PANTER, JENNA R.1; JONES, ANDREW P.2; VAN SLUIJS, ESTHER M.F.1; GRIFFIN, SIMON J.1; WAREHAM, NICHOLAS J.1
It is important to improve the health of middle-aged adults to reduce the incidence of disease and combat the substantial health care costs associated with an aging population (14). Engagement in physical activity is part of a healthy lifestyle and can prevent the development of obesity, certain types of cancers, and type 2 diabetes (13). However, few adults are sufficiently active. In the United Kingdom, 40% of men and 28% of women meet the recommended levels of at least 30 min of moderate-intensity activity five times a week, whereas in those aged ≥65 yr, only 14% of men and 11% of women meet this recommendation (15).
The most commonly recommended ways to promote physical activity in older adults are participation in walking and cycling. These activities are accessible to the majority of the population and can be undertaken for a variety of purposes, including recreation and transport. Transport-related walking or cycling, known as "active transport," is most frequently undertaken as a means of traveling to and from work ("active commuting" ). Thus, for many people, active commuting provides an accessible way to integrate physical activity into daily life. Yet, despite this, only 26% of trips are made by bicycle or on foot in the United Kingdom, a prevalence that is low compared with many other European countries; in The Netherlands, for example, 47% of trips are made by active modes (6).
To design effective interventions to encourage active commuting behavior, we need to better understand the reasons why people do or do not actively commute. Existing research that has examined the environmental and psychological predictors of the behavior is equivocal. Some studies suggest that certain environmental factors, such as short distance between home and work, diverse land use mix, and well-connected streets in the neighborhood, are associated with an increased likelihood of active commuting (3). Others have reported few or no environmental characteristics to be associated with the behavior (21,24). As for the psychological factors, high self-efficacy (11), positive intentions, and strong habits for walking and cycling (10,21) have been found to be important.
A recent review highlighted that few studies have simultaneously examined the psychological and environmental predictors of active commuting behavior (27). The results of these studies are inconsistent; some have found that both psychological and environmental factors are associated with active commuting (such as de Geus et al. ), whereas others have found that only psychological factors predicted active commuting (such as Lemieux and Godin ).
Conceptual models suggest that psychological factors may mediate the associations between environmental factors and physical activity behaviors (20). For example, a lack of safe cycle paths in a neighborhood may lead residents to exhibit lower self-efficacy toward cycling, and this may result in a decreased likelihood of cycling. Yet, these potential mediating effects have not been well tested. Furthermore, a major limitation of this previous work is its reliance environmental measures captured through participant-reported perceptions rather than the use of more objective measures that quantify characteristics of the environment either via street audits or via spatial data analyzed in a geographical information system (GIS). As a result, it is unknown whether interventions should be focused on changing the actual environment or how it is perceived among those who may use it. Finally, most of the evidence to date predominantly comes from North America (e.g., Lemieux and Godin ), Australia (e.g., Ball et al. ), and Europe (e.g., de Bruijn et al. ). It may be that evidence from these areas is not generalizable to other settings such as the United Kingdom. In The Netherlands, for example, there is a strong tradition for cycling, and the United Kingdom and Australia both have distinctive urban areas that are designed with an emphasis on car use.
To address the limitations outlined, this study explores the associations between environmental and psychological factors and active commuting in a sample of older English working adults from the European Prospective Investigation into Cancer (EPIC) cohort in the county of Norfolk, England.
The EPIC-Norfolk study was designed as a prospective cohort study, and the methods of recruitment, sampling, and overall sample representativeness have been described in detail elsewhere (7). Briefly, 25,639 participants aged 45-74 yr who were registered at 121 general practices within Norwich and surrounding towns were recruited into the study (1993-1997). After the baseline health check visit, there were several follow-up assessments including a repeat health check visit (1998-2000), which was completed by 15,276 participants. In 2006, those who had not refused further participation and were available to approach (n = 13,696) were invited to complete two postal questionnaires. The first asked about domain-specific activity and is known as EPAQ2 (39) (available at http://www.srl.cam.ac.uk/epic/questionnaires/epaq2/epaq2.pdf), and the second focused on their perceptions of the environment and views on physical activity. Participants provided written informed consent, and ethical approval for the study was given by the Norfolk Research Ethics Committee.
Travel Behavior Measures
Participation in active commuting was assessed using responses to EPAQ2 (39). Participants were asked to report how often they used four types of travel mode to get to their main job (car, work or public transport, bicycle, or on foot) using the response categories of "always," "usually," "occasionally," and "never or rarely." Participants were classified as active commuters if they reported "always" or "usually" traveling to work by bicycle or on foot. Some reported multimodal travel; those who reported "always" or "usually" traveling by car or bus as well as on foot or by bicycle were recorded as nonactive commuters.
Selection of Hypothesized Correlates of Active Commuting
The environmental and psychological measures tested were based on conceptual models of behavior and previous literature. Psychological measures from the theory of planned behavior (2) and those environmental measures included in the framework developed by Pikora et al. (29) were used. Additional measures were used, which captured constructs that had been recommended as an area for future research, such as habit (21), or which were hypothesized to be associated with active commuting behavior, such as route-related environmental measures. The existing literature suggested that both perceived and objective environmental measures may be associated with active commuting behavior, and hence, both types were included here. Table 1 provides an overview of those used in the study; the derivation of which is described below.
Participants were asked to report their agreement with the following seven statements about their habits for walking and cycling for transport: Walking or cycling to get somewhere (e.g., shops, work, school) is something… 1) "I do frequently," 2) "I do automatically," 3) "I have been doing a long time," 4) "I would find it hard no to do," 5) "that belongs to my (daily, weekly, or monthly) routine," 6) "that would require effort not to do," and 7) "that is typically me." These items were derived from the Habit Strength Index (38), which assesses self-identity and automaticity of behavior. It has shown high test-retest and internal reliability (9,38) and has been validated against other measures of habit strength (37). In this study, it also had high internal reliability with a Cronbach α of 0.92.
A previously validated questionnaire (18) was used to measure perceived behavioral control (PBC), intention, instrumental attitude, affective attitude, and subjective norms for active travel behaviors. Each was assessed using two items. In addition, three items were newly developed to assess social support for walking. These consisted of statements describing situations that may encourage someone to walk regularly: seeing other people walking, having encouragement from friends or relatives, and having friends and family to walk with. All items were tested for face validity in a pilot study and were understood and completed correctly. Respondents gave agreement using a five-point Likert scale, from which mean scores were calculated.
Respondents were asked to report their level of agreement with 16 statements that could be used to describe their residential neighborhood environment. These were adapted from the Neighborhood Environment Walkability Survey (NEWS), which has been shown to be valid and reliable in US (31) and Belgian samples (8) and has been used previously in a UK sample (26). The perceptions assessed were (i) residential density, (ii) land use mix diversity, (iii) access to services, (iv) street connectivity, (v) provision of walking and cycling facilities, (vi) aesthetics, (vii) traffic safety, and (viii) safety from crime.
Objective neighborhood and route environment.
Objective assessments of neighborhood and route environmental characteristics were computed using a GIS (ESRI ArcGIS 9.2). Participants reported their home postcodes, and these were converted into a map location using Code Point, a data set that identifies the center point for all postcodes in Great Britain (25). The neighborhood of each adult was defined using a modified digital representation of the Norfolk street network (Ordnance Survey Integrated Transport Network), which was interrogated to identify the area within an approximate 10-min walk (corresponding to 800 m) of their postcode. This distance is commonly used in research examining associations between neighborhood characteristics and walking (36). The network was modified to include publicly accessible roads and pedestrian streets as well as the locations of public footpaths from maps supplied by local government.
The work locations of participants either were identified using the full address or were provided the postcode using the method previously described. The shortest route between home and work locations via the modified street network was identified using the GIS. The length of this route was calculated, and seven measures representing environmental characteristics of the zone within 100 m surrounding it were estimated (Table 1). This distance was chosen because this was felt to capture the environment that users of the route would experience, and this has been used previously in similar research (28).
Date of birth and social class were collected at health check 1. Social class was measured according to the Registrar General's occupation-based classification that uses six categories: "professional," "managerial or technical," "skilled-nonmanual," "skilled-manual," "partly skilled," and "unskilled" (16). For the purposes of analysis, participants were assigned to one of three categories: "professional, managerial, and technical," "skilled-manual and nonmanual," and "partly skilled or unskilled." Height and weight were measured by trained nurses at health check 2 and were used to derive body mass index (BMI).
Of the 13,696 invited participants, 11,050 (89.4%) and 10,883 (94.3%) responded to the EPAQ2 and the environment questionnaires, respectively. Also, 10,665 (77.9%) participants completed both EPAQ2 and the environment questionnaire and provided data on social class, gender, and date of birth at health check 1. As the cohort was recruited into the study at middle age between 1993 and 1997, by 2006 (when later follow-up surveys were administered), many of the participants were retired. For these analyses, we excluded participants who reported that they did not work (n = 7177), had a limitation that precluded walking (n = 289), failed to provide any travel data (n = 495), failed to provide either a home or work location or reported the same home and work location (n = 687), or who lived more than 10 km from work (720). Participants in the latter category were excluded because they were deemed unlikely to actively commute. This left 1297 participants for this analysis.
If participants answered less than two-thirds of the psychological and perceived environmental items, which comprised a composite score, the composite score was coded as missing. Otherwise, missing responses were conservatively imputed with the response that was least likely to be associated with active transport on the basis of findings reported in a recent review of the literature (27).
Descriptive statistics were generated to characterize participants in these analyses. Independent t-tests and χ2 tests were used to compare scores of individual, psychological, and environmental characteristics between active commuters and nonactive commuters. Simple associations were explored between all potential predictors and active commuting using logistic regression. Predictors were then selected for inclusion in multivariable regression models using a P value cut point of <0.05. Where the psychological, distance, and environmental predictors showed strong correlations with each other (r > 0.5), only the strongest predictor of active commuting was carried forward.
As the literature suggests that the prevalence of active commuting (34) and the importance of environmental predictors for walking may vary by gender (17), interactions were fitted to test for any differences by gender in the selected individual or environmental predictors.
Statistically significant differences were found for many predictors, and therefore the analyses were stratified by gender. Selected predictors were then added into multiple logistic regression models to examine the associations between active commuting and all psychological predictors (model 1), distance between home and work (model 2), and environmental predictors (model 3). In all multiple models, adjustment was made for age, BMI, and social class. To create a combined best-fit model, backward stepwise regression was used to identify the predictors from models 1 to 3 that were statistically significantly associated with active commuting (model 4).
Using the combined best-fit model, the potential mediating effects of psychological factors on the relationship between distance, environmental predictors, and active commuting were assessed using the method described by Baron and Kenny (5). Linear and logistic regression analyses were conducted (dependent on whether the factors assessed were continuous or binary) to test the associations between (i) the predictor and potential mediators and (ii) between potential mediators and active commuting, adjusting for the predictor. If statistically significant associations (P < 0.05) were observed in both these models, associations between the predictor and active commuting were compared with the potential mediator included and omitted. The percentage change in odds ratios (OR) associated with active commuting for each predictor was then calculated and these were used to assess the strength of the possible mediation (19). All analyses adjusted for other predictors included in the final model. Predictors were modeled in the same way as in the main analysis, except for the distance variable, which was modeled as a continuous measure. All analyses were performed in SPSS version 16 (SPSS, Inc., Chicago, IL).
Compared with all potentially eligible participants (who reported working and did not report a limitation; n = 3199), the participants included in these analyses (n = 1297, aged 49-80 yr) were younger (mean age = 60.4 vs 61.18 yr), had a lower BMI (25.6 vs 26.08 kg·m−2), and were more likely to be female (61.1% vs 53.1%), all P < 0.01. The majority were employed in professional, managerial or technical roles (44.2%), with 30.8% undertaking skilled work (manual and non-manual) and 15% in partly skilled or unskilled professions. Levels of active commuting in the sample were not significantly different between men and women (26.8% vs 26.5%). For both genders, the prevalence of active commuting was highly dependent on distance to work, with decreasing prevalence as distance increased (P = 0.01; Table 1). There were few differences in sample characteristics according to commuting behavior (Table 2). Two exceptions were that female active commuters were more likely to be of lower social class than female nonactive commuters and male active commuters were more likely to have lower BMI scores than their nonactive commuting counterparts.
In men, the prevalence of active commuting declined with each increase in unit of BMI (OR = 0.88, 95% confidence interval (CI) = 0.82-0.95, P = 0.01). Only social class was a statistically significant predictor of women's active commuting. Compared with women in professional or managerial roles, those having skilled (OR = 1.48, 95% CI = 1.04-2.11, P = 0.02) or partly skilled or unskilled (OR = 2.14, 95% CI = 1.35-3.38, P = 0.01) occupations were more likely to actively commute.
Table 1 shows that compared with nonactive commuters, active commuters report higher scores for all the psychological predictors except social support, indicating more positive attitudes and intentions toward active commuting (P < 0.05). They also generally lived in neighborhoods that were more supportive for walking according to both perceived and objective measures and had a shorter distance to travel between home and work (Table 2).
Table 3 presents multivariable models 1-3. In model 1, it is noteworthy that, for both men and women, habit is the strongest predictor of active commuting, and none of the other psychological predictors is statistically significant. When habit was excluded from model 1 (results not presented in the table), both perceived behavioral control (in men: OR = 2.35, 95% CI = 1.48-3.71, P = 0.01; in women: OR = 1.42, 95% CI = 1.06-1.90, P = 0.01) and intention (in men: OR = 1.30, 95% CI = 1.02-1.66, P = 0.03; in women: OR = 1.51, 95% CI = 1.23-1.85, P < 0.01) became statistically significant. Distance to work was a very strong predictor of active commuting behavior for both genders (model 2), whereas very few of the environmental predictors were statistically significant in model 3. In the combined model (Table 4), both men and women reporting stronger habits for walking and cycling and living a shorter distance from work were more likely to actively commute. In men, urban-rural status was the only additional predictor of active commuting. Women living in neighborhoods with higher road density were more likely to actively commute, whereas having a main or secondary road on the route to work was associated with a decreased likelihood.
Because habit was the only psychological factor that was featured in the final model, this was the only variable that was tested as a mediator. In men, the inclusion of habit in the regression model resulted in a 4% decrease in OR for the association between distance to work and active commuting. In women, this association was reduced by 21%. Furthermore, in women, OR for the association between active commuting and the presence of a main or secondary road on the route and road density were reduced by 5% and 8%, respectively. These reductions in OR suggest that habit may partly mediate the association between environmental factors and active commuting, although the direct effects of environmental factors on active commuting remained statistically significant.
This is one of the first studies to investigate the associations between psychological and environmental factors and active commuting among a sample of older British working adults. In both men and women, short distance to work and stronger habit scores were associated with higher odds of active commuting. In addition, men living in more rural areas were less likely to actively commute, whereas high road density in the neighborhood and having no busy road on the route to work was associated with an increased likelihood of women's active commuting.
The findings reported here are generally consistent with the existing literature, despite the slightly older sample used here. Similar to previous work (e.g., Lemiuex and Godin ), this study found that few environmental measures were statistically significant predictors of active commuting after adjustment. In addition, the environmental predictors identified explained a small proportion of the variance in commuting behavior, supporting the findings of Ogilvie et al. (24) that the environment may be a relatively minor determinant of commuting behavior.
The fact that distance was the strongest predictor of behavior suggests that the application of interventions to encourage walking or cycling for short distance may be particularly efficacious. In the United Kingdom, several "park-and-stride" schemes have been implemented around schools to encourage children to walk a short distance to school (33). Parents are encouraged to park away from the school and then walk with or allow their children to walk the last part of the journey to school. This type of intervention could be adapted to adults via the use of off-site car parks which are within walking distance of the workplace, although the effectiveness of this strategy would require careful evaluation.
Of the few environmental predictors that persisted in final models after adjustment for distance, rural location was associated with a decreased likelihood of active commuting in men, possibly reflecting greater availability of personal motorized transport among rural males (12). Those women who lived in neighborhoods with high road density were more likely to actively commute, which may reflect the effects of improved road connectivity and hence greater walkability (22). The presence of a main or secondary road on the route to work, however, was associated with a decreased odds of women reporting active commuting. Such presence of a principal road on route may be a reflection of high-traffic volumes or speeds on these roads, and this might result in heightened safety concerns. This may be particularly important for older women who more commonly report fears about safety, including fast traffic (40). The provision of facilities such as pedestrian crossings to improve traffic safety could be an important component of a broader intervention to promote active commuting in busy neighborhoods.
Like Lemieux and Godin (21), we found some evidence that habit may act to at least partly mediate the associations between environmental factors and active commuting. This observation supports the concept that habitual activities, such as commuting, may be somewhat environmentally cued (1), although in this cross-sectional study, it was not possible to assess the direction of causality, and as a result, we cannot say whether habit acted as an influence on behavior or was a consequence of it. Aarts et al. (1) presented a model of exercise and habit formation where the social and physical environment is thought to influence decision-making. This occurs when perceptions of a behavior and intention are formed, when people reflect on their experiences of a behavior and when habits are developed. However, it is unknown at which point the environment is most influential in the formation of habits. Thus, further longitudinal research is required to explore the potential role of the environment in habit formation.
This work has several strengths and limitations. Strengths include the use of data collected from a well-characterized sample of adults living and working in both urban and rural environments. The study also uses where possible a wide range of perceived and objective environmental indicators and combines these with validated psychological measures, allowing the possible mediating effects of psychological factors on the environment to be explored.
In terms of the limitations, this study uses cross-sectional data and therefore causality cannot be inferred from the associations observed. Furthermore, we have no information on self-selection bias whereby some participants may choose to live in areas that were more conducive to active travel or work in areas, which are proximate to home (35). In this study, participants self-reported their usual travel mode to work in the last year. This masks day-to-day variations and may have lead to some overreporting of active travel. Our sample of working adults were slightly older than a typical working age population, and all lived in Norfolk, which is a predominantly rural county with a largely British white population (96.2% at the 2001 UK Census ), which may limit the generalizability of these results to other populations. Although we excluded participants who were not working and reported some difficulty walking, the fact that our sample is older than the population average means that we anticipate the group would find active travel more difficult than a younger cohort would. Whether this may translate into the environment being a more or less important determinant of commuting behavior is unknown. Greater family commitments in this age group may further moderate the importance of psychological or environmental factors; for example, considerations such as the need for children to be driven to school may be pertinent in behavioral choices.
In this study, we used data on covariates such as social class and BMI, which were collected around 9 and 6 yr before the collection of exposure and outcome measures and may have changed in the interim. We also used participant's postcodes rather than exact addresses. On average, one unique postcode covers about 15 addresses; however, in some rural areas, they can cover up to 80 addresses (30). Hence, this may limit the accuracy of our objectively assessed measures. Furthermore, our modeled routes were based on the assumption that participants would choose the shortest route between home and work, and although this provides a measure of the environmental potential of the route environment, it may not reflect the actual routes used. Another limitation was that the number of respondents reporting exclusively walking or cycling to work was too small to separate these groups. This is coupled with the fact that we had no information on workplace facilities for walkers and cyclists (e.g., lockers or showers), which might be important influences on behavior (32).
Because this work was exploratory in nature, we chose not to use particular a theory in the statistical modeling of active commuting behavior but rather used a best-fit approach on the basis of observed associations within the data. This more data-driven approach may have influenced our findings and therefore conclusions. We choose to adjust for BMI in our analysis because this has been found to be negatively correlated with active travel behavior (6). It may be that lower BMI is a result of engagement in active travel, in which case the inclusion of BMI in the models may attenuate the observed effects. However, we undertook a sensitivity analysis by removing BMI and found our results to be largely unchanged.
This study identified several individual characteristics, psychological and environmental measures, as correlates of older adult's active commuting. The findings suggest that interventions designed to encourage the development of habitual behaviors for active commuting may be particularly effective, especially among those living shorter distances from work.
J.P. is funded through a grant from the National Prevention Research Initiative, (http://www.npri.org.uk), consisting of the following funding partners: British Heart Foundation; Cancer Research UK; Department of Health; Diabetes UK; Economic and Social Research Council; Medical Research Council; Health and Social Care Research and Development Office for the Northern Ireland; Chief Scientist Office, Scottish Government Health Directorates; Welsh Assembly Government; and World Cancer Research Fund.
A.J. is supported through the Higher Education Funding Council for England, and E.v.S., S.G., and N.W. are funded through the Medical Research Council. All authors work within the Centre for Diet and Activity Research (CEDAR), a UKCRC Public Health Centre of Excellence.
Funding from the British Heart Foundation, Department of Health, Economic and Social Research Council, Medical Research Council, and the Wellcome Trust, under the auspices of the UK Clinical Research Collaboration, is gratefully acknowledged.
The results of the present study do not constitute endorsement by the American College of Sports Medicine.
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