t-tests comparing pre-post-education stroke knowledge scores, while repeated measures ANOVA, adjusting for covariates, assessed mean changes.

Results

The total number of participants was 2,721, mainly younger than 55 years. The majority had a university degree and were not healthcare professionals. A significant improvement was noted in the total knowledge score in all countries from a mean average (Mpretest = 21.11; Mposttest = 23.70) with p < 0.001. Identification of the stroke risks (Mpretest = 7.40; Mposttest = 8.75) and warning signs (Mpretest = 4.19; Mposttest = 4.94), understanding the preventive measures (Mpretest = 5.27; Mposttest = 5.39) and the importance of acting fast (Mpretest = 0.82; Mposttest = 0.85) improved from baseline with (p < 0.001) for all score components.

Conclusion

The educational tool successfully enhanced public understanding of stroke risks, the identification of stroke signs, and the critical need for emergency action. The advantages of this video include its short length, free online access, use of evidence-based content in lay language, and reflective images. The ultimate goal remains the long-term improvement of sustainability by mandating full-scale trials.

Assessing the individual risk of stroke in caregivers of patients with stroke

BACKGROUND

 Genetic factors influence the risk of developing stroke. Still, it is unclear whether this risk is intrinsically high in certain people or if nongenetic factors explain it entirely.

OBJECTIVE

 To compare the risk of stroke in kin and nonkin caregivers.

METHODS

 In a cross-sectional study using the Stroke Riskometer app (AUT Ventures Limited, Auckland, AUK, New Zealand), we determined the 5- and 10-year stroke risk (SR) among caregivers of stroke inpatients. The degree of kinship was rated with a score ranging from 0 to 50 points.

RESULTS

 We studied 278 caregivers (69.4% of them female) with a mean age of 47.5 ± 14.2 years. Kin caregivers represented 70.1% of the sample, and 49.6% of them were offspring. The median SR at 5 years was of 2.1 (range: 0.35-17.3) versus 1.73 (range: 0.04-29.9), and of 4.0 (range: 0.45-38.6) versus 2.94 (range: 0.05-59.35) at 10 years for the nonkin and kin caregivers respectively. In linear logistic regression controlled for the age of the caregivers, adding the kinship score did not increase the overall variability of the model for the risk at 5 years (R2 = 0.271; p = 0.858) nor the risk at 10 years (R2 = 0.376; p = 0.78).

CONCLUSION

 Caregivers of stroke patients carry a high SR regardless of their degree of kinship.

Usability and feasibility of PreventS-MD web app for stroke prevention

BACKGROUND

Most strokes and cardiovascular diseases (CVDs) are potentially preventable if their risk factors are identified and well controlled. Digital platforms, such as the PreventS-MD web app (PreventS-MD) may aid health care professionals (HCPs) in assessing and managing risk factors and promoting lifestyle changes for their patients.

METHODS

This is a mixed-methods cross-sectional two-phase survey using a largely positivist (quantitative and qualitative) framework. During Phase 1, a prototype of PreventS-MD was tested internationally by 59 of 69 consenting HCPs of different backgrounds, age, sex, working experience, and specialties using hypothetical data. Collected comments/suggestions from the study HCPs in Phase 1 were reviewed and implemented. In Phase 2, a near-final version of PreventS-MD was developed and tested by 58 of 72 consenting HCPs using both hypothetical and real patient (n = 10) data. Qualitative semi-structured interviews with real patients (n = 10) were conducted, and 1 month adherence to the preventive recommendations was assessed by self-reporting. The four System Usability Scale (SUS) groups of scores (0-50 unacceptable; 51-68 poor; 68-80.3 good; >80.3 excellent) were used to determine usability of PreventS-MD.

FINDINGS

Ninety-nine HCPs from 27 countries (45% from low- to middle-income countries) participated in the study, and out of them, 10 HCPs were involved in the development of PreventS before the study, and therefore were not involved in the survey. Of the remaining 89 HCPs, 69 consented to the first phase of the survey, and 59 of them completed the first phase of the survey (response rate 86%), and 58 completed the second phase of the survey (response rate 84%). The SUS scores supported good usability of the prototype (mean score = 80.2; 95% CI [77.0-84.0]) and excellent usability of the final version of PreventS-MD (mean score = 81.7; 95% CI [79.1-84.3]) in the field. Scores were not affected by the age, sex, working experience, or specialty of the HCPs. One-month follow-up of the patients confirmed the high level of satisfaction/acceptability of PreventS-MD and (100%) adherence to the recommendations.

INTERPRETATION

The PreventS-MD web app has a high level of usability, feasibility, and satisfaction by HCPs and individuals at risk of stroke/CVD. Individuals at risk of stroke/CVD demonstrated a high level of confidence and motivation in following and adhering to preventive recommendations generated by PreventS-MD.

Primordial and Primary Prevention of Ischemic Stroke in Saudi Arabia: A Combination Approach and Evolving Concepts

Ischemic stroke is a considerable public health hazard and a significant cause of disability and mortality in Saudi Arabia. Primary prevention strategies in the country are currently limited. With the health sector transformation program that depends on the principles of value-based care and applying the new model of care in disease prevention, aggressive and serious steps for primary stroke prevention are expected to be implemented. This article reviews primordial and primary prevention of ischemic stroke in Saudi Arabia and suggests a combination approach and framework for implementation. We provide a pragmatic solution to implement primordial and primary stroke prevention in Saudi Arabia and specify the roles of the government, health professionals, policymakers, and the entire population. Currently, there are several key priorities for primordial and primary stroke prevention in Saudi Arabia that should target people at different levels of risk. These include an emphasis on a comprehensive approach that includes both individual and population-based strategies and establishing partnerships across health-care providers to share responsibility for developing and implementing both strategies. This is an urgent call for action to initiate different strategies suggested by experts for primary stroke prevention in Saudi Arabia.

The Feasibility of Make My Day-A Randomized Controlled Pilot Trial of a Stroke Prevention Program in Primary Healthcare

Incorporating and sustaining engaging everyday activities (EEAs) in everyday life holds potential for improving health and wellbeing; thus, there is reason to explore EEAs as a behavioral change technique in stroke prevention. The aim of this study was to evaluate the feasibility of the stroke prevention program Make My Day (MMD) for people with moderate-to-high risk for stroke in a primary healthcare setting, where EEAs are utilized to promote healthy activity patterns. A randomized controlled pilot trial was designed to evaluate the feasibility of MMD. Twenty-nine persons at risk for stroke were recruited and randomized into either an intervention group (n = 14) receiving MMD or a control group (n = 15) receiving brief health advice and support with goal setting. The results suggest that MMD is feasible, with timely recruitment, overall high response rates and study completion, and sensitivity to change in key outcome measures. Moreover, the results demonstrate that the application of EEAs can be useful for promoting behavioral change in stroke prevention. Recommendations for improvements for a full-scale trial include recruiting a relevant sample, using reliability- and validity-tested outcome measures, and implementing strategies to limit missing data.

A Pilot Study of Application of the Stroke Riskometer Mobile App for Assessment of the Course and Clinical Outcomes of COVID-19 among Hospitalized Patients

INTRODUCTION

Early determination of COVID-19 severity and health outcomes could facilitate better treatment of patients. Different methods and tools have been developed for predicting outcomes of COVID-19, but they are difficult to use in routine clinical practice.

METHODS

We conducted a prospective cohort study of inpatients aged 20-92 years, diagnosed with COVID-19 to determine whether their individual 5-year absolute risk of stroke at the time of hospital admission predicts the course of COVID-19 severity and mortality. The risk of stroke was determined by the Stroke Riskometer mobile application.

RESULTS

We examined 385 patients hospitalized with COVID-19 (median age 61 years). The participants were categorized based on COVID-19 severity: 271 (70.4%) to the "not severe" and 114 (29.6%) to the "severe" groups. The median risk of stroke the next day after hospitalization was significantly higher among patients in the severe group (2.83, 95% CI: 2.35-4.68) versus the not severe group (1.11, 95% CI: 1.00-1.29). The median risk of stroke and median systolic blood pressure (SBP) were significantly higher among non-survivors (12.04, 95% CI: 2.73-21.19) and (150, 95% CI: 140-170) versus survivors (1.31, 95% CI: 1.14-1.52) and (134, 95% CI: 130-135), respectively. Those who spent more than 2.5 h a week on physical activity were 3.1 times more likely to survive from COVID-19. Those who consumed more than one standard alcohol drink a day, or suffered with atrial fibrillation, or had poor memory were 2.5, 2.3, and 2.6 times more likely not to survive from COVID-19, respectively.

CONCLUSIONS

High risk of stroke, physical inactivity, alcohol intake, high SBP, and atrial fibrillation are associated with severity and mortality of COVID-19. Our findings suggest that the Stroke Riskometer app could be used as a simple predictive tool of COVID-19 severity and mortality.

Machine learning, artificial intelligence and the prediction of dementia

PURPOSE OF REVIEW

Artificial intelligence and its division machine learning are emerging technologies that are increasingly applied in medicine. Artificial intelligence facilitates automatization of analytical modelling and contributes to prediction, diagnostics and treatment of diseases. This article presents an overview of the application of artificial intelligence in dementia research.

RECENT FINDINGS

Machine learning and its branch Deep Learning are widely used in research to support in diagnosis and prediction of dementia. Deep Learning models in certain tasks often result in better accuracy of detection and prediction of dementia than traditional machine learning methods, but they are more costly in terms of run times and hardware requirements. Both machine learning and Deep Learning models have their own strengths and limitations. Currently, there are few datasets with limited data available to train machine learning models. There are very few commercial applications of machine learning in medical practice to date, mostly represented by mobile applications, which include questionnaires and psychometric assessments with limited machine learning data processing.

SUMMARY

Application of machine learning technologies in detection and prediction of dementia may provide an advantage to psychiatry and neurology by promoting a better understanding of the nature of the disease and more accurate evidence-based processes that are reproducible and standardized.

Digital Health in Primordial and Primary Stroke Prevention: A Systematic Review

The stroke burden continues to grow across the globe, disproportionally affecting developing countries. This burden cannot be effectively halted and reversed without effective and widely implemented primordial and primary stroke prevention measures, including those on the individual level. The unprecedented growth of smartphone and other digital technologies with digital solutions are now being used in almost every area of health, offering a unique opportunity to improve primordial and primary stroke prevention on the individual level. However, there are several issues that need to be considered to advance development and use this important digital strategy for primordial and primary stroke prevention. Using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines we provide a systematic review of the current knowledge, challenges, and opportunities of digital health in primordial and primary stroke prevention.