Impacts of study design on sample size, participation bias, and outcome measurement: A case study from bicycling research

Michael Branion-Calles, Meghan Winters, Trisalyn Nelson, Audrey de Nazelle, Luc Int Panis, Ione Avila-Palencia, Esther Anaya-Boig, David Rojas-Rueda, Evi Dons, Thomas Götschi

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


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.

Original languageEnglish (US)
Article number100651
JournalJournal of Transport and Health
StatePublished - Dec 2019


  • Bias
  • Bicycling
  • Cross-sectional
  • Exposure
  • Longitudinal
  • Study design
  • Survey participation

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Transportation
  • Pollution
  • Safety Research
  • Health Policy
  • Public Health, Environmental and Occupational Health


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