Transport mode choice and body mass index: Cross-sectional and longitudinal evidence from a European-wide study.

BACKGROUND: In the fight against rising overweight and obesity levels, and unhealthy urban environments, the renaissance of active mobility (cycling and walking as a transport mode) is encouraging. Transport mode has been shown to be associated to body mass index (BMI), yet there is limited longitudinal evidence demonstrating causality. We aimed to associate transport mode and BMI cross-sectionally, but also prospectively in the first ever European-wide longitudinal study on transport and health. METHODS: Data were from the PASTA project that recruited adults in seven European cities (Antwerp, Barcelona, London, Oerebro, Rome, Vienna, Zurich) to complete a series of questionnaires on travel behavior, physical activity levels, and BMI. To assess the association between transport mode and BMI as well as change in BMI we performed crude and adjusted linear mixed-effects modeling for cross-sectional (n = 7380) and longitudinal (n = 2316) data, respectively. RESULTS: Cross-sectionally, BMI was 0.027 kg/m2 (95%CI 0.015 to 0.040) higher per additional day of car use per month. Inversely, BMI was -0.010 kg/m2 (95%CI -0.020 to -0.0002) lower per additional day of cycling per month. Changes in BMI were smaller in the longitudinal within-person assessment, however still statistically significant. BMI decreased in occasional (less than once per week) and non-cyclists who increased cycling (-0.303 kg/m2, 95%CI -0.530 to -0.077), while frequent (at least once per week) cyclists who stopped cycling increased their BMI (0.417 kg/m2, 95%CI 0.033 to 0.802). CONCLUSIONS: Our analyses showed that people lower their BMI when starting or increasing cycling, demonstrating the health benefits of active mobility

The impact of black carbon (BC) on mode-specific galvanic skin response (GSR) as a measure of stress in urban environments

Previous research has shown that walking and cycling could help alleviate stress in cities, however there is poor knowledge on how specific microenvironmental conditions encountered during daily journeys may lead to varying degrees of stress experienced at that moment. We use objectively measured data and a robust causal inference framework to address this gap. Using a Bayesian Doubly Robust (BDR) approach, we find that black carbon exposure statistically significantly increases stress, as measured by Galvanic Skin Response (GSR), while cycling and while walking. Augmented Outcome Regression (AOR) models indicate that greenspace exposure and the presence of walking or cycling infrastructure could reduce stress. None of these effects are statistically significant for people in motorized transport. These findings add to a growing evidence-base on health benefits of policies aimed at decreasing air pollution, improving active travel infrastructure and increasing greenspace in cities

Wearable sensors for personal monitoring and estimation of inhaled traffic-related air pollution: evaluation of methods

Physical activity and ventilation rates have an effect on an individual’s dose and may be important to consider in exposure–response relationships; however, these factors are often ignored in environmental epidemiology studies. The aim of this study was to evaluate methods of estimating the inhaled dose of air pollution and understand variability in the absence of a true gold standard metric. Five types of methods were identified: (1) methods using (physical) activity types, (2) methods based on energy expenditure, METs (metabolic equivalents of task), and oxygen consumption, (3) methods based on heart rate or (4) breathing rate, and (5) methods that combine heart and breathing rate. Methods were compared using a real-life data set of 122 adults who wore devices to track movement, black carbon air pollution, and physiological health markers for 3 weeks in three European cities. Different methods for estimating minute ventilation performed well in relative terms with high correlations among different methods, but in absolute terms, ignoring increased ventilation during day-to-day activities could lead to an underestimation of the daily dose by a factor of 0.08–1.78. There is no single best method, and a multitude of methods are currently being used to approximate the dose. The choice of a suitable method for determining the dose in future studies will depend on both the size and the objectives of the study

Evaluation of Different Recruitment Methods: Longitudinal, Web-Based, Pan-European Physical Activity Through Sustainable Transport Approaches (PASTA) Project

BACKGROUND: Sufficient sample size and minimal sample bias are core requirements for empirical data analyses. Combining opportunistic recruitment with a Web-based survey and data-collection platform yields new benefits over traditional recruitment approaches. OBJECTIVE: This paper aims to report the success of different recruitment methods and obtain data on participants' characteristics, participation behavior, recruitment rates, and representativeness of the sample. METHODS: A longitudinal, Web-based survey was implemented as part of the European PASTA (Physical Activity through Sustainable Transport Approaches) project, between November 2014 and December 2016. During this period, participants were recruited from 7 European cities on a rolling basis. A standardized guide on recruitment strategy was developed for all cities, to reach a sufficient number of adult participants. To make use of the strengths and minimize weakness, a combination of different opportunistic recruitment methods was applied. In addition, the random sampling approach was applied in the city of Örebro. To reduce the attrition rate and improve real-time monitoring, the Web-based platform featured a participant's and a researchers' user interface and dashboard. RESULTS: Overall, 10,691 participants were recruited; most people found out about the survey through their workplace or employer (2300/10691, 21.51%), outreach promotion (2219/10691, 20.76%), and social media (1859/10691, 17.39%). The average number of questionnaires filled in per participant varied significantly between the cities (P<.001), with the highest number in Zurich (11.0, SE 0.33) and the lowest in Örebro (4.8, SE 0.17). Collaboration with local organizations, the use of Facebook and mailing lists, and direct street recruitment were the most effective approaches in reaching a high share of participants (P<.001). Considering the invested working hours, Facebook was one of the most time-efficient methods. Compared with the cities' census data, the composition of study participants was broadly representative in terms of gender distribution; however, the study included younger and better-educated participants. CONCLUSIONS: We observed that offering a mixed recruitment approach was highly effective in achieving a high participation rate. The highest attrition rate and the lowest average number of questionnaires filled in per participant were observed in Örebro, which also recruited participants through random sampling. These findings suggest that people who are more interested in the topic are more willing to participate and stay in a survey than those who are selected randomly and may not have a strong connection to the research topic. Although direct face-to-face contacts were very effective with respect to the number of recruited participants, recruiting people through social media was not only effective but also very time efficient. The collected data are based on one of the largest recruited longitudinal samples with a common recruitment strategy in different European cities

Measuring spatial inequalities in the access to station-based bike-sharing in Barcelona using an Adapted Affordability Index

Bike-sharing schemes have been spreading globally during the last years. These should be publicly available schemes, servicing all groups of population. But the literature shows there are underrepresented population groups amongst their users. The physical access to bike-sharing stations and the supporting network of cycle lanes seems to influence the use of the schemes, especially of lower-income communities. This paper applies an index as a tool to evaluate spatial inequalities in the access to station-based bike-sharing schemes and the cycle network. The index aggregates several variables related to the population level of affordability, including mobility-related variables. The Adapted Affordability Index was inspired in an existing one, produced by the city council, in an attempt to ensure its usability for policymaking. The index was calculated and applied to the case of the bike-sharing scheme in Barcelona, at the geographical level of census tracts. The index shows a strong correlation with income, a variable not always publicly available at such a small geographical level. This study shows that there are inequalities in spatial access to the Barcelona bike-sharing scheme; the wealthier the population group, the more they have access to cycling infrastructure, especially to bike-sharing stations. The bike-sharing trend is accentuated in the hilly areas of the city. The successful application of the Adapted Affordability Index to the city of Barcelona is a promising avenue to provide a robust and easy to use bike-sharing spatial equity evaluation tool for policymaking

The effects of traveling in different transport modes on galvanic skin response (GSR) as a measure of stress: an observational study

Background Stress is one of many ailments associated with urban living, with daily travel a potential major source. Active travel, nevertheless, has been associated with lower levels of stress compared to other modes. Earlier work has relied on self-reported measures of stress, and on study designs that limit our ability to establish causation. Objectives To evaluate effects of daily travel in different modes on an objective proxy measure of stress, the galvanic skin response (GSR). Methods We collected data from 122 participants across 3 European cities as part of the Physical Activity through Sustainable Transport Approaches (PASTA) study, including: GSR measured every minute alongside confounders (physical activity, near-body temperature) during three separate weeks covering 3 seasons; sociodemographic and travel information through questionnaires. Causal relationships between travel in different modes (the “treatment”) and stress were established by using a propensity score matching (PSM) approach to adjust for potential confounding and estimating linear mixed models (LMM) with individuals as random effects to account for repeated measurements. In three separate analyses, we compared GSR while cycling to not cycling, then walking to not walking then motorized (public or private) travel to any activity other than motorized travel. Results Depending on LMM formulations used, cycling reduces 1-minute GSR by 5.7% [95% CI: 2.0–16.9%] to 11.1% [95% CI: 5.0–24.4%] compared to any other activity. Repeating the analysis for other modes we find that: walking is also beneficial, reducing GSR by 3.9% [95% CI: 1.4–10.7%] to 5.7% [95% CI: 2.6–12.3%] compared to any other activity; motorized mode (private or public) in reverse increases GSR by up to 1.1% [95% CI: 0.5–2.9%]. Discussion Active travel offers a welcome way to reduce stress in urban dwellers’ daily lives. Stress can be added to the growing number of evidence-based reasons for promoting active travel in cities

Impacts of study design on sample size, participation bias, and outcome measurement: A case study from bicycling research

Introduction Measuring bicycling behaviour is critical to bicycling research. A common study design question is whether to measure bicycling behaviour once (cross-sectional) or multiple times (longitudinal). The Physical Activity through Sustainable Transport Approaches (PASTA) project is a longitudinal cohort study of over 10,000 participants from seven European cities over two years. We used PASTA data as a case study to investigate how measuring once or multiple times impacted three factors: a) sample size b) participation bias and c) accuracy of bicycling behaviour estimates. Methods We compared two scenarios: i) as if only the baseline data were collected (cross-sectional approach) and ii) as if the baseline plus repeat follow-ups were collected (longitudinal approach). We compared each approach in terms of differences in sample size, distribution of sociodemographic characteristics, and bicycling behaviour. In the cross-sectional approach, we measured participants long-term bicycling behaviour by asking for recall of typical weekly habits, while in the longitudinal approach we measured by taking the average of bicycling reported for each 7-day period. Results Relative to longitudinal, the cross-sectional approach provided a larger sample size and slightly better representation of certain sociodemographic groups, with worse estimates of long-term bicycling behaviour. The longitudinal approach suffered from participation bias, especially the drop-out of more frequent bicyclists. The cross-sectional approach under-estimated the proportion of the population that bicycled, as it captured ‘typical’ behaviour rather than 7-day recall. The magnitude and directionality of the difference between typical weekly (cross-sectional approach) and the average 7-day recall (longitudinal approach) varied depending on how much bicycling was initially reported. Conclusions In our case study we found that measuring bicycling once, resulted in a larger sample with better representation of sociodemographic groups, but different estimates of long-term bicycling behaviour. Passive detection of bicycling through mobile apps could be a solution to the identified issues

Concern over health effects of air pollution is associated to NO<inf>2</inf>in seven European cities

Subjective perception of air pollution is important and can have impacts on health in its own rights, can lead to protective behaviour, or it can be leveraged to engage citizens and stakeholders in support of cleaner air policies. The aim of the current analysis was to examine associations between level of concern over health effects of air pollution and personal and environmental factors. In seven European cities, 7622 adult participants were recruited to complete an online questionnaire on travel and physical activity behaviour, perceptions and attitudes on active mobility and the environment, and sociodemographics. Air pollution at the home address was determined using Europe-wide PM2.5and NO2land use regression models. Mixed effects logistic regression was used to model concern over air pollution (worried versus not worried; city as random effect). Fifty-eight percent of participants were worried over health effects of air pollution with large differences across cities (Antwerp 78%, Barcelona 81%, London 64%, Orebro 11%, Rome 72%, Vienna 43%, Zurich 33%). Linking mean modelled air pollution to mean level of concern per city gave a good correlation for NO2(r2= 0.75), and a lower correlation for PM2.5(r2= 0.49). In the regression model, sex, having children in the household, levels of physical activity, and NO2at the home address were significantly linked to individual concern over health effects of air pollution. We found that NO2but not PM2.5at the home address was associated with concern over health effects of air pollution

Concern over health effects of air pollution is associated to NO2 in seven European cities

Subjective perception of air pollution is important and can have impacts on health in its own rights, can lead to protective behaviour, or it can be leveraged to engage citizens and stakeholders in support of cleaner air policies. The aim of the current analysis was to examine associations between level of concern over health effects of air pollution and personal and environmental factors. In seven European cities, 7622 adult participants were recruited to complete an online questionnaire on travel and physical activity behaviour, perceptions and attitudes on active mobility and the environment, and sociodemographics. Air pollution at the home address was determined using Europe-wide PM2.5 and NO2 land use regression models. Mixed effects logistic regression was used to model concern over air pollution (worried versus not worried; city as random effect). Fifty-eight percent of participants were worried over health effects of air pollution with large differences across cities (Antwerp 78%, Barcelona 81%, London 64%, Orebro 11%, Rome 72%, Vienna 43%, Zurich 33%). Linking mean modelled air pollution to mean level of concern per city gave a good correlation for NO2 (r2 = 0.75), and a lower correlation for PM2.5 (r2 = 0.49). In the regression model, sex, having children in the household, levels of physical activity, and NO2 at the home address were significantly linked to individual concern over health effects of air pollution. We found that NO2 but not PM2.5 at the home address was associated with concern over health effects of air pollution

The impact of black carbon (BC) on mode-specific galvanic skin response (GSR) as a measure of stress in urban environments

Previous research has shown that walking and cycling could help alleviate stress in cities, however there is poor knowledge on how specific microenvironmental conditions encountered during daily journeys may lead to varying degrees of stress experienced at that moment. We use objectively measured data and a robust causal inference framework to address this gap. Using a Bayesian Doubly Robust (BDR) approach, we find that black carbon exposure statistically significantly increases stress, as measured by Galvanic Skin Response (GSR), while cycling and while walking. Augmented Outcome Regression (AOR) models indicate that greenspace exposure and the presence of walking or cycling infrastructure could reduce stress. None of these effects are statistically significant for people in motorized transport. These findings add to a growing evidence-base on health benefits of policies aimed at decreasing air pollution, improving active travel infrastructure and increasing greenspace in cities