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 t1 and t2 and change scores (t2 −t1) 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 Vorinostat 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). 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 and Saelens and Handy, 2008). Date of birth, gender, highest educational qualification, housing tenure, household composition, access to cars and bicycles, possession of a driving check details 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). 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 nearly 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 and Verplanken and Orbell, 2003). 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). Using a geographical information system (ArcGIS, version 9.3), characteristics of the areas surrounding the home, workplace and route to work were derived using t1 postcodes (Appendix B).