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Concept: Commuting

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Objective To investigate the association between active commuting and incident cardiovascular disease (CVD), cancer, and all cause mortality.Design Prospective population based study. Setting UK Biobank.Participants 263 450 participants (106 674 (52%) women; mean age 52.6), recruited from 22 sites across the UK. The exposure variable was the mode of transport used (walking, cycling, mixed mode v non-active (car or public transport)) to commute to and from work on a typical day.Main outcome measures Incident (fatal and non-fatal) CVD and cancer, and deaths from CVD, cancer, or any causes.Results 2430 participants died (496 were related to CVD and 1126 to cancer) over a median of 5.0 years (interquartile range 4.3-5.5) follow-up. There were 3748 cancer and 1110 CVD events. In maximally adjusted models, commuting by cycle and by mixed mode including cycling were associated with lower risk of all cause mortality (cycling hazard ratio 0.59, 95% confidence interval 0.42 to 0.83, P=0.002; mixed mode cycling 0.76, 0.58 to 1.00, P<0.05), cancer incidence (cycling 0.55, 0.44 to 0.69, P<0.001; mixed mode cycling 0.64, 0.45 to 0.91, P=0.01), and cancer mortality (cycling 0.60, 0.40 to 0.90, P=0.01; mixed mode cycling 0.68, 0.57 to 0.81, P<0.001). Commuting by cycling and walking were associated with a lower risk of CVD incidence (cycling 0.54, 0.33 to 0.88, P=0.01; walking 0.73, 0.54 to 0.99, P=0.04) and CVD mortality (cycling 0.48, 0.25 to 0.92, P=0.03; walking 0.64, 0.45 to 0.91, P=0.01). No statistically significant associations were observed for walking commuting and all cause mortality or cancer outcomes. Mixed mode commuting including walking was not noticeably associated with any of the measured outcomes.Conclusions Cycle commuting was associated with a lower risk of CVD, cancer, and all cause mortality. Walking commuting was associated with a lower risk of CVD independent of major measured confounding factors. Initiatives to encourage and support active commuting could reduce risk of death and the burden of important chronic conditions.

Concepts: Epidemiology, Disease, Statistics, Death, Median, Sustainable transport, Bicycle, Commuting

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People with low income often experience higher exposures to air pollutants. We compared the exposure to particulate matter (PM1, PM2.5 and PM10), Black Carbon (BC) and ultrafine particles (PNCs; 0.02-1μm) for typical commutes by car, bus and underground from 4 London areas with different levels of income deprivation (G1 to G4, from most to least deprived). The highest BC and PM concentrations were found in G1 while the highest PNC in G3. Lowest concentrations for all pollutants were observed in G2. We found no systematic relationship between income deprivation and pollutant concentrations, suggesting that differences between transport modes are a stronger influence. The underground showed the highest PM concentrations, followed by buses and a much lower concentrations in cars. BC concentrations in the underground were overestimated due to Fe interference. BC concentrations were also higher in buses than cars because of a lower infiltration of outside pollutants into the car cabin. PNCs were highest in buses, closely followed by cars, but lowest in underground due to the absence of combustion sources. Concentration in the road modes (car and bus) were governed by the traffic conditions (such as traffic flow interruptions) at the specific road section. Exposures were reduced in trains with non-openable windows compared to those with openable windows. People from less income-deprived areas have a predominant use of car, receiving the lowest doses (RDD<1μgh(-1)) during commute but generating the largest emissions per commuter. Conversely, commuters from high income-deprived areas have a major reliance on the bus, receiving higher exposures (RDD between 1.52 and 3.49μgh(-1)) while generating less emission per person. These findings suggest an aspect of environmental injustice and a need to incorporate the socioeconomic dimension in life-course exposure assessments.

Concepts: Pollution, Particulate, Road, Air pollution, Diesel particulate matter, Bus, Bicycle, Commuting

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Active commuting - walking and bicycling for travel to and/or from work or educational addresses - may facilitate daily, routine physical activity. Several studies have investigated the relationship between active commuting and commuting stress; however, there are no studies examining the relationship between solely bicycle commuting and perceived stress, or studies that account for environmental determinants of bicycle commuting and stress. The current study evaluated the relationship between bicycle commuting, among working or studying adults in a dense urban setting, and perceived stress.

Concepts: Cycling, Automobile, Sustainable transport, Bicycle, Bicycle helmet, Utility cycling, Commuting, Human-powered transport

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We measured real-time exposure to PM(2.5), ultrafine PM (particle number) and carbon monoxide (CO) for commuting workers school children, and traffic police, in Jakarta, Indonesia. In total, we measured exposures for 36 individuals covering 93days. Commuters in private cars experienced mean (st dev) exposures of 22 (9.4) ppm CO, 91 (38) μg/m(3)PM(2.5), and 290 (150)×10(3) particlescm(-3). Mean concentrations were higher in public transport than in private cars for PM(2.5) (difference in means: 22%) and particle counts (54%), but not CO, likely reflecting in-vehicle particle losses in private cars owing to air-conditioning. However, average commute times were longer for private car commuters than public transport commuters (in our sample, 24% longer: 3.0 vs. 2.3h per day). Commute and traffic-related exposures experienced by Jakarta residents are among the highest in the world, owing to high on-road concentrations and multi-hour commutes.

Concepts: Carbon dioxide, Mode, Arithmetic mean, Carbon monoxide, Mean, Automobile, Commuting, Park and ride

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Introduction The ‘Safety in Numbers’ (SiN) phenomenon refers to a decline of injury risk per time or distance exposed as use of a mode increases. It has been demonstrated for cycling using cross-sectional data, but little evidence exists as to whether the effect applies longitudinally -that is, whether changes in cycling levels correlate with changes in per-cyclist injury risks.Methods This paper examines cross-sectional and longitudinal SiN effects in 202 local authorities in Britain, using commuting data from 1991, 2001 and 2011 censuses plus police -recorded data on ‘killed and seriously injured’ (KSI) road traffic injuries. We modelled a log-linear relationship between number of injuries and number of cycle commuters. Second, we conducted longitudinal analysis to examine whether local authorities where commuter cycling increased became safer (and vice versa).Results The paper finds a cross-sectional SiN effect exists in the 1991, 2001 and 2011 censuses. The longitudinal analysis also found a SiN effect, that is, places where cycling increased were more likely to become safer than places where it had declined. Finally, these longitudinal results are placed in the context of changes in pedestrian, cyclist and motorist safety. While between 1991 and 2001 all modes saw declines in KSI risk (37% for pedestrians, 36% for cyclists and 27% for motor vehicle users), between 2001 and 2011 pedestrians and motorists saw even more substantial declines (41% and 49%), while risk for cyclists increased by 4%.Conclusion The SiN mechanism does seem to operate longitudinally as well as cross-sectionally. However, at a national level between 2001-11 it co-existed with an increase in cyclist injury risk both in absolute terms and in relation to other modes.

Concepts: Injury, Physical trauma, Traffic, Driving, Automobile, Pedestrian, Bicycle, Commuting

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Although commuting provides an opportunity for incorporating physical activity into daily routines, little is known about the effect of active commuting upon depressive symptoms. This study aimed to determine whether changes in commute mode are associated with differences in the severity of depressive symptoms in working adults. Commuters were selected from the UK Biobank cohort if they completed ≥2 assessment centre visits between 2006 and 2016. Modes of travel to work were self-reported at each visit. Participants were categorised as ‘inactive’ (car only) or ‘active’ commuters (any other mode(s), including walking, cycling and public transport). Transitions between categories were defined between pairs of visits. The severity of depressive symptoms was defined using the two-item Patient Health Questionnaire (PHQ-2). Scores were derived between zero and six. Higher values indicate more severe symptoms. Separate analyses were conducted in commuters who were asymptomatic (zero score) and symptomatic (non-zero score) at baseline. The analytical sample comprised 5474 participants aged 40-75 at baseline with a mean follow-up of 4.65 years. Asymptomatic commuters who transitioned from inactive to active commuting reported less severe symptoms at follow-up than those who remained inactive (β -0.10, 95% CI [-0.20, 0.00]; N = 3145). A similar but non-significant relationship is evident among commuters with pre-existing symptoms (β -0.60, 95% CI [-1.27, 0.08]; N = 1078). After adjusting for transition category, longer commutes at baseline were associated with worse depressive symptoms at follow-up among symptomatic participants. Shifting from exclusive car use towards more active commuting may help prevent and attenuate depressive symptoms in working adults.

Concepts: Cycling, Travel, Transportation, Train, Bicycle, Commuting, Mode of transport, Park and ride

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Active commuting may help to increase adults' physical activity levels. However, estimates of its energy cost are derived from a small number of studies which are laboratory-based or use self-reported measures. Adults working in Cambridge (UK) recruited through a predominantly workplace-based strategy wore combined heart rate and movement sensors and global positioning system (GPS) devices for one week, and completed synchronous day-by-day travel diaries in 2010 and 2011. Commuting journeys were delineated using GPS data, and metabolic intensity (standard metabolic equivalents; MET) was derived and compared between journey types using mixed-effects linear regression. 182 commuting journeys were included in the analysis. Median intensity was 1.28 MET for car journeys; 1.67 MET for bus journeys; 4.61 MET for walking journeys; 6.44 MET for cycling journeys; 1.78 MET for journeys made by car in combination with walking; and 2.21 MET for journeys made by car in combination with cycling. The value for journeys made solely by car was significantly lower than those for all other journey types (p<0.04). On average, 20% of the duration of journeys incorporating any active travel (equating to 8 minutes) was spent in moderate-to-vigorous physical activity (MVPA). We have demonstrated how GPS and activity data from a free-living sample can be used simultaneously to provide objective estimates of commuting energy expenditure. On average, incorporating walking or cycling into longer journeys provided over half the weekly recommended activity levels from the commute alone. This may be an efficient way of achieving physical activity guidelines and improving population health.

Concepts: Navigation, Global Positioning System, Global navigation satellite system, GPS, Positioning system, Commuting, Wide Area Augmentation System, Navigational equipment

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Understanding the drivers of urban mobility is vital for epidemiology, urban planning, and communication networks. Human movements have so far been studied by observing people’s positions in a given space and time, though most recent models only implicitly account for expected costs and returns for movements. This paper explores the explicit impact of cost and network topology on mobility dynamics, using data from 2 city-wide public bicycle share systems in the USA. User mobility is characterized through the distribution of trip durations, while network topology is characterized through the pairwise distances between stations and the popularity of stations and routes. Despite significant differences in station density and physical layout between the 2 cities, trip durations follow remarkably similar distributions that exhibit cost sensitive trends around pricing point boundaries, particularly with long-term users of the system. Based on the results, recommendations for dynamic pricing and incentive schemes are provided to positively influence mobility patterns and guide improved planning and management of public bicycle systems to increase uptake.

Concepts: City, Dynamics, Computer network, Urban decay, Urban planning, Network topology, Commuting, Grid plan

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The societal need for a mobile workforce increases time spent commuting and thus also the total workday. How this affects individual well-being and social life is, however, surprisingly little known. We investigated the relation between commuting time and mode, and social participation and general trust in other people as measures of social capital, using data from public health surveys conducted in 2004 and 2008 in Scania, Sweden: in all, 21,088 persons ages 18 to 65 and working at least 30 hr per week. Commuting by car was significantly associated with a higher prevalence of low social participation and low general trust compared with active commuting, and the association increased with the duration of commuting time. In contrast, public commuting was not significantly associated with decreased social capital measures except among long-duration commuters, who reported lower social participation. The overall pattern was similar for men and for women.

Concepts: Sociology, Sweden, Society, Social capital, Commuting, Social, Telecommuting

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BACKGROUND: Perceptions of the environment appear to be associated with walking and cycling. We investigated the reasons for walking and cycling to or from work despite reporting an unsupportive route environment in a sample of commuters. METHODS: This mixed-method analysis used data collected as part of the Commuting and Health in Cambridge study. 1164 participants completed questionnaires which assessed the travel modes used and time spent on the commute and the perceived environmental conditions on the route to work. A subset of 50 also completed qualitative interviews in which they discussed their experiences of commuting. Participants were included in this analysis if they reported unsupportive conditions for walking or cycling on their route (e.g. heavy traffic) in questionnaires, walked or cycled all or part of the journey to work, and completed qualitative interviews. Using content analysis of these interviews, we investigated their reasons for walking or cycling. RESULTS: 340 participants reported walking or cycling on the journey to work despite unsupportive conditions, of whom 15 also completed qualitative interviews. From these, three potential explanations emerged. First, some commuters found strategies for coping with unsupportive conditions. Participants described knowledge of the locality and opportunities for alternative routes more conducive to active commuting, as well as their cycling experience and acquired confidence to cycle in heavy traffic. Second, some commuters had other reasons for being reliant on or preferring active commuting despite adverse environments, such as childcare arrangements, enjoyment, having more control over their journey time, employers' restrictions on car parking, or the cost of petrol or parking. Finally, some survey respondents appeared to have reported not their own environmental perceptions but those of others such as family members or ‘the public’, partly to make a political statement regarding the adversity of active commuting in their setting. CONCLUSIONS: Participants report walking and cycling to work despite adverse environmental conditions. Understanding this resilience might be just as important as investigating ‘barriers’ to cycling. These findings suggest that developing knowledge of safe walking and cycling routes, improving cycling confidence and restricting workplace parking may help to encourage walking and cycling to and from work.

Concepts: Environment, Natural environment, Environmental science, Automobile, Bicycle, Commuting, Park and ride, Journey to work