[Purpose] Although much researches have been conducted on the hippotherapy, the intervention methods of the previous studies focus on the pelvis posture. Thus, this study analyzed the electromyogram (EMG) of trunk muscle and lower limb muscle to analyze the kinetic factors. Based on the analysis, this study aims to compare the muscle load and suggest effective exercise method. [Participants and Methods] This study checked the muscle activity of Rectus abdominis (RA), Erector spinae (ES), Rectus femoris (RF), Adductor magnus (AM) during the exercise using horse riding machine in dorsiflexion position by bending 20 degrees and in neutral position. Each position was performed for 5 minutes and the speed of the horse riding machine was set to medium speed. [Results] Rectus abdominis showed higher muscle activity in dorsiflexion position and the groups had significant differences. Elector spinae showed higher muscle activity in dorsiflexion position and the groups had significant differences. Rectus femoris showed higher muscle activity in dorsiflexion position and the groups had significant differences. Adductor magnus also showed higher muscle activity in dorsiflexion position and the groups had significant differences. [Conclusions] The study result showed that exercise with horse riding machine in dorsiflexion position activates trunk muscle and thigh muscle effectively. Thus, the study suggests more effective posture for the modern people who exercise with horse riding machine for strengthening physical health.

Background: We aimed to evaluate whether the degree of F-18 fluorodeoxyglucose (FDG) uptake in the lungs is associated with an increased risk of lung cancer and develop lung cancer risk prediction models using metabolic parameters on F-18 FDG positron emission tomography (PET). Methods: We retrospectively included 585 healthy individuals who underwent F-18 FDG PET/CT scans for a health check-up. Individuals who developed lung cancer within 5 years of the PET/CT scan were classified into the lung cancer group (n=100); those who did not were classified into the control group (n=485). Clinical factors including age, sex, body mass index (BMI), and smoking history were collected. The standardized uptake value ratio (SUVR) and metabolic heterogeneity (MH) index were obtained in the bilateral lungs. Logistic regression models with clinical factors, SUVR and MH index were generated to quantify the probability of lung cancer development. The prediction models were validated using internal data set (n=210). Results: The lung SUVR and lung MH index in the lung cancer group were significantly higher than those in the control group (p<0.001 and p<0.001, respectively). In the combined prediction model 1, age, sex, BMI, smoking history, and lung SUVR were significantly associated with lung cancer development (age: OR 1.07, p <0.001; male: OR 2.08, p=0.015; BMI: OR 0.93, p =0.057; current or past smoker: OR 5.60, p <0.001; lung SUVR: OR 1.13, p <0.001). In the combined prediction model 2, age, sex, BMI, smoking history, and lung MH index showed a significant association with lung cancer development (age: OR 1.06, p<0.001; male: OR 1.87, p=0.045; BMI: OR 0.93, p =0.010; current or past smoker: OR 4.78, p<0.001; lung MH index: OR 1.33, p<0.001). In the validation data, combined prediction model 1 and 2 exhibited very good discrimination (area under the receiver operator curve [AUC]: 0.867 and 0.901, respectively). Conclusion: The metabolic parameters on F-18 FDG PET are related to an increased risk of lung cancer. Metabolic parameters can be used as biomarkers that provide information independent of clinical parameters related to lung cancer risk.

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Background: We aimed to evaluate whether the degree of F-18 uorodeoxyglucose (FDG) uptake in the lungs is associated with an increased risk of lung cancer and develop lung cancer risk prediction models using metabolic parameters on F-18 FDG positron emission tomography (PET).Methods: We retrospectively included 585 healthy individuals who underwent F-18 FDG PET/CT scans for a health check-up. Individuals who developed lung cancer within 5 years of the PET/CT scan were classi ed into the lung cancer group (n=100); those who did not were classi ed into the control group (n=485). Clinical factors including age, sex, body mass index (BMI), and smoking history were collected.The standardized uptake value ratio (SUVR) and metabolic heterogeneity (MH) index were obtained in the bilateral lungs. Logistic regression models with clinical factors, SUVR and MH index were generated to quantify the probability of lung cancer development. The prediction models were validated using internal data set (n=210).Results: The lung SUVR and lung MH index in the lung cancer group were signi cantly higher than those in the control group (p<0.001 and p<0.001, respectively). In the combined prediction model 1, age, sex, BMI, smoking history, and lung SUVR were signi cantly associated with lung cancer development (age: