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74th session of the WHO Regional Committee for Europe

Cycling and walking can help reduce physical inactivity and air pollution, save lives and mitigate climate change

Solid evidence to promote cycling and walking .

walking for 30 minutes or cycling for 20 minutes on most days reduces mortality risk by at least 10%;
active commuting is associated with about a 10% decrease in risk for cardiovascular disease and a 30% decrease in type 2 diabetes risk; and
cancer-related mortality is 30% lower among bike commuters.

What countries can do

It is crucial to redesign urban spaces that meet daily needs related to accessing jobs, education, health care, food and goods, recreation, and other amenities within distances that can be safely covered using active mobility means and public transport.
Infrastructure for safe walking and cycling plays a central role in promoting active travel.
Trip-end facilities, such as changing rooms at workplaces and secure parking for bikes at destinations and in the proximity of public transport, provide a backup option for active travelers.
Green spaces, parks and trails, and forms of urban revitalization are further options to promote walking and cycling indirectly.
Schools should be safely reachable by walking and biking, and children should learn about the importance of regular exercise and the environmental impacts of traffic.
Reducing car dependency through better land use and urban planning, efficient public transport and disincentivizing driving can lead to more walking and cycling.
Countries should develop national cycling and walking plans, secure resources and allocate responsibilities to support their implementation.

Walking and cycling: latest evidence to support policy-making and practice

Pan-European Master Plan for Cycling Promotion

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Health benefits of cycling: a systematic review

Affiliation.

1 UKK Institute, Tampere, Finland. [email protected]
PMID: 21496106
DOI: 10.1111/j.1600-0838.2011.01299.x

The purpose of this study was to update the evidence on the health benefits of cycling. A systematic review of the literature resulted in 16 cycling-specific studies. Cross-sectional and longitudinal studies showed a clear positive relationship between cycling and cardiorespiratory fitness in youths. Prospective observational studies demonstrated a strong inverse relationship between commuter cycling and all-cause mortality, cancer mortality, and cancer morbidity among middle-aged to elderly subjects. Intervention studies among working-age adults indicated consistent improvements in cardiovascular fitness and some improvements in cardiovascular risk factors due to commuting cycling. Six studies showed a consistent positive dose-response gradient between the amount of cycling and the health benefits. Systematic assessment of the quality of the studies showed most of them to be of moderate to high quality. According to standard criteria used primarily for the assessment of clinical studies, the strength of this evidence was strong for fitness benefits, moderate for benefits in cardiovascular risk factors, and inconclusive for all-cause mortality, coronary heart disease morbidity and mortality, cancer risk, and overweight and obesity. While more intervention research is needed to build a solid knowledge base of the health benefits of cycling, the existing evidence reinforces the current efforts to promote cycling as an important contributor for better population health.

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Purposeful exercise, including bicycle transportation, improves health. Maitland ME. Maitland ME. Clin J Sport Med. 2012 May;22(3):292-3. doi: 10.1097/JSM.0b013e318256e797. Clin J Sport Med. 2012. PMID: 22544063 No abstract available.

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Open access
Published: 12 January 2023

Intended cycling frequency and the role of happiness and environmental friendliness after COVID-19

Natalia Barbour 1 &
Fred Mannering 2

Scientific Reports volume 13 , Article number: 636 ( 2023 ) Cite this article

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Psychology and behaviour

Although the COVID-19 pandemic has contributed to an increase in cycling in many countries worldwide, it is not yet known whether this increase becomes a long-lasting change in mobility. The current study explores this increase by analyzing data collected in a U.S. nationwide longitudinal survey. Using a total of 7421 observations, a mixed logit model with heterogeneity in the means of random parameters was estimated. In the resulting sample, nearly 14 percent of the respondents stated that they were planning to cycle more while only 4 percent of the respondents stated that they were planning to cycle less post COVID-19 pandemic. The estimation results provide insights into socio-demographic and psychological factors that play a role in planned cycling behavior post COVID-19. The study also establishes that age, race, employment status, gender, and household size impact intended cycling frequency. The model estimation results further indicate that workers (full time and part time), individuals with a high degree of life satisfaction, and individuals who are environmentally friendly all have higher cycling-frequency probabilities relative to others. The findings can be used to support policies that target sustainable mobility and further our understanding of the transportation, psychology, and well-being relationships.

Longitudinal associations between going outdoors and mental health and wellbeing during a COVID-19 lockdown in the UK

Area-level and individual correlates of active transportation among adults in Germany: A population-based multilevel study

What we can learn from five naturalistic field experiments that failed to shift commuter behaviour

Introduction.

Transportation is no longer just about moving large numbers of cars in the shortest amount of time or improving the efficiency of public transit systems—the focal point of discussions have turned to equity, health, access, and social justice. Broadening the transportation landscape by considering additional modes and dimensions that are found in urban design, health, and psychology recognizes the importance that transportation systems play in daily lives of people worldwide 1 . The need to rethink the composition of the sector, its priorities, operations, and performance has been magnified by the two global events: the ongoing climate crisis and the COVID-19 pandemic. Transportation has long been one of the main contributing sectors to climate change, in the United States it accounts for approximately 30 percent of greenhouse gasses 2 , 3 . While transportation-related emissions and the need to decarbonize the sector are central to achieving Paris Agreement goals 4 , 5 , the COVID-19 pandemic has been a major disruptor, fundamentally changing how the transport system is perceived, affecting daily commutes, and encouraging many users to try alternate, and often non-motorized, transportation modes 6 , 7 , 8 , 9 .

In recent years, the shift to cycling has gained a substantial attention in research and practice 6 . Even before the pandemic, research pointed to the many virtues of non-motorized options like walking, cycling, and other micromobility modes as options to increase transport efficiency and reduce emissions 5 , 10 Buehler and Pucher 6 examined these trends during the pandemic over time and by location, and concluded that there had been considerable variation in the percentage changes in cycling levels between 2019 and 2020 among EU countries as well as among regions in the U.S. By comparing both full years, including periods of lockdown in 2020, they found that the 11 EU countries averaged an overall 8 percent increase in cycling while the U.S. averaged 16 percent increase.

Research that addresses cycling and explores it in the context of public gains has found its benefits to include cost savings, savings on journey time, convenience, health, and perceived utility benefits to well-being 11 , 12 , 13 , 14 . Benefits also include improvements in the livability of cities and public health 15 . From an environmental perspective, numerous studies have highlighted the potential for cycling to reduce motor vehicle use and the associated external social costs that are imposed in terms of pollution, greenhouse gases, dirt, noise, and congestion 14 , 16 .

Past work has also stressed the relationship between cycling and the design of the active mobility infrastructure, and the ways in which transportation planning, policy, behavioral economics, and engineering fields might advance to best support the shift from auto-centric to human-centric designs 7 , 17 . With observed shifts toward human-centric transport, the growing need for human-centric designs and how they might support and encourage such shifts has begun to be explored in the literature 6 , 18 , 19 . However, comparatively little is known about the specifics responsible for human-centric shifts or how the underlying dynamics will affect potential changes in the future 18 . Studies of travel behavior in context of COVID-19 pandemic, which have to varying degrees explored shifts in cycling behavior, have found that factors such as age, education, gender, employment, household size, and car ownership were influential in determining cycling use 17 , 19 .

With the cycling revolution reflected in the increased use and advocacy of this transport mode 20 there is acknowledgement that understanding cycling use and trends is a multifaceted problem influenced by global perspectives, preferences, and attitudes. For example, studies have started to explore the relationship between environmental friendliness, health as well as lifestyle and the propensity to cycle, with many concluding a clear connection between psychological and lifestyle related variables and the willingness to cycle 21 , 22 , 23 . Over the years, the impacts of physical infrastructure on cycling have been well documented and, in addition, the impacts of attitudes and social norms have been recognized as important considerations 24 , 25 . For example, previous studies have highlighted the fact that significant changes in transport-related attitudes, norms, and behaviors have the potential to result in immediate reductions in fossil fuel consumption, and establish the foundations for further policy, public investment, and industry shifts 26 , 27 . Work by Wang et al. 28 found that attitudes towards greenness and environmental concerns were significantly and positively correlated to cycling adoption intentions. Past research has also confirmed the substantial impact of environmental concerns on various behaviors 29 , 30 .

Growing societal awareness about climate change mitigation, coupled with a worldwide encouragement for sustainable behaviors, have been identified as factors influencing the public opinions about cycling. The evolution of public opinions towards cycling was underscored by Willis et al. 31 who argued that social factors clearly affect the decision to cycle, and it is essential to look beyond the role of physical and built environment factors when attempting to understand or predict use.

Especially in the context of intended cycling post COVID-19 pandemic, social and psychological factors, and their role must be considered to gain substantive insights. Because pandemic related measures were often dictated by the government, the modifications to one’s mobility did not necessarily arise primarily from free will, but instead were a response to various externally imposed constraints, resulting in many people trying modes and behaviors they would not have tried otherwise 32 .

The current work applies an advanced econometric modeling approach to study the effect of specific socio-demographic characteristics of cyclers who plan to change their cycling frequency after the pandemic. The analysis does not only examine the evolution of cycling frequency during the COVID-19 pandemic (which is particularly important when considering the need to reduce emissions) but also links human well-being, life satisfaction, and environmental friendliness to intended cycling frequency. Thus, the empirical work herein moves beyond the traditional set of socio-demographic variables to explore the relationship between lifestyle and psychological factors and the role they play in the growth in sustainable mobility. The aim of the current study is to provide a better understanding of the shifts toward cycling and relate these shifts to policy recommendations.

Data and methods

The publicly available data used for the forthcoming analysis are from a survey that was a joint project of Arizona State University and the University of Illinois Chicago with support from the National Science Foundation. The survey includes responses from the adult population of United States citizens and was conducted using a nationwide longitudinal questionnaire that gathered information about travel-related behaviors and attitudes before, during, and after the COVID-19 pandemic. To capture shifts in behavior the survey was divided into waves that gathered information at different phases of the pandemic. In addition, the data were weighted to be representative of national and regional demographics. The survey questions covered a wide range of topics including commuting, daily travel, air travel, working from home, online learning, shopping, and risk perception, along with attitudinal, socioeconomic, and demographic information.

The survey was deployed over multiple waves to the same respondents to monitor how behaviors and attitudes evolved over time. The questionnaire was disseminated digitally during April 2020 and October 2020. The final sample used for the analysis contained answers from 7421 respondents. The summary statistics for variables used in the model estimation is available in Table 1 .

To capture the shift in cycling behavior, each respondent was asked about their intended cycling frequency when COVID-19 is no longer a threat, and three response categories were considered; planning to cycle more (that combined somewhat more than before and much more than before), planning to cycle less (that combined somewhat less than before and much less than before), and planning to cycle about the same amount as before the pandemic.

Using these three response categories, a random parameters multinomial logit model with heterogeneity in means and variances was estimated (no heterogeneity in the variances was detected). This approach allows the mean and variance of random parameters to be functions of explanatory variables and thus provides additional accuracy in studying unobserved heterogeneity. Researchers from the fields of travel behavior and highway safety regularly apply this statistical approach to capture unobserved effects by allowing the parameters to vary across the sample population. In the transportation field, this approach of accounting for unobserved heterogeneity, and enabling novel findings that deliver more insights into the interactions among the variables, has been successfully applied in the past 32 , 33 .

To estimate model that offers the best fit for the data, a function that determines the probability of either a respondent intending to cycle the same amount post pandemic, cycle less, or cycle more is defined as:

where X kn is a vector of explanatory variables that affect the probability of observation n being a respondent’s intended cycling frequency alternative k , β k is a vector of estimable parameters for observation k , and ε kn is a disturbance term. If the disturbance term is assumed to be generalized extreme-valued distributed, a logit model results as 34 ,

where P n ( k ) is the probability of the respondent n intended cycling behavior k (stay the same, increase, or decrease) when COVID-19 is no longer a threat.

To account for the possibility that one or more parameter estimates in the vector β may vary across respondents due to unobserved heterogeneity, a distribution of these parameters can be assumed, and Eq. ( 2 ) can be rewritten as 35 :

where, f ( β k | φ k ) is the density function of β k and φ k is a vector of parameters describing the mixing density function (mean and variance), and all other terms are as previously defined.

To provide more flexibility in accounting for unobserved heterogeneity, with the mixing distribution now allowing parameters to vary across observations n , the β kn vector can be made to be a function of variables that affect its mean and variance as 33 , 36 , 37

where, β k is the mean parameter estimate across all cycling alternatives, Z kn is a vector of observation-specific explanatory variables that captures heterogeneity in the mean that affects cycling alternative k , Θ kn is a corresponding vector of estimable parameters, W kn is a vector of observation-specific explanatory variables that captures heterogeneity in the standard deviation (variance) σ kn with corresponding parameter vector Ψ kn , and v kn is a disturbance term.

The model estimation was done by simulated maximum likelihood approach and used 1000 Halton draws as they can deliver more efficient distribution of simulation draws than purely random draws 34 , 38 . Just like in other studies in travel behavior to achieve the most superior estimation, the normal distribution was assumed for random parameters 39 , 40 . Marginal effects were calculated to determine the effect of individual explanatory variables on probabilities of intended cycling frequencies. Marginal effects were averaged over all observations and are presented in Table 3 . The marginal effect of an explanatory variable shows the effect that of a one-unit increase in that explanatory variable has on the response probabilities. For indicator variables (that assume values of zero or one), marginal effects will give the effect of the explanatory variable going from zero to one 35 .

Insights into intended post pandemic cycling behavior

The largest category of the respondents included those who did not plan to change their behavior and this group represented 82 percent of the sample. The second largest category (13.5 percent of the sample, or 1002 observations), was the group, where the respondents stated that they were planning to cycle more (somewhat more or much more than before the pandemic). Only 4 percent of the respondents stated that they were planning to cycle less (somewhat less than or much less than before). This rather large difference in percentage points between the respondents who intended to cycle more (13.5 percent) and those who intended to cycle less (4 percent) likely reflects the fact that the COVID-19 pandemic provided an opportunity to experiment with new transportation modes such as cycling, and this experimentation may have long-lasting impact on mobility behavior. When asked about the reasons for their intention to increase cycling, the majority of the respondents stated that they realized they liked biking, and this was followed by their expectation to bike more in their neighborhoods (Table 2 ). The increase in the intended cycling frequency in a respondent’s neighborhood is likely dictated by the changes to the infrastructure or right-of-way in their immediate community in response to the COVID-19 pandemic 19 .

The mixed logit model with heterogeneity in the means of random parameters estimation results (Table 3 ) will provide additional insights into the variables playing a role in intended cycling frequency post COVID-19 pandemic and show that both socio-demographic and various lifestyle choices and preferences were statistically significant.

Model estimation results: Socio-demographic factors

Turning to the model estimation results, gender was found to be a significant factor in determining respondents’ cycling behavior. This finding is not novel as it has been confirmed by a large body of research 41 , 42 , 43 . The nuance that the current analysis offers, however, is that the effect of the women indicator variable is heterogeneous across women respondents, as indicated by the statistically significant standard deviation of the random parameter shown in Table 3 . Furthermore, Table 3 shows that women respondents without a vehicle in their households had an increase in their mean, implying they were more likely to indicate that they intend to cycle more post the pandemic than their vehicle-owning female counterparts. Respondents who were men, on the other hand, were found to have a higher probability of intending to cycle less (as indicated by the marginal effect). The gender differences present in intended cycling frequency likely reflect a much broader issue around gender and mobility.

Particularly in the light of the fact that, globally, women have been found to cycle less than men 41 , 44 , the outcome of the current analysis suggests an ongoing shift and possible increase in cycling by women post pandemic.

Disparities in cycling behavior are not only linked to gender but are also present among minority groups. Former research argues that the COVID-19 pandemic has created opportunities for cities to close streets to automobile traffic to promote public health and other civic objectives. Although these interventions promised numerous benefits, neighborhood activists and scholars of color suggest they can perpetuate structurally racist inequities 45 . Findings herein, to some extent, confirm these concerns as the model results indicate that the respondents who were Black and those who were Asian will not cycle more but rather less. Turning to the abovementioned results regarding gender and race, and intended post COVID-19 cycling frequency, it is essential to broaden the conversation and emphasize that it is rarely a single element that determines mobility behavior, and the totality of distributional justice, accessibility, and safety, and their varied impacts on different socio-demographic groups, must be given full consideration 45 .

Respondent’s age and their household size were also found statistically significant variables suggesting as the individual gets older, they will have a lower probability of intending to cycle more (as indicated by the marginal effect equal to − 0.0759 in Table 3 ), whereas as their household size increases, they will have a higher probability of intending to cycle less. Respondents who rent their residence were found to have a lower probability of intending to cycle more post COVID-19 pandemic. The findings relating to age are not surprising, in a sense that with age, the propensity to engage in active mobility modes decreases and the probability of injury increases 46 . Nevertheless, the insights on the household size and residential renting and their impact on intended cycling frequency are interesting, as they capture the complexity of human behavior with regard to personal mobility. Particularly, the impact of increasing household size and its role in increasing the probability of intending to cycle less, likely captures the number of children in households, which has been confirmed to have a strong relationship to cycling behavior of adults 22 , 31 .

Interestingly, both groups of workers in the sample (those who work full time, and those who work part time), were found to have a 0.0047 and 0.0206 higher probability of intending to cycle more after the pandemic (as indicated by the marginal effects in Table 3 ). Because cycling has become a much more appealing transportation alternative to many, especially during the peak of COVID-19 cases, some workers throughout that time found themselves using this mode 47 . Combining these results with the findings from Table 2 , which indicate that a significant number of respondents who indicated an intended increase in cycling, marked ‘I realized I really like biking’ as one of their reasons, provides an explanation that experiencing a particular phenomenon can drastically change how one feels about it, and whether they will adopt a new behavior in the long term 32 .

The last set of variables relating to the socio-demographic factors includes respondents who were students and those with a graduate level education. Both groups had a higher probability of intending to cycle more post COVID-19 pandemic. This is predominantly important in the case of students, who are still forming their long-term mobility behaviors that they will carry into their adult lives. The fact that they had a 0.0158 higher probability of intending to cycle more as indicated by marginal effects, offers an attractive opportunity for the universities to continue to build momentum around cycling. Even before the pandemic, it was not unusual for university campuses and college towns to invest in micromobility modes and networks to mitigate congestion and emissions as well as nudge the students toward physical activity through active transportation 48 .

Model estimation results: The psychology of cycling

In addition to the standard socio-demographic variables that are often considered in travel behavior research, this study explored a set of lifestyle and psychological variables that likely capture a wide variety of unobserved factors found to dictate how people behave and what choices they make 49 .

Environmental friendliness reflected in respondents’ self-reported commitment to live environmentally friendly lifestyle as well as respondent’s strong commitment to minimizing pollution related to transportation were both statistically significant factors in the model. Respondents from these two groups were found to have a higher probability of intending to cycle more post pandemic as indicated by the marginal effects in Table 3 (0.0078 for the respondents committed to environmentally friendly lifestyle and 0.0132 for the respondents committed to minimizing pollution from transport). Interestingly, the magnitude of the probability of the respondents who are committed to minimizing pollution coming from transport is much larger than of the ones committed to the environmentally friendly lifestyle, which suggests opportunities to emphasize the role of transportation in promoting sustainable lifestyles.

Lastly, respondents who indicated a high satisfaction with their life had a slightly 0.007 higher probability of intending to cycle more than those who did not indicate such satisfaction. This finding is consistent with the literature arguing that life satisfaction is directly and indirectly related to satisfaction with travel 50 . Abou-Zeid and Ben-Akiva 51 found that activity happiness and travel satisfaction are strongly correlated with activity participation with the greater the activity happiness and the greater the satisfaction with travel to the activity, the higher is the propensity of conducting the activity. Perhaps, what is the most interesting about this result is the opportunity to trigger further discussion about how cycling can be leveraged to support public health and well-being on a systemic level.

The findings do not only deliver insights into intended cycling frequency post COVID-19 but also stress out the importance of heterogeneous cycling behavior among different groups. Particularly considering the ongoing climate crisis and the efforts to decarbonize transportation, as well as the need to shift from auto-centric to human-centered designs, understanding which factors are critical in the further uptake of cycling is essential for planning urban cores. Although, systemic changes in how people travel are difficult to achieve, some cities around the world were able to gain momentum and overtime transform their downtowns. Even before the pandemic, cycling infrastructure expenditure that was supported by policies targeting less sustainable modes, was found to be associated with more cycling among commuters 52 . Nonetheless, work herein looks beyond the infrastructure and examines how different groups of people have responded to COVID-19 measures and how that affected their intended cycling frequency. Considering the complexity of human behavior, fully grasping travel preferences, and supporting the findings with statistically significant results is not an easy proposition. Because each person has arguably an unlimited number of factors impacting their behavior, identifying one observable factor, and combining it with other identified variables allowed a clearer profile of a cyclist who intends to cycle more. Looking beyond the system and focusing on individual travelers allows a broadening of the ongoing cycling dialogue by examining this shift through multiple lenses including environmental justice, equal access, gender equality, and various psychological factors.

Although cycling is an inexpensive and accessible mode of travel, its adoption does not happen uniformly as it is often tied to the built environment, social acceptance, perceived safety, and cycling culture. Savan et al. 27 identified strategic population segmentation as one of the key approaches to promote cycling. The authors argued that the importance of recognizing the needs of individual preferences is crucial to create successful cycling policies. The model findings herein, to some extent, deliver this information, as they were able to capture those differences in individual behaviors and preferences. Particularly interesting is the fact, that workers (full time and part time) have a higher probability of intending to cycle more after the pandemic. This result points towards the significance of evaluating cycling in the context of transportation as opposed to recreation. Supporting workers and creating policies that would further codify the shift triggered by the pandemic, could provide long term benefits in decreasing congestion, minimizing emissions, and increasing physical activity. As former research has shown, personalized travel programs usually involve strategically targeted information, events, and incentives to achieve most optimal results in causing mode shift 53 , 54 . Others argue that cycling promotion programs should take advantage of life course transition periods as opportunities to target the intended behavior change 55 . With that said, the disruption caused by COVID-19 pandemic serves as a perfect opportunity to capture groups who intend to cycle more. Policies and practices are needed to reduce the distance travelled by vehicles, support active travel like cycling and walking, public transit, and compact development to nurture this global transition 56 .

However, there is also another side of the story that needs to be told. Interventions to close streets to auto traffic and promote open streets to allow walking and cycling have promised numerous benefits, but some have indicated that these could preserve structurally racist inequities 45 , 57 . It is without a question that the pandemic has opened a new window of opportunity for original solutions untethered to former constraints, but also brought potential paradoxes emerging from open street implementation. Such nuances are important to consider in the light of the findings of this study, which concluded that people who are Black and Asian have a lower probability of intending to cycle after the pandemic. Instances of environmental injustices include both, the concentration of environmental amenities in Whiter communities and the concentration of environmental hazards in BIPOC (Black, Indigenous and People of Color) communities 45 . Therefore, it is essential to bring such considerations to the forefront in the conversations and advocacy relating to cycling, urban design, and access.

The results of this study can be also used to understand the barriers to cycling and as bases for policy formation. Given the potential reduction in greenhouse gas emissions reduction that cycling offers, the cycling mode merits more consideration, investment, and political support if Paris Agreement goals are going to be met.

Lastly, looking from a system-level perspective, achieving radical reductions in car use will require deep carbon reductions in all locations and a reduction in car use that is reflected in national average per capita statistics, not just in a select few inner-city locations 58 . To target emission reductions, alternatives such as e-bikes have shown promise, with research arguing that e-bikes could play a major role in carbon reduction of land-based transport and offer even larger CO 2 savings, especially when considering a mode switch from a personal vehicle to e-bike 59 , 60 .

Data availability

The datasets generated and analyzed during the current study are available in the COVID-19 and the Future Survey repository, https://covidfuture.org/data/ .

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Mother and kids are enjoying a bike trip together. Summer sunset.

HEALTHY KIDS

Why bicycling might keep mental health in high gear

The science behind why riding a bike might boost mental well-being, plus some ideas to get pedaling.

Shannon Brescher Shea’s nine-year-old struggled with focus and following directions at home and at school. But after riding his bike—whether around the park or to school—he felt calmer.

“We called it moving meditation,” says Shea, who is a family biking advocate and author in Maryland. “We saw how incredibly helpful it was for his focus, emotional regulation, and ability to follow directions.”

Shea’s experience isn’t unique. Science has repeatedly proven that physical activity contributes to improved mental health. “Exercise, no matter your age, is the single best thing you can do for every organ in your body, including your brain,” says Allan Reiss , a psychiatry and pediatrics professor and director of the Division of Interdisciplinary Brain Sciences at Stanford University School of Medicine.

And while any exercise helps, a growing body of research shows bicycling is among the activities that might provide even more of a mental health boost. “Our research shows that kids who get out for a bike ride at least once a week report higher levels of mental well-being,” says cognitive scientist Esther Walker , research program manager at Outride, a nonprofit organization that conducts cycling research and supports programming for youth.

At a time when youth mental health is in crisis, bicycling is one avenue families might not have fully leveraged. If you have a bike—or access to one—here is what you need to know to reap the benefits of riding.

The brain on bicycling

Recent research has shown that aerobic exercise is related to improved cognitive functioning like attention and academic performance. But some experts believe that when we hop on a bike, the improvements might be even more pronounced.

Scientists aren’t yet sure why, but it might have something to do with all the executive-function skills cyclists use. “You need to maintain your balance and process a lot of information from your environment, like knowing whether you can squeeze past a tree or how hard you need to brake,” says Reiss, who is among the researchers shifting focus to a younger subset of bikers. “You've got to coordinate, sense, process, integrate, inhibit, and continually make decisions.”

For everyone, but kids especially, honing those parallel processing skills is key. Outride, through primary research as well as research conducted through university partners , is starting to look at how cycling can provide that brain boost . According to Walker, “Research suggests that physical activity like cycling likely encourages new cell growth in areas of the brain linked to memory and problem solving, and can support stronger connections between neurons, ultimately impacting memory and learning.”

Meanwhile, Reiss and his team at Stanford are in the throes of a brand new study to measure changes in the brain while someone is exercising. (Most research looks at the brain’s activity before and after.)

This will allow the team to understand, among other things, how cycling changes attention, whether someone has a known attention-related issue like ADHD or not. That’s important, he says, because improved attention, derived from something as simple as a bike ride, has the potential to help at home, with friends, at school for kids, and at work for adults.

How to get in—or back in—the bike saddle

Luckily, it’s not too hard to get on a bike. And children, in particular, “They don’t see it as work,” Walker says. “They see it as fun and freedom.” Here are some ideas on how your family can take advantage of this powerful mental health supercharge.

Give kids the reins. How, exactly, does one hand over control to children on a bike ride? “We’re guiding kids but giving them agency to make decisions,” says Thomas Clanton , assistant professor of sport and recreation studies at Young Harris College.

So, for instance, parents might lay out a map of the city, highlight 10 kid-friendly destinations, and let the child decide where to go and how to get there on the bike. Your role? Weigh in on safety and logistics, depending on your biker’s age and capabilities.

Offer them jobs. Assigning roles on the bike ride can also provide a sense of control. For instance, whoever is leading the ride gets to be in charge of a pre-ride check: Does everyone have their helmet secured, lights on, and water bottle filled? During the ride, a leader can call out things to watch for, like big puddles or a stop sign. If anyone is too tired, the leader decides whether to take a break or turn back.

“Allowing actions to be youth-led gives kids a sense of ownership,” says Ajoa Abrokwa , founder of She Is Focused, a cycling-centric exercise and community engagement program for women and girls in Philadelphia. “They develop abilities on the bike to plan, execute, lead, and support.”

Focus on the fun. To ensure riders don’t get tired too quickly (or too bored), build in a fun pit stop rather than making the ride the sole focus. “Self-paced exploration and adventure are key on the bike,” says Charles Chancellor, an associate professor at Clemson University’s College of Behavioral, Social, and Health Sciences. (He also heads up the Bicycle Research Team , which conducts research on bicycling.)

Adventure can be urban or rural, too. If you live in a city with access to bike parks or pump tracks, join in the fun. For rural landscapes or those with access to bike trails, search for local flora and fauna on the ride.

Start small. Some riders will be naturally hesitant, especially when it comes to things like speeding down a hill or trying a bumpy trail. Try starting with a “snail race”: Who can ride the slowest without tipping over, from point A to point B?

Incorporate eco-stewardship. Whether your biker enjoys the smooth pavement of city streets or dirt and root-covered trails, the ride provides an opportunity to talk about caring for the Earth. City streets and dusty forest trails need care and concern to remain safe for cyclists. “We develop an increased awareness of, and care for, trailways and roads naturally while we’re on the bike,” Abrokwa says.

Bring some friends. Reiss says that the benefits of cycling can multiply when it becomes a social endeavor: “Our brains evolved to be socially involved, and exercise in groups is often more motivating for people.”

In neighborhoods where bike lanes or other safe-to-ride pathways exist, organizing a group ride with pals might be just the ticket to less stressed, more relaxed individuals.

How to write a nature prescription for your family

Don’t let your stress derail your kids’ eating habits

Outdoor fun + mindfulness = mental health boosts for kids

Want better behaved kids? Give them more sunshine

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Bicycling, also referred to as biking or cycling, is a form of transportation and a popular leisure-time physical activity. Health benefits include improved cardiovascular fitness, stronger muscles, greater coordination and general mobility, and reduced body fat. As with other types of exercise, it can also help improve mental health by lowering stress levels and stimulating feel-good endorphins.

Although not listed as an independent category in the American College of Sports Medicine’s annual top fitness trends, bicycling can be included in several of their top categories: outdoor activities, exercise for weight loss, group training (i.e., spinning classes), and fitness programs for older adults. [1] Bicycling is versatile—you can ride alone or with a group, indoors on a stationary bicycle or outdoors in nature, and at any age. It is a low-impact activity that reduces stress on the hips, knees, and feet and may be performed by people who cannot do higher-impact activities such as jogging or running.

Bicycling for transportation, such as commuting to work, provides the benefits of incorporating exercise into everyday life, reducing costs associated with driving a car or taking public transportation, and lessening road congestion and air/noise pollution, as seen in urban areas. [2] In one survey of cyclists, 21% reported biking to work. [3] Studies show that people who bike to work are more likely to meet national physical activity guidelines. Due to the intensity and energy expenditure of cycling, they are also more likely to meet recommended guidelines for cardiovascular fitness and health benefits. [3]

Bicycling and Health

Much research on the health benefits of bicycling focuses on “active commuting” which entails walking or cycling to work or other places that usually involve driving. These studies find that cycling reduces the risk of cardiovascular disease, diabetes, and early deaths, and may prevent weight gain or obesity. Most of the research is observational or cross-sectional, using self-reported surveys of cycling frequency. A possible bias in these studies is that cycling requires a minimum level of existing physical fitness, so adults who are healthier at baseline may be more likely to choose cycling or active commuting than less healthy individuals, which may affect mortality and disease rates. [4]

A meta-analysis of 23 prospective studies found that participants who engaged in active commuting (cycling or walking to work) had a significantly lower risk of cardiovascular disease. [5]
A large Swedish study following more than 23,000 men and women for ten years found that those who commuted to work riding a bicycle compared with those who drove or took public transportation had a decreased risk of several cardiovascular risk factors: obesity, high blood pressure, high triglycerides, and high blood glucose. [2]
A large prospective study followed more than 53,700 Danish older men and women ages 50 to 65 years for 20 years and controlled for underlying health conditions to observe the effect of cycling on heart health. [6] The results showed that participants who changed from no cycling to cycling had a 26% lower risk of heart disease compared with those who never cycled.
An observational study in 2364 younger adults (ages 18-30 years) found that men who actively commuted had reduced risk of obesity and elevated levels of triglycerides, blood pressure, and insulin. [7] An association was not as strong in women, possibly due to lower overall levels of active commuting.
The association between cycling and weight changes were studied in a cohort of more than 18,000 premenopausal women from the Nurses’ Health Study II. [8] After 16 years, the findings showed significantly less weight gain in those who walked briskly or cycled. Those who did not cycle at the start of the study but increased by as little as 5 minutes a day gained less weight than those who never cycled.
A large observational study including more than 72,000 men and 82,000 women showed that active commuting was significantly associated with a lower body mass index and percentage body fat than those who drove or took public transportation. [9]
Studies of older adults have shown that cycling can enhance opportunities to socialize, enjoy the outdoors, increase self-confidence by learning a skill, and recapture a sense of childhood joy and freedom. [10,11] Increased energy, better mood, and improved sleep are other reported benefits. [10] In another study, stress reduction in young and older active commuters who bicycled to work was found to be greater than in those who drove or commuted by other means. [12]
In an observational study following 7,459 adults with diabetes from the European Prospective Investigation into Cancer and Nutrition group, cycling was associated with a 35% lower risk of deaths from any cause compared with non-cyclists, and a 24% lower risk compared with motorcyclists. [13] The authors noted that their findings were similar to studies that had included healthy people and those with other types of chronic disease.
A meta-analysis of 17 studies looking at the association of walking or cycling to work and mortality found a 21% lower risk of deaths from any cause and 33% lower risk of cardiovascular deaths in cyclists. [14]

Bicycling Safety

A major reason people may not participate in biking is a belief that it is unsafe, which is not unfounded. [15] Bicycle riders in the U.S. have much higher fatality and serious injury rates than in comparable high-income countries. [16] According to the Centers for Disease Control and Prevention, about 1,000 bicyclists die in the U.S. each year in crashes involving motor vehicles, and more than 130,000 are injured on roads. [17] Male adults ages 55-69 are most likely to die from bicycle deaths, whereas male adolescents and young adults have the highest bicycle-related injuries. Deaths tend to occur in urban areas where there is heavier motor vehicle traffic; high speeds (of bicyclists and car drivers) and alcohol are major risk factors for accidents.

Moving toward a better bicycling environment

painted protected bike lane with bicyclist riding on it

Before 2011, no studies had been conducted in North America to show that cycle tracks, common in the Netherlands or Denmark, were safer than biking in the road with cars. This changed after a study in Montreal, Canada, revealed that cycle tracks had a 28% lower injury rate and attracted 2.5 times as many bicyclists compared to parallel roads without these bicycle facilities. [18] Additional research on 19 cycle tracks in the U.S. also found a lower risk of bicycle-vehicle than published crashes on roadways. [19] In Boston, Massachusetts, over a seven-year period with the initiation of its “Boston Bikes” program, newly constructed bicycle lanes, improved bicycle signage and parking, and a new bike share program led to a reduction in cycling injuries and an increase in people cycling. [20]

Beyond cycle tracks, some countries including the Netherlands and Denmark have implemented traffic signals for bicycles, special precautions at traffic intersections, and even express superhighways for cyclists riding longer distances to work. [16] Providing additional motorist training and traffic safety education in schools can further reduce accidents. Amenities along bike lanes such as bike-specific signals, smooth surfaces with low resistance, parks, sidewalk cafes, art, and trees planted between the lanes and street may attract more people to cycle. [21]

Still, financial and other challenges exist in creating bike-friendly environments. In the U.S., government funding is primarily for infrastructure that benefits motorized vehicles, leaving communities and businesses to fund their own bicycle infrastructure. [21] Indeed, there also remains a resistance from “car culture” to relinquish road space (including roadside parking spaces) to bikes, and criticism often arises when vehicles are constantly observed on roadways while cyclists may only appear sporadically on bike-dedicated infrastructure. However, rather than considering drivers “losing” and bicyclists “winning,” a win-win might be found in creative solutions that enhance the overall streetscape . For example, building a sidewalk-level cycle track reduces the need for painted lines, additional signs, cobra head lights that light the road, or plastic delineator posts. A raised, wide cycle track can also be built on a foundation that supports tree roots as well as a grass strip between the track and sidewalk to lessen the urban heat island effect. [22] Soft and low LED lighting can be an attractive edge to the road and, by shining on the pedestrians and bicyclists, show the human scale to a community. [23]

Rules of the road

Both cyclists and car drivers should be aware of cycling rules on the road. Check with your local state and municipal biking laws. Below are examples of common regulations.

Cyclists are allowed on any public roads and sidewalks unless specifically prohibited, such as on express state highways or private roads. Ride in the direction of traffic.
Cyclists must obey automobile road rules such as stopping at red lights and stop signs. Slow down at traffic intersections even if a light is green. An exception is that cyclists can pass cars on the right side.
Cyclists may ride on sidewalks outside business districts but must yield to pedestrians and signal when passing, such as with a bell or horn.
In certain states, cyclists must use hand signals when turning unless it is dangerous to remove hands from handlebars. At least one hand should be kept on the handlebars at all times. Learn more about using hand signals from the National Highway Traffic Safety Administration .
Cyclists 16 years or younger must wear a helmet with chin strap meeting the U.S. Consumer Product Safety Commission requirements. This protects against brain and head injuries. An exception is if a child is riding with an adult in an enclosed trailer. Regardless of requirements, use of helmets by riders of all ages can substantially reduce risk of brain injuries.
Bikes may be parked on sidewalks, but never blocking crosswalks, bus stops, fire hydrants, parking spaces, or walking access by pedestrians. Allow four feet clearance.
Theft of bikes or biking equipment is very common, so take with you any parts that can be easily removed from the bike. Ideally lock the bike lock to a bike rack with a lock that secures the frame and wheels, such as with U-locks. Do not lock bikes to trees, handrails, or private fences.
Some states require bike lights and reflectors for night use, and this is highly desirable even if not required. Display a white light on the front of the bicycle and a red light or reflector on the back. Wear ankle reflectors if the pedals do not have reflectors.
Be alert at all times for pedestrians, cars, other cyclists, animals, and changes in the road terrain.

What Are E-Bikes?

Regarding safety, some international studies report increased injuries related to e-bikes compared with conventional bicycles and which are more likely to require hospitalization. These more serious injuries may be likely due to the greater speed and weight of many e-bikes. [24,25] In the U.S., a study using data from the U.S. National Electronic Injury Surveillance System found that most e-bike injuries occurred in men (as was the case with bicycles) and, compared with standard bikes, e-bike injuries were twice as likely to involve a collision with a motor vehicle and three times more likely to involve a collision with a pedestrian. [26] Other studies have found that head trauma accidents involving e-bikes tended to involve older adult riders, which may be due to slower reaction times and less control with the e-bike. [25] Therefore bike helmets may be an important safety precaution. States have differing rules about wearing helmets when using e-bikes, most of which are age-dependent, but some also specify the type of e-bike. Check with your state’s bicycling regulations, but use of helmets is to be encouraged regardless of requirements because they can substantially reduce the risk of brain injury. Because e-bikes will not provide the same health benefits as traditional cycling and are associated with similar or increased risks from crashes, their use is best limited to situations where traditional bicycle riding is not a reasonable option.

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Open access
Published: 27 January 2010

The effectiveness of community-based cycling promotion: findings from the Cycling Connecting Communities project in Sydney, Australia

Chris E Rissel 1 , 2 ,
Carolyn New 1 ,
Li Ming Wen 1 ,
Dafna Merom 2 ,
Adrian E Bauman 2 &
Jan Garrard 3

International Journal of Behavioral Nutrition and Physical Activity volume 7 , Article number: 8 ( 2010 ) Cite this article

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Encouraging cycling is an important way to increase physical activity in the community. The Cycling Connecting Communities (CCC) Project is a community-based cycling promotion program that included a range of community engagement and social marketing activities, such as organised bike rides and events, cycling skills courses, the distribution of cycling maps of the area and coverage in the local press. The aim of the study was to assess the effectiveness of this program designed to encourage the use of newly completed off-road cycle paths through south west Sydney, Australia.

The evaluation used a quasi-experimental design that consisted of a pre- and post-intervention telephone survey (24 months apart) of a cohort of residents (n = 909) in the intervention area (n = 520) (Fairfield and Liverpool) and a socio-demographically similar comparison area (n = 389) (Bankstown). Both areas had similar bicycle infrastructure. Four bicycle counters were placed on the main bicycle paths in the intervention and comparison areas to monitor daily bicycle use before and after the intervention.

The telephone survey results showed significantly greater awareness of the Cycling Connecting Communities project (13.5% vs 8.0%, p < 0.05) in the intervention area, with significantly higher rates of cycling in the intervention area (32.9%) compared with the comparison area (9.7%) amongst those aware of the project. There was a significant increase in use of bicycle paths in the intervention area (28.3% versus 16.2%, p < 0.05). These findings were confirmed by the bike count data.

Despite relatively modest resources, the Cycling Connecting Communities project achieved significant increases in bicycle path use, and increased cycling in some sub-groups. However, this community based intervention with limited funding had very limited reach into the community and did not increase population cycling levels.

Riding a bicycle has considerable health benefits, with longitudinal studies reporting 30-40% decreases in mortality for regular riders [ 1 , 2 ] and decreased risk of diabetes [ 3 ]. Health benefits from commuter cycling include less likelihood of being overweight or obese [ 4 ], and considerable savings (estimated at $237 (AUD) million per annum) to the health budget [ 5 ]. Cycling for transport also has benefits for the environment, producing zero carbon emissions, contributes to less traffic congestion, and results in lower exposure of the rider to traffic pollution [ 4 , 6 ].

Despite cycling being the third most popular recreational activity in Australia [ 7 ], the proportion of trips by bicycle in Australia is about one per cent [ 8 ], similar to New Zealand and the USA, but far lower than in many European cities [ 9 ]. Although often poorly evaluated, Australian interventions to increase levels of cycling have generally been successful within the populations studied [ 10 ].

There has been very little Australian or international research evaluating the effectiveness of infrastructure and environmental changes upon increasing population levels of physical activity [ 11 ]. One example that building and promoting adequate cycleway facilities increases regular cycling comes from Bikewest in the Western Australian Department of Transport. They have used the mass marketing message Cycle Instead , complemented by an individualised marketing program conducted by a 'Travelsmart' team from the same Department, and reported a 53% increase in bike trips at 12 month follow-up [ 12 ].

A new Sydney Roads and Traffic Authority (RTA) built shared pedestrian and bicycle path, the Parramatta-Liverpool Rail-Trail was recently evaluated [ 13 ], one of the few such studies internationally. With only minimal promotion of the Rail-Trail, moderate increases in trail use and small increases in cycling activity among residents who live within 1.5 kms to trail were found [ 13 ]. However, there was no control area/trail and those increases that were observed may have been due to general increases in cycling in NSW [ 14 ].

It is unknown if promotion of bicycle paths leads to an increase in the proportion of adults who meet the physical activity recommendation, or whether the new cycle path simply attracts existing cyclists away from other routes and away from other modes of exercise. A prospective study in the US found that the building of a multi-use trail did not demonstrate an increase in physical activity among adults living near the trail [ 15 ]. Further, while often suggested, it is not clearly documented that an increase in cycling leads to an increase in population physical activity levels. Therefore, the two research questions of the Cycling Connecting Communities (CCC) project were 1) Does promoting new infrastructure increase cycling? and 2) Would an increase in cycling result in an increase in population levels of physical activity?

The community based intervention

The CCC project interventions were supported by a large number of partners through an Advisory Committee, including representatives from two local government areas (Liverpool and Fairfield City Councils) who supported and promoted CCC activities. Fairfield City Council had already initiated their own cycling related projects consisting of a Bicycle Recycle project to improve access to cheap bikes and the setup of a local bicycle group in the Fairfield area, called the Western Sydney Cycling Network.

The intervention program was based on a social marketing framework applied locally and used behaviour change theories including the transtheoretical model and stages of change [ 16 ] (see Table 1 ).

The project was implemented in the local government areas of Liverpool and Fairfield, with a third adjoining local government area (Bankstown) as the comparison area. All three areas are characterised by higher levels of non-English speaking residents compared to the rest of Sydney, and higher levels of social disadvantage [ 17 ]. Addressing social equity issues was a condition of funding approval.

A range of project resources was produced or purchased and branded with the project name and logo. A map titled 'Discover Fairfield and Liverpool by Bike' showing the bicycle paths and useful cycling routes in the area was considered the key resource in raising awareness for non and infrequent cyclists by illustrating the extent of local bike paths. 20,000 maps were produced. A general information booklet addressing concerns of potential cyclists titled 'Thinking about cycling' was created to complement the map (n = 5,000). Water bottles (n = 2,000) and reflective slap bands (n = 2,000) were designed with specific project images to serve as cues to engage in cycling.

As part of the CCC project, a one-hour presentation was developed and delivered to 351 people attending 24 community or workplace groups between February and September 2008. The objective was to raise awareness of cycling, the benefits of physical activity, the CCC project activities and resources, and to generate discussion of how to progress to riding a bike or to riding a bike more.

One of the main interventions in the early stages of CCC was the offer of free cycle skills courses. These courses were designed for members of the public who wanted to ride but did not, and focused on basic skills and confidence [ 18 ].

National Ride to Work Day is a national event which is part of a behaviour change program run by Bicycle Victoria to encourage workers to commute to work by bike on that day [ 19 ]. The CCC project trialled this as a broader community event in 2007, with a community breakfast held in a park adjacent to a major teaching Hospital in Liverpool. As this was considered successful, the event was replicated in 2008 with a higher level of marketing to local businesses.

Community rides

A number of community rides were organised, some as part of NSW Bike Week, a state-wide NSW Government initiative. Councils and other organisations are encouraged to run organised bicycle events in a safe and supported environment. The RTA provides start-up funding to assist in the promotion of these events, and rides were organised in each of the intervention area councils each year. Approximately 100 people participated in these rides.

The City of Sydney Spring Cycle is an annual event that is run by Bicycle New South Wales (NSW) [ 20 ]. While it has historically run from North Sydney to Olympic Park, additional starts were proposed for 2008. The CCC project lobbied Bicycle NSW to include the Liverpool start in 2008, and this was agreed upon with volunteer support from the CCC project. Several hundred people participated in the inaugural Liverpool start.

Australian Better Health Initiative funded community rides

The success of the Liverpool Bike Week event provided a model that could be replicated in other local communities in Liverpool and Fairfield. To make it more accessible to lower socio-economic areas, it was also desirable to provide free bike hire. A grant from the Australian Better Health Initiative (ABHI) provided the opportunity to run four such events over a four month period in 2009.

Four localities were chosen where there was good access to a network of cycle paths. Two were identified in the Liverpool area and two in Fairfield, and each site could be supported by the relevant local bicycle user group. Resources available on the day included a leaflet describing the route, healthy recipe books, and Measure Up booklets and measuring tapes, and CCC project resources. Participation varied on these rides, depending on the weather, ranging from 10-100.

The CCC was awarded $292,000 (AUS) from 2007 to 2009, which included evaluation, project coordination and intervention costs.

The impact evaluation used two approaches (Study 1 and 2) and two different data sources.

Study 1: Research questions related to telephone surveys

1. Is there a significant increase in self-reported cycle path use for cycling or walking, in the percentage of cyclists who used the cycle path in the past month and did this use vary across population sub-groups (age, sex, education attainment, ethnicity, car owners)?

2. Did the intervention campaign result in a significant increase in unprompted and prompted awareness of the cycle path?

3. Did the intervention result in a significant increase in cycling commuting or recreational cycling and who are more likely to change these behaviours?

The evaluation design was quasi-experimental with a cohort study with two data collection points in the intervention and comparison areas. The cohort evaluation focused on a random sample of adults, aged 18 years or older, living within two kilometres from the cycleway in suburbs that were defined as the intervention area or the comparison area, a different but demographically similar part of Sydney adjacent to the intervention area.

Respondents were selected using a three-stage sampling process. In the first stage postcodes within two kilometres from the two bicycle paths were identified. In the second sampling stage households in these areas were linked to the Electronic White Page Directory (EWPD) to randomly select telephone numbers for each sample group. In the third stage each household was telephoned and screened for eligible respondents. Eligible respondents were aged 18 years or older, and spoke English. If there was more than one eligible person per household, respondents were selected randomly using the most recent birthday technique.

Data collection

Data were collected using standard computer assisted telephone interview techniques (CATI). The baseline interview (approximately 10 minutes) was conducted in May-June 2007. Respondents who consented to participate in a follow-up interview were re-contacted 24 months later, with follow-up interviews conducted in May-June 2009 (see Figure 1 ). Interviews were conducted using a commercial market research company Socio-demographic characteristics (including age, sex, educational attainment, income, marital status, presence of children in the household and car ownership) were asked only at baseline using questions previously used in the NSW Health Survey [ 21 ]. These questions were replaced with campaign process evaluation questions in the follow-up interview.

Design of impact evaluation using a telephone survey .

Main outcome measures

Frequency of cycling - When was the last time you rode a bicycle? Was it today, in the last week, in the last month, in the last year, longer than a year, or never?

Physical activity (PA) behaviour -

Sufficiently active: sufficient to confer health benefit if total time is greater or at least 150 minutes (using the Active Australia questionnaire).

Total time cycling per week: estimated time spent on cycling in the past week.

Total sessions of cycling per week: number of times spent on cycling continuously for at least 10 minutes in the past week.

Usage of bicycle paths - whether respondent had ever used the new bicycle paths for any purpose.

Statistical analysis

All data analysis was conducted using STATA [ 22 ]. For the cohort of survey respondents for whom there was both baseline and follow-up data, regression analyses (general linear regression was used for continuous measures and logistic regression was used for categorical measures) tested the significance of differences between the intervention and comparison areas adjusting for baseline differences, socio-demographic characteristics and potential confounders. Pre-post changes in the cohort were examined with paired t-tests for continuous variables and McNemar's test for categorical measures.

Study 2: Bike count monitoring

1. Is there a significant overall increase in the daily means of bike counts along the cycleway not explained by seasonal, weekend and weather variations?

Four 'Trafficorders', devices that are designed to monitor traffic volumes by type and speed with a reliability range between 95%-98%, were placed at different points along each of the bicycle paths. The devices recorded activity continuously for every quarter of an hour, hourly, and 24 hours for each day during the monitored period. The data were retrieved from the devices as Excel files, separately for each location, and contained all the segmented readings for each day. The 24 hours readings for each location were plotted by dates to check for outliers and to observe time patterns. In addition, precipitation level and the minimum or maximum temperature for each day during the monitored period were provided by the nearest meteorology stations and were included in the data sets. These data were compared over the 24 months of the project.

Negative binomial regression analysis (STATA command 'nbreg') compared the area daily bicycle counts between the intervention and comparison areas over time (using an interaction term) and tested for statistical differences. Negative binomial regression is a regression technique used for nonnegative count variables where the count variation is expected to be greater than that of a true Poisson. The average daily means and the variance over the project period were also calculated for each location and for the intervention and comparison areas as totals.

Telephone surveys

A total of 1450 interviews were completed, with a response rate of 64.7 per cent. There was little difference between the intervention and comparison areas in terms of basic demographics at baseline, although there was a higher level of cycling in the intervention area (25% had cycled in the past 12 months compared with 19% in the comparison area). Most respondents (n = 1,254, 86.5%) agreed to be re-contacted 24 months later and to be asked similar questions.

At baseline there was higher bicycle ownership in the intervention area (p = 0.02) (excluding those with a disability), greater use of bicycle paths in the intervention area (p < 0.01) and a slight tendency for respondents in the intervention area to have cycled more recently (data not shown). There were no differences in self-reported health, physical activity levels, minutes riding a bicycle in the past week, and whether respondents had seen any advertising about cycling.

Of the 1,254 respondents at baseline who agreed to be re-contacted, 80.8% (n = 1,013) were able to be contacted, of which 909 agreed to be interviewed (89.7% response rate).

There was a greater proportion of older respondents in the comparison area at the follow-up survey (see Table 2 ), but otherwise no difference between areas. There was a loss of younger people at the follow-up, as well as students and respondents not born in Australia.

At follow-up, almost a quarter (25.8%) of respondents in the intervention group had cycled in the last year compare with 19.4% of respondents cycling in the last year in the comparison area (p = 0.06) (see Table 3 ). However, this difference is largely explained by the higher level of cycling in the intervention area at baseline (25.2%) compared with the control area (19.3%).

At follow-up, there were no differences between the intervention and comparison areas in the proportion of respondents who had cycled in the past year overall (see Table 3 ) or when the data were stratified by age and sex sub-groups. When type of rider was examined, there were significantly more people who described themselves as novice or beginner riders who had ridden in the past year in the intervention area (11.5%) compared with 1.4% in the comparison area (p = 0.013).

Despite similar path use at baseline, there was a significantly greater use of the bicycle paths in the intervention area (28.3%) at follow-up compared with the comparison area (16.2%) (p < 0.001) (see Table 4 ) and path use was significantly associated with an almost ten per cent increase in having cycled in the past year (29.1% in the intervention area compared with 20.6% in the comparison area (p = 0.010) (data not shown). There was also a significantly greater proportion of respondents in the intervention area who were likely to use the paths in the future (28.6%) compared with the comparison area (17.8%) (p < 0.001).

A greater proportion of respondents (13.5%) in the intervention area had heard of the Cycling Connecting Communities project compared with the comparison area (8.0%) (p = 0.013) (see Table 4 ). Among those people who had heard of the CCC project, there was a significantly higher proportion of respondents who had ridden in the last year in the intervention area (32.9%) compared with the comparison area (9.7%) (p = 0.014). This relationship remained significant after adjusting for baseline cycling (p = 0.021). There were no differences by age or sex in the profile of those respondents who recalled awareness of the CCC project, although respondents who described themselves as occasional riders at baseline in the intervention area were most likely to recall awareness of the CCC project (73.7%) compared with the comparison area (23.5%) (p = 0.004). Path use in the intervention area was greater than in the comparison area (p < 0.001) after adjusting for baseline differences, highlighting a greater increase in path use in the intervention area.

Minutes riding in the last week

In the intervention area, among those that had ridden in the past week there was a slight decrease in the mean minutes cycling for recreation or exercise (169.5 minutes to 152.1 minutes per week), but a large increase in the mean minutes cycling for transport (76.9 minutes to 174.2 minutes per week). In the comparison area there was a much bigger drop in the mean minutes of recreational cycling (190.3 minutes to 121.3 minutes per week) and a large drop in mean minutes of cycling for transport (197.6 minutes to 71.7 minutes per week).

For the small subset of respondents that had ridden in the previous week at both baseline and follow-up (n = 18) a similar pattern was observed (see Table 5 ).

Overall, among those that had ridden in the past week at baseline or follow-up, there was an increase in the total mean minutes cycled in the past week from 188.6 minutes to 233.0 minutes in the intervention area, compared with a decrease in the comparison area from 274.3 minutes to 134.1 minutes. Using the small subset of paired data (riding in past week at both baseline and follow-up), after adjusting for baseline levels of minutes riding, there was a significant increase in the total mean number of minutes riding in the intervention area compared with the comparison area (p = 0.039).

The increase in minutes riding can be explained in part because of an increase in the number of times participants went riding in the past week in the intervention area (2.9 to 4.8 times), and a slight decrease in the comparison area (4.6 to 4.5).

There was no significant difference between the intervention and comparison area with regard to the total mean minutes of physical activity. There was a similar amount of change in the mean minutes of physical activity - from 234.1 to 260.7 minutes per week in the comparison area, and 210.9 to 242.2 minutes per week in the intervention area. Mean minutes of cycling in the past week was significantly associated with total mean minutes of physical activity per week (p < 0.001), after adjusting for area of intervention, age and sex.

There was no statistical difference between the intervention area (48.7%) and the comparison area (53.7%) (p = 0.130) in the proportion of respondents meeting physical activity guidelines of 150 minutes of moderate intensity physical activity per week. However, of those people who met the physical activity guidelines, 28.1% had cycled in the past year (16.0% in the past month) compared with 16.8% of those not meeting the guidelines having cycled (6.5% in the past month) (p < 0.001 for both past year and past month comparisons). Forty per cent of people riding in the past week achieved the recommended minimum physical activity level just by cycling.

Bicycle count monitoring

Bicycle count data indicate increases in both the comparison and intervention area, with a significantly greater increase in the intervention area from 23.6 per day (95% confidence interval 21.9 - 25.4) in the first year of the project and which was maintained at the end of the project with 28.3 bicycles counted per day (95% confidence interval 25.6 - 31.1). This represents a 19.9% increase in the intervention area, and is compared with a 12.0% increase in the comparison area. Figure 2 shows the average daily bicycle count by intervention area over time (using westward data).

Bicycle counts in the intervention and comparison areas over time .

These results are confirmed in the multivariate analyses (using negative binomial regression and adjusting for weekends, rainfall, minimum and maximum temperatures) with the interaction between area of intervention and time being statistically significant (p = 0.021).

In the intervention area the Cycling Connecting Communities project appears to have increased awareness of the project, increased use of bicycle paths, increased cycling among novice or beginner riders, and increased the mean number of minutes cycled in the past week among participants riding at both baseline and follow-up. However, there was no overall increase in the population frequency of cycling, or overall increase in physical activity levels.

The increased use of bicycle paths in the intervention area may have resulted from increased awareness of the network of cycling paths through distribution of project resources such as the new bicycle map ( Discover Fairfield and Liverpool by Bicycle ). As there was no overall increase in the frequency of cycling, it is likely that the project redirected existing cyclists to bicycle paths. The bicycle paths (in both the intervention and comparison areas), while relatively new, already had one in five respondents using them. This level of use indicates that they were not really new facilities.

The stable level of cycling in the intervention areas may represent a positive achievement given the generally declining levels of cycling (8.6% decrease from 1996 to 2006) in the outer areas of Sydney [ 14 , 23 ]. Previous monitoring of travel modes for the journey to work using Australian Bureau of Statistics Census data indicate that there was a relative decline of 27% in bicycle trip mode share in Liverpool from 1996 to 2001 (10% decline in Fairfield) [ 23 ]. There were further declines in Liverpool (13%) from 2001 to 2006 while the Fairfield bicycle mode share for the journey to work increased 11% back to 1996 levels [ 14 ].

Among those people who had cycled in the past week, there was an increase in the mean number of minutes cycling in the intervention area, with those people using the bike paths and cycling more therefore gaining a health benefit. It is possible that an increase in the overall community prevalence of cycling would lead to an overall increase in population physical activity [ 24 ], but this conclusion cannot be reached in this study. Cycling was a significant component of their total minutes of weekly physical activity for those people who cycled, with 40% of cyclists achieving all the minimum 150 hours of moderate intensity physical activity just from cycling. However, there were not sufficient respondents cycling in the past week to influence the overall levels of physical activity.

A US study found that sixty per cent of the cyclists surveyed rode for more than 150 minutes per week during the study and nearly all of the cycling was for utilitarian purposes, not exercise [ 25 ]. A disproportionate share of this cycling occurred on streets with bicycle lanes, separate paths, or bicycle boulevards. 25 Other research from the US has found positive associations between miles of bicycle pathways per 100, 000 residents and the percentage of commuters using bicycles [ 26 ], and that new bicycle lanes in large cities will be used by commuters [ 27 ].

Being aware of the CCC project was also associated with a higher frequency of cycling in the intervention area, but the relatively low recall of the project in the community would have minimised possible impacts. A much stronger communication strategy is needed to have an impact at a community level. The overall budget for this project was about $300,000 (AUS) over three years, with the pre- and post- evaluation telephone surveys costing a third of the budget. Crudely, this represents about 35c (AUS) per person per year. This meant there were limited funds for the communication strategy, which had to rely on editorial stories in local newspapers, advertising, letterboxing, and other forms of distributing written information. By comparison, demonstration cycling towns as part of the Cycling England project, received funding of €500,000 per year (approximately €5 per head of population per year), starting in October 2005, and matched by the respective local authorities so that the total level of investment in cycling was at least €10 per head per year (equal to about $25 AUD) [ 28 ]. These funds were spent on a mix of infrastructure and behavioural programs. While there is reasonable evidence that the individual project strategies are effective in increasing cycling, the limited project resources meant that only a relatively small proportion of the population were exposed to or participated in project activities. Early results from the Cycling England project indicate increases in cycling and increases in population levels of physical activity [ 28 ].

It was disappointing that there was no overall increase in the frequency of cycling in the intervention area. Possible explanations were low levels of exposure to the project and its activities, and long distances to destinations of interest (identified in the baseline survey as a barrier) [ 29 ]. Use of higher exposure media such as television or radio may be necessary to achieve adequate dissemination of the message, but this will make the definition of comparison areas more important. It is also possible that a longer period of time is needed to allow for diffusion of innovations to translate into new behaviours.

At baseline, there was an association between cycling in the past year and being sufficiently physically active for men, but not for women. This is consistent with other health survey research that found that men who cycled to work, but not women, were less likely to be overweight or obese compared with other journey to work modes [ 4 , 30 ]. Cycling to work for weight loss or management could be a marketing angle, if it were perceived to be safe.

At baseline the factor most predictive of cycling in the past year was perceived ease of cycling in the respondent's neighbourhood [ 29 ]. Having good cycling infrastructure will obviously increase the perception that cycling is easy. Being close to destinations was another significant factor associated with recent cycling [ 29 ].

This study highlights that in this outer western Sydney intervention area, which is heavily car dependent, a shift to cycling will require a change in urban planning and density (making destinations of interest much closer), and greater investment in cycling infrastructure where riders want to go, behavioural programs and social marketing. It would be important to repeat this study in a more densely populated urban area, where trip distances were not so great a barrier.

This project raises some questions about the value of limited local social marketing. Policy changes that make car use less appealing (eg increased costs of fuel, less parking availability) are likely to have as much, if not more, impact as information and persuasion campaigns. If only a small amount of resources are available, then maps and bicycle path signage may be a better investment than other forms of communication. Alternatively, targeting a more narrowly defined target group might achieve better results within that sub-population.

The bike count data confirmed the self-reported use of the bicycle paths in the intervention area, confirming the lack of change in the frequency of cycling before and after the intervention. Limitations of these counters were that they were prone to damage and took some time to be repaired, and that they were only in two specific locations in the intervention.

A limitation of the evaluation was that the actual number of people who had cycled in the past week, month or even past year, was relatively low. This meant that statistical power to compare the intervention area with the comparison area was weak. A much larger sample was needed. However, a strength of this project has been the high degree of rigour involved in conducting the pre- and post- evaluation with a control group, with excellent response rates for both surveys, and a high quality data-set provided to the investigators for analysis. The use of bike counters to cross-calibrate the self-reported data is also a strength of the study.

Conclusions

This study shows that use of cycling infrastructure can be increased with a combination of social marketing and opportunities for people to ride in a safe and social context. Communication strategies that inform potential users of where the infrastructure is located (such as maps and route signposting) are critical. Users of this infrastructure are likely to be existing cyclists and novice or beginning riders who are trialling a new behaviour. Those people who use the cycling infrastructure will tend to cycle for longer if encouraged to ride. However, without sufficient resources, the effectiveness of a community based intervention in increasing population cycling and physical activity is limited.

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Rissel C, Merom D, Bauman A, Garrard J, New C, Wen LM: Current cycling, bicycle path use and willingness to cycle more - finding from a community survey of cycling in south-west Sydney, Australia. Journal of Physical Activity and Health.

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Acknowledgements

The Cycling Connecting Communities project was funded by NSW Health through the NSW Health Promotion Demonstration Research Grants Scheme. We wish to thank the members of the Advisory Committee: Andrew Milat, Ming Lin, Steve Soelistio, Mark Pepper, Janelle Borg, Jeni Bindon, Sheila Pham, Annette Stafford, Louise McKenzie, Alison Mortimer, Owen Hodgson, Melissa Brancato and the volunteer members of the Western Sydney Cycling Network and the Liverpool Bicycle User Group

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Chris E Rissel, Carolyn New & Li Ming Wen

Sydney Medical School, The University of Sydney, K25 - Medical Foundation Building, NSW, 2006, Australia

Chris E Rissel, Dafna Merom & Adrian E Bauman

School of Health & Social Development, Deakin University, Burwood Highway, Burwood, Victoria, 3125, Australia

Jan Garrard

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Correspondence to Chris E Rissel .

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The authors declare that they have no competing interests in this study.

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CR conceived the idea of this study and undertook data analysis and interpretation and wrote the original draft. CN collected process data and contributed to writing. LMW, DM, JG and AB contributed to the evaluation design, and writing this manuscript. All authors have read and approved the final manuscript.

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Rissel, C.E., New, C., Wen, L.M. et al. The effectiveness of community-based cycling promotion: findings from the Cycling Connecting Communities project in Sydney, Australia. Int J Behav Nutr Phys Act 7 , 8 (2010). https://doi.org/10.1186/1479-5868-7-8

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Published : 27 January 2010

DOI : https://doi.org/10.1186/1479-5868-7-8

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Here’s Why You Should Train Your Muscle Power to Elevate Your Cycling Performance

Unlock higher watts and faster speeds by focusing on this goal.

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If one of your goals is to improve your ability to cruise up a hill or sprint to win a race, you might do lower-body moves in the gym to build strength, such as squats and lunges . That’s helpful, of course, but another way to crank out higher watts with ease is to focus on training your muscle power.

Consider muscle power the ability to mesh strength and speed. While muscular strength means you can lift, for example, 50 pounds, muscular power is being able to throw that 50 pounds away from your body.

“Anytime you need to put a lot of force on the pedal, you need power,” explains Renee Eastman, C.S.C.S., cycling coach with Carmichael Training Systems in Colorado Springs, Colorado tells Bicycling . “[Muscular power is] all about force over time. And in order to be able to do something in a shorter amount of time—whether it’s sprinting to the end of the race or simply sprinting to catch up with your buddy—it requires your ability to generate force [quickly].”

How Muscle Power Benefits Cyclists

Being a powerful rider can help you flip the switch between simply enduring a ride to actually enjoying a ride. Cycling coach Tom Holland , C.S.C.S., recounts a client who came to him in need of a power-boosting workout to help her enjoy her cycling-related travels. “She wanted to get up those hills faster with less effort,” Holland, author of The Micro-Workout Plan tells Bicycling . “That’s where power comes into play. Once we focused on building that power, it helped her enjoy her rides more.”

Power also trains your muscles to adapt quickly when you start and stop suddenly, which is handy when you encounter obstacles. “If you have to quickly cross an intersection if a car is coming... having powerful muscles can help you get out of the way of danger fast,” Holland says.

Aside from how muscle power can help your cycling, research has also found that it may contribute to quality of life as you age. A 2022 systematic review and meta-analysis published in European Review of Aging and Physical Activity found that power training is more beneficial than traditional strength training for older adults, as it helps improve performance on activity tests, therefore demonstrating it can support functional movement as you age.

Of course, you can’t move something quickly if you can’t lift it, so before you work on muscle power, start with building your strength .

“You need to have the foundational strength and movement patterns first before doing power moves safely and effectively,” Eastman says. For example, focus on mastering movement patterns such as the squat , hinge , push, pull, and lunge with weight, and then you can add power by making those moves more explosive.

Fortunately, you are likely already building power on your bike, but here are more details on how to focus on that skill both on and off the bike.

How to Build Muscle Power on the Bike

The best power-building workout is one you do in the saddle, says Eastman. “If you’re not already doing power workouts on the bike, you’re missing out on a key ingredient,” she adds.

Eastman recommends this bike workout for muscle power:

10-minute warmup of low-intensity cycling
8-10 x 10-12 seconds of all-out sprint, with 3 minutes of low-intensity cycling between
10-minute cooldown of low-intensity cycling

“These short spurts help you improve your peak force,” Eastman says. This also helps you go from minimal to maximal effort in quick duration.

Another great muscular power workout on the bike? Hill repeats , says Eastman. As anyone who has climbed multiple hills in a row knows, you rely on the power of your legs, as well as stability from your core and upper body, when fighting gravity and an incline. Find a hill near you to crank out reps, or turn up the resistance on your indoor trainer to mimic hills outside and build your muscle power.

6 Exercises to Build Muscle Power

We typically consider plyometric exercises , or explosive moves, to be the road to muscular power, because they teach you to create force quickly. According to the International Journal of Sports Physical Therapy , plyometric movement enhances the ability of muscle fibers to generate more tension and force. They also target fast-twitch muscle fibers , which are crucial for sprint performance.

Some research indicates that lifting heavy loads—about 70 to 90 percent of your one-rep max or how much weight you can lift for just one rep—will also enhance your muscle power.

To add power exercises to your own routine, pick one or two below to incorporate into your strength training days, and then add more as you build more power.

Why it works : This explosive plyometric move helps your body become accustomed to changing directions quickly, and builds lateral hip strength while strengthening the glutes and boosting balance and coordination.

How to do it :

Stand with feet hip-width apart, knees bent into a slight squat.
Push off right foot, to hop laterally to the left, landing softly to lower onto left heel and bend into a partial squat, right leg coming behind and across body.
Now push quickly off left foot to hop back to right side, landing softly and bending right knee, left leg coming behind and across body. Make sure to keep knees over toes and keep posture upright throughout the move.
Continue alternating. Do 3 sets of 10-12 reps on each leg.

2. Bulgarian Split Squat

Why it works : Holland swore by the Bulgarian split squat to help him build power and reduce fatigue in his quads during Ironman training (he’s completed 26!). Because you are relying on the strength of one leg to power up, driving force through that leg, it builds power in the glutes and quads , along with the hamstrings and calves.

Start sitting in a chair.
Extend standing left leg out, and place heel on ground.
Stand up, keeping left leg in place and planting foot.
Place back right leg on the chair behind you.
Hold a heavy dumbbell or kettlebell at chest, making sure to keep shoulders down and back. Look straight ahead.
Lean slightly forward at hips and with left front foot planted firmly on ground, take an inhale and bend left leg to lower toward floor. Lower until back knee hovers just above the floor or as close to it as you can go while keeping left knee tracking over toes.
Exhale and drive left foot into floor to stand up, straightening front leg.
Repeat. Do 3 sets of 5 reps per side.

3. Plyo Lunge

Why it works : This plyometric move helps build explosive power and proprioception, which is your awareness of your body in space. Also, the burst of movement that you use to switch your stance mimics moves—and uses the same muscles —that you use to pedal.

Stand tall, arms at sides and feet hip-width apart.
Jump up and land with right foot forward, both knees bending 90 degrees with right knee tracking over toes and left knee hovering just above floor.
Now jump again, reversing the position of legs.
Continue alternating. Do 3 sets of 5 reps per leg.

4. Medicine Ball Slam

Why it works : This move provides a dynamic, explosive total-body exercise and utilizes the same foundational muscles that help stabilize you when cycling, such as your core, back, and chest, as well as your lower-body muscles .

Stand with feet shoulder-width apart, knees slightly bent. Hold a medicine ball in both hands.
Squat down by sending hips down and back.
Then drive through feet to stand up, lifting ball above head and coming onto toes.
Slam the ball down as you lower back into a squat.
Catch the ball and repeat. Do 3 sets of 6-8 reps.

5. Kettlebell One-Arm Swing

Why it works : The one-armed kettlebell swing strengthens your lower body, and requires serious power from your hips to get the bell to swing forward and up. It also targets your core.

Start standing, feet slightly wider than hip-width apart, kettlebell in front of you, about arm’s length away.
Hinge at hips and grab the kettlebell with right hand.
Drag it back and up, behind you, right at groin.
Drive feet into ground and powerfully extend hips to swing the bell forward and up. Hit a plank-like position at the top, shoulders right over hips. Avoid leaning back.
Allow momentum of the bell to swing back down, send hips straight back for the hinge as it lowers.

Why it works : There’s a reason burpees have been standard gym-class fare for a long time: The burpee is a full-body, plyometric move that builds core stability , too. You’ll work everything from the shoulders and arms to core, glutes, and legs.

Stand with feet hip-width apart.
Squat down and lower hands to floor in front of you, just inside ankles.
Put weight on hands as you jump feet back into a plank position, shoulders over wrists, forming a straight line from head to heels.
Bend elbows to lower body to floor.
Press back up, keeping body in one straight line.
Jump feet back up to hands.
Jump quickly into air, arms reaching overhead.
Land softly.
Repeat. Do 3 sets of 5 reps.

How to Safely Train Muscular Power

Ready to put some power into your pedaling? Keep these tips in mind first.

1. Build in recovery time , both between sets and between days of power training. If you’re doing an explosive move, you want to give your muscles enough time to recover for the next set, says Eastman.

Don’t hesitate to rest between moves. A small study with 10 male basketball players found that one minute of recovery between sets was effective, but Eastman says you can rest for up to two minutes.

Finally, your muscles need 48 to 72 hours to recover from plyometric moves, according to the International Journal of Sports Physical Therapy .

2. Build a base . “Power is like icing on the cake in terms of strength training, like a finishing touch,” says Eastman. “Before you build power, though, you need the movement patterns, you need to build the stronger connective tissues and muscle volume, and the foundational work.” You should be comfortable and experienced with weight training for at least six months before you start plyometric training.

3. Don’t do it all at once. “If you’re doing a strength day, you might do two power moves and three or four strength moves . It’s not all or nothing. And I certainly wouldn’t suggest that someone starts with five to eight power moves, you really only need two or three in a workout,” says Eastman.

Megan McMorris has been writing for sports magazines since the 1990s, when she was a regular contributor to Sports Illustrated Women . In recent years, she has branched out into other writing-related fields—from PR copywriting to ghostwriting to corporate communications—but has recently returned to her first love: writing for magazines on subjects she knows and loves. A former editor at Fitness , she’s written for Runner’s World, Men’s Health, SELF, Glamour, Real Simple, Prevention, and Shape , among many others. She lives in Portland, Oregon, near the base of Mount Tabor, one of her favorite spots on earth. Follow her tales at meganmcmorris.substack.com .

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The role of motivation and goal setting in cycling: How to set goals and stay motivated without burning out

Finding your reason to ride can go a long way to keep you excited to press on the pedals

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Cycling is a sport that heavily rewards consistency with regular training as the cornerstone of any good training plan. Motivation is the underpinning element that is arguably the most important if you are looking to maximise your performance with everything else stemming from a place of motivation.

Naturally, sometimes heading out for a ride will feel difficult or even not worth doing. Finding ways to motivate yourself can help keep you on track even when the weather or other factors in your life are making it tough to head out and turn the pedals.

To find out exactly how to keep on top of your motivation and how best to set yourself goals that will light the fire within you, we caught up with professional coach Jacob Tipper. Having raced professionally himself, before coaching riders such as Ben Healy and Hannah Payton he has a unique perspective to shine some light on the intricate and often delicate subject of finding a healthy balance with motivation and goal setting.

Why is having a goal to work towards important in cycling?

Having a goal in mind whether that is to ride your local 30-mile loop at a certain average speed or to build up to your first race or a grand fondo is a great way to give each ride a purpose beyond itself.

“It's giving you an excuse, to plan and have a bit of structure and stimulus beyond just wanting to ride hard on a Tuesday night to give yourself a good workout. It becomes that those Tuesday night hard rides are working towards something bigger.” Tipper explains.

If each ride is viewed individually, it is harder to stick with it when you are tired or if the weather isn’t great. When you set yourself a goal you can clearly see how missing a week of riding or not committing to a session is going to have a knock-on effect either delaying you from reaching your goal or underperforming when it comes to an event.

Tipper also explains that when people have a goal to work towards, “you really see the added motivation of people turning it on and suddenly starting to chase the process you can see that they seem to start enjoying it a bit more.”

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Cycling is a sport that at times is characterised by suffering and pushing yourself into uncomfortable places, if you do not have a good reason as to why you are doing this the motivation will only last so long before you no longer see the fun of going out and pushing your limits.

SMART targets really are the best way to set your goals

You have almost certainly come across the SMART acronym for goal setting, which is for good reason. It stands for Specific, Measurable, Achievable, Realistic and Time-sensitive. By setting your goals using this method it becomes easy to define a path from where you are to where you want to be.

“As a coach I get people coming to me and saying their goal is to just be fitter.” These open-ended broad goals are very hard to achieve because there is no yardstick for what is exactly meant by ‘fitter’. Instead breaking the goal of being fitter down to something measurable gives you a performance metric to evaluate your riding against.

Working out if you want to ride faster over a specific course, ride further, increase your power or complete a sportive will allow for a plan to be formed around it. The more specific you can be the easier it is to work towards as you can target exactly what needs to be done to improve in the right direction.

For those newer to the sport, knowing what is achievable can be a tough ask as it is hard to know how you are going to progress. “This is where enlisting the help of a coach can be really beneficial to people.” Having an experienced pair of hands to help guide you through your goal-setting process is a great way to ensure that you don’t set lofty goals that are so far out of reach that they actually dim your motivation.

It is a fine line to set a goal that will push you and present a challenge without being unrealistic. “You should have a direction to go in, and it's not always super important what that direction is. It's just good to know that you've got a direction rather than just aimlessly riding.” Not all goals have to be massive long-term goals, setting some smaller intermediate goals is a great way to keep you on track and to visibly show that you are making progress toward the bigger goal.

Give yourself enough time to achieve your goals

It is easy to get motivated once you have a goal in mind and you want to immediately go out and chase it as quickly as possible. This is a surefire way to end up with both emotional and physical burnout, it is far better to take steady small steps to achieving a target rather than trying to force your body to adapt.

“10 to 12 weeks gives you a chance to really have more of a plan. It depends on where your fitness is at. If you're not particularly fit and you're getting back into training then having some intermediate goals every three to four weeks will most likely show some change.” This can be great for motivation as you consistently see your ability increasing and it reaffirms that what you are doing is working.

“If you are already highly trained and you're already doing as much as you can do with the time you have and the goal is to squeeze that last couple of per cent, then you're not going to see a couple of per cent change within three or four weeks. It will take you a bit longer to get there.”

Setting goals in 10-12 week blocks gives your body time to adapt to the new training stimulus as well as go through a few periods of recovery to really let the training benefit take effect.

For particularly lofty ambitions it might be that you have a series of 12-week training blocks that are all gradually building to an ultimate goal. It is good to keep each block fairly short as otherwise it is easy to lose track of what you are working towards, for example, setting a goal for six months down the road will make it hard to stay on track and measure your progress without a series of intermediate goals.

It is just as important to avoid over-doing it as it is to have a goal

Much like anything in life, there can be too much of a good thing and for those who find themselves hyper-motivated to achieve their goals, it can be easy to end up burning out.

The key to avoiding burnout is being able to identify it as early as possible. One of the ways that Tipper monitors athletes that he coaches is simply to listen to the language they use when discussing training, if things start to sound more negative than before and there is generally less energy coming from the athlete this is an early tell that maybe things need to be switched up to prevent the rider from burning out.

An increase in irritability is another indicator that you might need to take an easier week or mix in some cross-training to stave off burnout. If you find yourself getting easily frustrated about things that typically wouldn’t bother you, it might be time to assess your training load and how you are conducting your sessions.

This is another place where a coach can offer tailored insights to help you train to your maximum potential without burning out. For riders who aren’t coached, enlisting a strategy of two or three weeks of structured training with a week of easier-intensity riding can help balance things. Another great method to prevent burnout is to add in some cross-training. If you find yourself struggling to get out on the road bike, swap out a few sessions for running, mountain biking, swimming or strength training.

If you have lost motivation, it is important to first understand why

If you have lost your drive to get out and go for a ride it might not be simply a case of lacking motivation for cycling in particular. Tipper explains that the first step in finding your motivation is to first understand why you are currently not as motivated as you once were.

“Often there's sometimes an underlying reason rather than just the cycling.” The reality is that everything we do affects everything else so if other areas in your life are becoming more stressful, this has a knock-on effect on your mood and motivations. “The world's getting harder to live in, with the additional pressures and stress, more things are taking energy away from us, be it in traffic or social media or our jobs. It's giving people less and less energy to focus on other areas of life and to enjoy their hobbies so much.”

If this is the case, then riding should be seen as an outlet rather than an additional stressor and heading out for some riders that have no focus other than feeding the soul can be exactly what you need at that time.

Once other areas of your life calm down and free up some bandwidth, the best way to spark your motivation for riding is to find a challenge that genuinely excites you. These don’t have to be big grandiose goals but they can be to ride more miles than your friends each week or to ride up your local climb at a target speed or power. Having these small, achievable but also low-pressure goals will likely have you excited to head out and press on the pedals.

The number one thing that Tipper believes is key to finding your motivation is to make sure that the goals you set and work towards are purely for yourself. If you are setting goals to impress other people or to try and be better than someone else it is very easy for the goal to lose its appeal.

Data is great but it is only part of the equation

There is no denying it, as a collective group, cyclists love to nerd out on data, whether that is the average speed and power of your last ride or if it is breaking down your local hill climb segment to see where you can squeeze out a little bit more speed. Tipper is the first to admit that for a lot of riders, this can be part of the fun and definitely shouldn’t be something to be concerned about doing.

This does change, however, when riders begin to obsess about their power, weight or speed. It can become a dangerous path to associating your self-worth with your ability on the bike which, even for a professional rider, is not a healthy state of mind to be in.

It is easy to get hung up on your speed or power from ride to ride but it is only part of the equation. Tipper makes the point that “people will now go for a ride and suddenly worry about the data whereas before they wouldn't have known, so they couldn't have worried and would have just finished the ride.”

It is great to have and adds another level of specificity to your training but cycling is a complex equation that is more than just what the numbers on the screen say. It can be all too easy if you start to fixate on the data of each ride rather than other things that matter just as much. Something to never lose sight of is the enjoyment of each ride and discovering new roads under your own steam.

Another important factor to remember when it comes to data analysis is that you are very seldom comparing like for like. If you have changed power meters then there will be some level of discrepancy between them. Equally, if you used to have 12 hours a week to train and now you can only commit five then comparing your performances is irrelevant. Instead, you should focus on being the best you can be given your current life setting.

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Do the Health Benefits of Cycling Outweigh the Risks?

Jeroen johan de hartog.

1 University of Utrecht, Institute for Risk Assessment Sciences, Utrecht, the Netherlands

Hanna Boogaard

Hans nijland.

2 Netherlands Environmental Assessment Agency, Bilthoven, the Netherlands

Gerard Hoek

Although from a societal point of view a modal shift from car to bicycle may have beneficial health effects due to decreased air pollution emissions, decreased greenhouse gas emissions, and increased levels of physical activity, shifts in individual adverse health effects such as higher exposure to air pollution and risk of a traffic accident may prevail.

We describe whether the health benefits from the increased physical activity of a modal shift for urban commutes outweigh the health risks.

Data sources and extraction

We have summarized the literature for air pollution, traffic accidents, and physical activity using systematic reviews supplemented with recent key studies.

Data synthesis

We quantified the impact on all-cause mortality when 500,000 people would make a transition from car to bicycle for short trips on a daily basis in the Netherlands. We have expressed mortality impacts in life-years gained or lost, using life table calculations. For individuals who shift from car to bicycle, we estimated that beneficial effects of increased physical activity are substantially larger (3–14 months gained) than the potential mortality effect of increased inhaled air pollution doses (0.8–40 days lost) and the increase in traffic accidents (5–9 days lost). Societal benefits are even larger because of a modest reduction in air pollution and greenhouse gas emissions and traffic accidents.

Conclusions

On average, the estimated health benefits of cycling were substantially larger than the risks relative to car driving for individuals shifting their mode of transport.

Recently, policy interest in promoting cycling as a mode of transport has increased substantially within Europe. Several capitals, such as Copenhagen, Denmark (in 1995), Helsinki, Finland (2000), Oslo, Norway (2002), Stockholm, Sweden (2006), Barcelona, Spain (2007), Paris, France (2007), and Brussels, Belgium (2009), have implemented low-cost rental systems aimed at stimulating commuters to use bicycles for the typically short urban trips. Motive for these policies is more often the reduction of traffic congestion than promotion of health. In 2005, the European Union formulated an important area of action: “addressing the obesogenic environment to stimulate physical activity” ( Commission of the European Communities 2005 ). Attitudes and policies toward active commuting have recently been discussed ( Lorenc et al. 2008 ; Ogilvie et al. 2004 ). The Transport, Health, and Environment Pan-European Programme (THE PEP) provides guidance to policy makers and local professionals on how to stimulate cycling and walking ( THE PEP 2009 ). The promotion of walking and cycling is a promising way to increase physical activity across the population by integrating it into daily life.

Promoting cycling for health reasons implies that the health benefits of cycling should outweigh the risks of cycling. Although society may benefit from a shift from private car use to bicycle use (e.g., reduced air pollution emission), disadvantages to individuals may occur. Although individuals may benefit from increased physical activity, at the same time they inhale more pollutants because of increased breathing rates. The risks of being involved in traffic accidents may increase, as well as the severity of an accident. A study in Vancouver, Canada ( Marshall et al. 2009 ), illustrated that, especially in the city center, high-walkability neighborhoods had high traffic density, leading to high air pollution concentrations for a traffic-related primary pollutant [nitric oxide (NO)] but not for a secondary pollutant (ozone). For cycling, similar issues may occur.

The aim of this review is to assess quantitatively whether the health benefits of the use of a bicycle instead of a private car for short trips outweigh the health risks. The risks and benefits are evaluated both for the individuals who shift from car driving to cycling and for society as a whole.

Materials and Methods

We focus on the comparison of private car driving versus cycling because most trips are made by car, and the use of the private car is related to many negative aspects, including congestion, use of physical space, reduction of outdoor activities, air pollution, and noise. In the Netherlands, 20% and 30% of total car trips (totaling 15.9 million trips/day) are, respectively, for shopping and commuting purposes ( Beckx et al. 2009a , 2009b ; Mobiliteitsonderzoek Nederland 2007 ). Approximately 50% of all car trips are < 7.5 km, which is short enough to make travel by bicycle a feasible alternative.

In the quantitative comparison between car driving and cycling, we considered air pollution, traffic accidents, and physical activity as main exposures. We summarize the relevant evidence of health effects related to air pollution, traffic accidents, and physical activity separately. For these sections, we made use of published (systematic) reviews, supplemented with more recent key studies.

Health effects related to air pollution, traffic accidents, and physical activity differ—for example, traffic accidents resulting in injuries and physical activity affecting cardiovascular disease. Therefore, we compare potential effects of these exposures (in conjunction with driving or cycling) on mortality rather than morbidity. In addition, epidemiologic evidence of associations of these exposures with mortality is stronger than associations with other outcomes, particularly for physical activity. All three exposures have been associated with mortality, so a common metric can be used to quantify their potential effects, and mortality is reported more consistently than other health outcomes. In particular, minor injuries associated with traffic accidents are much more likely to be underreported than are deaths due to traffic accidents.

For deriving the relative risks comparing car driving and cycling, we specified a hypothetical scenario based on statistics in the Netherlands. The scenario assumes a transition from car driving to cycling for 500,000 people 18–64 years of age for short trips on a daily basis in the Netherlands. We made calculations for a daily traveled distance of 7.5 km and 15 km—for example, people commuting to and from work for 3.75 km (the average short trip) or 7.5 km (the maximum short trip). Our scenario implies a shift of about 12.5% of the 7.95 million short car trips, an ambitious yet not unrealistic percentage. In the Netherlands, 40.8% of persons > 18 years of age own both a car and a bicycle and therefore may be able to shift modes on a daily basis. In this review, we focus on the Dutch situation because of data availability, but in the overall discussion we illustrate that the use of this Dutch scenario has not substantially affected our conclusions. The scenario is used mostly to calculate travel time and kilometers driven, inputs needed to calculate air pollution, physical activity, and accident impacts, combined with more generic concentration–response functions.

We express mortality impacts in life-years gained or lost estimated with life table calculations ( Miller and Hurley 2003 ). For the calculation we used a population of 500,000 people 18–64 years of age, distributed in age categories comparable to the 2008 Dutch population [Statistics Netherlands (CBS) 2008]. We estimated the effects on this population for a lifetime.

Air Pollution Exposures and Health Effects

Air pollution exposure during cycling and car driving.

Since the 1990s various studies have measured air pollution exposure levels associated with different modes of transport ( Kaur et al. 2007 ). In recent studies, the emphasis has been on fine and ultrafine particulate matter [aerodynamic diameter ≤ 2.5 μm (PM 2.5 ) and ≤ 0.1 μm (UFP), respectively], because these are the main pollutants related to human health effects. Driving or cycling in traffic may result in air pollution exposures that are substantially higher than overall urban background concentrations ( Kaur et al. 2007 ). Consequently, even relatively short times spent in traffic may contribute significantly to daily exposures ( Beckx et al 2009a , 2009b ; Fruin et al. 2004 ; Marshall et al. 2006 ; Van Roosbroeck et al. 2007 ). Table 1 summarizes studies that specifically compared exposures during car driving and cycling within the same study.

Air pollution exposures during cycling and car driving.

City	Study design	Pollutant	Mean concentration car (μg/m )	Mean concentration cycling (μg/m )	Ratio car/cycle
Amsterdam	Two inner-city routes traveled for about 1 hr in January and May 1990 ( = 55 and 41)	CO BTEX	4,833 332	1,730 99	2.8 3.4
Copenhagen	Two cars and two cyclists on a 7.6-km inner-city route in the morning of two days in summer 1998	BTEX TSP	44 44	150 75	0.3 0.6
London	Three routes from the center (one central, two to more outward sections) in July 1999 and February 2000 ( = 96 cycle trips and 54 car trips)	PM EC	37 29	28 18	1.32 1.6
London	Two short (~ 1 km) routes (one heavy traffic, one mixed) traveled in spring 2003 during early morning, lunchtime, and afternoon	EC	39	25	1.6
London	Two short (~ 1 km) routes (one heavy traffic, one mixed) traveled in spring 2003 during early morning, lunchtime, and afternoon	PM UFP CO	38 99,736 1,300	34 93,968 1,100	1.12 1.06 1.18
Huddersfield, UK	7-mile journey from village to Huddersfield, cycle along a major highway and a separate bicycle path (six samples in September/October 1996)	Abs	7.6	2.7 6.3	2.6 1.2
11 Dutch cities	Simultaneous cycle and car drives between same start and end points in afternoon in 11 large Dutch cities, ~ 12 routes in each city; sampling duration, ~ 3 hr/city (1 day per city in autumn 2006)	UFP PM	25,545 49	24,329 45	1.05 1.11
Arnhem, the Netherlands	2-hr morning rush hour exposures of cyclists and car and bus passengers on an urban route in a medium-size city	UFP PM Abs	40,351 78 8.8	44,258 72 6.0	0.91 1.09 1.48
Mean	Simple mean of ratios from applicable studies	PM EC and Abs UFP			1.16 1.65 1.01

Abbreviations: Abs, absorbance (10 −5 m), a marker for (diesel) soot; BTEX, sum of benzene, toluene, ethylbenzene and xylene; CO, carbon monoxide; EC, elemental carbon, equivalent to (diesel) soot; TSP, total suspended dust; UFP, ultrafine particle count (per cubic centimeter).

Overall, air pollution exposures experienced by car drivers were modestly higher than those experienced by cyclists, with mean ratios of 1.16 for PM 2.5 , 1.01 for UFP, and 1.65 for elemental carbon or soot. However, increased physical activity results in higher minute ventilation (volume of air inhaled in one minute) for cyclists than for car drivers, with estimates from two Dutch studies reporting that the minute ventilation of cyclists was 2.3 times ( van Wijnen et al. 1995 ) and 2.1 times ( Zuurbier et al. 2009 ) higher than that of car drivers. Therefore, inhaled doses of PM 2.5 and, to a lesser extent, elemental carbon may be higher in cyclists. The difference in exposure between cyclists and car drivers depends on a large number of factors, such as selected route, car speed, trip duration, car type, ventilation status (open windows, mechanical ventilation), driving behavior, street configuration, and weather conditions ( Kaur et al. 2007 ). Trip duration might also be higher for cyclists, although this may be highly dependent on the setting. For example, in a study conducted in 11 Dutch cities, there was no difference in the time required to bicycle versus drive short distances ( Boogaard et al. 2009 ), but for longer trips cars were faster than cyclists ( Zuurbier et al. 2010 ).

Health effects of in-traffic exposures

The short exposures typical for commuting have not been studied extensively in air pollution epidemiology, in contrast to 24-hr average exposures or long-term (annual average) exposures [ World Health Organization (WHO) 2006 ]. Several studies have documented that long-term exposure to traffic-related air pollution is associated with adverse health effects, including increased mortality ( WHO 2006 ).

Table 2 summarizes the few epidemiologic studies of in-traffic air pollution exposures, suggesting that these exposures result in physiologic changes (including airway and systemic inflammation and lung function decrements) in healthy adults and asthmatics and possibly more severe adverse effects (myocardial infarction).

Epidemiological studies of air pollution exposure in traffic.

Study population	Design	Main findings	Comments
Sixty mild to moderate asthmatic adults in London	Exposure during 2 hr walking in OS or HP, pre/postexposure physiologic measurements: median PM concentration, 28 (OS) vs. 11 μg/m (HP); median EC, 7.5 vs. 1.3 μg/m ; median UFP, 63,700 vs. 18,300 particles/cm	Asymptomatic decrease in lung function and increase in inflammation after walking in OS compared with HP; changes most consistently associated with EC and UFP; per 1-μg/m significant increase in EC decrement in lung function of ~ 1% decrement in lung function and ~ 2% increase in exhaled NO (inflammation)	OS has diesel traffic only
Subjects ( = 691) with MI in Augsburg	Case–crossover study comparing the frequency of participation in traffic in the hours before the MI and a control period (24–72 hr before MI)	RR = 2.92 for participation in traffic in the hour before the MI; increased risk found for all transport means (car, bicycle, public transport)	May be stressors other than air pollution
Nine healthy young U.S. policemen	Physiologic measurements before and after 8-hr work shift; average in-vehicle PM , 24 μg/m	Significant increases of heart rate variability, ectopic beats, blood inflammatory and coagulation markers, and red blood cell volume; per 10-μg/m PM effect on C-reactive protein, +32%; neutrophils, +6%; von Willebrand factor, +12%; and ectopic beats, +20%.
Twelve healthy young subjects	Physiologic measurements before and after 1-hr cycling trip from city center to university in Utrecht	Statistically nonsignificant 1–3% decrements in lung function per 10 /m soot concentration and a 15% increase in exhaled NO per 38,000 particles/cm

Abbreviations: EC, elemental carbon; HP, Hyde Park; MI, myocardial infarction; NO, nitric oxide; OS, Oxford Street; RR, relative risk; UFP, ultrafine particle count.

Furthermore, there is a fairly substantial body of evidence of human controlled exposure studies in which volunteers have been exposed for 1–2 hr to diesel exhaust and to filtered air for comparison [see Supplemental Material, Table 1 (doi:10.1289/ehp.0901747)]. Typically, the evaluated exposures (100–300 μg/m 3 ) are higher than those encountered in ambient air, although not excessively. Because of ethical concerns, only physiologic effects have been studied with this study design. These studies have documented airway and systemic inflammation after exposure to diesel exhaust in patients and in healthy subjects.

Assessment of the modal shift impact on mortality related to air pollution exposure

Individual effects.

Because the physiologic changes observed in epidemiologic and controlled exposure studies likely play a role in the pathway to cardiac events of long-term exposure, it is plausible that these more adverse effects may occur in susceptible subjects. We calculated the potential impact on mortality of a transition from using a car to a bicycle for a 30-min (7.5-km) or 1-hr (15-km) commute based upon relative risk estimates from long-term exposure studies of mortality in association with PM 2.5 ( Pope et al. 2002 ) and black smoke (BS) ( Beelen et al. 2008 ).

The derivation of these risk estimates is provided in the Supplemental Material, Table 2 (doi:10.1289/ehp.0901747); Table 3 shows the results. We assumed that the actual risk related to long-term air pollution exposure is determined by the inhaled daily dose of PM 2.5 or BS. First, we calculated the inhaled pollution dose during commuting (car driving or cycling) and noncommuting hours based on prior information concerning minute ventilation rates (liters per minute) and PM 2.5 and BS exposures (micrograms per cubic meter) during sleep, rest, driving, or cycling. Next, we estimated the total daily dose for PM 2.5 and BS (micrograms per day) for driving or cycling. We then used the ratio of the total daily doses for the two travel modes to derive an “equivalent” change in PM 2.5 or BS concentration (micrograms per cubic meter) that could be normalized to the 10-μg/m 3 increase in long-term exposures used by Pope et al. (2002) and Beelen et al. (2008) to estimate the relative risk associated with the estimated change in long-term PM 2.5 and BS exposures that would result from a shift to commuting by bicycle instead of by car.

Potential mortality impact of cycling compared with car driving, for 0.5- and 1-hr commute, estimated for PM 2.5 and BS. a

Travel mode	Duration of travel (hr/day)	PM /BS concentration (μg/m )	Inhaled dose (μg/day)	Total dose for car or bicycle (μg/day)	Equivalent change in PM or BS (μg/m )	RR mortality, equal toxicity	RR mortality, traffic 5× more toxic
PM
Car	0.5	40.0	12.0	246
Cycle	0.5	34.5	22.8	257	0.9	1.005	1.026
Car	1.0	40.0	24.0	252
Cycle	1.0	34.5	45.5	274	1.8	1.010	1.053

BS
Car	0.5	30.0	9.0	126
Cycle	0.5	18.2	12.0	129	0.2	1.001	1.006
Car	1.0	30.0	18.0	132
Cycle	1.0	18.2	24.0	138	0.5	1.002	1.012

RR, relative risk.

Assuming equal toxicity of particles, the estimated relative risk associated with the change in PM 2.5 inhalation due to cycling instead of car driving ranges from 1.005 to 1.010. If we assume that traffic PM is more toxic than ambient PM 2.5 in general, these relative risk estimates range from 1.026 to 1.053. This assumption is supported by an analysis of PM from different sources, indicating the strongest associations with mortality from traffic particles ( Laden et al. 2000 ). If the assessment is based on BS, relative risk estimates are smaller (between 1.001 and 1.012).

Societal effects

The modal shift will reduce overall air pollution levels, which may result in health benefits of the general city population. An indication of the potential reduction in air pollution was obtained by using the Dutch dispersion model CAR (Calculation of Air pollution from Road traffic) ( Eerens et al. 1993 ). For a typical major urban street with a traffic intensity of 10,000 vehicles/day, for a 12.5% reduction in traffic intensity, concentration reductions were 1.3 μg/m 3 for nitrogen dioxide (NO 2 ) and 0.4 μg/m 3 for particles with aerodynamic diameter ≤ 10 μm (PM 10 ). The relative risk of long-term exposure to NO 2 expressed per 10-μg/m 3 increase on all-cause mortality is 1.10 ( Tonne et al. 2008 ). This implies that for the approximately 800,000–1,600,000 subjects living in major streets in the Netherlands, mortality rates could be 1.012 times lower. This relative risk is of the same order of magnitude as the estimated increased risk to the cyclist described in the previous section and applies to a larger population.

According to the WHO (2004) , road traffic injuries accounted for approximately 2% of all global deaths, making them the 11th leading cause of global deaths. The rates of road traffic death vary considerably among countries, transport mode, type of area (urban or rural), and person. Among several European countries, the highest fatality rates are about 3.5 times higher than the lowest figures [see Supplemental Material, Figure 1 (doi:10.1289/ehp.0901747)] ( International Transport Forum 2010 ).

How safe is cycling compared with car driving for an individual?

Table 4 shows the estimated numbers of traffic deaths per age category per billion passenger kilometers traveled by bicycle and by car (driver and passenger) in the Netherlands for 2008 ( CBS 2008 ). These data suggest that there are about 5.5 times more traffic deaths per kilometer traveled by bicycle than by car for all ages, and that cycling is riskier than travel by car for all age groups except young adults (15–30 years of age), with about 9 times more deaths among those < 15 years of age, and 17 times more deaths among those > 80 years of age. The comparison in Table 4 probably overestimates the difference between cyclists and car drivers for short trips, because the relatively safe long car trips driven on highways are included. Across Europe, 8% of traffic deaths occur on the motorways, whereas 25% of the kilometers driven are on motorways ( European Road Transport Safety 2008 ). Risks for nonfatal accidents are higher for cyclists than for car drivers, as well [Supplemental Material, Table 3 (doi:10.1289/ehp.0901747)].

Traffic deaths per age category per billion passenger kilometers by bicycle and by car in the Netherlands. a

Age category (years)	Bicycle	Car	Ratio
< 15	4.9	0.6	8.6
15–20	5.4	7.4	0.7
20–30	4.2	4.6	0.9
30–40	3.9	2.0	2.0
40–50	6.6	1.0	6.9
50–60	9.6	1.2	7.9
60–70	18.6	1.6	11.7
70–80	117.6	7.6	15.4
> 80	139.6	8.1	17.1
Total average (all ages)	12.2	2.2	5.5
Total average (20–70 years of age)	8.2	1.9	4.3

Data from CBS (2008) .

How safe is cycling compared with car driving for society?

For society, the risk that car drivers present to cyclists and pedestrians must also be taken into account. For the Netherlands, an analysis has compared the risks of a fatal accident for car drivers and cyclists, including the risk to other road users ( Dekoster and Schollaert 1999 ). The analysis excluded motorways, because cyclists cannot use these roads. Mortality rates were similar for car drivers and cyclists (20.8 vs. 21.0 deaths per million kilometers traveled). People older than 50 years are less frequently involved in fatal accidents when driving a car than when driving a bicycle, but the opposite is true for people 18–49 years of age [Supplemental Material, Table 4 (doi:10.1289/ehp.0901747)]. Jacobsen (2003) showed that in different European countries, the number of traffic deaths of cyclists is inversely related to the amount of cycling [Supplemental Material, Figure 2 (doi:10.1289/ehp.0901747)], suggesting a “safety-in-numbers” effect.

Assessment of the modal shift impact on traffic accidents related mortality

For 18- to 64-year-old individuals, the risk of a fatal accident while cycling is about 4.3 times higher compared with the same distance by car ( Table 4 ). The fatal traffic accident rate for cyclists 20–70 years of age is about 8.2 deaths per billion passenger kilometers traveled, whereas the risk for car drivers and passengers the rate is 1.9 deaths per billion passenger kilometers traveled ( Table 4 ). A population of 500,000 commuting 7.5 km/day will commute 1.36785 billion km/year (7.5 km/day × 365 days/year × 500,000). From the data shown in Table 4 , we estimate that this amount of car travel would result in approximately 2.6 deaths/year (1.9 × 1.36785). An equivalent amount of bicycle travel would result in approximately 11.2 deaths/year (8.2 × 1.36785). In the Netherlands, the all-cause mortality rate for 18- to 64-year-old persons is 235.1 per 100,000 per year ( CBS 2008 ) or 1,176 persons per 500,000 per year. Hence, among 18- to 64-year-olds, the relative risk of all-cause mortality associated with a 7.5 km/day shift from driving to cycling would be [1,176 + (11.2 – 2.6)]/1,176 = 1.007. When we use age-specific data, relative risks ranged from 0.996 to 1.010. For the 15-km scenario, age specific relative risks ranged from 0.993 to 1.020.

The societal impact of a modal switch on the number of fatal accidents largely depends on which people switch from car to bicycle. If it is the average population, the impact (including risk presented to other road users) would be practically zero [Supplemental Material, Table 4 (doi:10.1289/ehp.0901747)], but if young car drivers switched to a bicycle, it would decrease the number of fatal accidents. The opposite is true for elderly car drivers.

Physical Activity

Levels of inactivity are high in virtually all developed and developing countries.a The WHO (2007a) estimates that 60–80% of the world’s population does not meet the recommendations required to induce health benefits. For Europe 62.4% inactive adults are estimated ranging from 43.3% (Sweden) to 87.7% (Portugal) ( Varo et al. 2003 ). In the Netherlands about 62% of the population is sedentary ( Varo et al. 2003 ). The WHO estimates that the prevalence of physical inactivity accounts for 22% of cardiovascular disease prevalence globally ( WHO 2007a ). There is sufficient evidence for an association between physical activity and mortality, cardiovascular disease (hypertension), diabetes, obesity, cancer (colon and breast), osteoporosis, and depression ( Bauman 2004 ; Warburton et al. 2006 ). Because only a few studies specifically reported on the beneficial health effects of cycling, we also summarized the quantitative evidence of beneficial health effects of physical activity, making use of review papers.

Cycling and physical activity recommendation

Recently, the American College of Sports Medicine and the American Heart Association published an updated recommendation for physical activity ( Haskell et al. 2007 ). To promote and maintain health, all healthy adults 18–65 years of age need moderate-intensity aerobic physical activity for a minimum of 30 min on 5 days each week or vigorous-intensity aerobic activity for a minimum of 20 min on 3 days each week. Also, combinations of moderate- and vigorous-intensity activity can be performed to meet this recommendation. For young people, 60 min of moderate to vigorous physical activity on a daily basis is recommended ( Strong et al. 2005 ). In several physical activity studies, metabolic equivalent of task (MET) is used as an indicator of physical activity, and the minimum goal should be in the range of 500–1,000 MET min/week. Leisure cycling or cycling to work (15 km/hr) has a MET value of 4 and is characterized as a moderate activity ( Ainsworth et al. 2000 ). Hence, a person shifting from car to bicycle for a daily short distance of 7.5 km would meet the minimum recommendation (7.5 km at 15 km/hr = 30 min) for physical activity in 5 days (4 MET × 30 min × 5 days = 600 MET min/week).

Health effects and assessment of the modal shift impact on mortality

Table 5 provides summary estimates from reviews for the impact of physical activity on all-cause mortality, and includes only estimates that are relevant to compare the risks for cyclists and car drivers. It is difficult to synthesize information across studies because investigators have measured physical activity in different ways and classified physical activity according to different dose schemes that often are difficult to compare directly ( Lee and Skerrett 2001 ). Several reviews have suggested that the relative risk of mortality for those who meet the recommended levels of physical activity compared with the inactive group is between 0.65 and 0.80 ( Bauman 2004 ; Lee and Skerrett 2001 ; Warburton et al. 2006 ).

Potential impact of physical activity on all-cause mortality in various reviews a and cohort studies.

Source	Definition of physical activity	Relative risk	Comments
Reviews
	Meeting moderate physical activity recommendation (1,000 kcal/week)	0.70–0.80	Review, excluding papers examining only two levels of physical activity
	Expending of 1,000 kcal/week	0.70	Based on a symposium; invited experts reviewed the existing literature
	Meeting physical activity recommendation	0.70	Review of peer-reviewed studies published between 2000 and 2003
	Different definitions of physical activity	0.70–0.87 (moderate) 0.46–0.92 (vigorous)	Review
	Meeting physical activity recommendation	0.65–0.80	Review
	Different definitions including moderate exercise (4,100–7,908 kJ/week), vigorous exercise, and different distances walked	0.50–0.77	Review of adult cohort studies with a mean > 60 years of age

Studies on cycling
	Cycling to work for 3 hr/week	0.55–0.72	Based on a Danish cohort, adjusted for leisure time physical activity (among others)
	Walking and cycling to work	0.71–0.79	Based on a Finnish cohort study among subjects with type 2 diabetes; estimates without adjusting for other domains in physical activity
	Cycling to work (MET-hours/day)	0.66–0.79	Based on a Chinese women cohort in Shanghai, adjusted for other physical activity
Overall summary		0.50–0.90

Three studies have directly assessed mortality related to cycling to work. In a prospective study in Copenhagen, the relative risk of the group bicycling to work was 0.72 [95% confidence interval (CI), 0.57–0.91] compared with other modes of transport after multivariate adjustment, including leisure-time physical activity ( Andersen et al. 2000 ). The relative risk for physically active groups compared with the sedentary group decreased with activity level: 0.68, 0.61, and 0.53 ( Andersen et al. 2000 ). In the Shanghai Women’s Health Study, exercise and cycling for transportation were both inversely and independently associated with all-cause mortality ( Matthews et al. 2007 ). Hazard ratios were 0.79 (95% CI, 0.61–1.01) for the group cycling 0.1–3.4 metabolic equivalent hours per day and 0.66 (95% CI, 0.40–1.07) for the group cycling > 3.4 metabolic equivalent hours per day, compared with the noncycling group. A Finnish study that combined cycling and walking to work versus nonactive commuting also showed significantly lower relative risks for active commuters in the range of 0.71 and 0.79 ( Hu et al. 2004 ). According to the reviews and the three cycling studies, the relative risk for all-cause mortality is in the range of 0.50–0.90 ( Table 5 ).

An expert panel determined a generally linear relationship between physical activity level and the rates of all-cause mortality, total cardiovascular disease, and coronary heart disease incidence and mortality ( Kesaniemi et al. 2001 ). There is thus evidence that health gains occur for physically active and nonactive persons, although the magnitude of these benefits may differ.

To calculate the potential impact of the modal shift on mortality, we directly used the range of relative risk estimates (0.50–0.90) presented in Table 5 .

Comparison of Life Years Gained or Lost

For the people who shift from car to bicycle use for short trips, we estimated that the beneficial effect on all-cause mortality rates of the increased physical activity due to cycling is substantially larger (relative risk, 0.50–0.90) than the potential mortality effect of increased inhaled air pollution doses (relative risk, 1.001–1.053) and the effect on traffic accidents (age-specific relative risk, 0.993–1.020). The estimated gain in life expectancy per person from an increase in physical activity ranged from 3 to 14 months ( Table 6 ). The estimated life expectancy lost because of air pollution (0.8–40 days) and traffic accidents (5–9 days) was much smaller. On average, the benefits of cycling were about 9 times larger than the risks of cycling, compared with car driving for the individuals making the shift, calculated as 337,896/(28,135 + 9,639). The estimated number of life years gained still exceeded the losses when the lowest estimate for physical activity was compared with the highest estimate for air pollution and traffic accidents (benefits/risks ratio of 2).

Summary of impact on all-cause mortality for subjects shifting from car to bicycle.

Stressor	Relative risk	Gain in life years	Gain in life days/months per person
Air pollution	1.001 to 1.053	−1,106 to −55,163 (−28,135)	−0.8 to −40 days (−21 days)
Traffic accidents	0.996 to 1.010 0.993 to 1.020	−6,422 to −12,856 (−9,639)	−5 to −9 days (−7 days)
Physical activity	0.500 to 0.900	564,764 to 111,027 (337,896)	14 to 3 months (8 months)

The largest estimated gain in life years was for the elderly [Supplemental Material, Table 6 (doi:10.1289/ehp.0901747)]. The ratio of life years gained to lost was 8.4 for persons < 40 years of age, 8.6 for persons 40–64 years of age, and 10.8 for persons ≥ 65 years of age.

The relative benefits of a 7.5-km versus 15-km distance are probably similar. A 15-km distance (1-hr commute) increases the life-years lost for air pollution from 20 to 40 days based on PM 2.5 and increases the life-years lost for traffic accidents from 5 to 9 days. The total estimated number of days lost per person is thus 49 for a 15-km distance and 25 for a 7.5-km distance. The relative risk of physical activity is difficult to quantify with the approach employed here. Using the data from Matthews et al. (2007) , the relative risk would be 0.79 for the 7.5-km distance and 0.66 for the 15-km distance, assuming 4 MET associated with cycling. These relative risks translate in 280 and 173 days gained, respectively.

Overall Discussion

Principal findings.

We quantitatively compared the health benefits from physical activity with the risks related to air pollution and traffic accidents between cycling and car driving for short trips, distinguishing the individuals who shift modes of travel from society as a whole. Estimated inhaled air pollution doses were higher in cyclists. The risk of a fatal traffic accident is higher for cyclists than for car drivers. Substantial benefits of physical activity have been demonstrated, including decreased cardiovascular disease and mortality.

For the people who shift from car to bicycle, we estimated that the well-documented beneficial effect of increased physical activity due to cycling resulted in about 9 times more gains in life-years than the losses in life years due to increased inhaled air pollution doses and traffic accidents. For the society as a whole this can be even larger because of reduced air pollution emissions. If the risk presented to other road users is included, the risk of a fatal traffic accident is virtually the same for cyclists and car drivers.

Strengths and weaknesses

The strength of our assessment is especially the quantitative comparison of benefits and risks, in a common scenario for the three stressors evaluated. It could be argued that the Copenhagen ( Andersen et al. 2000 ) and Chinese studies ( Matthews et al. 2007 ) of the effects of bicycling on mortality have already demonstrated the net effect of physical activity on all-cause mortality, including the negative effects of fatal traffic accidents and air pollution. However, the size of the potential negative health effects was not quantified separately in those studies. Therefore it is difficult to transfer the net effect of these studies to other locations, where traffic accident rates and air pollution may be different. Because in our assessment the separate risks have been disentangled, it is possible to make assessments for different settings, by using other input data (e.g., traffic mortality rates).

We performed our calculations for the Netherlands, where an extensive cycling infrastructure exists and priority is given to cyclists over other traffic—factors that contribute to regular cycling. Restrictions to car use through traffic calming in residential areas and car-free zones influence cycling behavior as well ( Pucher and Dijkstra 2003 ). Apart from the highest average distance cycled per person, the Netherlands is also one of the safest countries in terms of fatal traffic accidents. In such countries as the United Kingdom, Spain, and France, the risk of a fatal traffic accident for cyclists is substantially higher, probably also relative to car driving [Supplemental Material, Figure 2 (doi:10.1289/ehp.0901747)]. When we repeated the traffic accident calculations for the United Kingdom, where the risk of dying per 100 million km for a cyclist is about 2.5 times higher [Supplemental Material, Figure 2 (doi:10.1289/ehp.0901747)] and assuming the same fatality risk for car drivers as in the Netherlands, resulting life expectancy losses were approximately 14 days/person, based on 2005 population data from the United Kingdom and Wales. Overall, benefits of cycling are still 7 times larger than the risks.

Calculations on mortality impacts were performed for people 18–64 years of age, because people in that age range are more likely to make the modal shift. Age-specific analysis showed that the relative benefits of cycling are highest in the older age categories. This may have been even more pronounced if we had taken into account that the relative risks of physical activity may be larger for the elderly ( Vogel et al. 2009 ). The empirical evidence for higher relative risks in elderly related to long-term exposure to air pollution is weak; for example, in the large American Cancer Society study there were no differences in relative risk for PM 2.5 ( Pope et al. 2002 ). We did not include children in our assessment because they are unable to drive a car, so a modal shift is not possible. Because of our focus on mortality effects (being extremely rare in children), we could not quantitatively compare risks for children as car passengers or as cyclists for physical activity and air pollution. The benefits of physical activity in children are considered important, however, both for current and for future health.

Overall relative risks may largely reflect the response from sensitive subgroups. For all stressors, the elderly are likely more susceptible, and we documented in an additional analysis that the ratio of benefits and risks was highest for ≥ 65-year-olds. For air pollution, subjects with preexisting cardiorespiratory disease may be more susceptible, and for physical activity, sedentary people may be more susceptible; these are subgroups that may partly overlap. Hence, both the risks and benefits may be higher than in the population average analysis.

In summary, it is unlikely that the conclusion of substantially larger benefits from cycling than risks is strongly affected by the assumptions made in the scenario, including the use of data from the Netherlands. Because concentration–response functions are mostly based on studies in Europe and North America, they may not apply in developing countries. For air pollution, there are no studies on long-term mortality effects in developing countries. The generally higher ambient air pollution concentrations could lead to higher losses in life-years comparing cycling and car driving. Traffic accident statistics for the Netherlands are probably not transferable to developing countries. For physical activity, there is evidence from a Chinese study ( Matthews et al. 2007 ), with very similar benefits. Hence, very large differences in concentration–response functions for air pollution and traffic accidents from the functions we used would be necessary to tip the balance between benefits and risks.

For air pollution, there is considerable evidence that long-term and short-term exposures are related to increased cardiopulmonary mortality ( Brunekreef and Holgate 2002 ). There are no studies of mortality effects specifically related to in-traffic exposures. We estimated the effect of shifting mode using two major long-term mortality cohort studies ( Beelen et al. 2008 ; Pope et al. 2002 ), making assumptions about the contribution of traffic participation to the total inhaled dose of PM 2.5 and (diesel) soot. Relative risks comparing cycling and car driving were small for both approaches, with the lower estimates based upon BS probably most realistic, because this component is more specific for traffic emissions.

The actual risk may be smaller because cyclists could more easily choose a low-traffic route. The substantial influence of route has been documented in various monitoring and modeling studies ( Adams et al. 2001 ; Hertel et al. 2008 ; Kingham et al. 1998 ; Strak et al. 2010 ). A study in Utrecht found 59% higher UFP exposure for cyclists along a high-traffic route compared with a low-traffic route ( Strak et al. 2010 ). Walking close to the curb in London greatly increased personal exposures ( Kaur et al. 2005 ). For cyclists, position on the road is likely important as well, because it determines distance to motorized traffic emissions. Urban planning may also contribute by separating cycle lanes from heavily trafficked roads ( Thai et al. 2008 ).

For society, reduced overall air pollution levels may result in lower mortality from long-term exposure of city dwellers. The potential benefits we estimated based on NO 2 reductions were in the same order of magnitude as the potential risks for the individuals shifting.

Table 4 shows that the modal shift will lead to an increase in traffic accident deaths. The relative risk may be lower than we used because of the “safety-in-numbers” effect [Supplemental Material, Figure 2 (doi:10.1289/ehp.0901747)]. Car drivers may take more account of cyclists, resulting in fewer accidents per car-kilometer, when cyclists form a bigger part of the traffic ( Jacobsen 2003 ). Traffic fatality and injury rates in Germany and the Netherlands (with relatively high levels of cycling and walking) were relatively low compared with those of the United States ( Pucher and Dijkstra 2003 ). However, whether this reduction is attributable to a safety-in-numbers effect or a result of more biking lanes cannot easily be disentangled. The WHO concluded that if promotion of active commuting is accompanied by suitable transport planning and safety measures, active commuters are likely to benefit from the safety-in-numbers effect ( WHO 2007b ). The relative risks could also be higher because the less experienced cyclists making the shift could be more vulnerable to accidents. We cannot quantify this effect.

Even when origin and destination are the same, cars and bicycles often take different routes ( Witlox 2007 ). The same short trip for a car may be 20–50% longer than for a bicycle (our calculations are based on comparisons per kilometer). If we could make a trip-based comparison, a lower relative risk for fatal accidents for cyclists compared with car drivers would be found. Furthermore, we did not take into account the concept of constant travel time budgets ( van Wee et al. 2006 ): A change of travel time will be compensated by a change of destination. When taking the bicycle, the shop next door is preferred over the shop with greater choices farther away. These factors would lead to lower relative risks than we used.

Relative risks for different physical activity definitions (total physical activity, meeting the physical activity guideline, active commuting) were quite consistent. An important issue is whether the comparison between subjects with lower and higher physical activity can be used to assess the health effects of a change in physical activity related to a shift toward active commuting. Bauman (2004) showed that persons who were already in the highest quartile of fitness at baseline had a significantly lower mortality when they became even more active. In another study, people who went from unfit to fit over a 5-year period had 44% relative risk reduction compared with people who remained unfit ( Blair et al. 1995 ). The largest improvements in health status are seen in inactive persons who change their lifestyle and become physically active ( Warburton et al. 2006 ). A review by Erikssen et al. (1998) suggested similar health benefits from an increase in physical activity for active and sedentary persons. Already active persons could have lower benefits of the extra physical activity, leading to relative risks up to 0.90. If only active persons shift mode of transport, lower overall benefits of cycling compared with car driving will be found (ratio of life-years gained vs. lost, 4 instead of 9).

An increase in cycling does not necessarily lead to an increase in total physical activity, if it is associated with reduced activity in another domain ( Forsyth et al. 2008 ; Thomson et al. 2008 ). The empirical evidence for substitution is weak, and increased fitness could also lead to more physical activity in leisure time. If we assume that for 25% of the population no health gains occur because of substitution, the ratio of benefits to risks (central estimates from Table 6 ) would be reduced from 8.9 to 6.7. Only if for 89% of the population no increase in total physical activity occurs because of substitution would benefits and risk become equal.

We have not considered the negative effects of physical activity on health—namely, musculoskeletal injury and fatal and nonfatal cardiac events ( Institute of Medicine 2007 ). Cycling can be considered a moderate type and duration of sport and has lower injury risk than do more vigorous types (running, scholastic athletics) and longer durations of physical activity ( Hootman et al. 2001 ; Parkkari et al. 2004 ). Exercise has acute cardiac risks as well, but the absolute risk of a cardiac event during exercise seems to be low ( Institute of Medicine 2007 ). Regular physical activity also reduces the acute risk of a cardiovascular event ( Tofler and Muller 2006 ).

Restriction to mortality

We limited the quantitative assessment to mortality. It is difficult to evaluate the comparison between cycling and car driving if morbidity is included because of the lack of solid concentration–response relationships for air pollution and physical activity for morbidity outcomes. A meta-analysis reported a consistent positive association between physical activity and health-related quality of life ( Bize et al. 2007 ). The largest cross-sectional study showed that people meeting the recommended levels of physical activity had an adjusted odds ratio of “having 14 or more unhealthy days during the previous months” of 0.4 (95% CI, 0.36–0.45) over the inactive subjects ( Bize et al. 2007 ). Quality of life may even further improve apart from the increases in life-years. Concentration–response functions for air pollutants and morbidity outcomes such as hospital admissions are lower than for mortality: in the range of 1% compared with 6% per 10-μg/m 3 increase in PM 2.5 ( WHO 2006 ). Traffic injuries may differ even more between cyclists and car drivers than fatal accidents [Supplemental Material, Table 4 (doi:10.1289/ehp.0901747)], if underreporting of especially cyclist accidents is accounted for. This would reduce the ratio between benefits and risks.

We did not include all stressors in the quantitative evaluation. Cycling contributes to other benefits, including reduced emissions of carbon dioxide relevant for reducing climate change, reduced use of physical space (e.g., related to parking), and reduced traffic noise for city dwellers, which may result in less annoyance. We are not aware of exposure studies or health effects studies that have compared traffic noise during transport for cyclists and car drivers.

Suggestions for policy

Our study suggests that policies stimulating cycling likely have net beneficial effects on public health. Policies should be accompanied by safety measures and efforts to limit hazards, for example, by infrastructural choices (building cycling lanes away from major roads to limit cyclists’ air pollution exposures) or limitations such as a ban on car traffic during school start and end hours near schools. Policies may take the age dependence of the traffic accident relative risks into account—for example, by stimulating especially the young to increase cycling. However, this may not be the optimal choice for the beneficial effects of cycling.

Assessing what traffic policies are effective in promoting a population shift from using cars toward cycling (and walking) is beyond the scope of this review. A recent review showed that targeted behavior change programs can change the behavior of motivated subgroups, resulting in a 5% shift of all trips at the population level in the largest study ( Ogilvie et al. 2004 ). However, effects of similar programs on the general, less motivated population are unclear. Those programs may benefit from taking the public’s views into account and learning from good practices (e.g., THE PEP 2009 ). In particular, perceptions of walking and cycling as dangerous activities are an important barrier to the promotion of active transport ( Lorenc et al. 2008 ).

On average, the estimated health benefits of cycling were substantially larger than the risks of cycling relative to car driving. For the society as a whole, this can be even larger because there will be a reduction in air pollution emissions and eventually fewer traffic accidents. Policies stimulating cycling are likely to have net beneficial effects on public health, especially if accompanied by suitable transport planning and safety measures.

This research was conducted within the framework of INTARESE (Integrated Assessment of Environmental Stressors in Europe), funded by European Union 6th Framework Programme grant 018385-2.

Supplemental Material is available online (doi:10.1289/ehp.0901747 via http://dx.doi.org/ ).

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Greater Good Science Center • Magazine • In Action • In Education

How to Stop Overthinking Your Happiness

All humans strive to be happy in some form. While there are intriguing variations in what exactly it means to be happy , this tenet is one of the rare human universals, transcending differences in culture, geographic location, age, ethnicity, and gender. As the Dalai Lama put it , simply, “The purpose of life is to be happy.”

That might lead to the expectation that we should all be happy, at least when circumstances afford it. Yet this is not the case. Even when people’s lives are good, many feel less than happy, and may be beset by anxiety and depression.

Thus there is a paradox: The pursuit of happiness is one of the prime values people hold, and they often fall short of attaining it. There might even be a further vexing twist on this happiness paradox, by which the more fervently people pursue happiness the further they get from it. In the words of the philosopher Eric Hoffer, “The search for happiness is one of the chief sources of unhappiness.”

The idea is that the more we value happiness the higher expectations we set for our happiness—high expectations we are more likely to miss. When we miss them, we may become disappointed and discontented. Such feelings are incompatible with happiness. And voila! Like in quicksand, the more we want to be happy, the less happy we become.

Fortunately, our research points to a solution—and the solution is pretty simple to state, if tricky to implement: When you’re experiencing something positive, don’t judge yourself.

How tracking happiness makes us unhappy

In earlier empirical research , we showed that intensely valuing happiness indeed seems to backfire. For example, people who endorsed statements like, “Happiness is extremely important to me,” were more likely to have lower well-being and greater depressive symptoms.

Intriguingly, this was especially the case when the circumstances of people’s lives were good. This is in line with the idea that the happiness paradox trap becomes engaged when expectations for happiness are activated—when we think everything is good and we ought to feel happy.

A recent New York Times opinion piece elaborates on the ways in which this happens, and puts its finger on one particular aspect of pursuing happiness that might interfere with attaining it: tracking it. It asked: “Could tracking happiness make us feel worse?” The answer to the question finds a resounding yes, it could and it does.

Tracking happiness may interfere with attaining happiness for two key reasons. First, when we track our happiness we are pulled out of the moment, which interferes with experiencing happiness to its fullest. This follows a suspicion memorably voiced by John Stuart Mill: “Ask yourself whether you are happy and you cease to be so.”

The second reason why tracking happiness might be harmful is that it invites comparison. And comparison—to our own high expectations, to other people’s blissful Instagram feeds—breeds discontent. This leads the happiness hunter directly into the place they wanted to avoid.

At this point, we might conclude that we should let go of our lofty goals to become happier. Maybe it is not in the cards for us and we should let go of the goal, and make do with whatever happiness scraps fall to us. But this conclusion does not accord with a large body of research that examines whether and how people can become happier.

Take, for example, UC Riverside psychologist Sonya Lyubomirsky’s research , which has found that Undermine Well-Being">happiness interventions can work in helping people be happier , at least sometimes. Meaning, when people want to feel happier, they can get there. The mystery is further deepened in that Lyubomirsky and her colleagues found this is especially true for people who are highly motivated and put forth more effort, as evidenced by selecting to be part of a happiness-enhancing intervention (compared to cognitive exercises).

Thus, there is a puzzle: How can valuing happiness be bad and pursuing happiness be good?

Roots of dissatisfaction

That puzzle led us to believe that the story must be more complicated. Perhaps valuing happiness—even intensely—is not inherently and always problematic. Rather, the problem might lie in how people approach happiness. There might be some bad and some good ways. That is, whether or not valuing happiness is associated with bad outcomes depends on the way in which people approach and think about happiness.

What might those ways be? UC Berkeley psychology alumni Felicia Zerwas and Brett Ford proposed a model of pursuing happiness that provides cues by taking a closer look at what happens psychologically when people pursue happiness. They proposed that it is OK to aspire to happiness, even intensely.

Where things start to get dicey is a bit further down the path where there is a fork in the road: On one path, someone can simply be OK with the level of happiness they have reached. But on the other path, someone can judge their experiences and worry about how much happiness they do or don’t have.

Going down this second path infuses negativity into their experiences, ultimately leading them further away from happiness. We can call this tendency concern about happiness. Concern about happiness, rather than simply aspiring to happiness, might lie at the heart of self-defeat.

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Because this is a bit abstract, let us illustrate the two approaches with an example. Let’s say you are at a birthday party—your own! Your friends planned it for you and invited all your favorite people, who brought your favorite foods, treats, and beverages. You feel lots of positive emotion—contentment, excitement, gratitude, joy, and happiness. So far so good.

Now comes the key moment for our happiness hunter, where the path forks. On the one hand, you could simply aspire to being happy. Period. You enjoy the moment and dance the night away at your birthday party. End of story. On the other hand, however, you could be concerned about your happiness, adding judgment to your experience and with it a layer of overthinking. You have everything that should make you happy, and yet you wonder, you worry, This is perfect, why aren’t I happier? A disappointment sets in which might spiral into further disappointment.

Scientists call these “negative meta-emotions”: feelings we have about feelings. And so even when happiness is most within reach—or perhaps precisely because it is within reach—you get in your own way.

Now add to this the fact that few experiences are purely and unadulteratedly happy. Most events—even the best—have elements of ambiguity and mixed emotion. The cake might not be perfect or one of the guests might misbehave. We can easily see how the person who is concerned about happiness will latch on to those flies in the ointment and let them spoil the entire experience.

Four ways to not ruin happiness

So to recap, when people who aspire to happiness have positive events, they can simply roll with it and enjoy their experiences. Even if there IS a fly in the ointment, that’s OK. In contrast, when people who are concerned about happiness have positive events, they cannot simply enjoy them. They yuck their own yum: They judge and add negative meta-emotions.

That all means that the problem may not lie in how happy people are or how happy they want to be—it lies in how people respond to their happiness.

We put these ideas to an empirical test in a but Not Aspiring to Happiness Is Linked With Negative Meta-Emotions and Worse Well-Being">recent series of studies involving 1,815 participants from across the U.S. We found that, indeed, people fall into two types, with some scoring high on aspiring to happiness, and some scoring high on concern about happiness.

In our survey, they endorsed statements like, “I am concerned about my happiness even when I feel happy,” and, “If I don’t feel happy, maybe there is something wrong with me.” People who were more concerned about their happiness experienced lower satisfaction with their lives, lower psychological well-being, and higher depression symptoms.

And, based on diary entries they completed, we found that this link was explained by how they responded to positive events: they were more likely to have negative meta-emotions like disappointment about their own feelings. It’s like a slow drip of weak poison, where every single experience doesn’t harm overall well-being, but repeated instances over many months do.

Meanwhile, aspiring to happiness—considering happiness very important but without a tendency to judge—was innocuous and did not interfere with attaining happiness.

What does our research teach us about whether the pursuit of happiness is possible? We believe the studies point to a solution to the happiness paradox. From the concerned people, we can learn which pitfalls to avoid , and from the aspirers we can learn how to make happiness attainable. Four of these lessons are supported by science:

A first, most fundamental lesson is not to judge our emotions. As our walk through the process of pursuing happiness illustrates, the path to happiness goes awry when we judge . This is easier said than done, especially as judgments can be deeply ingrained. But it is possible to learn an accepting perspective: viewing our emotions, positive and negative, as natural and valuable parts of human life. Accepting our emotions, in turn, is associated with greater well-being . Acceptance can help us become happier and enjoy life more, and it also is a helpful strategy to be resilient when we encounter adversity.
Next, consider counteracting one of the main tributaries to judgment: monitoring how we feel. Monitoring itself is not harmful but it makes it a lot more likely that we will judge. When we don’t monitor our feelings, we are less likely to judge—and more likely to enjoy.
A third strategy unites the first and second, and it is: Don’t treat activities—or life—as a means to an end. If we can live our lives fully, mindfully, without looking beyond, true happiness might emerge. This idea is captured in a quote attributed to Nathaniel Hawthorne: “Happiness is like a butterfly which, when pursued, is always beyond our grasp, but, if you will sit down quietly, may alight upon you.”
Finally, if there is any common theme to research on what makes people happier, it is that social connection is helpful . This might be because social connection invites us to judge and monitor less and be in the moment more.

This is not to say the only paths toward happiness are psychological. Our cultures, systems, and societies play a key role in individual happiness . First, they directly create happiness. For example, giving people money , supporting social connection, and combating inequality and injustice are some of the best ways to make people happier. Second, they shape how people approach happiness . For example, we learn from our culture how to think about happiness and how to go about pursuing it, whether we simply aspire or are concerned.

Happiness is a—maybe THE—core value throughout human history and across cultures. While there are pitfalls, attaining greater happiness is possible.

About the Authors

Iris Mauss, Ph.D. , is the Thomas and Ruth Ann Hornaday Professor of Psychology and the director of the Institute of Personality and Social Research at the University of California, Berkeley. Her lab’s research focuses on emotions and emotion regulation, with an emphasis on their links to psychological health.

Brett Q. Ford

Brett Q. Ford, Ph.D. , is an associate professor of psychology at the University of Toronto, where she directs the Affective Science and Health Laboratory. Her research examines how people manage emotions and cope with stress, exploring both the benefits and the costs of striving to feel good.

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Benefits, risks, barriers, and facilitators to cycling: a narrative review
The economic benefits and costs of cycling to wider society. There is a growing body of evidence indicating a net benefit to society of increasing participation in cycling as summarized in the following sections relating to cycling impacts on health, pollution and congestion, duty and taxation and employment and retail. 5.3.1.
Health Benefits of Indoor Cycling: A Systematic Review
The total sample size of the studies was 372 (306 women). Results revealed that indoor cycling may improve aerobic capacity, blood pressure, lipid profile, and body composition. These enhancements may be achieved as standalone intervention or combined with other physical exercises or diet. Conclusions: The combination of indoor cycling and diet ...
Cycling for health
Active transportation, which is any form of human-powered transportation, can mitigate the health effects of the climate crisis while simultaneously improving the health of people. Physical activity improves overall well-being, as well as physical and mental health. Active transportation, particularly cycling, is a convenient way to meet ...
Health Benefits of Cycling and Do's and Don'ts
Improves strength and flexibility. Like other aerobic exercises, cycling can build up your muscular strength and endurance. According to Dr. Kubiak, research has shown that indoor cycling helps ...
Cycling as a Part of Daily Life: A Review of Health Perspectives
Health aspects of day-to-day cycling have gained attention from the health sector aiming to increase levels of physical activity, and from the transport and planning sector, to justify investments in cycling. We review and discuss the main pathways between cycling and health under two perspectives — generalizable epidemiological evidence for ...
Cycling and walking can help reduce physical inactivity and air
Cycling and walking can help fight overweight and reduce physical inactivity, which causes one million deaths per year in the European Region. Both means of active transport can also help to reduce air pollution that claims more than half a million deaths every year. Evidence shows that investments in policies that promote safe cycling and walking can play a crucial role in shaping health ...
Cycling for health: Improving health and mitigating the ...
Active transportation, which is any form of human-powered transportation, can mitigate the health effects of the climate crisis while simultaneously improving the health of people. Physical activity improves overall well-being, as well as physical and mental health. Active transportation, particularly cycling, is a convenient way to meet ...
Health benefits of cycling: a systematic review
The purpose of this study was to update the evidence on the health benefits of cycling. A systematic review of the literature resulted in 16 cycling-specific studies. Cross-sectional and longitudinal studies showed a clear positive relationship between cycling and cardiorespiratory fitness in youths.
Health benefits of cycling: a systematic review
Humans. Male. Middle Aged. Physical Fitness. The purpose of this study was to update the evidence on the health benefits of cycling. A systematic review of the literature resulted in 16 cycling-specific studies. Cross-sectional and longitudinal studies showed a clear positive relationship between cycling and cardiorespiratory fitness in youths ….
Intended cycling frequency and the role of happiness and ...
Research that addresses cycling and explores it in the context of public gains has found its benefits to include cost savings, savings on journey time, convenience, health, and perceived utility ...
Cycling Benefits: 12 Reasons Cycling Is Good for You
5. Cycling boosts mental health and brain power. Cycling can ease feelings of stress, depression, or anxiety. Focusing on the road or your cadence when cycling can help you develop concentration ...
11 Research-Backed Health Benefits of Cycling
11. Less Stress. Everyone knows that exercise can help reduce stress, but a 2018 study in the Lancet of over one million (!) participants confirmed that cycling is one of the top stress-busting ...
Why bicycling might keep mental health in high gear
According to Walker, "Research suggests that physical activity like cycling likely encourages new cell growth in areas of the brain linked to memory and problem solving, and can support stronger ...
Bicycling
Bicycling. Bicycling, also referred to as biking or cycling, is a form of transportation and a popular leisure-time physical activity. Health benefits include improved cardiovascular fitness, stronger muscles, greater coordination and general mobility, and reduced body fat. As with other types of exercise, it can also help improve mental health ...
The top 5 benefits of cycling
The top 5 benefits of cycling. August 11, 2016. Going for a ride is good for your heart and muscles, and it may improve how you walk, balance, and climb stairs. Image: DTStockPhotos. They say you never forget how to ride a bike, so maybe it's time to climb aboard a two- or three-wheeler and enjoy the health benefits of cycling.
The effect of cycling on cognitive function and well-being in older
Given the associations between cognitive function, well-being and exercise in older adults, the aim of the study reported in this paper was to investigate the effect of cycling outdoors on cognitive function and mental health and well-being of older adults (over 50 years old). Compared to other studies, which measure effect of indoor exercise ...
The effectiveness of community-based cycling promotion: findings from
Background Encouraging cycling is an important way to increase physical activity in the community. The Cycling Connecting Communities (CCC) Project is a community-based cycling promotion program that included a range of community engagement and social marketing activities, such as organised bike rides and events, cycling skills courses, the distribution of cycling maps of the area and coverage ...
5 Health benefits of cycling
Cycling relieves stress. Chronic stress can have big health impacts. But physical activities like cycling can help reduce daily stress. "Any time you exercise, it releases endorphins," says Thoman. Endorphins can help you feel better when you are under stress. And, exercising outdoors has added benefits.
Feeling stressed? Bike to work
Study shows how a pedal-powered commute can set you up for the whole day. Cycling can help reduce stress and improve your work performance, new research confirms. New research from Concordia's ...
How Training Muscle Power Can Help Conquer Hills and Sprints
Being a powerful rider can help you flip the switch between simply enduring a ride to actually enjoying a ride. ... Aside from how muscle power can help your cycling, research has also found that ...
The role of motivation and goal setting in cycling: How to set goals
For riders who aren't coached, enlisting a strategy of two or three weeks of structured training with a week of easier-intensity riding can help balance things. Another great method to prevent ...
Cycling in older adults: a scoping review
In addition, cycling can help to protect the environment and reduce greenhouse gas emissions. The primary aim of this scoping review is to identify the currently available scientific evidence and gaps of research in this field. ... However, it seems to be highly relevant to conduct more research on cycling among older adults in low- and middle ...
When is VeloSano 2024 in Cleveland?
VeloSano 'Bike to Cure' event returns to Cleveland: How you can help raise money for cancer research. 100% of the money VeloSano raises is used to benefit cancer research at the Cleveland Clinic.
Radiologists to Research How AI Can Help Detect Breast Cancer
Breast radiologists at Kaiser Permanente will research how an artificial intelligence tool may help detect breast cancers at earlier stages, before cancers can be seen by the human eye, and when it is most responsive to treatment. ... Radiologists to Research How AI Can Help Detect Breast Cancer September 4, 2024 . Kaiser Permanente Mid ...
Do the Health Benefits of Cycling Outweigh the Risks?
A Finnish study that combined cycling and walking to work versus nonactive commuting also showed significantly lower relative risks for active commuters in the range of 0.71 and 0.79 (Hu et al. 2004). According to the reviews and the three cycling studies, the relative risk for all-cause mortality is in the range of 0.50-0.90 (Table 5).
How to Stop Overthinking Your Happiness
Acceptance can help us become happier and enjoy life more, and it also is a helpful strategy to be resilient when we encounter adversity. Next, consider counteracting one of the main tributaries to judgment: monitoring how we feel. Monitoring itself is not harmful but it makes it a lot more likely that we will judge.

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