South East Asian Nutrition Surveys (SEANUTS) II - a multi-country evaluation of nutrition and lifestyle indicators in children aged 12 years and below: rationale and design.

OBJECTIVE: To assess the nutritional status, growth parameters and lifestyle behaviours of children between 0·5 and 12 years in nationally representative samples in Malaysia, Indonesia, Thailand and Vietnam. DESIGN: A cross-sectional study was conducted in the four countries, between May 2019 and April 2021. Data collected can be categorised into four categories: (1) Growth - anthropometry, body composition, development disorder, (2) nutrient intake and dietary habits - 24-h dietary recall, child food habits, breast-feeding and complementary feeding, (3) socio-economic status - food insecurity and child health status/environmental and (4) lifestyle behaviours - physical activity patterns, fitness, sunlight exposure, sleep patterns, body image and behavioural problems. Blood samples were also collected for biochemical and metabolomic analyses. With the pandemic emerging during the study, a COVID-19 questionnaire was developed and implemented. SETTING: Both rural and urban areas in Malaysia, Indonesia, Thailand and Vietnam. PARTICIPANTS: Children who were well, with no physical disability or serious infections/injuries and between the age of 0·5 and 12 years old, were recruited. RESULTS: The South East Asian Nutrition Surveys II recruited 13 933 children. Depending on the country, data collection from children was conducted in schools and commune health centres, or temples, or sub-district administrative organisations. CONCLUSIONS: The results will provide up-to-date insights into nutritional status and lifestyle behaviours of children in the four countries. Subsequently, these data will facilitate exploration of potential gaps in dietary intake among Southeast Asian children and enable local authorities to plan future nutrition and lifestyle intervention strategies.

Optimized Signal Quality Assessment for Photoplethysmogram Signals Using Feature Selection.

OBJECTIVE: With the increasing use of wearable healthcare devices for remote patient monitoring, reliable signal quality assessment (SQA) is required to ensure the high accuracy of interpretation and diagnosis on the recorded data from patients. Photoplethysmographic (PPG) signals non-invasively measured by wearable devices are extensively used to provide information about the cardiovascular system and its associated diseases. In this study, we propose an approach to optimize the quality assessment of the PPG signals. METHODS: We used an ensemble-based feature selection scheme to enhance the prediction performance of the classification model to assess the quality of the PPG signals. Our approach for feature and subset size selection yielded the best-suited feature subset, which was optimized to differentiate between the clean and artifact corrupted PPG segments. CONCLUSION: A high discriminatory power was achieved between two classes on the test data by the proposed feature selection approach, which led to strong performance on all dependent and independent test datasets. We achieved accuracy, sensitivity, and specificity rates of higher than 0.93, 0.89, and 0.97, respectively, for dependent test datasets, independent of heartbeat type, i.e., atrial fibrillation (AF) or non-AF data including normal sinus rhythm (NSR), premature atrial contraction (PAC), and premature ventricular contraction (PVC). For independent test datasets, accuracy, sensitivity, and specificity rates were greater than 0.93, 0.89, and 0.97, respectively, on PPG data recorded from AF and non-AF subjects. These results were found to be more accurate than those of all of the contemporary methods cited in this work. SIGNIFICANCE: As the results illustrate, the advantage of our proposed scheme is its robustness against dynamic variations in the PPG signal during long-term 14-day recordings accompanied with different types of physical activities and a diverse range of fluctuations and waveforms caused by different individual hemodynamic characteristics, and various types of recording devices. This robustness instills confidence in the application of the algorithm to various kinds of wearable devices as a reliable PPG signal quality assessment approach.