Urinary Nitric Oxide Levels Are Associated with Blood Pressure, Fruit and Vegetable Intake and Total Polyphenol Excretion in Adolescents from the SI! Program.

Nitric oxide (NO) is important to cardiovascular health (CVH), and its bioavailability could be regulated by the antioxidant effect of polyphenols, improving endothelial function and consequently blood pressure (BP). However, scant research has been carried out on NO and CVH correlates in adolescent populations. Therefore, our aim was to investigate the association between NO and the CVH status and other health factors in adolescents. NO, total polyphenol excretion (TPE), anthropometric measurements, BP, blood lipid profile, blood glucose, diet, physical activity, and smoking status were recorded, while CVH score was classified as ideal, intermediate, and poor. Negative associations were observed between NO and body mass index, body fat percentage, BP, and triglycerides; and positive associations between NO and skeletal muscle percentage, HDL-cholesterol, fruit and vegetable intake, and TPE was observed. To capture more complex interactions among different factors, multiple linear regression was performed, obtaining a significant association between NO and fruit and vegetable intake (ß = 0.175), TPE (ß = 0.225), and systolic BP (ß = -0.235). We conclude that urinary NO levels are positively associated with the consumption of fruits and vegetables rich in antioxidants such as polyphenols and negatively associated with systolic BP.

Long-term activity recognition from wristwatch accelerometer data.

With the development of wearable devices that have several embedded sensors, it is possible to collect data that can be analyzed in order to understand the user's needs and provide personalized services. Examples of these types of devices are smartphones, fitness-bracelets, smartwatches, just to mention a few. In the last years, several works have used these devices to recognize simple activities like running, walking, sleeping, and other physical activities. There has also been research on recognizing complex activities like cooking, sporting, and taking medication, but these generally require the installation of external sensors that may become obtrusive to the user. In this work we used acceleration data from a wristwatch in order to identify long-term activities. We compare the use of Hidden Markov Models and Conditional Random Fields for the segmentation task. We also added prior knowledge into the models regarding the duration of the activities by coding them as constraints and sequence patterns were added in the form of feature functions. We also performed subclassing in order to deal with the problem of intra-class fragmentation, which arises when the same label is applied to activities that are conceptually the same but very different from the acceleration point of view.