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Changes in an athlete’s physiological and health metabolic profiles after detraining have not been studied in elite Taekwondo (TKD) athletes. To enable a better understanding of these physiological changes to training cessation, this study examined the effects of 8-weeks detraining on the aerobic capacity, body composition, inflammatory status and health metabolic profile in elite TKD athletes. Sixteen elite TKD athletes (age: 21.0 ± 0.8 yrs, BMI: 22.4 ± 3.9 kg/m2; Mean ± SD; 11 males and 5 females) participated in this study. Physical activity level assessment using computerized physical activity logs was performed during the competitive preparation season (i.e. one-week before national competition) and at two week intervals throughout the detraining period. Participant aerobic capacity, body fat, and blood biomarkers were measured before and after detraining, and the blood biomarker analyses included leukocyte subpopulations, blood glucose, insulin, dehydroepiandrosterone-sulfate (DHEA-S), and cortisol. Eight-week detraining increased DHEA-S/cortisol ratio (+57.3%, p = 0.004), increased insulin/cortisol ratio (+59.9%, p = 0.004), reduced aerobic power (–2.43%, p = 0.043), increased body fat accumulation (body fat%: +21.3%, p < 0.001), decreased muscle mass (muscle mass%: –4.04%, p < 0.001), and elevated HOMA-IR (the biomarker of systemic insulin resistance; +34.2%, p = 0.006). The neutrophil-to-lymphocyte ratio (NLR), a systemic inflammatory index, increased by 48.2% (p = 0.005). The change in aerobic capacity was correlated with the increased fat mass (r = –0.429, p = 0.049) but not with muscle loss. An increase in the NLR was correlated to the changes in HOMA-IR (r = 0.44, p = 0.044) and aerobic capacity (r = –0.439, p = 0.045). We demonstrate that 8-week detraining suppresses physiological stress but rapidly results in declines in athletic performance and health metabolic profiles, including reduced aerobic capacity, increased body fat, muscle loss, insulin resistance development and elevated systemic inflammatory status in these young elite TKD athletes. The inflammation state was positively associated with insulin resistance development, fat mass, WHR (the index for central fat accumulation), and the decline in VO2max.

We propose a new prototype classification method that can be combined with support vector machines (SVM) [2] for recognizing handwritten numerals and Chinese characters. This method employs a learning process for determining both the number and location of prototypes. The possible techniques used in this process for adjusting the location of prototypes include the K-means (KM) algorithm and the fuzzy c-means (FCM) algorithm [1]. When the prototype classification method is applied, the SVM method can be used to process top-rank candidates obtained in the prototype learning or matching process. We apply this hybrid method to the recognition of handwritten numerals and Chinese characters. Experiment results show that this hybrid method saves great amount of training and testing time when the number of character types is large, and achieves comparable accuracy rates to those achieved by using SVM solely. Our results also show that the proposed method performs better than the nearest neighbor (NN) classification method. These outcomes suggest that the proposed method can serve as an effective solution for large-scale multiclass classification.