Purpose: To assess validity of the Personal Activity Location Measurement System (PALMS) for deriving time spent walking/running, bicycling, and in vehicle, using SenseCam as the comparison.
Methods: 40 adult cyclists wore a Qstarz BT-Q1000XT GPS data logger and SenseCam (camera worn around neck capturing multiple images every minute) for a mean of 4 days. PALMS used distance and speed between GPS points to classify whether each minute was part of a trip (yes/no), and if so, the trip mode (walking/running, bicycling, in vehicle). SenseCam images were annotated to create the same classifications (i.e., trip yes/no and mode). 2x2 contingency tables and confusion matrices were calculated at the minute-level for PALMS vs. SenseCam classifications. Mixed-effects linear regression models estimated agreement (mean differences and intraclass correlations [ICCs]) between PALMS and SenseCam with regards to minutes/day in each mode.
Results: Minute-level sensitivity, specificity, and negative predictive value were >=88%, and positive predictive value was >=75% for non mode-specific trip detection. 72-80% of outdoor walking/running minutes, 73% of bicycling minutes, and 74-76% of in-vehicle minutes were correctly classified by PALMS. For minutes/day, PALMS had a mean bias (i.e., amount of over or under estimation) of 2.4-3.1 minutes (11-15%) for walking/running, 2.3-2.9 minutes (7-9%) for bicycling, and 4.3-5 minutes (15-17%) for vehicle time. ICCs were >=.80 for all modes.
Conclusions: PALMS has validity for processing GPS data to objectively measure time walking/running, bicycling, and in vehicle in population studies. Assessing travel patterns is one of many valuable applications of GPS in physical activity research that can improve our understanding of the determinants and health outcomes of active transportation as well as its impact on physical activity.
(C) 2015 American College of Sports Medicine