Agent-based modeling of physical activity behavior and environmental correlations: an introduction and illustration.

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.

Multilevel modeling of walking behavior: advances in understanding the interactions of people, place, and time.

It has become increasingly clear that the influences on walking as well as other forms of regular physical activity are complex and require an increased understanding of factors across multiple levels of influence. Ecological frameworks have provided the field with a heuristic means of capturing potential impacts on behavior across diverse domains, including personal, behavioral, social or cultural, and environmental. We discuss advances in both understanding and applying this framework through the inclusion of previously ignored dimensions of impact (e.g., time), the application of state-of-the-art statistical methods for understanding interactions among multiple domains (e.g., signal detection), and the development of computer technologies (e.g., agent-based modeling) aimed at simulating the complex relationships between multiple levels of impact and walking behavior. We conclude with suggestions for future research in this emerging field.