ABSTRACT
The artificial light at night (ALAN) exposure has emerged as a significant environmental and public health concern globally. However, there is far less evidence on the health effects of indoor ALAN than on outdoor ALAN. Moreover, evidence on cardiovascular effects of indoor ALAN is more limited. To evaluate the association between short-term exposure to ALAN during sleep with heart rate variability (HRV) in young healthy adults, as well as the mediating role of blood oxygen saturation (SpO2), and to further explore the intervention effects of shading habits, this prospective repeated measurement study was conducted among 81 adults with 150 nights (1324h) of HRV monitoring. HRV and SpO2 were monitored during sleep, concurrently with the measurement of indoor and outdoor ALAN. Shading habits were defined as whether to wear blindfolds or draw bed curtains during sleep, and were collected by questionnaires. Linear mixed-effect model was conducted to assess the association between ALAN exposure and HRV indices. The role of SpO2 in the association was analyzed using mediation analyses. We found that indoor ALAN exposure reduced parasympathetic activity and imbalanced cardiac autonomic function. We also found that the use of outdoor ALAN may underestimate or misestimate the potential health effects of ALAN. A significant mediation effects were observed on standard deviation of normal-to-normal intervals (SDNN; p-value for ACME = 0.014) and the ratio of low frequency power to high frequency power (LF/HF; p-value for ACME = 0.026) through minimum SpO2 after indoor ALAN exposure. The association between indoor ALAN and HRV was more pronounced among participants without shading habits during sleep. This study provides general population-based evidence that short-term exposure to indoor ALAN was significantly associated with impaired HRV, and SpO2 partially mediated the association. Improve shading habits during sleep may mitigate the adverse effects of indoor ALAN.
ABSTRACT
Body fat assessment is required as part of an objective health assessment, both for nonobese and obese people. Image-based body fat assessment will enable faster diagnosis. Body fat analysis that accounts for age and sex will help in both diagnosis and correlating diseases and fat distribution. After evaluating computed tomography imaging algorithms to identify and segment human abdominal and subcutaneous fat, we present an improved region growing scale-invariant feature transform algorithm. It applies Naive Bayes image thresholding for key point selection and image matching. This method enables rapid and accurate comparison and matching of images from multiple databases and improves the efficiency of image processing.