PURPOSE This study sought to establish obesity diagnosis criteria by using the Body Volume Index (BVI) by body part extracted through 3D BodyScanner. METHODS The body fat percentage was measured using Dual Energy X-ray Absorptiometer (DEXA) for 225 participants (male = 119, female = 106), and BVI for eight body parts was measured using 3D BodyScanner. Independent t-test and Receiver Operating Characteristic (ROC) analysis were conducted. ROC analysis calculated the Area Under the Curve (AUC), and the optimal cut-point by Youden's J index. Sensitivity, Specificity, Accuracy, Balanced Classification Rate (BCR), and F1-score (harmonic mean of recall and precision) values were calculated to verify the validity of the optimal cut-point. RESULTS A statistically significant difference was observed in BVI by body part according to whether obesity was present for both men and women, and the obese group higher than the normal group. The optimal cut-point for each body part to diagnose obesity was 7.96 for shoulder, 9.79 for chest, 7.15 for upper abdominal, 7.71 for lower abdominal, 14.89 for total abdominal, 9.79 for thigh, 5.70 for calf, and 74.96 for total body volume in men. In case of women, this was 6.04 for shoulder, 9.82 for chest, 4.96 for upper abdominal, 6.23 for lower abdominal, 11.63 for total abdominal, 8.88 for thigh, 4.05 for calf, and 58.15 for total body volume, and the accuracy was 0.6~0.9. CONCLUSIONS BVI is a useful indicator for diagnosing obesity. However, this can be applicable only to Asian adults since there may be differences depending on race or age.
PURPOSE The purpose of this study is to analyze the type of and interest in home training video contents using the YouTube platform. METHODS Web crawling was performed using Python and a total of 3,937 sets ofvideo information (title, content, number of views, upload date) were obtained, 3,155 of which were finally selected for the study material. Overlapping and unrelated content were excluded. The data of text underwent 3 stages of preprocessing, the TF and TF-IDF of the keywords were calculated to identify the main keywords, and the LDA algorithm was applied in the topic modeling to successfully identify the types. In order to understand the level of interest by type, the number of views was subdivided into the percentage of the assigned type. RESULTS First, the types of home training videos were classified into bare whole body exercise for aerobic and muscular power strengthening, Pilates exercise for core and upper body strengthening, upper body exercise using tools, lower body line exercise, posture correction and upper body stretching exercise for pain relief, hip-up exercise, dance and tabata exercise for diet, diet and lower body correction stretching exercise for diet, and bare body exercise for core and lower body strengthening. Second, it was found that the proportion and interest were high in the contents of bare whole body exercise for aerobic and muscular power strengthening, dance and tabata exercise for diet, diet and lower body correction stretching exercise for diet. CONCLUSIONS The findings of this study may provide baseline data about the development of the active online home training videos in the market.
