Annals of Behavioral Medicine

, Volume 28, Issue 3, pp 158-162

First online:

A Preliminary study of one year of pedometer self-monitoring

  • Catrine Tudor-LockeAffiliated withDepartment of Exercise and Wellness, Arizona State University Email author 
  • , David R. BassettJr.Affiliated withDepartment of Health and Exercise Science, The University of Tennessee
  • , Ann M. SwartzAffiliated withDepartment of Human Movement Sciences, University of Wisconsin-Milwaukee
  • , Scott J. StrathAffiliated withDepartment of Human Movement Sciences, University of Wisconsin-Milwaukee
  • , Brian B. ParrAffiliated withExercise and Sports Science, University of South Carolina Aiken
  • , Jared P. ReisAffiliated withGraduate School of Public Health, San Diego State University
  • , Katrina D. DuBoseAffiliated withCenter for Physical Activity & Weight Management, Schiefelbusch Institute for Lifespan Studies University of Kansas
  • , Barbara E. AinsworthAffiliated withDepartment of Exercise & Nutritional Sciences, San Diego State University

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Background: Long-term pedometer monitoring has not been attempted.Purpose: The purpose of this project was to collect 365 days of continuous self-monitored pedometer data to explore the natural variability of physical activity.Methods: Twenty-three participants (7 men, 16 women; M age = 38 ± 9.9 years; M body mass index = 27.7± 6.2 kg/m2) were recruited by word of mouth at two southern U.S. universities. Participants were asked to wear pedometers at their waist during waking hours and record steps per day and daily behaviors (e.g., sport/exercise, work or not) on a simple calendar. In total, participants wore pedometers and recorded 8,197 person-days of data (of a possible 8,395 person-days, or 98%) for a mean of 10,090± 3,389 steps/day. Missing values were estimated using the Missing Values Analysis EM function in SPSS, Version 11.0.1.Results: A mean of 10,082± 3,319 steps/day was computed. Using the corrected data, differences in steps/day were significant for season (summer > winter, F = 7.57, p = .001), day of the week (weekday > weekend, F = 3.97, p = .011), type of day (workday vs. nonworkday, F = 9.467, p = .008), and participation in sport/exercise (day with sport/exercise > day without sport/exercise, F = 102.5, p < .0001).Conclusions: These data suggest that surveillance should be conducted in the spring/fall or that an appropriate correction factor should be considered if the intent is to capture values resembling the year-round average.