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Robert W. Motl, Weimo Zhu, Youngsik Park, Edward McAuley, Jennifer A. Scott and Erin M. Snook

We examined the reliability of scores from physical activity monitors in a sample of 193 individuals with multiple sclerosis (MS) who wore a pedometer and an accelerometer for a 7-day period. There were no significant differences among days for the pedometer (p = .12) or the accelerometer (p = .15) indicating that week and weekend days can be analyzed in a single intra-class correlation (ICC) analytic model. The 7 days of monitoring yielded ICC estimates of .93 for both the pedometer and accelerometer, and a minimum of 3 days yielded a reliability of .80 for both the pedometer and accelerometer. Results indicated that physical activity monitor scores are reliable measures of physical activity for individuals with MS.

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Weimo Zhu, Zorica Nedovic-Budic, Robert B. Olshansky, Jed Marti, Yong Gao, Youngsik Park, Edward McAuley and Wojciech Chodzko-Zajko

Purpose:

To introduce Agent-Based Model (ABM) to physical activity (PA) research and, using data from a study of neighborhood walkability and walking behavior, to illustrate parameters for an ABM of walking behavior.

Method:

The concept, brief history, mechanism, major components, key steps, advantages, and limitations of ABM were first introduced. For illustration, 10 participants (age in years: mean = 68, SD = 8) were recruited from a walkable and a nonwalkable neighborhood. They wore AMP 331 triaxial accelerometers and GeoLogger GPA tracking devices for 21 days. Data were analyzed using conventional statistics and highresolution geographic image analysis, which focused on a) path length, b) path duration, c) number of GPS reporting points, and d) interaction between distances and time.

Results:

Average steps by subjects ranged from 1810−10,453 steps per day (mean = 6899, SD = 3823). No statistical difference in walking behavior was found between neighborhoods (Walkable = 6710 ± 2781, Nonwalkable = 7096 ± 4674). Three environment parameters (ie, sidewalk, crosswalk, and path) were identified for future ABM simulation.

Conclusion:

ABM should provide a better understanding of PA behavior’s interaction with the environment, as illustrated using a real-life example. PA field should take advantage of ABM in future research.