Physical inactivity during adolescence increases risk for a number of serious health conditions. Surveillance, epidemiological, and intervention studies seeking to increase physical activity and/or decrease sedentary behaviors in adolescents could be enhanced through more informative and accurate methods of measuring these behaviors. Concerns about the validity of retrospective self report of physical activity and sedentary behavior have led to increasing use of objective measures, e.g., accelerometers and Global Positioning System (GPS) loggers, which can be complemented by subjective or contextual information using self-initiated event-contingent electronic ecological momentary assessment (EMA) after exercise or other critical activities. Regardless of the technique used, device non-wear, equipment malfunction, and participant non-response result in missing and ambiguous data that complicate statistical analysis. Adolescents recruited into objective PA monitoring studies will increasingly have so-called smart phones, which are miniature computers with built in motion sensors and location-finding capabilities. Sophisticated programs (i.e., apps) can be easily installed on the phones. The overall objective of this project is to develop new software for common mobile phones that can both reduce and explain missing data collected during objective and EMA activity monitoring studies with free living adolescents. This technology will supplement objective monitors already used today, with minimal additional cost. Our solution will have three novel components: (1) A phone app that uses the mobile device's built-in sensors to detect major transitions in type of movement or location, after which timely, context-sensitive questions and reminders are triggered that will reduce and explain missing or incomplete activity, location, and event- contingent EMA data, regardless of whether built-in or external objective monitors are used, (2) A second phone app that has an entertaining, game-like feel and allows adolescents to interactively fill in gaps in their own data at the end of the day using cues from automatically-detected major transitions to explain this missing data, and (3) Server-side software that will remotely collect data from the two apps in real-time and provide researchers with a cost-efficient way to reduce missing data and improve characterization of transition in activity. The system's feasibility, acceptability, and performance will be compared to the current state of the art in a within-subjects study with a free-living sample 40 low-to-middle income, ethnically diverse adolescents in 9-12th grade. The source code for all the software will be made freely available to other researchers.