Validity of PALMS GPS Scoring of Active and Passive Travel Compared with SenseCam

Abstract

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). 2×2 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.

Bicycle Trains, Cycling, and Physical Activity: A Pilot Cluster RCT

“…For the GPS data, Personal Activity Location Measurement System, version 4 filtered invalid GPS fixes as a result of satellite interference. 41,42 Because this study applied concurrent accelerometer and GPS data in the estimation of cycling activity, 3 days were used as the minimum amount of valid days for inclusion of combined accelerometer and GPS data to estimate cycling activity, the criteria set by Jerrett et al 43 (i.e., >4 hours of valid combined data for a minimum of 3 days).…”

A Framework for Using GPS Data in Physical Activity and Sedentary Behavior Studies

“…Using algorithms that utilize the signal-to-noise-ratio (SNR), PALMS can categorize measurements as occurring indoors or outdoors. Furthermore, PALMS can identify and categorize trips into three modes: walking, biking, and in a vehicle, based on speed(5). PALMS is currently available as a free online system, providing researchers with rapid access to combined accelerometer and GPS data.…”

Using GPS, GIS, and Accelerometer Data to Predict Transportation Modes

“…More recently, SenseCam, a camera worn around the neck that takes pictures at regular intervals or when triggered by imbedded sensors, has been suggested to improve data collection of daily activities including trips (6,16,30). Pictures are then used to identify transportation modes or other trip characteristics.…”