BACKGROUND: Physical activity is an effective modifiable behavior for preventing recurrent strokes. This study aims to determine the adherence to physical activity recommendations among stroke survivors in the United States. We further compared our findings with the adherence observed among myocardial infarction (MI) survivors and healthy adults, each assessed against distinct physical activity guidelines specific to their respective populations. METHODS: We utilized data from the 2011 to 2019 Behavioral Risk Factor Surveillance System, a nationally representative survey. To establish benchmarks for stroke survivors, we referenced 3 different physical activity recommendations outlined in 2011, 2014, and 2021 American Heart Association stroke prevention guidelines. Similarly, for MI survivors and healthy adults, we used the 2011 guidelines for secondary prevention of coronary vascular diseases and the 2020 World Health Organization physical activity guidelines, respectively. Adherence to recommendations was determined by the respondents’ self-reported intensity, duration, and frequency of physical activity. Multivariate logistic regression compared adherence in stroke survivors, MI survivors, and healthy adults. RESULTS: Among 48 222 stroke survivors in the United States, the overall adherence rates to 2011, 2014, and 2021 physical activity guidelines were 75.4%, 40.2%, and 69.2%. For independently mobile stroke survivors, the adherence rates increased to 78.1%, 42.1%, and 69.9%. Among MI survivors and healthy adults, the adherence rates were 42.7% and 72%. When 2021 recommendations were used as a benchmark, older (aged ≥65 years) stroke survivors were more likely to adhere to recommendations than younger survivors (71.9% versus 62.3%; P <0.0001). After adjusting for sociodemographic factors and comorbidities, non-Hispanic Black survivors were less likely to adhere to recommendations (adjusted odds ratio, 0.81 [95% CI, 0.7–0.94]), whereas older and higher educated stroke survivors were more likely to adhere to recommendations. Geographically, stroke belt and nonrural residents were less likely to adhere to recommendations ([63.5% versus 67.9%; P <0.0001]; [53.8% versus 58.7%; P <0.0001]). Stroke and MI survivors were less likely to adhere to the latest recommendations than healthy adults (adjusted odds ratio, 0.74 [95% CI, 0.69–0.8]; (adjusted odds ratio, 0.24 [95% CI, 0.22–0.26]). CONCLUSIONS: A substantial number of stroke survivors do not meet physical activity recommendations. Tailored interventions should be designed for at-risk populations, for example, non-Hispanic Black survivors and lower educated stroke survivors.

Background: Self-management among stroke survivors is effective in mitigating the risk of a recurrent stroke. This study aims to determine the prevalence of self-management and its associated factors among stroke survivors in the United States. Methods: We analyzed the Behavioral Risk Factor Surveillance System (BRFSS) data from 2016 to 2021, a nationally representative health survey. A new outcome variable, stroke self-management (SSM = low or SSM = high), was defined based on five AHA guideline-recommended self-management practices, including regular physical activity, maintaining body mass index, regular doctor checkups, smoking cessation, and limiting alcohol consumption. A low level of self-management was defined as adherence to three or fewer practices. Results: Among 95,645 American stroke survivors, 46.7% have low self-management. Stroke survivors aged less than 65 are less likely to self-manage (low SSM: 56.8% vs. 42.3%; p < 0.0001). Blacks are less likely to self-manage than non-Hispanic Whites (low SSM: 52.0% vs. 48.6%; p < 0.0001); however, when adjusted for demographic and clinical factors, the difference was dissipated. Higher education and income levels are associated with better self-management (OR: 2.49, [95%CI: 2.16–2.88] and OR: 1.45, [95%CI: 1.26–1.67], respectively). Further sub-analysis revealed that women are less likely to be physically active (OR: 0.88, [95%CI: 0.81–0.95]) but more likely to manage their alcohol consumption (OR: 1.57, [95%CI: 1.29–1.92]). Stroke survivors residing in the Stroke Belt did not self-manage as well as their counterparts (low-SSM: 53.1% vs. 48.0%; p < 0.001). Conclusions: The substantial diversity in self-management practices emphasizes the need for tailored interventions. Particularly, multi-modal interventions should be targeted toward specific populations, including younger stroke survivors with lower education and income.

Background: Artificial intelligence (AI) is increasingly being used in healthcare. Despite its promise, physicians and trainees remain cautiously optimistic. This systematic review and meta-analysis aimed to assess knowledge and attitudes toward AI and to provide recommendations for AI buy-in by physicians. Methods: Searches of PubMed-OVID-IEEE-Scopus, and Web-of-Science for studies in 2013–2024 identified 11,437 records. One-hundred-and-fifteen met inclusion criteria. Fifty-three studies reported quantitative data on physicians’/trainees’ knowledge and were included in the meta-analysis. Results: Our meta-analysis estimated that only 19.6% of physicians and trainees have high overall AI knowledge, while 36.3% have low knowledge. Fifty-five studies evaluated the depth of AI knowledge. These studies consistently concluded that most physicians or trainees possess only moderate conceptual knowledge of AI, and their technical knowledge is usually limited. Qualitative evaluations also highlighted that a high level of conceptual AI knowledge is associated with greater receptiveness to AI implementation in medicine. We identified five major barriers to translating AI to the bedside with physician buy-in. Conclusion: Although physicians and trainees are generally receptive to AI, many barriers hinder adoption. To address them, we recommend establishing standardized AI education and workforce training, involving clinicians early in AI design, clarifying legal and regulatory issues, leveraging insights from clinical decision support system implementation to reduce workflow challenges, and integrating patient-centered communication principles to enhance trust and transparency.

Predicting paroxysmal atrial fibrillation (PAF) is challenging due to its transient nature. Existing methods often rely solely on electrocardiogram (ECG) waveforms or Electronic Health Record (EHR)-based clinical risk factors. We hypothesized that explicitly balancing the contributions of these heterogeneous data sources could improve prediction accuracy. We developed a Transformer-based deep learning model that integrates 12-lead ECG signals and 47 structured EHR variables from 189 patients with cryptogenic stroke, including 49 with PAF. By systematically varying the relative contributions of ECG and EHR data, we identified an optimal ratio for prediction. Best performance (accuracy: 0.70, sensitivity: 0.72, specificity: 0.87, Area Under Curve - Receiver Operating Characteristics (AUROC): 0.65, Area Under the Precision-Recall Curve (AUPRC): 0.43) was achieved using a 5-fold cross-validation when EHR data contributed one-third and ECG data two-thirds of the model’s input. This multimodal approach outperformed unimodal models, improving accuracy by 35% over EHR-only and 5% over ECG-only methods. Our results support the value of combining ECG and structured EHR information to improve accuracy and sensitivity in this pilot cohort, motivating validation in larger studies.