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.
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.
Authors: Yong Yang; Amy H Auchincloss; Daniel A Rodriguez; Daniel G Brown; Rick Riolo; Ana V Diez-Roux Journal: Comput Environ Urban Syst Date: 2015-05-01
Authors: Kosuke Tamura; Steven D Langerman; Joniqua N Ceasar; Marcus R Andrews; Malhaar Agrawal; Tiffany M Powell-Wiley Journal: Curr Cardiovasc Risk Rep Date: 2019-03-08
Authors: Francesca L Pontin; Victoria L Jenneson; Michelle A Morris; Graham P Clarke; Nik M Lomax Journal: Int J Behav Nutr Phys Act Date: 2022-09-14 Impact factor: 8.915