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Front Public Health. 2014 Feb 28;2:12. doi: 10.3389/fpubh.2014.00012. eCollection 2014.

Development of a smartphone application to measure physical activity using sensor-assisted self-report.

Author information

1
Department of Preventive Medicine, University of Southern California , Los Angeles, CA , USA ; Department of Psychology, University of Southern California , Los Angeles, CA , USA.
2
Department of Preventive Medicine, University of Southern California , Los Angeles, CA , USA.
3
College of Computer and Information Science, Northeastern University , Boston, MA , USA.
4
College of Computer and Information Science, Northeastern University , Boston, MA , USA ; Bouvé College of Health Sciences, Northeastern University , Boston, MA , USA.

Abstract

INTRODUCTION:

Despite the known advantages of objective physical activity monitors (e.g., accelerometers), these devices have high rates of non-wear, which leads to missing data. Objective activity monitors are also unable to capture valuable contextual information about behavior. Adolescents recruited into physical activity surveillance and intervention studies will increasingly have smartphones, which are miniature computers with built-in motion sensors.

METHODS:

This paper describes the design and development of a smartphone application ("app") called Mobile Teen that combines objective and self-report assessment strategies through (1) sensor-informed context-sensitive ecological momentary assessment (CS-EMA) and (2) sensor-assisted end-of-day recall.

RESULTS:

The Mobile Teen app uses the mobile phone's built-in motion sensor to automatically detect likely bouts of phone non-wear, sedentary behavior, and physical activity. The app then uses transitions between these inferred states to trigger CS-EMA self-report surveys measuring the type, purpose, and context of activity in real-time. The end of the day recall component of the Mobile Teen app allows users to interactively review and label their own physical activity data each evening using visual cues from automatically detected major activity transitions from the phone's built-in motion sensors. Major activity transitions are identified by the app, which cues the user to label that "chunk," or period, of time using activity categories.

CONCLUSION:

Sensor-driven CS-EMA and end-of-day recall smartphone apps can be used to augment physical activity data collected by objective activity monitors, filling in gaps during non-wear bouts and providing additional real-time data on environmental, social, and emotional correlates of behavior. Smartphone apps such as these have potential for affordable deployment in large-scale epidemiological and intervention studies.

KEYWORDS:

context-sensitive ecological momentary assessment; experience sampling; mobile phone; physical activity; sedentary behavior; smartphone

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