Scholarly article on topic 'Patterns and predictors of changes in active commuting over 12months'

Patterns and predictors of changes in active commuting over 12months Academic research paper on "Social and economic geography"

CC BY
0
0
Share paper
Academic journal
Preventive Medicine
OECD Field of science
Keywords
{"Health promotion" / Walking / Cycling / "Longitudinal study" / "Health promotion" / "Behavioural change" / "Physical activity and health" / Walking / Epidemiology / "Environment design" / Adults / "Follow-up studies"}

Abstract of research paper on Social and economic geography, author of scientific article — Jenna Panter, Simon Griffin, Alice M. Dalton, David Ogilvie

Abstract Objective To assess the predictors of uptake and maintenance of walking and cycling, and of switching to the car as the usual mode of travel, for commuting. Methods 655 commuters in Cambridge, UK reported all commuting trips using a seven-day recall instrument in 2009 and 2010. Individual and household characteristics, psychological measures relating to car use and environmental conditions on the route to work were self-reported in 2009. Objective environmental characteristics were assessed using Geographical Information Systems. Associations between uptake and maintenance of commuting behaviours and potential predictors were modelled using multivariable logistic regression. Results Mean within-participant changes in commuting were relatively small (walking: +3.0min/week, s.d.=66.7; cycling: −5.3min/week, s.d.=74.7). Self-reported and objectively-assessed convenience of public transport predicted uptake of walking and cycling respectively, while convenient cycle routes predicted uptake of cycling and a pleasant route predicted maintenance of walking. A lack of free workplace parking predicted uptake of walking and alternatives to the car. Less favourable attitudes towards car use predicted continued use of alternatives to the car. Conclusions Improving the convenience of walking, cycling and public transport and limiting the availability of workplace car parking may promote uptake and maintenance of active commuting.

Academic research paper on topic "Patterns and predictors of changes in active commuting over 12months"

.JL^äke.

ELSEVIER

Patterns and predictors of changes in active commuting over 12 months^

Jenna Panter a* Simon Griffin a, Alice M. Dalton b, David Ogilvie a

a Medical Research Council Epidemiology Unit & UKCRC Centre for Diet and Activity Research (CEDAR), Institute of Public Health, University of Cambridge, UK b Norwich Medical School, University ofEastAnglia, Norwich, UK & UKCRC Centre for Diet and Activity Research (CEDAR), Institute of Public Health, University of Cambridge, UK

ARTICLE INFO ABSTRACT

Objective. To assess the predictors of uptake and maintenance of walking and cycling, and of switching to the car as the usual mode of travel, for commuting.

Methods. 655 commuters in Cambridge, UK reported all commuting trips using a seven-day recall instrument in 2009 and 2010. Individual and household characteristics, psychological measures relating to car use and environmental conditions on the route to work were self-reported in 2009. Objective environmental characteristics were assessed using Geographical Information Systems. Associations between uptake and maintenance of commuting behaviours and potential predictors were modelled using multivariable logistic regression.

Results. Mean within-participant changes in commuting were relatively small (walking: +3.0 min/ week, s.d. = 66.7; cycling: — 5.3 min/week, s.d. = 74.7). Self-reported and objectively-assessed convenience of public transport predicted uptake of walking and cycling respectively, while convenient cycle routes predicted uptake of cycling and a pleasant route predicted maintenance of walking. A lack of free workplace parking predicted uptake of walking and alternatives to the car. Less favourable attitudes towards car use predicted continued use of alternatives to the car.

Conclusions. Improving the convenience of walking, cycling and public transport and limiting the availability of workplace car parking may promote uptake and maintenance of active commuting.

© 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Contents lists available at ScienceDirect

Preventive Medicine

journal homepage: www.elsevier.com/locate/ypmed

Available online 9 August 2013

Keywords: Health promotion Walking Cycling

Longitudinal study

Introduction

Everyday physical activity is important for health (Das and Horton, 2012). Active commuting (walking and cycling to work) is specifically associated with reduced morbidity and mortality (Hamer and Chida, 2008), and cross-sectional studies have shown that those who walk or cycle to work - either alone, or in combination with the car - or who commute by public transport are more physically active than those who use only the car (Pratt et al., 2012). Promoting a shift away from car use in general, and towards walking and cycling for transport in particular, therefore has potential as a public health strategy and merits further research (Das and Horton, 2012) — not least because systematic reviews of interventions have found limited evidence of effectiveness (McCormack and Shiell, 2011; Ogilvie et al., 2004, 2007; Yang et al., 2010).

Using the ecological model as a framework (Sallis and Owen, 2002), reviews of predominantly cross-sectional studies have highlighted the

☆ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.

* Corresponding author at: UKCRC Centre for Diet and Activity Research (CEDAR), Box 296, Institute of Public Health, Robinson Way, Forvie Site, Cambridge, CB2 0SR, UK. Fax: +441223 330316.

E-mail address: jenna.panter@mrc-epid.cam.ac.uk (J. Panter).

potential importance of a range of individual, social, and environmental factors for walking and cycling (Bauman et al., 2012; Heinen et al., 2009; Panter and Jones, 2010; Saelens and Handy, 2008). To inform the development and targeting of more effective interventions, we need to quantify changes in walking and cycling and understand the relative importance of different factors in predicting those changes, but our knowledge of these is limited (NICE, 2012; Shephard, 2008). Perceptions of the neighbourhood environment were associated with uptake and maintenance of walking for transport (Cleland et al., 2008), while proximity to facilities for physical activity was associated with more favourable trends in walking in older adults (Li et al., 2005; Michael et al., 2010). Studies of people relocating to new residential environments found that those moving to areas with higher street connectivity reported more walking,(Wells and Yang, 2008), while those moving to areas with higher residential density, street connectivity and park access were more likely to take up cycling (Beenackers et al., 2012).

These few previous studies are limited by small sample sizes (Wells and Yang, 2008) or a focus on specific population groups (Cleland et al., 2008; Li et al., 2005; Michael et al., 2010) or behaviours (Beenackers et al., 2012). Using data from the Commuting and Health in Cambridge study, we aimed to describe changes in walking and cycling to and from work in a cohort of commuters and assess the predictors of uptake and maintenance of walking, cycling and use of alternatives to the car for commuting.

0091-7435/$ - see front matter © 2014 The Authors. Published by Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.ypmed.2013.07.020

Methods

Study setting, participant recruitment and data collection

Cambridge has a distinct cycling culture related to its flat topography and large university population. The Commuting and Health in Cambridge study protocol, recruitment and data collection procedures and baseline results have been reported elsewhere (Ogilvie et al., 2010; Panter et al., 2011; Yang et al., 2012). Briefly, adults aged 16 and over who lived within 30 km of the city centre and travelled to work in Cambridge were recruited, predominantly through workplaces, and received postal questionnaires between May and October 2009 (ti) and again one year later (t2). Individual data collection was matched to the same week of the year wherever possible to minimise any seasonal differences in behaviour. To avoid breaching data protection legislation and to assure participants of the study's independence, commuters were not recruited using employer-based sampling frames such as staff databases but were invited to opt in to the study through a variety of strategies including recruitment stands, advertisements and emails distributed through corporate mailing lists. A variety of workplaces contributed to participant recruitment. These included local authorities, healthcare providers, retail outlets and institutions of higher and further education distributed across a range of city centre and urban fringe locations in Cambridge. Of the 2163 people who registered their interest in taking part in the study, 1582 met the inclusion criteria and were sent a questionnaire at ti; of these, 1164 (74%) provided consent and returned a completed baseline questionnaire.

Outcomes: uptake and maintenance of walking, cycling and use of alternatives to the car

At both time points participants were asked to report the travel modes used on each commuting journey over the last seven days. If participants walked or cycled for any part of their journeys they reported the average time spent doing so per trip, from which total weekly times spent walking and cycling at ^ and t2 and change scores (t2-ti) were computed. Change scores of >± 300 min/week (n = 9) were truncated to 300. The most frequently reported travel mode or combination of modes (hereafter referred to as 'usual' mode(s)) used at each time point was also computed (Appendix A). Six binary outcome measures -uptake and maintenance of walking and of cycling (based on time) and of use of alternatives to the car (based on usual mode) - were subsequently derived (Table 1).

Predictors Overview

Potential predictors were measured at baseline and chosen because they represented constructs within the socio-ecological model (Sallis and Owen,

2002) and had support in the literature (Heinen et al., 2009; Panter and Jones, 2010; Saelens and Handy, 2008).

Individual and household characteristics

Date of birth, gender, highest educational qualification, housing tenure, household composition, access to cars and bicycles, possession of a driving licence and self-reported height and weight were assessed by questionnaire. Age and body mass index (BMI) (kg/m2) were calculated and participants were assigned to one of three categories of weight status (World Health Organisation, 2000).

Psychological measures relating to car use

Using a five-point Likert scale, participants reported their agreement with eight statements on using the car for the commute next time (for example: 'It would be good to use the car') representing four constructs (perceived behavioural control, intention, attitude and subjective norms; two items per construct) from the theory of planned behaviour (Hardeman et al., 2009). Habit strength for car commuting was summarised using a binary variable derived from participants' agreement on the same scale with seven statements derived from the habit strength index (Panter et al., 2013; Verplanken and Orbell, 2003).

Perceptions of the environment

Using a five-point Likert scale, participants reported their level of agreement with seven statements describing the environment along their commuting route (for example: 'There is little traffic'). Responses to positively worded items were collapsed such that those who 'strongly agreed' or 'agreed' with an item were compared to those who 'strongly disagreed', 'disagreed' or 'neither disagreed or agreed', and vice versa for negatively worded items. Participants also reported the car parking provision at their workplace (free, paid or no parking) and the distance between their home and workplace, summarised as a categorical measure (<5 km, 5-20 km and >20 km) to distinguish relatively long or short trips (Panter et al., 2013).

Objectively assessed measures of the environment

Using a geographical information system (ArcGIS, version 9.3), characteristics of the areas surrounding the home, workplace and route to work were derived using tj postcodes (Appendix B). Variables were included if they were associated with travel behaviour in cross-sectional analyses of the baseline sample: those relating to the home location (urban-rural status, area-level deprivation, road junction density, distance to the nearest railway station and the nearest bus stop, and frequency of bus services), the workplace location (density of destinations within walking distance) and the geographical context of the commuting route (Dalton et al., 2013; Panter et al., 2011).

Table 1

Details of outcome measures used.

Outcome Variable used to define change Predictor group Reference group Sample size used in analysisa

Description Sample sizeb Description Sample sizeb

Uptake of walking Weekly time spent walking Increased walking (from 0 at ti to >0 at t2) 72 Spent no time walking at either 401 470

('took up walking') time point ('no walking')

Uptake of cycling Weekly time spent cycling Increased cycling (from 0 at t1 to >0 at t2) 33 Spent no time cycling at either 268 293

('took up cycling') time point ('no cycling')

Uptake of alternatives Most frequently reported mode(s) Shifted from car to alternative usual mode 37 Car user at both time points 137 174

to the car

Maintenance of Weekly time spent walking Reported same time walking at both time points, 73 Decreased time spent walking 109 181

walking where time > 0 OR ('reduced or gave up walking')

increased walking, where time >0 at t1

('maintained their walking')

Maintenance of cycling Weekly time spent cycling Reported same time cycling at both time points, 186 Decreased time spent cycling 168 347

where time > 0 OR ('reduced or gave up cycling')

increased cycling, where time >0 at t1

('maintained their cycling')

Maintenance of use of Most frequently reported mode(s) Used alternative to car at both time points 444 Switched to car as usual mode 37 462

alternatives to the car

Data collected in 2009 and 2010 in Cambridge, UK. a Sample size refers to actual number of participants used in maximally adjusted models (those with complete data for all predictors included in the model). b Sample size refers to potential numbers of participants in each group (not accounting for missing data in potential predictors).

Analysis

All analyses were conducted in Stata 11.1. Differences in baseline characteristics between participants with and without follow-up data were tested using t tests, x2 tests or Mann-Whitney U tests. One-way analysis of variance was used to test for differences between change in usual mode(s) and in time spent walking or cycling.

Associations between potential predictors and all outcomes were assessed using logistic regression models, initially adjusted for age and sex. Route characteristics were matched to the behaviour of interest; thus walking models included pleasantness and convenience of routes for walking and convenience of public transport, while cycling models included convenience of routes for cycling. All variables significantly associated at p < 0.25 (in the case of categorical variables, p < 0.25 for heterogeneity between groups) (Hosmer and Lemeshow, 1989) were carried forward into multivariable regression models. No adjustment was made for clustering by workplace, as preliminary multilevel models suggested no evidence of this.

Relocation can alter the length of a commute or the route taken. As a sensitivity analysis, we identified participants who reported different home or work postcodes at t1 and t2 corresponding to different locations. Excluding these movers(n = 155)fromanalysismadenosubstantial difference to thedirection or size of associations, hence the results presented include these participants.

Results

Sample characteristics

Of the 1164 participants who returned questionnaires at t-i, 704 (60.5%) completed questionnaires at t2 and 655 provided information on commuting at both t1 and t2 and were included in this analysis (Table 2). Those included were more likely to be older (mean age of 43.6 years versus 40.5 years, p = 0.01) and to own their own home (75.1% versus 71.8%, p = 0.01) than those who did not participate at t2. There were no significant differences in gender, educational qualifications, weight status, car ownership or time spent walking or cycling at baseline.

Changes in weekly time spent walking and cycling and usual commuting mode(s)

Table 2

Characteristics of participants with data at both time points.

Percentage (n)

Individual characteristics Gender(n = 655)

Male 31.6 (207)

Female 68.4 (448)

Mean age (s.d.) 43.65 (11.3) Highest educational qualification (n = 655)

Less than degree 26.3 (172)

Degree or higher 73.7 (483) Weight status (n = 655)

Normal or underweight 63.3(415)

Overweight or obese 36.7 (240)

Household characteristics Number of children in household (n = 655)

None 72.0 (472)

One or more 28.0(183) Home ownership (n = 655)

Does not own home 24.9 (163)

Home owner 75.1 (492) Number of cars in household (n = 655)

None 14.8 (97)

One car or more 84.2 (558) Home location (n = 655)

Urban 64.7 (424)

Rural 35.3 (231)

Mean (s.d.) self-reported distance between home and work (km) 13.1 (11.3)

Walking and cycling

Change in time spent walking to and from work (n = 654; median = 0 min/week, IQR = 0,0)

No walking reported at either time point 61.2 (401)

Exactly the same non-zero time at both time points 2.1 (14)

Increase in weekly walking time 20.0 (131)

Decrease in weekly walking time 16.7 (108) Change in time spent cycling to and from work (n = 655; median = 0 min/week, IQR = -10,0)

No cycling reported at either phase 1 or phase 2 41.0 (268)

Exactly the same non-zero time at both time points 9.6 (63)

Increase in weekly cycling time 23.0 (151)

Decrease in weekly cycling time 26.4 (173)

IQR: interquartile range. Data collected in 2009 and 2010 in Cambridge, UK.

Changes in time spent walking and cycling were symmetrically distributed. Many participants had change values of 0 min/week, reflecting either: (i) no walking (or cycling) at t-i and t2 or (ii) exactly the same number of trips and average duration of walking (or cycling) per trip at t1 and t2. Mean change values were relatively small (walking: + 3.0 min/week, s.d. = 66.7, p = 0.24; cycling: — 5.3 min/week, s.d. = 74.7, p = 0.07). Those who reported more time walking or cycling on the journey to work at t1 tended to report less at t2 (Fig. 1). Generally, changes reflected a combination of changes in trip frequency and average duration per trip, although many cyclists reported the same number of trips but different durations (Appendix C).

Most participants reported the same usual mode at t1 and t2.21% and 68% used the car and alternatives to the car at both t1 and t2 respectively, whilst 6% switched to the car at t2 and 6% switched away from the car. Changes in time spent walking and cycling differed according to change in usual mode (p < 0.001 for both walking and cycling; Fig. 2). Those who switched away from the car reported substantial mean increases in walking and cycling, whereas those switching to the car reported substantial mean decreases.

Predictors of uptake and maintenance of walking, cycling and use of alternatives to the car

Results for uptake and maintenance of walking, cycling and use of alternatives to the car are presented in Tables 3, 4 and 5 respectively. Commuters with no children in the household or who reported

convenient public transport or a lack of free workplace parking were more likely to take up walking. Those reporting convenient cycle routes or living in areas objectively assessed to have more frequent bus services were more likely to take up cycling. Older participants, those with a degree, and those who reported convenient cycle routes or a lack of free workplace parking were more likely to take up alternatives to the car.

In general, only a few of the potential predictors were associated with maintenance of more active travel behaviours. Only those who reported that it was pleasant to walk on the route to work were significantly more likely to maintain walking, whereas none of the potential predictors were associated with maintenance ofcycling. Area-level deprivation and less favourable attitudes towards car use predicted continued use of alternatives to the car.

Discussion

Principal findings

Small average changes in weekly time spent walking or cycling on the commute were observed over the 12-month period. However, among participants who switched from the car to an alternative as their usual mode of transport, the mean increases in active travel time were substantial and of a similar order of magnitude as the effect sizes reported in controlled studies of interventions to promote walking for transport (15-30 min/week) (Ogilvie et al., 2007). Sociodemographic factors predicted uptake and maintenance of use

Fig. 1. Scatterplot of change spent in time against time reported at baseline for A) walking and B) cycling on the commute.

of alternatives to the car, and having no children in the household predicted uptake of walking. Supportive transport environments predicted uptake of walking and cycling. Lack of free workplace

parking predicted uptake of walking and of alternatives to the car. Less favourable attitudes towards car use predicted maintenance of using alternatives to the car.

Fig. 2. Mean changes in computed time spent walking and cycling according to modal shift category.

Uptake and maintenance of walking.

Uptake of walking OR ( 95% CI) Maintenance ofwalking OR ( 95% CI)

Minimally Maximally Minimally Maximally

adjusted+ adjustedj adjusted+ adjustedj

Personal and household characteristics

Age ( years) n/a 1.01 (0.98,1.03) n/a 1.00 ( 0.97,1.02)

Gender Male 1.0 1.0

Female n/a 1.11 ( 0.61,2.03) n/a 1.55 ( 0.74,3.23)

Weight status Overweight or obese 1.0 1.0

Normal or underweight 1.37 0.79, 2.40) - 1.11 0.60, 2.06) -

Highest educational qualification Less than degree 1.0 1.0 1.0

Degree or higher 0.70 0.40,1.22) 0.74 (0.41,1.35) 1.12 0.57, 2.23) -

Number of children One or more 1.0 1.0 1.0 1.0

None 2.20 1.56,4.17) 2.18 ( 1.08,4.39) 1.87 0.86, 4.09) 1.74 ( 0.79, 3.85)

Cars One or more 1.0 1.0 1.0

None 1.62 0.80, 3.29) 1.10 (0.49,2.46) 0.63 0.28,1.38) -

Home ownership Does not own home 1.0 1.0 1.0

Owns home 1.67 0.90, 3.08) 1.30 ( 0.66,2.53) 1.59 0.72, 3.51) -

Objectively measured environment

Home location Rural 1.0 1.0 1.0

Urban 1.41 0.82, 2.46) 1.18 ( 0.61,2.28) 0.94 0.49,1.80) -

Area-level deprivation More affluent 1.0 1.0

Less affluent 0.88 0.53,1.47) - 1.26 0.69, 2.31) -

Junction density around home Lower 1.0 1.0 1.0

Higher 1.51 0.91,2.52) 1.13 (0.63, 2.02) 1.15 0.63, 2.09) -

Distance to nearest railway station from home Further 1.0 1.0

Closer 0.99 0.60,1.64) - 1.00 0.55,1.84) -

Distance to nearest bus stop from home Further 1.0 1.0

Closer 1.11 0.67,1.83) - 1.05 0.57,1.93) -

Frequency of bus services around home Less frequent 1.0 1.0

More frequent 1.00 0.60,1.66) - 0.87 0.48,1.58) -

Destinations within walking distance around work Lower density 1.0 1.0

Higher density 1.30 0.78, 2.15) - 0.93 0.51,1.71) -

Geographical context of commute Commuting to the heart from within the city 1.0 1.0

Commuting to the outskirts from within the city 0.77 0.37,1.59) 0.76 0.31,1.90) -

Commuting to the heart from outside the city 1.43 0.68, 3.00) - 0.78 0.34,1.78) -

Commuting to the outskirts from outside the city 0.78 0.38,1.62) 1.10 0.49,2.44)

Self-reported measures of the environment

Pleasant to walk SD/D/N 1.0 1.0 1.0

A/SA 1.06 0.63,1.78) - 2.48 0.76,8.15) 2.34 ( 1.07,5.11)

Convenient public transport SD/D/N 1.0 1.0 1.0

A/SA 2.46 1.47,4.13) 2.47 (1.44,4.25) 0.72 0.39,1.31) -

No convenient walking routes A/SA 1.0 1.0

SD/D/N 0.88 0.53,1.46) - 1.82 0.42, 7.86) -

Little traffic SD/D/N 1.0 1.0

A/SA 0.70 0.29,1.71) - 1.17 0.63,2.16) -

Safe to cross the road SD/D/N 1.0 1.0

A/SA 1.24 0.75, 2.07) - 0.94 0.51,1.73) -

Self-reported distance from home to work Over 20 km 1.0 1.0

5.0-20 km 0.45 0.24, 0.87) - 0.97 0.46,2.07)

Under 5 km 0.72 0.40,1.33) - 0.79 0.39,1.60) -

Workplace car parking Free 1.0 1.0 1.0

None or paid-for 2.35 1.34,4.12) 2.04 ( 1.12,3.71) 1.17 0.58, 2.36) -

Psychological measures relating to car use

Intention score Strong intentions 1.0 1.0

Weak intentions 0.96 0.57,1.62) - 1.35 0.74, 2.47) -

Attitude score More favourable attitudes 1.0 1.0

Less favourable attitudes 1.07 0.64,1.80) - 1.08 0.60,1.97) -

PBC score Higher PBC score 1.0 1.0 1.0

Lower PBC score 1.51 0.90, 2.53) 0.94 (0.51,1.73) 0.85 0.46,1.56) -

Social norm score Higher social norms 1.0 1.0

Lower social norms 1.17 0.69,1.98) - 0.72 0.40, 1.33) -

Habit strength Higher habit strength 1.0 1.0

Lower habit strength 0.97 0.58,1.63) - 1.14 0.62,2.07) -

PBC: perceived behavioural control; +: adjusted for age and sex only; J: adjusted for all other variables included in the model; SA: strongly agree; A: agree; N: neither; SD: strongly disagree; D: disagree. -: not significant in minimally adjusted models; n/a: models adjusted only for age and sex not presented. Data collected in 2009 and 2010 in Cambridge, UK.

Quantifying change in walking and cycling

We cannot be certain to what extent the computed changes in travel time represent true changes or the effects of measurement error. Although there are no validated measures of transport-

specific physical activity behaviours, the fact that few participants reported small non-zero changes ( ±15min/week) suggests that commuters' estimates of such a frequently-performed and relatively habitual behaviour may well have been relatively accurate.

Table4

Uptake and maintenance of cycling.

Uptake of cycling OR (95% CI) Maintenance of cycling OR (95% CI)

Minimally Maximally Minimally Maximally

adjusted + adjustedJ adjusted+ adjusted J

Personal and household characteristics

Age (years) n/a 1.00 (0.96,1.04) n/a 0.99 (0.97,1.01)

Gender Male 1.0 1.0

Female n/a 1.38 (0.51,3.74) n/a 1.21 (0.77,1.88)

Weight status Overweight or obese 1.0 1.0

Normal or underweight 0.98 (0.89,1.08) - 0.85 (0.60,1.22) -

Highest educational qualification Less than degree 1.0 1.0 1.0

Degree or higher 1.67 (0.71,3.89) 1.75 (0.68,4.51) 1.24(0.73, 2.10) -

Number of children One or more 1.0 1.0

None 0.77 (0.34,1.71) - 1.01 (0.63,1.59) -

Cars One or more 1.0 1.0 1.0

None 2.06 (0.80, 5.30) 0.50 (0.13, 2.00) 1.05 (0.60,1.86) -

Home ownership Does not own 1.0 1.0 1.0

Owns home 3.04(1.34,6.94) 2.32 (0.87, 6.19) 0.95 (0.54,1.68) -

Objectively measured environment

Home location Rural 1.0 1.0

Urban 1.44(0.68, 3.05) - 1.15(0.70,1.91) -

Area-level deprivation More affluent 1.0 1.0

Less affluent 1.04(0.50,2.17) - 1.20 (0.78,1.85) -

Junction density around home Lower 1.0 1.0

Higher 1.03 (0.50,2.15) - 0.86 (0.56,1.31) -

Distance to nearest railway station from home Further 1.0 1.0

Closer 1.64(0.79, 3.41) 0.94 (0.35, 2.55) 0.99 (0.65,1.53) -

Distance to nearest bus stop from home Further 1.0 1.0

Closer 0.3 (0.45,1.94) - 1.06 (0.70,1.63)

Frequency of bus services around home Less frequent 1.0 1.0 1.0 -

More frequent 3.64 (1.73, 7.67) 2.59 (0.99, 6.78) 0.91 (0.58,1.43)

Destinations within walking distance around work Lower density 1.0 1.0

Higher density 1.03 (0.49,2.16) - 0.88 (0.58,1.34) -

Geographical context of commute Commuting to the heart from within the city 1.0 1.0 1.0

Commuting to the outskirts from within the city 1.34(0.42,4.30) 1.27 (0.33,4.85) 0.76 (0.31,1.90) -

Commuting to the heart from outside the city 0.36 (0.10,1.27) 1.53 ( 0.23,10.09) 0.78 (0.34,1.78) -

Commuting to the outskirts from outside the city 0.43 (0.15,1.26) 1.34(0.22, 8.10) 1.10 (0.49,2.44)

Self-reported measures of the environment

Dangerous to cycle SD/D/N 1.0 1.0 1.0

A/SA 2.16 (0.88, 5.29) 1.49 (0.52,4.22) 0.93 (0.59,1.46) -

Convenient cycle routes SD/D/N 1.0 1.0 1.0

A/SA 2.79 (1.34,5.84) 2.48 (1.04,5.93) 1.14(0.71,1.84) -

Little traffic A/SA 1.0 1.0

SD/D/N 1.88 (0.38,9.35) - 1.12 (0.61,2.06) -

Safe to cross the road SD/D/N 1.0 1.0

A/SA 1.40 (0.67,2.95) - 1.14 (0.74,1.74) -

Self-reported distance from home to work Over 20 km 1.0 1.0 1.0 1.0

5.0-20 km 0.96 (0.36, 2.54) 0.85 (0.29, 2.56) 1.12 (0.51,2.48) 1.14(0.50,2.56)

Under 5 km 3.94(1.67,9.31) 2.36 (0.32,17.60) 1.45 (0.67,3.16) 1.57 (0.70,3.53)

Workplace car parking Free 1.0 1.0 1.0 1.0

None or paid-for 1.83 ( 0.83,4.03) 1.91 (0.73,4.99) 0.69 (0.44,1.08) 0.67 (0.42,1.05)

Psychological measures relating to car use

Intention score Strong intentions 1.0 1.0 1.0

Weak intentions 2.29 (1.08,4.86) 1.32 (0.27, 6.53) 1.19 (0.76,1.87) -

Attitude score More favourable attitudes 1.0 1.0 1.0

Less favourable attitudes 2.51 (1.18,5.33) 1.32 (0.37,4.76) 1.17 (0.74,1.87) -

PBC score Higher PBC score 1.0 1.0 1.0 1.0

Lower PBC score 1.97 (0.94,4.14) 1.26 (0.36,4.39) 0.76 (0.49,1.18) 0.70 (0.44,1.10)

Social norm score Higher social norm 1.0 1.0 1.0

Lower social norm 2.05 ( 0.93,4.53) 0.51 (0.14,1.82) 1.06 (0.69,1.62) -

Habits Higher habit strength 1.0 1.0 1.0

Lower habit strength 2.10 (0.98,4.51) 0.64 (0.13, 3.29) 1.10 (0.70,1.72) -

PBC: perceived behavioural control; +: adjusted for age and sex only; J: adjusted for all other variables included in the model; SA: strongly agree; A: agree; N: neither; SD: strongly disagree; D: disagree. -: not significant in minimally adjusted models; n/a: models adjusted only for age and sex not presented. Data collected in 2009 and 2010 in Cambridge, UK.

Modest increases in individuals' daily walking or cycling time could have important public health implications when aggregated at a population level (Rose, 1992). They may also be important for individual health outcomes, although more rigorous longitudinal evidence is required to assess whether increases in active commuting result in increases in overall physical activity and health at an individual level (Shephard, 2008).

Potential targets for intervention

Previous reviews of the environmental correlates of walking and cycling have generally reported inconsistent or null associations (Heinen et al., 2009; Panter and Jones, 2010; Saelens and Handy, 2008). In keeping with the findings of one more recent review, however (McCormack and Shiell, 2011), our longitudinal findings suggest several plausible

Predictors of uptake and maintenance of use of alternatives to the car.

Uptake of alternatives to the car OR Maintenance of alternatives to the (95% CI) car OR (95% CI)

Minimally Maximally Minimally Maximally

adjusted + adjusted' adjusted + adjusted*

Personal and household characteristics

Age (years) n/a 1.09(1.03,1.15) n/a 0.98 (0.95,1.02)

Gender Male 1.0

Female n/a 0.47 (0.15,1.45) n/a 0.83 (0.34,2.03)

Weight status Overweight or obese 1.0 1.0

Normal or underweight 1.41 (0.66,3.05) - 1.48 ( 0.75,2.95) -

Highest educational qualification Less than degree 1.0 1.0 1.0

Degree or higher 1.83 (0.78,4.29) 3.52 (1.01,12.26) 1.30 (0.61,2.75) -

Number of children One or more 1.0 1.0 1.0

None 1.17 (0.50,2.71) - 1.91 (0.94,3.89) 0.49 (0.22,1.12)

Home ownership Does not own 1.0 1.0 1.0

Owns home 4.43 (1.69,11.63) 3.33 (0.84,13.25) 1.53 (0.60,3.94) -

Neighbourhood characteristics

Home location Rural 1.0 1.0 1.0

Urban 1.44 (0.68, 3.04) - 2.14(1.06,4.29) 1.42 (0.42,4.74)

Area-level deprivation More affluent 1.0 1.0 1.0 1.0

Less affluent 1.85 (0.87,3.94) 1.64(0.56,4.85) 2.78 (1.32,5.85) 2.49 (1.02,6.07)

Junction density around home Lower 1.0 - 1.0 -

Higher 1.39 (0.67,2.89) 1.08 (0.55,2.13)

Distance to nearest railway station from home Further 1.0 - 1.0 1.0

Closer 1.07 (0.47,2.42) 2.37 (1.19,4.74) 1.28 (0.50,3.26)

Distance to nearest bus stop from home Further 1.0 - 1.0 1.0

Closer 0.95 (0.44,2.02) 1.67 (0.84,3.30) 1.86 (0.82,4.24)

Frequency of bus services around home Less frequent 1.0 1.0 1.0 -

More frequent 1.87 (0.84,4.17) 1.86(0.48, 7.11) 0.72 (0.36,1.47)

Destinations within walking distance around work Lower density 1.0 1.0 1.0 1.0

Higher density 1.56 ( 0.74,3.27) 5.37(0.02,146.71) 1.56 ( 0.79,3.09) 1.52 (0.27,8.66)

Workplace characteristics

Self-reported distance from home to work Over 20 km 1.0 1.0 1.0 1.0

5.0-20 km 0.76 (0.33,1.77) 0.60(0.17, 2.11) 0.98 (0.43,2.23) 0.61 (0.19,1.99)

Under 5 km 8.88 (2.41,32.67) 6.22 (0.38,101.25) 2.89 (1.13,7.41) 0.61 (0.12,2.98)

Workplace car parking Free 1.0 1.0 1.0

No or paid for 4.42 (1.97,9.95) 22.62 (4.42,115.78) 0.81 (0.38,1.72)

Geographical context of commute Commuting to the heart from within the city 1.0 1.0 1.0 1.0

Commuting to the outskirts from within the city 0.49 (0.09,2.75) 0.86(0.01,532.09) 0.69 (0.24,2.00) 1.36 (0.20,9.17)

Commuting to the heart from outside the city 0.21 (0.04,1.05) 1.01 (0.04,24.82) 0.43 (0.15,1.25) 1.37 (0.26, 7.31)

Commuting to the outskirts from outside the city 0.18 (0.04,0.85) 0.79(0.00,419.06) 0.29 (0.11,0.81) 1.52(0.14,16.88)

Perceptions of route environment

It is pleasant to walk SD/D/N 1.0 1.0

SA/A 1.08 (0.49,2.39) - 1.37 (0.69,2.72) -

It is dangerous to cycle SA/A 1.0 1.0

SD/D/N 0.47 (0.13,1.74) - 1.22 (0.54,2.77) -

There are convenient cycle routes SD/D/N 1.0 1.0 1.0

SA/A 3.81 (1.70,8.52) 4.65 (1.45,14.92) 1.43 (0.72,2.84) -

There is little traffic SD/D/N 1.0 1.0

SA/A 1.92 (0.44,8.42) - 2.22 (0.52,9.54) -

There is convenient public transport SD/D/N 1.0 1.0

SA/A 1.02 (0.41,2.54) - 1.44(0.71,2.94) -

There are no convenient routes for walking SA/A 1.0 1.0 1.0

SD/D/N 1.60 ( 0.70,3.64) - 2.68 (1.34,5.39) 1.73 (0.77,3.86)

It is safe to cross the SD/D/N 1.0 1.0 1.0

road SA/A 1.76 (0.82,3.77) 0.85 (0.28, 2.63) 1.06 (0.54,2.10) -

Psychological measures relating to car use

Intention score Strong intentions 1.0 1.0 1.0

Weak intentions 2.41 (0.39,14.74) - 4.09 (1.93,8.68) 1.58 (0.49,5.09)

Attitude score More favourable attitudes 1.0 1.0 1.0 1.0

Less favourable attitudes 2.98 (0.94,9.44) 1.22(0.17, 9.09) 5.06 (2.35,10.87) 5.01 (1.52,16.55)

PBC score Higher PBC score 1.0 1.0 1.0 1.0

Lower PBC score 3.43 (1.06,11.11) 1.33 (0.16,11.33) 2.00 (1.00,4.03) 0.66 (0.26,1.65)

Social norm score Higher social norm 1.0 1.0 1.0 1.0

Lower social norm 10.48(1.88,58.40) 2.29(0.13,41.25) 3.00 (1.40,6.42) 0.84 (0.29,2.38)

Habits Higher habit strength 1.0 1.0 1.0 1.0

Lower habit strength 10.30(1.64,64.62) 1.60(0.08, 30.65) 4.48 (2.14,9.36) 1.79 (0.58,5.52)

PBC: perceived behavioural control; +: adjusted for age and sex only, 'adjusted for all other variables included in the model. SA: strongly agree; A: agree; N: neither; SD: strongly disagree; D: disagree. n.s.: not significant; -: not significant in minimally adjusted models; n/a: Models adjusted only for age and sex not presented. Data collected in 2009 and 2010 in Cambridge, UK.

targets for environmental interventions, such as restricting workplace parking and providing convenient routes for cycling, convenient public transport and pleasant routes for walking (Ogilvie et al., 2007; Yang et al., 2010). Their effects on commuting behaviour and physical activity are largely unknown and should be assessed in future studies.

We also found that commuters with less favourable attitudes towards car use were more likely to continue using alternatives to the car, possibly due to perceived lack of choice. Changing attitudes may be difficult, however, particularly in the car-orientated environments that typify many developed countries. The provision of more supportive environments for walking and cycling may itself result in changes in attitudes or perceptions over time and this seems an important avenue for future research. While a combination of observational analyses of longitudinal data of this kind may strengthen the evidence base for a causal pathway linking environmental change to behaviour change, further research should also elucidate the mediating mechanisms in quasi-experimental studies of actual interventions.

Other characteristics were also important predictors of behaviour. Those who lived in more deprived areas were more likely to continue using alternatives to the car, while older adults and those without children were more likely than those with children to take up walking to work. Qualitative research in this sample and elsewhere (Cleland et al., 2008; Guell et al., 2012; Pooley et al., 2012) has highlighted the importance of the social context in shaping travel behaviour. The tailoring and evaluation of interventions to promote walking and cycling should take account of these contextual considerations.

Strengths and limitations

This is one of the few longitudinal studies to provide a detailed quantification of changes in active commuting or to assess the predictors of uptake and maintenance of walking, cycling and use of alternatives to the car on the commute. Our use of a range of self-reported and objectively measured potential predictors specific to commuting, in a large cohort of healthy working commuters from urban and rural areas is an important strength. We also classified change using two complementary metrics: a detailed continuous measure of time spent walking or cycling; and a categorical measure based on the usual mode of travel, that might more accurately reflect habitual travel behaviour.

Our findings may not be generalisable to other contexts where cycling is less prevalent. Only 56% of participants provided data at follow-up, and although travel mode was not associated with dropout, the attrition of the cohort limits the generalisability of our observations. Our sample also contained a higher proportion of participants educated to degree level and a smaller proportion of obese adults than the population of Cambridgeshire (Office of National Statistics, 2011). While our measure of time spent walking and cycling improves on many instruments used previously (Ogilvie et al., 2004), we did not collect information on the time spent walking or cycling on each day. We also lacked information on measures of socio-economic status or workplace facilities for cyclists, which may influence commuting behaviour. Relatively few participants had changed their usual travel mode(s), which may have limited our power to detect associations. Further investigation in larger samples with data collected at multiple time points over a longer time period would be warranted.

Conclusions

In this longitudinal study, we found a lack of empirical support for many of the putative predictors of travel behaviour change suggested by findings from cross-sectional studies. Only a few were found to be important; based on these findings, interventions to restrict workplace parking and provide convenient routes for cycling, convenient public transport and pleasant routes for walking to work appear to hold

promise. Their effects on travel behaviour are, however, largely unknown and further studies are required to establish these.

Conflict of interest statement

The authors declare that there are no conflicts of interest.

Acknowledgments

The Commuting and Health in Cambridge study was developed by David Ogilvie, Simon Griffin, Andy Jones and Roger Mackett and initially funded under the auspices of the Centre for Diet and Activity Research (CEDAR), a UKCRC Public Health Research Centre of Excellence. Funding from the British Heart Foundation, Economic and Social Research Council, Medical Research Council, National Institute for Health Research and the Wellcome Trust, under the auspices of the UK Clinical Research Collaboration, is gratefully acknowledged. The study is now funded by the National Institute for Health Research Public Health Research programme (project number 09/3001/06: see http://www.phr.nihr.ac.uk/ funded_projects). David Ogilvie and Simon Griffin are supported by the Medical Research Council [Unit Programme number MC_UP_1001/ 1].Jenna Panter is now supported by an NIHR post-doctoral fellowship. The views and opinions expressed herein are those of the authors and do not necessarily reflect those of the NIHR PHR programme or the Department of Health. The funders had no role in study design, data collection and analysis, the decision to publish, or the preparation of the manuscript. The study was approved by the Hertfordshire Research Ethics Committee (reference numbers 08/H0311/208 and 09/H0311/ 116). We thank all staff from the MRC Epidemiology Unit Functional Group Team, in particular for the study coordination and data collection (led by Cheryl Chapman), physical activity data processing and data management.

Appendix A. Supplementary data

Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.ypmed.2013.07.020.

References

Bauman, A.E., Reis, R.S., Sallis, J.F., Wells, J.C., Loos, R.J.F., Martin, B.W., 2012. Correlates of physical activity: why are some people physically active and others not? Lancet 380, 258-271.

Beenackers, MA., Foster, S., Kamphuis, C.B.M., et al., 2012. Taking up cycling after residential relocation: built environment factors. Am. J. Prev. Med. 42, 610-615. Cleland, V.J., Timperio, A., Crawford, D., 2008. Are perceptions of the physical and social environment associated with mothers' walking for leisure and for transport? A longitudinal study. Prev. Med. 47,188-193. Dalton, A., Jones, A., Panter, J., Ogilvie, D., 2013. Neighbourhood, route and workplace-related environmental characteristics predict adults' mode of travel to work. PLoS One 8, e67575.

Das, P., Horton, R., 2012. Rethinking our approach to physical activity. Lancet 380, 189-190.

Guell, C., Panter, J., Jones, N., Ogilvie, D., 2012. Towards a differentiated understanding of active travel behaviour: using social theory to explore everyday commuting. Soc. Sci. Med. 75, 233-239.

Hamer, M., Chida, Y., 2008. Active commuting and cardiovascular risk: a meta-analytic review. Prev. Med. 46,9-13. Hardeman, W., Kinmonth, A., Michie, S., Sutton, S., the ProActive Project Team,, 2009. Impact of a physical activity intervention program on cognitive predictors of behaviour among adults at risk of type 2 diabetes (ProActive randomised controlled trial). Int. J. Behav. Nutr. Phys. Act. 6,16. Heinen, E., van Wee, B., Maat, K., 2009. Commuting by bicycle: an overview of the literature. Transp. Rev. 30, 59-96. Hosmer, D., Lemeshow, S., 1989. Model-building Strategies and Methods for Logistic

Regression, Applied Regression. Wiley, New York 82-134. Li, F., Fisher, J., Brownson, R., 2005. A multilevel analysis of change in neighborhood walking activity in older adults. J. Aging Phys. Act. 13,45-59. McCormack, G., Shiell, A., 2011. In search of causality: a systematic review of the relationship between the built environment and physical activity among adults. Int. J. Behav. Nutr. Phys. Act. 8,125. Michael, Y.L., Perdue, L.A., Orwoll, E.S., Stefanick, M.L., Marshall, L.M., 2010. Physical activity resources and changes in walking in a cohort of older men. Am. J. Public Health 100,654-660.

National Institute for Health and Clinical Excellence, 2012. Walking and cycling: local measures to promote walking and cycling as forms of travel or recreation. NICE public health guidance 41. http:///www.guidance.nice.org.uk/ph41 (Last accessed: 20.09.13).

Office for National Statistics, 2011. Neighbourhood statistics. Available at: http://www. neighbourhood.statistics.gov.uk/dissemination/ (Last accessed: 20.09.13).

Ogilvie, D., Egan, M., Hamilton, V., Petticrew, M., 2004. Promoting walking and cycling as an alternative to using cars: systematic review. BMJ 329, 763-766.

Ogilvie, D., Foster, C., Rothnie, H., et al., 2007. Interventions to promote walking: systematic review. BMJ 334,1204-1207.

Ogilvie, D., Griffin, S., Jones, A., et al., 2010. Commuting and health in Cambridge: a study of a 'natural experiment' in the provision of new transport infrastructure. BMC Publ. Health 10, 703.

Panter, J.R., Jones, A.P., 2010. Attitudes and the environment: what do and don't we know? J. Phys. Act. Health 7, 551-561.

Panter, J., Griffin, S., Jones, A., Mackett, R., Ogilvie, D., 2011. Correlates of walking and cycling to work: baseline results from the commuting and health in Cambridge study. Int. J. Behav. Nutr. Phys. Act. 8,124.

Panter, J., Desousa, C., Ogilvie, D., 2013. Incorporating walking or cycling into car journeys to and from work: the role of individual, workplace and environmental characteristics. Prev. Med. 56 (3-4), 211-217.

Pooley, C., Tight, M., Jones, T., Horton, D., Schieldeman, G., Jopson, A., 2012. Understanding Walking and Cycling: Summary of Key Findings and Recommendations.

Pratt, M., Sarmiento, O., Montes, F., et al., 2012. The implications of megatrends in information and communication technology and transportation for changing global physical activity. Lancet 380,282-293.

Rose, G., 1992. The Strategy of Preventive Medicine. Oxford University Press, New York

Saelens, B.E., Handy, S.L., 2008. Built environment correlates of walking: a review. Med. Sci. Sports Exerc. 40, 550-566.

Sallis, J.F., Owen, N., 2002. Ecological models of health behavior. In: Glanz, K., Lewis, F.M., Rimer, B.K. (Eds.), Health Behaviour and Health Education: Theory, Research, and Practice. Jossey-Bass, San Francisco, pp. 462-484.

Shephard, RJ., 2008. Is active commuting the answer to population health? Sports Med. 38, 751-758.

Verplanken, B., Orbell, S., 2003. Reflections on past behavior: a self reported index of habit strength. J. Appl. Soc. Psychol. 33,1313-1330.

Wells, N.M., Yang, Y., 2008. Neighborhood design and walking: a quasi-experimental longitudinal study. Am. J. Prev. Med. 34,313-319.

World Health Organisation, 2000. Obesity: preventing and managing the global epidemic. Report of a WHO Consultation. World Health Organisation, Geneva, pp. 1-253.

Yang, L., Sahlqvist, S., McMinn, A., Griffin, S., Ogilvie, D., 2010. Interventions to promote cycling: systematic review. BMJ 341, c5293.

Yang, L., Griffin, S., Chapman, C., Ogilvie, D., 2012. The feasibility of rapid baseline objective physical activity measurement in a natural experimental study of a commuting population. BMC Publ. Health 12,841.