Future studies are needed to identify factors related to quality of life among women with incontinence and to use validated instruments according to specific subjects.

ObjectivesHeart failure (HF) is a progressive syndrome that marks the end-stage of heart diseases, and it has a high mortality rate and significant cost burden. In particular, non-adherence of medication in HF patients may result in serious consequences such as hospital readmission and death. This study aims to identify predictors of medication adherence in HF patients. In this work, we applied a Support Vector Machine (SVM), a machine-learning method useful for data classification.MethodsData about medication adherence were collected from patients at a university hospital through self-reported questionnaire. The data included 11 variables of 76 patients with HF. Mathematical simulations were conducted in order to develop a SVM model for the identification of variables that would best predict medication adherence. To evaluate the robustness of the estimates made with the SVM models, leave-one-out cross-validation (LOOCV) was conducted on the data set.ResultsThe two models that best classified medication adherence in the HF patients were: one with five predictors (gender, daily frequency of medication, medication knowledge, New York Heart Association [NYHA] functional class, spouse) and the other with seven predictors (age, education, monthly income, ejection fraction, Mini-Mental Status Examination-Korean [MMSE-K], medication knowledge, NYHA functional class). The highest detection accuracy was 77.63%.ConclusionsSVM modeling is a promising classification approach for predicting medication adherence in HF patients. This predictive model helps stratify the patients so that evidence-based decisions can be made and patients managed appropriately. Further, this approach should be further explored in other complex diseases using other common variables.

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Many studies have reported the negative effects of depression on adherence to antihypertensive medication. However, little is known about the mechanism underlying this relationship in elderly patients with hypertension. The aim of this cross-sectional study is to examine the mediating role of self-efficacy in the relationship between depression and medication adherence among older patients with hypertension. The data were collected from October to December 2014. A total of 255 older patients with hypertension were assessed using the Geriatric Depression Scale, the Self-efficacy for Appropriate Medication Use Scale, and the Morisky Medication Adherence Scale. Hierarchical linear regression analysis and the Sobel test were used to examine the mediating role of self-efficacy in the relationship between depression and medication adherence. Depression and self-efficacy were statistically significant predictors of medication adherence in older patients with hypertension. Self-efficacy partially mediated the relationship between depression and medication adherence. Interventions targeting self-efficacy could increase the confidence of patients in their ability to actively take their medicines. Moreover, health care providers should be aware of the importance of early detection of depression in older patients with hypertension. Future studies with longitudinal data are warranted to clarify the multidirectional relationships between depression, self-efficacy, and medication adherence.

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