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AIMS AND OBJECTIVES: To explore the association between self-efficacy and self-management by modelling three types of social support as mediators among stroke high-risk populations. BACKGROUND: Self-efficacy and social support (i.e. objective support, subjective support and support utilisation) are important for self-management among stroke high-risk populations. Self-efficacy activates three types of social support, and the effect of social support on self-management varies by types among chronic patients. Therefore, social support may act as a mediator between self-efficacy and self-management, and the mediating role may vary by types of social support. Disentangling the role of these different types of social support can guide tailored interventions. DESIGN: A cross-sectional study. METHODS: This study was conducted among 448 Chinese adults at high risk for stroke. Self-efficacy, self-management and social support were assessed using the Self-Efficacy Scale, the Stroke Self-management Scale and the Social Support Rating Scale respectively. The PROCESS SPSS Macro version 3.3, model 4 was used to explore the mediating role of different types of social support in the relationship between self-efficacy and self-management. This study followed STROBE checklist for cross-sectional studies (Appendix S1). RESULTS: Self-efficacy improved three types of social support, and subjective support and support utilisation promoted self-management, but objective support hindered self-management. The specific indirect effect of objective support and subjective support was significant but not that of support utilisation. Objective support, subjective support and support utilisation attenuated the total effect of self-efficacy on self-management by -23.8%, 23.8% and 7.7% respectively. CONCLUSIONS: Mediating effect of social support in the relationship between self-efficacy and self-management varies by type, and the positive effect of subjective support is offset by the detrimental effect of objective support. RELEVANCE TO CLINICAL PRACTICE: Among stroke high-risk populations, interventions should target objective support and subjective support as well as self-efficacy to efficiently improve their self-management.
Assuntos
Autogestão , Acidente Vascular Cerebral , Adulto , Humanos , Autoeficácia , Estudos Transversais , Apoio SocialRESUMO
Background: Stroke patients have to face a high risk of recurrence, especially for those with comorbid T2DM, which usually lead to much more serious neurologic damage and an increased likelihood of death. This study aimed to explore determinants of stroke relapse among patients with comorbid T2DM. Materials and methods: We conducted this case-control study nested a prospective cohort of ischemic stroke (IS) with comorbid T2DM. During 36-month follow-up, the second stroke occurred in 84 diabetic IS patients who were allocated into the case group, while 613 patients without recurrence were the controls. We collected the demographic data, behaviors and habits, therapies, and family history at baseline, and measured the variables during follow-up. LASSO and Logistic regression analyses were carried out to develop a prediction model of stroke recurrence. The receiver operator characteristic (ROC) curve was employed to evaluate the performance of the prediction model. Results: Compared to participants without recurrence, the higher levels of pulse rate (78.29 ± 12.79 vs. 74.88 ± 10.93) and hypertension (72.6 vs. 61.2%) were recorded at baseline. Moreover, a lower level of physical activity (77.4 vs. 90.4%), as well as a higher proportion of hypoglycemic therapy (36.9 vs. 23.3%) was also observed during 36-month follow-up. Multivariate logistic regression revealed that higher pulse rate at admission (OR = 1.027, 95 %CI = 1.005-1.049), lacking physical activity (OR = 2.838, 95% CI = 1.418-5.620) and not receiving hypoglycemic therapy (OR = 1.697, 95% CI = 1.013-2.843) during follow-up increased the risk of stroke recurrence. We developed a prediction model using baseline pulse rate, hypoglycemic therapy, and physical activity, which produced an area under ROC curve (AUC) of 0.689. Conclusion: Physical activity and hypoglycemic therapy play a protective role for IS patients with comorbid diabetes. In addition to targeted therapeutics, the improvement of daily-life habit contributes to slowing the progress of the IS.
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Ischemic stroke (IS) is often comorbid with type 2 diabetes mellitus (T2DM) wherein the determinants of long-term outcomes, beyond the acute stroke phase, are not adequately known. This study identified the determinants of long-term outcomes for diabetic IS patients through a prospective nested case-control study in 624 patients treated with conservative measures (38.60% females, mean age: 63.85 years). After 36-month follow-up, 117 (18.8%) patients with poor outcome were enrolled in the case group. The poor outcome was defined with a modified Rankin Scale (mRS) score ≥3. Meanwhile, 374 (59.9%) patients with good outcome, defined as (mRS score <3), were included in the control group. Patients who died (n = 32) or lost to follow-up (n = 101) were excluded in analysis. Poor prognostic outcome was positively associated with (1) the pulse rate at admission, (2) diastolic blood pressure (DBP), and (3) fasting blood glucose (FBG) during follow-up, whereas physical activity and lipid-lowering treatment during follow-up were negatively associated. Importantly, a forecasting model with these indicators distinguished the patients with good versus poor outcomes with 70.1% sensitivity and 73.5% specificity. Health care professionals and laboratory medicine scholars may want to monitor an increase in DBP and FBG during follow-up, as well as physical activity and lipid-lowering treatment, in relationship to the prognosis of IS with comorbid T2DM after conservative therapies. The proposed predictive model for personalized/precision medicine requires field testing in independent studies, and might help risk stratification with theranostic tests for patients with acute IS who also have a diagnosis of T2DM.
