[Purpose] This study evaluated the predictive power of Body Mass Index (BMI) for metabolic syndrome in older adults across pre-, during-, and post-COVID-19 periods, and examined the effects of metabolic syndrome factors on BMI by income level, aiming to inform elderly health management and crisis-related policies. [Methods] Data from 6,242 older adults (aged 65–80) were drawn from the 2019–2022 Korea National Health and Nutrition Examination Survey. Income was divided into quartiles, and time was segmented into pre-, during-, and post-pandemic periods. Multiple linear regression was used to assess the effects of metabolic syndrome factors (diabetes, abdominal obesity, low HDL, hypertension, hypertriglyceridemia) on BMI by income and period. Receiver Operating Characteristic (ROC) analysis evaluated BMI’s predictive power for metabolic syndrome. Significance was set at .05. [Results] Abdominal obesity and low HDL consistently influenced BMI across all groups. In the lowest income group, hypertension increasingly affected BMI during and after the pandemic. BMI Area Under the Curve (AUC) values peaked during the pandemic in this group, while the highest income group showed stable predictive power. [Conclusion] The COVID-19 pandemic had a differential impact on the association between BMI and metabolic syndrome among older adults according to income level. In low-income older adults, the predictive power of BMI for metabolic syndrome increased during the mid-pandemic period, while it remained stable across all periods in high-income groups. Systematic health management programs and policy interventions targeting low-income older adults are required to reduce health disparities during public health crises.
PURPOSE This study optimized stopword removal to enhance topic modeling performance. We propose an objective method combining normalized pointwise mutual information (NPMI) with median-based term frequency–inverse document frequency (TF–IDF) to automatically generate stopwords. METHODS Using text data from 443 research papers on “Taekwondo sparring,” we selected stopword candidates based on NPMI and identified 30 words with the lowest TF–IDF scores. We examined the impact of removing 1–30 stopwords on u_mass coherence scores. RESULTS The NPMI–TF–IDF method significantly improved coherence (R² = .456; p < .001). However, excessive removal led to diminishing returns, with the optimal coherence score (−11.442) achieved at 200 stopwords. In contrast, manually selected stopwords yielded a lower coherence score (−16.001). The findings indicate that integrating TF–IDF with NPMI effectively preserves meaningful words and outperforms PMI2 and PMI3 approaches. CONCLUSIONS Manual stopword selection can reduce reproducibility. Optimizing stopword removal based on domain-specific characteristics is essential. Future research should validate this method across diverse fields to establish a more generalizable standard.
