E-scooters are a relatively new mode of travel in the UK and their impacts on physical and mental health are uncertain. Although their use does not involve physical activity directly, through walking or cycling, they might provide other well-being benefits. Such effects will likely vary according to the user and the context. We aim to assess whether use of shared e-scooters is associated with wider well-being and mental health, arising from, for example access to local services, exposure to the natural environment, reduced stress, and a perception of having done some exercise. Our secondary question is whether potential well-being impacts vary across population sub-groups. A total of 2,402 responses to an online survey were completed during a one-month period (August to September 2021) by shared e-scooters users operated by a sole UK provider. Personal well-being from e-scooter use was assessed using questions on general levels of stress and mood (before, during or after e-scooter journeys), and features of the journey such as exposure to the natural environment and perception of air quality. All well-being questions were reported using a five-point Likert scale. Analysis indicates that people with protected characteristics and those who have personal challenges, for example with respect to personal mobility, are more likely to incur well-being benefits. The results presented are part of on-going research, with the next steps being to measure changes over time. The findings may be of interest to policymakers and the research community.
Whilst the origins of e-scooters stretch back to the early 20th century (
One of the reasons for the delay in introducing e-scooters to the UK has been the mixed set of hypothesised and potential impacts arising from e-scooter use, with many past case studies based in the USA. Some of the benefits include potential for increased accessibility (
Published literature based on empirical evidence from e-scooter use in the UK is still sparse. Some are prospective, covering for example potential impacts from e-scooters prior to the introduction of shared e-scooter schemes (
A large variety of definitions of well-being have been proposed, covering both objective and subjective measures. A comprehensive summary is that by Forgeard et al. (
To date, little has been reported on the wider impacts (such as well-being aspects) of e-scooters. The few publications in the public domain comprise reviews rather than analysis of primary data (e.g.,
The structure of the paper is as follows. In section 2, we describe the method and analysis approach used to investigate the research question, based on the use of an online survey. The results are presented in section 3, followed finally by conclusions and discussion.
With little other published work in this field, the research started with the hypothesis that well-being impacts from shared e-scooter use could be both positive and negative and that different types of users may have different experiences. The method adopted is a social science approach, based on self-reported well-being via an online questionnaire. This method was chosen to generate early evidence on the direction and nature of impacts in the spirit of a pilot study, which could be the basis of a more formal assessment in future research.
Based on the literature and drawing on reviews, findings from related modes and the conceptual frameworks (section 1), the well-being related questions were grouped as follows: (a) Feeling less stressed before, during or after a journey and good mood; (b) Subjective changes in activity levels, i.e. feeling more active, cycling less, walking more; (c) Self-reported accessibility and efficiency measures, for example, higher journey time reliability, benefits with respect to journey cost; and (d) perceived environmental impacts concerning local air quality and personal safety. A range of independent variables were asked, including basic demographic information (e.g., age category, gender, ethnicity group, household income, education level as examples). Respondents also voluntarily provided information on their frequency of e-scooter use, mobility (ability to walk for approximately 400 metres), their access to other modes and trip purpose by category. The wording in the questionnaire for these variables is presented in
The online questionnaire was delivered to potential respondents in the Voi e-scooter suppliers’ database of registered users on August 2021 and who had agreed to receive messaging on initial registration to use the scooters. Participants were offered entry to a prize draw to acknowledge the time and inconvenience of taking part, with prizes of e-shopping vouchers, helmets and scooter use credits. A total of 2,402 responses were received during the one-month period that the survey was live (August to September 2021). The data included respondents from 15 cities and towns in the UK and includes first-time users, occasional users (1–3 rides per month), regular users (4–6 rides per month) and frequent users (6+ rides per month). All well-being questions were reported using a five-point Likert scale ranging from strongly disagree to strongly agree.
The 15 UK cities and towns where respondents were located are as follows: Bath, Birmingham, Bristol, Cambridge, Corby, Coventry, Higham Ferrers, Kettering, Liverpool, Northampton, Oxford, Peterborough, Portsmouth, Southampton, and Wellingborough. Different no-ride and slow-ride zones were implemented in these cities and towns to ensure that riding occurs in relatively safe and monitored areas.
The dependent variables used in the questionnaire were presented on a five-point Likert scale, that is (1) Strongly disagree, (2) Disagree, (3) Neither agree nor disagree, (4) Agree, and (5) Strongly agree. To capture and utilise the explicit ordering information in the answers, an Ordinal Logistic Regression (OLR) model (
In this research, relatively low frequencies of “Strongly disagree” responses are observed in a number of questions (introduced in section 3.2). Therefore, the adjacent “Strongly disagree” and “Disagree” are collapsed (
The models were applied using R language and the MASS package, with the OR and
We begin with a summary of the basic demographic and socioeconomic characteristics of the e-scooter users who responded, including behaviour related to e-scooter usage. The percentage values of each variable and categories within are shown in
Demographic, socioeconomic characteristics, and frequency of e-scooter use.
Variable | Categories | ||
---|---|---|---|
Age band | 18–30 years | 1144 | 47.6 |
31–40 years | 649 | 27 | |
41–60 years | 525 | 21.9 | |
61+ years | 54 | 2.3 | |
Prefer not to say | 30 | 1.2 | |
Gender identity | Female | 826 | 34.4 |
Male | 1513 | 63 | |
Prefer not to say | 63 | 2.6 | |
Ethnicity | White | 1960 | 81.6 |
BAME | 369 | 15.4 | |
Prefer not to say | 73 | 3 | |
Long-term illness that limits daily activities or work | Yes | 243 | 10.1 |
No | 2095 | 87.2 | |
Prefer not to say | 64 | 2.7 | |
Any difficulty walking for a quarter of a mile? | Yes | 99 | 4.1 |
No | 2236 | 93.1 | |
Prefer not to say | 67 | 2.8 | |
Educational attainment | No formal qualifications | 56 | 2.3 |
Level 1 and 2 (e.g. GCSE’s, O Levels) | 264 | 11 | |
Level 3 (e.g. A Levels, BTEC) | 554 | 23.1 | |
Level 4 (e.g. University Degree) or higher | 1403 | 58.4 | |
Prefer not to say | 125 | 5.1 | |
Employment status | Employed | 1733 | 72.1 |
Self-employed | 244 | 10.2 | |
Unemployed | 90 | 3.7 | |
Full-time student/pupil | 209 | 8.7 | |
Not working (Others) | 45 | 1.9 | |
Prefer not to say | 81 | 3.4 | |
Social Grade (UK National Statistics Socio-economic classification, NS-SEC) | AB | 379 | 15.8 |
C1 | 1285 | 53.5 | |
C2 | 266 | 11.1 | |
DE | 203 | 8.4 | |
Prefer not to say | 269 | 11.2 | |
Cars available to household | Have car(s) | 1781 | 74.1 |
No cars | 550 | 22.9 | |
Prefer not to say | 71 | 3 | |
Annual household income (AHI) | Less than £15,000 | 248 | 10.3 |
£15,000–£24,999 | 322 | 13.4 | |
£25,000–£37,499 | 389 | 16.2 | |
£37,500–£49,999 | 321 | 13.4 | |
£50,000–£74,999 | 351 | 14.6 | |
£75,000–£99,999 | 213 | 8.9 | |
Over £99,999 | 173 | 7.2 | |
Prefer not to say | 385 | 16 | |
How regularly have you used e-scooters? | One time only | 557 | 23.2 |
Occasionally (1–3 times a month) | 1013 | 42.2 | |
Regularly (4–6 times a month) | 356 | 14.8 | |
Frequently (more than 6 times a month) | 476 | 19.8 |
The youngest age group (age 18–30) consists of the highest proportion (47.6%) in the survey, with 2.2% of respondents aged over 61. These are overall consistent with other e-scooter studies, for example, Almannaa et al. (
There is also an evident higher proportion of males (63%) and users having good educational attainment, with 58.4% having at least a level 4 degree (higher education or equivalent). The proportion with no educational qualifications is 2.3%, compared with a 2018 national average figure of 9% (details of UK qualification levels in the Appendix 3). In the sample, those reporting no educational qualifications were generally in the higher age categories. This may be attributable to the relationship between age and educational achievement, with more younger people having a degree.
The results show that 10.1% of the respondents have a long-term illness, which limits daily activities or work (Limiting long-term illness, LLTI), and 4.1% consider themselves to have difficulties in walking for one-quarter mile (approximately 400 metres). The majority (42.2%) of participants are occasional users who use e-scooter 1–3 times a month; this is followed by one-time only users (23.2%). Interesting, this survey attracted a slightly higher percentage of frequent e-scooter users (19.8%) than regular users (14.8%). The survey not only attracted frequent users but also those who are not more likely to be in favour of the scheme. Some of the one-time only and occasional users may not be familiar with the scheme or do not consider e-scooter as an attractive travel model that well fits their needs. Qualitative feedback using open ended questions was also collected alongside the quantitative/ordinal responses, and people used this survey to express their suggestions and experience (both positive and negative).
Overall, respondents’ demographic and socioeconomic profile is consistent with other micromobility studies (
Respondents were initially asked about the rationale for choosing an e-scooter on their last trip (
Response to “Why did you choose to ride an e-scooter on your last trip?”
Trip origin and destination types.
Proportion of Trip origin and destination types.
Destination | ||||||||
---|---|---|---|---|---|---|---|---|
% | HOME | W&S | PT | LEISURE | VFR | OTHERS | Total | |
Origin | HOME | 0.65 | 14.11 | 3.17 | 7.35 | 11.23 | 11.93 | 48.44 |
W&S | 9.47 | 2.70 | 0.82 | 0.41 | 0.71 | 1.12 | 15.23 | |
PT | 2.47 | 1.82 | 0.18 | 0.71 | 0.82 | 0.82 | 6.82 | |
LEISURE | 1.88 | 0.24 | 0.41 | 11.35 | 0.35 | 0.53 | 14.76 | |
VFR | 4.53 | 0.18 | 0.71 | 1.00 | 1.06 | 0.53 | 8.00 | |
OTHERS | 3.88 | 0.82 | 0.12 | 0.12 | 0.35 | 1.47 | 6.76 | |
Total | 22.87 | 19.87 | 5.41 | 20.93 | 14.52 | 16.40 | 100.00 |
HOME: Home; W&S: Work/Business/School/College/University; PT: Bus/Train/Tram/Other public transport; LEISURE: Just rode for fun/On holiday, leisure; VFR: Visiting friends or relatives; OTHERS: Any other types.
In the following sections (3.2.2–3.2.5), we investigate e-scooter related changes in subjective well-being change, physical activity and other wider questions on accessibility. For each, a summary of responses is given, followed by fitting OLR models on several key questions (section 3.3).
Two groups of questions were used to explore changes in firstly personal stress levels (before, during and after the journey) and secondly, some wider indicators of well-being (mood, energy, feeling closer to nature). With the same question format:
Summary responses to questions concerning feelings of stress and other well-being indicators.
In
The lowest level of agreement was towards the statement that as a result of using e-scooters the participants felt less stressed before the journey. This aligns with the sense that the impacts of e-scooter use are largely incurred during the use of the mode (
E-scooters can help to increase accessibility by providing a flexible urban travel mode. Respondents were asked about four indicators of accessibility, access to local services and amenities, the speed and reliability of journeys, and the costs involved, using the following question: “
Survey results on various benefits in accessibility, journey time and cost brought by e-scooter.
E-scooters are sometimes advertised as an “effort-free” transport mode, which may imply that their use offers few, if any fitness benefits (
E-scooter use and self-reported physical activity.
42.4% of respondents strongly agreed or agreed that e-scooter is not completely “effort-free”, and they feel like they have done some exercise after the e-scooter trip, whilst only 29.2% reported that they disagreed with the statement. However, it is important to note that this is a perception rather than based on measured activity.
There are also more people who consider themselves to be walking more, but cycling less, as part of the e-scooter journey. Overall, respectively 17.1% and 24.6% of participants strongly agree and agree that e-scooter trip “involves more physical activity than other ways I usually travel”. They are higher than the proportion of people who disagree or strongly disagree. We can not completely disentangle the relationship between scooter use and walking or cycling because our statements were framed around walking more or cycling less.
Two questions about personal safety and air quality were asked to detect the likelihood of negative well-being outcomes from e-scooter use. Respondents were again asked to respond on the Likert scale to the statement
Safety concerns could impede people’s willingness of e-scooter (
Safety and air quality hazards concerns.
This study further investigated the relationship between e-scooter use and subjective well-being changes. Hence, the following key questions (listed in
Key Questions that are chosen to fit the OLR model.
Group | Question |
Stress level | Feeling less stressed during the journey if use e-scooter? |
Feeling less stressed after the journey | |
Feelings and wider well-being | I’m usually in a good mood after an e-scooter trip |
Feeling closer to the natural environment with e-scooter | |
Feeling less sluggish when I arrive using e-scooter | |
Accessibility | Accessibility to services and amenities has improved with e-scooter |
Reliability of journey time has improved with e-scooter |
Section 3.2.2 indicated that the impacts of e-scooter use on stress levels are mainly incurred during the use, and could happen immediately after the journey (
Ordinal logistic regression results of stress level and well-being.
From
“Get fresh air” were significantly more likely to feel less stressed both during and after the trip, where the odds ratios are 1.69 and 1.56, respectively. Working people and students tend to benefit from less stress while riding e-scooters. Similar relationships were found in people of lower educational attainment (Level 1, 2 and below) and income (annual household income between £ 15,000 and £24,999).
To avoid the reverse causality problem, not all models have utilised the same group of independent variables. The purpose of “have fun” is dropped when modelling the relationship between input features and “in good mood” (dependent). Hence, the round symbol (in
Ordinal logistic regression results of feelings and wider well-being.
The feeling of “self-efficacy” and “control” in mobility and travelling helps improve psychological well-being and life satisfaction (
Ordinal Logistic Regression results of feelings about accessibility and travel time reliability.
From
Although a substantial body of research has been conducted into some of the impacts of e-scooter use, for example on safety issues, the literature around a wider set of impacts including well-being is much more sparse. Due to the very recent introduction of shared e-scooter schemes in the UK, this paper has particular novelty in reporting preliminary perceived well-being outcomes from shared e-scooter use in the UK. Building on a framework for a social science approach to self-reporting of well-being, the results are not intended to reflect a clinical assessment of mental health. However, the findings at the level of summary trends and Ordinal Logistic Regression modelling have consistency. Those users with protected characteristics, i.e. ethnic minority, those with lower educational outcomes, mobility issues, and who do not have cars are more likely (higher odds ratio) to agree on well-being benefits according to responses on the Likert scale. 2.3% of respondents are aged 61 or older, although a relatively small proportion, it is interesting to note that the age range extends to this older group, given (media) stereotypes of e-scooters as this mode is only used by younger groups. For example, the work of Bieliński & Ważna (
The strengths of the study arise from the large sample of complete responses obtained with a relatively lengthy questionnaire, allowing analysis to consider a wide number of variables and determinants which have not been considered in related work, such as individuals’ health status. A high proportion of respondents gave permission for the research to link their questionnaire responses to their empirical individual e-scooter trip data. This will exploration of further characteristics such as time of departure by demographic and differences in route choice. It will also allow analysis by the geographies involved, such as whether responses vary significantly by location within the UK and the extent to which the demographic varies by location. There are inevitably constraints on the work presented here, including a lack of validation or comparator data, largely due to the early stage of the e-scooter trials in the UK overall. Because registration to use a shared e-scooter requires ICT (information and communications technology) access, the issue of bias against non-digital users that arises with other online questionnaires is not a factor here. However, the users surveyed are from a single suppliers’ database and the extent to which this is representative of the experience of all shared e-scooter users is unknown.
This research focused on e-scooter’s users’ use and their subjective well-being, while the wider impacts on the whole transport and urban system are not investigated. The synergies and trade-offs between e-scooter users and other travel modes/people (car drivers, pedestrians) are beyond the scope of this research. Future work on this interesting topic might provide a comprehensive picture of the whole urban transport system and urban inhabitants.
Respondents reported a higher proportion of agreeing that e-scooter involves more physical activities, but the links between e-scooters and physical activity are not thoroughly explored in this work, since the focus of this research is the subjective well-being and e-scooter use. Future work may study the relationship by looking into the reasons for doing more physical activity. It should be noted that the use of e-scooter may not only directly leads to more physical activities, in the long-term, this new type of micromobility might also promote infrastructure changes (
The changes in subjective well-being might vary in different contexts and local environment. Respondents were from 15 different cities and towns in the UK (all outside of Greater London), but this study did not incorporate spatial variables (e.g. city and town name, trip origin and destination coordinates, land use). Future work utilising spatial and context information could help shed more light on the relationship between people’s well-being changes and e-scooter use.
The survey was conducted between August and September 2021, when COVID-19 was of concern. The pandemic changed how people travel and their opinion about different modes, in the short-term but may also in the long-term. In future work, it can be beneficial to re-survey the cohort and understand how their perspectives may vary at different stages of the pandemic.
Similar to the observation in many other micro-mobility studies (
The research scope didn’t encompass a policy study, given that at the time of study and paper production the schemes were still subject to formal evaluation and policy review, led by the Government. However the main implications for policy comprise two aspects. First, the work indicates that shared e-scooter schemes engender a set of well-being impacts for the users that can be considered alongside other impacts more traditionally included in scheme evaluation, positive or negative (i.e. efficiency, environmental, financial). A standard cost-benefit analysis (CBA) framework would rarely include these well-being impacts due to the challenges in monetising them. Hence an expanded joint cost-benefit and multicriteria analysis (CBA-MCA) approach would be needed with the user well-being impacts enumerated using a similar approach to that adopted here. Second, those users most likely to incur some types of well-being benefits are likely to have protected characteristics or personal challenges (for example in terms of ethnicity or personal mobility). This has implications where the development of transport policy considers equity outcomes and the distribution of impacts amongst sub-sets of the population. A further analysis could usefully consider the role of e-scooter use well-being impacts alongside the wider set of personal health benefits or costs to specific groups of individuals with protected characteristics or personal challenges – particularly if the use of e-scooters could form a ‘gateway’ mode to increased use of more active travel forms or increased outdoor activity more generally. The survey responses as a whole form part of a wider study, with further analysis, for example, on mode shift and potential environmental impacts, to be reported in future papers.
For the purposes of open access, the author has applied a creative commons license (CC BY) license to any author accepted manuscript version arising.
The data can not be openly available because of ethical and legal considerations. Aggregate data will be made available in the Research Data Leeds Repository.
The additional files for this article can be found as follows:
DOI:
Appendix 2 to 3. DOI:
The authors would like to acknowledge the contribution by Voi scooters, without whom the survey wouldn’t have been possible. The opinions expressed in the paper are those of the authors only and do not necessarily reflect the views of Voi scooters.
This research has been sponsored by the Alan Turing Institute under grant number R-LEE-006 and from the Medical Research Council (grant number MC_UU_00006/7). James Woodcock has received funding from the European Research Council (ERC) under the Horizon 2020 research and innovation programme (grant agreement No 817754). This material reflects only the author’s views and the Commission is not liable for any use that may be made of the information contained therein. No funds were received from commercial partners. The prize draw rewards to compensate participants for the inconvenience of participation were partly funded by The Alan Turing Institute and Voi.
The authors have no competing interests to declare.
Susan Grant-Muller: conceptualisation, methodology, investigation, resources, data curation, writing – original draft, writing – review & editing, Writing – review & editing, supervision, project administration, funding acquisition. Yuanxuan Yang: methodology, formal analysis, writing – original draft, writing – review & editing, software, validation, visualization. Jenna Panter: conceptualisation, methodology, writing – review and editing. James Woodcock: Conceptualisation, Methodology, Writing – editing and review.