Association of outdoor artificial light at night exposure with overweight and obesity among children and adolescents aged 9 to 18 years in China / 北京大学学报(医学版)

OBJECTIVE@#To analyze the association between outdoor artificial light-at-night (ALAN) exposure and overweight and obesity among children and adolescents aged 9 to 18 years in China.@*METHODS@#Using follow-up data of 5 540 children and adolescents aged 9 to 18 years conducted from November 2019 to November 2020 in eight provinces of China, latitude and longitude were determined based on school addresses, and the mean monthly average nighttime irradiance at the location of 116 schools was extracted by the nearest neighbor method to obtain the mean outdoor ALAN exposure [unit: nW/(cm2·sr)] for each school. Four indicators of overweight and obesity outcomes were included: Baseline overweight and obesity, persistent overweight and obesity, overweight and obesity progression and overweight and obesity incidence. Mixed effects Logistic regression was used to explore the association between ALAN exposure levels (divided into quintiles Q1-Q5) and baseline overweight and obesity, persistent overweight and obesity, overweight and obesity progression and overweight and obesity incidence. In addition, a natural cubic spline function was used to explore the exposure response association between ALAN exposure (a continuous variable) and the outcomes.@*RESULTS@#The prevalence of baseline overweight and obesity, persistent overweight and obesity, overweight and obesity progression and overweight and obesity incidence among the children and adolescents in this study were 21.6%, 16.3%, 2.9% and 12.8%, respectively. The OR value for the association between ALAN exposure and baseline overweight and obesity was statistically significant when ALAN exposure levels reached Q4 or Q5, 1.90 (95%CI: 1.26-2.86) and 1.77 (95%CI: 1.11-2.83), respectively, compared with the children and adolescents in the Q1 group of ALAN exposure. Similar to the results for baseline overweight and obesity, the OR values for the association with persistent overweight and obesity were 1.89 (95%CI: 1.20-2.99) and 1.82 (95%CI: 1.08-3.06) when ALAN exposure levels reached Q4 or Q5, respectively, but none of the OR values for the association between ALAN and overweight and obesity progression and overweight and obesity incidence were statistically significant. Fitting a natural cubic spline function showed a non-linear trend between ALAN exposure and persistent overweight and obesity.@*CONCLUSION@#There is a positive association between ALAN exposure and overweight and obesity in children and adolescents, and the promotion of overweight obesity in children and adolescents by ALAN tends to have a cumulative effect rather than an immediate effect. In the future, while focusing on the common risk factors for overweight and obesity in children and adolescents, there is a need to improve the overweight and obesity-causing nighttime light exposure environment.

Heartbeat-aware convolutional neural network for R-peak detection of wearable device ECG data / 南方医科大学学报

OBJECTIVE@#To develop a method for R-peak detection of ECG data from wearable devices to allow accurate estimation of the physiological parameters including heart rate and heart rate variability.@*METHODS@#A fully convolutional neural network was applied to predict the R-peak heatmap of ECG data and locate the R-peak positions. The heartbeat-aware (HA) module was introduced to enable the model to learn to predict the heartbeat number and R-peak heatmap simultaneously, thereby improving the capability of the model for extraction of the global context. The R-R interval estimated by the predicted heartbeat number was adopted to calculate the minimum horizontal distance for peak positioning. To achieve real-time R-peak detection on mobile devices, the deep separable convolution was adopted to reduce the number of parameters and the computational complexity of the model.@*RESULTS@#The proposed model was trained only with ECG data from wearable devices. At a tolerance window interval of 150 ms, the proposed method achieved R peak detection sensitivities of 100% for both wearable device ECG dataset and a public dataset (i.e. LUDB), and the true positivity rates exceeded 99.9%. As for the ECG signal of a 10 s duration, the CPU time of the proposed method for R-peak detection was about 23.2 ms.@*CONCLUSION@#The proposed method has good performance for R-peak detection of both wearable device ECG data and routine ECG data and also allows real-time R-peak detection of the ECG data.