@article{ref1,
title="Impacts of study design on sample size, participation bias, and outcome measurement: a case study from bicycling research",
journal="Journal of transport and health",
year="2019",
author="Branion-Calles, Michael and Winters, Meghan and Nelson, Trisalyn and de Nazelle, Audrey and Panis, Luc Int and Avila-Palencia, Ione and Anaya-Boig, Esther and Rojas-Rueda, David and Dons, Evi and Gotschi, Thomas",
volume="15",
number="",
pages="e100651-e100651",
abstract="Introduction Measuring bicycling behaviour is critical to bicycling research. A common study design question is whether to measure bicycling behaviour once (cross-sectional) or multiple times (longitudinal). The Physical Activity through Sustainable Transport Approaches (PASTA) project is a longitudinal cohort study of over 10,000 participants from seven European cities over two years. We used PASTA data as a case study to investigate how measuring once or multiple times impacted three factors: a) sample size b) participation bias and c) accuracy of bicycling behaviour estimates.   Methods We compared two scenarios: i) as if only the baseline data were collected (cross-sectional approach) and ii) as if the baseline plus repeat follow-ups were collected (longitudinal approach). We compared each approach in terms of differences in sample size, distribution of sociodemographic characteristics, and bicycling behaviour. In the cross-sectional approach, we measured participants long-term bicycling behaviour by asking for recall of typical weekly habits, while in the longitudinal approach we measured by taking the average of bicycling reported for each 7-day period.   Results Relative to longitudinal, the cross-sectional approach provided a larger sample size and slightly better representation of certain sociodemographic groups, with worse estimates of long-term bicycling behaviour. The longitudinal approach suffered from participation bias, especially the drop-out of more frequent bicyclists. The cross-sectional approach under-estimated the proportion of the population that bicycled, as it captured 'typical' behaviour rather than 7-day recall. The magnitude and directionality of the difference between typical weekly (cross-sectional approach) and the average 7-day recall (longitudinal approach) varied depending on how much bicycling was initially reported.   Conclusions In our case study we found that measuring bicycling once, resulted in a larger sample with better representation of sociodemographic groups, but different estimates of long-term bicycling behaviour. Passive detection of bicycling through mobile apps could be a solution to the identified issues.<p /> <p>Language: en</p>",
language="en",
issn="2214-1405",
doi="10.1016/j.jth.2019.100651",
url="http://dx.doi.org/10.1016/j.jth.2019.100651"
}