Abstract
Background: Metabolic syndrome is a cluster of metabolic disorders including elevated blood pressure, high plasma glucose, excess body fat around the waist, and abnormal cholesterol or triglyceride levels. These conditions cause serious complications such as heart disease, stroke and type 2 diabetes. In Japan, specific health checkups and specific health guidance which focused on metabolic syndrome has been performed since 2008. Those who fall under certain criteria need to receive a medical treatment guidance from doctor, public health nurse or dietitian. Those who received health guidance receives a reassessment of improvement of their life-style 3-6 months later. However, the efficacy of this approach has not been elucidated. In addition, many persons who have metabolic syndrome do not receive this instruction. Recently, the image analysis technology using the artificial intelligence (AI) progresses rapidly. The smart device application “Asken” has an AI-powered photo analysis system which analyzes the photo of the entire meal, and delivers individualized messages and dietary feedbacks. In this study, we utilized the Internet of Things (IoT) device which includes Asken app, body composition analyzer and sphygmomanometer that can connect wirelessly. Objective: Our aim is to assess the efficacy of specific health guidance adding on IoT device. This is a multicenter, unblinded, non-randomized controlled study. Results: At the end of January 2020, we recruited 219 participants including 105 participants with IoT devices. We used 48 participants (32 with IoT and 16 without IoT) who had finished a reassessment 3 to 6 months after initial guidance. Results: Age, body weight (BW), body mass index (BMI), blood pressure (BP), fasting plasma glucose (FPG), hemoglobin A1c (HbA1c), total cholesterol (T-Chol), high density lipoprotein cholesterol (HDL), low density lipoprotein cholesterol (LDL), non-HDL cholesterol (n-HDL), and triglyceride (TG), did not differ between IoT-use and control group. 6 months after initial guidance, the quantity of decrease of BW in IoT-use group was significantly larger than control (-2.5 ± 4.1 kg vs. 0.6±4.4, p = 0.03). In addition, the quantities of decrease of both T-Chol and n-HDL in IoT-use group were also significantly larger than control (T-Chol, -5.9 ± 32.0 vs. 14.3 ± 31.6, p = 0.02; n-HDL, -7.6 ± 29.0 vs. 9.4 ± 27.5, p = 0.01). Conclusion: Using IoT device might be useful for body weight loss and the improvement of mild hypercholesterolemia in those with metabolic syndrome.
574 The development of cost-effective health guidance courses for male workers with the risk of metabolic syndrome
