A predictive model for identifying low medication adherence among older adults with hypertension: A classification and regression tree model.

Various individual characteristics may affect medication adherence; however, few studies have investigated the effect of interrelationships among these various individual characteristics on medication adherence. This cross-sectional study explored the interrelationships among risk factors for medication adherence and established a predictive model of low medication adherence among older adults with hypertension. Convenience sampling was used to recruit 300 older adults with hypertension. The following parameters were recorded: demographic and disease characteristics, health beliefs, self-efficacy, social support, and medication adherence of antihypertensive drugs. Classification and regression tree (CART) analysis was performed to develop a predictive model of low medication adherence. The CART model revealed that health belief, disease duration, self-efficacy, and social support interacted to contribute to various pathways of low medication adherence. The predicted accuracy of the model was validated with a low misclassification rate of 26%. The proposed classification model can help identify risk cases with low medication adherence. Suitable health education programs based on these risk factors to manage and improve medication adherence for older adults with hypertension could be considered.

Applying classification and regression tree analysis to identify risks of developing sarcopenia in the older population.

BACKGROUND AND OBJECTIVE: Variations in the risk factors for sarcopenia can lead to differences in the likelihood of developing sarcopenia among older adults; however, few studies have explored the interactions among the risk factors. This study examined the interactions among risk factors and identified a discriminative pathway for groups at risk of sarcopenia in community-dwelling older adults. METHODS: A cross-sectional study was conducted between July and August 2019 to recruit 200 older adults from an outpatient department of a hospital providing care for older people. Data on various risk factors, namely demographics (age, gender, education, comorbidities, and body mass index [BMI]), dietary habits (weekly consumption of milk, coffee, and meat), lifestyle behaviours (vitamin D supplementation, smoking, drinking, and physical activity), and depression symptoms were collected. Sarcopenia was defined according to the Asian Working Group for Sarcopenia criteria. A classification and regression tree (CART) model was used to examine interactions among these factors and identify groups at risk of sarcopenia. FINDINGS: The prevalence of sarcopenia was 38.5%. The CART model identified two end groups at differential risks of sarcopenia, with a minimum of one and a maximum of three risk factors. In the first group, low BMI (<18.5 kg/m2 ) was a predominant risk factor for sarcopenia among older people. In the second group, older adults with a normal BMI, aged ≥68 years, and without a regular walking habit had a higher probability of developing sarcopenia than did their counterparts. CONCLUSIONS: The interactive effects among older age, BMI, and walking may cause different probabilities of developing sarcopenia in the older population. IMPLICATIONS FOR PRACTICE: Older adults with a low or normal BMI but without a regular walking habit could be a predominant risk group for sarcopenia. The appropriate maintenance of body weight and regular walking activity is suggested to prevent sarcopenia in community-dwelling older adults.

[Risk factor management in respect of type 2 diabetic complications: ABC management].

Type 2 diabetes (T2DM) is one of the most costly and burdensome chronic diseases in Taiwan and is a condition increasing in epidemic proportions all over the world. The symptoms of T2DM, including hyperglycemia, hypertension, and hyperlipidemia are among the highest risk factors for the development of complications with the disease. Blood sugar control alone is not enough for T2DM management. Proper HbA1C, blood pressure, and cholesterol level have been proven by many researchers to decrease T2DM morbidity and mortality. Therefore, the public and health care providers should know this latest ABC management to decrease the complications in T2DM.