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BackgroundLimited information is available about the present characteristics and dynamic clinical changes that occur in patients with COVID-19 during the early phase of the illness. ObjectiveThis study aimed to develop and validate machine learning models based on clinical features to assess the risk of severe disease and triage for COVID-19 patients upon hospital admission. MethodsThis retrospective multicenter cohort study included patients with COVID-19 who were released from quarantine until April 30, 2020, in Korea. A total of 5628 patients were included in the training and testing cohorts to train and validate the models that predict clinical severity and the duration of hospitalization, and the clinical severity score was defined at four levels: mild, moderate, severe, and critical. ResultsOut of a total of 5601 patients, 4455 (79.5%), 330 (5.9%), 512 (9.1%), and 301 (5.4%) were included in the mild, moderate, severe, and critical levels, respectively. As risk factors for predicting critical patients, we selected older age, shortness of breath, a high white blood cell count, low hemoglobin levels, a low lymphocyte count, and a low platelet count. We developed 3 prediction models to classify clinical severity levels. For example, the prediction model with 6 variables yielded a predictive power of >0.93 for the area under the receiver operating characteristic curve. We developed a web-based nomogram, using these models. ConclusionsOur prediction models, along with the web-based nomogram, are expected to be useful for the assessment of the onset of severe and critical illness among patients with COVID-19 and triage patients upon hospital admission.

BackgroundWeight loss interventions using mobile phone apps have recently shown promising results. ObjectiveThis study aimed to analyze the short-term weight loss effect of a mobile coaching intervention when it is integrated with a local public health care center and a regional hospital’s antiobesity clinic as a multidisciplinary model. MethodsA total of 150 overweight or obese adults signed up to complete an 8-week antiobesity intervention program with human coaching through a mobile platform. Paired t tests and multiple linear regression analysis were used to identify the intervention factors related to weight change. ResultsAmong the 150 participants enrolled in this study, 112 completed the 8-week weight loss intervention. Weight (baseline: mean 77.5 kg, SD 12.9; after intervention: mean 74.8 kg, SD 12.6; mean difference −2.73 kg), body mass index, waist circumference, fat mass (baseline: mean 28.3 kg, SD 6.6; after intervention: mean 25.7 kg, SD 6.3; mean difference −2.65 kg), and fat percentage all showed a statistically significant decrease, and metabolic equivalent of task (MET) showed a statistically significant increase after intervention. In multiple linear regression analysis, age (beta=.07; P=.06), △MET (beta=−.0009; P=.10), number of articles read (beta=−.01; P=.04), and frequency of weight records (beta=−.05; P=.10; R2=0.4843) were identified as significant factors of weight change. Moreover, age (beta=.06; P=.03), sex (female; beta=1.16; P=.08), △MET (beta=−.0009; P

BackgroundDue to the prevalence of the westernized dietary pattern and lack of physical activity, the numbers of overweight or obese individuals are increasing, resulting in a growing health burden because of various related diseases. A lifestyle modification approach has additional advantages compared with pharmacological therapies or bariatric surgery. In our randomized controlled trial conducted in 2015, we successfully used a ubiquitous health care (SmartCare) service for patients with metabolic syndrome to achieve a significant weight loss effect. Various useful apps have been developed for the SmartCare Service, which involves using a mobile phone to manage chronic diseases, minimizing time and space restrictions. Many studies have demonstrated weight loss effects using a SmartCare service, but limited data are available regarding the effect of active participation in relation to weight loss. ObjectiveWe aimed to assess the weight loss effect achieved after using the SmartCare service in terms of adherence and participation. We divided the intervention group of the previous study according to participation level, and analyzed whether there was a significant difference in the outcome. MethodsWe classified participants into 3 groups according to their adherence. Within the intervention group using the SmartCare service, the active group comprised those transmitting anthropometric measurement data using a mobile phone 3 or more times per week or who had a health consultation 5 or more times during a 24-week period. The passive group comprised those who did not adhere to these levels of engagement. The control group comprised those who did not use the SmartCare service. We compared changes in body weight, body mass index (BMI), body fat percentage, waist circumference, and lipid profile among the 3 groups. ResultsWe identified 422 participants and analyzed 405, excluding 17 who were missing necessary data for analysis. The active group consisted of 116 participants, compared with 80 in the passive group and 209 in the control group (without SmartCare service). There was a statistically significant difference in improvements to body weight, BMI, body fat percentage, and waist circumference among active participants compared with less active participants and the control group (P

BackgroundOverweight and obesity, due to a Westernized diet and lack of exercise, are serious global problems that negatively affect not only personal health, but national economies as well. To solve these problems, preventative-based approaches should be taken rather than medical treatments after the occurrence of disease. The improvement of individual life habits, through continuous care, is thus a paramount, long-term treatment goal. This study describes the effects of ubiquitous health care (uHealth care) or SmartCare services in the treatment of weight loss and obesity. ObjectiveThe aim of this study is to evaluate the effect of SmartCare services on weight loss compared to the effects of existing outpatient treatments in obese patients with metabolic syndrome. MethodsMetabolic syndrome patients who met the inclusion/exclusion criteria were enrolled in the study and randomized into an intervention or control group. The intervention group was provided with remote monitoring and health care services in addition to the existing treatment. The control group was provided with only the existing treatment. Pedometers were given to all of the patients. Additionally, mobile phones and body composition monitors were provided to the intervention group while body weight scales were provided to the control group. The patients visited the hospitals at 12 and 24 weeks following the baseline examination to receive efficacy and safety evaluations. ResultsMean weight reduction from baseline to week 24 was measured as a primary efficacy evaluation parameter and was found to be 2.21 kg (SD 3.60) and 0.77 kg (SD 2.77) in the intervention and control group, respectively. The intervention group had a larger decrement compared to the control group (P

East Asian populations exhibit a genetic predisposition to obesity, yet comprehensive research on these traits is limited. We conducted a genome-wide association study (GWAS) with 93,673 Korean subjects to uncover novel genetic loci linked to obesity, examining metrics such as body mass index, waist circumference, body fat ratio, and abdominal fat ratio. Participants were categorized into non-obese, metabolically healthy obese (MHO), and metabolically unhealthy obese (MUO) groups. Using advanced computational methods, we developed a multifaceted polygenic risk scores (PRS) model to predict obesity. Our GWAS identified significant genetic effects with distinct sizes and directions within the MHO and MUO groups compared with the non-obese group. Gene-based and gene-set analyses, along with cluster analysis, revealed heterogeneous patterns of significant genes on chromosomes 3 (MUO group) and 11 (MHO group). In analyses targeting genetic predisposition differences based on metabolic health, odds ratios of high PRS compared with medium PRS showed significant differences between non-obese and MUO, and non-obese and MHO. Similar patterns were seen for low PRS compared with medium PRS. These findings were supported by the estimated genetic correlation (0.89 from bivariate GREML). Regional analyses highlighted significant local genetic correlations on chromosome 11, while single variant approaches suggested widespread pleiotropic effects, especially on chromosome 11. In conclusion, our study identifies specific genetic loci and risks associated with obesity in the Korean population, emphasizing the heterogeneous genetic factors contributing to MHO and MUO. [ABSTRACT FROM AUTHOR]