Previous studies have shown that unusual eating times, light exposure at night, and night/rotating shift work contribute to the disruption of the circadian rhythm resulting in higher risks of sleep disorder, obesity, cancers, in addition to adverse effects on mental status and academic performance. The present review explores how changes in the light and food environments with the times influence human biological rhythm and their potential relationships to metabolic syndromes as well as the slim body trend among young women, which is becoming a social problem in modern Japanese society. Additionally, previous studies on the effects of food and light environments have been introduced　for insights on the problems triggered by biological rhythm disruption.

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This study investigated the relationships between age and walking motion of female adults (19-86 years) with independent walking ability. The walking motion with self-selected speed was measured with 3D motion analysis systems. Generally, the cadence was increased while step length was decreased with advanced age. In contrast to younger female, a larger hip joint flexion and extension with a smaller ankle joint plantar-flexion were observed among older female. In summary, this study presented the differences in gait characteristics of female at different age groups. There were strong relationships between age, and increased hip flexion and decreased ankle plantar-flexion from terminal stance phase to initial swing phase.

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To clarify whether the increase in the conduit artery blood flow during dynamic exercise with a small skeletal muscle groups was inhibited by aging, we compared femoral artery blood flow at rest and during the submaximal knee extension exercise (0 W, 10 W, 20 W and 30 W) between older (O group) and young (Y group) males. Absolute blood flow of femoral artery at rest and during exercise at low intensity was smaller in O than Y groups. However, femoral artery blood flow corrected by skeletal muscle mass during exercise at 10~30 W was not attenuated by aging. These results suggested that, during the knee extension exercise within the range of the load intensity that we used in this study, even the older males can maintain the femoral artery blood flow that is equal to young males, in other words, the aging does not inhibit increase of the femoral artery blood flow.

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This study aimed to investigate the association between the skeletal muscle mass index (SMI), skipping meal(s), and lack of physical activity among female university students who desire to lose weight. The body composition of 74 female students who desire to lose weight were measured using the bioelectrical impedance analysis (BIA) method, and their ideal weight and eating and physical activity behaviors were noted through a questionnaire-based survey. The ideal weight (percentage difference between the current weight) was significantly correlated with body mass index or body fat (r = -0.64 or -0.58, P < 0.01), but not with SMI (r = -0.18). Insufficient physical activity was associated with higher body fat and lower SMI. Further, participants who skipped meal(s) and had insufficient physical activity had markedly lower SMI. These results suggest that an individual’s ideal weight is determined based on their current weight (not muscle mass). Therefore, if individuals who skipped meal(s) and had insufficient physical activity try to lose weight based on their current weight, their muscle mass might greatly decrease.

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Methods of human motion tracking for acquiring biometric information are needed in order to apply knowledge of physiological anthropology. However, previous methods have difficulty in control of video shooting conditions. Thus, we developed a more robust motion tracking method using machine learning. In this report, we evaluated the accuracy of our method by comparing palpebral fissure height measured using manually selected data and tracking data obtained using YOLOv3. The results indicate that our method has practical accuracy in measuring palpebral fissure height and suggest that including noise in training data contributes to its accuracy.

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