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Keywords:

  • active travel;
  • body mass index;
  • walking;
  • cycling

Abstract

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. Conclusion
  7. References

Objective: This study describes the prevalence of walking and cycling to work in New South Wales (NSW) from 2005–2010. It examines the demographic characteristics of those people walking and cycling to work and the association of walking and cycling with body mass index (BMI).

Methods: Data from the NSW Continuous Health Survey, a telephone survey of health indicators among a representative sample of residents aged 16 years or over, were used.

Results: There were no changes in the proportions of employed respondents walking or cycling to work in NSW from 2005 to 2010, with estimates ranging from 5.1–7.3% usually walking, and 1.4–1.8% usually cycling. People who walked (adjusted odds ratio [AOR]=1.07, 95%CI 1.00–1.14) or cycled (AOR=1.22, 95%CI 1.14–1.32) to work had higher levels of education, after adjusting for age, sex, income and residence.

Conclusions: There has been no overall increase in active commuting in NSW (2005–2010). Better efforts to communicate the benefits of active travel and less sedentary travel are warranted, in particular among those with lower levels of education.

Implications: More interventions are needed to encourage walking and cycling to work, in order to gain significant benefits in terms of maintaining a healthy weight.

Walking and cycling for transport are increasingly being recognised as important strategies to increase levels of physical activity in the community.1,2 Substituting some part of a sedentary travel trip (mainly driving) with walking and cycling can contribute to individuals reaching recommended levels of physical activity (i.e. 150 minutes of moderate-intensity physical activity per week).3,4 Even half the recommended amount of physical activity, i.e. 15 min per day, 6 days a week, can provide significant health benefits.5 The New South Wales (NSW) Government has plans to encourage increased walking and cycling,6,7 and active travel should be of interest to both the health and transport sectors.

Achieving adequate physical activity is known to have many health benefits.8 Actively commuting to work by walking or cycling has clear health benefits, independent of leisure time physical activity.9–11 Those countries with a high proportion of trips taken by bicycle or walking tend to have lower rates of obesity.12 In the US, cities and counties with higher levels of walking and cycling tend to have lower rates of diabetes.13

Walking is the most common form of sport or recreation for physical activity in Australia, and cycling is the fourth most popular (after running and swimming).14 Analysis of the Exercise, Recreation and Sport Survey data indicates that the proportion of people walking and cycling has significantly increased over the past 10–15 years,15,16 although it represents small absolute gains at the population level.

However, walking and cycling for recreation or sport has not translated to increases in active travel to work. Census data for the journey to work indicate that the proportion of adults in capital cities using a bicycle to get to work has remained flat over the past 25 years.17,18 Despite some variation by local government areas and pockets of high levels of walking and cycling, including proximity to the central business district in Sydney,19 walking and cycling to work has not yet substantially increased its mode share in NSW, or most of Australia, according to Census data.17

While Census data is an important long-term surveillance system for transport planners, it is limited by infrequent data collection, since it is collected on just one day of the year, with weather potentially having an impact on walking and cycling trips. More frequent data collections are needed to monitor active commuting. Further, little is known about the characteristics of those people who do actively commute.

There is some evidence that the journey to work is associated with body mass index (BMI). An earlier study reported that people who drove to work were 13% more likely to be overweight compared with those travelling by other modes.20 Further, men who rode a bicycle to work or used public transport were significantly less likely to be overweight or obese than those who drove.21 The study was not able to provide reliable data for women because of the small numbers involved in the analysis, and also did not consider any nutritional variables.

In this study, we extended our previous work by reporting on the prevalence of walking or cycling to work in NSW from 2005 to 2010, describing the demographic characteristics of those people walking and cycling to work, and examining the association of walking and cycling with BMI.

Methods

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. Conclusion
  7. References

We used data from the NSW Continuous Health Survey, which is a telephone survey of health indicators among a representative sample22 of residents aged 16 years or over in NSW, Australia.23 The main issues covered in the survey include health behaviours (e.g. physical activity and nutrition) and health status (e.g. self-rated health status, overweight or obesity), as well as socio-demographic characteristics. In 2005, 13,701 respondents completed the survey (57.7% response rate); 7,962 in 2006; 13,178 in 2007; 10,296 in 2008; 10,719 in 2009 and 10,245 in 2010. Response rates were 59.3%, 63.6%, 63.4%, 58.7%, and 57.2%, respectively. Of these respondents, 49.2%, 49.0%, 49.4%, 49.3%, 47.7%, and 46.9% (respectively) reported that they had a job and were included in this analysis. A job was defined very broadly and included full or part time, and paid or voluntary work.

Study variables

To examine trends in walking and cycling to work over time, we determined mode of transport to work by responses to the question: ‘How do you usually get to work?’ which allowed for multiple responses. The responses included travel by train, bus, ferry, tram, bicycle or walking only, travel by car as the driver or passenger, or working from home. To identify active commuters, we created three groups: walkers, cyclists and others. There was minimal overlap with walking and cycling and, where this occurred, respondents were assigned as walkers.

To examine the demographic characteristics of walkers and cyclists, study variables included age, sex, completion of tertiary education, household income, socio-economic indexes for areas (SEIFA), and Accessibility/Remoteness Index of Australia (ARIA). SEIFA provides summary measures derived from the Census to measure different aspects of socioeconomic conditions by geographic area. ARIA provides a nationally consistent measure of geographic remoteness of a residence.

The study health outcome variable was weight status based on BMI. BMI was derived from self-reported height and weight, which was calculated by dividing the weight (in kilograms) by the height (in metres) squared. We also examined the proportion of the sample with adequate levels of physical activity (greater than 150 minutes of at least moderate-intensity physical activity a week), and nutrition variables including daily fruit and vegetable intake and times fast food is usually eaten per week, and times meat products are usually eaten per week. Full descriptions of variable definitions can be found on the NSW Health Survey website.23

Statistical analysis

Prevalence estimates of walking and cycling were weighted for the probability of selection based on the household size, and for age and sex based on the NSW component of the 2011 Australian Census of Population and Housing. Weighted data were used for all statistical analyses.

Generalised linear models were fitted to weighted survey data with a logit link and a quasibinomial error distribution, to deal with over-dispersion and non-integer successes and failures in the data. As there were no differences in prevalence across years, and to increase sample size, data from the 2005 to 2010 NSW Health Surveys were pooled, and models were fitted to the resulting data-set. All statistical analyses were done using the R software package.24

Results

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. Conclusion
  7. References

Trends over time

Between 2005 and 2010, an estimated average of 1.6% of NSW residents usually cycled to work and an estimated average of 5.7% usually walked to work. There was no difference in the level of commuter walking or cycling across the study years (Table 1).

Table 1. Proportions of employed adults walking or cycling to work in NSW, 2005–2010.
YearWalked to work (%, 95%CI*)Cycled to work (%, 95%CI*)
  1. * Confidence Interval

20057.22 (6.23–8.20)1.67 (1.13–2.21)
20065.03 (4.13–5.93)1.37 (0.80–1.94)
20075.13 (3.67–6.59)1.42 (0.65–2.20)
20085.77 (4.70–6.83)1.61 (0.95–2.26)
20095.29 (4.50–6.09)1.80 (1.23–2.37)
20104.96 (3.94–5.97)1.48 (0.92–2.03)

Characteristics of walkers and cyclists

Walking

Adjusting for all other variables in the table (Table 2), respondents with a tertiary education were more likely to walk to work than those with less education (adjusted odds ratio [AOR]=1.07, 95%CI 1.00–1.14). Respondents living in outer regional areas (AOR=1.55, 95%CI 1.39–1.73), remote (AOR=2.42, 95%CI 1.96–2.99) or very remote locations (AOR=4.39, 95%CI 3.07–6.26) were also more likely to walk to work. As age increases, walking decreases.

Table 2. Factors associated with walking to work, Source: NSW Health Survey 2005–2010.
FactorN% WalkingOR (95%CI)AOR (95%CI)
  1. OR = odds ratio

  2. AOR = Adjusted odds ratio, adjusting for other variables in the table

  3. SEIFA =Socio-Economic Index for Advantage

Age (years)  16–35  35–50  50–65  65+ 4,677 7,246 8,017 1,289 6.8 4.82 5.01 6.18 1 0.69 (0.68–0.70) 0.72 (0.71–0.73) 0.90 (0.89–0.92) 1 0.75 (0.64–0.87) 0.75 (0.64–0.87) 0.68 (0.53–0.87)
Sex  Male  Female 9,751 11,478 5.23 6.09 1 1.17 (1.17–1.18) 1 1.01 (0.95–1.07)
Education  Not tertiary-educated  Tertiary-educated 13,760 7,469 5.72 5.41 1 0.94 (0.94–0.95) 1 1.07 (1.00–1.14)
Remoteness  Major Cities  Inner Regional  Outer Regional  Remote  Very Remote 10,226 5,887 4,140 523 104 5.09 4.98 8.58 13.09 19.13 1 0.98 (0.97–0.98) 1.75 (1.74–1.76) 2.81 (2.76–2.86) 4.41 (4.27–4.57) 1 0.75 (0.67–0.84) 1.55 (1.39–1.73) 2.42 (1.96–2.99) 4.39 (3.07–6.26)
Household income  More than $80,000  $60,000–$80,000  $40,000–$60,000  $20,000–$40,000  less than $20,000  Less than $10,000 7,894 3,615 4,321 3,714 1,518 167 4.56 5.24 5.85 7.77 9.05 14.49 1 1.14 (1.14–1.14) 1.32 (1.29–1.28) 1.79 (1.75–1.79) 2.07 (2.04–2.07) 3.57 (3.45–3.57) 1 1.09 (1.07–1.12) 1.09 (1.03–1.16) 1.00 (0.91–1.10) 0.51 (0.44–0.57) 1.01 (1.11–0.92)
SEIFA  Most advantaged  Moderately advantaged  Neither advantaged nor disadvantaged  Moderately disadvantaged  Most disadvantaged 3,495 4,062 4,950 4,776 3,580 5.5 5.69 6.1 5.23 5.08 1 1.04 (1.03–1.04) 1.12 (1.11–1.13) 0.95 (0.94–0.96) 0.92 (0.91–0.93) 1 0.83 (0.73–0.95) 0.98 (0.83–1.14) 0.87 (0.76–1.00) 1.01 (0.89–1.15)
Cycling

Adjusting for all other variables in the table (Table 3), women were less likely than men to cycle to work (AOR=0.34, 95%CI 0.32–0.37). There was no clear pattern of association between household income or socioeconomic advantage and cycling to work. Respondents with a tertiary education were more likely to cycle to work (AOR=1.22, 95%CI 1.14–1.32), as were respondents in very remote areas of NSW (AOR=5.89, 95%CI 4.30–8.06). Respondents in inner regional areas (AOR=0.77, 95%CI 0.68–0.87) or outer regional areas (AOR=0.76, 95%CI 0.66–0.87) and aged between 50 and 65 years (AOR=0.52, 95%CI 0.35–0.76) were less likely to cycle to work (Table 3).

Table 3. Factors associated with cycling to work, Source: NSW Health Survey 2005–2010.
FactorN% CyclingOR (95%CI)AOR (95%CI)
  1. OR = odds ratio

  2. AOR = Adjusted odds ratio, adjusting for other variables in the table

  3. SEIFA =Socio-Economic Index for Advantage

Age (years)  16–35  35–50  50–65  65+ 4,677 7,246 8,017 1,289 1.6 2.09 0.82 0.37 1 1.32 (1.31–1.33) 0.51 (0.50–0.52) 0.23 (0.21–0.24) 1 1.26 (0.91–1.76) 0.52 (0.35–0.76) 0.31 (0.13–0.65)
Sex  Male  Female 9,751 11,478 2.36 0.54 1 0.23 (0.22–0.23) 1 0.34 (0.32–0.37)
Education  Not tertiary-educated  Tertiary-educated 13,760 7,469 1.35 1.95 1 1.45 (1.44–1.47) 1 1.22 (1.14–1.32)
Remoteness  Major Cities  Inner Regional  Outer Regional  Remote  Very Remote 10,226 5,887 4,140 523 104 1.6 1.59 1.17 1.24 9.51 1 1.00 (0.99–1.01) 0.73 (0.72–0.74) 0.78 (0.74–0.81) 6.46 (6.17–6.76) 1 0.77 (0.68–0.87) 0.76 (0.66–0.87) 0.88 (0.63–1.23) 5.89 (4.30–8.06)
Household income  More than $80,000  $60,000–$80,000  $40,000–$60,000  $20,000–$40,000  Less than $20,000  Less than $10,000 7,894 3,615 4,321 3,714 1,518 167 1.79 1.26 1.38 1.67 1.08 1.01 1 0.70 (0.70–0.70) 0.77 (0.77–0.77) 0.93 (0.93–0.93) 0.60 (0.60–0.60) 0.56 (0.52–0.60) 1 0.72 (0.68–0.76) 1.06 (0.90–1.24) 0.47 (0.37–0.61) 0.93 (0.68–1.25) 0.74 (0.65–0.83)
SEIFA  Most advantaged  Moderately advantaged  Neither advantaged nor disadvantaged  Moderately disadvantaged  Most disadvantaged 3,495 4,062 4,950 4,776 3,580 1.82 1.43 1.76 1.56 1.15 1 0.78 (0.77–0.79) 0.97 (0.96–0.98) 0.86 (0.85–0.87) 0.63 (0.62–0.64) 1 1.02 (0.88–1.19) 0.82 (0.69–0.98) 0.96 (0.82–1.13) 1.15 (1.00–1.32)

Associations with body mass index

For men and women, walking to work was independently and significantly associated with lower BMI, after adjusting for all other variables in the model, including: education, income, overall minutes of physical activity per week and a number of nutrition variables (Table 4).

Table 4. Standardised regression adjusted co-efficients for factors associated with body mass index by sex. Source: NSW Health Survey 2005–2010.
 MenWomen
 Co-efficient95%CICo-efficient95%CI
  1. Adjusted co-efficient, adjusting for other variables in the table

Bicycle commuter−2.15−4.11 – −0.19−1.22−3.18 – 0.74
Pedestrian commuter−2.47−4.43 – −0.51−2.95−4.91 – −0.99
Age10.678.71 – 12.6313.4411.48 – 15.40
Less than $10,000 (Reference Level)   less than $20,000   $20,000–$40,000   $40,000–$60,000   $60,000–$80,000   More than $80,000 0.00 5.26 −2.19 0.75 −2.50 1.39 3.30 – 7.23 −4.15 – −0.23 −1.21 – 2.71 −4.46 – −0.54 −0.57 – 3.35 0.00 0.97 −0.84 −1.11 −0.07 −0.87 −0.99 – 2.93 −2.80 – 1.12 −3.07 – 0.85 −2.03 – 1.89 −2.83 – 1.09
Tertiary-educated−6.88−8.84 – −4.92−6.31−8.27 – −4.35
Inner Regional (Reference Level)   Major Cities   Outer Regional   Remote   Very Remote 0.00 −0.92 −1.57 1.52 1.87 −2.88 – 1.04 −3.53 – 0.39 −0.44 – 3.48 −0.09 – 3.83 0.00 −0.92 1.88 3.43 2.04 −2.88 – 1.04 −0.08 – 3.84 1.47 – 5.39 0.08 – 4.00
Moderately advantaged (Reference Level)   Most advantaged   Neither advantaged nor disadvantaged   Moderately disadvantaged   Most disadvantaged 0.00 −2.10 1.18 1.44 2.61 −4.06 – −0.14 −0.78 – 3.14 −0.52 – 3.40 0.65 – 4.57 0.00 −3.84 2.81 0.88 2.40 −5.80 – −1.88 0.85 – 4.77 −1.08 – 2.84 0.44 – 4.36
Serves of fruit eaten per day−0.03−1.99 – 1.93−1.24−3.20 – 0.72
Times fast food usually eaten per week1.17−0.79 – 3.132.030.07 – 3.99
Serves of vegetables eaten per day0.72−1.24 – 2.684.062.10 – 6.02
Times meat products usually eaten per week1.14−0.82 – 3.103.501.54 – 5.46
Minutes of physical activity per week−5.59−7.55 – −3.63−6.47−8.43 – −4.51

Cycling to work for men was significantly associated with lower BMI, but not for women. As expected, for both men and women, increases in BMI were associated with age, moderate socioeconomic advantage and low levels of education. For women, higher weekly meat consumption and lower daily vegetable intake was associated with increased BMI. Minutes of physical activity per week was inversely associated with BMI.

Of those respondents walking to work, 31.4% were overweight (BMI 25–30) and a further 13.2% were obese (BMI>30), compared to 34.5% and 18.3% of non-walkers respectively. Of those respondents cycling to work, 38.7% were overweight and a further 11.0% were obese, compared to 34.3% and 18.5% of non-cyclists respectively.

Discussion

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. Conclusion
  7. References

The prevalence of walking or cycling to work in NSW did not increase from 2005 to 2010. People most likely to be active commuters had higher levels of education and lived in remote locations. Lower BMI was associated with cycling to work for men, and walking to work for both men and women, after adjusting for all other variables in the model.

The lack of change in the prevalence of walking and cycling to work in NSW over the past six years is disappointing, but not surprising. Such changes require a much greater investment in environmental infrastructure to support these activities, and individual behavioural programs to encourage them.25,26 Those few local government areas (e.g. City of Sydney) that have invested in infrastructure for cycling have reported significant increases.27 ABS Census journey-to-work data indicate increases in cycling in the inner city, but decreases in outer Sydney, with little overall change.28 An investment to promote cycling in the United Kingdom has led to increased population physical activity.29 NSW has a Bike Plan,7 but it is not well funded. NSW also has a draft ‘Walking Strategy’ but this has not yet been implemented.6

Profiles of people walking and cycling to work are not common in the public health literature, although they are more common in the transport sector in terms of understanding consumer behaviours.30 They are potentially important for public health practitioners because they can help increase understanding about that section of the public who do actively commute, and therefore the profiles help develop programs to increase the size of this group. For example, in some circles, riding a bicycle has a ‘low status’ image. It is an activity that is stopped when someone comes of age and can afford a car, or an activity resorted to if a person were unlucky enough to lose a driving licence.31 In other circles, cycling can be associated with expensive sport bicycles. Instead, our data have identified that those people cycling to work have high levels of education, and it is most likely a deliberate choice.

People walking or riding bikes to work had a higher level of education, after adjusting for where they lived and income, and this may indicate that they are more aware of the mental and physical health and environmental benefits of walking and cycling. This suggests that stronger public communication about the benefits of active travel, to encourage more active commuting and less sedentary travel, is warranted – in particular among those less educated.

Women tend to walk rather than cycle and this may be a reflection of aversion to risk among women. Garrard has observed that the proportion of women cycling can be a proxy measure of the quality and safety of a bicycle network.32 Consistent with our earlier findings,20,21 cycling was associated with lower BMI in men, but not in women – even with a larger sample, and after taking into account overall physical activity and some nutritional variables. The proportion of women cycling remains lower than men, and may have contributed to low power in this analysis. Walking to work was associated with lower BMI for both men and women, but the relationship with BMI was not as strong as that of cycling, probably due to the lower physical intensity of walking compared with cycling.

Strengths and weakness

A strength of the present analysis is the use of high-quality and consistent data over time (six years). By pooling these data, we were able to build a much bigger sample size than the previous analyses, and therefore have more power to enable stratification of the data. Further, the inclusion of nutritional variables in the model enhances the analysis, although the nutritional component is not comprehensive. A further strength of the NSW Health Survey data is that data are collected year-round. This means the data are not susceptible to variable weather conditions on a single measurement day (such as on Census day), which is known to affect walking and cycling to work.

However, the association between active commuting and BMI remains cross sectional and we cannot assign causality. Further, there may be errors due to the use of self-reported height and weight to compute BMI or the possibility of social desirability bias or measurement bias. Other factors not included in the analysis may have contributed significantly to BMI. Nonetheless, our study is consistent with many others,12,33 and the relationship can be plausibly explained by the sedentary time spent in driving, or being driven on a bus or train.34 A further limitation is that walking and cycling to work are only a subset of all walking or cycling, and does not represent a complete picture of these activities.

Conclusion

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. Conclusion
  7. References

Despite apparent weight benefits of active commuting, and locally specific increases in the inner city, there has been no overall change in walking or cycling to work across NSW between 2005 and 2010. Much more needs to be done to increase active travel to work.

References

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. Conclusion
  7. References
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