Merging self‐reported with technically sensed data for tracking mobility behavior in a naturalistic intervention study. Insights from the GISMO study

Abstract

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 x¯ = 8,458 min (σ = 6,427 min) for IG‐PT, x¯ = 8,444 min (σ = 5,961 min) for IG‐C, and x¯ = 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.

Health effects of active commuting to work: The available evidence before GISMO

“…The aim of this study was to synthesize and critically review the available intervention studies on active commuting and beneficial health effects before the publication of the GISMO study (Sareban et al, 27 Reich et al, 28 Niederseer et al, 29 Loidl et al, 30 Schmied et al, 31 Neumeier et al, 32 Fernandez La Puente de Battre et al, 33 Sareban et al, 34 Reich et al, 35 ). Only eight articles on a total of six studies were detected, and the researchers reported a diverse range of results relating to active commuting type, duration, and output 16‐23 .…”

Effects of active commuting on cardiovascular risk factors: GISMO—a randomized controlled feasibility study

“…Participants were active even during wintertime and did not use their cars more often than during summer. A detailed analysis on the seasonal aspects of active commuting in the GISMO study is provided elsewhere 26 . This is in contrast to Stigell et al 38 who found lower cycling frequencies during winter than during summer.…”

“…Participants were asked to wear these devices during 2 weeks at the beginning and 2 weeks toward the end of the intervention period. With this combined approach, the validity of the mobility documentation could have been increased substantially compared to self‐reported approaches 26 …”

Effects of active commuting to work for 12 months on cardiovascular risk factors and body composition

“…Each subject kept a diary to evaluate distances covered during active commuting. Additionally, subjects were given a wearable device with an integrated GPS tracker and optical heart rate sensor (Polar M200, Polar Electro Oy, Finland) for two weeks at the beginning as well as at the end of the study, to validate diary entries 17 …”