Mobile Device Biomonitoring to Prevent and Treat Obesity in Underserved Youth

Part 1: Developing a Mobile Software Suite for Biomonitoring The software suite proposed in this research will be implemented using a three-tier architecture. The front tier of this architecture is the data collection sensors coupled with mobile phones that acts as data transmission device. The middle tier is a web server that receives and processes information and sends it to a back-end database server that stores the information.

The sensor layer is a collection of off-the-shelf devices that measure the metabolic activity. In particular, we propose to use heart rate, blood pressure, and blood glucose monitors currently available from Alive Technologies25. In addition to measuring the metabolic activity all these sensors are also capable of wirelessly transmitting this data over Bluetooth interface. We propose to use feature rich Nokia N95 as the mobile phone platform. N95 supports Bluetooth 2.0 +EDR for quick pairing with external Bluetooth sensors, and has 3G and WiFi radios for high bandwidth data transfer. All the external sensors listed above stream data to the Bluetooth enabled N95 mobile phone. In addition to the high bandwidth radio capabilities, the N95 mobile phone platform has a highly accurate built-in assisted GPS unit that uses a combination of GPS satellites, cellular tower and WiFi scanning to obtain a GPS position lock in less than 10 seconds. The stated location accuracy of GPS unit is 30 meters, while in practice the observed accuracy is around 3 meters.

Part 1a: Testing and Initial Deployment of sensors in target populations: We will use unit testing to extensively test our mobile software suite. We will recreate several usage scenarios and environmental conditions that our deployment test bed is likely to encounter. A total of 50 Hispanic and African American youth will be recruited to assist in technology development: 1) an advisory group of 10 youth, 2) 20 youth to participate in laboratory testing of the biomonitors and 3) 20 youth to wear the biomonitors and provide data and feedback. The advisory group of 5 African American and 5 Hispanic youth will be retained throughout the developmental phase to periodically visit our laboratory and test-run the sensors (50% female, 12-17 years of age). We will recruit a separate group of 10 African American and 10 Hispanic youth (50% female, 12-17 years of age) to participate in idea building sessions to ensure that sensors will be attractive and wearable, and to test the ease of usability of our mobile software suite. The initial deployment phase includes training the children on how to wear and remove the sensors. For laboratory testing of sensors, 10 Hispanic and 10 African American youth will spend 3-6 hours wearing the sensors and following protocols for walking, sitting, standing and doing various daily activities either at Dr. Spruijt-Metz's Physical Activity Observation Laboratory at USC HSA, or to the Motion Capture Laboratory at the Viterbi School of Engineering on USC Main Campus. Once the software and hardware is determined to be robust enough for deployment we will conduct our initial monitoring study with 10 African American and 10 Hispanic youth (50% female, 12-17 years of age). Children will wear the devices for three periods of one week (7 days), after which they will participate in brief individual interviews and surveys to ascertain ease of wear and to find ways to motivate and incentivize teens for wearing the sensors. Data collected from these weeks of wear will be used for the remaining data analysis and web presentation phases of the study.

Part 2: Data Capture and Transmission to a Back-End Server: We propose t a comprehensive mobile software suite that will allow the mobile phone to use Bluetooth to pair with wireless monitoring devices to collect vital health and behavioral data along with reading the built-in GPS data. The BodyMedia and MemSensse units will provide accelerometry data on physical activity and sleep. These measures will be validated in our physical activity lab against the Actigraph accelerometer (which has been extensively validated in youth) and Continuous Observation using the SOFIT system, a gold standard for physical activity measurement in youth 32, 33. By using time stamps the sensor data from Accelerometer can be correlated to the vital signs data collected from wearable sensors. We will use the data collected from all these sensors to automatically classify the user's activity. In particular, the software creates user specific movement signatures to account for differences in user's gait, walking/running/bicycling speed, usual route taken between work and home etc. The software will be able to use a combination of GPS and accelerometers to recognize the differences between driving on road and walking. This sensor information will be recorded continuously on the local storage on the mobile phone. For reference, our mobile device platform has an 8GB in-built flash memory that can be used for storing sensor information. Table 1 shows the approximate data rate of sensors. Using these data rates, we estimate that our 8GB local storage can store approximately 1000 days worth of data.

While data may be stored locally on the mobile phone the real value of our approach is the fact that these mobile devices can transmit the sensor information to any remote server using cellular data network or even WiFi. The information collected from the sensors in the mobile phone is sent to the web server for processing. The web server acts as data integrity manager that prevents illegal data read/writes by using simple authentication mechanisms, such as personal authentication. The web server utilizes HTTP/SMTP protocol to receive information from the mobile phones. The web server also provides web enabled access to the data for physician's around the globe.