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There is an increasing number of metabolic syndrome (MetS) patients worldwide, and there is no exception in South Korea. The risk complications of metabolic syndrome have been investigated by many previous research studies, while no data on any current trends of MetS are available. Therefore, the present study investigates the recent prevalence of MetS and its associated risk complications in Korean adults by using the Korean National Health and Nutrition Examination Survey (KNHANES). The Survey respondents (n = 4744) were adults over the age of 30, and they had participated in KNHANES 2016, which is a health survey of a national representative sample of non-institutionalized civilian South Koreans. The cross-tabulation analysis was applied to figure out the general characteristics impacting on the prevalence of MetS; furthermore, the odds ratios and 95% confidence intervals (CIs) using multivariate logistic regression analysis were presented for the risk complications of MetS. Findings from this study indicated that subjective health status, family structure, age, income level, use of nutrition labelling and gender showed significant connections with the prevalence of MetS. The risk diseases, stroke (OR = 2.174, 95% CI = 1.377–3.433, p < 0.01), myocardial infarction (MI) (OR = 2.667, 95% CI = 1.474–4.824, p < 0.01) and diabetes (OR = 6.533, 95% CI = 4.963, p < 0.001) were explored and verified attributable to the prevalence of MetS. The findings in this study suggest that sociodemographic characteristics-concentrated strategies are vital to prevent the prevalence of MetS in South Korea, and relative risk complications ought to be cautiously dealt with as well.

Purpose This study aims to explore the hidden connectivity among words by semantic network analysis, further identify salient factors accounting for customer satisfaction of coffee shops through analysis of online reviews and, finally, examine the moderating effect of business types of coffee shops on customer satisfaction. Design/methodology/approach Two typical major procedures of big data analytics in the hospitality industry were adopted in this research: one is data collection and the other is data analysis. In terms of data analysis, frequency analysis with text mining, semantic network analysis, CONCOR analysis for clustering and quantitative analysis with dummy variables were performed to dig new insights from online customer reviews both qualitatively and quantitatively. Findings Different factors were extracted from online customer reviews contributing to customer satisfaction or dissatisfaction, and among these factors, the brand-new factor “Sales event” was examined to be significantly associated with customer satisfaction. In addition, the moderating effect of business types on the relationship between “Value for money” and customer satisfaction was verified, indicating differences between customers from different types of coffee shops. Research limitations/implications The present study broadened the research directions of coffee shops by adopting online customer reviews through relative analytics. New dimensions such as “Sales event” and detailed categorization of “Coffee quality”, “Interior” and “Physical environment” were revealed, indicating that even new cognition could be generated with new data source and analytical methods. The industry professionals could develop their decision-making based on information from online reviews. Originality/value The present study used online reviews to understand coffee shop costumer experience and satisfaction through a set of analytical methods. The textual reviews and numeric reviews were concerned simultaneously to unearth qualitative perception and quantitative data information for customers of coffee shops.

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Background Due to the aging population worldwide, diseases that frequently attack elderly people, such as sarcopenia and osteoporosis, are major public health issues. Methods This study used a systematic review and meta-analysis to examine the associations among body mass index (BMI), sarcopenia, and bone mineral density (BMD) in a group of adults older than 60 years. Eight studies with a total of 18,783 subjects were examined using a random effect model. Results In sarcopenia patients, total hip BMD (d=0.560; 95% confidence interval [CI], 0.438 to 0.681; P <0.01; I 2 =53.755%), femoral neck BMD (d=0.522; 95% CI, 0.423 to 0.621; P <0.01; I 2 =77.736%) and lumbar spine BMD (d=0.295; 95% CI, 0.111 to 0.478; P <0.01; I 2 =66.174%) were lower than in control subjects. Additionally, BMI (d=0.711; 95% CI, 0.456 to 0.996; P <0.01; I 2 =97.609%) correlated with the BMD of the total hip, femoral neck, and lumbar spine. That is, sarcopenia patients with low BMD levels in the total hip, femoral neck, and lumbar spine also had low fat levels. Thus, sarcopenia patients with low BMD in the total hip, femoral neck and lumbar spine and low BMI could have a higher than average risk of osteosarcopenia. No sex effects were significant ( P >0.05) for any variable. Conclusion BMI could be a key point in osteosarcopenia, suggesting that a low body weight could be facilitate the transition from sarcopenia to osteosarcopenia.