title: Validity of PALMS GPS scoring of active and passive travel compared with SenseCam.
abstract: OBJECTIVE
  The objective of this study is to assess validity of the personal activity location measurement system (PALMS) for deriving time spent walking/running, bicycling, and in vehicle, using SenseCam (Microsoft, Redmond, WA) as the comparison.
  
  
  METHODS
  Forty adult cyclists wore a Qstarz BT-Q1000XT GPS data logger (Qstarz International Co., Taipei, Taiwan) and SenseCam (camera worn around the neck capturing multiple images every minute) for a mean time of 4 d. PALMS used distance and speed between global positioning system (GPS) points to classify whether each minute was part of a trip (yes/no), and if so, the trip mode (walking/running, bicycling, or in vehicle). SenseCam images were annotated to create the same classifications (i.e., trip yes/no and mode). Contingency tables (2 × 2) and confusion matrices were calculated at the minute level for PALMS versus SenseCam classifications. Mixed-effects linear regression models estimated agreement (mean differences and intraclass correlation coefficients) between PALMS and SenseCam with regard 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. Seventy-two percent to 80% of outdoor walking/running minutes, 73% of bicycling minutes, and 74%-76% of in-vehicle minutes were correctly classified by PALMS. For minutes per day, PALMS had a mean bias (i.e., amount of over or under estimation) of 2.4-3.1 min (11%-15%) for walking/running, 2.3-2.9 min (7%-9%) for bicycling, and 4.3-5 min (15%-17%) for vehicle time. Intraclass correlation coefficients were ≥0.80 for all modes.
  
  
  CONCLUSIONS
  PALMS has validity for processing GPS data to objectively measure time spent 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 effect on physical activity.
authors.affiliation: Family and Preventive Medicine, University of California, San Diego, San Diego, CA.
authors.forename: Jordan A
authors.initials: JA
authors.lastname: Carlson
authors.forename: Marta M
authors.initials: MM
authors.lastname: Jankowska
authors.forename: Kristin
authors.initials: K
authors.lastname: Meseck
authors.forename: Suneeta
authors.initials: S
authors.lastname: Godbole
authors.forename: Loki
authors.initials: L
authors.lastname: Natarajan
authors.forename: Fredric
authors.initials: F
authors.lastname: Raab
authors.forename: Barry
authors.initials: B
authors.lastname: Demchak
authors.forename: Kevin
authors.initials: K
authors.lastname: Patrick
authors.forename: Jacqueline
authors.initials: J
authors.lastname: Kerr
mesh_terms: D000328:Adult; D001336:Automobiles; D001642:Bicycling; D015444:Exercise; D005260:Female; D040362:Geographic Information Systems; D006801:Humans; D008297:Male; D009043:Motor Activity; D012420:Running; D012984:Software / Q000592:standards*; D014195:Travel; D016138:Walking