1. Impacts of study design on sample size, participation bias, and outcome measurement: A case study from bicycling research
- Author
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Trisalyn A. Nelson, Luc Int Panis, Michael Branion-Calles, Evi Dons, Thomas Götschi, Esther Anaya-Boig, Ione Avila-Palencia, David Rojas-Rueda, Meghan Winters, Audrey de Nazelle, and Commission of the European Communities
- Subjects
Technology ,CYCLISTS ,Population ,Transportation ,Sample (statistics) ,Exposure ,1117 Public Health and Health Services ,03 medical and health sciences ,1507 Transportation and Freight Services ,0302 clinical medicine ,Bias ,Cross-sectional ,Survey participation ,0502 economics and business ,11. Sustainability ,030212 general & internal medicine ,Longitudinal cohort ,Safety, Risk, Reliability and Quality ,Prospective cohort study ,education ,Public, Environmental & Occupational Health ,050210 logistics & transportation ,education.field_of_study ,Science & Technology ,Participation bias ,Recall ,Health Policy ,05 social sciences ,Public Health, Environmental and Occupational Health ,Study design ,Pollution ,Outcome (probability) ,Bicycling ,PROSPECTIVE COHORT ,Sample size determination ,1205 Urban and Regional Planning ,Longitudinal ,FOLLOW-UP ,Psychology ,Life Sciences & Biomedicine ,Safety Research ,Demography - 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.
- Published
- 2019