Identifying walking trips from GPS and accelerometer data in adolescent females.

BACKGROUND Studies that have combined accelerometers and global positioning systems (GPS) to identify walking have done so in carefully controlled conditions. This study tested algorithms for identifying walking trips from accelerometer and GPS data in free-living conditions. The study also assessed the accuracy of the locations where walking occurred compared with what participants reported in a diary. METHODS A convenience sample of high school females was recruited (N = 42) in 2007. Participants wore a GPS unit and an accelerometer, and recorded their out-of-school travel for 6 days. Split-sample validation was used to examine agreement in the daily and total number of walking trips with Kappa statistics and count regression models, while agreement in locations visited by walking was examined with geographic information systems. RESULTS Agreement varied based on the parameters of the algorithm, with algorithms exhibiting moderate to substantial agreement with self-reported daily (Kappa = 0.33-0.48) and weekly (Kappa = 0.41-0.64) walking trips. Comparison of reported locations reached by walking and GPS data suggest that reported locations are accurate. CONCLUSIONS The use of GPS and accelerometers is promising for assessing the number of walking trips and the walking locations of adolescent females.

[1]  J. Jobe,et al.  Promoting physical activity in middle school girls: Trial of Activity for Adolescent Girls. , 2008, American journal of preventive medicine.

[2]  J. R. Landis,et al.  The measurement of observer agreement for categorical data. , 1977, Biometrics.

[3]  Henk Meurs,et al.  Biases in response over time in a seven-day travel diary , 1986 .

[4]  J. Wolf Applications of New Technologies in Travel Surveys , 2006 .

[5]  Charles E Matthews,et al.  Prediction of activity mode with global positioning system and accelerometer data. , 2008, Medicine and science in sports and exercise.

[6]  Terry E. Duncan,et al.  A Multilevel Approach to Youth Physical Activity Research , 2004, Exercise and sport sciences reviews.

[7]  Kai Elgethun,et al.  Comparison of global positioning system (GPS) tracking and parent-report diaries to characterize children's time–location patterns , 2007, Journal of Exposure Science and Environmental Epidemiology.

[8]  Philip J Troped,et al.  Portable global positioning units to complement accelerometry-based physical activity monitors. , 2005, Medicine and science in sports and exercise.

[9]  Francisca Galindo Garre,et al.  Evaluation of neural networks to identify types of activity using accelerometers. , 2011, Medicine and science in sports and exercise.

[10]  Song Yang,et al.  Imputation of missing data when measuring physical activity by accelerometry. , 2005, Medicine and science in sports and exercise.

[11]  Tae-Seong Kim,et al.  A Triaxial Accelerometer-Based Physical-Activity Recognition via Augmented-Signal Features and a Hierarchical Recognizer , 2010, IEEE Transactions on Information Technology in Biomedicine.

[12]  J. Staudenmayer,et al.  Development of novel techniques to classify physical activity mode using accelerometers. , 2006, Medicine and science in sports and exercise.

[13]  Diane J Catellier,et al.  Design of the Trial of Activity in Adolescent Girls (TAAG). , 2005, Contemporary clinical trials.

[14]  P. Stopher,et al.  Assessing the accuracy of the Sydney Household Travel Survey with GPS , 2007 .

[15]  Jean Wolf Defining GPS and Its Capabilities , 2004 .

[16]  E. Murakami,et al.  Can using global positioning system (GPS) improve trip reporting , 1999 .

[17]  Stephen Greaves,et al.  Exploring variability in pedestrian exposure to fine particulates (PM2.5) along a busy road , 2008 .

[18]  Bernard F Fuemmeler,et al.  Accelerometer data reduction: a comparison of four reduction algorithms on select outcome variables. , 2005, Medicine and science in sports and exercise.

[19]  Lisa Chasan-Taber,et al.  Estimating physical activity using the CSA accelerometer and a physical activity log. , 2003, Medicine and science in sports and exercise.

[20]  A. Raftery Bayesian Model Selection in Social Research , 1995 .

[21]  John Staudenmayer,et al.  An artificial neural network to estimate physical activity energy expenditure and identify physical activity type from an accelerometer. , 2009, Journal of applied physiology.

[22]  Xi Long,et al.  Single-accelerometer-based daily physical activity classification , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[23]  Pierre Abraham,et al.  Study of human outdoor walking with a low-cost GPS and simple spreadsheet analysis. , 2007, Medicine and science in sports and exercise.

[24]  Carl J Caspersen,et al.  Relationship of walking to mortality among US adults with diabetes. , 2003, Archives of internal medicine.

[25]  M. Duncan,et al.  GIS or GPS? A comparison of two methods for assessing route taken during active transport. , 2007, American journal of preventive medicine.

[26]  J. Wolf,et al.  Impact of Underreporting on Mileage and Travel Time Estimates: Results from Global Positioning System-Enhanced Household Travel Survey , 2003 .

[27]  G. Welk,et al.  Reliability of accelerometry-based activity monitors: a generalizability study. , 2004, Medicine and science in sports and exercise.

[28]  Jianhe Du,et al.  Increasing the accuracy of trip rate information from passive multi-day GPS travel datasets: Automatic trip end identification issues , 2007 .

[29]  J. Curnow,et al.  Technical reliability of the CSA activity monitor: The EarlyBird Study. , 2002, Medicine and science in sports and exercise.

[30]  Noreen C. McDonald,et al.  Exploratory Analysis of Children's Travel Patterns , 2006 .

[31]  M. Murphy,et al.  Walking: the first steps in cardiovascular disease prevention , 2010, Current opinion in cardiology.

[32]  R. J. Shephard,et al.  Utility of Global Positioning System to Measure Active Transport in Urban Areas , 2008 .

[33]  S. Going,et al.  Defining accelerometer thresholds for activity intensities in adolescent girls. , 2004, Medicine and science in sports and exercise.

[34]  B. Ainsworth,et al.  Estimation of energy expenditure using CSA accelerometers at hip and wrist sites. , 2000, Medicine and science in sports and exercise.

[35]  Ilkka Korhonen,et al.  Detection of Daily Activities and Sports With Wearable Sensors in Controlled and Uncontrolled Conditions , 2008, IEEE Transactions on Information Technology in Biomedicine.

[36]  Kelly R Evenson,et al.  Identifying Walking Trips Using GPS Data. , 2011, Medicine and science in sports and exercise.