Physical activity accrued as part of public transport use in

EnglandAuthors:

*Mr R Patterson, PhD student, Public Health Policy Evaluation Unit, School of Public Health, Imperial

College London, London W6 8RP, UK. r.patterson@imperial.ac.uk

Dr. E Webb, Honorary Senior Research Associate, Department of Epidemiology and Public Health,

University College London, London WC1E 7HB, UK. elizabeth.webb@ucl.ac.uk

Professor C Millett, Professor of Public Health, Public Health Policy Evaluation Unit, School of Public

Health, Imperial College London, London W6 8RP, UK. c.millett@imperial.ac.uk

Dr. A A Laverty, Research Fellow, Public Health Policy Evaluation Unit, School of Public Health,

Imperial College London, London W6 8RP, UK. a.laverty@imperial.ac.uk

*Corresponding author

ABSTRACTWord count: 197 (Max 200)

BackgroundWalking and cycling for transport (active travel) is an important source of physical activity with

established health benefits. However, levels of physical activity accrued during public transport

journeys in England are unknown.

MethodsUsing the English National Travel Survey 2010-2014 we quantified active travel as part of public

transport journeys. Linear regression models compared levels of physical activity across public

transport modes, and logistic regression models compared the odds of undertaking 30 minutes a day

of physical activity.

ResultsPublic transport users accumulated 20.5 (95% confidence interval=19.8, 21.2) minutes a day of

physical activity as part of public transport journeys. Train users accumulated 28.1 minutes (26.3,

30.0) with bus users 16.0 minutes (15.3, 16.8). 34% (32%, 36%) of public transport users achieved 30

minutes a day of physical activity in the course of their journeys; 21% (19%, 24%) of bus users and

52% (47%, 56%) of train users.

ConclusionPublic transport use is an effective way to incorporate physical activity into daily life. One in three

public transport users meet physical activity guidelines suggesting that shifts from sedentary travel

modes to public transport could dramatically raise the proportion of populations achieving

recommended levels of physical activity.

Word count 2,997(Max 3000)

INTRODUCTIONActive travel (walking and cycling for transport) is an important potential source of physical activity

enabling it’s accumulation in the course of daily life, rather than requiring intentional activity such as

exercise or sport.(1) Insufficient levels of physical activity are responsible for 3.2-5.3 million global

deaths annually and 39% of adults in England do not meet physical activity guidelines.(2, 3) Known

benefits of active travel predominantly focus on walking and cycling, sometimes aggregated into a

combined measure for research purposes. E.g., walking and cycling for transport is associated with

11% lower risk of all-cause mortality,(4) an 11% reduction in cardiovascular risk(1) and lower

adiposity.(5, 6)

In addition to walking and cycling, recent evidence suggests that public transport use is associated

with lower adiposity (5, 7-9) and that this is due to incidental physical activity.(8, 9) Encouraging

public transport use may be especially important among people who would otherwise travel by

sedentary modes and those who are unable to walk or cycle entire journeys. E.g., the average

commute distance in England and Wales (15km in 2011) equates to >2.5 hours of brisk walking each

way.(10, 11) Whilst walking this distance may be impractical, and cycling is not seen as a viable

option for many commuters in England and Wales(12), public transport use may enable the

introduction of some incidental physical activity into the journey. A review of the association

between public transport use and physical activity estimated that public transport use was

associated with 8-33 minutes/day of physical activity.(13) However, this review noted a lack of data

on whole populations or from large samples, as the majority of existing research was based on small

samples or specific subgroups.

The current study used detailed travel diary data from a large representative sample of the general

population in England to quantify walking and cycling as part of public transport journeys and

whether this varies by public transport mode (i.e. between users of trains, buses and other forms).

METHODSThe National Travel Survey (NTS) is an annually repeated cross-sectional survey of residents of a

representative sample of households in England commissioned by the UK Department for Transport.

A two-stage, stratified random sample of private households with all residents of selected

households invited to participate, regardless of age. Participants answer questions about travel,

personal and household characteristics and complete a diary of all journeys made in one week

including mode of transport, distance and duration. The diary separately records information on

each stage of multi-stage journeys, e.g. the time and distance walking to the train station in addition

to details of the train journey. Response rates for the NTS range from 59 to 63% annually.(14)

Sample DefinitionTo maximise sample size and statistical power we pooled NTS data from 2010-2014 for these

analyses. While it is possible that the same individuals were sampled in multiple waves, the data do

not allow identification of this. It seems unlikely given the small numbers sampled in each year, and

so we treat all data points as individuals in these analyses. NTS participants only provide details of

short walking journeys (<1 mile) on one day of the diary, therefore analyses were restricted to data

from this day as these short journeys may form an important part of physical activity accumulated

during public transport. Of the 61,612 participants aged 17+ years, 48,662 recorded a journey on this

day, 7,458 of whom recorded a journey for which public transport was the main mode (by distance).

4,312 of these participants provided details of how they accessed public transport. In order to

quantify the physical activity accumulated by those who walk or cycle to access public transport, we

excluded those who accessed public transport using exclusively private vehicles (n=671). Three

participants were excluded due to missing data for public transport mode, leaving 3,638 participants

for inclusion in analyses.

DataThe outcomes of interest were the number of minutes of physical activity (walking or cycling)

accumulated in the course all of public transport journeys that day, and meeting a 30 minutes/day

threshold. This threshold reflects World Health Organization (WHO) global physical activity

guidelines of 150 minutes/week of moderate intensity physical activity, which has been used in

previous work.(15, 16) Journeys were classified as either public transport or not. NTS designates that

journeys with >1 stage are assigned a main mode, which is the mode used for the stage which is

longest by distance. Within public transport journeys, each stage of a journey was categorised as

either active (walking or cycling) or inactive (e.g. bus or train). The duration of all these active stages

were summed to give the total number of minutes/day of physical activity accumulated in the

course of public transport journeys.

In order to investigate whether the level of physical activity in the course of public transport

journeys varied by mode of public transport, participants were categorised into users of: i) buses, ii)

trains, iii) light-rail or iv) multimode (those using >1 public transport mode). Light-rail includes:

London Underground, Blackpool Tramway and Tyne and Wear Metro among others. Modes were

assigned based on any recorded use of that mode during the diary day.

NTS additionally collects participant data including: sex, age, ethnicity, household car access, size of

conurbation of residence (categorised as: London, metropolitan built-up areas/large urban >250k

population, small/medium urban 50k-250k population and <50k population), and population density

in persons/hectare of the household’s post code sector (post code sectors have a mean population

of 7000). Socioeconomic position was assessed using household income, housing tenure

(categorised into those who own their home and those who rent) and a household measure of

occupation-related socioeconomic position (highest National Statistics Socio-economic Classification

(NS-SEC) of any resident of the household).

AnalysesThe baseline characteristics of public transport users and non-users were summarised alongside

those of participants by mode of public transport used (bus, train, light rail, multimode). Groups

were compared using Pearson’s Chi-squared test.

We log-transformed minutes of physical activity due to a right skew.(17) Linear regression was used

with antilogs taken in order to predict the number of minutes/day by mode of public transport used

and for each covariate. A logistic regression model was used to estimate the predicted percentage

accumulating 30 minutes/day of physical activity by mode of public transport used and for each

covariate. We present separate bivariable regressions between each explanatory variable and the

outcome in addition to multivariable regression adjusted for all variables simultaneously. All

analyses took account of the NTS complex survey design and included survey weights to take

account of non-response and differences in travel diary completion across days of the week.(18)

RESULTS3,638 participants undertook any physical activity as part of public transport use, comprising 8% of

those who made a journey on the diary day (Table 1). When comparing our sample of public

transport users to NTS participants who did not use public transport, public transport users differed

from non-users on all characteristics investigated, except gender. Younger participants and minority

ethnic participants were more likely to be users of public transport (p<0.001). Only 5% of those with

household access to a car used public transport, compared with 26% of those with no car access.

London residents and those living in more densely populated areas were more likely to user public

transport (p=0.001).

47% of public transport users were bus users (1,732), 21% were train users (749), 10% were light-rail

users (350) and 22% were multimode public transport users (816). Women and those aged 60+ years

were more likely to be bus users than men and younger participants (both p<0.001). 33% of those

living in a household where the highest NS-SEC was a managerial occupation were bus users while

26% used the train. Corresponding figures for those living in a household where the highest NS-SEC

was a routine occupation were 76% and 10% respectively (p<0.001). London residents were the

most likely to be light-rail users and multimode public transport users (p<0.001).

The predicted number of minutes/day of activity among all public transport users is 20.5, this was

unaffected by adjustment for covariates (Table 2). There was variation between travel modes, with

train users accumulating 28.1 minutes (95% confidence interval=26.3, 30.0) compared with bus users

at 16.0 minutes (15.3, 16.8). Light-rail and multimode public transport users achieved 23.8 (21.9,

25.8) and 23.3 minutes (21.7, 25.2) respectively (p<0.001). Frequency of minutes/day of physical

activity ranged from <5 to >100 minutes/day, with a large peak for bus users accumulating 15-20

minutes/day (Figure 1).

The percentage achieving 30 minutes physical activity was 34% (32%, 36%) for all public transport

users. This ranged from 21% (19%, 24%) for bus users and 52% (47%, 56%) for train users. Figures for

light-rail and multimode public transport users were 42% (35%, 48%) and 39% (35%, 44%)

respectively. Minutes of physical activity differed between several individual characteristics,

however, these differences were of relatively small magnitude (<2.8 minutes). However, there were

notable differences in the percentage meeting the recommended physical activity threshold, e.g.

31% aged 17-25 years met the 30 minutes/day threshold compared with 36% aged 40-59 years and

31% of London residents compared with 39% of those living in areas of <50,000 people.

DISCUSSIONMain finding of this studyThis nationally representative study found that public transport use is associated with an average of

21 minutes/day of physical activity and that one in three public transport users achieve

recommended levels of physical activity from this alone. Train users accumulate the highest levels of

physical activity with bus users accruing the lowest levels. These results suggest that public transport

use can be an effective way to incorporate physical activity into daily life and raise population

physical activity levels.

What is already known on this topicThese findings are consistent with previous research. Our finding of 21 minutes/day of physical

activity for public transport users is in line with the 19 minutes found by a US study of similar design

in 2005.(19) This estimate is slightly higher than estimates from studies using objective measures,

such as Global Positioning System (GPS) and accelerometers, which have found that public transport

use is associated with 15 minutes/day of walking(20) and 10.2 minutes/trip of moderate-to-vigorous

physical activity (MVPA).(21) A non-systematic review in 2012 reported 8-33 minutes/day, spanning

from a modest contribution to potentially exceeding physical activity recommendations through

public transport travel alone.(13) This review covered articles published 2002-2012 and primarily

based on small samples and selected populations. We additionally found differences across public

transport modes, with higher levels of physical activity among train users and lower levels in bus

users. Other investigators found similar differences when measuring the distance travellers were

willing to travel, using data from Australia and Canada.(22, 23) These findings support the suggestion

that people may be willing to walk further to reach a train station than a bus stop, and may be

required to do so due to the lower density of train stations compared with bus stops.(22)

Moving from sedentary journey modes to public transport has potential effects commensurate with

existing physical activity interventions. A systematic review of interventions to increase physical

activity, including supervised exercise and train-the-trainer interventions, found a mean effect of 15

minutes/week.(24) In a separate review of interventions to increase walking for transport, the most

effective intervention involved providing a booklet of tailored advice, leading to an increase of 10

minutes/day.(25, 26) Despite being an effective method of scaling up physical activity, there is

limited information on how to implement physical activity interventions through transport systems.

(27, 28) Promising research has emerged from a new busway in Cambridgeshire, where buses run on

a new custom built segregated track alongside a traffic free path for cyclists and pedestrians.

Proximity to the guided busway was associated with an increase in active commute trips.(29)

However, it is unclear how much of this was associated with public transport use and how much was

from people cycling or walking the new route. Similarly, studies of the impact of new light-rail in the

USA have found that, when combined with walkable streets, public transport infrastructure is

associated with increase of four minutes of objectively measured MVPA.(30, 31) It is uncertain

whether the modest magnitude is because of the restriction to MVPA rather than simply walking or

whether the duration of active travel in this study was shorter. Studies routinely record significantly

lower levels of objectively measured physical activity when compared with self-reported data.(32,

33) The majority of studies of increasing physical activity through transport have reported only

modest effects and further research is needed to identify effective policies(25).

What this study addsThis is the first UK study to quantify the physical activity accumulated during public transport using

nationally representative data. Other key strengths include the detailed data collected as part of the

NTS, including comprehensive information available in the travel diary data and detailed data on

potentially confounding participant characteristics. We have included information on all transport

journeys, in contrast to many other studies which restrict to the journey to work.(34-36)

Public transport use can be an effective way to incorporate physical activity into daily life and raise

population activity levels. In the England and Wales census of 2011, only 16% of commuters

travelled to work using public transport.(37) This higher level of public transport use compared with

our sample was likely to be due to our use of all journeys rather than restricting to commutes only.

We also measured public transport use on a specific day rather than usual mode and we restricted

to participants who provided details on how they accessed public transport, both of which may have

affected our estimates. With an average commute distance of 15km, walking and/or cycling the

entire journey is not practicable for most people.(10) Promoting public transport commuting as an

alternative to private motorised transport may therefore be an effective way to encourage these

individuals to incorporate physical activity into their daily routine. NTS data from 2014 show that

over 68% of English adults do not use public transport in any given week, and national health survey

data shows 39% of adults do not engage in the recommended level of physical activity.(38) If 10% of

those who don’t currently use public transport were to take it up, our findings suggest that 34% of

them would meet the recommendations, this would mean 1.2 million more adults in England

meeting physical activity guidelines. However, some forms of public transport, most obviously bus

use in England is in decline outside of London, a trend linked by some to privatisation following the

1985 Transport Act.(14, 39) Increasing public transport use in England would require large changes

to UK transport policy, including changes to local authority travel funding.

Increased availability of public transport as an alternative to car use is important if more people are

going to use public transport, although availability of alternatives alone will be insufficient to change

the behaviour of all car users.(40, 41) Increasing the proportion of the population achieving physical

activity recommendations through public transport will necessitate input from city planning and

transport professionals. Access to public transport has been identified as one of eight key

interventions to improve health through city planning, taking into account risks to health associated

with air and noise pollution, in addition to physical inactivity.(42) This is reflected in current

guidance in the UK and globally which includes recommendations to facilitate public transport use in

combination with active forms of transport to enable the inclusion of physical activity in longer

journeys.(43-47) Any increase in physical activity is beneficial and recent studies demonstrate that

the greatest benefits come to the most sedentary in society when they move from little or no to

some physical activity.(48, 49).

The increasing proportion of the global population living in urban centres underscores the

importance of public transport.(50) Unplanned urban sprawl has been blamed for unhealthy and

environmentally detrimental conurbations.(51, 52) This increase in urban sprawl means that many

journeys, are too long for people to walk or cycle. Improved urban design, including higher

residential density and dense, efficient, well organised public transport systems are positively

associated with physical activity (53). Increases in public transport use also have beneficial impacts

including reduced congestion and pollution, in line with the environmental co-benefits agenda.(54,

55)

Limitations of this studyNevertheless, our study has some weaknesses including the use of self-reported travel data. A

previous study using a travel diary based on the NTS diary found it to overestimate journey times by

12%, when compared with a data from a wearable camera. However, it also reported that nearly

15% of journeys, 63% of which were walking journeys, are not recorded at all in a travel diary

suggesting that we are likely to have underestimated total walking time.(56) By excluding

participants who did not give details of how they accessed public transport we aimed to reduce the

effect of this underreporting. We were only able to use data from a single day for each participant,

which may not be representative of their usual travel behaviour. Finally, the intensity of the physical

activity quantified here is unknown and will depend on a number of factors including pace and level

of fitness.(15) Studies using accelerometers and GPS have found that walking for transportation,

including walking to access public transport, is associated with the accumulation of MVPA, although

the measured MVPA is less than the self-reported walking time.(20, 21, 30) Thus, although our

measure of active minutes may not be of an intensity to reflect MVPA, the 30 minute threshold has

been used extensively in other research on physical activity and health and is strongly linked to

reductions in disease risk.(1, 49, 57)

CONCLUSIONPublic transport can build substantial amounts of physical activity into people’s day, and one in three

public transport users accumulate ≥30 minutes/day of physical activity. Public transport use can be

an effective way to incorporate physical activity into daily life and meet physical activity guidelines.

Shifts from sedentary travel modes to public transport could dramatically raise the proportion of

populations achieving recommended levels of physical activity. The accumulation of evidence on the

health benefits of public transport suggest that a refocusing of transport policy to encourage more

public transport use may be required.

FundingThis report is independent research supported by the National Institute for Health Research

(Research Professorship, Dr Christopher Millett, RP_2014-04- 032). The Public Health Policy

Evaluation Unit at Imperial College London is grateful for support from the NIHR School of Public

Health Research. EW is funded by the ESRC International Centre for Lifecourse Studies

(ES/J019119/1). The views expressed in this publication are those of the author(s) and not

necessarily those of the NHS, the National Institute for Health Research or the Department of Health

and Social Care

REFERENCES1. Hamer M, Chida Y. Active commuting and cardiovascular risk: a meta-analytic review. Prev Med. 2008; 46:9-13.2. Lee IM, Shiroma EJ, Lobelo F, Puska P, Blair SN, Katzmarzyk PT. Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy. The Lancet. 2012; 380:219-29.3. Lim SS, Vos T, Flaxman AD, Danaei G, Shibuya K, Adair-Rohani H, et al. A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010. The Lancet. 2012; 380:2224-60.4. Kelly P, Kahlmeier S, Gotschi T, Orsini N, Richards J, Roberts N, et al. Systematic review and meta-analysis of reduction in all-cause mortality from walking and cycling and shape of dose response relationship. International Journal of Behavioral Nutrition and Physical Activity. 2014; 11.5. Flint E, Cummins S. Active commuting and obesity in mid-life: cross-sectional, observational evidence from UK Biobank. The Lancet Diabetes & Endocrinology. 2016; 4:420-35.6. Flint E, Cummins S, Sacker A. Associations between active commuting, body fat, and body mass index: population based, cross sectional study in the United Kingdom. BMJ. 2014; 349:g4887.7. Laverty AA, Mindell JS, Webb EA, Millett C. Active travel to work and cardiovascular risk factors in the United Kingdom. Am J Prev Med. 2013; 45:282-8.8. Flint E, Webb E, Cummins S. Change in commute mode and body-mass index: prospective, longitudinal evidence from UK Biobank. The Lancet Public Health. 2016.9. Liao Y, Tsai H-H, Wang H-S, Lin C-P, Wu M-C, Chen J-F. Travel mode, transportation-related physical activity, and risk of overweight in Taiwanese adults. Journal of Transport & Health. 2016; 3:220-5.10. Office for National Statistics. 2011 Census Analysis - Distance Travelled to Work. 2014; Available from: http://webarchive.nationalarchives.gov.uk/20160105160709/http://www.ons.gov.uk/ons/dcp171776_357812.pdf.11. Ainsworth BE, Haskell WL, Herrmann SD, Meckes N, R. BJD, Tudor-Locke C, et al. The Compendium of Physical Activities Tracking Guide. Healthy Lifestyles Research Center, College of Nursing & Health Innovation, Arizona State University; [16 February, 2017]; Available from: https://sites.google.com/site/compendiumofphysicalactivities/home.12. Deaprtment for Transport. British Social Attitudes Survey 2015: Public attitudes towards transport2015.13. Rissel C, Curac N, Greenaway M, Bauman A. Physical Activity Associated with Public Transport Use—A Review and Modelling of Potential Benefits. International Journal of Environmental Research and Public Health. 2012; 9:2454-78.14. Department for Transport. NTS Statistical Tables. 2016 [updated 12 April, 201612 April, 2016]; Available from: https://www.gov.uk/government/collections/national-travel-survey-statistics.15. Ogilvie D, Mitchell R, Mutrie N, Petticrew M, Platt S. Personal and environmental correlates of active travel and physical activity in a deprived urban population. Int J Behav Nutr Phys Act. 2008; 5:43.16. World Health Organisation. Global Recommendations on Physical Activity for Health. Geneva, Switzerland2010.17. Kirkwood B, Sterne J. Essential Medical Statistics. 2nd Edition ed. Oxford, UK.: Blackwell Science; 2013.18. Morris S, Humphrey A, Byron C, Templeton I, Hurn J. National Travel Survey 2014 - Technical Report: The Department for Transport2015.19. Besser LM, Dannenberg AL. Walking to public transit: steps to help meet physical activity recommendations. Am J Prev Med. 2005; 29:273-80.

20. Saelens BE, Moudon AV, Kang B, Hurvitz PM, Zhou C. Relation Between Higher Physical Activity and Public Transit Use. American Journal of Public Health. 2014; 104:854-9.21. Chaix B, Kestens Y, Duncan S, Merrien C, Thierry B, Pannier B, et al. Active transportation and public transportation use to achieve physical activity recommendations? A combined GPS, accelerometer, and mobility survey study. International Journal of Behavioral Nutrition and Physical Activity. 2014; 11:124.22. Daniels R, Mulley C. Explaining walking distance to public transport: The dominance of public transport supply. Journal of Transport and Land Use. 2013; 6:5.23. O'Sullivan S, Morrall J. Walking Distances to and from Light-Rail Transit Stations. Transportation Research Record. 1996; 1538:19-26.24. Conn VS, Hafdahl AR, Mehr DR. Interventions to increase physical activity among healthy adults: meta-analysis of outcomes. Am J Public Health. 2011; 101:751-8.25. Ogilvie D, Foster CE, Rothnie H, Cavill N, Hamilton V, Fitzsimons CF, et al. Interventions to promote walking: systematic review. BMJ. 2007; 334:1204.26. Mutrie N, Carney C, Blamey A, Crawford F, Aitchison T, Whitelaw A. “Walk in to Work Out”: a randomised controlled trial of a self help intervention to promote active commuting. J Epidemiol Commun H. 2002; 56:407-12.27. Reis RS, Salvo D, Ogilvie D, Lambert EV, Goenka S, Brownson RC. Scaling up physical activity interventions worldwide: stepping up to larger and smarter approaches to get people moving. The Lancet. 2016; 388:1337-48.28. Global Advocacy for Physical Activity (GAPA) the Advocacy Council of the International Society for Physical Activity and Health (ISPAH). NCD Prevention: Investments that Work for Physical Activity - A complementary document to The Toronto Charter for Physical Activity: A Global Call to Action 2011.29. Ogilvie D, Panter J, Guell C, Jones A, Mackett R, Griffin S. Health impacts of the Cambridgeshire Guided Busway: a natural experimental study. Public Health Res. 2016; 4.30. Brown BB, Tharp D, Smith KR, Jensen WA. Objectively measured active travel and uses of activity-friendly neighborhood resources: Does change in use relate to change in physical activity and BMI? Preventive Medicine Reports. 2017; 8:60-6.31. Brown BB, Werner CM, Tribby CP, Miller HJ, Smith KR. Transit Use, Physical Activity, and Body Mass Index Changes: Objective Measures Associated With Complete Street Light-Rail Construction. American Journal of Public Health. 2015; 105:1468-74.32. Hagstromer M, Oja P, Sjostrom M. Physical activity and inactivity in an adult population assessed by accelerometry. Med Sci Sports Exerc. 2007; 39:1502-8.33. Troiano RP, Berrigan D, Dodd KW, Masse LC, Tilert T, McDowell M. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc. 2008; 40.34. Sahlqvist S, Song Y, Ogilvie D. Is active travel associated with greater physical activity? The contribution of commuting and non-commuting active travel to total physical activity in adults(). Preventive Medicine. 2012; 55:206-11.35. Lachapelle U, Frank L, Saelens BE, Sallis JF, Conway TL. Commuting by public transit and physical activity: Where you live, where you work, and how you get there. J Phys Act Health. 2011; 8 Suppl 1:S72-82.36. Morabia A, Mirer FE, Amstislavski TM, Eisl HM, Werbe-Fuentes J, Gorczynski J, et al. Potential Health Impact of Switching From Car to Public Transportation When Commuting to Work. American Journal of Public Health. 2010; 100:2388-91.37. Gower TL. 2011 Census Analysis - Method of Travel to Work in England and Wales Report: Office for National Statisitcs2013.38. Scholes S, Mindell J. Chapter 2 - Physical activity in adults. In: Craig R, Mindell J, editors. Health Survey for England 2012 Volume 1: Health, social care and lifestyles. Health and Social Care Information Centre, Leeds2013.

39. Simpson BJ. Deregulation and privatization: the British local bus industry following the transport act 1985. Transport Reviews. 1996; 16:213-23.40. Jensen M. Passion and heart in transport — a sociological analysis on transport behaviour. Transport Policy. 1999; 6:19-33.41. Beirão G, Sarsfield Cabral JA. Understanding attitudes towards public transport and private car: A qualitative study. Transport Policy. 2007; 14:478-89.42. Giles-Corti B, Vernez-Moudon A, Reis R, Turrell G, Dannenberg AL, Badland H, et al. City planning and population health: a global challenge. The Lancet. 2016; 388:2912-24.43. NICE. Physical activity: walking and cycling. 2012.44. Public Health England. Working Together to Promote Active Travel A briefing for local authorities2016.45. National Physical Activity Plan Alliance. U.S. National Physical Activity Plan2016.46. Sustrans, Transport Scotland. Active Travel Strategy Guidance2014.47. World Health Organisation. Action Plan for implementation of the European Strategy for the Prevention and Control of Noncommunicable Diseases 2012−2016 Copenhagan, Denmark2012.48. Smith AD, Crippa A, Woodcock J, Brage S. Physical activity and incident type 2 diabetes mellitus: a systematic review and dose-response meta-analysis of prospective cohort studies. Diabetologia. 2016.49. Woodcock J, Franco OH, Orsini N, Roberts I. Non-vigorous physical activity and all-cause mortality: systematic review and meta-analysis of cohort studies. Int J Epidemiol. 2011; 40:121-38.50. United Nations DoEaSA, Population Division,. World Urbanization Prospects: The 2014 Revision, Highlights 2014.51. McGranahan G, Satterthwaite D. Urbanisation concepts and trends. IIED Working Paper. London: IIED2014.52. Ewing R, Meakins G, Hamidi S, Nelson AC. Relationship between urban sprawl and physical activity, obesity, and morbidity - update and refinement. Health Place. 2014; 26:118-26.53. Sallis JF, Cerin E, Conway TL, Adams MA, Frank LD, Pratt M, et al. Physical activity in relation to urban environments in 14 cities worldwide: a cross-sectional study. Lancet. 2016.54. Chapman L. Transport and climate change: a review. Journal of Transport Geography. 2007; 15:354-67.55. Department for Transport. The Bus Services Bill: An Overview2016.56. Kelly P, Doherty A, Mizdrak A, Marshall S, Kerr J, Legge A, et al. High group level validity but high random error of a self-report travel diary, as assessed by wearable cameras. Journal of Transport & Health. 2014; 1:190-201.57. Ekelund U, Steene-Johannessen J, Brown WJ, Fagerland MW, Owen N, Powell KE, et al. Does physical activity attenuate, or even eliminate, the detrimental association of sitting time with mortality? A harmonised meta-analysis of data from more than 1 million men and women. The Lancet. 2016; 388:1302-10.

FIGURE CAPTION

Figure 1 - The number of minutes of daily physical activity for each participant by mode of public transport

Table 1 - Characteristic of NTS participants 2010-2014 by mode of transport used

Public Transport

Users

Non Public Transport

Users p-value* Bus Train Light Rail Multimode p-value*All 3638

(8%)41204 (92%)

1723 (47%)

749(21%)

350 (10%)

816 (22%)

Gender Male 8% 92% 0.024 39% 25% 11% 25% <0.001Female 8% 92% 55% 17% 8% 20%Age 17-25 15% 85% <0.001 51% 22% 9% 18% <0.00126-39 10% 90% 33% 25% 13% 28%40-59 6% 94% 40% 24% 11% 26%60+ 6% 94% 72% 9% 4% 14%Ethnicity White 7% 93% <0.001 47% 23% 9% 21% <0.001South Asian 14% 86% 44% 16% 14% 26%Black 27% 73% 59% 11% 6% 25%Other 17% 83% 41% 16% 13% 31%Household NS-SEC - Social Class Managerial 8% 92% <0.001 33% 26% 13% 28% <0.001Intermediate 7% 93% 55% 17% 8% 20%Routine 8% 92% 76% 10% 4% 11%Unclassified 15% 85% 59% 14% 6% 21%Household Income £50,000 and over 9% 91% <0.001 25% 29% 14% 31% <0.001£25,000 to £49,999 7% 93% 48% 22% 8% 22%Less than £25,000 8% 92% 67% 12% 6% 15%Housing Tenure Owns 7% 93% <0.001 42% 25% 10% 24% <0.001Rents 12% 88% 55% 14% 10% 21%Household Car Access Access to a car 5% 95% <0.001 38% 27% 11% 24% <0.001No car access 26% 74% 61% 11% 8% 20%Population density** 0-9.99 4% 96% <0.001 48% 31% 4% 17% <0.00110-24.99 6% 94% 52% 27% 4% 17%25-39.99 7% 93% 52% 23% 5% 20%40-74.99 14% 86% 47% 17% 11% 25%75+ 31% 69% 39% 10% 20% 31%Conurbation Type London 25% 75% <0.001 36% 15% 17% 32% <0.001Metropolitan & >250k 7% 93% 66% 20% 5% 10%50k-250k 6% 94% 57% 25% 2% 16%<50k 3% 97% 48% 34% 1% 17%NSSEC - National Statistic Socio-economic classification* - Peason's Chi-squared test for difference in distribution by characteristics**Population density of the post code sector of the household address, postcode sectors have a mean population of 7,000.

Table 2 – Regression analysis showing the number of minutes of daily physical activity accumulated in the course of public transport use and the percentage reaching at least 30 minutes a day

Minutes of daily physical activity during public transport

use (95% CI)Percentage achieving 30 minutes of daily physical activity

during public transport use (95% CI)

Unadjusted* Adjusted** Unadjusted* (%) Adjusted** (%)

20.5 (19.8, 21.2) 20.5 (19.8, 21.2) 34 (32, 36) 34 (32, 36)Public Transport Mode p-value p-value p-value p-value

Bus 16.2 (15.5, 16.8) ref 16.0 (15.3, 16.8) ref 21 (19, 23) ref 21 (19, 24) ref

Train 28.7 (27.0, 30.6) <0.001 28.1 (26.3, 30.0) <0.001 54 (50, 58) <0.001 52 (47, 56) <0.001

Light rail 22.9 (21.2, 24.7) <0.001 23.8 (21.9, 25.8) <0.001 40 (34, 46) <0.001 42 (35, 48) <0.001

Multimode 22.9 (21.3, 24.7) <0.001 23.3 (21.7, 25.2) <0.001 39 (34, 43) <0.001 39 (35, 44) <0.001Sex

Male 21.9 (21.0, 22.8) ref 21.1 (20.2, 22.0) ref 38 (35, 40) ref 36 (33, 38) ref

Female 19.3 (18.5, 20.1) <0.001 19.9 (19.1, 20.7) 0.024 31 (28, 33) <0.001 33 (30, 35) 0.066Age

17-25 19.8 (18.6, 21.0) ref 19.9 (18.8, 21.1) ref 30 (26, 34) ref 31 (27, 34) ref

26-39 20.8 (19.5, 22.1) 0.251 19.8 (18.6, 21.1) 0.903 37 (34, 40) 0.008 35 (32, 38) 0.103

40-59 22.1 (21.0, 23.3) 0.005 21.5 (20.4, 22.6) 0.054 38 (35, 41) 0.001 36 (33, 40) 0.027

60+ 18.7 (17.5, 19.9) 0.194 21.1 (19.7, 22.6) 0.210 28 (25, 32) 0.567 34 (29, 38) 0.291Ethnicity

White 20.9 (20.2, 21.6) ref 20.5 (19.7, 21.3) ref 36 (34, 38) ref 35 (32, 37) ref

South Asian 19.8 (17.7, 22.2) 0.396 20.6 (18.3, 23.1) 0.961 32 (25, 39) 0.391 35 (28, 41) 0.996

Black 17.8 (16.3, 19.5) <0.001 20.5 (18.7, 22.4) 0.997 24 (19, 29) <0.001 31 (25, 37) 0.299

Other 20.1 (17.6, 23.0) 0.590 20.4 (17.9, 23.1) 0.918 29 (20, 37) 0.120 30 (22, 38) 0.241Household NS-SEC - Social Classification

Managerial 22.0 (21.2, 22.9) ref 20.8 (19.9, 21.7) ref 38 (36, 41) ref 34 (32, 37) ref

Intermediate 19.7 (18.0, 21.7) 0.035 20.3 (18.5, 22.3) 0.659 34 (29, 38) 0.082 35 (31, 40) 0.694

Routine 17.7 (16.6, 19.0) <0.001 20.0 (18.6, 21.5) 0.386 25 (22, 29) <0.001 32 (27, 37) 0.442

Unclassified 18.7 (16.6, 21.2) 0.014 20.3 (18.1, 22.7) 0.713 27 (20, 34) 0.007 32 (25, 39) 0.543Household Income

≥£50,000 22.6 (21.5, 23.7) ref 20.8 (19.7, 22.0) ref 40 (37, 43) ref 34 (31, 38) ref£25,000-£49,999 20.6 (19.5, 21.7) 0.014 20.6 (19.6, 21.7) 0.811 34 (30, 38) 0.017 34 (30, 37) 0.769

<£25,000 18.5 (17.5, 19.6) <0.001 20.1 (18.7, 21.5) 0.499 28 (26, 31) <0.001 34 (30, 37) 0.799Housing Tenure

Owns 21.8 (20.9, 22.6) ref 20.9 (20.0, 21.8) ref 38 (35, 40) ref 35 (33, 38) ref

Rents 19.0 (18.1, 20.0) <0.001 20.0 (19.0, 21.1) 0.273 30 (27, 32) <0.001 32 (29, 36) 0.190Car Access

Access to a car 21.6 (20.8, 22.5) ref 20.0 (19.1, 21.0) ref 38 (35, 40) ref 33 (31, 36) ref

No car access 19.1 (18.2, 20.1) <0.001 21.1 (20.0, 22.2) 0.156 30 (27, 33) <0.001 35 (32, 39) 0.363Population Density***

<10 21.3 (19.8, 22.9) ref 19.7 (18.0, 21.6) ref 38 (34, 42) ref 33 (28, 38) ref

10-24.99 22.2 (20.7, 23.8) 0.446 21.8 (20.3, 23.3) 0.065 39 (34, 43) 0.742 37 (32, 41) 0.182

25-39.99 20.0 (18.5, 21.6) 0.242 19.9 (18.4, 21.5) 0.851 33 (28, 37) 0.084 32 (28, 36) 0.794

40-74.99 20.6 (19.4, 21.9) 0.502 21.2 (20.0, 22.4) 0.202 32 (29, 36) 0.059 35 (31, 38) 0.602

75+ 19.2 (17.9, 20.7) 0.053 19.7 (18.2, 21.4) 0.975 31 (27, 36) 0.035 34 (29, 38) 0.800Conurbation Type

London 20.1 (19.2, 21.1) ref 19.4 (18.5, 20.4) ref 33 (30, 35) ref 31 (28, 34) refMetropolitan & >250k 20.6 (19.2, 22.1) 0.554 22.2 (20.8, 23.8) 0.003 32 (28, 36) 0.943 37 (33, 41) 0.031

50k-250k 19.9 (18.3, 21.6) 0.838 20.2 (18.6, 21.9) 0.470 36 (31, 41) 0.250 37 (32, 42) 0.075

<50k 22.5 (20.9, 24.3) 0.012 22.2 (20.2, 24.4) 0.025 41 (36, 45) 0.002 39 (33, 45) 0.030NSSEC - National Statistic Socio-economic classification. CI – Confidence Interval. *Separate bivariable regression between each explanatory variable and the outcome. **Multivariable regression adjusted for all variables presented simultaneously. ***Population density of the post code sector of the household address, postcode sectors have a mean population of 7,000.