How many days of pedometer use predict the annual activity of the elderly reliably?

Med Sci Sports Exerc. 2008 Jun;40(6):1058-64. doi: 10.1249/MSS.0b013e318167469a.

Abstract

Purpose: Daily variations of physical activity in the elderly remain unclear. We thus used a uniaxial accelerometer/pedometer to examine the variability of step counts for 1 yr, determining the minimum number of days observation needed to obtain reliable estimates of annual physical activity.

Methods: Subjects were 37 males and 44 females, healthy Japanese, aged 65-83 yr. The pedometer was worn on the waistband throughout 1 yr, accumulating information on the individual's daily step count.

Results: The step count spectrum showed peaks with periods of 2.3, 3.5, and 7.0 d and an aperiodic component that had a greater power at low frequencies (i.e., non-white noise). These characteristics were absent in randomly resequenced data. To ensure that 80% of total variance was attributable to between-subjects variance, 25 and 8 consecutive days of observation were needed in male and female subjects, respectively. To achieve 90% on this same measure of reliability, 105 and 37 consecutive days of observation were required. In contrast, 4 d of randomly timed observations yielded 80% reliability for both men and women, and 11 and 9 d gave 90% reliability in men and women, respectively. If sampling also took account of season and day of the week, the respective observation periods for men and women were reduced to 8 and 4 d (i.e., 2 and 1 consecutive days of sampling every 89 d) for 80% and to 16 and 12 d (i.e., 4 and 3 consecutive days every 89 d) for 90% reliability.

Conclusion: When estimating annual step counts, seasonal and/or random sampling of data allows collection of reliable data during substantially fewer days than needed for consecutive observations.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aged / physiology*
  • Female
  • Fourier Analysis
  • Humans
  • Leisure Activities
  • Longitudinal Studies
  • Male
  • Monitoring, Ambulatory / methods*
  • Motor Activity
  • Predictive Value of Tests
  • Seasons
  • Walking*