BACKGROUND The rapid increase in one-person Korean households has led to an outbreak of metabolic syndrome. This calls for an analysis of the complex effects of metabolic syndrome risk factors in one-The present study aimed to identify the factors affecting metabolic syndrome in one-person households using machine-learning techniques and categorically characterize its risk factors through latent class analysis.person households, which vary from individual to individual. OBJECTIVE The present study aimed to identify the factors affecting metabolic syndrome in one-person households using machine-learning techniques and categorically characterize its risk factors through latent class analysis. METHODS This cross-sectional study included 10-year secondary data of the National Health and Nutrition Survey (2009–2018). We selected 1,371 participants belonging to one-person households. Data were analyzed using SPSS 25.0 (IBM, New York), Mplus 8.0 (Muthen & Muthen, Los Angeles), and Python 3.0 (Plone & Python, Montreal). RESULTS Machine-learning techniques investigated the factors affecting metabolic syndrome in one-person households. We categorized the metabolic syndrome risk factors in one-person households hierarchically into four classes. Results showed that those with obesity and abdominal obesity in middle adulthood exhibited the highest probability, indicating that they are the most vulnerable and at-risk group (P < .001). CONCLUSIONS This study identified the factors affecting metabolic syndrome in one-person households using machine-learning techniques and latent class analysis. Customized interventions prepared for each risk factor for one-person households can prevent metabolic syndrome.

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BACKGROUND Recently, the survey shows that the health habits of young adults has been crippled. In addition, the modification of health habits has been considered nearly impossible in the older adult group. Therefore, health management strategies should be built up to prevent chronic diseases. In order to prevent one of chronic diseases, metabolic syndrome (MetS), prevention and intervention programs should be developed for health habit change. OBJECTIVE This study investigated the effects of the e-Motivate4Change (EMC) program using mobile applications and wearable devices, which were developed for the prevention and management of MetS in young adult university students. METHODS This experimental study utilized a non-equality control group. Fifty-seven female second graders and two male students from two universities in D metropolitan city in Korea participated (n = 30, experimental group; n = 29, control group). The experimental group received a 12-week EMC program intervention, and the control group received MetS education and booklets without the EMC intervention. RESULTS After the program, the experimental group had significantly higher scores for health-related lifestyle (t = 3.86, p < .001) and self-efficacy (t = 6.00, p < .001) than did the control group. Concerning body mass index, there were significant effects for group (F = 1.01, p < .001) and the group × time interaction (F = 4.71, p = .034); concerning cholesterol, there were significant main effects for group (F = 4.32, p = .042) and time (F = 9.73, p < .001). CONCLUSIONS : The EMC program effectively improved participants’ health-related lifestyle and self-efficacy and significantly reduced their obesity and cholesterol levels. The program can be used to identify and prevent MetS among young adults. CLINICALTRIAL Institutional Review Board of Keimyung University (no. 40525-201704-HR-020-02).