The purpose of the present investigation was to examine the relationships between the exercise history and the bone mineral density (BMD) and bone mineral content (BMC) in female Japanese young adults using dual X-ray absorptiometry (DXA). One-hundred twenty females, aged between 18 to 28 years, participated in the present investigation. The BMD at the lumbar spine (L-BMD), whole body BMD and BMC (WB-BMD and WB-BMC), lean body mass (LBM) and fat mass (FM) were measured by DXA. Using a self-administrered questionnaire, the exercise habits during preschool (4-6 years), primary school (7-12 years), junior high school (13-15 years), high school (16-18 years), and the current habits (>18 years) were eveluated. The L-BMD, WB-BMD and WB-BMC were significantly higher in the subjects with exercise habits during both the period of <18 years and >18 years compared with those in the subjects without an exercise history during all periods (p<0.05). In a separate analysis with the data stratified by the school age, the subjects with an exercise history during primary school, junior high school, or high school had significantly higher BMD and BMC values compared with the non-exercisers (each, p<0.05). In contrast, the BMD and BMC did not differ significantly according to either the exercise history during pre-school nor the current exercise status. A multiple stepwise regression analysis revealed that the body weight, LBM, FM, age of menarche, and exercise habits during high school were significant determinants of the L-BMD, WB-BMD and WB-BMC (p<0.001). The results of the present investigation show that both the exercise history during school age and the current exercise habits affect the BMD and BMC in young adults. In particular, high school females should be encouraged to participate in the regular exercise to increase their bone health. Future studies will be needed to confirm the targeted age-group(s) for participation in sports/exercise for the improvement of bone health, including an analysis of the type and intensity of exercise/sports.

We have previously reported on a predictive model for deficiency-excess pattern diagnosis that was unable to predict the medium pattern. In this study, we aimed to develop predictive models for deficiency, medium,and excess pattern diagnosis, and to confirm whether cutoff values for diagnosis differed between the clinics. We collected data from patients' first visit to one of six Kampo clinics in Japan from January 2012 to February 2015. Exclusion criteria included unwillingness to participate in the study, missing data, duplicate data, under 20 years old, 20 or less subjective symptoms, and irrelevant patterns. In total, 1,068 participants were included. Participants were surveyed using a 153-item questionnaire. We constructed a predictive model for deficiency, medium, and excess pattern diagnosis using a random forest algorithm from training data, and extracted the most important items. We calculated predictive values for each participant by applying their data to the predictive model, and created receiver operating characteristic (ROC) curves with excess-medium and medium-deficiency patterns. Furthermore, we calculated the cutoff value for these patterns in each clinic using ROC curves, and compared them. Body mass index and blood pressure were the most important items. In all clinics, the cutoff values for diagnosis of excess-medium and medium-deficiency patterns was > 0.5 and < 0.5, respectively. We created a predictive model for deficiency, medium, and excess pattern diagnosis from the data of six Kampo clinics in Japan. The cutoff values for these patterns fell within a narrow range in the six clinics.