Examining health apps and wearable use in improving physical and mental well-being across U.S., China, and Singapore.

Health apps and wearables are touted to improve physical health and mental well-being. However, it is unclear from existing research the extent to which these health technologies are efficacious in improving physical and mental well-being at a population level, particularly for the underserved groups from the perspective of health equity and social determinants. Also, it is unclear if the relationship between health apps and wearables use and physical and mental well-being differs across individualistic, collectivistic, and a mix of individual-collectivistic cultures. A large-scale online survey was conducted in the U.S. (individualist culture), China (collectivist culture), and Singapore (mix of individual-collectivist culture) using quota sampling after obtaining ethical approval from the Institutional Review Board (IRB-2021-262) of Nanyang Technological University (NTU), Singapore. There was a total of 1004 respondents from the U.S., 1072 from China, and 1017 from Singapore. Data were analyzed using multiple regression and negative binomial regression. The study found that income consistently had the strongest relationship with physical and mental well-being measures in all three countries, while the use of health apps and wearables only had a moderate association with psychological well-being only in the US. Health apps and wearables were associated with the number of times people spent exercising and some mental health outcomes in China and Singapore, but they were only positively associated with psychological well-being in the US. The study emphasizes the importance of considering the social determinants, social-cultural context of the population, and the facilitating conditions for the effective use of digital health technologies. The study suggests that the combined use of both health apps and wearables is most strongly associated with better physical and mental health, though this association is less pronounced when individuals use only apps or wearables.

FedDdrl: Federated Double Deep Reinforcement Learning for Heterogeneous IoT with Adaptive Early Client Termination and Local Epoch Adjustment.

Federated learning (FL) is a technique that allows multiple clients to collaboratively train a global model without sharing their sensitive and bandwidth-hungry data. This paper presents a joint early client termination and local epoch adjustment for FL. We consider the challenges of heterogeneous Internet of Things (IoT) environments including non-independent and identically distributed (non-IID) data as well as diverse computing and communication capabilities. The goal is to strike the best tradeoff among three conflicting objectives, namely global model accuracy, training latency and communication cost. We first leverage the balanced-MixUp technique to mitigate the influence of non-IID data on the FL convergence rate. A weighted sum optimization problem is then formulated and solved via our proposed FL double deep reinforcement learning (FedDdrl) framework, which outputs a dual action. The former indicates whether a participating FL client is dropped, whereas the latter specifies how long each remaining client needs to complete its local training task. Simulation results show that FedDdrl outperforms the existing FL scheme in terms of overall tradeoff. Specifically, FedDdrl achieves higher model accuracy by about 4% while incurring 30% less latency and communication costs.

Prevalence of Overweight among Secondary School Students in Klang District, Selangor.

Overweight and obesity place children and adolescents at increased risk of significant health problems, both during their early life and adult life. A cross sectional study was carried out to determine the prevalence of overweight among secondary school students aged 13-17 years in the Klang district and to determine the association between overweight and age, sex, ethnicity, religion and blood pressure. Random cluster proportionate to size sampling technique was used to select the respondents. Weight was recorded using TANITA model HD-309 and height was measured using SECA Body meter Model 208. A mercury sphygmomanometer was used to measure blood pressure manually. Statistical analysis was carried out using SPSS version 13. Out of the 3,333 respondents, 11.4% were found to be at risk of overweight and 8.2% were overweight. The prevalence of overweight was significantly higher in the males (10.6%) as compared to females (6.0%). Prevalence of overweight was highest in Malays (10.7%) followed by the Indians (7.1%) and the Chinese (5.9%). Prevalence of 'risk of overweight' was highest in the Indians (13.7%) followed by Chinese (12.0%) and Malays (9.8%). There was a moderate direct significant relationship between overweight and systolic blood pressure (r=0.5, r2=0.25, p<0.001) and also diastolic blood pressure (r=0.42, r2=0.18, p<0.001). The relationship between BMI and overweight was very weak (r=0.11, r2=0.01 and df=3331, p<0.001). In conclusion the prevalence of overweight among the secondary school students is high and there is a need for a comprehensive integrated population-based intervention program.