Geographical Information Support for Health Mobility-Promoting active commuting as a novel option to counteract sedentary lifestyle

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

Sedentary lifestyle is a major modifiable risk factor for many chronic diseases. Global guidelines recommend for maintaining health in adults, at least 150 minutes of moderate intensity of physical activity throughout the week, but compliance is insufficient and health problems arise. One obvious way to overcome this is to integrate physical activity into the daily routine for example by active commuting to work. Scientific evidence, however, is scarce and therefore we set out to perform this systematic review of the available literature to improve understanding of the efficiency of active commuting initiatives on health. Literature searches were performed in PubMed and Cochrane database. Altogether, 37 studies were screened. Thereof, eight publications were reviewed, which included 555 participants. The mean study duration of the reviewed research was 36 ± 26 (8-72) weeks. Overall, active commuting in previously untrained subjects of both sexes significantly improved exercise capacity, maximal power, blood pressure, lipid parameters including cholesterol, high-density lipoprotein, and waist circumference. Improvement was independent of the type of active commuting. Despite relatively few studies that were previously performed, this review revealed that active commuting has health beneficial effects comparable to those of moderate exercise training.

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

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.