Qiuli Zhao

Background: Poststroke depression is common and includes depressive and somatic symptoms. However, few studies have confirmed the influence of family functioning on poststroke depression or explored the association among daily activities, family functioning, and poststroke depression. Objectives: We examined the independent risk factors of daily activities and family functioning for poststroke depression and identified the mediating effect of family functioning on the association between daily activities and poststroke depression. Methods: This cross-sectional study design used convenience sampling to recruit 422 stroke survivors from the neurology department of a hospital in Harbin, China, from February to July 2018. We assessed participants’ demographic and clinical variables, including depression, daily activities, and family functioning. Pearson’s correlations and multiple linear regression analyses were conducted, and a path analysis with bootstrapping was utilized to define direct/indirect effects. Results: Daily activities and family functioning had a significant and direct negative effect on participants’ depression. The indirect effect of 1,000 bootstrap samples after bias correction with a 95% confidence interval was below zero, indicating that family function had a significant mediating effect on the association between depression and daily activities. Discussion: This study revealed the importance of family functioning in the association between depression and daily activities in stroke survivors. To the best of our knowledge, this study was the first to explore the mediating role of family functioning in poststroke depression, emphasizing the importance of family for the mental health of stroke patients. To reduce the incidence of poststroke depression, interventions that enhance daily activities and family functioning may include nurses, family therapists, rehabilitation physicians, and community workers.

Objectives: This study aimed to identify the influencing factors associated with long onset‐to‐door time and establish predictive models that could help to assess the probability of prehospital delay in populations with a high risk for stroke.Materials and Methods: Patients who were diagnosed with acute ischemic stroke (AIS) and hospitalized between 1 November 2018 and 31 July 2019 were interviewed, and their medical records were extracted for data analysis. Two machine learning algorithms (support vector machine and Bayesian network) were applied in this study, and their predictive performance was compared with that of the classical logistic regression models after using several variable selection methods. Timely admission (onset‐to‐door time < 3 hr) and prehospital delay (onset‐to‐door time ≥ 3 hr) were the outcome variables. We computed the area under curve (AUC) and the difference in the mean AUC values between the models.Results: A total of 450 patients with AIS were enrolled; 57 (12.7%) with timely admission and 393 (87.3%) patients with prehospital delay. All models, both those constructed by logistic regression and those by machine learning, performed well in predicting prehospital delay (range mean AUC: 0.800–0.846). The difference in the mean AUC values between the best performing machine learning model and the best performing logistic regression model was negligible (0.014; 95% CI: 0.013–0.015).Conclusions: Machine learning algorithms were not inferior to logistic regression models for prediction of prehospital delay after stroke. All models provided good discrimination, thereby creating valuable diagnostic programs for prehospital delay prediction.

Objective. Dyslipidemia is one of the most important modifiable risk factors for cardiovascular disease. Diet and lifestyle interventions positively contribute to the management of dyslipidemia. Adequate knowledge of the correct diet for dyslipidemia is an effective preventive strategy against cardiovascular disease. Method. This study developed a questionnaire to test dyslipidemia dietary knowledge. The initial, multiple-choice dyslipidemia dietary knowledge questionnaire (DDKQ) was formed based on a literature review of previous studies. Two Delphi rounds were performed to ensure content validity and face validity, items were pretested and filtered through item analysis, and the reliability and validity of the final questionnaire were checked. Results. The DDKQ consisted of 5 parts, with 25 items in total. It demonstrated adequate content validity (0.94), face validity, internal consistency (KR20 = 0.67), and test-retest reliability (0.74). The mean item difficulty (P) was 0.55 and ranged from 0.15 to 0.83, and the mean item discrimination index (D) was 0.36, ranging from 0.21 to 0.58. The questionnaire was also able to distinguish between participants with/without a medical background; medical workers produced significantly higher total scores (16.70±2.84 vs. 14.57±4.26, p< 0.05). Conclusion. The DDKQ is a reliable and valid measure of dyslipidemia dietary knowledge. It is suitable for providing scientific assessments for targeted health education interventions.

Objective Many factors influence pre-hospital delays in the event of stroke. This study aimed to develop and evaluate a comprehensive educational program for decreasing pre-hospital delays in high-risk stroke population. Methods We enrolled 220 high-risk stroke population and caregivers from six urban communities in Harbin from May 2013 to May 2015, and randomly divided them into intervention and control groups. We implemented a comprehensive educational program (intervention group), comprising public lectures, instructional brochures, case videos, simulations, and role-playing from May 2013 to May 2015. We delivered conventional oral education in the control group. We compared stroke pre-hospital delay behavioral intention (SPDBI), pre-hospital stroke symptom coping test (PSSCT), and stroke pre-symptoms alert test (SPSAT) results between the groups before and 6, 12, and 18 months after health intervention. Results There were significant differences between before and after intervention (P < 0.01). SPDBI, PSSCT, and SPSAT scores were significantly different between the groups (P < 0.01). The interaction between time and intervention method was significant (P < 0.01). According to multivariate repeated measures analysis of variance, SPDBI, PSSCT, and SPSAT scores were significantly different at each time after intervention (P < 0.05). Conclusion The comprehensive educational program was significantly effective in decreasing SPDBI, improving knowledge, enhancing stroke pre-symptoms alert, and reducing the possibility of pre-hospital delays.

Several stroke impairment scales are currently available for stroke patients but none of them provide information regarding the pre-stroke behavioral intentions of high-risk stroke patients and their relatives. This study’s objective was to generate and validate a new written tool, the Stroke Pre-hospital Delay Behavior Intention (SPDBI) scale. It is suitable for use with high-risk stroke patients and their relatives to predict the likelihood of pre-hospital delay. From a review of related studies, we formulated a prototype scale. We interviewed ten stroke patients in a semi-structured iterative process that included interviews with experts, high-risk patients, and their family members. Then, we pretested and filtered items. We next used a large sample size and factor analysis to determine the scale’s structure. Finally, we checked the reliability and validity of the scale. We identified five sub-domains (stroke warning signs, non-treatment justification, symptom attributions, habitual response style, and emergency system use). The SPDBI demonstrated good internal consistency and test-retest reliability (Cronbach’s α =0.808; Intraclass Correlation Coefficient [ICC] =0.797). This SPDBI scale is a reliable, and valid measure of the likeliness of pre-hospital delay in high-risk stroke patients and their family members. It may provide scientific assessment for targeted health education intervention.

Measures of specific knowledge of coping with pre-hospital stroke symptoms can help educate high-risk patients and family caregivers. This study aimed to develop and validate the Pre-hospital Stroke Symptoms Coping Test (PSSCT). Reliability and validity were analyzed using multiple data sources. The Delphi expert consultation method was applied to assess the test’s surface validity and content validity index. The final edition of the 19-item PSSCT contained 3 sections assessing coping with typical symptoms and symptoms associated with vomiting and twitching. Its psychometric properties were investigated in a community sample of 300 high-risk patients and family members. The PSSCT was readily accepted by participants. It demonstrated adequate surface validity and content validity, and good internal consistency (KR₂₀ = 0.822) and test-retest reliability (0.769), with difficulty (P) and degree of differentiation (D) ranges of 0.28–0.83 and 0.15–0.66, respectively. It was also able to distinguish between individuals who had/had not experienced a stroke. Experienced individuals scored significantly higher overall and on coping with typical symptoms and twitching (P<0.01). The PSSCT can practically and directly assess critical knowledge regarding coping with pre-hospital stroke symptoms and has good reliability and validity.