title: Assessing the potential for bias in direct observation of adult commuter cycling and helmet use.
abstract: OBJECTIVE
  Bicycling and helmet surveillance, research, and programme evaluation depend on accurate measurement by direct observation, but it is unclear whether weather and other exogenous factors introduce bias into observed counts of cyclists and helmet use.
  
  
  METHODS
  To address this issue, a time series was created of cyclists observed at two observation points in Washington, DC, at peak commuting times and locations between September 2012 and February 2013. Using multiple linear regression with Newey-West SEs to account for possible serial correlation, the association between various factors and cyclist counts and helmet use was investigated.
  
  
  RESULTS
  The number of cyclists observed per 1 h session was significantly associated with predicted daily high temperature, chance of rain, and actual rain. Additionally, fewer cyclists were observed on Fridays. Helmet use was significantly lower during evening commutes than morning and also lower on Fridays. Helmet use was not associated with weather variables. Controlling for observable cyclists characteristics weakened the association between helmet use and the time of day and day of the week, but it did not eliminate that association.
  
  
  CONCLUSIONS
  Direct observation to measure commuter cycling trends or evaluate interventions should control for weather and day of week. Measurement of helmet use is unlikely to be meaningfully biased by weather factors, but time of day and day of week should be taken into account. Failing to control for these factors could lead to significant bias in assessments of the level of, and trends in, commuter cycling and helmet use.
authors.affiliation: Department of Health Systems Administration, Georgetown University, Washington, DC, USA.
authors.forename: John D
authors.initials: JD
authors.lastname: Kraemer
authors.affiliation: Department of Health Systems Administration, Georgetown University, Washington, DC, USA.
authors.forename: Heather N
authors.initials: HN
authors.lastname: Zaccaro
authors.affiliation: Department of Health Systems Administration, Georgetown University, Washington, DC, USA.
authors.forename: Jason S
authors.initials: JS
authors.lastname: Roffenbender
authors.affiliation: Department of Health Systems Administration, Georgetown University, Washington, DC, USA.
authors.forename: Sabeeh A
authors.initials: SA
authors.lastname: Baig
authors.affiliation: Department of Health Systems Administration, Georgetown University, Washington, DC, USA.
authors.forename: Megan E
authors.initials: ME
authors.lastname: Graves
authors.affiliation: Department of Health Systems Administration, Georgetown University, Washington, DC, USA.
authors.forename: Katherine J
authors.initials: KJ
authors.lastname: Hauler
authors.affiliation: Department of Health Systems Administration, Georgetown University, Washington, DC, USA.
authors.forename: Aamir N
authors.initials: AN
authors.lastname: Hussain
authors.affiliation: Department of Health Systems Administration, Georgetown University, Washington, DC, USA.
authors.forename: Faith E
authors.initials: FE
authors.lastname: Mulroy
mesh_terms: D000063:Accidents, Traffic / Q000331:legislation & jurisprudence / Q000517:prevention & control / Q000706:statistics & numerical data*; D000328:Adult; D001642:Bicycling / Q000293:injuries / Q000331:legislation & jurisprudence / Q000706:statistics & numerical data*; D006259:Craniocerebral Trauma / Q000453:epidemiology / Q000517:prevention & control*; D005260:Female; D006260:Head Protective Devices / Q000706:statistics & numerical data*; D006801:Humans; D008297:Male; D009042:Motivation; D015588:Observer Variation; D011159:Population Surveillance; D015397:Program Evaluation; D012307:Risk Factors; D013997:Time Factors; D014186:Transportation; D014887:Weather