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Sound exposure data are central for any intervention study. In the case of utilitarian mobility, where studies cannot be conducted in controlled environments, exposure data are commonly self-reported. For short-term intervention studies, wearable devices with location sensors are increasingly employed. We aimed to combine self-reported and technically sensed mobility data, in order to provide more accurate and reliable exposure data for GISMO, a long-term intervention study. Through spatio-temporal data matching procedures, we are able to determine the amount of mobility for all modes at the best possible accuracy level. Self-reported data deviate ±10% from the corrected reference. Derived modal split statistics prove high compliance to the respective recommendations for the control group (CG) and the two intervention groups (IG-PT, IG-C). About 73.7% of total mileage was travelled by car in CG. This share was 10.3% (IG-PT) and 9.7% (IG-C), respectively, in the intervention groups. Commuting distances were comparable in CG and IG, but annual mean travel times differ between (Formula presented.) = 8,458 min (σ = 6,427 min) for IG-PT, (Formula presented.) = 8,444 min (σ = 5,961 min) for IG-C, and (Formula presented.) = 5,223 min (σ = 5,463 min) for CG. Seasonal variabilities of modal split statistics were observable. However, in IG-PT and IG-C no shift toward the car occurred during winter months. Although no perfect single-method solution for acquiring exposure data in mobility-related, naturalistic intervention studies exists, we achieved substantially improved results by combining two data sources, based on spatio-temporal matching procedures.
Loidl, M., Stutz, P., Fernandez Lapuente de Battre, M. D., Schmied, C., Reich, B., Bohm, P., … Niederseer, D. (2020). Merging self-reported with technically sensed data for tracking mobility behavior in a naturalistic intervention study. Insights from the GISMO study. Scandinavian Journal of Medicine and Science in Sports, 30(S1), 41–49. https://doi.org/10.1111/sms.13636