# Citizen-centric design in mHealth: Concept mapping for active transportation promotion

Nuttanun Siriaporn, Audrey de Nazelle, Anne Vuillemin

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Nuttanun Siriaporn, Audrey de Nazelle, Anne Vuillemin. Citizen-centric design in mHealth: Concept mapping for active transportation promotion. Transportation Research Interdisciplinary Perspectives, 2025, 30, 10.1016/j.trip.2025.101367. hal-04984110

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# Citizen-centric design in mHealth: Concept mapping for active transportation promotion

Nuttanun Siriaporn<sup>a,*</sup>, Audrey de Nazelle<sup>b</sup>, Anne Vuillemin<sup>a</sup>

## ARTICLE INFO

**Keywords:**

Active transportation

Concept mapping

Participatory approach

Mobility

Behavior change

Systems thinking

## ABSTRACT

**Purpose:** Active transportation (AT) offers a sustainable means of enhancing daily physical activity and mitigating transportation-related pollution. Leveraging mobile health (mhealth) technology can be a valuable tool to promote AT behavior. However, existing interventions often lack evidence-based behavior change techniques and rarely involve potential end-users in the development process. This study aims to explore the promotion of AT through mhealth interventions using a participatory approach.

**Methods:** We conducted online concept mapping sessions with residents of the Alpes-Maritimes Department in France. Participants engaged in brainstorming, sorting, and rating of ideas related to app features designed to encourage AT. We employed multidimensional scaling and hierarchical cluster analysis to visualize the participants' collective conceptual thinking on this subject. Data was stratified by participant demographics, including age, education, and typical mode of transportation.

**Results:** Participants collectively generated 44 ideas perceived as useful to fostering AT behaviors. These ideas formed six main concepts, ranked by efficiency to encourage AT as follows: Infrastructure (n = 9 ideas), Itinerary (n = 9), Contact with government (n = 4); Data (n = 12); App usability (n = 6); and Legislation and code of conduct (n = 4).

**Conclusions:** The study highlights the pivotal role of infrastructure in fostering AT. Travelers could also be effectively engaged through app features such as tailored information on the presence and quality of AT infrastructure, hybrid multimodal journey planning, and time and cost savings by different travel modes. Engaging with local governments via the app is identified as a novel and promising approach to AT promotion. Indications of specific tailoring opportunities for different demographic groups need to be further investigated.

# 1. Introduction

## 1.1. Active transportation

Insufficient physical activity leads to an increased risk in non-communicable diseases such as cardiovascular diseases, some types of cancer, type 2 diabetes, dementia, and depression (2018 Physical Activity Guidelines Advisory Committee, 2018). Active transportation (AT) is defined as modes of travel in which “the sustained physical exertion of the traveler directly contributes to their motion” (Cook et al., 2022). This is typically walking and cycling, but can also include electric assisted cycling, kick scooting, skateboarding and wheelchair use for those with reduced mobility. Engaging in AT allows people to integrate

physical activity into their daily lives and reach recommended levels (Chaix et al., 2014; Giles-Corti et al., 2016; Schoeppe et al., 2013). AT modes also do not produce traffic noise, which has been linked to cardiovascular diseases (Babisch, 2014) and mental health (Seidler et al., 2019). Similarly, AT does not produce any tailpipe, brake and tire wear emissions, and can hence reduce harmful air pollutants when replacing motorized travel, thus benefiting population health (Castro et al., 2017; Khreis et al., 2023) and biodiversity (Harrison et al., 2005). The transition from motorized vehicles will also reduce CO2 emission and contribute to climate action (Brand et al., 2021b, 2021a).

A wealth of evidence has demonstrated the highly beneficial nature of engaging in AT for both human and planetary health (WHO Regional Office for Europe, 2022). However, mobility data from 28 EU member

<footnote><sup>a</sup> Université Cote d'Azur, LAMHESS, 261 Bd du Mercantour, 06200 Nice, France</footnote>

<footnote><sup>b</sup> Centre for Environmental Policy, Imperial College London, 16-18 Prince's Gardens, London SW7 1NE, United Kingdom</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail addresses: nuttanun.siriaporn@univ-cotedazur.fr (N. Siriaporn), anazelle@imperial.ac.uk (A. de Nazelle), anne.vuillemin@univ-cotedazur.fr (A. Vuillemin).</footnote>
states reveals that cars remain the dominant mode of daily trans-
portation (56 %), while walking and cycling remain at a low 16 % of
mode share (Fiorello et al., 2016). Promoting individual behavioral
shifts towards AT will require the establishment of an enabling envi-
ronment supportive of change (Götschi et al., 2017). Engaging society
towards these transformations, both at individual and policy levels, is
part of the solution.

1.2. Mhealth

Health interventions delivered via mobile apps, or mhealth, have
been successful in promoting physical activity and weight loss (Flores-
Mateo et al., 2015; Pfaeffli Dale et al., 2016; Schippers et al., 2017).
Given their technological capabilities, accessibilities, and success in
promoting health, mobile apps have the potential to facilitate changes in
travel behavior, encouraging individuals to engage in AT for the benefits
of their health and the health and sustainability of cities.

1.3. *AT digital interventions and citizen participation*

App-based AT promotion interventions documented and evaluated
in previous studies have used a variety of engagement and evaluation
methods. Self-reported evaluations have consistently demonstrated
success in motivating behavioral intentions (Anagnostopoulou et al.,
2020; Ross et al., 2015) and raising awareness about the impacts of
travel choices (Ek et al., 2020; Hasshu et al., 2015; Jariyasunant et al.,
2012; Park et al., 2017; Roider et al., 2019). Impacts on travel behavior,
however, have been less consistent, resulting in only partial or no sig-
nificant effects on the population (Bopp et al., 2017; Castellanos, 2016;
Goetz et al., 2023). These apps employed a variety of theories and
behavioral change techniques to engage travelers, predominantly: So-
cial Cognitive Theory (Bandura, 1989) (applied in Bopp et al., 2017; Ek
et al., 2018; Ross et al., 2015), Theory of Planned Behavior (TPB; Ajzen,
1991) (applied in Merlin et al., 2022; Semanjski et al., 2016), and
Transtheoretical Model of Behavior Change (TTM; Prochaska and
DiClemente, 1982) (applied in Bopp et al., 2017; Cellina et al., 2019;
Máca et al., 2020). As an exception, Cellina et al. (2020) used co-
creation with a living lab and incorporated user perspectives into their
app design.

Citizen engagement and participation is when members of the pop-
ulation, whether individuals or organized communities, engage in and
provide input into decision making (Baum, 2001). Active citizen
involvement in the co-creation of interventions tailored to their needs
enhances the likelihood of both the acceptance and success of these
innovations (Eysenbach, 2008; Leask et al., 2019). Participatory ap-
proaches allow individuals whose lives are impacted by the outcome to
be directly involved in the decision-making process. In transportation,
participatory and citizen engagement approaches have been used for
example to co-create cycling policies in Berlin, Germany (von Schnei-
demesser et al., 2020), to co-create a framework for future mobility
services in Sweden (Ebbesson, 2022), and to assess community needs for
transportation planning in California, United States (Pinski and
McCarthy, 2024).

Concept mapping is a system thinking approach where inputs from relevant stakeholders provide a common framework that can be used for planning and evaluation (Kane and Trochim, 2007). In previous studies, concept mapping has been successfully used to identify intervention strategies to increase physical activity in African Americans in an urban community (Kelly et al., 2007), and to understand gender differences between children and adolescents biking to school in Germany (Schönbach et al., 2020).

Our study aims to explore the promotion of AT through mhealth
interventions using a participatory approach. As such, we investigated
mhealth features which might encourage and facilitate citizens to shift
their mobility behaviors towards AT.

2. Material and methods

We conducted a participatory study using concept mapping to
explore how citizens envision an app to encourage modal shifts toward
walking and cycling. Our study has an exploratory sequential mixed-
method design, initially delving into the topic through qualitative
exploration, and subsequently quantifying trends and preferences. We
based the study in the department of Alpes-Maritime, France, and
collected data between April 2022 and June 2022. The region hosts a
population of 1.09 million, with its capital of Nice being the fifth largest
city in France.

## 2.1. Concept mapping

We used concept mapping to generate, group, and rank ideas around
app-based AT promotion. The iterative data collection approach,
detailed in the Participation Steps section 2.4 below, involved two
questions to guide the online discussion activities; the first (i) asked for
ideas of features in an AT promoting app, and the second (ii) questioned
their perceived effectiveness. These questions were methodically
generated following the guideline by Kane and Trochim (Kane and
Trochim, 2007) and were validated by three researchers working in
physical activity and health promotion.

GroupWisdom (groupwisdomTM, 2022) is an online service and
platform that specializes in concept mapping. Recruited participants
were enrolled on the platform and all communication throughout the
study took place through the platform. GroupWisdom provides user-
friendly features which guided participants through each step of the
study. It supports desktop and mobile participation in multiple lan-
guages. We conducted our study in English and French. The Group-
Wisdom platform was also used for data analysis and visualization.

## 2.2. Participants

Our target population was the general adult population (over 18), as
mobility behavior – our primary interest – constitutes a universal aspect
of daily life. Our study sought to represent diverse mobility behaviors,
age groups, and physical abilities.

2.3. Recruitment

Participant recruitment was conducted in multiple manners:

- Physical posters with information about the study and a QR code to register were posted throughout the region's largest university, Université Côte d'Azur.

- Digital posters with links to both information about the study and the process of registering were posted on the first author's Twitter (now X) account and shared by 13 additional accounts. These included those belonging to local cycling and environmental associations, physical activity researchers, media pages, and the Université Côte d'Azur's sport science campus.

- One motor handicap, one senior, one environmental and two student associations in the region were contacted via email and phone and agreed to forward the study invitation to their members and followers.

Recruitment remained ongoing throughout the study duration, with
various participants joining at different stages, facilitating the ongoing
integration of new knowledge and insights (Johnsen et al., 2000).

2.4. Participation steps

After the online registration, participants received a link to join the
online GroupWisdom platform. Once informed consent was provided,
they were asked to complete the participant demographic survey, which
asked about their age, postal code, level of education, if they had motor
disabilities, and typical mode of transportation. Every participant was
required to complete this initial survey, after which they were invited to
contribute to the stage of the concept mapping exercise, which was
underway at the time of their enrolment, without being required to
complete the previous steps. The questions and prompts used in the
concept mapping activities are provided in Appendix 1.

Step 1: Generating ideas/brainstorming.

In the first step, participants were asked to brainstorm ideas for app
features that could encourage AT. This step was open for one month,
during which participants worked on the task on their own time and
were also able to view other participants' responses.

Step 2: Sorting the ideas.

To prepare for the next step, the researchers gathered the ideas
collected in Step 1, refined them for clarity, combined similar or
repeating ones, separated those containing more than one attribute, and
translated them into English or French. Participants were then directed
to organize the ideas into themes, based on their perceived meanings,
and to name the created themes. The participants worked on this task
during their own time and were not able to see other participants' work.
This step was open for six weeks, with the final three weeks overlapping
with Step 3.

Step 3: Rating the ideas.

The last step was the rating of ideas. The participants were presented
with all the ideas (but not the sorting) and asked to rate them based on a
five-point scale. The prompt asked for their opinions about how effective
the ideas might be in encouraging AT behaviors, with ratings ranging
from extremely effective to not at all. This step was open for three weeks,
during this time participants could engage in Steps 2 and 3 simulta-
neously. Participants worked on the tasks on their own time and were
not able to see others' ratings.

2.5. Analysis

Once data collection was completed and the data cleaned, the
analysis steps were performed using GroupWisdom analytical tools:

- Each participant's sorting data (how they understood and grouped ideas in Step 2) was represented in a binary matrix, illustrating whether they had sorted one idea with another (example in Fig. A.1).

- To show how similarly participants grouped each idea, all the binary matrices were combined into a similarity matrix (Fig. A.2). The similarity matrix shows every possible pair of ideas and indicates how many participants grouped each pair.

- A point map (Fig. A.3) visualizes the ideas in relation to each other: each point represents an idea, and the proximity between the points represents how likely they were to be sorted together. The software used a multidimensional scaling algorithm to calculate the coordinates of each idea from the similarity matrix.

- The key diagnostic of multidimensional scaling is the stress value, which indicates the discrepancy between the similarity matrix and the two-dimensional point map. The lower the stress value, the better the fit. A stress value lower than 0.35 is generally accepted as indicating a good fit in concept mapping studies (Kane and Rosas, 2017).

- Bridging values indicate how well an idea reflects its adjacent neighbors in the point map (Fig. A.3). Ranging from zero to one, a lower bridging value (closer to zero) indicates that the idea was often sorted with the others in the same area of the plot, meaning it is well related; a higher bridging value (closer to one) means that the idea was also sorted with other ideas further away in the plot, and was hence less related to its neighbors, serving as a bridge between areas.

- Ward's method of hierarchical cluster analysis was used to organize the ideas into concepts. This method starts with the point map and step-by-step merges the points into larger clusters, creating a cluster map. It merges ideas based on their sorting pattern, comparing its consistency with other ideas'. In an iterative process, three

researchers agreed on the final number of mergers and clusters that
best reflects a cohesive and manageable conceptualization of the
topic (Fig. A.4). The goal was to minimize the number of clusters
while still producing coherent clusters.

- For each cluster, the researchers assigned the most relevant title chosen from the list that was provided by participants during Step 3.

- The coherence and cohesiveness of the ideas within each cluster can be observed by averaging the bridging value of the ideas within each cluster. A lower average bridging value indicates consistency and coherence within the cluster, and vice versa for a higher average bridging value.

- Average ratings indicate the extent to which participants perceived the content of each cluster to be effective in encouraging AT. In the cluster map (Fig. 2), this is demonstrated through the number of layers on each cluster: the greater the number of layers the higher the average rating.

The shape and size of the clusters reveals some relational charac-
teristics between the ideas. A large spread of points within a cluster
demonstrates a loosely related grouping of ideas. Quantitatively, points
in a large cluster would have bridging values closer to one. In reverse,
tighter clusters show a narrow focus of ideas, and would have a low
average bridging value, closer to zero.

Further analyses were then carried out to disaggregate participant
characteristics:

- Based on how they answered the typical mode of transportation question from the participant survey, the participants were categorized into five travel mode groups: AT users, mass transportation users, private mode users, multimodal users with private motorized modes, and multimodal users without private motorized modes. AT users mainly used non-motorized modes (e.g. walking, electric and non-electric bike, scooter, skateboard, etc.). Mass transportation users mainly use public transportation or carpooling. Private mode users mainly drive cars or ride motorcycles or use private car services. Multimodal users combine more than one mode; we distinguish between those who included or not private motorized modes in the mix.

- We used Pattern Match analysis to compare average cluster ratings for different mode users.

- As AT may have more physical requirements than other modes, people of different ages may have different preferences to comfortably engage it. To observe this discrepancy, Go-Zone bivariate mapped and compared how two age groups, younger adults (<40) and middle-aged to older adults (>40), rated each idea. The diagram is divided into four quadrants above and below the average rating on each axis to show which ideas were rated above average for both groups, below average for both groups, above average for one group and below for another, and vice versa.

2.6. Ethics

This work has been supported by the French government, through
the UCAJEDI Investments in the Future project managed by the National
Research Agency (ANR) (reference number ANR-15-IDEX-01), and the
European Union's Horizon 2020 research and innovation program, co-
funded by Region Provence-Alpes-Côte d'Azur and IDEX UCAJEDI
(reference number N° 847581). All participants provided digitally
signed consent before participating in the study. The ethics approval was
acquired from the Comité d'Éthique de la Recherche, at the Université
Côte d'Azur: AVIS n° 2022-009.

3. Results

A total of 67 participants registered for the study and were enrolled
in the online platform, GroupWisdom. Throughout the study, emails
were sent to all 67 participants to notify them about the opening of each step, remind them of extensions, and provide notice before each step closed. Of these participants, 46 completed the participant survey. Twenty-nine participants engaged in Step 1, which generated 45 ideas. After refining the data, 44 ideas remained and were sorted by 17 participants in Step 2. Four participants' inputs were rejected, however, because they were sorted based on opinions of the ideas (i.e., sorting the idea based on whether they agree with it) rather than thematically. Lastly, 13 participants successfully rated all 44 ideas in Step 3. Some participants completed all three steps, and some completed only one or two (Fig. 1).

This Sankey diagram shows the number of participants in each step of the study.

Participant demographics varied throughout the study: in Step 1 (brainstorming) around a third of participants (34.5 %) were solely AT users and two participants had motor disabilities; Step 2 (sorting) had slightly more multimodal users without private mode than solely AT users; Step 3 (rating) had predominantly (69 %) participants who were AT users or multimodal users without private modes (Table 1). The participation rate of those 60 + years old in all three steps was no more than 10 %.

Many of the participants were multimodal users (n = 23, at Step 1). Only a few were mass transport or private mode users. The private mode users were all car drivers, no participants regularly rode motorcycles, nor used private car services. There were two car drivers in Step 3, one of which also rode bikes and was categorized as a multimodal with private mode user, and the other was solely a private mode user (Table 1). Due to the low number of private mode users, an accurate conclusion based on transport modes cannot be drawn.

## 3.1. Clusters

A low stress point map of 0.18 reflected how similar the participants were in grouping the ideas. A 6-cluster plot was chosen to best represent the ideas and concepts that emerged from the study (Fig. 2 and Fig. A.4). The most cohesive cluster as indicated by the lowest bridging values was *Infrastructure* (0.06), followed by *Itinerary* (0.19), *Data* (0.19), *Contact with the government* (0.34), *App usability* (0.42), and *Legislation and code of conduct* (0.61) (Table A.1).

*Infrastructure* had the highest average rating of perceived efficiency in promoting AT, 3.84 (out of 5). Next in efficiency ratings were *Itinerary* (3.76), *Contact with the government* (3.27), *Data* (3.13), *App usability* (2.67), and *Legislation and code of conduct* (2.63) (Fig. 2).

There were nine ideas in the *Infrastructure* cluster, three of which were about cycling lanes (#17, 18, and 19 in Table A.1). These suggestions had the highest rating (4.1 to 4.5) in the study. The other *Infrastructure* ideas were about street furniture for bicycles (21), bike parking spaces (24), infrastructure to separate cyclists and pedestrians (42), dedicated spaces to bring bikes onto public transport (22, 23), and lastly, charging for motorcycle and bike parking in order to fund cycling policies (44).

*Itinerary* contained ideas about providing various itineraries and navigation based on users' needs. The top three of nine ideas in the cluster were providing route options with fewer cars (12), creating safer routes for children (16), and enhancing the ability to choose between different types of cycling routes (43). The other ideas were about providing multimodal itineraries (11), changes in slopes (13), popular routes (36), and creating a map of bike and scooter parking (37).

*Contact with the government* included ideas regarding communication with the local authorities to provide decision-making support from citizens. The top-rated of four ideas was to create visualizations that can be used to make the case for AT and to inform local authorities about the number of AT network users (31).

*Data* focused on providing data and information to users about various impacts based on their travel behaviors. This was the biggest cluster, with twelve ideas. The three top-rated ideas included comparing the amount of time saved between different modes of transportation (1), the cumulative amount of money saved (4), and a map indicating how to avoid high-pollution areas (35). Other ideas were about impacts such as the amount of CO2 emissions avoided (8, 9, 10), calories spent for different itineraries and modes (6), and comparisons between impacts of different modes (3, 7, 15).

*App usability* included ideas about social functions, gamification, and challenges. The top-rated of six ideas were evenly split between having in-app challenges around the target modes of transportation (28) and being able to rank different modes in a city (39). All the ideas in this cluster were rated below 3 (moderately effective) out of 5.

Lastly, *Legislation and code of conduct* introduced ideas about providing guidelines or best practices for cyclists and pedestrians, as well as support for those with motor disabilities. The cluster's four ideas were all rated below 3, with the top idea relating to the development of a code for best pedestrian practices (41). This cluster had the least agreement between its ideas, as indicated by the highest average bridging value, 0.61 (Table A.1), and the broad shape with ideas all in different corners (Fig. 2).

This map shows six clusters of ideas to support AT. The number of layers of each cluster corresponds to its average rating. The greater the number of layers, the higher its average rating.

## 3.2. Pattern matches

Caution must be used when comparing typical modes of transportation, given that Step 3 (rating) only had two car drivers (one of which was a multimodal user who also biked) and two mass transport users. When disaggregated by travel mode groups, *Infrastructure* received the overall highest rating with AT users and multimodal users. Mass transport users rated it second and private mode users third (Fig. 3), both of whom rated ideas within the Itinerary cluster as the most effective means to promote AT. Data ideas were rated as second most efficient for private mode users and ranked fourth for the other groups (Fig. 3).

This diagram compares travel mode groups and how they rated each

<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Sankey diagram of participation.</figcaption></figure>
<figure><img src="image_3.png" /><figcaption>Fig. 2. Point cluster rating map.</figcaption></figure>

cluster on average. AT users (n = 5), mass transport users (n = 2), private mode users (n = 1), and multimodal users (n = 5).

### 3.3. Go-zones

Go-Zone analysis was conducted to compare how participants above and below 40 years old rated each idea (Fig. 4). The ideas from the *Infrastructure* (grey dots) and *Itinerary* (yellow dots) clusters had the highest average effectiveness rating for both groups. Both groups collectively supported these two Infrastructure ideas: for all towns with 60,000+ inhabitants to build an additional 10 km of cycling road every year (18) and for more bike lanes in urban areas (19). The only discrepancies in the *Infrastructure* cluster were an idea on installing bicycle racks on buses to help reach difficult (e.g., hilly) or out-of-the-way areas (22), which was more favorable in the older age group (4.2 vs 2.8); additionally, an idea to impose fees on motorcycle and bicycle parking to finance cycling policies (44) was equally poorly ranked in both groups.

Legislation and code of conduct ideas (green dots) were collectively ranked low for both age groups (e.g. having armchairs for those with reduced mobility (20), controls on the pedestrian/bike paths (32), test for new cyclists (40), and pedestrian codes (41)). The largest discrepancies between age groups were for *Contact with the government* (orange dots), where younger age groups ranked the ideas relatively higher than the older age groups (reporting infrastructural issues to municipal services (30), tracking AT user density to support policy changes (31), and locating supportive retailers and favorable active modes in the area (34)). Two of the App usability ideas (red dots) were ranked slightly more favorably in the younger age group (gamification (27) and in-app challenges (28)), but otherwise ranked low. Data ideas (blue dots) were quite dispersed, although none were ranked above average for both groups. Monitoring total money saved (4) and time saved (5) stood out as being better ranked by the younger age group, and in similar fashion, the older age group ranked the very similar ideas on trip planning information with relative time and cost savings of different modes slightly higher (1 and 15).

This analysis compares how the older (n = 9, x-axis) and younger (n = 4, y-axis) groups rated each idea. The color of each point indicated the clusters they belonged to: Infrastructure (grey), Itinerary (yellow), Contact with government (orange), Data (blue), App usability (red), and Legislation and code of conduct (green). Average ratings lines split the quadrants: the green quadrant shows ideas rated above the average for both groups, blue quadrant below average for both groups, orange quadrant represents above average ratings for the younger group and below average for the older group, and vise-versa for the yellow quadrant.

## 4. Discussion

Through concept mapping, our study aimed to explore citizens' views on features which might be included in a mobile app oriented towards encouraging AT. The main concepts that emerged, ranked in order of most to least effective, were the following: having more and better AT infrastructure; providing various itinerary and journey planning information; enabling contact with the government to report back on AT conditions; providing trip data, such as time and cost savings, as well as environmental indicators; including social and gaming features; and finally, having codes of conduct for cyclists and pedestrians, as well as assistance for people with disabilities. Different age groups were largely in agreement with one another, although younger research participants were slightly more enthusiastic about both local government interaction and gaming features.

### 4.1. Infrastructure and code of conduct

Perhaps the most surprising result was a unified voice regarding the need for better AT infrastructure. While the exercise prompts specifically asked research participants about mobile app features, participants nonetheless intentionally pointed out the need for infrastructure change, which an app cannot provide. This call for better infrastructure chimes well with the large body of research showing the necessity of establishing an enabling environment for AT (Evans et al., 2022; Javaid
<table><caption>**Table 1**
The demographics of the participants disaggregated by each step.</caption><thead><tr><th></th><th>Participant survey (N = 46)</th><th>Step 1: Brainstorming (N = 29)</th><th>Step 2: Sorting (N = 17)</th><th>Step 3: Rating (N = 13)</th></tr><tr><th></th><th colspan="4">Frequency (%)</th></tr></thead><tbody><tr><td><strong>Age</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>19-39</td><td>17 (37.0)</td><td>9 (31.0)</td><td>8 (47.1)</td><td>4 (30.8)</td></tr><tr><td>40-59</td><td>25 (54.3)</td><td>17 (58.6)</td><td>8 (47.1)</td><td>8 (61.5)</td></tr><tr><td>&gt;60</td><td>4 (8.7)</td><td>3 (10.3)</td><td>1 (5.9)</td><td>1 (7.7)</td></tr><tr><td><strong>Highest level of education</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Received a technical degree or high school diploma</td><td>1 (2.2)</td><td>1 (3.5)</td><td>0 (0)</td><td>0 (0)</td></tr><tr><td>Received a 2-year college degree</td><td>5 (10.9)</td><td>4 (13.8)</td><td>2 (11.8)</td><td>2 (15.4)</td></tr><tr><td>Received a bachelor's degree</td><td>9 (19.6)</td><td>6 (20.7)</td><td>4 (23.5)</td><td>2 (15.4)</td></tr><tr><td>Received a master's degree</td><td>21 (45.7)</td><td>12 (41.4)</td><td>7 (41.2)</td><td>4 (30.8)</td></tr><tr><td>Received a doctoral degree or higher</td><td>10 (21.7)</td><td>6 (20.7)</td><td>4 (23.5)</td><td>5 (38.5)</td></tr><tr><td><strong>Motor disability</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Yes, and it affects my ability to get around</td><td>2 (4.3)</td><td>2 (6.9)</td><td>0 (0)</td><td>1 (7.7)</td></tr><tr><td>Yes, but it does not affect my ability to get around</td><td>0 (0)</td><td>0 (0)</td><td>0 (0)</td><td>0 (0)</td></tr><tr><td>No</td><td>44 (95.7)</td><td>27 (93.1)</td><td>17 (100)</td><td>12 (92.3)</td></tr><tr><td><strong>Regular mode of transport</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Active transport user</td><td>17 (37.0)</td><td>10 (34.5)</td><td>5 (29.4)</td><td>5 (38.5)</td></tr><tr><td>Mass transport user</td><td>3 (6.5)</td><td>3 (10.3)</td><td>1 (5.9)</td><td>2 (15.4)</td></tr><tr><td>Private mode user</td><td>3 (6.5)</td><td>3 (10.3)</td><td>1 (5.9)</td><td>1 (7.7)</td></tr><tr><td>Multimodal with private mode</td><td>9 (19.6)</td><td>4 (13.8)</td><td>3 (17.6)</td><td>1 (7.7)</td></tr><tr><td>Multimodal without private mode</td><td>14 (30.4)</td><td>9 (31.0)</td><td>7 (41.2)</td><td>4 (30.8)</td></tr></tbody></table>

et al., 2020). Safety concerns from traffic injuries and infrastructure-related falls are major barriers to cycling (Campos Ferreira et al., 2022; Pearson et al., 2023). A well-designed infrastructure for walking and cycling that meets the needs of both experienced and inexperienced users can provide comfort and safety (Akgün-Tanbay et al., 2022; Hull and O'Holleran, 2014; Li et al., 2012). While social, cultural, and personal factors influence travel mode choices (Götschi et al., 2017), proper infrastructure is essential to promote walking and cycling, which participants in this study recognized.

Ideas from the poorest ranking cluster, Legislation and code of conduct, didn't necessarily lend themselves well to being included as app features either. Its dominant idea, clarifying the best practices for pedestrians, was primarily put forth by older participants. This suggestion is in line with research on conflict between pedestrians and cyclists, which is of particular concern for older populations due to increased rates of injury and the severity of crashes which involve them (Polders et al., 2015). Better infrastructure – such as separation between cyclists and pedestrians – might help, although proper conduct by both cyclists and pedestrians is also needed to ensure maximum safety (Hatfield and Prabhakharan, 2016).

## 4.2. App related solutions

### 4.2.1. Itinerary

The Itinerary features were rated second most efficient in encouraging the adoption of AT, emphasizing once more the role of infrastructure in promoting walking and cycling. Ideas emerging from this cluster fit well with multiple health behavioral constructs, such as the TPB's perceived behavioral control (Ajzen, 1991), the Health Belief Model's (HBM) perceived barriers (Rosenstock, 1974), and COM-B's (Capability, Opportunity, Motivation, Behavior model) Capability (Michie et al., 2011). Proposing navigation, route suggestions, and locations of bicycle parking provide actionable information which addresses some of the major barriers to AT. This information can potentially provide a sense of control (TPB), reducing perceived obstacles (HBM), and boosting one's ability to engage in the behavior (COM-B). Sottile et al. (2021), Semanski et al. (2016), Anagnostopoulou et al. (2020), and Tsirimpa et al. (2019) implemented AT route suggestions within their app-based interventions and found that they were able to nudge participants towards breaking their habits and trying alternatives. Providing multiple itineraries and options for choosing different types of walking and cycling paths, such as types of bike lanes, children-friendly, and peaceful streets, were also identified by our participants. Trip planning apps typically try to provide route options to users, such as on the GeoVelo app (available in France); however, there first needs to be the infrastructure to support such variations. Practical, aesthetic, and emblematic street designs, such as variety of walking and cycling routes, attractive urban spaces, and presence of traffic lights and signs, have previously been shown to influence AT behaviors (Blitz and Lanzendorf, 2020). A novel idea in this cluster is the provision of hybrid, multimodal itineraries combining active and public transportation. Such constructs have not previously been reported in AT app evaluations, and therefore offer promising ideas to be tested in future app developments.

### 4.4.2.2. Contact with the government

The construct rated third for effectiveness is perhaps the most novel contribution to the AT promotion field. Participants recognized a need for engagement with government, and also envisioned a role to be played in this regard by an app. In cases in which citizens feel powerless to engage in sustainable and healthy transportation behavior due to a lack of options (e.g. no safe AT infrastructure), contacting elected officials is achievable for most – if not all – adults. These actions (e.g. reporting of cycling paths problems) would provide an outlet that removes a potential sense of powerlessness, all while engaging citizens in an action which could help in the long term with addressing infrastructure barriers (the first area of concern identified) (Riley et al., 2021). While such an active approach of directly interacting with policy makers and local authorities has not been tested in AT-promotion apps, it has nonetheless been explored more generally for citizen engagement in governance processes in governance and city management apps (Bastos et al., 2022; Gagliardi et al., 2017). Technology-based direct citizen participation in city governance and management, e.g. through reporting issues, monitoring environmental factors, and sharing opinions, shows some promise for more effective engagement. In particular, two-way communication and collaboration between citizens and government can facilitate transparency and trust (Cooper et al., 2006; Yang, 2005), important factors for engagement in behavior changes (Riley et al., 2021). Robust evaluations of such systems, however, are still lacking (Bastos et al., 2022).

The idea put forth by participants to passively collect travel data to engage with local officials has previously been incorporated in AT apps. Crowd-sourced AT data stemming from apps or other sources have shown some promise as a tool for researchers and practitioners to inform decision making (Nelson et al., 2021). The effect of citizen contribution to AT data on their own behavior has not yet been documented, but it is known from broader applications that contributing to data collection through apps can positively impact the attitude and behavior of
<figure><img src="image_4.png" /><figcaption>Fig. 3. Pattern matches.</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>Fig. 4. Go-zones map.</figcaption></figure>

participants (Hognogi et al., 2023). Making these data (e.g. travel pattern and mobility behavior) open to the public has been shown to stimulate and facilitate digital citizen engagement and in turn produces more open data (Hasselwander et al., 2022). Fostering this cycle can also lead to innovations and promote change in mobility behaviors (Hasselwander et al., 2022). Major challenges still hamper their current use, however, including issues of representativeness, security, privacy, quality and of local capacity to manage, process and make use of novel but complex crowd-sourced data (Nelson et al., 2021).

The younger participants in our study seemed more enthusiastic than the older group about the potential for direct policy engagement through an app. This chimes well with previous findings on youth involvement in politics, which have found that young people's higher sensitivity to cause-oriented political interests can lead them – in contrast to their older counterparts – to participate in less institutionized forms of participation, such as apps (Soler-i-Martí, 2015).

#### 4.4.2.3. Data

Findings on the potential for data provided as feedback to app users aligned well with health motivation constructs, such as perceived benefits from the HBM; subjective norm form the TPB; or motivation from the COM-B model. Features such as amount of CO2 emissions, time, and money saved based on travel modes are often included in travel apps (Marquart and Schuppan, 2022). These feedback features are effective in increasing awareness of personal impacts and changing behavioral intentions (Di Dio et al., 2020; Jariyasunant et al., 2012; Park et al., 2017), which can disrupt undesirable habits (Hermsen et al., 2016). Our participants valued personal impact on time and money slightly above environmental impact of CO2 emissions. In the transportation field, the individual level determinants (time and money saved) are well studied and are consistent with our results (Javaid et al., 2020). In the *Data* cluster, two ideas were related to health. They were about air pollution and calories expended during travel. Despite strong evidence on health
benefits of AT, including self-perceived health (Avila-Palencia et al., 2017), our study did not indicate that health was perceived as a strong motivator to encourage AT. In three reviews on the determinants of transportation mode choice, while travel-related health impact was acknowledged, health benefits were not explicitly found to be determinants of choice (Chaudhry et al., 2023; Heinen et al., 2010; Javaid et al., 2020). It may be, however, that participants were not highly aware of such impacts.

Participants also suggested utilizing community statistics, such as the overall rate of CO2 emissions avoided across all app users, as a means of encouragement. One campaign tested community contributions towards a common goal, in this case winning cargo bikes for the community, and found that it had significant effects as to increase rates of cycling (Minnich, 2023). Contribution to a community goal can provide a sense of social identity in a larger cultural context (Social Identity Theory, (Tajfel and Turner, 2004)), and allow users to identify with other users through shared goals. This can motivate individuals to adopt new behaviors and contribute to the subjective norm (TPB) and values of the community. Social features also appeared in App usability and are discussed further in section 4.2.4.

#### 4.4.2.4. App usability

The App usability findings were unexpected. Features such as gamification and in-app challenges, while emerging as possible ideas, ranked poorly in terms of perceived effectiveness. Previous findings have shown that social gamification (e.g., leaderboards where users can compare their point-based progress with others) alone does not produce significant AT behavior change, but could be effective when combined in with material incentives (Máca et al., 2020). It is worth considering that a large portion of our rating participants were already AT users who may have been already motivated without the need for gamified apps. The younger age group, however, did seem to value such approaches more highly than older age groups, in line with previous studies which have shown that ease of use in relation to gamified interventions decreases among older users (Koivisto and Hamari, 2014). The older age group in our study, on the other hand, rated social features (inviting friends onto the app) more highly; this also aligns with previous research showing that social interaction may motivate seniors to increase physical activity (Ehn et al., 2019). Mhealth features that foster social interaction and social capital, both on the app and in real life, have high potential to create a supportive social network, benefiting the process of healthy aging (Annear et al., 2014).

In the COM-B model for behavior change, one of the drivers of behavior change is social opportunity, which includes social norms, conformity, and influences (Michie et al., 2011). The social dimension of the determinants of low-carbon transport mode adoption is related to the perception of common travel norms (Javaid et al., 2020). An idea in this cluster suggested anonymously displaying other AT users nearby on a map, to provide a sense of community. Although this idea may encourage the perception of social norms, our participants rated it low (average 2.62). The TTM (Prochaska and Velicer, 1997) proposed that social support and norms could prompt self-reevaluation and facilitate the transition from the state of contemplation to preparation (i.e., from considering AT modes to actively planning or attempting to engage in it). A review has shown that social factors play a clear role in influencing attitudes and perceptions of cycling, which in turn influences decisions to bike for transportation (Willis et al., 2015). Campaigns and interventions that support social cohesion to improve perceptions of cycling as the norm can greatly encourage AT on a community level.

### 4.3. Limitation

Concept mapping takes an exploratory sequential approach, where the topic is explored qualitatively prior to the quantitative phase (Fetters et al., 2013). The method does not aim for population representation, but instead encourages diverse participation to foster new ideas (Kane

and Trochim, 2007). Despite targeting vulnerable transport users, senior citizens, and those with motor disability, we did not maintain their engagement. We faced a high dropout rate of 71.7%, from the first to the last step. Online participation can yield broad reach but is prone to dropout (Yetano and Royo, 2017). As our study comprised of a two-month multi-step research project, we assume that the high dropout rate may have been attributable to networked individualism, whereby individuals' commitments to the study remained limited due to their involvement in other online networks (Rainie and Wellman, 2012), and in turn, also to respondent fatigue. Nevertheless, we still reached the recommended amount of participation (Jackson and Trochim, 2002; Severens, 1995; Trochim, 1989).

Concept mapping utilizes Ward's method of hierarchical cluster analysis to develop the clustering from the sorting data. It uses an algorithm that provides the best fit for each idea based on the consistency of its sorting patterns, referring to how it was either sorted or not sorted with each of the other ideas (i.e., pattern can be viewed in Fig. A.2). For example, an idea on installing armchairs for people with motor disabilities (20) was most often sorted with ideas 21 to 24 from Infrastructure, but the cluster analysis placed it in Legislation and Code of Conduct, even though it was only sorted with these ideas (32, 40, 41) two to three times. Consequently, it should be acknowledged that this approach may overlook ideas that were most often sorted together should their sorting patterns lack overall similarity. As a result, this could cause some ideas to seem misplaced within certain clusters. In addition, cluster titles may not always seem suitable, as they were generated by participants for themes they created independently from the final clusters. Nonetheless, we still consider the ideas to have some level of consistency with the six proposed cluster concepts.

## 5. Conclusion

Available mobile technologies nowadays provide the potential to promote an active and low-carbon lifestyle. We asked the public how an app could be developed to support AT, and they made it clear that infrastructure remains the most important element. Our study confirms the potential of increasing engagement through in-app challenges and gamification, disrupting undesirable habits with relatable personal feedback, as well as nudging actions with route suggestions. Novel ideas emerged in communication with local authorities and citizen engagement and should be adapted to mhealth and tested in future studies. Providing information on multimodal journey planning is another unexplored suggestion for AT promotion. As we found that different demographics provided different responses, varying populations and stages of change should be considered for these interventions. Further studies are needed to evaluate what specific app features could effectively trigger the transition from contemplating AT to actively attempting it and making it a habit. Finally, it must be emphasized that transforming mobility behavior is a complex endeavor. While promoting AT through an app can encourage change on an individual level, significant changes are also needed on broader social, environmental, and policy levels. To navigate this complexity and to promote AT, systemic change through a multifaceted approach grounded in the target user's perception is necessary. Apps have a role to play in this too.

## Funding resources

**Funding:** This work has been supported by UCA<sup>JEDI</sup> Investments in the Future project managed by the National Research Agency (ANR) [reference number ANR-15-IDEX-01]; and the European Union's Horizon 2020 research and innovation program, co-funded by Region Provence-Alpes-Côte d'Azur and IDEX UCA<sup>JEDI</sup> [reference number N° 847581].
CRediT authorship contribution statement

**Nuttanun Siriaporn:** Writing – original draft, Visualization, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Conceptualization.
**Audrey de Nazelle:** Writing – review & editing, Validation, Supervision.
**Anne Vuillemin:** Writing – review & editing, Supervision, Funding acquisition, Conceptualization, Validation.

Declaration of competing interest

The authors declare that they have no known competing financial

interests or personal relationships that could have appeared to influence
the work reported in this paper.

Acknowledgements

Thank you to Dr. Laura Brody for her help with language and proof
reading of this article. And thank you to the participants for their
valuable contribution to the study.

Appendix

Appendix 1. *Questions and prompts in the study*

This is the list of questions and prompts of each step of the concept mapping study. These questions were available in English and French.
Participant survey:

1. How old are you?

a. <19

b. 20–39

c. 40–59

d. >60

2. What is your postal code?

3. What is the highest level of education you completed?

a. Never received a diploma

b. Finished middle school

c. Received a technical degree

d. Received a high school diploma

e. Received a 2-year college degree

f. Received a bachelor's degree

g. Received a master's degree

h. Received a doctoral degree or higher

4. Do you have a diagnosed motor disability?

a. Yes, and it affects my ability to get around

b. Yes, but it does not affect my ability to get around

c. No

5. In a regular week, which mode of transport do you use most? If you use multiple modes equally, you may choose more than one.

a. I drive my car

b. I ride my motorcycle

c. I use private car services (Uber, taxi, etc.)

d. I am a passenger of carpool (with friends, family, or carpool service e.g. BlaBlaCar)

e. I take the public transport (bus, train, tram, including specialized service for handicap and seniors)

f. I ride my bicycle, scooter, skateboard, roller blade, etc.

g. I ride my e-bike, e-scooter, e-skate, hoverboard, etc.

h. I walk

Step 1 (Brainstorming):

1. In order to create a mobile app to encourage AT, what app feature would encourage you to active transport (walk/bike/skate/scooter)?

Step 2 (Sorting):

1. In this activity you will categorize ideas into piles according to your view of their meaning. To do this, you will sort each idea into piles in a way that makes sense to you. Group the ideas on how similar in meaning they are to one another.

Step 3 (Rating):

1. On a scale of 1 to 5, how effective would this statement be in your decision to engage in active transport?
<figure><img src="image_6.png" /><figcaption>Fig. A1. Binary matrix.</figcaption></figure>

This is an example of binary matrix from one of our participants during Step 3 (sorting). “1” in green indicates that the corresponding ideas were grouped together. Vice versa for “0” in red. This participant did not rate three ideas (20, 32, 39).

<figure><img src="image_7.png" /><figcaption>Fig. A2. Similarity matrix.</figcaption></figure>

This matrix shows, by color and number, how often each idea was grouped with the others overall, during Step 3 (sorting). Green indicates higher frequency and red indicates lower frequency.
<figure><img src="image_8.png" /><figcaption>Fig. A3. Point map.</figcaption></figure>

This map has layers on each point, indicating a higher bridging value with more layers. Higher bridging value indicates that the idea was sorted with other ideas further away in the plot, and therefore is less related to its surroundings, and served as a bridge between areas.
<figure><img src="image_9.png" /><figcaption>Fig. A4. Clustering steps.</figcaption></figure>

This figure shows each step of the clustering analysis, starting from 20 clusters to the final 6 clusters. Each vertical line represents a merger from n clusters to n-1 clusters. For example, the top line represents the merger between 4&6 cluster with 8&9 cluster, going from 20 clusters to 19 clusters. These mergers are numbered in blue on the map.

<caption>Table A1</caption>

All the ideas resulted from the concept mapping study with the bridging value and average rating of each of the ideas and its cluster averages. The clusters are ordered based on the average bridging value and the ideas within the clusters are ordered based on its rating value.

<table><thead><tr><th>ID</th><th>Idea</th><th>Bridging value<br>(0-1)</th><th>Average Rating<br>(out of 5)</th></tr></thead><tbody><tr><td>1</td><td><strong>Cluster name: Infrastructures</strong></td><td>Avg 0.06</td><td>Avg 3.84</td></tr><tr><td>18</td><td>It must be mandatory for all town with 60,000 + inhabitants to build additional 10 km cycling road every year</td><td>0.04</td><td>4.4615</td></tr><tr><td>19</td><td>More bike lanes should be built in urban areas to encourage people not to use cars.</td><td>0</td><td>4.3846</td></tr><tr><td>17</td><td>More complete network of cycle paths! Particularly in Nice, the North-South/South-North cycling network is missing.</td><td>0.04</td><td>4.0769</td></tr><tr><td>23</td><td>Provide an area dedicated to bicycles on the tram to offer continuity of service on long and/or routes without bike lanes.</td><td>0.03</td><td>3.9231</td></tr><tr><td>42</td><td>More safety infrastructure between cyclist and pedestrian; the speed of cyclist is 5 times higher than that of pedestrian</td><td>0.05</td><td>3.9231</td></tr><tr><td>21</td><td>Increase the number of street furniture for bicycles (i.e. ergonomic bike parking, inflation terminals near cycle paths).</td><td>0.06</td><td>3.9231</td></tr><tr><td>24</td><td>Have bike parking on each block segment in town.</td><td>0.11</td><td>3.8462</td></tr><tr><td>22</td><td>Encourage the installation of bicycle racks on buses, in particular to facilitate access to difficult (e.g., hilly) or out-of-the-way areas.</td><td>0.02</td><td>3.7692</td></tr><tr><td>44</td><td>Charging for motorcycle and bicycle parking to fund cycling policies</td><td>0.19</td><td>2.2308</td></tr><tr><td>2</td><td><strong>Cluster name: Itinerary</strong></td><td>Avg 0.19</td><td>Avg 3.77</td></tr><tr><td>43</td><td>Ability to choose different types of cycling infrastructures sought (from 100 % safe routes to sharing lanes with car traffic)</td><td>0.14</td><td>4</td></tr><tr><td>12</td><td>Provide multiple itinerary options of road/path with less cars</td><td>0.14</td><td>4</td></tr><tr><td>16</td><td>In the itinerary, offer a "Children's route" with secure cycle paths and peaceful streets.</td><td>0.15</td><td>4</td></tr><tr><td>11</td><td>A navigation system that offers realistic hybrid routes (including active transport and public transport).</td><td>0.32</td><td>3.9231</td></tr><tr><td>33</td><td>Shows secure routes of network of cycle paths already made</td><td>0.14</td><td>3.8462</td></tr></tbody></table>

(continued on next page)
<table><caption>Table A1 (continued)</caption><thead><tr><th>ID</th><th>Idea</th><th>Bridging value<br>(0-1)</th><th>Average Rating<br>(out of 5)</th></tr></thead><tbody><tr><td>37</td><td>Map for parking bicycle, scooters, etc.</td><td>0.18</td><td>3.6923</td></tr><tr><td>38</td><td>Percentage of bike lane and avoid main roads dedicated for motorized vehicles</td><td>0.29</td><td>3.5833</td></tr><tr><td>36</td><td>Map of the most popular routes</td><td>0.14</td><td>3.4167</td></tr><tr><td>13</td><td>Provide multiple itinerary options, showing difference in slope</td><td>0.21</td><td>3.4167</td></tr><tr><td>3</td><td><strong>Cluster name: Data</strong></td><td>Avg 0.19</td><td>Avg 3.13</td></tr><tr><td>4</td><td>Indicate the balance of total amount of money saved.</td><td>0.14</td><td>3.4615</td></tr><tr><td>1</td><td>When planning for a trip, directly indicate how much time you save if you go there by bike, bus, or train (multiple modes)</td><td>0.29</td><td>3.4615</td></tr><tr><td>5</td><td>Indicate the total amount of time saved</td><td>0.24</td><td>3.3846</td></tr><tr><td>35</td><td>Map of high pollution areas to avoid</td><td>0.33</td><td>3.3077</td></tr><tr><td>7</td><td>Provide the total gas/and money saved compared to a similar travel by car.</td><td>0.16</td><td>3.2308</td></tr><tr><td>15</td><td>When looking for car journeys of less than 10 km, stats of cars should compare with other modes (ie. indicate how much time and money can be saved by bike or public transport)</td><td>0.26</td><td>3.2308</td></tr><tr><td>2</td><td>When planning for a trip, directly indicate how much money you save if you go there by bike, bus, or train (multiple modes)</td><td>0.15</td><td>3.1538</td></tr><tr><td>10</td><td>Provide the summed stats on CO2 avoided for all app users</td><td>0.2</td><td>3.0769</td></tr><tr><td>9</td><td>Present a balance of total CO2 avoided, for example every month</td><td>0.12</td><td>2.9231</td></tr><tr><td>8</td><td>At the end of each journey, indicate the CO2 saved compared to an equivalent journey by car</td><td>0.09</td><td>2.8462</td></tr><tr><td>3</td><td>The app requests the user to indicate their usual mode of transport so that for every time they engage in a different, more active modes, the app calculates and compares the personal impact of the more active modes of transport.</td><td>0.23</td><td>2.7692</td></tr><tr><td>6</td><td>Calories will spend, comparing modes of transport</td><td>0.12</td><td>2.6923</td></tr><tr><td>4</td><td><strong>Cluster name: Contact with the government</strong></td><td>Avg 0.34</td><td>Avg 3.27</td></tr><tr><td>31</td><td>Cumulative graphic interface informing about the density of active travel road network user, intended for local officials (Metropolis, Marie, etc.). To support requests and arguments with the decision-makers.</td><td>0.42</td><td>3.5385</td></tr><tr><td>14</td><td>Shows "facilities" on the itinerary along the path (parking, repair stations...)</td><td>0.22</td><td>3.4615</td></tr><tr><td>30</td><td>Method of reporting obstacles on cycle paths (vehicles in particular), black spots, deformed pavement, etc. with photo, to inform municipal services</td><td>0.27</td><td>3.1538</td></tr><tr><td>34</td><td>On a global map, locate partner merchants who support active mobility.</td><td>0.45</td><td>2.9167</td></tr><tr><td>5</td><td><strong>Cluster name: App usability</strong></td><td>Avg 0.42</td><td>Avg 2.67</td></tr><tr><td>28</td><td>Offer in-app challenges depending on the mode of transport chosen</td><td>0.3</td><td>2.7692</td></tr><tr><td>39</td><td>Add a ranking to the mode of travel in the city</td><td>0.69</td><td>2.7692</td></tr><tr><td>27</td><td>gamify the app (e.g., badges according to the distances traveled with active mobility, or by type of active mobility)</td><td>0.32</td><td>2.6923</td></tr><tr><td>25</td><td>Indicate regularly (for example once a day when launching the app) positive information on active mobility (e.g.: "moving for 30 min a day reduces such a risk to your health by X%")</td><td>0.49</td><td>2.6923</td></tr><tr><td>26</td><td>Anonymously show other users on the app on the map in a geo-localized way, to reinforce the feeling that we are legion to use active mobility</td><td>0.35</td><td>2.6154</td></tr><tr><td>29</td><td>Can invite friends on the app</td><td>0.37</td><td>2.4615</td></tr><tr><td>6</td><td><strong>Cluster name: Legislation and code of conduct</strong></td><td>Avg 0.61</td><td>Avg 2.63</td></tr><tr><td>41</td><td>Clarify the pedestrian code, to recommend best pedestrian practices</td><td>0.34</td><td>2.9231</td></tr><tr><td>32</td><td>Have pedestrian and cyclist point of contact per district in each city, and who could inspect bike lanes and sidewalks.</td><td>0.44</td><td>2.6154</td></tr><tr><td>20</td><td>Install armchairs for people with motor disabilities with more ecological materials</td><td>0.66</td><td>2.5833</td></tr><tr><td>40</td><td>Provide a test (e.g., ACSR civic road safety certificate) for first-time cyclists and scooter users, in the face of irresponsible or ignorant behavior observed.</td><td>1</td><td>2.3846</td></tr></tbody></table>

## Data availability

Data will be made available on request.

## References

2018 Physical Activity Guidelines Advisory Committee, 2018. 2018 Physical Activity Guidelines Advisory Committee Scientific Report. Washington, DC.

Ajzen, I., 1991. The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 50, 179-211. https://doi.org/10.1016/0749-5978(91)90020-T.

Akgün-Tanbay, N., Campisi, T., Tanbay, T., Tesoriere, G., Dissanayake, D., 2022. Modelling road user perceptions towards safety, comfort, and chaos at shared space: the via Maqueda case study. Italy. J. Adv. Transp. 2022. https://doi.org/10.1155/2022/4979496.

Anagnostopoulou, E., Urbančić, J., Bothos, E., Magoutas, B., Bradesko, L., Schrammel, J., Mentzas, G., 2020. From mobility patterns to behavioural change: leveraging travel behaviour and personality profiles to nudge for sustainable transportation. J. Intell. Inf. Syst. 54, 157-178. https://doi.org/10.1007/s10844-018-0528-1.

Annear, M., Keeling, S., Wilkinson, T., Cushman, G., Gidlow, B., Hopkins, H., 2014. Environmental influences on healthy and active ageing: A systematic review. Ageing Soc. 34, 590-622. https://doi.org/10.1017/S0144686X1200116X.

Avila-Palencia, I., De Nazelle, A., Cole-Hunter, T., Donaire-Gonzalez, D., Jerrett, M., Rodriguez, D.A., Nieuwenhuijsen, M.J., 2017. The relationship between bicycle commuting and perceived stress: a cross-sectional study. BMJ Open 7, 1-11. https://doi.org/10.1136/bmjopen-2016-013542.

Babisch, W., 2014. Updated exposure-response relationship between road traffic noise and coronary heart diseases: a meta-analysis. Noise Health 16, 1-9. https://doi.org/10.4103/1463-1741.127847.

Bandura, A., 1989. Human agency in social cognitive theory. Am. Psychol. 44, 1175-1184. https://doi.org/10.1037/0003-066x.44.9.1175.

Bastos, D., Fernández-Caballero, A., Pereira, A., Rocha, N.P., 2022. Smart city applications to promote citizen participation in city management and governance: a systematic review. Informatics 9, 1-29. https://doi.org/10.3390/informatics9040089.

Baum, H.S., 2001. Citizen Participation, in: Smelser, N.J., Baltes, P.B.B.T.-I.E. of the S.& B.S. (Eds.), . Pergamon, Oxford, pp. 1840-1846. 10.1016/B0-08-043076-7/04439-9.

Blitz, A., Lanzendorf, M., 2020. Mobility design as a means of promoting non-motorised travel behaviour ? A literature review of concepts and findings on design functions. J. Transp. Geogr. 87, 102778. https://doi.org/10.1016/j.jtrangeo.2020.102778.

Bopp, M., Sims, D., Matthews, S.A., Rovniak, L.S., Poole, E., Colgan, J., 2017. Development, implementation, and evaluation of active lions: a campaign to promote active travel to a university campus. Am. J. Heal. Promot. 32, 536-545. https://doi.org/10.1177/0890117117694287.

Brand, C., Götschi, T., Dons, E., Gerike, R., Anaya-Boig, E., Avila-Palencia, I., de Nazelle, A., Gascon, M., Gaupp-Berghausen, M., Iacorossi, F., Kahlmeier, S., Int Panis, L., Racioppi, F., Rojas-Rueda, D., Standaert, A., Stigell, E., Sulikova, S., Wegener, S., Nieuwenhuijsen, M.J., 2021. The climate change mitigation impacts of active travel: Evidence from a longitudinal panel study in seven European cities. Glob. Environ. Chang. 67. https://doi.org/10.1016/j.gloenvcha.2021.102224.

Brand, C., Dons, E., Anaya-Boig, E., Avila-Palencia, I., Clark, A., de Nazelle, A., Gascon, M., Gaupp-Berghausen, M., Gerike, R., Götschi, T., Iacorossi, F., Kahlmeier, S., Laeremans, M., Nieuwenhuijsen, M.J., Pablo Orjuela, J., Racioppi, F., Raser, E., Rojas-Rueda, D., Standaert, A., Stigell, E., Sulikova, S., Wegener, S., Int Panis, L., 2021. The climate change mitigation effects of daily active travel in cities. Transp. Res. Part D Transp. Environ. 93. https://doi.org/10.1016/j.trd.2021.102764.

Campos Ferreira, M., Dias Costa, P., Abrantes, D., Hora, J., Felício, S., Coimbra, M., Galvão Dias, T., 2022. Identifying the determinants and understanding their effect on the perception of safety, security, and comfort by pedestrians and cyclists: a systematic review. Transp. Res. Part F Traffic Psychol. Behav. 91, 136-163. https://doi.org/10.1016/j.trf.2022.10.004.
Castellanos, S., 2016. Delivering modal-shift incentives by using gamification and smartphones: a field study example in Bogota, Colombia. Case Stud. Transp. Policy 4, 297-278. https://doi.org/10.1016/j.cstp.2016.08.008.

Castro, A., Künzli, N., Götschi, T., 2017. Health benefits of a reduction of PM10 and NO2 exposure after implementing a clean air plan in the Agglomeration Lausanne-Morges. Int. J. Hyg. Environ. Health 220, 829-839. https://doi.org/10.1016/j.ijheh.2017.03.012.

Cellina, F., Bucher, D., Veiga Simão, J., Rudel, R., Raubal, M., 2019. Beyond limitations of current behaviour change apps for sustainable mobility: insights from a user-centered design and evaluation process. Sustainability 11, 2281. https://doi.org/10.3390/su11082281.

Cellina, F., Castri, R., Simao, J.V., Granato, P., 2020. Co-creating app-based policy measures for mobility behavior change: a trigger for novel governance practices at the urban level. Sustain. Cities Soc. 53, 101911. https://doi.org/10.1016/j.scs.2019.101911.

Chaix, B., Kestens, Y., Duncan, S., Merrien, C., Thierry, B., Pannier, B., Brondeel, R., Lewin, A., Karusisi, N., Perchoux, C., Thomas, F., Méline, J., 2014. Active transportation and public transportation use to achieve physical activity recommendations? A combined GPS, accelerometer, and mobility study. Int. J. Behav. Nutr. Phys. Act. 11, 1-11. https://doi.org/10.1186/s12966-014-0124-x.

Chaudhry, A.-G., Masoumi, H., Dienel, H.-L., 2023. A systematic literature review of mobility attitudes and mode choices: MENA and South Asian cities. Front. Sustain. Cities 4. https://doi.org/10.3389/frsc.2022.1085784 OPEN.

Cook, S., Stevenson, L., Aldred, R., Kendall, M., Cohen, T., 2022. More than walking and cycling: What is 'active travel'? Transp. Policy 126, 151-161. https://doi.org/10.1016/j.tranpol.2022.07.015.

Cooper, T.L., Bryer, T.A., Meek, J.W., 2006. Citizen-centered collaborative public management. Public Adm. Rev. 66, 76-88. https://doi.org/10.1111/j.1540-6210.2006.00668.x.

Di Dio, S., Massa, F., Nucara, A., Peri, G., Rizzo, G., Schillaci, D., 2020. Pursuing softer urban mobility behaviors through game-based apps. Heliyon 6, e03930. https://doi.org/10.1016/j.heliyon.2020.e03930.

Ebbesson, E., 2022. Towards a co-creation framework based on citizens' dreams of future mobility. Transp. Res. Interdiscip. Perspect. 16. https://doi.org/10.1016/j.trip.2022.100686.

Ehn, M., Johansson, A., Revenäs, Å., 2019. Technology-based motivation support for seniors ' physical activity - a qualitative study on seniors ' and health care professionals ' views. Int. J. Environ. Res. Public Health 16, 2418. https://doi.org/10.3390/ijerph16132418.

Ek, A., Alexandrou, C., Nyström, C.D., Direito, A., Eriksson, U., Hammar, U., Henriksson, P., Maddison, R., Lagerros, Y.T., Löf, M., 2018. The Smart City Active Mobile Phone Intervention (SCAMI) study to promote physical activity through active transportation in healthy adults: a study protocol for a randomised controlled trial. BMC Public Health 18, 1-11. https://doi.org/10.1186/s12889-018-5658-4.

Ek, A., Alexandrou, C., Söderström, E., Bergman, P., Delisle Nyström, C., Direito, A., Eriksson, U., Henriksson, P., Maddison, R., Trolle Lagerros, Y., Bendtsen, M., Löf, M., 2020. Effectiveness of a 3-month mobile phone-based behaviour change program on active transportation and physical activity in adults: randomized controlled trial. JMIR Med Health 8, e18531-e. https://doi.org/10.2196/18531.

Evans, J.T., Phan, H., Buscot, M.J., Gall, S., Cleland, V., 2022. Correlates and determinants of transport-related physical activity among adults: an interdisciplinary systematic review. BMC Public Health 22, 1-26. https://doi.org/10.1186/s12889-022-13937-9.

Eysenbach, G., 2008. Medicine 2.0: social networking, collaboration, participation, apomigration, and openness. J Med Internet Res 10, e22.

Fetters, M.D., Curry, L.A., Creswell, J.W., 2013. Achieving integration in mixed methods designs-principles and practices. Health Serv. Res. 48, 2134-2156. https://doi.org/10.1111/1475-6773.12117.

Fiorello, D., Martino, A., Zani, L., Christidis, P., Navajas-Cawood, E., 2016. Mobility data across the EU 28 member states: results from an extensive CAWI Survey. Transp. Res. Procedia 14, 1104-1113. https://doi.org/10.1016/j.trpro.2016.05.181.

Flores-Mateo, G., Granado-Font, E., Ferré-Grau, C., Montana-Carreras, X., 2015. Mobile phone apps to promote weight loss and increase physical activity: A systematic review and meta-analysis. J. Med. Internet Res. 17. https://doi.org/10.2196/jmir.4836.

Gagliardi, D., Schina, L., Lucio, M., Mangialardi, G., Niglia, F., Corallo, A., 2017. Information and communication technologies and public participation : interactive maps and value added for citizens. Gov. Inf. q. 34, 153-166. https://doi.org/10.1016/j.giq.2016.09.002.

Giles-Corti, B., Vernez-Moudon, A., Reis, R., Turrell, G., Dannenberg, A.L., Badland, H., Foster, S., Lowe, M., Sallis, J.F., Stevenson, M., Owen, N., 2016. City planning and population health: a global challenge. Lancet 388, 2912-2924. https://doi.org/10.1016/S0140-6736(16)30066-6.

Goetz, A., Marinica, I., Mayr, H., Mosetti, L., Schubert, R., 2023. Do Mobile Applications Foster Sustainable Mobility? Evidence From a Field Experiment. SSRN Electron. J. 10.2139/ssrn.4310386.

Götschi, T., de Nazelle, A., Brand, C., Gerike, R., Alasya, B., Anaya, E., Avila-Palencia, I., Banister, D., Bartana, I., Benvenuti, F., Boschetti, F., Brand, C., Buekers, J., Carniel, L., Carrasco Turigas, G., Castro, A., Cianfano, M., Clark, A., Cole-Hunter, T., Copley, V., De Boever, P., de Nazelle, A., Dimajo, C., Dons, E., Duran, M., Eriksson, U., Franzen, H., Gaupp-Berghausen, M., Gerike, R., Girmenia, R., Götschi, T., Hartmann, F., Iacorossi, F., Int Panis, L., Kahlmeier, S., Khreis, H., Laeremans, M., Martinez, T., Meschik, M., Michelle, P., Muehlmann, P., Mueller, N., Nieuwenhuijsen, M., Nilsson, A., Nussio, F., Orjuela Mendoza, J.P., Pisanti, S., Porcel, J., Racioppi, F., Raser, E., Riegler, S., Robrecht, H., Rojas Rueda, D., Rothballer, C., Sanchez, J., Schaller, A., Schuthof, R., Schweizer, C., Sillero, A.,

Smidfeltrosqvist, L., Spezzano, G., Standaert, A., Stigell, E., Surace, M., Uhlmann, T., Vancluysen, K., Wegener, S., Wennberg, H., Willis, F., Witzell, J., Zeuschner, V., 2017. Towards a comprehensive conceptual framework of active travel behavior: a review and synthesis of published frameworks. Curr. Environ. Heal. Reports 4, 286-295. https://doi.org/10.1007/s40572-017-0149-9.

groupwisdomTM, 2022. The Concept System® groupwisdomTM.

Harrison, M.A.J., Barra, S., Borghesi, D., Vione, D., Arsenie, C., Iulian Olariu, R., 2005. Nitrated phenols in the atmosphere: a review. Atmos. Environ. 39, 231-248. https://doi.org/10.1016/j.atmosenv.2004.09.044.

Hasselwander, M., Kiko, M., Johnson, T., 2022. Digital civic engagement, open data, and the informal sector: a think piece. Transp. Res. Interdiscip. Perspect. 16, 100700. https://doi.org/10.1016/j.trip.2022.100700.

Hassu, S., Chiclana, F., Passow, B.N., Elizondo, D., 2015. Encouraging Active Commuting through monitoring and analysis of commuter travel method habits. In: 2015 SAI Intelligent Systems Conference (IntelliSys). IEEE, pp. 179-186. https://doi.org/10.1109/IntelliSys.2015.7361142.

Hatfield, J., Prabhakharan, P., 2016. An investigation of behaviour and attitudes relevant to the user safety of pedestrian/cyclist shared paths. Transp. Res. Part F Traffic Psychol. Behav. 40, 35-47. https://doi.org/10.1016/j.trf.2016.04.005.

Heinen, E., van Wee, B., Maat, K., 2010. Commuting by bicycle: An overview of the literature. Transp. Rev. 30, 59-96. https://doi.org/10.1080/01441640903187001.

Hermens, S., Frost, J., Renes, R.J., Kerkhof, P., 2016. Using feedback through digital technology to disrupt and change habitual behavior: A critical review of current literature. Comput. Human Behav. 57, 61-74. https://doi.org/10.1016/j.chb.2015.12.023.

Hognogi, G.-G., Meltzer, M., Alexandrescu, F., Ștefăneșcu, L., 2023. The role of citizen science mobile apps in facilitating a contemporary digital ago. Humanit. Soc. Sci. Commun. 10, 863. https://doi.org/10.1057/s41599-023-02358-7.

Hull, A., O'Holleran, C., 2014. Bicycle infrastructure: can digital change encourage cycling? Urban. Plan. Transp. Res. 2, 369-406. https://doi.org/10.1080/21650020.2014.955210.

Jackson, K.M., Trochim, W.M.K., 2002. Concept mapping as an alternative approach for the analysis of open-ended survey responses. Organ. Res. Methods 5, 307-336. https://doi.org/10.1177/109442802237114.

Jariyasunant, J., Carrel, A., Ekamaram, V., Gaker, D.J., Kote, T., Sengupta, R., Walker, J.L., 2012. The Quantified Traveler: Using personal data to promote sustainable transport behavior. LK - https://worldcat.org/title/838247419.

Javaid, A., Creutzig, F., Bamberg, S., 2020. Determinants of low-carbon transport mode adoption: systematic review of reviews. Environ. Res. Lett. 15. https://doi.org/10.1088/1748-9326/aba032.

Johnsen, J.A., Biegel, D.E., Shafran, R., 2000. Concept mapping in mental health: Uses and adaptations. Eval. Program Plann. 23, 67-75. https://doi.org/10.1016/S0149-7189(99)00038-5.

Kane, M., Trochim, W., 2007. Concept Mapping for Planning and Evaluation. 10.4135/9781412983730.

Kane, M., Rosas, S., 2017. Conversations About Group Concept Mapping: Applications, Examples, and Enhancements. SAGE Publications.

Kelly, C.M., Baker, E.A., Brownson, R.C., Schootman, M., 2007. Translating research into practice: Using concept mapping to determine locally relevant intervention strategies to increase physical activity. Eval. Program Plann. 30, 282-293. https://doi.org/10.1016/j.evalprogplan.2007.05.007.

Khreis, H., Sanchez, K.A., Foster, M., Burns, J., Nieuwenhuijsen, M.J., Jaikumar, R., Ramani, T., Zietsman, J., 2023. Urban policy interventions to reduce traffic-related emissions and air pollution: A systematic evidence map. Environ. Int. 172, 107805. https://doi.org/10.1016/j.envint.2023.107805.

Koivisto, J., Hamari, J., 2014. Demographic differences in perceived benefits from gamification. Comput. Human Behav. 35, 179-188. https://doi.org/10.1016/j.chb.2014.03.007.

Leask, D., Sandlund, M., Skelton, D., Altenburg, T., Cardon, G., Paw, C.A.M., De Bourdeaudhuij, V.M., Chastin, S., 2019. Principles and recommendations for the application and reporting of participatory methodologies in the development and evaluation of public health interventions. Res. Involv. Engagem. 5, 1-16. https://doi.org/10.1186/s40900-018-0136-9.

Li, Z., Wang, W., Liu, P., Schneider, R., Ragland, D.R., 2012. Investigating bicyclists' perception of comfort on physically separated bicycle paths in Nanjing, China. Transp. Res. Rev. 2317, 76-84. https://doi.org/10.3141/2317-10.

Máca, V., Scasný, M., Zverínová, I., Jakob, M., Hrnčík, J., 2020. Incentivizing commuter cycling by financial and non-financial rewards. Int. J. Environ. Res. Public Health 17, 1-14. https://doi.org/10.3390/ijerph17176033.

Marquart, H., Schuppan, J., 2022. Promoting sustainable mobility: to what extent is "health" considered by mobility app studies? a review and a conceptual framework. Sustainability 14. https://doi.org/10.3390/su14010047.

Merlin, L.A., Freeman, K., Renne, J., Hoermann, S., 2022. Clustered randomized controlled trial protocol of a Mobility-as-a-Service app for College campuses. Transp. Res. Interdiscip. Perspect. 14, 100572. https://doi.org/10.1016/j.trip.2022.100572.

Michie, S., van Stralen, M.M., West, R., 2011. The behaviour change wheel: a new method for characterising and designing behaviour change interventions. Implement. Sci. 6, 42. https://doi.org/10.1186/1748-5908-6-42.

Minnich, A., 2023. Gamification in the transport sector: quasi-experimental evidence from a bicycle navigation app. Transp. Res. Part A Policy Pract. 167, 103552. https://doi.org/10.1016/j.tra.2022.11.012.

Nelson, T., Ferster, C., Laberee, K., Fuller, D., Winters, M., 2021. Crowdsourced data for bicycling research and practice. Transp. Rev. 41, 97-114. https://doi.org/10.1080/01441647.2020.1806943.

Park, H., Sanguinetti, A., Castillo Cortes, G., 2017. EcoTrips, in: Marcus, A., Wang, W. (Eds.), Design, User Experience, and Usability: Designing Pleasurable Experiences,
Lecture Notes in Computer Science. Springer International Publishing, Cham, pp. 60–76. 10.1007/978-3-319-58637-3_5.

Pearson, L., Berkovic, D., Reeder, S., Gabbe, B., Beck, B., 2023. Adults' self-reported barriers and enablers to riding a bike for transport: a systematic review. Transp. Rev. 43, 356–384. https://doi.org/10.1080/01441647.2022.2113570.

Pfaeffli Dale, L., Dobson, R., Whittaker, R., Maddison, R., 2016. The effectiveness of mobile-health behaviour change interventions for cardiovascular disease self-management: A systematic review. Eur. J. Prev. Cardiol. 23, 801–817. https://doi.org/10.1177/2047487315613462.

Pinski, M., McCarthy, L.N., 2024. Bridging the gap: do community needs assessments enhance public participation in transportation planning? Transp. Res. Interdiscip. Perspect. 25, 101114. https://doi.org/10.1016/j.trip.2024.101114.

Polders, E., Vlahogianni, E., Leopold, F., Durso, C., 2015. ElderSafe - Risks and countermeasures for road traffic of the elderly in Europe. Brussels.

Prochaska, J.O., DiClemente, C.C., 1982. Transtheoretical therapy: Toward a more integrative model of change. Psychother. Theory Res. Pract. 19, 276–288. https://doi.org/10.1037/h0088437.

Prochaska, J.O., Velicer, W.F., 1997. The transtheoretical model of health behavior change. Am. J. Heal. Promot. 12, 38–48. https://doi.org/10.4278/0890-1171-12.1.38.

Rainie, L., Wellman, B., 2012. Networked: The New Social Operating System. The MIT Press.

Regional, W.H.O., 2022. Walking and Cycling: Latest Evidence to Support Policy-Making and Practice. Office for Europe, Copenhagen.

Riley, R., de Preux, L., Capella, P., Mejia, C., Kajikawa, Y., de Nazelle, A., 2021. How do we effectively communicate air pollution to change public attitudes and behaviours? A review. Sustain. Sci. https://doi.org/10.1007/s11625-021-01038-2.

Roider, O., Wegener, S., Stark, J., Judmaier, P., Michelberger, F., Barberi, A., 2019. Merging virtual world with real-life behavior: a concept for a smartphone app to influence young people's travel behavior. Transp. Res. Rec. 2673, 241–250. https://doi.org/10.1177/0361198119835812.

Rosenstock, I.M., 1974. The health belief model and preventive health behavior. Health Educ. Monogr. 2, 354–386. https://doi.org/10.1177/109019817400200405.

Ross, T., Burris, A., Oliveira, L., Arnott, B., Araujo-Soares, V., 2015. A feasibility study of the effect of phone-based feedback of other commuters' subjective experiences on driver intentions to change. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 548–558. https://doi.org/10.1007/978-3-319-20886-2_51.

Schippers, M., Adam, P.C.G., Smolenski, D.J., Wong, H.T.H., de Wit, J.B.F., 2017. A meta-analysis of overall effects of weight loss interventions delivered via mobile phones and effect size differences according to delivery mode, personal contact, and intervention intensity and duration. Obes. Rev. 18, 450–459. https://doi.org/10.1111/obr.12492.

Schoeppe, S., Duncan, M.J., Badland, H., Oliver, M., Curtis, C., 2013. Associations of children's independent mobility and active travel with physical activity, sedentary behaviour and weight status: A systematic review. J. Sci. Med. Sport 16, 312–319. https://doi.org/10.1016/j.jsams.2012.11.001.

Schönbach, D.M.I., Vondung, C., Hidding, L.M., Altenburg, T.M., Chinapaw, M.J.M., Demetriou, Y., 2020. Gender influence on students, parents, and teachers' perceptions of what children and adolescents in Germany need to cycle to school: A concept mapping study. Int. J. Environ. Res. Public Health 17, 1–22. https://doi.org/10.3390/ijerph17186872.

Seidler, A., Schubert, M., Romero-Starke, K., Freiberg, A., Zeeb, H., Riedel-Heller, S., Hegewald, J., 2019. Traffic noise and mental illness – a systematic review. Environ. Epidemiol. 3, 360. https://doi.org/10.1097/01.EE9.0000609984.76129.9e.

Semanjski, I., Aguirre, A.J.L., De Mol, J., Gautama, S., 2016. Policy 2.0 platform for mobile sensing and incentivized targeted shifts in mobility behavior. Sensors (switzerland) 16. https://doi.org/10.3390/s16071035.

Severens, P., 1995. Handbook Concept Mapping met Ariadne [Handbook Concept Mapping using Ariadne]. Nederlands Centrum Geestelijke Volksgezondheid/Talcott BV, Utrecht.

Soler-i-Martí, R., 2015. Youth political involvement update: measuring the role of cause-oriented political interest in young people's activism. J. Youth Stud. 18, 396–416. https://doi.org/10.1080/13676261.2014.963538.

Sottile, E., Giacchetti, T., Tuveri, G., Piras, F., Calli, D., Concas, V., Zamberlan, L., Meloni, I., Carrese, S., 2021. An innovative GPS smartphone based strategy for university mobility management: a case study at the University of RomaTre, Italy. Res. Transp. Econ. 85, 100926. https://doi.org/10.1016/j.retrec.2020.100926.

Tajfel, H., Turner, J.C., 2004. The Social Identity Theory of Intergroup Behavior, Political psychology: Key readings., Key readings in social psychology. Psychology Press, New York, NY, US. 10.4324/9780203505984-16.

Trochim, W.M.K., 1989. An introduction to concept mapping for planning and evaluation. Eval. Program Plann. 12, 1–16.

Tsirimpa, A., Polydoropoulou, A., Pagoni, I., Tsouros, I., 2019. A reward-based instrument for promoting multimodality. Transp. Res. Part F Traffic Psychol. Behav. 65, 121–140. https://doi.org/10.1016/j.trf.2019.07.002.

von Schneidemesser, D., Herberg, J., Stasiak, D., 2020. Re-claiming the responsivity gap: The co-creation of cycling policies in Berlin's mobility law. Transp. Res. Interdiscip. Perspect. 8, 100270. https://doi.org/10.1016/j.trip.2020.100270.

Willis, D.P., Manaugh, K., El-Geneidy, A., 2015. Cycling under influence: summarizing the influence of perceptions, attitudes, habits, and social environments on cycling for transportation. Int. J. Sustain. Transp. 9, 565–579. https://doi.org/10.1080/15568318.2013.827285.

Yang, K., 2005. Public administrators' trust in citizens: a missing link in citizen involvement efforts. Public Adm. Rev. 65, 273–285. https://doi.org/10.1111/j.1540-6210.2005.00453.x.

Yetano, A., Royo, S., 2017. Keeping citizens engaged: a comparison between online and offline participants. Adm. Soc. 49, 394–422. https://doi.org/10.1177/0095399715581625.