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.

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.

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 β [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.

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