Ettema, D. (2016). Runnable Cities: How does the running environment influence perceived attractiveness, restorativeness, and running frequency? Environment and Behavior, 48(9), 1127–1147. https://doi.org/10.1177/0013916515596364
This article investigates the impact of the running environment on perceived satisfaction, restoration, and running participation based on a questionnaire distributed to 1,581 novice runners. The most frequently experienced impediments on running routes are poor lighting, unleashed dogs, and encounters with cyclists and cars. Regression analyses reveal that attractiveness and restorativeness are positively associated with the quality of the running surface and running in parks or outside towns and negatively by running on public roads in town, by running in larger cities (>250,000 inhabitants), and by other road users. However, attractiveness and restorativeness of running routes play only a minor role in the decision of how frequently to run. Practical considerations (proximity, threats) appear to have a larger impact on running frequency. Importantly, the most frequently mentioned impediments (poor lighting, cars, unleashed dogs) do not affect running frequency, whereas infrequent impediments (threats by other people) significantly affect running frequency.
This article investigates the relationship between the running environment on runners' perceived satisfaction, restoration, and running participation. The most commonly experienced impediments on running routes are poor lighting, unleashed dogs, and encounters with cyclists, and cars. The quality of running surfaces may also matter, as reported comfortable running surfaces are associated with more frequent reported running, as well as higher perceived attractiveness and restorativeness.
Description of method used in the article
A survey of participants (N = 1,581, 80% women) participating in a six-week introductory running course across 133 locations in the Netherlands. Questions on the survey included running frequency, running companions, time of day, a score of training route environmental qualities, frequency of nuisances encountered, degree of attractiveness of training route, and degree of restorativeness of training route. Results are analyzed using hierarchical regression analysis of attractiveness and restorativeness on spatial variables, also ordinal logic regression models to assess the relationship between the built environment on running frequency.
Of some practical use if combined with other research