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Sonja Kahlmeier, Paul Kelly, Charlie Foster, Thomas Götschi, Nick
Cavill, Hywell Dinsdale, James Woodcock, Christian Schweizer, Harry
Rutter, Christoph Lieb, Pekka Oja and Francesca Racioppi
2011, updated reprint 2014, viii + 39 pages
promotion of cycling and walking for everyday physical activity not
only promotes health but can also have positive effects on the
This booklet summarizes
the tools and guidance developed to facilitate this shift: the
methodology for the economic assessment of transport infrastructure and
policies in relation to the health effects of walking and cycling;
systematic reviews of the economic and health literature; and guidance
on applying the health economic assessment tools and the principles
This methodology and
user guide will be of key interest to professionals at both national and
local levels: transport planners, traffic engineers, and special
interest groups working on transport, walking, cycling or the
environment, as well as health economists, physical activity experts and
health promotion experts.
The development of HEAT for walking and cycling was supported by the European Union in the framework of the Health Programme 2008–2013 (Grant agreement 2009 52 02), the Austrian Federal Ministry of Agriculture, Forestry, Environment and Water Management, the Swiss Federal Office of Public Health, the Swedish Expertise Fund and a consortium of donors from the United Kingdom under the leadership of Natural England.
Methodological guidance on economic Appraisal of health effects related to walking and cycling
2.3. Interactions between transportrelated physical activity, air pollution and road traffic injuries Transport-related health effects include possible negative effects from exposure to ambient air pollution or road traffic injuries. Possible interactions between the positive effects of exercise through active transport and such
negative effects need to be considered. To date, no comprehensive review of active transport and physical activity is available that takes the possible negative effects of ambient air pollution into account.
Two recent scenario analyses showed that, in most cases, especially in western Europe, the positive health effects of cycling are likely to greatly outweigh the negative effects of air pollution and road traffic accidents suffered by cyclists (11,12). Also, the use of all-cause mortality estimates (see also below) rather
than cause- specific ones has the advantage of incorporating the possible detrimental effects associated with walking or cycling.
2.4. Mortality or morbidity?
Physical activity has beneficial effects on many aspects of morbidity such as coronary heart disease, stroke, diabetes, some types of cancer, musculoskeletal health, energy balance and aspects of mental health (including anxiety and depression) and improving functional health in elderly people (13). From a public health point of view, these benefits materialize more rapidly than reductions in mortality. They can also be important in motivating individuals to walk and/or cycle, as people may be more likely to increase their physical activity to improve their immediate health and well-being than to prolong their life.
The consensus meetings therefore recommended, for the time being, focusing only on all-cause mortality for HEAT for walking and for cycling. It should be noted that this method is likely to produce conservative estimates, since it does not account for diseaserelated benefits.
2.5. The nature of the relationship
between physical activity and health Epidemiological studies report relationships between different categories or levels of exposure and health outcomes. For example, a comparison of sedentary people with people who are active beyond a specific threshold (such as 150 minutes of activity per week) may show that active people are healthier.
The international advisory group concluded that, overall, a linear dose–response function is the most suitable one to use for HEAT. In this way, users do not have to know the baseline level of physical activity of their subjects, and a constant absolute risk reduction can be applied for all HEAT applications within the range of exposure for which an incremental reduction of mortality risk can be observed.
HEAT does not take into account differences in the pace (or intensity) of walking or cycling, or the possibility that less well-trained individuals may benefit more and better-trained individuals may benefit less from the same amount of walking or cycling.
2.8. Time needed for health benefits to build up
It is important to recognize that there will be a delay between increases in physical activity and measurable benefits to health. Based on the best available evidence, it was concluded that five years was a reasonable assumption to use for such “newly induced physical activity” to reach full effect, with an increment of 20% in benefits each year.
2.12. Costs applied
To conduct an economic appraisal of walking and cycling, it is necessary to agree on a method of valuing health (or life). There are a number of ways in which this can be done.
• A standard value of a statistical life (VSL)
This is often used in transport appraisals. It is most commonly derived using a method called willingness to pay. The willingness to pay shows how much a representative sample of the population (who, in this instance, are potential victims) would be willing to pay (in monetary terms) for example for a policy that
would reduce their annual risk of dying from 3 in 10 000 to 2 in 10 000.
• Cost of illness
This applies costs (for example costs to the national health service or loss of earnings) to each specific disease.
• Years of life lost (or gained)
This allows a more comprehensive assessment of health effects, as it takes the life expectancy of the participants into account.
• Quality-adjusted life-years (QALYs)
These are derived from years of life spent in ill health, multiplied by a weight measuring the relative undesirability of the illness state.
• Disability-adjusted life-years” (DALYs)
These measure the overall disease burden, expressed as the number of years lost due to ill health, disability or early death.
As this project was aimed primarily at transport appraisals, the VSL approach was used, as this is more common in transport appraisals.
It is recommended to use either a current, internationally agreed VSL or a local VSL, where available.
Since benefits occurring in the future are generally considered less valuable than those occurring in the present, economists apply a so called “discount rate” to future benefits. In many cases, the economic appraisal of health effects related to walking and cycling will be included as one component into a more comprehensive cost–benefit analysis of transport interventions or infrastructure projects. The final result of the comprehensive assessment would then be discounted to allow a calculation of the net present value.
2.14. Sensitivity analysis
It is strongly recommended that the uncertainties around an assessment are made explicit, and that the calculations are carried out with high and low estimates of the main variables in order to gain a better understanding of the possible range of the final results.
3.1 Economic Literature
Generally, the economic analyses showed positive benefit–cost ratios, the median being 5:1 with a range from –0.4 to 32.5. However, owing to the different methods applied in the studies, this value has to be viewed with caution. Some studies estimated the value attributed to each new walker or cyclist; these ranged from about €120 to €1300.
As explained in section 2.12, HEAT uses the VSL method to economically quantify the health benefits of reduced mortality from walking or cycling. Due to the dearth of official VSL studies, HEAT previously suggested using either a default value of €1.574 million (5,24) or a national VSL.
The Organisation for Economic Co-operation and Development (OECD) recently published a comprehensive review of VSL studies (23). Studies were only included if they were based on a representative population sample of at least 200 subjects (or 100 for subsamples of larger studies) and provided information on the size
of the risk change in question. About 400 values were selected to calculate the VSL for adults in 38 countries around the world. For the EU27 countries, an average VSL of US$ 3.6 million with a range from US$ 1.8 million to US$ 5.4 million (2005 US dollars).
Since the available studies used a range of different exposures, to conduct the metaanalysis it was necessary to estimate for each study the reduced risk at a common exposure level. For this purpose, the different cycling exposures used in the studies were converted into MET-hours per week (assuming a linear dose-response relationship and an average intensity of 6.8 METs for cycling if not otherwise stated). The common exposure level was set at 11.25 MET-hours per week. This value was derived from the global physical activity recommendations as corresponding to the recommended level of at least 150 minutes of moderate-intensity physical activity per week (14) using 4.5 METs as an average for moderate-intensity physical activity. Using 6.8 METs as an average intensity for cycling, this exposure represents about 100 minutes of cycling per week.
4. The HEAT for walking and for cycling: introduction
The principles and guidance set out in Chapter 2 have been developed into a practical tool for walking and for cycling, known as HEAT (6). The tool estimates the maximum and the mean annual benefit in terms of reduced mortality as a result of walking or cycling.
It can be applied in a number of situations, as further described in section 4.4, such as:
• when planning a new piece of cycling or walking infrastructure, helping to make the case for investment;
• It will help to answer the following question: If x people cycle or walk for y minutes on most days, what is the economic value of the health benefits that occur as a result of the reduction in mortality due to their physical activity?
4.2. Who is the tool for?
The tool is based on the best available evidence and transparent assumptions. It is intended to be simple to use by a wide variety of professionals at both national and local levels. These include primarily:
• transport planners;
• traffic engineers; and
• special interest groups working on transport, walking, cycling or the environment.
4.3. What can the tool be used for?
The tool provides an estimate of the economic benefits accruing from walking or cycling as a result of lower death rates. Ideally, for a comprehensive assessment, it would be supplemented with data on other potential health outcomes from walking or cycling (morbidity) and combined with other transport-related outcomes such as less congestion, reduced journey times or fewer road traffic injuries. These and other enhancements will be considered for inclusion in future versions of the tool.
4.5. Basic functioning of the tool
The tool is based on relative risk data from published studies. The included studies controlled for leisure-time physical activity as well as the usual socioeconomic variables (age, sex, smoking, etc.). This means that the relative risks reported for walking or cycling and mortality were independent of other forms of physical activity.
The tool uses these relative risks and applies them to the amount of walking or cycling entered by the user, assuming a linear relationship between walking or cycling and mortality. To illustrate this, the relative risk from the meta-analysis used for the updated version of HEAT for cycling is 0.90 for regular commuter cycling for 100 minutes per week for 52 weeks of the year (equivalent to 87 hours of cycling per year).
Thus, in any given year, regular cyclists receive a protective benefit of 10% (1.00 minus 0.90) – that is, they are 10% less likely to die from any cause than non-cyclists. If the user enters a cycling volume equivalent to 29 hours per year (i.e. three times less), the protective benefit of this amount of cycling will be roughly 3%. If the user enters 174 hours (twice the time cycled in the reference population), the resulting protective benefit is 20%. This is twice the protective benefit of the reference population.
To avoid inflated values at the upper end of the range, the risk reduction available from the HEAT is capped. Inspection of the data points of the new meta-analyses suggested that, after about 45% risk reduction for cycling and 30% for walking, no significant further risk reductions were achieved. HEAT will apply a maximum 45% risk reduction in the risk of mortality for cycling (corresponding to 450 minutes per week) and a maximum 30% risk reduction (corresponding to 458 minutes per week) for walking.
HEAT then uses population-level mortality data to estimate the number of adults who would normally be expected to die in any given year in the target population. Next, it calculates the reduction in expected deaths in this population that cycle or walk at the level specified by the user, using the adjusted relative risk. Finally, the tool produces an estimate of economic savings from this calculated reduction in deaths, as well as discounted and average savings.
The basic functioning of the tool is shown in fig. 1.
4.5.1. Applicable age range HEAT for cycling is designed for analyses of adult populations aged about 20–65 years. This is because HEAT should be used for regular behaviour such as commuting, and the retirement age is about 65 years in most countries.
If the age distribution in the assessed population is significantly different (much younger, much older), HEAT may underestimate or overestimate the resulting benefits. HEAT should not be applied to populations of children, very young adults or older people.
4.6. What input data are needed?
To use HEAT, the following data are needed:
• an estimate of how many people are walking or cycling, which might come from route user surveys, population surveys or roadside counts, or could be estimates from scenario analyses
• an estimate of the average time spent walking or cycling in the study population, which can again come from surveys or estimates and can be entered in a number of ways:
• duration (average time walked or cycled per person, e.g. 30 minutes walked on average per day), which is the most direct data entry route;
• distance (average distance walked or cycled per person, e.g. 10 km cycled on average per day);
• trips (average per person or total observed across a population, e.g. 250 bicycle trips per year); or
• steps (average number of steps taken per person, e.g. 9000 steps per day).
A number of default values are provided in HEAT; these have been derived from the literature and agreed on as part of the expert consensus process. They should be used unless more relevant data are available that more accurately reflect the situation under study, for the following variables:
• mortality rate (a European average can be used or a national rate from the WHO European Detailed Mortality Database (30) for an average population (about 20–74 years old), a younger average population (about 20–45 years old) or a predominantly older average population (about 46–74 years old), or the local mortality
rate can be entered);
• VSL (values commonly used across Europe are provided in the model but users may adapt this value by, for example, adopting agreed values for their own country; for more information see section 2.12);
• the period of time over which average benefits are to be calculated; and
• a discount rate, if so wished; the default value supplied can be used or an alternative rate can be entered.
In addition, details of the cost of promoting cycling or walking can be entered, which can be used to calculate a benefit–cost ratio.
Along the way, some assumptions may need to be taken where no data are available, such as on the supposed impact of an intervention on newly induced levels of walking and cycling. Input is provided for such assumptions, wherever possible with default values (and their sources). Explanations and further information on the different steps of the tool as well as a section with frequently asked questions are provided on the web site
4.7. Data sources
Input data for the model may come from a number of sources, including:
• route user surveys;
• population-level travel behaviour surveys;
• destination-based behaviour travel surveys (e.g. commuter behaviour); and
• traffic counts.
Alternatively, informed estimates may serve as surrogates for empirical data, such as in scenario calculations. In all cases, it is important to use the most reliable data possible and to validate these with secondary sources where available.
A few considerations will help to make the best use of the available data and avoid mistakes.
4.7.1. Use of short-term counts and surveys
The main concern with short-term counts is that they do not accurately capture variations in walking or cycling over time (i.e. time of the day, day of the week, season or weather). Since HEAT assumes that the entered data reflect long-term average levels of walking or cycling, data from short-term counts may distort the results. This issue will affect single-site evaluations (such as a footpath or a bridge) where counts are conducted at the site itself, or community- wide evaluations that are based on surveys conducted only during a certain time of the year.
4.7.2. Use of data from a few locations
The choice of location may strongly influence the count numbers, which may not be representative of the wider level of walking (or cycling). Results need to be
interpreted carefully, and should in general not be extrapolated beyond the locations where actual data were collected.
4.7.3. Use of trip or count data In HEAT, trip or count data need to be combined with an estimate of average trip length in order to calculate the volume of walking or cycling. An example is provided by counts conducted on a bridge, where it remains unknown how far people walk or cycle beyond the bridge. Average trip distance estimates may be derived from user surveys on a specific facility or from travel surveys.
There are several methods of estimating cycling and walking distances.
• Cyclists or pedestrians can be asked to draw their route on a map and to measure the distance.
• Cyclists and pedestrians can be asked to provide their starting and finishing points and to multiply the straight-line distance between the two points with a correction factor. One study has suggested a factor of 1.26.
• Another method is based on subjective estimates of distance travelled, although this has been shown to lead to distances being overestimated and not to be always reliable. Thus, if subjective measures are used, it is recommended that a correction be made for overestimation; a correction factor of 0.88 has been suggested.
• Making use of global positioning systems (GPS) has been shown to overestimate the distance; a correction factor of 0.95 has therefore been suggested.
• Making use of shortest- or fastest-route algorithms in geographical information systems has been shown to overestimate distance by between 12% and 21%, depending on the algorithm used. This corresponds to correction factors of 0.89 and 0.83, respectively.
4.8. What data will the tool produce?
The tool will produce an estimate of the following outputs:
• maximum annual benefit;
• mean annual benefit; and
• net present value of mean annual benefit.
The maximum annual benefit is the total value of reduced mortality due to the level of walking or cycling entered by the user. This is a maximum value, as it assumes that the maximum possible benefits to health will have occurred as a result of the entered level of walking or cycling. In reality, the health benefits are likely to accrue over time.
The mean annual benefit is therefore the key output of the model. It adjusts the maximum annual benefit (total value of lives saved due to the level of walking or cycling entered by the user) by three main factors:
• an estimate of the time it takes for the health benefits from regular walking or cycling to occur;
• a build-up period for uptake of walking or cycling, which allows the user to vary the projections in uptake if valuing a specific intervention such as for a new cycle path, and varies for full usage occurring between 1 and 50 years; and
• the net present value of mean annual benefit, which adjusts the above outputs to take the diminishing value of current savings over time into account (the model suggests a discount rate of 5% but this can be varied).
6. HEAT for cycling: instructions for users
6.2. How to use the tool: five simple steps
6.2.1. General features of the HEAT web site HEAT is composed of 16 questions in total; depending on the route you take, some questions will be skipped. On the left-hand side of the screen you will see the flow chart of questions to help you orientate where you are in the assessment process.
Click on “next question” or “back” to move between questions; do not use the back-button of your internet browser. You can also go back to a previous question by clicking on it in the flow chart of questions on the left-hand side of the screen. If you make changes, click on “save changes” before you continue.
Step 1: entering cycling data
First of all, the scope for the use of HEAT needs to be considered to make sure that is applicable for
an assessment. If HEAT is right for the study in question, a decision needs to be taken as to which of the two possible data types is going to be used for the assessment.
• Data from a single point in time are used when assessing the status quo, such as valuing current levels of cycling in a city, or if data on the results of an intervention only are available (no “before” data).
• Before and after data are used when assessing the impact of an actual intervention or hypothetical scenarios. Before and after data are required, and the tool evaluates the difference in levels of cycling between the two.
All assessments require two main parameters to be entered:
1. the amount of cycling done in the study area as duration (the most direct entry route), distance or trips per day; and
2. the number of people benefiting from this amount of cycling.
Amount of cycling: select the desired option for input data
Enter the average time spent cycling per person per day.
Enter the average time spent cycling per person per day.
המשפט דלעיל שגוי. צריך להיות נתון הקשור למרחק
If data are entered as trips, the average number of trips per person per day can be entered or the total number of trips observed in the study area (e.g. from a count of cyclists passing a sample point). If the total number of trips includes trips by modes of transport other than cycling, the mode share option can be used to take account of this by specifying the proportion of these trips that are cycling trips.
Then, either the total number of people taking these cycling trips or the proportion of these trips that are return journeys needs to be entered. For example, if 1000 trips a day are observed at a sample point, this could correspond to 1000 individuals each counted once or 500 individuals each counted twice (as they make a
return journey), or some combination of the two. Whenever possible, it is strongly recommended to use the actual number of people cycling. This is because alternative methods involve a number of assumptions, which would reduce the accuracy of the results. If the total number of people taking these trips is unknown, the tool will use the proportion of return journeys to estimate the number of individuals taking the trips. As the HEAT web site assumes that the trip data you have entered relates to a regular (i.e. daily or near-daily) pattern of cycling, the number of individual cyclists is calculated from the proportion of return journeys, using the daily average number of trips. On the HEAT for cycling web site, input is given to derive the best proportion of return journeys for different types of count data.
Finally, the duration or distance of the cycling trips has to be entered.
For all entry options, the user also has to enter how many days per year this amount of cycling is done. If this amount is done every day (or represents an average value per year, e.g. from a travel survey), 365 should be entered. However, most individuals do not cycle every day. If no long-term data are used and users are
unsure how many days they cycled in a year, 124 is recommended as a default, which is the observed number of days in Stockholm. This is a conservative value, which should be changed only if reliable local data are available, as it will influence the final calculation.
If data from a single point in time are assessed, the user can then enter the general parameters. Otherwise, users will be asked to enter the after-intervention data. They can choose to use a different metric for the after data (e.g. duration for the before data and distance for the after data).
Number of people benefiting
The tool requires information on the number of individuals doing the amount of cycling entered in the previous questions. In many cases, this figure will be the number of cyclists in the study area, city or country, or the number of people who stand to benefit from the reported levels of cycling entered if the data were entered as cycling trips (see above).
In some cases, cycling data may have been derived from a survey based on a representative sample of a larger population, where the findings apply to the whole population. For example, in the case of a national travel survey that is representative of the whole population, the total population should be used here rather than the sample size of the travel survey.
It is important to ensure that the correct population figure is entered, as this can substantially affect the resulting calculations.
Step 2: checking the cycling summary HEAT will now show a summary of the entries, allowing you to make corrections or to change entries. HEAT will also show the likely reduction in the risk of mortality in the study population, based on the entries.
Warning messages will appear here in two cases: (a) if levels of cycling have been entered that are above the suggested scope of HEAT for cycling of about 1 hour of cycling per day; and (b) if levels of cycling have been entered that would theoretically lead to very high reductions in the mortality rate.
Specifically, if an equivalent of 120 minutes or more of cycling per day is entered, users are requested to consider whether their entered volume of cycling truly represents long-term behaviour in an average adult population, as this is what HEAT is designed for. To avoid inflated values, the risk reduction available from the
HEAT is capped at 45%.
Step 3: Impact of an intervention or all current cycling?
In this step, users can decide whether they want to quantify the benefits of a current situation (or a scenario analysis) in a country, in a community or on a specific infrastructure. This means that HEAT will provide an estimate of the value of all the cycling data entered (and no build-up period for the health benefits to accrue will be applied).
If instead “impact of an intervention” is selected, the tool will ask for an estimate of the proportion of the cycling that can be attributed to the intervention. When assessing the impact of an intervention, it is prudent to assume that not all the cycling or increase in cycling observed is newly induced.
Data to estimate the proportion of newly induced cycling are rarely available. Therefore, the proportion of cycling to be attributed to the intervention (i.e. to be evaluated) needs to be estimated to the best of the user’s knowledge.
It is strongly advised to calculate various scenarios with higher and lower percentages, as this number significantly affects your results.
Note that if users wish to assess the value of an increase of cycling over time without a particular intervention, 100% should be entered.
Time needed to reach full level of cycling
This allows adjustment for the estimated time it will take to reach the full level of cycling entered. This can be particularly useful when assessing interventions. For example, if a new cycle path is built and it is estimated it will take 5 years for usage to reach a steady state, this figure should be changed to 5. The default value has been set at 1 year.
Step 4: checking the parameters
The parameters in step 4 have been set by the expert advisory group according to the best information currently available. They should be changed only if reliable local data are available, as changes to these parameters can have a significant impact on the final values.
Nevertheless, local values for the following two parameters should be used where available:
• For the value of a statistical life (in local currency), the standard value of a statisticallife used in the country of study should be entered; the preferred currency can be chosen. This will form the basis of the cost savings in the model. Whenever possible, enter a country-specific value or use a country value from the drop-down menu. If this is not known, European default values of €2.487 million (WHO European Region), €3.387 million (EU-27 countries) or €3.371 million (EU-28 countries including Croatia) can be used.
• The annual rate of the working-age population that dies each year (deaths per 100,000 people per year in the respective age group) can be derived from published mortality data for people of working age for the study country. The default value is set at the last available average for the WHO European Region according to the WHO European
Detailed Mortality Database. HEAT also provides national values as available in the WHO European Detailed Mortality Database. Users have the option to select default mortality rates for an average population (about 20–64 years old), a younger average population (about 20–45 years old) or a predominantly older average population (about 46–64 years old). It is suggested that the most recently available local rate be used wherever possible.
Users can also enter their own value. In this case, it is suggested to use the local crude annual death rate, as it reflects the age- and sex-specific mortality rates and the age and sex distribution of the population. Enter the number of deaths
per year per 100 000 people aged 20–64 years. It must be noted that HEAT is not appropriate for populations consisting mainly of children, very young adults or older people, as the underlying relative risk would not be appropriate.
The time frame for calculating mean annual benefit is the period over which the discounted mean annual benefit will be calculated. This is usually standardized within each country; the default value has been set at 10 years.
If it is known how much it cost to promote cycling in a particular case (such as a specific promotionproject or new infrastructure), and the user would like the tool to calculate a benefit–cost ratio for the local data, costs can be entered here.
The costs must include all relevant investments. For example, to assess the benefit–cost ratio of a promotion campaign for cycling, costs for the cycling infrastructure used by the target audience, which may be borne by the local
administration, will also need to be included. The time frame entered to calculate the benefit-cost ratio can differ from the time frame entered to calculate the average annual benefit.
For the discount rate, the rate to be used for calculating future benefits can be entered. Savings that occur in future years will be discounted by this percentage per year, and will be shown in the “present value” section of Step 5. A rate of 5% has been set as the default value. Common discount rates are usually available from government agencies; one option is to use interest rates on long-term government bonds.
Step 5. Reading the economic savings resulting from reduced mortality
Results are presented in three different ways.
The average annual benefit is the value of lives saved (mortality only) per year. It averages the benefit over the time frame entered to calculate the benefits. This takes into account the time periods selected for uptake of cycling and the
build up of health benefits (see also section 5.3).
In addition, the total benefit accumulated over the time period entered for averaging the result is given as well as the maximum annual benefit achieved when both health benefits and uptake of cycling have reached the maximum levels.
These should always be quoted as maximum rather than average values.
The current value of the average annual benefit is the second main output of the model, using the discount rate from Step 4 to calculate the net present value, taking into account the reduced value of benefits over time.
The current value of total benefit s accumulated over the time period entered is also shown.
If costs are entered, HEAT also provides a benefit–cost ratio.
The results of the assessment depend on a number of assumptions, which were agreed at the consensus meetings.
• The build-up of benefits is the estimated time it will take for cyclists in the model to realize the benefits in terms of mortality of the cycling entered at step 1. The default value is 5 years, based on expert consensus. If a steady-state situation is assessed (selecting “all current cycling”), no build-up period for the health benefits is applied.
• The average cycling speed is set at 14 km/h. This value is based on commuting time per week from a study in Copenhagen, combined with data from the Stockholm commuting studies on the number of trips per week over the year, distance and duration. Based on an estimated average of 4 km per trip, the observed distance–speed relationship produces an estimated average speed of 14 km/h.
• The relative risk data from the meta-analysis, which includes studies from China and Europe, can be applied to cyclists in other settings.
• There is a linear relationship between risk of death and cycling duration (assuming a constant average speed), in other words, each dose of cycling leads to the same absolute risk reduction.
• No thresholds have to be reached to achieve health benefits.
• Men and women have approximately the same level of relative risk reduction.
You are reminded that the HEAT tools provide you with an approximation of the level of health benefits. To get a better sense for the possible range of the results, you are strongly advised to rerun the model, entering slightly different values for variables where you have provided a “best guess”, such as entering high and low
estimates for such variables.