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The Added Benefit of Bicycle Commuting on the Regular Amount of Physical Activity Performed
The Added Benefit of Bicycle Commuting on the Regular Amount of Physical Activity Performed David Donaire-Gonzalez, MSc,1,2,3,4 Audrey de Nazelle, PhD,5 Tom Cole-Hunter, PhD,1,2,3 Ariadna Curto, MSc,1,2,3 Daniel A. Rodriguez, PhD,6 Michelle A. Mendez, PhD,6 Judith Garcia-Aymerich, PhD,1,2,3 Xavier Basagaña, PhD,1,2,3 Albert Ambros, BSc,1,2,3 Michael Jerrett, PhD,7 Mark J. Nieuwenhuijsen, PhD1,2,3 Introduction: Physical inactivity is a leading cause of death and disability globally. Active transportation such as bicycling may increase physical activity levels. It is currently uncertain whether a shift from motorized transport modes to bicycle commuting leads to increased physical activity overall or substitutes other forms of physical activity. The study aims to disentangle whether bicycle commuting adds to or replaces other physical activities by comparing the physical activity performed by bicycle and motorized commuters. Methods: Physical activity, travel behavior, health status, sociodemographic, and built environment characteristics were assessed for 752 adults, between June 2011 and May 2012, in Barcelona, Spain. Statistical analyses, performed in 2013–2014, included linear, non-linear, and mixture models to estimate disparities and the dose–response relationship between physical activity duration and commute mode. Results: Regular bicycle commuters traveled by bicycle an average of 3.1 (SD¼2.5) hours in the previous week. Bicycle commuting contributed positively to physical activity duration across participants (po0.05). It amounted to 2.1 (95% CI¼0.84, 3.55) hours/week extra of physical activity for bicycle commuters versus motorized commuters. Among bicycle travelers, there was a positive dose–response relationship between bicycle commuting and physical activity duration, with an average extra physical activity duration of 0.5 (95% CI¼0.4, 0.6) hours/week for every additional 1 hour/week of bicycle commuting. Conclusions: Bicycle commuting likely adds to overall physical activity. The extra physical activity performed by bicycle commuters is undertaken as moderate physical activity and follows a sigmoidal dose–response relationship with bicycle duration. (Am J Prev Med 2015;](]):]]]–]]]) & 2015 American Journal of Preventive Medicine
Introduction 1
From the Centre for Research in Environmental Epidemiology, Barcelona, Spain; 2CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain; 3Universitat Pompeu Fabra, Departament de Ciències Experimentals i de la Salut, Barcelona, Spain; 4Physical Activity and Sports Sciences Department, Fundació Blanquerna, Barcelona, Spain; 5Center for Environmental Policy, Imperial College London, London, United Kingdom; 6 Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; and 7Environmental Health Sciences, School of Public Health, University of California, Berkeley, California Address correspondence to: David Donaire-Gonzalez, MSc, Center for Research in Environmental Epidemiology (CREAL), Doctor Aiguader, 88, 08003 Barcelona, Spain. E-mail: [email protected]. 0749-3797/$36.00 http://dx.doi.org/10.1016/j.amepre.2015.03.036
& 2015 American Journal of Preventive Medicine
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hysical inactivity is a major public health challenge that affects one third of the adult population worldwide.1 Physical inactivity causes 6%–10% of the global burden of non-communicable diseases and is responsible for 9% of premature deaths globally.2 Physical inactivity also probably accounts for approximately 32 million disability-adjusted life years (DALYs) (representing about 2.1% of global DALYs) each year.3 Furthermore, global trends suggest a widespread reduction in physical activity, which raises concern about worsening health burdens in the future from physical inactivity.4
Published by Elsevier Inc.
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To reverse the global trend toward physical inactivity, a holistic intervention including individual, social and cultural, environmental, and policy levels has been recommended by many national and international organizations, including the Lancet Physical Activity Series Working Group.4,5 Implementing transportation policies that promote active modes such as bicycling has been proposed as a promising method because although cycling rates in most developed countries including Spain are below 5%, cycling rates reach 20% in countries such as Denmark and the Netherlands, which have wellestablished active transport policies.6 Moreover, active transport has been shown to have important health cobenefits such as decreasing the risk of obesity, cardiovascular disease, and all-cause mortality.7,8 Currently, and despite the increasing evidence relating active commute to higher physical activity levels,9 it remains uncertain whether bicycle commuting is related to higher overall physical activity or substitutes other forms of physical activity.10–13 Hence, this study aims to disentangle whether bicycle commuting adds to physical activity levels or replaces other forms of physical activity by comparing the physical activity performed by bicycling and motorized commuters and quantifying the dose of physical activity arising from bicycle commuting.
Methods Site and Design Barcelona, one of the most populated cities in Europe (total population, 1,621,537), is located in Northeastern Spain with a temperate Mediterranean climate. Although the climate and land use are conducive to bicycling, data show that the city has a low share of bicycle trips (2%).14 To promote more bicycling, the City began implementing a series of programs in 2007. Specifically, the City implemented an infrastructure improvement plan, educational program, and bicycling sharing program (called “Bicing”) to increase the ride share of bicycle commuting. To retrospectively assess the impact of this city-level bicycle sharing program and characterize pre- and post-travel behavior, current built environment, bicycle attitudes and perceptions toward cycling, and physical activity, a transportation and health survey was conducted as part of the Transportation, Air pollution and Physical ActivitieS (TAPAS) project between June 2011 and May 2012.
Study Sample To give the same recruitment opportunity to all individuals living in Barcelona and to reach the spatial representativeness of the City, 40 sampling points were randomly selected to cover the whole City of Barcelona, with four random points for each of the ten districts of the City. Each point was sampled by three trained interviewers between 7:45AM and 11:30AM during 4 weekdays (without rain) within a randomly selected week. To reach all commute modes, interviewers were randomly assigned to strategic locations to target
public transport users near public transport stations, bicycle commuters near private and public bicycle parking lots, and private transport users near vehicle parking lots. The interviewers were instructed to systematically invite all travelers but prioritizing the recruitment of bicycling travelers over other travel modes when both appeared at the same time. This strategy was employed to ensure sufficient numbers of bicyclists owing to the relatively low number of bicyclists commuters (o2%) compared with other modes. To study non-walking Barcelona commuters who were able to ride a bicycle and susceptible to Bicing implementation, several inclusion criteria were employed: being aged Z18 years; living in Barcelona since 2006 or earlier; currently working or studying in the City; self-report of being healthy enough to ride a bicycle for 20 minutes; 5. not having chronic disease that limited exercise capacity or efficiency (such as chronic obstructive pulmonary disease, ischemic cardiomyopathy, or congestive cardiac insufficiency); 6. having a commute equivalent to a Z10-minute walk; and 7. using at least one mode of transport other than walking to commute. 1. 2. 3. 4.
Of the 18,469 subjects approached across the 40 random sampling points, 6,701 (36.3%) agreed to participate, with no differences in response rate across commute modes. A total of 1,389 subjects fulfilled the inclusion criteria, of which 752 (54.1% of those eligible) completed the subsequent telephone survey and were included in the analysis. Our study protocol was approved by the Clinical Research Ethical Committee of the Parc de Salut Mar (CEIC-Parc de Salut Mar), and written informed consent was obtained from all participants.
Measures Study participants answered a telephone survey administered using computer-assisted telephone interviewing (Appendix, available online). Duration of overall physical activity was defined using the International Physical Activity Questionnaire short form and included time spent walking plus time spent in all other activities of moderate or vigorous intensity during the past 7 days. Moderate-to-vigorous activity duration comprised only moderateor vigorous-intensity activities. Commute mode was defined as bicycle, public transport, motorcycle, or car, according to the most frequent mode of commute, with motorized mixed-mode commuters classified according to their motorized mode (n¼2).15 Bicycle travelers and cycling duration were assessed using the Pedestrian and Bicycling Survey,16 with bicycle travelers defined as those participants who traveled by bicycle at least once in the previous week. Other measurements included sociodemographic factors; health status; smoking habits; nutritional status; willingness to cycle; perceived ability to cycle; performance of regular vigorous activity; perceptions toward bicycle commuting; and objective built environment assessment of participant’s home, working/studying, and commute route (Appendix). www.ajpmonline.org
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Statistical Analysis The relationship between the commute mode and physical activity level was evaluated using several approaches, including 1. a multivariate linear model between commute mode and
overall physical activity duration (log-transformed) was built to understand the effect of the commute mode upon overall physical activity duration; 2. a multivariate linear model between commute mode and overall physical activity duration (log-transformed), but accounting for active travel duration (bicycle and walk), was built to understand the effect of the commute mode upon nontravel physical activity duration; and 3. a multivariate mixture model between commute mode and duration of each type of physical activity (vigorous, moderate, and walking) was built following the Fletcher et al.17 procedure for the zero-inflated lognormal distributions to understand the effect of the commute mode upon each type of physical activity in overall and non-travel physical activity (Appendix). On the other hand, the dose–response and threshold relationship between bicycle commuting duration and moderate-tovigorous physical activity duration, among bicycle travelers, was evaluated using locally weighted scatterplot smoothing and segmented regressions, respectively (Appendix). Multivariate models included all variables that in bivariate analyses showed a statistically significant relationship either with physical activity or commute mode (at the po0.20 level), and in multivariate analysis reached statistical significance (at the po0.05 level) or changed by 410% the coefficient of the commute mode (Appendix Table 2, available online).18 Furthermore, all two-way interactions between physical activity duration and either age, gender, or health status were also tested in bivariate and multivariate analyses. As a sensitivity analysis, and in order to test if the substitution was happening only within the most active population, the comparison against bicycle commuters was restricted to those motorized participants who reported both willingness to cycle and performance of regular vigorous activity. Furthermore, as additional analysis, we assessed the representativeness of the TAPAS sample regarding 1. age and gender of the working-age population of Barcelona; and 2. neighborhood deprivation index and population and commer-
cial outlet densities of Barcelona (Appendix). All analyses were conducted during 2013–2014, using R, version 3.0.0, and ArcGIS Desktop 10 Service Pack 4.
Results Table 1 shows the main characteristics of the total sample of 752 participants, with a mean age of 37 (SD¼10) years, including 49% men, 87% workers, 70% with a bachelor’s degree or equivalent, 73% non-smokers, and 27% overweight based on BMI. Relative to the total sample, the 335 (45%) bicycling commuters were significantly more likely to be men, younger, single, childless, non-smokers, and have a ] 2015
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bachelor’s degree or higher. Bicycle commuters were also more physically active, and with a shorter, flatter, and bicycle-adapted commute route (Table 1; Appendix Table 1, available online). Of the motorized commuters (n¼417 using public transport, car or motorcycle), 232 (55%) had access to a bicycle in Barcelona, of which 92 (40%) rode a bicycle for travel a median of three times per week. Multivariate regression analysis showed that there were differences in overall physical activity duration according to the commute mode used after accounting for personal, neighborhood, and route characteristics (Table 2). Compared with bicycle commuters, public transport, motorcycle, and car commuters had a geometric mean ratio of weekly physical activity duration that was 25% (95% CI¼14%, 35%), 23% (95% CI¼6%, 36%), and 33% (95% CI¼6%, 52%) lower, respectively (Table 2). The analysis of vigorous, moderate, and walking physical activity duration showed that bicycle commuters participated in more moderate physical activity than motorized commuters, but bicycle commuters did less walking activity than public transport commuters (Figure 1B, Appendix Table 2, po0.05). By contrast, analysis for total duration of non-travel physical activity showed no differences according to commuting mode (Figures 1A and 1C). The analysis of time spent within each type of non-travel physical activity showed that public transport commuters were 60% (95% CI¼20%, 60%) less likely to participate in leisure-time moderate-intensity physical activity than bicycle commuters, but that their duration of recreational walking activity was 47% (95% CI¼27%, 69%) higher than that of bicycle commuters (Figure 1C). Finally, the analysis of the dose–response between bicycle commuting duration and moderate-to-vigorous physical activity duration showed that bicycle commuting accounted for 11% of the total variance explained by moderate-to-vigorous activity (adjusted R2¼0.17). Participants’ moderate-to-vigorous physical activity duration was on average 0.48 (95% CI¼0.40, 0.56) hours/ week higher for every extra hour per week of bicycle commuting (Figure 2; Appendix Table 3, available online). Furthermore, there was a lower and an upper threshold (o0.48 hours/week and 49.5 hours/week, respectively) in the positive relationship between bicycle commuting and physical activity (Figure 2). The results were the same when the bicycle commuters were compared with motorized commuters who reported both willingness to cycle and the performance of regular vigorous activity (data not shown). There were no statistically significant differences in age, gender, neighborhood deprivation index, population density, and commercial outlet density between the TAPAS sample and the active population of Barcelona (data not shown).
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Table 1. Sociodemographic, Bicycle Behavior and Perception, and Physical Activity Characteristics of Participants According to Commute Mode
ALL
Bicycle
Public transport
Motorcycle
Car
n¼752
335 (45%)
288 (38%)
102 (14%)
27 (4%)
p-value
365 (49)
182 (54)
109 (38)
58 (57)
16 (59)
o0.001
37 (10)
36 (10)
37 (12)
38 (10)
40 (8)
0.012
Civil status: single, n (%)a
142 (19)
78 (23)
44 (15)
19 (19)
1 (4)
0.002
a
271 (36)
109 (33)
99 (34)
47 (46)
16 (59)
0.005
652 (86)
297 (88)
238 (82)
90 (88)
27 (100)
0.028
523 (70)
257 (77)
178 (62)
73 (72)
15 (56)
o0.001
98 (13)
51 (15)
37 (13)
7 (7)
3 (11)
0.177
209 (28)
72 (22)
89 (31)
36 (35)
12 (44)
0.002
205 (27)
70 (21)
12 (44)
33 (32)
90 (31)
0.006
315 (42)
155 (46)
105 (37)
46 (45)
9 (33)
0.066
Bicycle access (own or public access): yes, n (%)
567 (75)
335 (100)
156 (54)
60 (59)
16 (59)
o0.001
Public shared bicycle registered: yes, n (%)
392 (52)
282 (84)
76 (26)
26 (26)
8 (30)
o0.001
Use bicycle for travel: yes, n (%)
422 (56)
330 (99)
65 (23)
18 (18)
9 (33)
o0.001
6 (4)
6 (3)
3 (3)
3 (3)
3 (3)
o0.001
2.5 (3)
2.9 (3)
1.2 (1)
1.0 (2)
0.7 (1)
o0.001
Self-efficacy: totally self-sufficient, n (%)
346 (46)
191 (57)
96 (33)
46 (45)
13 (48)
o0.001
Willingness to shift to bicycling: totally ready, n (%)
38 (9)
—
31 (11)
3 (3)
4 (17)
o0.001
600 (81)
324 (98)
18 (69)
58 (59)
200 (71)
o0.001
Traffic safety: yes, n (%)
545 (73)
303 (91)
158 (55)
68 (68)
16 (59)
o0.001
Keeping personal appearance: impossible, n (%)a
196 (26)
35 (10)
96 (33)
50 (51)
15 (56)
o0.001
Vigorous activity duration (hours/week)
1 (3)
2 (4)
0 (2)
2 (4)
1 (3)
o0.001
Moderate activity duration (hours/week)
1 (3)
3 (3)
0 (1)
0 (2)
0 (1)
o0.001
Walking activity duration (hours/week)
4 (4)
3 (3)
4 (5)
3 (4)
3 (3)
o0.001
Overall activity duration (hours/week)
7 (7)
8 (7)
7 (6)
6 (8)
5 (9)
o0.001
32 (34)
38 (31)
25 (31)
28 (43)
22 (41)
o0.001
Sociodemographic Sex: male, n (%) Age (years), M (SD)
a
Has child(ren): yes, n (%)
Working status: currently employed, n (%) Education level: more than secondary, n (%)
a
Foreigner: yes, n (%) Smoking status: current smoker, n (%) BMI (Z25), n (%)
a
a
Health status: very good or excellent, n (%) Bicycle behavior and perception
Bicycle travel frequency: days, median (IQR) Bicycle travel duration: hours/week, median (IQR)
Enjoy bicycle commuting: yes, n (%)a a
Physical activity, median (IQR)
Energy expenditure in activity (MET-hours/week) Built environment, median (IQR)
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Table 1. Sociodemographic, Bicycle Behavior and Perception, and Physical Activity Characteristics of Participants According to Commute Mode (continued)
Sociodemographic Commuting duration (minutes) Commuting network distance (km)a Bikeability index at home (0–10)
a
Walkability index at home (–4 to 11)
a
ALL
Bicycle
Public transport
Motorcycle
Car
n¼752
335 (45%)
288 (38%)
102 (14%)
27 (4%)
p-value
20 (15)
20 (13)
29 (20)
15 (10)
20 (15)
o0.001
3.5 (2.6)
3 (2.2)
3.9 (2.7)
3.6 (2.4)
4.7 (4.4)
o0.001
6.3 (2.2)
6.6 (1.9)
6.1 (2.4)
5.9 (2.3)
5.7 (2.4)
o0.001
0.5 (3.0)
0.5 (2.8)
0.6 (3.0)
0.4 (3.3)
0.4 (2.6)
0.833
Note: High stress levels, having a score Z4 in each question of the short form of Perceived Stress Scale ; p-value, ANOVA or Kruskal-wallis for continuous variables, χ2 for categorical variables. Boldface indicates statistical significance. a Some variables have missing values: 1 in age, 1 in civil status, 2 in child, 3 in education level, 1 in smoking status, 2 in BMI, 12 in stress, 4 in traffic safety, 3 in keeping personal appearance, and 24 in built environment. IQR, inter-quartile range. 16
Discussion The physical activity from bicycle commuting is seen to be an addition, rather than substitution, to the regular physical activity performed by individuals. The extra physical activity performed by bicycle commuters is the result of performing more moderate physical activity while traveling by bicycle. Furthermore, this extra physical activity follows a sigmoidal dose–response relationship with bicycle commuting duration.
Comparison With Previous Studies Higher overall physical activity duration was found in participants who performed their commute by bicycle, which is consistent with previous research regarding active travel among adults.19,20 In agreement with Dombois and colleagues,19 the differences were due to higher levels of moderate-intensity physical activities. In other studies focused on children and adolescents, the effect of an active commute mode on physical activity duration was shown to increase with commute distance.21–23 This was not the case in the TAPAS study, possibly because the distances traveled in this study were too short, with a median of 3.3 km (25th percentile, 2.1 km; 75th percentile, 4.7 km). On the other hand, as is expected from previous literature, perceiving bicycle commuting as enjoyable was related to more participation in this moderate physical activity, whereas perceiving bicycle commuting as traffic-risky was related to performing more walking activity.24 There was no difference in non-travel physical activity when comparing across commute modes, supporting the idea that active travel acts as an additional form of physical activity rather than a substitution of other forms of physical ] 2015
activity. This finding is consistent with studies conducted in England and the U.S.22,25 On the other hand, and in agreement with Terzano et al.,22 the participants performed different types of non-travel physical activity (walk versus other moderate-intensity activities) according to the commute mode used. Studies on children have found similar results on commute mode and types of leisure-time physical activity.21,26 We believe that differences found in the type of physical activity performed are explained by the flexibility and quickness associated with bicycle use, which allows bicycle commuters to engage in a larger variety of other forms of physical activity during the same day.
Table 2. Crude and Adjusteda Linear Association Between Commute Mode and Physical Activity Duration Commute mode (reference bicycle)
All physical activity duration GMR (95% CI)
Non-travel physical activity duration GMR (95% CI)
0.73 (0.65, 0.84)
0.97 (0.83, 1.14)
0.75 (0.66, 0.86)
0.99 (0.84, 1.17)
0.73 (0.61, 0.87)
1.07 (0.87, 1.32)
0.77 (0.64, 0.94)
1.17 (0.93, 1.46)
0.60 (0.44, 0.83)
0.89 (0.64, 1.23)
0.67 (0.48, 0.94)
0.99 (0.69, 1.40)
Public transport Crude Adjusted
a
Motorcycle Crude Adjusted
a
Car Crude Adjusted
a
Note: GMR was used because physical activity was log-transformed. a Detailed multivariate regression models are available in Appendix Table 2. GMR, geometric mean ratio.
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Figure 1. Distribution of adjusteda overall and non-travel physical activity duration, according to the commute mode. (A) This figure shows the adjusted geometric mean and the CI obtained from Fletcher and bootstrap approach of overall and non-travel physical activity. (B and C) These figures show the contribution of vigorous, moderate, and walking activity to the adjusted geometric mean of overall and nontravel physical. a Final models for each level and overall physical activity measures are available in Appendix Table 2.
To our knowledge, this is the first epidemiologic study to assess the dose–response relationship between bicycle commuting duration and moderate-to-vigorous physical activity duration. Bicycle commuting duration was found to explain most of the variance across participants regarding moderate-to-vigorous physical activity. The moderate-to-vigorous physical activity performed by participants followed a sigmoidal dose–response relationship with bicycle commuting duration. Furthermore, half of the physical activity dose provided by bicycle commuting, which in our sample was mainly on flat ground, was in moderate-to-vigorous intensity and half in light- or very light–intensity levels. Despite the fact that the current study design does not allow us to establish a causal inference relationship, these findings reinforce the evidence that bicycle commuting may add to current physical activity performed by an individual, rather than replace other forms of physical activity. In the setting of the current study, bicycle commuting accounted for 2 extra hours of physical activity per week compared to motorized commuting. This extra physical activity, by itself, is almost equal to the minimum recommended amount of physical activity (30 minutes
on 5 days each week) required to promote and maintain physical health (i.e., reducing the risk of chronic disease and premature mortality).27,28 The promotion of physical activity, apart from being effective in the prevention of disease,2 has proven to be financially beneficial for public health systems.29–31 Particularly, the inclusion of the bicycle as a means of transport can be a cost-effective intervention, assuming the provision of appropriate infrastructures and the possibility of integration into an individual’s daily routine.5,32 In addition, focusing on interventions toward promoting trips by bicycle instead of walking has been shown to provide a more healthbeneficial cardiovascular stimulus, even in young adults.13 Consequently, based on the current evidence, public health policymakers may wish to promote bicycle commuting.
Strengths and Limitations One of the main strengths of this study is that the sample is representative of the distribution of the active population of Barcelona with respect to personal, neighborhood, and route characteristics. Another strength is the www.ajpmonline.org
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Figure 2. Dose–response relationship between bicycle commuting and moderate-to-vigorous physical activity duration (locally weighted scatterplot smoothing regression). Note: Solid and dashed lines indicate the geometric mean and the 95% CIs of the weekly extra moderate-to-vigorous physical activity duration according to bicycle commuting duration, from a LOESS regression model with bicycle commuting duration, and adjusted for age and number of children. The serrated vertical lines depict the thresholds in the dose–response relationship identified by segmented regression analysis, which were 0.5 and 9.5 hours/week of bicycle commuting, respectively.
control for the most likely confounders (sociodemographic factors, smoking habits, nutritional and health status, bicycle commuting perceptions, and built environment) of the relationship between physical activity and bicycle commuting, which prevents the risk of residual confounding in the shown effect.33 In addition, to our knowledge, this is the first study to use independent questions to assess bicycling and physical activity duration. Finally, the statistical methods were the most appropriate and conservative statistical methods to quantify both the difference and the dose–response relationship of outcomes with log-normal zero inflated distributions, as is the case of physical activity outcomes. A limitation of the study is the low response rate from street recruitment. However, it is important to note that there were no differences in response rate by mode of commute, and the sample is representative of the active population and neighborhood distribution of Barcelona. Hence, we believe that in the worse scenario, the low response rate might have led to an over-representation of the active population within the TAPAS sample, but the relationship between physical activity levels and bicycling is still valid and original. A second limitation is the low number of sampled car users. Although it corresponds to ] 2015
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the low prevalence of car trips within Barcelona city (7%),34 it limited the accuracy of the estimates of car commuters. Another limitation of the study is the crosssectional design, which limits causal inference of the relationship between bicycle commuting and a higher physical activity level. The use of self-reported data could be understood as another limitation, but it is the most cost-effective method for measuring the effects of bicycling on physical activity, given the weakness of accelerometer assessment of bicycling and the high costs of doubly labeled water assessment. The selection of a recall questionnaire, which assigns 8 METs for any kind of selfreported vigorous activity, instead of a quantitative history questionnaire, which allows a detailed measurement of the intensity of physical activities performed, prevented the study of the relationship between the volume of physical activity performed (energy expenditure) and commute mode.35 Future studies on the added value of active transportation in the promotion of physical activity should include assessments of energy expenditure, as it takes into account the different intensities of each type of physical activity performed.
Conclusions Bicycle commuting likely adds to overall physical activity. The extra physical activity performed by bicycle commuters is undertaken as moderate physical activity and follows a sigmoidal dose–response relationship with bicycle duration. This work is part of the Europe-wide project Transportation Air pollution and Physical ActivitieS: an integrated health risk assessment program of climate change and urban policies (TAPAS), which has partners in Barcelona, Basel, Copenhagen, Paris, Prague, and Warsaw. TAPAS is a 4-year project funded by the Coca-Cola Foundation, AGAUR, and CREAL. www. tapas-program.org/. The funders did not have any role in study design, data collection, analysis and interpretation of data, and the writing of this article and the decision to submit it for publication. All researchers are independent from funders. No financial disclosures were reported by the authors of this paper.
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3. WHO. Prevalence of Insufficient Physical Activity. Geneva: WHO. www. who.int/gho/ncd/risk_factors/physical_activity_text/en/index.html. 4. Kohl HW 3rd, Craig CL, Lambert EV, et al. The pandemic of physical inactivity: global action for public health. Lancet. 2012;380(9838): 294–305. http://dx.doi.org/10.1016/S0140-6736(12)60898-8. 5. De Nazelle A, Nieuwenhuijsen MJ, Antó JM, et al. Improving health through policies that promote active travel: a review of evidence to support integrated health impact assessment. Environ Int. 2011;37(4): 766–777. http://dx.doi.org/10.1016/j.envint.2011.02.003. 6. Pucher J, Buehler R. Making cycling irresistible: lessons from The Netherlands, Denmark and Germany. Transp Rev. 2008;28(4): 495–528. http://dx.doi.org/10.1080/01441640701806612. 7. Xia T, Zhang Y, Crabb S, Shah P. Cobenefits of replacing car trips with alternative transportation: a review of evidence and methodological issues. J Environ Public Health. 2013;2013:e797312. 8. Hamer M, Chida Y. Active commuting and cardiovascular risk: a meta-analytic review. Prev Med. 2008;46(1):9–13. http://dx.doi.org/ 10.1016/j.ypmed.2007.03.006. 9. Wanner M, Götschi T, Martin-Diener E, Kahlmeier S, Martin BW. Active transport, physical activity, and body weight in adults: a systematic review. Am J Prev Med. 2012;42(5):493–502. http://dx.doi.org/10.1016/j. amepre.2012.01.030. 10. Yang L, Sahlqvist S, McMinn A, Griffin SJ, Ogilvie D. Interventions to promote cycling: systematic review. BMJ. 2010;341:C5293. http://dx.doi.org/10.1136/bmj.c5293. 11. Rissel CE, New C, Wen LM, Merom D, Bauman AE, Garrard J. The effectiveness of community-based cycling promotion: findings from the Cycling Connecting Communities project in Sydney, Australia. Int J Behav Nutr Phys Act. 2010;7:8. http://dx.doi.org/10.1186/1479-5868-7-8. 12. Rodríguez DA, Khattak AJ, Evenson KR. Can new urbanism encourage physical activity? Comparing a new urbanist neighborhood with conventional suburbs. J Am Plann Assoc. 2006;72(1):43–54. http://dx.doi.org/ 10.1080/01944360608976723. 13. Shephard RJ. Is active commuting the answer to population health? Sports Med Auckl NZ. 2008;38(9):751–758. http://dx.doi.org/10.2165/ 00007256-200838090-00004. 14. l’Institut d’Estudis Regionals i Metropolitans de Barcelona. Enquesta de Mobilitat En Dia Feiner. Barcelona, Spain: 2013. http://www.iermb. uab.es/htm/mobilitat/pdf/resultatsEMEF2013.pdf. 15. LaJeunesse S, Rodríguez DA. Mindfulness, time affluence, and journeybased affect: exploring relationships. Transp Res Part F Psychol Behav. 2012;15(2):196–205. http://dx.doi.org/10.1016/j.trf.2011.12.010. 16. Forsyth A, Krizek KJ, Agrawal AW, Stonebraker E. Reliability testing of the Pedestrian and Bicycling Survey (PABS) method. J Phys Act Health. 2012;9(5):677–688. 17. Fletcher D, MacKenzie D, Villouta E. Modelling skewed data with many zeros: a simple approach combining ordinary and logistic regression. Environ Ecol Stat. 2005;12(1):45–54. http://dx.doi.org/ 10.1007/s10651-005-6817-1. 18. Glidden DV, Shiboski SC, McCulloch CE. Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models. New York: Springer Science and Business Media, 2011. 19. Dombois OT, Braun-Fahrländer C, Martin-Diener E. Comparison of adult physical activity levels in three Swiss alpine communities with varying access to motorized transportation. Health Place. 2007;13(3): 757–766. http://dx.doi.org/10.1016/j.healthplace.2006.12.002. 20. Yang L, Panter J, Griffin SJ, Ogilvie D. Associations between active commuting and physical activity in working adults: cross-sectional results from the Commuting and Health in Cambridge study. Prev Med. 2012;55 (5):453–457. http://dx.doi.org/10.1016/j.ypmed.2012.08.019. 21. Cooper AR, Jago R, Southward EF, Page AS. Active travel and physical activity across the school transition: the PEACH project. Med Sci Sports
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Exerc. 2012;44(10):1890–1897. http://dx.doi.org/10.1249/MSS.0b013e 31825a3a1e. Terzano K, Morckel VC. Walk or bike to a healthier life: commuting behavior and recreational physical activity. Environ Behav. 2011;43(4): 488–500. http://dx.doi.org/10.1177/0013916510366644. Van Sluijs EMF, Fearne VA, Mattocks C, Riddoch C, Griffin SJ, Ness A. The contribution of active travel to children’s physical activity levels: cross-sectional results from the ALSPAC study. Prev Med. 2009;48(6): 519–524. http://dx.doi.org/10.1016/j.ypmed.2009.03.002. Heinen E, van Wee B, Maat K. Commuting by bicycle: an overview of the literature. Transp Rev. 2009;30(1):59–96. http://dx.doi.org/10. 1080/01441640903187001. Sahlqvist S, Song Y, Ogilvie D. Is active travel associated with greater physical activity? The contribution of commuting and non-commuting active travel to total physical activity in adults. Prev Med. 2012;55(3): 206–211. http://dx.doi.org/10.1016/j.ypmed.2012.06.028. Goodman A, Mackett RL, Paskins J. Activity compensation and activity synergy in British 8-13 year olds. Prev Med. 2011;53(4–5): 293–298. http://dx.doi.org/10.1016/j.ypmed.2011.07.019. Garber CE, Blissmer B, Deschenes MR, et al. American College of Sports Medicine position stand. Quantity and quality of exercise for developing and maintaining cardiorespiratory, musculoskeletal, and neuromotor fitness in apparently healthy adults: guidance for prescribing exercise. Med Sci Sports Exerc. 2011;43(7):1334–1359 . http://dx.doi.org/10.1249/MSS.0b013e318213fefb. Haskell WL, Lee I-M, Pate RR, et al. Physical activity and public health: updated recommendation for adults from the American College of Sports Medicine and the American Heart Association. Med Sci Sports Exerc. 2007;39(8):1423–1434. http://dx.doi.org/10.1249/mss.0b013e31 80616b27. Oldridge NB. Economic burden of physical inactivity: healthcare costs associated with cardiovascular disease. Eur J Cardiovasc Prev Rehabil. 2008;15(2):130–139. Janssen I. Health care costs of physical inactivity in Canadian adults. Appl Physiol Nutr Metab. 2012;37(4):803–806. http://dx.doi.org/ 10.1139/h2012-061. Stokes RJ, MacDonald J, Ridgeway G. Estimating the effects of light rail transit on health care costs. Health Place. 2008;14(1): 45–58. http://dx.doi.org/10.1016/j.healthplace.2007.04.002. Jarrett J, Woodcock J, Griffiths UK, et al. Effect of increasing active travel in urban England and Wales on costs to the National Health Service. Lancet. 2012;379(9832):2198–2205. http://dx.doi.org/ 10.1016/S0140-6736(12)60766-1. Bauman AE, Reis RS, Sallis JF, Wells JC, Loos RJF, Martin BW. Correlates of physical activity: why are some people physically active and others not? Lancet. 2012;380(9838):258–271. http://dx.doi.org/ 10.1016/S0140-6736(12)60735-1. Desarrollo Organización Movilidad. 2012. “Dades Bàsiques de Mobilitat 2011.” Barcelona. http://w110.bcn.cat/Mobilitat/Continguts/Docu ments/Fitxers/dadesbasiques2011complert.pdf. Accessed November 2012. Lamonte MJ, Ainsworth BE. Quantifying energy expenditure and physical activity in the context of dose response. Med Sci Sports Exerc. 2001;33(6suppl):S370–S378 discussion S419–S420.
Appendix Supplementary data Supplementary data associated with this article can be found at, http://dx.doi.org/10.1016/j.amepre.2015.03.036.
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Introduction 1
From the Centre for Research in Environmental Epidemiology, Barcelona, Spain; 2CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain; 3Universitat Pompeu Fabra, Departament de Ciències Experimentals i de la Salut, Barcelona, Spain; 4Physical Activity and Sports Sciences Department, Fundació Blanquerna, Barcelona, Spain; 5Center for Environmental Policy, Imperial College London, London, United Kingdom; 6 Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; and 7Environmental Health Sciences, School of Public Health, University of California, Berkeley, California Address correspondence to: David Donaire-Gonzalez, MSc, Center for Research in Environmental Epidemiology (CREAL), Doctor Aiguader, 88, 08003 Barcelona, Spain. E-mail: [email protected]. 0749-3797/$36.00 http://dx.doi.org/10.1016/j.amepre.2015.03.036
& 2015 American Journal of Preventive Medicine
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hysical inactivity is a major public health challenge that affects one third of the adult population worldwide.1 Physical inactivity causes 6%–10% of the global burden of non-communicable diseases and is responsible for 9% of premature deaths globally.2 Physical inactivity also probably accounts for approximately 32 million disability-adjusted life years (DALYs) (representing about 2.1% of global DALYs) each year.3 Furthermore, global trends suggest a widespread reduction in physical activity, which raises concern about worsening health burdens in the future from physical inactivity.4
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To reverse the global trend toward physical inactivity, a holistic intervention including individual, social and cultural, environmental, and policy levels has been recommended by many national and international organizations, including the Lancet Physical Activity Series Working Group.4,5 Implementing transportation policies that promote active modes such as bicycling has been proposed as a promising method because although cycling rates in most developed countries including Spain are below 5%, cycling rates reach 20% in countries such as Denmark and the Netherlands, which have wellestablished active transport policies.6 Moreover, active transport has been shown to have important health cobenefits such as decreasing the risk of obesity, cardiovascular disease, and all-cause mortality.7,8 Currently, and despite the increasing evidence relating active commute to higher physical activity levels,9 it remains uncertain whether bicycle commuting is related to higher overall physical activity or substitutes other forms of physical activity.10–13 Hence, this study aims to disentangle whether bicycle commuting adds to physical activity levels or replaces other forms of physical activity by comparing the physical activity performed by bicycling and motorized commuters and quantifying the dose of physical activity arising from bicycle commuting.
Methods Site and Design Barcelona, one of the most populated cities in Europe (total population, 1,621,537), is located in Northeastern Spain with a temperate Mediterranean climate. Although the climate and land use are conducive to bicycling, data show that the city has a low share of bicycle trips (2%).14 To promote more bicycling, the City began implementing a series of programs in 2007. Specifically, the City implemented an infrastructure improvement plan, educational program, and bicycling sharing program (called “Bicing”) to increase the ride share of bicycle commuting. To retrospectively assess the impact of this city-level bicycle sharing program and characterize pre- and post-travel behavior, current built environment, bicycle attitudes and perceptions toward cycling, and physical activity, a transportation and health survey was conducted as part of the Transportation, Air pollution and Physical ActivitieS (TAPAS) project between June 2011 and May 2012.
Study Sample To give the same recruitment opportunity to all individuals living in Barcelona and to reach the spatial representativeness of the City, 40 sampling points were randomly selected to cover the whole City of Barcelona, with four random points for each of the ten districts of the City. Each point was sampled by three trained interviewers between 7:45AM and 11:30AM during 4 weekdays (without rain) within a randomly selected week. To reach all commute modes, interviewers were randomly assigned to strategic locations to target
public transport users near public transport stations, bicycle commuters near private and public bicycle parking lots, and private transport users near vehicle parking lots. The interviewers were instructed to systematically invite all travelers but prioritizing the recruitment of bicycling travelers over other travel modes when both appeared at the same time. This strategy was employed to ensure sufficient numbers of bicyclists owing to the relatively low number of bicyclists commuters (o2%) compared with other modes. To study non-walking Barcelona commuters who were able to ride a bicycle and susceptible to Bicing implementation, several inclusion criteria were employed: being aged Z18 years; living in Barcelona since 2006 or earlier; currently working or studying in the City; self-report of being healthy enough to ride a bicycle for 20 minutes; 5. not having chronic disease that limited exercise capacity or efficiency (such as chronic obstructive pulmonary disease, ischemic cardiomyopathy, or congestive cardiac insufficiency); 6. having a commute equivalent to a Z10-minute walk; and 7. using at least one mode of transport other than walking to commute. 1. 2. 3. 4.
Of the 18,469 subjects approached across the 40 random sampling points, 6,701 (36.3%) agreed to participate, with no differences in response rate across commute modes. A total of 1,389 subjects fulfilled the inclusion criteria, of which 752 (54.1% of those eligible) completed the subsequent telephone survey and were included in the analysis. Our study protocol was approved by the Clinical Research Ethical Committee of the Parc de Salut Mar (CEIC-Parc de Salut Mar), and written informed consent was obtained from all participants.
Measures Study participants answered a telephone survey administered using computer-assisted telephone interviewing (Appendix, available online). Duration of overall physical activity was defined using the International Physical Activity Questionnaire short form and included time spent walking plus time spent in all other activities of moderate or vigorous intensity during the past 7 days. Moderate-to-vigorous activity duration comprised only moderateor vigorous-intensity activities. Commute mode was defined as bicycle, public transport, motorcycle, or car, according to the most frequent mode of commute, with motorized mixed-mode commuters classified according to their motorized mode (n¼2).15 Bicycle travelers and cycling duration were assessed using the Pedestrian and Bicycling Survey,16 with bicycle travelers defined as those participants who traveled by bicycle at least once in the previous week. Other measurements included sociodemographic factors; health status; smoking habits; nutritional status; willingness to cycle; perceived ability to cycle; performance of regular vigorous activity; perceptions toward bicycle commuting; and objective built environment assessment of participant’s home, working/studying, and commute route (Appendix). www.ajpmonline.org
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Statistical Analysis The relationship between the commute mode and physical activity level was evaluated using several approaches, including 1. a multivariate linear model between commute mode and
overall physical activity duration (log-transformed) was built to understand the effect of the commute mode upon overall physical activity duration; 2. a multivariate linear model between commute mode and overall physical activity duration (log-transformed), but accounting for active travel duration (bicycle and walk), was built to understand the effect of the commute mode upon nontravel physical activity duration; and 3. a multivariate mixture model between commute mode and duration of each type of physical activity (vigorous, moderate, and walking) was built following the Fletcher et al.17 procedure for the zero-inflated lognormal distributions to understand the effect of the commute mode upon each type of physical activity in overall and non-travel physical activity (Appendix). On the other hand, the dose–response and threshold relationship between bicycle commuting duration and moderate-tovigorous physical activity duration, among bicycle travelers, was evaluated using locally weighted scatterplot smoothing and segmented regressions, respectively (Appendix). Multivariate models included all variables that in bivariate analyses showed a statistically significant relationship either with physical activity or commute mode (at the po0.20 level), and in multivariate analysis reached statistical significance (at the po0.05 level) or changed by 410% the coefficient of the commute mode (Appendix Table 2, available online).18 Furthermore, all two-way interactions between physical activity duration and either age, gender, or health status were also tested in bivariate and multivariate analyses. As a sensitivity analysis, and in order to test if the substitution was happening only within the most active population, the comparison against bicycle commuters was restricted to those motorized participants who reported both willingness to cycle and performance of regular vigorous activity. Furthermore, as additional analysis, we assessed the representativeness of the TAPAS sample regarding 1. age and gender of the working-age population of Barcelona; and 2. neighborhood deprivation index and population and commer-
cial outlet densities of Barcelona (Appendix). All analyses were conducted during 2013–2014, using R, version 3.0.0, and ArcGIS Desktop 10 Service Pack 4.
Results Table 1 shows the main characteristics of the total sample of 752 participants, with a mean age of 37 (SD¼10) years, including 49% men, 87% workers, 70% with a bachelor’s degree or equivalent, 73% non-smokers, and 27% overweight based on BMI. Relative to the total sample, the 335 (45%) bicycling commuters were significantly more likely to be men, younger, single, childless, non-smokers, and have a ] 2015
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bachelor’s degree or higher. Bicycle commuters were also more physically active, and with a shorter, flatter, and bicycle-adapted commute route (Table 1; Appendix Table 1, available online). Of the motorized commuters (n¼417 using public transport, car or motorcycle), 232 (55%) had access to a bicycle in Barcelona, of which 92 (40%) rode a bicycle for travel a median of three times per week. Multivariate regression analysis showed that there were differences in overall physical activity duration according to the commute mode used after accounting for personal, neighborhood, and route characteristics (Table 2). Compared with bicycle commuters, public transport, motorcycle, and car commuters had a geometric mean ratio of weekly physical activity duration that was 25% (95% CI¼14%, 35%), 23% (95% CI¼6%, 36%), and 33% (95% CI¼6%, 52%) lower, respectively (Table 2). The analysis of vigorous, moderate, and walking physical activity duration showed that bicycle commuters participated in more moderate physical activity than motorized commuters, but bicycle commuters did less walking activity than public transport commuters (Figure 1B, Appendix Table 2, po0.05). By contrast, analysis for total duration of non-travel physical activity showed no differences according to commuting mode (Figures 1A and 1C). The analysis of time spent within each type of non-travel physical activity showed that public transport commuters were 60% (95% CI¼20%, 60%) less likely to participate in leisure-time moderate-intensity physical activity than bicycle commuters, but that their duration of recreational walking activity was 47% (95% CI¼27%, 69%) higher than that of bicycle commuters (Figure 1C). Finally, the analysis of the dose–response between bicycle commuting duration and moderate-to-vigorous physical activity duration showed that bicycle commuting accounted for 11% of the total variance explained by moderate-to-vigorous activity (adjusted R2¼0.17). Participants’ moderate-to-vigorous physical activity duration was on average 0.48 (95% CI¼0.40, 0.56) hours/ week higher for every extra hour per week of bicycle commuting (Figure 2; Appendix Table 3, available online). Furthermore, there was a lower and an upper threshold (o0.48 hours/week and 49.5 hours/week, respectively) in the positive relationship between bicycle commuting and physical activity (Figure 2). The results were the same when the bicycle commuters were compared with motorized commuters who reported both willingness to cycle and the performance of regular vigorous activity (data not shown). There were no statistically significant differences in age, gender, neighborhood deprivation index, population density, and commercial outlet density between the TAPAS sample and the active population of Barcelona (data not shown).
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Table 1. Sociodemographic, Bicycle Behavior and Perception, and Physical Activity Characteristics of Participants According to Commute Mode
ALL
Bicycle
Public transport
Motorcycle
Car
n¼752
335 (45%)
288 (38%)
102 (14%)
27 (4%)
p-value
365 (49)
182 (54)
109 (38)
58 (57)
16 (59)
o0.001
37 (10)
36 (10)
37 (12)
38 (10)
40 (8)
0.012
Civil status: single, n (%)a
142 (19)
78 (23)
44 (15)
19 (19)
1 (4)
0.002
a
271 (36)
109 (33)
99 (34)
47 (46)
16 (59)
0.005
652 (86)
297 (88)
238 (82)
90 (88)
27 (100)
0.028
523 (70)
257 (77)
178 (62)
73 (72)
15 (56)
o0.001
98 (13)
51 (15)
37 (13)
7 (7)
3 (11)
0.177
209 (28)
72 (22)
89 (31)
36 (35)
12 (44)
0.002
205 (27)
70 (21)
12 (44)
33 (32)
90 (31)
0.006
315 (42)
155 (46)
105 (37)
46 (45)
9 (33)
0.066
Bicycle access (own or public access): yes, n (%)
567 (75)
335 (100)
156 (54)
60 (59)
16 (59)
o0.001
Public shared bicycle registered: yes, n (%)
392 (52)
282 (84)
76 (26)
26 (26)
8 (30)
o0.001
Use bicycle for travel: yes, n (%)
422 (56)
330 (99)
65 (23)
18 (18)
9 (33)
o0.001
6 (4)
6 (3)
3 (3)
3 (3)
3 (3)
o0.001
2.5 (3)
2.9 (3)
1.2 (1)
1.0 (2)
0.7 (1)
o0.001
Self-efficacy: totally self-sufficient, n (%)
346 (46)
191 (57)
96 (33)
46 (45)
13 (48)
o0.001
Willingness to shift to bicycling: totally ready, n (%)
38 (9)
—
31 (11)
3 (3)
4 (17)
o0.001
600 (81)
324 (98)
18 (69)
58 (59)
200 (71)
o0.001
Traffic safety: yes, n (%)
545 (73)
303 (91)
158 (55)
68 (68)
16 (59)
o0.001
Keeping personal appearance: impossible, n (%)a
196 (26)
35 (10)
96 (33)
50 (51)
15 (56)
o0.001
Vigorous activity duration (hours/week)
1 (3)
2 (4)
0 (2)
2 (4)
1 (3)
o0.001
Moderate activity duration (hours/week)
1 (3)
3 (3)
0 (1)
0 (2)
0 (1)
o0.001
Walking activity duration (hours/week)
4 (4)
3 (3)
4 (5)
3 (4)
3 (3)
o0.001
Overall activity duration (hours/week)
7 (7)
8 (7)
7 (6)
6 (8)
5 (9)
o0.001
32 (34)
38 (31)
25 (31)
28 (43)
22 (41)
o0.001
Sociodemographic Sex: male, n (%) Age (years), M (SD)
a
Has child(ren): yes, n (%)
Working status: currently employed, n (%) Education level: more than secondary, n (%)
a
Foreigner: yes, n (%) Smoking status: current smoker, n (%) BMI (Z25), n (%)
a
a
Health status: very good or excellent, n (%) Bicycle behavior and perception
Bicycle travel frequency: days, median (IQR) Bicycle travel duration: hours/week, median (IQR)
Enjoy bicycle commuting: yes, n (%)a a
Physical activity, median (IQR)
Energy expenditure in activity (MET-hours/week) Built environment, median (IQR)
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Table 1. Sociodemographic, Bicycle Behavior and Perception, and Physical Activity Characteristics of Participants According to Commute Mode (continued)
Sociodemographic Commuting duration (minutes) Commuting network distance (km)a Bikeability index at home (0–10)
a
Walkability index at home (–4 to 11)
a
ALL
Bicycle
Public transport
Motorcycle
Car
n¼752
335 (45%)
288 (38%)
102 (14%)
27 (4%)
p-value
20 (15)
20 (13)
29 (20)
15 (10)
20 (15)
o0.001
3.5 (2.6)
3 (2.2)
3.9 (2.7)
3.6 (2.4)
4.7 (4.4)
o0.001
6.3 (2.2)
6.6 (1.9)
6.1 (2.4)
5.9 (2.3)
5.7 (2.4)
o0.001
0.5 (3.0)
0.5 (2.8)
0.6 (3.0)
0.4 (3.3)
0.4 (2.6)
0.833
Note: High stress levels, having a score Z4 in each question of the short form of Perceived Stress Scale ; p-value, ANOVA or Kruskal-wallis for continuous variables, χ2 for categorical variables. Boldface indicates statistical significance. a Some variables have missing values: 1 in age, 1 in civil status, 2 in child, 3 in education level, 1 in smoking status, 2 in BMI, 12 in stress, 4 in traffic safety, 3 in keeping personal appearance, and 24 in built environment. IQR, inter-quartile range. 16
Discussion The physical activity from bicycle commuting is seen to be an addition, rather than substitution, to the regular physical activity performed by individuals. The extra physical activity performed by bicycle commuters is the result of performing more moderate physical activity while traveling by bicycle. Furthermore, this extra physical activity follows a sigmoidal dose–response relationship with bicycle commuting duration.
Comparison With Previous Studies Higher overall physical activity duration was found in participants who performed their commute by bicycle, which is consistent with previous research regarding active travel among adults.19,20 In agreement with Dombois and colleagues,19 the differences were due to higher levels of moderate-intensity physical activities. In other studies focused on children and adolescents, the effect of an active commute mode on physical activity duration was shown to increase with commute distance.21–23 This was not the case in the TAPAS study, possibly because the distances traveled in this study were too short, with a median of 3.3 km (25th percentile, 2.1 km; 75th percentile, 4.7 km). On the other hand, as is expected from previous literature, perceiving bicycle commuting as enjoyable was related to more participation in this moderate physical activity, whereas perceiving bicycle commuting as traffic-risky was related to performing more walking activity.24 There was no difference in non-travel physical activity when comparing across commute modes, supporting the idea that active travel acts as an additional form of physical activity rather than a substitution of other forms of physical ] 2015
activity. This finding is consistent with studies conducted in England and the U.S.22,25 On the other hand, and in agreement with Terzano et al.,22 the participants performed different types of non-travel physical activity (walk versus other moderate-intensity activities) according to the commute mode used. Studies on children have found similar results on commute mode and types of leisure-time physical activity.21,26 We believe that differences found in the type of physical activity performed are explained by the flexibility and quickness associated with bicycle use, which allows bicycle commuters to engage in a larger variety of other forms of physical activity during the same day.
Table 2. Crude and Adjusteda Linear Association Between Commute Mode and Physical Activity Duration Commute mode (reference bicycle)
All physical activity duration GMR (95% CI)
Non-travel physical activity duration GMR (95% CI)
0.73 (0.65, 0.84)
0.97 (0.83, 1.14)
0.75 (0.66, 0.86)
0.99 (0.84, 1.17)
0.73 (0.61, 0.87)
1.07 (0.87, 1.32)
0.77 (0.64, 0.94)
1.17 (0.93, 1.46)
0.60 (0.44, 0.83)
0.89 (0.64, 1.23)
0.67 (0.48, 0.94)
0.99 (0.69, 1.40)
Public transport Crude Adjusted
a
Motorcycle Crude Adjusted
a
Car Crude Adjusted
a
Note: GMR was used because physical activity was log-transformed. a Detailed multivariate regression models are available in Appendix Table 2. GMR, geometric mean ratio.
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Figure 1. Distribution of adjusteda overall and non-travel physical activity duration, according to the commute mode. (A) This figure shows the adjusted geometric mean and the CI obtained from Fletcher and bootstrap approach of overall and non-travel physical activity. (B and C) These figures show the contribution of vigorous, moderate, and walking activity to the adjusted geometric mean of overall and nontravel physical. a Final models for each level and overall physical activity measures are available in Appendix Table 2.
To our knowledge, this is the first epidemiologic study to assess the dose–response relationship between bicycle commuting duration and moderate-to-vigorous physical activity duration. Bicycle commuting duration was found to explain most of the variance across participants regarding moderate-to-vigorous physical activity. The moderate-to-vigorous physical activity performed by participants followed a sigmoidal dose–response relationship with bicycle commuting duration. Furthermore, half of the physical activity dose provided by bicycle commuting, which in our sample was mainly on flat ground, was in moderate-to-vigorous intensity and half in light- or very light–intensity levels. Despite the fact that the current study design does not allow us to establish a causal inference relationship, these findings reinforce the evidence that bicycle commuting may add to current physical activity performed by an individual, rather than replace other forms of physical activity. In the setting of the current study, bicycle commuting accounted for 2 extra hours of physical activity per week compared to motorized commuting. This extra physical activity, by itself, is almost equal to the minimum recommended amount of physical activity (30 minutes
on 5 days each week) required to promote and maintain physical health (i.e., reducing the risk of chronic disease and premature mortality).27,28 The promotion of physical activity, apart from being effective in the prevention of disease,2 has proven to be financially beneficial for public health systems.29–31 Particularly, the inclusion of the bicycle as a means of transport can be a cost-effective intervention, assuming the provision of appropriate infrastructures and the possibility of integration into an individual’s daily routine.5,32 In addition, focusing on interventions toward promoting trips by bicycle instead of walking has been shown to provide a more healthbeneficial cardiovascular stimulus, even in young adults.13 Consequently, based on the current evidence, public health policymakers may wish to promote bicycle commuting.
Strengths and Limitations One of the main strengths of this study is that the sample is representative of the distribution of the active population of Barcelona with respect to personal, neighborhood, and route characteristics. Another strength is the www.ajpmonline.org
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Figure 2. Dose–response relationship between bicycle commuting and moderate-to-vigorous physical activity duration (locally weighted scatterplot smoothing regression). Note: Solid and dashed lines indicate the geometric mean and the 95% CIs of the weekly extra moderate-to-vigorous physical activity duration according to bicycle commuting duration, from a LOESS regression model with bicycle commuting duration, and adjusted for age and number of children. The serrated vertical lines depict the thresholds in the dose–response relationship identified by segmented regression analysis, which were 0.5 and 9.5 hours/week of bicycle commuting, respectively.
control for the most likely confounders (sociodemographic factors, smoking habits, nutritional and health status, bicycle commuting perceptions, and built environment) of the relationship between physical activity and bicycle commuting, which prevents the risk of residual confounding in the shown effect.33 In addition, to our knowledge, this is the first study to use independent questions to assess bicycling and physical activity duration. Finally, the statistical methods were the most appropriate and conservative statistical methods to quantify both the difference and the dose–response relationship of outcomes with log-normal zero inflated distributions, as is the case of physical activity outcomes. A limitation of the study is the low response rate from street recruitment. However, it is important to note that there were no differences in response rate by mode of commute, and the sample is representative of the active population and neighborhood distribution of Barcelona. Hence, we believe that in the worse scenario, the low response rate might have led to an over-representation of the active population within the TAPAS sample, but the relationship between physical activity levels and bicycling is still valid and original. A second limitation is the low number of sampled car users. Although it corresponds to ] 2015
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the low prevalence of car trips within Barcelona city (7%),34 it limited the accuracy of the estimates of car commuters. Another limitation of the study is the crosssectional design, which limits causal inference of the relationship between bicycle commuting and a higher physical activity level. The use of self-reported data could be understood as another limitation, but it is the most cost-effective method for measuring the effects of bicycling on physical activity, given the weakness of accelerometer assessment of bicycling and the high costs of doubly labeled water assessment. The selection of a recall questionnaire, which assigns 8 METs for any kind of selfreported vigorous activity, instead of a quantitative history questionnaire, which allows a detailed measurement of the intensity of physical activities performed, prevented the study of the relationship between the volume of physical activity performed (energy expenditure) and commute mode.35 Future studies on the added value of active transportation in the promotion of physical activity should include assessments of energy expenditure, as it takes into account the different intensities of each type of physical activity performed.
Conclusions Bicycle commuting likely adds to overall physical activity. The extra physical activity performed by bicycle commuters is undertaken as moderate physical activity and follows a sigmoidal dose–response relationship with bicycle duration. This work is part of the Europe-wide project Transportation Air pollution and Physical ActivitieS: an integrated health risk assessment program of climate change and urban policies (TAPAS), which has partners in Barcelona, Basel, Copenhagen, Paris, Prague, and Warsaw. TAPAS is a 4-year project funded by the Coca-Cola Foundation, AGAUR, and CREAL. www. tapas-program.org/. The funders did not have any role in study design, data collection, analysis and interpretation of data, and the writing of this article and the decision to submit it for publication. All researchers are independent from funders. No financial disclosures were reported by the authors of this paper.
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Appendix Supplementary data Supplementary data associated with this article can be found at, http://dx.doi.org/10.1016/j.amepre.2015.03.036.
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