# An Analysis of the Health Effects of Physical Activity due to Active Travel Policies in Rennes, France

Henry Fremont, Samuel Younkin, Anne Roué Le Gall, Natalie Levine, Jonathan Patz

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Henry Fremont, Samuel Younkin, Anne Roué Le Gall, Natalie Levine, Jonathan Patz. An Analysis of the Health Effects of Physical Activity due to Active Travel Policies in Rennes, France. Wellcome Open Research, 2024, 9, pp.154. 10.12688/wellcomeopenres.20917.2. hal-04811281

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RESEARCH ARTICLE

**REVISED**
# An Analysis of the Health Effects of Physical Activity due to Active Travel Policies in Rennes, France

[version 2; peer review: 3 approved]

Henry Fremont<sup>1</sup>, Samuel Younkin<sup>1</sup>, Anne Roué Le Gall<sup>2</sup>, Natalie Levine<sup>1</sup>,
Jonathan Patz<sup>1</sup>

V2

First published: 19 Mar 2024, 9:154

https://doi.org/10.12688/wellcomeopenres.20917.1

Latest published: 19 Aug 2024, 9:154

https://doi.org/10.12688/wellcomeopenres.20917.2

## Abstract

### Background

Rennes, a midsize city in France, features many opportunities for active travel. City officials seek to increase walking and cycling by 2030 to improve public health. Physical inactivity, a leading risk factor for premature mortality around the globe, has been shown to be associated with many chronic diseases including heart disease, type 2 diabetes, and cancer.

### Methods

Using the 2018 household travel survey of Rennes residents, we apply the Health-Oriented Transportation statistical model to assess health impacts associated with population-level rates of walking and cycling. We consider two proposed mobility and climate objectives which outline sustainable transportation goals by 2030. These include a shift in transportation mode share to increase walking and cycling trips, as well as a broad reduction in vehicle miles traveled (VMT) across the metropolitan area.

## Results

Our regression analysis demonstrated that factors of household car access and inner-city residency were predictors of prevalence (observed one-day proportion engaging in walking or cycling), participation (weekly proportion), and intensity (mean individual physical activity achieved through walking/cycling) of active travel.

## Open Peer Review

Approval Status

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1. **Alistair Woodward**<sup>1</sup>, The University of Auckland, Auckland, New Zealand

2. **Charles Musselwhite**<sup>1</sup>, Aberystwyth University, Aberystwyth, UK

3. **Anna Porter**, Ramboll U.S. Consulting, Raleigh, USA

Any reports and responses or comments on the article can be found at the end of the article.

<footnote n="1"><sup>1</sup>Nelson Institute for Environmental Studies, University of Wisconsin-Madison, Madison, Wisconsin, USA</footnote>

<footnote n="2"><sup>2</sup>Département Santé-Environnementale, École des Hautes Études en Santé Publique, Rennes, France</footnote>
Age and education were additionally associated with prevalence. The 2030 mobility objective (mode share: 9% cycle, 35% walk) was associated with a reduction of 1,051 DALYs (disability-adjusted life-years), translating to $73 million USD ($23-$177) in averted costs. The climate objective (10% reduction in VMT) was associated with a reduction of 369 DALYs when replaced entirely by walking and 714 DALYs with cycling, translating to $26 million ($8-$62) and $50 million ($15-$121) saved, respectively.

## Conclusions

Rennes residents experience high participation in active travel, particularly those in the inner city. If residents achieve the city's active travel goals for 2030, there is potential for a large reduction in health burden and subsequent costs. Reaching these goals may require significant investment in transportation programming and infrastructure to improve active travel opportunities.

## Keywords

Health-Oriented Transportation, Active Travel, Active Transportation, Public Health, Physical Activity, Sustainability, Walking, Cycling

This article is included in the **Complex Urban Systems for Sustainability and Health: CUSSH**

**CUSSH**

collection.

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**Corresponding author:** Henry Fremont (hfremont@wisc.edu)

**Author roles:** Fremont **H**: Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Software, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing; **Youkin **S**: Conceptualization, Formal Analysis, Investigation, Methodology, Project Administration, Software, Supervision, Writing – Review & Editing; **Roué Le Gall **A**: Data Curation, Resources, Writing – Review & Editing; **Levine **N**: Writing – Review & Editing; **Patz **J**: Conceptualization, Funding Acquisition, Project Administration, Resources, Supervision, Writing – Review & Editing

**Competing interests:** No competing interests were disclosed.

**Grant information:** This project was supported by Wellcome [209387].

The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

**Copyright:** © 2024 Fremont H et al. This is an open access article distributed under the terms of the [Creative Commons Attribution License](https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**How to cite this article:** Fremont H, Youkin S, Roué Le Gall A et al. An Analysis of the Health Effects of Physical Activity due to Active Travel Policies in Rennes, France [version 2; peer review: 3 approved] Wellcome Open Research 2024, 9:154 https://doi.org/10.12688/wellcomeopenres.20917.2

**First published:** 19 Mar 2024, 9:154 https://doi.org/10.12688/wellcomeopenres.20917.1
REVISED Amendments from Version 1

- The Introduction section is updated to incorporate more background on the relationship between transport and health as well as similar models.

- With the Methods section we further justify the data age, as well as choices around subsetting to adults and USD vs Euro as our final currency cost metric.

- In the Limitations section we elaborate on the assumptions within the Mode Share Shift and Travel Time Substitution models.

- We expand on the possibility for future research which might seek to disaggregate travel and health outcomes by social or environmental variables, which were not available in the EMD data, for subgroup analysis of health equity impacts in Rennes.

Any further responses from the reviewers can be found at the end of the article

Introduction

Physical inactivity is the fourth leading risk factor for premature death across the globe¹. Insufficient physical activity is associated with higher all-cause mortality and incidence of non-communicable diseases, like cardiovascular disease and some cancers, as well as a multi-billion USD global economic burden¹⁻³. Increased physical activity can not only attenuate these health risks, but lead to health co-benefits like improved sleep quality, prevention of injuries from falls, and cognitive improvements³,⁴. Environmental co-benefits of increased physical activity, particularly through increasing active travel methods, include large reductions in carbon emissions and improved air quality⁵.

Active travel, or transportation by walking, cycling, or other active methods, can be an integral aspect of keeping a commu- nity active and healthy. Transportation is linked to urban popu- lations' health through pathways of air pollution, noise, green space, injury, and physical activity⁶. Active travel is consistently associated with reductions in all-cause mortality, cardiovascu- lar disease, and other non-communicable diseases, even after adjusting for demographic factors, recreational physical activ- ity, and other comorbidities⁷. Active travel is also associated with benefits to mental health, reduced stress, and improved social measures⁸,⁹. While benefits from active travel exist globally and across age groups, there is limited research on the differ- ent impacts across socio-economic groups and race/ethnicity populations¹⁰,¹¹.

This article aims to consider active travel-related health outcomes in an urban environment using the Health-Oriented Transportation (HOT) statistical model¹². The HOT model considers scenarios in which an urban population achieves physical activity through transportation, i.e. active travel. The model is a transparent and easily accessible tool that allows users to assess the current and potential health benefits of active travel using data from a one-day travel survey¹². These open and communicable methods allow researchers and policymakers to be informed about potential health outcomes of various transportation policies, even when there is limited data availability. Additional modeling approaches have been

developed for estimating population active travel's health impacts, such as the Health Economic Assessment Tool (HEAT) and the Integrated Transportation and Health Impacts Model (ITHIM)¹³,¹⁴. Each model includes its own set of varying assumptions and mixed methods for connecting transportation to health. Both HEAT and ITHIM incorpo- rate air pollution and road injuries to their health outcome analysis, whereas the latest HOT statistical methods are inten- tionally simplified to solely include the health benefits from physical activity associated with walking and cycling. The HOT model was recently used to explore discrepan- cies in health outcomes based on different socioeconomic characteristics¹⁰. For the current study, the HOT model is applied to data from the city of Rennes, France. Beyond our application of HOT statistical modeling to Rennes, we intro- duce novel methods which consider policy scenarios involv- ing active transportation mode share and reduction in personal vehicle use.

Rennes, a mid-size city in northwest France, features many opportunities for physical activity¹⁵. Rennes policymakers have an opportunity to greatly improve health through investment in active transportation for the nearly 300,000 adult population, as measured in 2018¹⁶. In 2022, the city committed 7% of its entire budget to a wide range of actions to promote physical activity among its citizens, such as sports facilities and active mobility interventions¹⁷. To aid attainment of the Rennes global transport policy that focuses on simultaneously reducing air pollution and promoting active mobility, the city commis- sioned the Household Travel Survey in 2018, which is used in the current analysis¹⁷. Rennes was selected for these analyses both for the cities' prioritization of transport and health as well as a recent research collaboration under the Complex Urban System for Sustainability and Health (CUSSH) project with the École des Hautes Études en Santé Publique (EHESP)¹⁸.

In addition to our assessment of walking and cycling in Rennes using the Health-Oriented Transportation statistical model, we employ novel methods to compute health impacts associated with two proposed policies affecting transporta- tion mode share in an urban environment¹⁹. We consider two policy scenarios specific to Rennes, both relating to city-wide goals by the year 2030. First, a policy which aims for a trans- portation mode share of 40% of trips by car or motorcycle, 9% of trips by cycle, 35% of trips by walking, and 16% of trips by public transit. Second, a policy which calls for the total vehicle-miles traveled (VMT) by Rennes residents to be reduced by 10% by the year 2030. We refer to these two policies as ‘Mobility’ and ‘Climate’ policies, corresponding to the mode share and VMT reduction policies, respectively.

We seek to understand the extent to which walking and cycling affect population health in Rennes. We assess the city's current state of active travel and additionally perform scenario impact analysis of proposed policies. We use regression tech- niques and the HOT model with household travel survey data from Rennes to estimate public health benefits, and hope these may assist in policymaker decisions around promoting active travel in Rennes going forward.
# Methods

## Data description

We consider the 2018 household travel survey, Enquête Ménages-Déplacements (EMD), conducted by Rennes Métropole. The survey provides a single-day snapshot of individuals’ travel activity, and is carried out approximately every 10 years using a French national standard for household travel surveys²⁰. Using survey data reported from 7,978 households, we include characteristics from 10,256 participants in our health impact analyses. The following social and environmental factors are considered from the travel survey: sex, age, education, household car access, and inner-city/suburban residency.

We formulate the Rennes household travel survey for use with the Health-Oriented Transportation model. We subset this to adults aged 18–65 to correspond to the physical activity exposure-response functions used in our health impacts analysis. We further differentiate between inner-city Rennes and the surrounding suburban area which is still within the metropolitan area limits. We differentiate these using the household-level ‘secteur de tirage’, which roughly translates to the inner-city vs. suburban districts. We label inner-city Rennes as “Commune de Rennes” and the surrounding suburban area as “Zone Suburbaine”. To determine weekly participation in active travel, we utilize questions specific to individuals’ walk and cycle frequency of use. Participants were asked how often they walk and cycle, for each selecting one from four options: several days per week, several days per month, occasionally, or never. We consider the first option to indicate individuals who participate weekly in active travel.

## HOT metrics

As seen in the London-based analysis by Younkin et al., several health-oriented transportation (HOT) metrics are introduced for estimation and communication of population-level walking and cycling analysis¹². “Active travel” is defined as physical activity as a result of transportation of the mode walk or cycle. Travel activity is the weekly rate of active travel in terms of Metabolic Equivalent per Task (MET)-hours per week. Prevalence of active travel is the observed one-day snapshot of the proportion of survey participants who engage in walking or cycling. This measure is built directly from the logged walk and cycle trips in the travel diary. Participation is the same proportion, but of active travelers on a weekly basis. This is estimated from participants’ responses to the multiple-choice questions of walk and cycle frequency (i.e., the proportion which selected ‘Several Days per Week’). We scale from the single-day travel diary to a weekly estimate of activity using a measure of active travel frequency. As the Rennes travel survey included a measure of weekly walk and cycle participation, we calculate frequency using the ratio between prevalence and participation. Travel intensity is defined as the mean individual travel activity achieved by a population’s active travelers, measured in MET-hours per week.

## Modeling

**Subgroup Analysis.** We make use of three regression models, prevalence, participation, and intensity. We include social and

environmental factors to control for confounding among predictors which may have a strong effect on active travel measures. We employ active travel metrics (prevalence, participation, intensity) used in the HOT statistical model¹². The following demographic and environmental variables are included: sex, age, education, household car access, and inner-city/suburban residency. Sex was reported as a binary variable, with participants selecting male or female. We dichotomize education and household car access. Education is split into Primary School through Secondary Baccalaureate versus Superior Baccalaureate through Apprenticeship, the latter categorized as ‘Highly Educated’. We split household car access into two categories, representing households with no personal vehicles and households with one or more personal vehicles. Survey participants who selected “NA” or did not respond to any demographic and environmental variables were not included in the analysis.

**Travel Time Substitution.** Many government entities are prioritizing the reduction of greenhouse gas emissions to combat climate change, including investment in reductions of personal vehicle usage. We introduce the Travel Time Substitution (TTS) model, developed for quantifying the increase in active travel corresponding to a population decrease in personal motor-vehicle travel and resulting reduced vehicle miles traveled (VMT). We model a substitution of time spent in motorized travel by time spent in active travel. Let $t = (t_w, t_c, t_o)$ be the average weekly time (in minutes) spent walking, cycling, or using other transportation modes (including all non-active travel modes). Of the non-active travel time being replaced ($t_o$), we assume a fraction ($\alpha$) of this time is replaced by an active mode, i.e., walk or cycle. The change in travel activity can then be expressed as follows: $\delta_{T_A} = \frac{\alpha t_o}{60(6-3\gamma_w)}$ where $\gamma_w = \frac{t_w}{t_w+t_c}$, using 3 METs for walking and 6 METs for cycling.

**Mode Share Shift.** As local and regional policymakers continue to promote walking and cycling for health and transport, mode share (i.e., the proportion of the population using each travel method) is increasingly used as a metric of success when evaluating a population’s levels of active travel. We introduce the Mode Share Shift (MSS) model, with which we estimate the change in travel activity associated with such a shift in walk and cycle mode share. Given the mode share at baseline $\alpha_b = (\alpha_w, \alpha_c, \alpha_o)$ where $\sum \alpha_j = 1$, we propose mode share of a proposed travel scenario different from baseline, $\alpha_c = (\alpha_w + \delta_w, \alpha_c + \delta_c, \alpha_o - \delta_w - \delta_c)$. Using the weekly average total number of trips, $N$, and the median duration of a trip by each mode (in minutes), $t = (t_w, t_c, t_o)$, we estimate the associated change in population travel activity as follows: $\delta_{T_A} = \frac{N}{60}(3t_w\delta_w + 6t_c\delta_c)$, again using 3 METs for walking and 6 METs for cycling.

## Health estimates

**Mortality and DALY Rates.** For estimates of 2018 mortality and disability-adjusted life year (DALY) rates in Rennes, we use the Global Burden of Disease (GBD) Results Tool, a data resource of the Global Health Data Exchange²¹. For calculating
a monetary cost corresponding to a change in travel activity and its associated health impacts, we reference the 2021 report by Daroudi et al. which estimates the cost per averted DALY based on various country-level metrics. Daroudi's report uses US Dollars (USD) as their currency to measure the cost associated with DALYs, so we continue this translation using USD as our final cost unit²².

**Simplified Comparative Risk Assessment.** Drawing on the method developed by Symonds et al., we perform a simplified comparative risk assessment by computing the population attributable fraction using the estimated population mean travel activity (rather than integrating over the distribution of physical activity, as these distributions, particularly the distribution for the scenario, are not accessible)¹⁹.

$$ \rho = \frac{R(x_b + \delta_{TA})}{R(x_b)} - 1 $$

We use all-cause mortality as our response and assume the exposure-response function is exponential on a power transformed physical activity variable (MET-hours/week), $x^p$, i.e., $R(x) = e^{-\alpha x^p}$, a similar method of linking population active transport to health impacts as with the Health Economic Assessment Tools (HEAT) and the Integrated Transport and Health Modelling Tool (ITIHIM)¹³,²³. We fit our exponential decay curve to data from Arem et al. to estimate the shape parameter, $\alpha = 0.107$, and power transformation, $p = 0.436$. The baseline physical activity for our Rennes adult population is simulated using additional data from Arem et al., resulting in a baseline level of approximately $x_b = 12$ MET-hours/week. To generate this baseline population value, we adopt both the leisure-time physical activity intervals reported by Arem et al. and their corresponding distribution across the authors' overall survey population (i.e., 0.1 to <7.5, 7.5 to <15.0 MET-hours/week). We then simulate uniform distributions within each interval with $n = 10,000$ total values, ultimately calculating the median activity for our simulated distribution of individuals and their baseline physical activity²⁴.

Moving beyond estimating averted deaths as a result of active travel scenarios, we expand our methods to include measurement of DALYs. It is challenging to model the relationship between population physical activity and DALY rates. Active travel modelers have used disease-specific ERFs in order to build an estimate of DALYs, but we have not yet found a peer-reviewed methodology explicitly linking physical activity and the rate of DALYs among a large-scale, adult, urban population⁵.

To circumvent this lack of explicit measurement for an exposure-response link between population physical activity and annual DALY rates, we model the relationship between death and DALY rates in France for the years 1990–2019 (all available years in the GBD Results Tool) for individuals aged 15–64. We model the annual proportional change in deaths versus the annual proportional change in DALYs. A visualization of the death and DALY rates for those years is shown in

**Figure 1.** A linear regression between annual proportional change in deaths and DALYs, with intercept term set to zero, results in a statistically significant linear association between the two, with the model coefficient approximately equal to 0.45 (95% CI: 0.41–0.5). As a result, for aged 15–64 individuals in France, we estimate that a 1% reduction in all-cause mortality would correspond to a 0.45% reduction in DALYs.

Building on this modeled relationship between deaths and DALYs for France, we incorporate this translation to our simplified comparative risk assessment. We utilize the Arem curve for all-cause mortality to estimate an active travel scenario's associated impact on the Rennes population death rate by performing our simplified comparative risk assessment calculation as previously outlined. From this, we additionally compute the associated proportional change in Rennes' rate of DALYs using our demonstrated linear association between adult death and DALY rates in France.

Increased physical activity is linked with benefiting a variety of noncommunicable health outcomes¹. While this translation between all-cause mortality and disability-adjusted years of life is by no means an exact measure, we acknowledge that all-cause mortality and DALYs are not built via identical algorithms, but are intended as large-scale umbrella metrics for gauging population health. Through our modeling approach we seek to translate health impacts not only to averted deaths, but averted DALYs, to better capture health impacts associated with active travel in Rennes. With a more comprehensive understanding of physical activity's population health impacts, we show how active travel affects a more inclusive subsection of a population's health ecosystem.

# Results

## Participant characteristics

Introductory summary statistics of participant characteristics are included in Table 1. Among 10,256 survey participants, 1,584 (15%) individuals resided in Commune de Rennes while 8,672 (85%) individuals resided in Zone Suburbaine. When considering sex, age, and education, the darkest difference was observed across age of Commune de Rennes versus Zone Suburbaine residents, with mean ages of 40 (SD = 13) and 46 (12) years, respectively. Education, in this table, includes all possible six categories, rather than its dichotomized values used in the regression analysis. When considering household car access, individual walk frequency, and cycle frequency, all three factors featured differences across inner-city and suburban survey participants. Perhaps most alarming are the reported rates of cycling frequency for 'Several Days per Year' and 'Never', totaling 76% and 84% of all individuals for both Commune de Rennes and Zone Suburban, respectively. Additionally, the lower prevalence of 'Several Days per Week' walk and cycle frequency among Zone Suburbaine residents (55% walk, 7.7% cycle) relative to Commune de Rennes residents (76% walk, 17% cycle) could be another possible point of focus for local health professionals.
<figure><img src="image_10.png" /></figure>

**Figure 1.** Modeling the relationship between France's annual proportional change in deaths and DALYs for individuals aged 15-64 in years 1990-2019, Global Burden of Disease Results Tool. In addition to the visualization of death and DALY rates for those years, we include linear regression results (with intercept set to zero) demonstrating the statistically significant association between the two metrics.

<table><caption>**Table 1.** Participant Characteristics, Rennes Household Travel Survey 2018. Statistics are split between Rennes' inner city ("Commune de Rennes") and outer suburban area ("Zone Suburbaine").</caption><thead><tr><th colspan="3">Participant Characteristics, EMD 2018</th></tr><tr><th rowspan="2">Characteristic</th><th colspan="2">Rennes Métropole</th></tr><tr><th>Commune de Rennes, N = 1,584<sup>1</sup></th><th>Zone Suburbaine, N = 8,672<sup>1</sup></th></tr></thead><tbody><tr><td><strong>Sex</strong></td><td></td><td></td></tr><tr><td>M</td><td>756 (48%)</td><td>4,154 (48%)</td></tr><tr><td>F</td><td>828 (52%)</td><td>4,158 (52%)</td></tr><tr><td><strong>Age</strong></td><td>40 (13)</td><td>46 (12)</td></tr><tr><td><strong>Education</strong></td><td></td><td></td></tr><tr><td>Primary School</td><td>18 (1.3%)</td><td>120 (1.4%)</td></tr><tr><td>Secondary, from 6th to 3rd</td><td>170 (12%)</td><td>1,480 (18%)</td></tr><tr><td>Secondary, beyond 2nd Without Completing Baccalaureate</td><td>140 (10%)</td><td>1,350 (16%)</td></tr><tr><td>Secondary Baccalaureate</td><td>199 (15%)</td><td>1,527 (18%)</td></tr><tr><td>Superior Baccalaureate, up to 2</td><td>217 (16%)</td><td>1,561 (19%)</td></tr><tr><td>Superior Baccalaureate, 3+ OR Apprenticeship</td><td>623 (46%)</td><td>2,327 (28%)</td></tr></tbody></table>
<table><thead><tr><th colspan="3">Participant Characteristics, EMD 2018</th></tr><tr><th rowspan="2">Characteristic</th><th colspan="2">Rennes Métropole</th></tr><tr><th>Commune de Rennes, N = 1,584<sup>1</sup></th><th>Zone Suburbaine, N = 8,672<sup>1</sup></th></tr></thead><tbody><tr><td><strong>Access to Car</strong></td><td></td><td></td></tr><tr><td>Yes</td><td>607 (71%)</td><td>3,391 (93%)</td></tr><tr><td>No</td><td>253 (29%)</td><td>260 (7.1%)</td></tr><tr><td><strong>Walk Frequency</strong></td><td></td><td></td></tr><tr><td>Several days per week</td><td>935 (76%)</td><td>2,990 (55%)</td></tr><tr><td>Several days per month</td><td>114 (93%)</td><td>674 (12%)</td></tr><tr><td>Several days per year</td><td>134 (11%)</td><td>1,137 (21%)</td></tr><tr><td>Never</td><td>46 (3.7%)</td><td>642 (12%)</td></tr><tr><td><strong>Cycle Frequency</strong></td><td></td><td></td></tr><tr><td>Several days per week</td><td>203 (17%)</td><td>420 (7.7%)</td></tr><tr><td>Several days per month</td><td>101 (8.2%)</td><td>404 (7.4%)</td></tr><tr><td>Several days per year</td><td>216 (18%)</td><td>1,707 (31%)</td></tr><tr><td>Never</td><td>709 (58%)</td><td>2,912 (53%)</td></tr></tbody></table>

## Prevalence & participation

Active travel prevalence is calculated independently across all characteristics included in these analyses with weighted estimates. The overall prevalence was 24.7% (95% confidence interval: 23.7%, 25.8%). The most dramatic differences in prevalence were observed across household car access and location (inner-city vs. suburban residency). Individuals with no access to cars demonstrated an active travel prevalence of 65.3% (60.2%, 70.3%) compared to 28.6% (26.9%, 30.3%) among individuals with household access to one or more cars. Survey participants living in the inner-city, Commune de Rennes, reported a prevalence of 43.2% (40.3%, 46.2%) compared to Zone Suburbaine residents reporting 19.8% (18.7%, 20.8%).

Participation in active travel is synthesized using individuals who answered “Several Days per Week” for walking or cycling in the two survey questions and is calculated across all characteristics with weighted estimates. The overall participation was 64.5% (63.1%, 65.9%), dramatically different from the prevalence values calculated from recorded walk and cycle trips. The participation calculation excludes those who did not answer the two questions about walk and cycle frequency (54% of participants did not answer either question). This, or low frequency of weekly active travel, may account for these differences across prevalence and participation. Digging deeper into participation and differentiating between walking and cycling provides additional insight. Reported individual

cycling participation, 9.7% (8.8%, 10.6%), is similar to the observed prevalence of cycling, 3.6% (3.2%, 4.1%). Examining individual weekly walk rates, however, there is a larger difference between the reported walk participation, 62.1% (60.6%, 63.5%), and the observed prevalence of walking, 46.1% (44.9%, 47.3%). If we examine the ratios between prevalence and participation (aka the HOT active travel metric ‘frequency’), these may provide more useful information. We observe the frequency for cycling to be about 0.4, whereas the same value for walking is about 0.7. This may somewhat explain the difference between observed and subjective measures of active travel in Rennes, making the raw differences less dramatic from how they may appear at first glance.

Performing a weighted chi-squared test of association for both prevalence and participation yielded statistically significant results for both household car access and inner-city residency. Additionally, education was associated with active travel prevalence. Number of survey participants in each household was added to the analysis for further insight and was not shown to be significantly associated with prevalence or participation.

## Regression analysis

Results from multivariate regression analysis are presented in Table 2, including outcomes of prevalence (multivariate logistic regression), participation (multivariate logistic regression), and intensity (multivariate log-linear regression) of active travel. The factors of household car access and inner-city residents

<footnote n="1">¹n (%); Mean (SD)</footnote>
<table><caption>**Table 2.** Multivariate Regression Analysis of Individual Factors with Prevalence, Participation, and Intensity, Rennes Household Travel Survey 2018. Split into three regression models, highlighted rows demonstrate factors which demonstrated statistical significance. Parentheticals include 95% confidence intervals.</caption><thead><tr><th colspan="3">Multivariate Logistic Regression: Active Travel Prevalence, Participation, and Intensity Rennes Métroploe Household Travel Survey, 2018</th></tr><tr><th></th><th>Odds Ratio<sup>1</sup></th><th>p-value</th></tr></thead><tbody><tr><td><strong>Prevalence</strong></td><td></td><td></td></tr><tr><td>Female</td><td>1.07 (0.93-1.22)</td><td>0.369</td></tr><tr><td>Age</td><td><strong>1.02 (1.01-1.02)</strong></td><td><strong>&lt; 1e-3</strong></td></tr><tr><td>Highly Educated</td><td><strong>1.46 (1.27-1.69)</strong></td><td><strong>&lt; 1e-3</strong></td></tr><tr><td>No Access to Car</td><td><strong>4.37 (3.42-5.57)</strong></td><td><strong>&lt; 1e-3</strong></td></tr><tr><td>Commune de Rennes Resident</td><td><strong>2.31 (1.93-2.75)</strong></td><td><strong>&lt; 1e-3</strong></td></tr><tr><td><strong>Participation</strong></td><td></td><td></td></tr><tr><td>Female</td><td>0.94 (0.83-1.06)</td><td>0.322</td></tr><tr><td>Age</td><td>1.01 (1-1.01)</td><td>0.105</td></tr><tr><td>Highly Educated</td><td>1.08 (0.95-1.23)</td><td>0.244</td></tr><tr><td>No Access to Car</td><td><strong>5.63 (4-7.92)</strong></td><td><strong>&lt; 1e-3</strong></td></tr><tr><td>Commune de Rennes Resident</td><td><strong>2.73 (2.24-3.32)</strong></td><td><strong>&lt; 1e-3</strong></td></tr><tr><td><strong>Intensity</strong></td><td></td><td></td></tr><tr><td>Female</td><td>-0.05 (-0.16-0.08)</td><td>0.446</td></tr><tr><td>Age</td><td>0 (0-0.01)</td><td>0.12</td></tr><tr><td>Highly Educated</td><td>0.09 (-0.04-0.25)</td><td>0.173</td></tr><tr><td>No Access to Car</td><td><strong>0.56 (0.32-0.85)</strong></td><td><strong>&lt; 1e-3</strong></td></tr><tr><td>Commune de Rennes Resident</td><td><strong>1.74 (1.38-2.16)</strong></td><td><strong>&lt; 1e-3</strong></td></tr></tbody></table>

were significant in all three regression models, while age and education (dichotomized) were additionally predictors of prevalence. For all significant factors in the three models, the resulting coefficient, whether odds ratio (prevalence, participation) or proportional change in the log-transformed coefficient (intensity), represented a positive association with active travel.

### Policy objective: mobility

We consider a Rennes policy scenario designed to increase active modes of transport, specifically walking and cycling. For impact analysis of this policy, we apply the TTS and MSS models. The proposed policy would have Rennes' mode share, by the year 2030, achieve the following: 40% of all transportation trips by car or motorcycle, 9% of trips by bicycle, 35% of trips by walk, and 16% of trips by public transit. To begin, we estimate the 2018 mode share of Rennes' metropolitan

area. This is shown in Figure 2, displaying the 2018 mode share by trip count and the 2030 mobility objective's goal, as well as a 2018 comparison between the mode shares of subregions Commune de Rennes (City of Rennes) and Zone Suburbaine (Surrounding suburban area).

From the mode share comparison, we may observe that in order to reach the 2030 goal, the most dramatic change is required in Zone Suburbaine; in most modes Commune de Rennes is already achieving the 2030 goal. We see that Zone Suburbaine has significant ground to gain in walking, cycling, and public transit.

In Table 3, we apply the Mode Share Shift (MSS) model to the 2030 mode share scenario of 35% of trips by walk and 9% by cycle. This translates to a total of 3,319 new daily trips by walking and 1,994 trips by cycle. We estimate that the associated mean individual change in weekly activity is approximately 3.91 MET-hours/week (~40 mins of cycling/week). If Rennes were to achieve such a shift in active travel mode share usage, we estimate that this increase in activity is associated with averting 25 deaths per year or 1,051 DALYs. These averted DALYs translate to averting $73 million USD ($23–$171) in associated costs.

### Policy objective: climate

We consider a Rennes policy scenario in which vehicle miles traveled (VMT) are reduced by 10% by the year 2030. To estimate such a policy's corresponding change in walking and cycling, and associated health impacts, we apply the Travel Time Substitution (TTS) model. In Table 3, we include 4 scenarios of VMT replacement: 100% cycling (an upper bound for possible new travel activity), 80% cycling and 20% walking (the 2030 mode share goal outlined in the mobility policy), 89% walking and 11% cycling (the current 2018 survey's observed ratio of cycling to walking by trip count), and 100% walking (a lower bound for possible new travel activity). The new travel activity due to this policy scenario is shown as relative proportions of new walk and cycle trips, compared to the mobility policy objective which lists the raw total new walk and cycle trips.

For this climate objective, we estimate that the mean individual change in weekly travel activity ranges from ~1.3 MET-hours/week (30 mins of walking/week) to ~2.6 MET-hours/week (30 mins of cycling/week), corresponding to full replacement of the VMT each by walking and cycling, respectively. When 10% of Rennes' VMT is replaced entirely by time spent walking, we estimate that this increase in activity is associated with averting 9 deaths per year or 369 DALYs. These averted DALYs translate to saving $26 million USD ($8–$62) in associated costs. When 10% of Rennes' VMT is replaced entirely by time spent cycling, we estimate that this increase in activity is associated with averting 17 deaths per year or 714 DALYs. These averted DALYs translate to $50 million USD ($15–$121) saved. This range of measures, representing VMT replacement by some division of walking and cycling, can serve to provide lower and upper bounds for health impacts of similar transportation policies.

<footnote n="1">¹ For Intensity, Proportional Change in Log(Intensity) is reported rather than OR.</footnote>
<figure><img src="image_11.png" /></figure>

**Figure 2.** Mode Share Visualization, 2018 Rennes vs 2030 Mobility Objective, Rennes Household Travel Survey 2018. In addition to visualizing the 2018 and proposed 2030 mode shares for the entire Rennes metropolitan area, figure includes Rennes' 2018 inner-city ("Commune de Rennes") and suburban ("Zone Suburbaine") comparison for greater context. The Mobility Objective's proposed 2030 mode share aims for 40% of trips to be taken by car or motorcycle, 9% by cycle, 35% by walk, and 16% by public transit.

**Table 3.** Health and Economic Impacts of 2030 Active Travel Policies, Rennes Household Travel Survey 2018. Impacts are split into sections for each policy. The Mobility Objective section displays the impacts due to an increase in walk/cycle trips required to meet the target mode share. Within this section, the first two columns display the increase in daily walk/cycle trips associated with the Mobility Objective (proposed trip mode share of 40% car or motorcycle, 9% cycle, 35% walk, and 16% public transit). The Climate Objective requires 10% of Vehicle Miles Traveled (VMT) to be replaced by active modes. We illustrate this replacement by walk and cycle modes through various possible proportions within that replacement, each shown with their associated impacts (see first two columns). The replacement proportion of 0.89 by walk and 0.11 by cycle is representative of the current 2018 mode share in Rennes, and the 0.80/0.20 proportions correspond to the Mobility Objective's proposed goal mode share for 2030.

<table><thead><tr><th>Walk</th><th>Cycle</th><th>Change in Mean Travel Activity (MET-hrs/week)</th><th>Averted Deaths</th><th>Averted DALYs</th><th>Cost Reduction (USD, millions)</th></tr></thead><tbody><tr><td colspan="6"><strong>Mobility Objective: New Walk and Cycle Trips</strong></td></tr><tr><td>3,319</td><td>1,994</td><td>3.91</td><td>25</td><td>1,051</td><td>$73 (23-177)</td></tr><tr><td colspan="6"><strong>Climate Objective: Proportion of VMT Replacement by Walking and Cycling</strong></td></tr><tr><td>0.00</td><td>1.00</td><td>2.57</td><td>17</td><td>714</td><td>$50 (15-121)</td></tr><tr><td>0.80</td><td>0.20</td><td>1.55</td><td>10</td><td>442</td><td>$31 (10-75)</td></tr><tr><td>0.89</td><td>0.11</td><td>1.43</td><td>10</td><td>409</td><td>$28 (9-69)</td></tr><tr><td>1.00</td><td>0.00</td><td>1.28</td><td>9</td><td>369</td><td>$26 (8-62)</td></tr></tbody></table>
# Discussion

## Conclusion

With both univariate and multivariate regression analyses of Rennes subgroups, two characteristics arose as particularly influential predictors of travel activity: household car access and inner-city residency. We observed low rates of cycling among suburban survey participants. Additionally, household car access was dramatically higher in the surrounding suburban area, whose residents were also, on average, older.

Considering the 2030 policy objectives for Rennes, among suburban residents there will need to be substantive increases in active travel, particularly with cycling, in order to achieve such transformative change in both active travel modes and reduction of VMT. Achieving these goals provides a significant opportunity for not only reducing health burden on Rennes residents, but also for averting large associated costs ($104 million USD saved between both policies). Although increased active travel will require individual behavior change across populations, these improvements will not be possible without measures to support suburban active travel through innovations in infrastructure or other transportation programming.

## Limitations

The all-cause mortality dose-response curves used in the HOT statistical model are developed from large-scale cohort study data specific to adults, thus we restrict our analysis to Rennes individuals aged 18–65²⁴. If an increase in walking and cycling is achieved among adults aged 18–65, it would not be naive to suggest that there may be some additional activity by those outside that age range who continue to engage in regular transportation trips. Additionally, the modeled relationship between death and DALY rates is based on French adults aged 15–64 in years 1990–2019. These ages do not exactly align with the 18–65 restriction of Rennes but represent the closest approximation using available data to match the same population’s demographic makeup.

We observe a difference between prevalence, built from logged walk or cycle trips in the one-day travel diary, and participation in active travel, synthesized from two individual-based survey questions about how often each individual engages in walking and cycling. Even after differentiating active travel between walking and cycling, the discrepancy between trip-based and questionnaire-based survey subsections remains. We observe much higher participation rates compared to prevalence rates and attribute this to low active travel frequency, demonstrating that a one-day trip diary does not sufficiently measure a population’s travel activity. Additionally, this difference between participation and prevalence could be due to the subjectivity of the individual-based walk/cycle participation questions, social desirability bias, or the possibility that participants may be including negligible walk trips in their assessment of how often they walk (e.g., walks from one’s apartment to the bus stop outside, or from one’s house to the household car parked outside) which were not logged in the trip diary.

The MSS and TTS models require several underlying assumptions. First, with all mode share shifts considered in these analyses, the population's total number of transportation trips is kept

constant. As Rennes officials' mobility policy objective outlined mode share, a proportional measure, as its outcome of choice, maintaining a constant value for total trips avoids an additional level of complexity in how the population might achieve the policy's desired increase in walking and cycling mode share proportions. Without this constant trip total, we might consider scenarios where Rennes simply increases their number of walk or cycle trips (alongside no change in other modes), or conversely, simply decreases their non-walk and non-cycle trips (with no change in walking or cycling, meaning no change in travel activity). By keeping total transportation trips constant, we choose to avoid this ambiguity and additional dimension of analysis, though challenging this assumption provides an interesting possibility for future research. Second, for the climate policy objective's VMT reduction we assume a full replacement by active modes walk and cycle, with no change in public transportation use by Rennes residents. We incorporate this assumption for similar reasoning as with mode share shift. Reduction in vehicle use may not directly translate to any change in active travel modes, but our analysis is concerned with just that: the health impacts of scenarios in which the VMT reduction does directly translate to an increase in walking and cycling. The additional dimensionality added by incorporating public transit is interesting but requires a level of complexity not pursued in this analysis. Future research in health-oriented transportation might consider spreading VMT reduction scenarios across walking, cycling, as well as public transit.

Analysis of Rennes' current state of active travel might seek to utilize data more recent than 2018, but the frequency of which these EMD surveys are conducted by Rennes Métropole (historically every 10 years) led our research team to be satisfied with the 2018 travel survey's ability to capture active travel behavior in Rennes. More recently, Buehler et al. demonstrate that in France cycling increased in 2020 compared to levels in 2019²⁵. Through research collaboration with Rennes Métropole, we are grateful for the opportunity to access these 2018 data to better understand how physical activity through travel impacts health in Rennes.

The EMD data did not include many social or environmental variables such as race, ethnicity, income, or disability status, some of which have been shown to affect participation in active travel¹⁰. Incorporation of these variables in the household survey could allow researchers a subgroup-level measure of access to active travel, which in turn may contribute to health inequity. Active travel researchers in the future may seek to disaggregate the population health outcomes by subgroups, giving additional valuable insight into how transport affects health inequity within Rennes. This would require additional statistical tuning; while subgroups' population totals might be usable to scale the overall health impacts to specific social or environmental variables, this avenue also invites questions around the baseline distribution of physical activity of the sample population, and at this time we are not aware of any studies which measure Rennes residents' physical activity habits at the subgroup level.

## Public health implications

Walking and cycling mode share and distance traveled are both useful measures when considering the impact of transportation
choices on population health. The city of Rennes' goal of increasing active travel by 2030 is an important step towards improving public health. To reach their specified goals, this requires an emphasis on providing safe and easy access to active travel in the outer city. Policies targeting VMT reduction, as well as those directly aimed at changes in mode share, will not only help improve air quality and mitigate climate change, which is perhaps the primary motivation of the policies, but will also improve public health through physical activity and save money via these health impacts' significant associated costs. Other urban policy makers would do well to adopt Rennes' strategy to improve health through transportation. Even by simply reducing or replacing short trips in personal vehicles, there are dramatic health benefits that a population may achieve by investing in active travel²⁶.

## Data availability

### Underlying data

Data used in this study is licensed by a third party and is not freely available to the public. All travel diary data were de-identified prior to procurement by our research team and are licensed and maintained by the Rennes Métropole

organization. Data were shared with our research team as part of our collaboration in the Complex Urban Systems for Sustainability and Health (CUSSH) Consortium alongside researchers from the École des Hautes Études en Santé Publique (EHESP), who facilitated procurement of these travel diary data for Rennes.

## Software availability

Source code available from: https://gitlab.com/GHI-UW/hot.

Archived source code at time of publication: https://doi.org/10.5281/zenodo.10436883²⁷.

## Acknowledgements

We would like to gratefully recognize the Complex Urban Systems for Sustainability and Health (CUSSH) Consortium supported by the Wellcome Trust as well as the Nelson Institute for Environmental Studies at the University of Wisconsin-Madison for their support and contributions towards completing this manuscript. Additionally, we would like to express gratitude toward the École des Hautes Études en Santé Publique (EHESP) for their role in facilitation and procurement of the Rennes travel data used in this study.

## References

1. Lee IM, Shiroma EJ, Lobelo F, et al.: Effect of physical inactivity on major Non-Communicable Diseases worldwide: An analysis of burden of disease and life expectancy. *Lancet*. 2012; 380(9838): 219-229.
*PubMed Abstract* | *Publisher Full Text* | *Free Full Text*

2. Ding D, Lawson KD, Kolbe-Alexander TL, et al.: The economic burden of physical inactivity: a global analysis of major Non-Communicable Diseases. *Lancet*. 2016; 388(10051): 1311-1324.
*PubMed Abstract* | *Publisher Full Text*

3. Katzmarzyk PT, Friedenreich C, Shiroma EJ, et al.: Physical inactivity and Non-Communicable Disease burden in low-income, middle-income and high-income countries. *Br J Sports Med*. 2022; 56(2): 101-106.
*PubMed Abstract* | *Publisher Full Text* | *Free Full Text*

4. Bull FC, Al-Ansari SS, Biddle S, et al.: World Health Organization 2020 guidelines on physical activity and sedentary behaviour. *Br J Sports Med*. 2020; 54(24): 1451-1462.
*PubMed Abstract* | *Publisher Full Text* | *Free Full Text*

5. Maizlish N, Rudolph L, Jiang C: Health benefits of strategies for carbon mitigation in US transportation, 2017–2050. *Am J Public Health*. 2022; 112(3): 426-433.
*PubMed Abstract* | *Publisher Full Text* | *Free Full Text*

6. Nieuwenhuizen MJ, Khreis H, Verlinghieri E, et al.: Transport and health: a marriage of convenience or an absolute necessity. *Environ Int*. 2016; 88: 150-152.
*PubMed Abstract* | *Publisher Full Text*

7. Dinu M, Pagliai G, Macchi C, et al.: Active commuting and multiple health outcomes: a systematic review and meta-analysis. *Sports Med*. 2019; 49(3): 437-452.
*PubMed Abstract* | *Publisher Full Text*

8. Martin A, Goryakin Y, Suhrcke M: Does active commuting improve psychological wellbeing? longitudinal evidence from eighteen waves of the British household panel survey. *Prev Med*. 2014; 69: 296-303.
*PubMed Abstract* | *Publisher Full Text* | *Free Full Text*

9. Avila-Palencia I, Int Panis L, Dons E, et al.: The effects of transport mode use on self-perceived health, mental health, and social contact measures: a cross-sectional and longitudinal study. *Environ Int*. 2018; 120: 199-206.
*PubMed Abstract* | *Publisher Full Text*

10. Younkin S, Fremont H, Bratburd J, et al.: The influence of socioeconomic characteristics on active travel in US metropolitan areas and the contribution to health inequity [version 1; peer review: 1 approved, 1 approved with reservations]. *Wellcome Open Res*. 2023; 8: 266.
*Publisher Full Text*

11. Hansmann KJ, Grabow M, McAndrews C: Health equity and active transportation: a scoping review of active transportation interventions and their impacts on health equity. *J Transp Health*. 2022; 25: 101346.
*Publisher Full Text*

12. Younkin SG, Fremont HC, Patz JA: The health-oriented transportation model: estimating the health benefits of active transportation. *J Transp Health*. 2021; 22: 101103.
*Publisher Full Text*

13. Woodcock J, Givoni M, Morgan AS: Health impact modelling of active travel visions for England and wales using an Integrated Transport and Health Impact Modelling Tool (ITHIM). *PLoS One*. 2013; 8(1): e51462.
*PubMed Abstract* | *Publisher Full Text* | *Free Full Text*

14. Health Economic Assessment Tool (HEAT) for walking and for cycling: v5.2. Methods and user guide on physical activity, air pollution, injuries and carbon impact assessments. 2023; accessed Jul. 12, 2024.
*Reference Source*

15. Office de Tourisme: Destination Rennes. Destination Rennes: Office de Tourisme. Accessed October 19, 2022.
*Reference Source*

16. Comparateur de territoires – Comparez les territoires de votre choix - Résultats pour Intercommunality-Metropolis of Rennes Métropole (243500139) Insee. Institut national de la statistique et des études économiques (INSEE). September 22, 2022; Accessed October 19, 2022.
*Reference Source*

17. Rennes Métropole: Se déplacer à vélo. Accessed September 14, 2023.
*Reference Source*

18. Davies M, Belesova K, Crane M, et al.: The CUSSH programme: supporting cities' transformational change towards health and sustainability [version 2; peer review: 2 approved], *Wellcome Open Res*. 2021; 6: 100.
*PubMed Abstract* | *Publisher Full Text* | *Free Full Text*

19. Symonds P, Milner J, Mohajeri N, et al.: A tool for assessing the climate change mitigation and health impacts of environmental policies: the Cities
Rapid Assessment Framework for Transformation (CRAFT) [version 2; peer review: 3 approved]. *Wellcome Open Res.* 2021; 5: 269.

PubMed Abstract | Publisher Full Text | Free Full Text

20. Rennes Métropole: Enquête Ménages - Déplacements en Ille-et-Vilaine 2018. Published online February 2019.

Reference Source

21. Institute for Health Metrics and Evaluation (IHME): Global Health Data Exchange. Global Burden of Disease Study 2019 (GBD 2019) Data Resources. GHDX. Accessed October 19, 2022.

Reference Source

22. Daroudi R, Akbari Sari A, Nahvijou A, et al.: Cost per DALY averted in low, middle- and high-income countries: evidence from the global burden of disease study to estimate the cost-effectiveness thresholds. *Cost Eff Resour Alloc.* 2021; 19(1): 7.

PubMed Abstract | Publisher Full Text | Free Full Text

23. Kahlmeier S, Cavill N, Dinsdale H, et al.: Health Economic Assessment Tools

(HEAT) for walking and for cycling. 2011; Accessed December 4, 2023.

Reference Source

24. Arem H, Moore SC, Patel A, et al.: Leisure time physical activity and mortality: a detailed pooled analysis of the dose-response relationship. *JAMA Intern Med.* 2015; **175**(6): 959-967.

PubMed Abstract | Publisher Full Text | Free Full Text

25. Buehler R, Pucher J: COVID-19 impacts on cycling, 2019–2020. *Transport Rev.* 2021; **41**(4): 393–400.

Publisher Full Text

26. Grabow ML, Spak SN, Holloway T, et al.: Air quality and exercise-related health benefits from reduced car travel in the Midwestern United States. *Environ Health Perspect.* 2012; **120**(1): 68–76.

PubMed Abstract | Publisher Full Text | Free Full Text

27. Fremont H, Younkin S: Analysis of Active Travel in Rennes, FR - Source Code. Zenodo. 2023.

http://www.doi.org/10.5281/zenodo.10436883
# Open Peer Review

Current Peer Review Status:

<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>

## Version 2

Reviewer Report 03 December 2024

https://doi.org/10.21956/wellcomeopenres.25316.r111733

© 2024 **Porter A.** This is an open access peer review report distributed under the terms of the [Creative Commons Attribution License](https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

<figure><img src="image_15.png" /></figure>

### Anna Porter

Biostatistics, Ramboll U.S. Consulting, Raleigh, USA

This is a well written and comprehensive examination of the health effects of policy related proposed change in active travel in Rennes, France.

I have a few minor suggestions: In the results section, restate what the definitions of prevalence as well as participation are.

State the direction of the associations between sociodemographic factors (car access, inner-city residency, education) and active travel prevalence. Are these associations presented in a table?

Numbers corresponding to these associations would also be useful, either in a table/figure or in the text.

Is it stated anywhere how statistical significance is defined?

Table 2 is confusing with OR as the heading, and intensity being proportional change. The footnote I find insufficient for addressing this. Would consider other ways to present the findings, such as presenting the three models in columns rather than rows (wide rather than long).

**Is the work clearly and accurately presented and does it cite the current literature?**

Yes

**Is the study design appropriate and is the work technically sound?**

Yes

**Are sufficient details of methods and analysis provided to allow replication by others?**

Yes

**If applicable, is the statistical analysis and its interpretation appropriate?**

Yes

**Are all the source data underlying the results available to ensure full reproducibility?**

Yes

**Are the conclusions drawn adequately supported by the results?**
Yes

**Competing Interests:** No competing interests were disclosed.

**Reviewer Expertise:** Physical activity epidemiology, active transportation, bicycling

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

Reviewer Report 05 September 2024

https://doi.org/10.21956/wellcomeopenres.25316.r94013

© 2024 Musselwhite C. This is an open access peer review report distributed under the terms of the [Creative Commons Attribution License](https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Charles Musselwhite ID

Department of Psychology, Aberystwyth University, Aberystwyth, Wales, UK

I am satisfied with the changes made to the article which have improved the context of this important study. I visited Rennes in the summer (not for this peer review, haven't gone that far for the cause yet!). I was impressed by how easy it was to walk around the centre. No need for a car at all! But the outskirts (outer city) were pretty car dominated. I wish Rennes the best with the recommendations from this excellent study.

**Is the work clearly and accurately presented and does it cite the current literature?**

Yes

**Is the study design appropriate and is the work technically sound?**

Yes

**Are sufficient details of methods and analysis provided to allow replication by others?**

Yes

**If applicable, is the statistical analysis and its interpretation appropriate?**

Yes

**Are all the source data underlying the results available to ensure full reproducibility?**

Yes

**Are the conclusions drawn adequately supported by the results?**

Yes

**Competing Interests:** No competing interests were disclosed.
**Reviewer Expertise:** Environmental and Community Psychology. Transport and health. Transport and ageing. Social research methods.

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

## Version 1

Reviewer Report 13 June 2024

https://doi.org/10.21956/wellcomeopenres.23143.r84551

© 2024 Musselwhite C. This is an open access peer review report distributed under the terms of the [Creative Commons Attribution License](https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

<figure><img src="image_17.png" /></figure>

Charles Musselwhite

<figure><img src="image_18.png" /></figure>

Department of Psychology, Aberystwyth University, Aberystwyth, Wales, UK

This is a very well written paper documenting a new approach to identify potential health outcomes of transport policies and interventions. A slightly elaborated introduction to the paper would be very interesting around two points (1) what other transport and health models are there that could have been used and how does HOT develop it and; (2) bring out the known relationships between transport and health in more detail.

More detail on the methods would be useful: The household survey data used is 6 years out of date, so some discussion on why this is being used is needed and some discussion on what might have changed in terms of the data over the past 6 years is needed. Why only use data on 18-65 year old's? Older people being excluded from analyses is Why is US dollars rather than Euros used for health costs and benefits?

The findings are discussed well.

In the limitations, I understand the models have assumptions but maybe a comment on how realistic such assumptions are and how much they might influence the model outcomes would be useful (e.g. "First, with all mode share shifts considered in these analyses, the population's total number of transportation trips is kept constant. Second, for the climate policy objective's VMT reduction we assume a full replacement by active modes walk and cycle, with no change in public transportation use by Rennes residents.")

A comment on what would it take to disaggregate the outcomes by sub-groups of the population would be useful given how transport and health disadvantage follow one another.

**Is the work clearly and accurately presented and does it cite the current literature?**

Partly

**Is the study design appropriate and is the work technically sound?**

Yes
**Are sufficient details of methods and analysis provided to allow replication by others?**

Yes

**If applicable, is the statistical analysis and its interpretation appropriate?**

Yes

**Are all the source data underlying the results available to ensure full reproducibility?**

Partly

**Are the conclusions drawn adequately supported by the results?**

Yes

**Competing Interests:** No competing interests were disclosed.

**Reviewer Expertise:** Environmental and Community Psychology. Transport and health. Transport and ageing. Social research methods.

**I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.**

Author Response 14 Aug 2024

**Henry Fremont**

We thank the reviewer for their comment about expanding the introduction to include information around existing other transport and health models, as well as more evidence surrounding the relationship between transport and health.

To address the comment with transport and health models, we have added the following text to the Introduction section's third paragraph:

> "Additional modeling approaches have been developed for estimating population active travel's health impacts, such as the Health Economic Assessment Tool (HEAT) and the Integrated Transportation and Health Impacts Model (ITHIM). Each model includes its own set of varying assumptions and mixed methods for connecting transportation to health. Both HEAT and ITHIM incorporate air pollution and road injuries to their health outcome analysis, whereas the latest HOT statistical methods are intentionally simplified to solely include the health benefits from physical activity associated with walking and cycling."

For these two models' citations, and for all references going forward added as a result of this revision, we note these new references in brackets alongside a corresponding citation added in the References section at the end of the document (e.g., "[NEW REFERENCE: HEAT, 2013]"). We do not include these bracketed statements in this response to reviewer document so as to avoid clutter.

We further include the following text at the end of the same paragraph to better illustrate how this analysis introduces novel methods compared to previous applications of HOT methods: "*Beyond our application of HOT statistical modeling to Rennes, we introduce novel methods which consider policy scenarios involving active transportation mode share and*
reduction in personal vehicle use.”

To address the comment requesting more information surrounding the relationship
between transport and health, we have added the following text to the Introduction
section's second paragraph: "*Transportation is linked to urban populations' health through
pathways of air pollution, noise, green space, injury, and physical activity.*" This adds highly
cited evidence which explicitly links transport and health through several health pathways.
The Introduction section already includes numerous references which link physical
(in)activity to health, as well as active travel, but did not directly link all transportation to
health. Thank you for the suggestion to do so.

We appreciate the reviewer's comment on the age of the data and to better discuss why it is
being used and how the population's travel habits may have changed since it was collected
6 years ago.

To address this comment, we add a new, fourth paragraph to the Limitations section: "Analysis of Rennes' current state of active travel might seek to utilize data more recent than 2018, but the frequency of which these EMD surveys are conducted by Rennes Métropole (historically every 10 years) led our research team to be satisfied with the 2018 travel survey's ability to capture active travel behavior in Rennes. More recently, Buehler et al. demonstrate that in France cycling increased in 2020 compared to levels in 2019. Through research collaboration with Rennes Métropole, we are grateful for the opportunity to access these 2018 data to better understand how physical activity through travel impacts health in Rennes."

We thank the reviewer for their suggestion for the Methods section to explain our choice to
subset the data to 18-65 year old participants. We already include a paragraph addressing
this at the start of the Limitations section, and do not want to repeat or move this text
within the manuscript, so we have made the following change to the second paragraph of
the Methods section in order to quickly summarize the justification for using 6-year-old
data: "We subset this to adults aged 18-65 and differentiate between inner-city Rennes and the
surrounding area which is still within the metropolitan area limits." Was changed to: "We
subset this to adults aged 18-65 to correspond to the physical activity exposure-response
functions used in our health impacts analysis. We further differentiate between inner-city Rennes
and the surrounding suburban area which is still within the metropolitan area limits."

We are grateful for the reviewer's question around the use of US Dollars as opposed to
Euros as the currency for associated costs. To address this comment, we add the following
text to the Methods section's *Health estimates* subsection: "*Daroudi's report uses US Dollars
(USD) as their currency to measure the cost associated with DALYs, so we continue this translation
using USD as our final cost unit.*"

We thank the reviewer for the comment on better outlining our model assumptions and
how these might influence outcomes. To address this comment, we alter the third
paragraph of the Limitations section as follows: "*The MSS and TTS models require several
underlying assumptions. First, with all mode share shifts considered in these analyses, the
population's total number of transportation trips is kept constant. Second, for the climate policy
objective's VMT reduction we assume a full replacement by active modes walk and cycle, with no
change in public transportation use by Rennes residents." Was changed to: "The MSS and TTS models require several underlying assumptions. First, with all mode share shifts considered in these analyses, the population's total number of transportation trips is kept constant. As Rennes officials' mobility policy objective outlined mode share, a proportional measure, as its outcome of choice, maintaining a constant value for total trips avoids an additional level of complexity in how the population might achieve the policy's desired increase in walking and cycling mode share proportions. Without this constant trip total, we might consider scenarios where Rennes simply increases their number of walk or cycle trips (alongside no change in other modes), or conversely, simply decreases their non-walk and non-cycle trips (with no change in walking or cycling, meaning no change in travel activity). By keeping total transportation trips constant, we choose to avoid this ambiguity and additional dimension of analysis, through challenging this assumption provides an interesting possibility for future research. Second, for the climate policy objective's VMT reduction we assume a full replacement by active modes walk and cycle, with no change in public transportation use by Rennes residents. We incorporate this assumption for similar reasoning as with mode share shift. Reduction in vehicle use may not directly translate to any change in active travel modes, but our analysis is concerned with just that: the health impacts of scenarios in which the VMT reduction does directly translate to an increase in walking and cycling. The additional dimensionality added by incorporating public transit is interesting but requires a level of complexity not pursued in this analysis. Future research in health-oriented transportation might consider spreading VMT reduction scenarios across walking, cycling, as well as public transit." We choose to not actively investigate how "realistic" each of our scenarios might be, as we find that to be beyond the scope of our statistical modeling effort. We are translating proposed policy to estimated health impacts and believe that diving into the feasibility of these policies is certainly a very intriguing avenue for future research but is not within the bounds of this analysis.

We also thank the reviewer for their suggestion to discuss disaggregation of health outcomes by subgroups and how that might illuminate links between transport and health disadvantage. To address this comment, we make the following change: "*The ENTD data did not include many social or environmental variables such as race, ethnicity, income, or disability status, some of which have been shown to affect participation in active travel.* Incorporation of these variables in the household survey could provide insight into inequities in access to active modes of travel which may contribute to health inequity." Was changed to: "*The EMD data did not include many social or environmental variables such as race, ethnicity, income, or disability status, some of which have been shown to affect participation in active travel. Incorporation of these variables in the household survey could allow researchers a subgroup-level measure of access to active travel, which in turn may contribute to health inequity. Active travel researchers in the future may seek to disaggregate the population health outcomes by subgroups, giving additional valuable insight into how transport affects health inequity within Rennes. This would require additional statistical tuning; while subgroups' population totals might be usable to scale the overall health impact to specific social or environmental variables, this avenue also invites questions around the baseline distribution of physical activity of the sample population, and at this time we are not aware of any studies which measure Rennes residents' physical activity habits at the subgroup level.*" We correct a typo in the dataset's acronym and suggest avenues for future research which might seek to disaggregate health outcomes by subgroups in Rennes.
Unrelated to any reviewer comments, we add the "EMD" acronym to the Methods section when the dataset's name is introduced, as we reference that acronym several times later in the manuscript. Finally, we add all relevant new citations to the bottom of the References section with suggested numbering, but we defer to editor's citation manager for determining those. We include proper formatting for all new citations.

**Competing Interests:** No competing interests were disclosed.

Reviewer Report 16 May 2024

https://doi.org/10.21956/wellcomeopenres.23143.r79273

© 2024 Woodward A. This is an open access peer review report distributed under the terms of the [Creative Commons Attribution License](https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

<figure><img src="image_19.png" /></figure>

Alistair Woodward

<figure><img src="image_20.png" /></figure>

Department of Epidemiology and Biostatistics, School of Population Health, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand

A well-written and robust study. I have suggestions for minor improvements.

Some important points and relevant additional references regarding the introduction:

- increasing active travel reduces carbon emissions only if walking and cycling replaces travel by motor vehicles. It is easier to do the first (increase prevalence of PA), than the second (cut VMT). What is the basis for the mode share shift assumptions in Rennes?

- it is true that few studies have looked closely at equity, but this has been done, and what's more, published work includes empirical studies which are in many ways more persuasive than modelling exercises (eg Keall et al July 2022 Transportation Research Part D)[Ref1]

- we all hope, those of us working in the field, that documenting potential health gains of well-chosen transport policies will make it more likely that these policies are implemented. But is there any evidence that this really happens? It is 15 years since the first substantial co-benefits studies were published (eg Woodcock 2009 Lancet)[Ref 2], yet transport emissions continue upwards, Japan is, to my knowledge, the only country in which body weight has not risen, and car dominated cities prevail in must parts of the world.

- electric bicycles out-number push bikes now, in many cities, and are the biggest selling electric vehicles world-wide. I expect similar trends apply in France? If so, the assumptions about prevalence, intensity and mode-substitution all require re-thinking. E-bikes typically require less PA per km than push bikes, but are ridden further and more frequently, and are more likely to replace vehicle trips.

I was intrigued by the approach to estimating, indirectly, the relation between physical activity and disability adjusted life years. I presume PA will reduce DALYs only if the diseases causing disability are sensitive to the effects of exercise. Secular trends in deaths and DALYs represent the effects of all causes of ill-health, at least some of which are not relevant. (Is dementia strongly affected by
physical activity, for instance?) Did the authors test the relationship for a limited range of causes of death and lost DALYs?

Why are the health costs and gains expressed in terms of US dollars, rather than Euro? And would it be more appropriate to use metric measures such as Km rather than miles?

In the HOT, what assumptions are made about road crash injuries associated with increased walking and cycling? And are these assumptions tuned to city circumstances? There are signs, from multi-city studies, that the changes in road trauma associated with change in active travel depend strongly on the local context, including infrastructure and vehicle fleet characteristics. (eg Stevenson 2016 Lancet)

The relations between inner city residence, active travel and household car access are important, and expected. But what comes first? Is there any sign from the Rennes data, which are the biggest levers for transformation? Is it urban density, or car access, or popularity of walking and cycling?

## References

1. Transportation Research Part D: Transport and Environment. https://shop.elsevier.com/journals/transportation-research-part-d-transport-and-environment/1361-9209#full-description. 2022.

2. Keall M, Randal E, Abrahamse W, Chapman R, et al.: Equity and other effects of a program facilitating and promoting active travel. *Transportation Research Part D: Transport and Environment*. 2022; **108**. Publisher Full Text

**Is the work clearly and accurately presented and does it cite the current literature?**

Partly

**Is the study design appropriate and is the work technically sound?**

Yes

**Are sufficient details of methods and analysis provided to allow replication by others?**

Yes

**If applicable, is the statistical analysis and its interpretation appropriate?**

I cannot comment. A qualified statistician is required.

**Are all the source data underlying the results available to ensure full reproducibility?**

Yes

**Are the conclusions drawn adequately supported by the results?**

Yes

**Competing Interests:** No competing interests were disclosed.

**Reviewer Expertise:** public health, epidemiology, transport and health

I confirm that I have read this submission and believe that I have an appropriate level of
expertise to confirm that it is of an acceptable scientific standard.