Comparing bicyclists who use smartphone apps to record rides with those who do not: implications for representativeness and selection bias.

Increasing population levels of cycling has the potential to improve public health by increasing physical activity. As cyclists have begun using smartphone apps to record trips, researchers have begun using data generated from these apps to monitor cycling levels and evaluate cycling-related interventions. The goal of this research is to assess the extent to which app-using cyclists represent the broader cycling population to inform whether use of app-generated data in bike-infrastructure intervention research may bias effect estimates. Using an intercept survey, we asked 95 cyclists throughout Atlanta, Georgia, USA about their use of GPS-based smartphone apps to record bike rides. We asked respondents to draw their common bike routes, from which we assessed the proportion of ridership captured by app-generated data sources overall and on types of bicycle infrastructure. We measured socio-demographics and bike-riding habits, including cyclist type, ride frequency, and most common ride purpose. Cyclists who used smartphone apps to record their bike rides differed from those who did not across some but not all socio-demographic characteristics and differed in several bike-riding attributes. App users rode more frequently, self-classified as stronger riders, and rode proportionately more for leisure. Although groups had similar infrastructure preferences at the person level, differences appeared at the level of the estimated ride, where, for example, the proportion of ridership captured by an app on protected bike lanes was lower than the overall proportion of ridership captured. A sample calculation illustrated how such differences may induce selection bias in smartphone-data-based research on infrastructure and motor-vehicle-cyclist crash risk. We illustrate in the sample scenario how the bias can be corrected, assuming inverse-probability-of-selection weights can be accurately specified. The presented bias-adjustment method may be useful for future bike-infrastructure research using smartphone-generated data.