Psychosocial and environmental factors associated with cycling for transport among a working population

HEALTH EDUCATION RESEARCH Vol.23 no.4 2008 Pages 697 708 Advance Access publication 17 October 2007 Psychosocial and environmental factors associated with cycling for transport among a working population Bas de Geus 1, Ilse De Bourdeaudhuij 2, Caroline Jannes 2 and Romain Meeusen 1 * Abstract The aim of this study was to examine psychosocial and environmental predictors of cycling for transportation. A sample of 343 Flemish adults (43% men) living at maximum 10 km from their workplace was surveyed. Self-report measures of cycling, demographic variables, psychosocial variables, self-efficacy, perceived benefits and barriers and environmental attributes (destination, traffic variables and facilities at the workplace) of cycling for transport were obtained by means of a mailing questionnaire. Modeling and social support by accompanying, external self-efficacy, ecological economic awareness and lack of time and interest were positively associated with the likelihood of cycling for transport and varied in importance between cyclists and non-cyclists. Cyclists estimate the time to destination shorter than noncyclists and indicate to have more facilities for cyclists at the workplace. The results suggest that when people live in a setting with adequate bicycle infrastructure, individual determinants (psychosocial, self-efficacy, perceived benefits and barriers) outperform the role of environmental determinants in this sample. Promotion campaigns aimed at increasing cycling for transportation should focus on creating social support by encouraging cycling with partners, 1 Department of Human Physiology and Sports Medicine, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium and 2 Department of Movement and Sport Sciences, Ghent University, Watersportlaan 2, 9000 Ghent, Belgium *Correspondence to: R. Meeusen. E-mail: rmeeusen@vub.ac.be increasing self-efficacy, raising ecological and economic awareness, decreasing lack of time and interest barriers and providing facilities for cyclists at the workplace. Introduction Despite several decades of warning about the potentially negative health consequences of a sedentary lifestyle, a large proportion of adults in Europe [1] and North America [2] are physically inactive. In Flanders (Belgium), minimum 66% of women and 40% of men in all age groups are physically inactive [3]. There is incontestable evidence from observational and randomized trials that regular physical activity contributes to the primary and secondary prevention of cardiovascular disease and several other chronic conditions and that it is associated with a reduced risk of premature death [4 6]. Regular participation in physical activity and exercise are also associated with a decrease in mental health problems such as anxiety and depression [4] and are also associated with better quality of life and health outcomes [7]. Public health recommendations emphasize the need to accumulate physical activity of at least moderate intensity (three to six metabolic equivalents) on most days of the week, including walking and cycling [2]. Commuter cycling is an excellent way of exercising, which fits well into the daily life routine [8, 9]. Cycling during commuting to work provides a possibility for frequent and regular health-enhancing physical activity to large numbers of people in the working population [10]. Ó The Author 2007. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oxfordjournals.org doi:10.1093/her/cym055

B. de Geus et al. To influence commuter cycling, it is necessary to identify the factors that can be changed, so that relevant policies and effective interventions can be developed [11]. Possible correlates of commuter cycling include individual factors and environmental factors. Compared with individual-based intervention strategies, community and environmental approaches may increase the chance of affecting a greater percentage of the underactive population [12] with potentially lower costs per person [13]. Studies based on a variety of models have shown the importance of attitudes, motives, perceived benefits and barriers, self-efficacy, social influence of family and friends and the intention to change behavior (for an overview, see Sallis and Owen [14]) for participation in general physical activity. Most of the studies have been conducted in the United States [15 17] or in Australia [18 22], with only a few in Europe or Flanders (Belgium) [23, 24]. Reviews concerning these topics indicate that environmental factors, like accessibility, aesthetic attributes, convenience of facilities or others, have an influence on general physical activity [25] and walking for particular purposes [26]. Several authors [17, 19, 27] highlight the importance of distinguishing between transportation and recreational physical activity because the environmental factors influencing these activities tend to differ. Hoehner et al. [17] suggest that the physical environment may affect transportation activity more than recreational activity. There has been little health research examining factors that influence transport-related cycling [19, 25, 28]. Therefore, the aim of the present study was to investigate the difference in perception between cyclists (C) and non-cyclists (NC) and to look for the prediction of four major psychosocial factors on cycling status: (i) psychosocial variables (social influence, social norm, modeling and social support), (ii) self-efficacy, (iii) perceived benefits and (iv) perceived barriers, together with environmental correlates such as (i) destination, (ii) traffic variables in the neighborhood and on the road to work and (iii) facilities for cyclists during cycling for transport. Methods Procedure A cross-sectional design was used, analyzing selfreported survey data collected from a questionnaire. A National Health surveillance company announced the study in their newsletter, distributed in Flanders, asking the members to fill out the questionnaire online. If the respondents had no possibility to fill out the questionnaire online, a paper form was filled out. In order to be sure that enough cyclists would fill out the questionnaire, local cycle communities, concerned with cycling for transport, were contacted. No cycling unions organizing recreational or competitive cycling trips were contacted. The Vrije Universiteit Brussel ethical committee approved the study. Participants Eight hundred and twenty one questionnaires were returned. Inclusion criteria for this study were as follows: age between 18 and 65 years, living in Flanders (Belgium), having a paid job outside home, living at <10 km from the workplace and having no injury or illness affecting the person s ability to be physically active. After applying the selection criteria and data cleaning, 343 participants remained (42%). Characteristics of the study population are included in Table I. The total group was divided into a cycling group and a non-cycling group. The cycling group was defined as those who cycled at least once a week to work in the last 6 months prior to the start of the Table I. Self-reported anthropometric data Cyclists (n = 189) Non-cyclists (n = 154) Men (% total) 25 18 Women (% total) 30 27 Age [mean years (SD)] 40.4 (8.8) 41.9 (9.5) Height [mean cm (SD)] 172.6 (9.1) 172.5 (8.3) Weight [mean kg (SD)] 71.5 (13.6) 73.0 (14.2) BMI [mean kgm 2 (SD)] 23.9 (3.5) 24.4 (3.8) 698

Cycling in Flanders, Belgium study. In the final sample of 343 answers, 189 (55%) were C and 154 (45%) were NC. Fifty-seven percent of the respondents were women, which is comparable with the Flemish population (51%) [29]. In our study population, more respondents had a higher education in comparison with the Flemish population (30%) [29]. The body mass index (BMI) is somewhat higher in the Flemish population (24.8 kmm 2 ) [29] in comparison with our study population. Measures The development of the questionnaire was based on two existing questionnaires [23, 30]. Each participant completed a questionnaire, which consisted of three parts to obtain information on (i) demographic variables, (ii) the psychosocial correlates of cycling for transport and (iii) the environmental correlates of cycling for transport. Cycling for transport was assessed with a single question and defined as those who cycled at least once a week to work in the last 6 months prior to the start of the study. The demographic variables This part of the questionnaire requested details about gender, age, height, weight, the highest level of education, working situation, distance and frequency of traveling to work and living area. Dichotomous variables were constructed for education (lower: vocational or technical training and general education; higher: college or university) and living area (town of <30 000 inhabitants or town of >30 000 inhabitants). Demographic variables are listed in Table II. The psychosocial correlates of cycling for transport Each participant completed a questionnaire including social variables, self-efficacy and perceived benefits and barriers, as previous research showed that these were the modifiable variables with the strongest evidence of association with physical activity [14]. All psychosocial correlates were asked in relation to cycling for transportation. A summary of the measures of psychosocial correlates is presented in Table III. Table II. Demographic variables Cyclists (n = 189) Education* Lower (technical and vocational) 35.4 54.5 Higher (general and higher) 64.6 45.5 Living environment* City (>100 000 inhabitants) 45.7 32.2 City (30 000 100 000 inhabitants) 16.0 15.1 Suburbs (1000 30 000 inhabitants) 36.2 41.1 Countryside (<1000 inhabitants) 2.1 8.6 Distance to work (km one way) <3 km 14.3 11.1 3 5 km 17.8 14.6 >5 km 23.0 19.2 Frequency to work per week (one way)* <3 times a week 0.0 2.6 3 to 5 times a week 38.9 26.0 >5 times a week 16.4 16.1 Non-cyclists (n = 154) Values are percentage of total. Pearson chi-square showed a statistical difference for education, living environment and frequency to work: * P <0.01. The environmental correlates of cycling for transport Variables of interest were neighborhood environmental variables believed to be related to cycling for transportation in the neighborhood and on the road to work. To measure for the hypothesized environmental correlates of cycling in the neighborhood, an existing questionnaire was used [23]. The scales, scale composition, sample items and response categories are listed in Table III. Neighborhood environmental variables included destinations (time to go to destinations with the bicycle and access to local shopping or other destinations) and traffic variables in the neighborhood. To measure the hypothesized environmental correlates of cycling to work, the same questionnaire of De Bourdeaudhuij et al. [23] was used. Questions related to cycling facilities at the workplace and traffic variables on the road to work were constructed (Table III). Statistical analyses Analyses were carried out using SPSS 14.0 software (SPSS Inc., Chicago, IL, USA). Independent 699

B. de Geus et al. Table III. Summary of measures of psychosocial correlates and facilities for cyclists, safety in the neighborhood and safety on the road to work Scale (composition) Response category Cronbach s a Cycling general Individual Psychosocial Total score Social influence Five-point scale a 0.73 Social norm Five-point scale b 0.84 Modeling Five-point scale a 0.54 Social support: accompany Five-point scale a 0.52 Social support: encourage Five-point scale a 0.65 Self-efficacy Total score Three-point scale c 10 items 0.86 Internal self-efficacy 6 items 0.82 External self-efficacy 4 items 0.74 Perceived benefits Total score Five-point scale b 21 items 0.92 Physical well-being 9 items 0.90 Psychosocial 6 items 0.80 Ecological economic awareness 3 items 0.86 Body image 3 items 0.74 Perceived barriers Total score Five-point scale a 17 items 0.87 Lack of skills and health 5 items 0.77 Lack of time 4 items 0.76 Lack of motivation 4 items 0.76 External obstacles 4 items 0.74 Environmental Destinations Total score Five-point scale d 12 items 0.93 Food shops 4 items 0.85 Non-food shops 6 items 0.89 Work 1 item Bus, tram or metro stop 1 item Traffic variables in the neighborhood Total score Five-point scale b 15 items 0.52 Traffic danger 6 items 0.79 Bicycle lanes 3 items 0.74 Crime 3 items 0.64 Traffic safety 2 items 0.40 Cycling to work Facilities for cyclers at the workplace Total score Two-point scale e 5 items 0.58 Traffic variables on the road to work Total score Five-point scale b 11 items 0.43 Traffic danger 3 items 0.71 Bicycle lanes 4 items 0.51 Crime 4 items 0.42 a Five-point scale from 1 (never) to 5 (very often). b Five-point scale from 1 (strongly disagree) to 5 (strongly agree). c Three-point scale from 1 (know I can do it) to 3 (know I cannot do it). d Five-point scale from 1 (1 5 min) to 5 (>30 min). e Two-point scale 1 (yes) and 2 (no). 700

Cycling in Flanders, Belgium t-tests were carried out to analyse for differences in descriptive variables (age, length, weight, body mass index) between C and NC. To study for differences in demographic variables such as gender, education, living environment, distance and frequency to work between the C and NC group, Chi-Square tests were carried out. Items in each factor were summed to provide a total score for each category of psychosocial variables, self-efficacy, perceived benefits and barriers, destinations, facilities for cyclists at the workplace and traffic variables in the neighborhood and on the road to work. These summed scores were then divided by the number of items in each category. To investigate the difference in perception between C and NC, one-way analyses of variance were carried out. Outcome measures for psychosocial variables, self-efficacy, perceived benefits and barriers, destinations, facilities for cyclists at the workplace and traffic variables in the neighborhood and on the road to work were dichotomized at the median score and analyzed separately. Further, to study for the prediction of the four major groups of psychosocial determinants to cycling status, logistic regression analysis was conducted. Since education level was significantly different between the C and the NC, it was incorporated as a covariate, but did not influence the results. Results The values of C and NC for the anthropometric variables are reported in Table I. No statistical difference was found for gender, age, length, weight and BMI between C and NC. C report more often to be in good health and non-smokers or ex-smokers than NC. The percentage of C and NC for the demographic variables are reported in Table II. Pearson chisquare showed a statistical difference for living environment (v 2 = 12.16; P < 0.01). The larger the living environment, the more participants cycled. Living environment was further dichotomized into high and low categories using a median split. People reporting to live outside a big city (<30 000 inhabitants) were 56% (P < 0.008) more likely to be NC [confidence interval (CI 95%): 0.36 0.86]. Although no statistical difference was found between C and NC for distance to work (v 2 = 0.076; P = 0.963), C traveled more frequently to work (v 2 = 14.10; P = 0.01) than NC. Pearson chi-square showed a statistical difference for education (v 2 = 12.56; P = 0.001). Differences between C group and NC group Psychosocial The C group perceives more psychosocial support from their surrounding (Table IV). The C group indicated to have more often a cycling partner (social influence) [F (1,268) = 4.03, P < 0.05; g 2 = 0.015] who cycles with them than the NC group. The C group also indicated that significant others stimulate them (social norm) [F (1,268) = 5.35, P < 0.05; g 2 = 0.020], go cycling with them (social support accompany) [F (1,268) = 19.94, P < 0.001; g 2 = 0.067] and/or without them (modeling) [F (1,268) = 10.63, P < 0.001; g 2 = 0.040]. Self-efficacy The C group indicated a stronger internal [F (1,287) = 19.33, P < 0.001; g 2 = 0.064] and external [F (1,287) = 42.23, P < 0.001; g 2 = 0.129] self-efficacy than the NC group. Perceived benefits For the perceived benefits of cycling, the difference between C and NC was not significant, except for the ecological economic awareness. The C group perceives the economic and ecological advantage of cycling as more important than the NC group [F (1,298) = 6.49, P < 0.011; g 2 = 0.021]. Perceived barriers Perceived barriers seem to be different between both groups. The different subscales measuring perceived barriers to cycle were all significantly different between C and NC at the 0.1% level. NC perceived more lack of skills and health problems, 701

B. de Geus et al. Table IV. Differences between cyclists and non-cyclists and ORs and CIs of multiple binary logistic regressions for psychosocial factors, self-efficacy and perceived benefits and barriers for cycling in the neighborhood for transport and on the road to work Scale Cyclists mean (SD) Non-cyclists mean (SD) F P OR P CI 95% Cycling general Individual Psychosocial Social influence 2.57 (1.23) 2.26 (1.29) 4.03* 0.046 0.98 0.94 0.58 0.167 Social norm 3.74 (1.23) 3.38 (1.34) 5.46* 0.020 1.30 0.377 0.73 2.33 Modeling 2.95 (1.16) 2.50 (1.08) 11.07*** 0.001 1.83* 0.043 1.02 3.27 Social support: 2.74 (0.93) 2.23 (0.89) 19.16*** 0.000 2.26** 0.012 1.20 4.27 accompany Social support: 2.68 (1.13) 2.47 (1.02) 2.47 0.109 0.66 0.183 0.35 1.22 encourage Self-efficacy Internal self-efficacy 1.52 (0.47) 1.79 (0.52) 19.33*** 0.000 0.61 0.078 0.35 1.10 External self-efficacy 1.56 (0.51) 1.99 (0.59) 42.23*** 0.000 0.32*** 0.001 0.19 0.56 Perceived benefits Physical well-being 4.09 (0.68) 3.96 (0.80) 1.89 0.170 1.05 0.845 0.62 1.80 Psychosocial 3.01 (0.83) 2.88 (0.93) 1.98 0.161 1.25 0.388 0.75 2.10 Ecological economic 4.48 (0.79) 4.21 (1.00) 6.19* 0.011 1.71* 0.029 1.06 2.78 awareness Body image 2.66 (0.95) 2.88 (1.04) 2.79 0.096 0.63 0.080 0.35 1.06 Perceived barriers Lack of skills and 1.37 (0.54) 1.61 (0.74) 11.11** 0.003 0.92 0.777 0.53 1.60 health Lack of time 1.96 (0.71) 2.60 (0.89) 52.26*** 0.000 0.26*** 0.001 0.15 0.45 Lack of interest 1.89 (0.76) 2.58 (0.98) 48.50*** 0.000 0.45** 0.003 0.27 0.76 External obstacles 2.23 (0.79) 2.66 (0.83) 19.31*** 0.000 1.03 0.916 0.59 1.81 Environmental Destinations (min) Food shops 7.95 (4.59) 10.77 (6.67) 19.61*** 0.000 0.60 0.058 0.35 1.02 Other shops 12.05 (6.51) 15.67 (8.68) 17.82*** 0.000 0.75 0.278 0.44 1.27 Work 21.76 (10.24) 23.66 (11.62) 6.89** 0.009 0.77 0.293 0.49 1.42 Bus, tram or metro stop 6.91 (4.92) 8.70 (7.14) 2.37 0.124 0.83 0.494 0.48 1.25 Traffic variables in the neighborhood Traffic danger 2.81 (0.53) 2.73 (0.61) 1.70 0.194 1.02 0.93 0.63 1.65 Bicycle lanes 2.05 (0.64) 2.08 (0.70) 0.20 0.658 0.70 0.13 0.43 1.12 Crime 1.76 (0.48) 1.80 (0.53) 0.47 0.495 0.63 0.14 0.34 1.16 Traffic safety 2.49 (0.55) 2.42 (0.56) 1.08 0.299 1.27 0.36 0.76 2.11 Cycling to work Facilities for cyclists at the workplace 1.56 (0.24) 1.74 (0.25) 38.90*** 0.000 0.28*** 0.001 0.18 0.45 Traffic variables on the road to work Traffic danger 2.76 (0.63) 2.72 (0.68) 0.10 0.556 1.30 0.26 0.83 2.03 Bicycle lanes 2.42 (0.52) 2.34 (0.53) 2.13 0.145 1.48 0.12 0.90 2.41 Crime 1.96 (0.42) 1.90 (0.49) 1.20 0.273 1.06 0.80 0.66 1.70 * P < 0.05; ** P < 0.01; *** P < 0.001. 702

Cycling in Flanders, Belgium more external obstacles and more lack of time and lack of interest to cycle compared with C. Destinations When asking participants to estimate the time it would take them to go with their bicycle to different destinations like food shops or other shops, the NC group estimated the time to be significantly longer than the C group [F (1,315) = 19.61, P < 0.001; g 2 = 0.059 and F (1,315) = 17.82, P < 0.001; g 2 = 0.054, respectively]. Also, the estimated time to go to work with the bicycle differed between C and NC [F (1,315) = 6.89, P < 0.01; g 2 = 0.022]. The mean estimated time difference was ;3 min for all destinations, except for public transport stops. Facilities for cyclists at the workplace The C group indicated more often than the NC group that facilities, like showers or financial support, are available at the workplace [F (1,319) = 38.90, P < 0.001; g 2 = 0.109]. Traffic variables in the neighborhood and on the road to work No differences were found between C and NC for traffic variables in the neighborhood and on the road to work. Prediction Psychosocial Participants with relatives who cycle (modeling) and give social support through cycling with them (accompany) are more likely to cycle for transport [odds ratio (OR) = 1.83, CI 95%, 1.02 3.27, P < 0.05 and OR = 2.26, CI 95%, 1.20 4.27, P < 0.05, respectively]. Social influence, social norm and encouragement did not predict C or NC. Self-efficacy Participants reporting high levels of external selfefficacy (cycling even if the weather is bad, have to do shopping, etc.) are more likely to take the bicycle for transport (OR = 0.32, CI 95%, 0.19 0.56, P < 0.001). Perceived benefits Ecological economic awareness (cycling is cheaper, better for the environment, etc.) seems to play an important role in predicting cycling (OR = 1.71, CI 95%, 1.06 2.78, P < 0.05). Perceived barriers Lack of time (job and family requirements, etc.) (OR = 0.26, CI 95%, 0.15 0.45, P < 0.001) and lack of interest (no interest, self-discipline, etc.) (OR = 0.45, CI 95%, 0.27 0.76, P < 0.005) are important reasons why participants are less likely to cycle for transport. Destinations Examination of the ORs showed that the estimated time to go to destinations seemed not to predict participation of cycling. Facilities for cyclists at the workplace The availability of cycle facilities at the workplace seems to be associated with cycling for transport (OR = 0.28, CI 95%, 0.18 0.45, P < 0.001). Traffic variables in the neighborhood and on the road to work Examination of the ORs showed that traffic variables in the neighborhood and on the road to work seemed not to predict participation of cycling to work. Discussion The current study incorporated psychosocial and environmental factors to explain participation in cycling for transport. Higher education was associated with more cycling to work. This finding is in accordance with previous studies that have found higher physical activity levels in more educated people [4], the use of Bikeway [15], a more frequent use of walking paths [31] and an increased proportion of walking for exercise within the past 2 weeks [16]. In this study, people reporting high levels of social support and modeling were also more likely to 703

B. de Geus et al. cycle. These results are in accordance with the review by Trost et al. [27] in which social support emerged as a consistently important correlate for physical activity in general. De Bourdeaudhuij et al. [24] found that walking and cycling for transportation and walking for recreation were related to social support from family and/or friends. Also, Duncan and Mummery [21] reported that people reporting high levels of social support were 65% more likely to participate in recreational walking than those who reported low levels of social support when adjusting for the identified sociodemographic variables. Social support could be an important influence on the likelihood of attaining sufficient activity and of participating in walking in the presence of environmental influences when adjusting for other psychosocial variables [21]. As De Bourdeaudhuij and Sallis [30] stated, this social supportive environment must be differentiated from the perceived social benefits for physical activity in general. The perceived social benefits refer to the social reasons people give for their exercise participation such as being together with family members or friends, etc., which did not contribute significantly to physical activity in general [30] and more specific to cycling for transport. As the most important social variable was support from significant others who accompanied the participant in physical activity [30], McAuley et al. [32] argued that the formation of buddy groups for cycling may also provide a strong source of social support. In this study, people reporting high levels of external self-efficacy were more likely to cycle. In a recent review by Trost et al. [27], physical activity self-efficacy emerged as the most consistent correlate of general physical activity behavior. Selfefficacy was also related to physical activity in Flanders (Belgium) [30]. Amidst all sorts of activity (walking, moderate-intensity activity and vigorousintensity activity), self-efficacy is the strongest direct correlate of physical activity [33]. Hence, it would be useful to enhance self-efficacy to stimulate the non-cycling population into cycling activity. However, enhancing self-efficacy is a complex task. A gradual increase in cycling in sedentary individuals living close to their worksite may permit avoidance of aversive emotional states threatening self-efficacy [30]. In this study, results suggest that the ecological economic awareness (cycling is cheaper and better for the environment) as sole perceived benefit was associated with cycling. This shows that cycling can be considered as an economic or moral choice of participants. More focus could be given to both aspects in commuter cycling interventions. Perceived barriers (psychological and health, lack of time, lack of interest and external obstacles) differed significantly (P < 0.001) between C and NC. Also, the ORs indicated that those who perceive barriers (lack of time and lack of interest) for cycling are more often the NC. The current findings seem to be supported by previous results. In a review by Trost et al. [27], barriers (lack of time, too tiring, bad weather, etc.) to general physical activity emerged as a strong influence. In the study of King et al. [34] in a sample of women 40 years or older, it was found that being too tired and having a lack of energy was associated with less activity. Furthermore, Wilcox et al. [35] investigated the influence of the living environment (rural versus urban) in a similar population. They found that for both groups, perceived barriers (as a combined scale for different items) were associated with less leisure time physical activity. Factors that influence the choice to use motorized or non-motorized transport are based primarily on two fundamental aspects of the land use: (i) proximity (distance) and (ii) connectivity (directness of travel) [36]. In order to show that cycling to work and other destinations (bakery, super market, etc.) is feasible in Flanders, we asked the participants to estimate the time they would spend or spend for going to destinations by bicycle. The results showed that the mean estimated time spent on traveling by bicycle to food and other shops and stops for public transport is <15 min. This is in accordance with figures from other studies in Flanders where two-third of the car trips are <7.5 km [37] and in the United States, where the majority of non-working trips are within walking and cycling distance [38]. Moreover, it was found that the NC always seem to estimate the time as longer than 704

Cycling in Flanders, Belgium the C. A possible hypothesis would be that this resulted from the fact that more C lived in the city, where shops and other destinations like stops for public transport are closer to each other. However, including the living environment as a covariate did not show differences in the results. Another explanation could be that NC have a wrong perception of the time it would take to do the trip by bicycle. Those participants who cycle for transportmaybemore aware of the time it takes to cycle and report their perception of the environment accordingly. The time it would take the participants to go to stops for public transport is for C and NC <10 min. It could be a positive indicator to stimulate those who live too far from their work to go by bicycle and to combine public transport and the use of a bicycle. In the study of Hoehner et al. [17], having public transit stops nearby (<400 m) was associated with engaging in active transportation, although statistical significance was not achieved for all ORs. In this study, traffic variables asked participants about traffic danger (risk of accident with a motorized vehicle, busy streets, etc.), cycle lanes (cycling lanes are present in the neighborhood and in good condition, etc.), crime (fear for crime makes cycling not possible, etc.) and traffic safety (the speed of motorized vehicle is mostly slow and streetlights are present). No significant difference in perceived traffic variables was found between C and NC, and these environmental factors did not influence participation in cycling for transport. These results differ from other studies [15, 17, 25, 28, 38, 39]. In the study of Hoehner et al. [17], cycling for transportation was significantly associated with perceiving bicycle lanes to be present on most streets in the community. Also, Troped et al. [15] found that increases in self-reported and objective [Geographic Information System (GIS)] distance were associated with decreased likelihood of bicycle use. People walk [28, 38] and cycle [38] more for transport when their neighborhoods have higher residential density, a mixture of land uses (e.g. shops are within distance of homes), connected streets (e.g. grid-like pattern of many culde-sacs) and visual aesthetics. In the study of Wendel-Vos et al. [39], participants living in a neighborhood with a larger square area of sport grounds close to home spend more time on cycling in general and those living in a neighborhood with a larger square area of parks close to home spent more time on cycling for commuting purposes. Additionally, cycling for commuting purposes was associated, independent of gender, age and educational level, with the square area of parks in neighborhoods with a 300-m radius. In a review of Humpel et al. [25], it was stated that accessibility, opportunities and aesthetic attributes seemed to be significantly associated with physical activity in general. To our knowledge, no other studies are available looking at safety and crime factors related to cycling for transportation in adults. Studies investigating these factors for general physical activity or walking showed no or mixed results [17, 18, 21, 25, 34, 35]. It could be important to distinguish between transportation and recreational physical activity when looking for the contribution of the environment in engaging in walking and physical activity [17, 26]. The results of the study of Hoehner et al. [17] suggested that the physical environment might affect transportation activity more than recreational activity. When investigating the influence of the environment on cycling for transportation, the environmental setting plays a crucial role. We believe that in Flanders, there is at least a good possibility to cycle. The basic infrastructure is available in most places, and roads and cities are constructed with the idea that there might be cyclists on the road, which is not the case in many US metropolitan regions [40]. However, data from a random sample of residents in Flanders [37] showed that citizens are mostly not satisfied with the availability and condition of bicycle lanes. Only 1 in 10 households was satisfied. This is in accordance with our finding. Both the C and the NC indicate that they are not satisfied with the bicycling lanes. The decision to cycle for transport will also be influenced by factors like private car ownership, compatible distances for walking or cycling and the availability of mass transit. Data from the NIS [37] indicate that in Flanders, there is approximately one car for two citizens (all ages confound), which means that private car ownership is high. 705

B. de Geus et al. About 70% of all trips are <10 km and of all the trips made by car 67% are <10 km. Mass transit is readily available, with 50% of the citizens in Flanders being satisfied with public transport in their neighborhood [37]. The results suggest that physical environmental factors are not essential in predicting cycling for transportation or travel to work in a population living in Flanders at <10 km from their workplace. The influence of individual and social factors seems to be more predictive in distinguishing between C and NC. Our results are largely in accordance with Moudon et al. [40] who states that cycling takes place irrespective of environmental prompts and barriers independently from traffic conditions and seems to rest largely on personal factors. In the study of De Bourdeaudhuij et al. [24] on physical activity, the variance explained by environmental factors was lower (1 to 8%) than by psychosocial factors (maximum 42%). However, we believe that in other environmental settings, changes in the environment might be a first necessary step to make cycling possible. Efforts need to be made by companies and/or the authorities to stimulate the presence of favorable conditions for cyclists at the workplace (showers, shelters, safe bicycle parks, etc.) since these facilities were important in distinguishing between both groups. This study is unique since it is the first time that psychosocial (social influence, perceived benefits and barriers and self-efficacy) and environmental correlates have been determined for commuter cycling. Cycling to work was defined as cycling at least once a week to work in the last 6 months prior to the start of study. All respondents lived at maximum 10 km from their workplace. There were several limitations of the present study. First, the data relied on self-reports of psychosocial and environmental variables and did not contain objective measures of cycling behavior or the environment. Second, the cross-sectional nature of the study did not allow any interpretation in terms of causality. Third, the recruitment of the respondents was done in an indirect way, which did not allow us to calculate the response rate. Finally, the respondents in our study had a higher education in comparison with the mean of the Flemish population [29]. Conclusion The scarcity of research examining possible correlates of cycling for transportation makes these findings important in the field. Overall, we can state that individual factors (psychosocial, self-efficacy, perceived benefits and barriers) outperformed the environmental determinates in this sample of adults living in Flanders, in which a basic cycling infrastructure is available. However, ecological models suggest that the combination of psychosocial and environmental variables will best explain physical activity [41]. The results of this study suggest that promotion campaigns aimed at increasing cycling for transportation should focus on creating social support by encouraging cycling with cycling partners, increasing self-efficacy, raising ecological and economic awareness, decreasing lack of time and interest barriers and providing facilities for cyclists at the workplace. Funding Policy Research Centre Sport, Physical Activity and Health, Flemish Government, Steunpunt Sport, Beweging en Gezondheid; Vrije Universiteit Brussel VLV 62. Acknowledgements The authors wish to acknowledge the Liberale Mutualiteit van Oost-Vlaanderen for their assistance in respondents recruitment. Conflict of interest statement None declared. References 1. Steptoe A, Wardle J, Fuller R et al. Leisure-time physical exercise: prevalence, attitudinal correlates, and behavioural correlates among young Europeans from 21 countries. Prev Med 1997; 26: 845 54. 706

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