Physical Activity, Screen Time, and Sleep Trajectories From Childhood to Adolescence: The Influence of Sex and Body Weight Status

Background: Behavioral trajectories from childhood to adolescence may differ and are poorly understood. The authors estimated the trajectories of moderate to vigorous physical activity (MVPA), screen time, and sleep duration during this period, by sex and initial weight status. Methods: Data from Quebec Adiposity and Lifestyle Investigation in Youth, an ongoing cohort study in Canada on the natural history of obesity, were used. Participants predisposed to obesity attended baseline (8–10 y old, n = 630) and follow-up visits 2 years (n = 564) and 7 years (n = 359) after baseline. Participants with completed self-reported and accelerometer-based data were included in the analyses (n = 191, 353, and 240 for MVPA, screen time, and sleep, respectively). The authors performed group-based trajectory analyses and multinomial logistic regression models. Results: Two MVPA, 3 screen time, and 2 sleep trajectories were identified. Girls were more likely than boys to belong to trajectory with lower MVPA means (odds ratio [OR] = 6.45; 95% confidence interval [CI], 3.08 to 13.49), yet less likely to belong to the trajectory with higher screen time (OR = 0.47; 95% CI, 0.23 to 0.97) and lower sleep duration (OR = 0.46; 95% CI, 0.27 to 0.78). Overweight or obesity at baseline was associated with a greater likelihood of belonging to the trajectory with lower MVPA (OR = 10.99; 95% CI, 1.31 to 91.14) and higher screen time (OR = 2.01; 95% CI, 1.04 to 4.06), respectively. Conclusions: It appears to be gender- and weight-based determinants of behavioral trajectories in this sample. These results may provide guidance for interventions in similar populations.

AntProphet: an Intention Mining System behind Alipay's Intelligent Customer Service Bot

We create an intention mining system, named AntProphet, for Alipay's intelligent customer service bot, to alleviate the burden of customer service. Whenever users have any questions, AntProphet is the first stop to help users to answer their questions. Our system gathers users' profile and their historical behavioral trajectories, together with contextual information to predict users' intention, i.e., the potential questions that users want to resolve. AntProphet takes care of more than 90% of the customer service demands in the Alipay APP and resolves most of the users' problems on the spot, thus significantly reduces the burden of manpower. With the help of it, the overall satisfaction rate of our customer service bot exceeds 85%.