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PW 1797 Digitizing data collection for roadside observational studies: the process and experience
  1. Nino Paichadze,
  2. Amber Mehmood,
  3. Andres Vecino Ortiz,
  4. Abdulgafoor M Bachani,
  5. Adnan A Hyder
  1. Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA


The diversity of mobile-health (mHealth) applications has generated immense interest among researchers to test innovative ideas. To facilitate real time data collection in a roadside environment, mHealth tools were developed for population-level observational studies on three road safety risk factors: speed, helmet and seatbelt use. The digitization was employed to improve efficiency of the process through rapid aggregation and analysis, to enhance the quality of data through standardization, and to monitor adherence to protocols. The process involved: (1) selecting proper mobile data capture software application and a device; (2) setting up the server; (3) developing data collection forms; (4) deploying and pre-testing the forms; and (5) pilot-testing in the field. We selected KoBoToolbox software as it supports specific features of the data collection forms (capture of repeated vehicle-specific information). We use KoBoCollect app on Android tablets from where completed forms are sent to the secure cloud server. KoBoCollect forms were developed based on paper forms using XLM language. Digitizing has several advantages: KoBo supports data of all types (text, images, GPS); such features of the KoBo form as hints, constraints, required decrease the errors during data entry; XLM forms are easily customized and offer multilingual support; cloud server enables multi-location data collection; touch-screen Android tablets and simple KoBo app are easily adopted by users without special IT skills. mHealth-based apps enable access to ready-to-use data on hundreds-of-thousands of roadside observations in a short period of time. This eliminates the efforts of double data entry and data cleaning and thus proves to be cost-effective. On a larger scale, these benefits translate into improved quality and accuracy of data and overall efficiency of the process. This is especially important for the field of road safety where robust data essential for monitoring trends, developing effective interventions and assessing the progress, is required.

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