Whilst the origins of e-scooters stretch back to the early 20^th century (Mansky, 2019), the widespread phenomena of shared e-scooters in major cities worldwide is far more recent and dates to the early 1990s with the advent of the lithium-ion battery. Shared e-scooter schemes grew rapidly throughout the 2000s, with scooters available for generally short-term rental. Companies separated into those providing either docked scooters (with fixed locations to pick up and park the scooter) or dockless (scooters are tagged by the supplier and can be left anywhere within a defined radius). US cities were early adopters of e-scooters, with parts of Europe following soon after, and shared e-scooters are now available in major cities internationally. In many cities, there are competing suppliers operating in the same locations. The UK has been slower to uptake the new mode. Currently, using privately owned e-scooters on public roads and pavements is illegal in the UK, but they can be used on private land. In July 2020, a series of trials were announced by the Department for Transport via competitive tender. By 2022, there were 32 shared e-scooter schemes under trial in a mixture of cities, towns and local authorities. E-scooters are legal in public areas if they are from a government-approved shared scheme trial in the UK, and there are many restrictions on how and where they can be used. Shared e-scooter users will need to hold a valid driving license (a full or provisional UK licence for categories AM, A, or B includes entitlement for category Q), and wearing a helmet is advised. It is prohibited to use shared e-scooters on the pavement, footpaths and pedestrianised zones, and they can be used on cycle lanes. There is a maximum speed set at 15.5 miles per hour, and some lower limits may be imposed in certain areas by geofencing. E-scooters should be used by one person at a time, and riding under the influence (e.g. drunk) is prohibited (Department for Transport, 2020).

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 (Smith and Schwieterman, 2018) and improvements to journey time, convenience and practicality in hot weather (Sanders, Branion-Calles, and Nelson, 2020). However, there have been concerns about the safety of both riders and other road space users (Kobayashi et al., 2019, Sikka et al., 2019) and that use of e-scooters may displace more active modes such as walking and cycling (Sanders, Branion-Calles, and Nelson, 2020). Yang et al. (2020) used a meta-analysis approach to understand the e-scooter related incidents and safety concerns. The work (Yang et al., 2020) concludes that the key concerns (risks) in the US are helmet use and riding under the influence. Siman-Tov et al. (2016) investigated a total of 795 patients who were admitted for e-bike and e-scooter-related (private rather than shared) injuries and incidents in Israel, and 8.4% of the Injuries were pedestrians. The hypothesis that e-scooters and other e-micromobility could have negative consequences of reducing physical activity is still disputed in academic and non-academic works (Bozzi, 2021). Glenn et al. (2020) reported that e-scooter has a negative effect on health due to users’ lower level of physical activity. But Jones, Harms, and Heinen (2016) suggested that the overall physical activity effect of switching to e-micromobility remains unclear, with e-micromibility users tend to perceive (self-report) increases in physical activity (Jones, Harms, and Heinen, 2016). Sanders, da Silva Brum-Bastos, and Nelson (2022) concluded that e-scooter use is likely to reduce active travel, but the mode is healthier than driving and replaced some car trips. Hence, the use of e-scooters and its relationship with physical activity is moderated by transportation habits, options, and context (Sanders, da Silva Brum-Bastos, and Nelson, 2022).

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 (Cottell, Connelly, and Harding, 2021). Studies involving empirical evidence from the UK are predominantly in the medical literature, such as injury patterns from private e-scooter use in London (Cruz et. al, 2021; Rashed, Vassiliou, and Barber, 2022). Two early reports from the trial implementations (Brown, Klein, and Thigpen, 2021; Sherriff et al., 2021) focus on users’ behaviour and attitudes. Brown, Klein, and Thigpen (2021) includes findings from Milton Keynes around misparking and the potential to incentivise correct parking. Sherriff et al. (2021) reports on a mixed sample from Salford, that is, users, undecided potential users and those who would not use scooters. The number of e-scooter users in the sample is low (fewer than 200) and the implementation area is small, but the report gives early evidence that although current use purpose is largely “for fun”, there is a propensity for e-scooters to be incorporated as a mode within regular trips (with male users more so than females).

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. (2011), who conclude that “a useful way to convey information on a wide array of subjective and objective indicators is to …. not attempt to reduce well-being to one number, but instead ….. to find ways to present information on a variety of objective and subjective facets of wellbeing” (Forgeard et al., 2011). A number of studies have been concerned with methods to assess well-being impacts from travel in general terms, but are not directly linked to e-scooters. Mokhtarian (2019) gives a broad overview, examining terminology, related concepts and issues in measurement of well-being and travel. Gärling et al. (2020) propose that travel behaviour research should focus more on emotional well-being. The paper reviews and assesses current measurement methods for emotional well-being and argues that the best method is recurrent measures of current mood. For multi-mode and multi-stage journeys, aggregation of point estimates can be used. Gärling et al. (2020) suggests that where retrospective is necessary, an amendment to Ettema’s (2011) satisfaction with travel scale may be used as a standardised retrospective measure of emotional well-being during travel. Chatterjee et al. (2020) propose a conceptual model for well-being related to the commute, but not specifically for e-scooters as a mode. Although a significant number of e-scooter trips are for non-commute purposes, the model proposed by Chatterjee et al. (2020) is a useful, loose framework to investigate e-scooter well-being impacts in response to the research question. The model includes subjective (e.g. stress, eudaimonic) and objective (e.g. time, money) constructs and temporal aspects (i.e. mood during travel, afterwards and long term). It also extends to life objective factors such as life conditions and other modes used by the traveller. With the lack of substantive research in the field, in this study, the nature of well-being impacts is hypothesised based on published findings from other related modes (for example, bicycle). The source of positive e-scooter well-being outcomes are anticipated from a mixture of characteristics including being fun to use, creating accessibility, forming a semi-active mode, exposure to fresh-air and others. Negative well-being impacts from e-scooter use may arise from, for example, the stress of being in traffic, near misses and the need to concentrate on driving and riding skills.

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., Glenn et al., 2020; Milakis et al., 2020). This is the originality and knowledge gap that we contribute towards with the research reported in this paper. The overarching research question is: What are the significant factors underpinning self-reported changes in subjective well-being indicators as a result of e-scooter use? The results will be of interest to policy makers, operators and the general public in informing assessment of the wider costs and benefits of shared e-scooter schemes alongside the core outcomes such as safety, efficiency, environmental and financial factors.

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.

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 (2020) reported no e-scooter sharing users in their survey aged over 61.

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 (Bozzi and Aguilera, 2021) and attracting more investments into creating a better environment and culture for facilitating various low speed and active travel mode, such as cycling and walking. There is also a potential bias in the “cycling less” responses, since the other questions (e.g. “walking more” and “more physical activity”) are for positive effects, while the “disagree” and “strongly disagree” with “cycling less” is not entirely clear does it mean “I am cycling more” or “I am cycling the same”. Therefore, the negative effect on cycling is not fully captured in the questionnaire. The outcome of cycling less due to use of the e-scooter can, however, be detected by analysis of associated questions collected in the questionnaire which, due to space limitations, are not reported here. These concern mode switch and in particular, participants were asked to indicate a switch from other modes including more active modes such as walking and cycling in their last journey and journeys in the previous month. That analysis will form part of a subsequent paper. More generally, although many of the questions have a positive orientation, e.g., ‘feeling less stressed’, other questions that have a negative orientation include those related to air quality and personal safety e.g ‘I’m nervous about my safety’. Due to the length of the survey and considering user response burden, it wasn’t possible to fully balance the number of positive and negative orientated questions. The survey does not fully capture all the variance in the questions of gender. Binary gender options were provided to keep consistency with open UK census data. Although a “prefer not to say” option is available, it does not effectively allow people to express other gender identities, such as non-binary and transgender.

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 (Bozzi & Aguilera, 2021), the users and survey samples differ from the national census average, with more younger, male, affluent people of relatively higher educational qualifications. Different groups may have varying travel behaviours and associated subjective well-being changes. As in any survey, the findings only reflect the opinion of the respondents. The conclusions should not be over-generalised to non-scooter users or users of scooters that are not part of a shared e-scooter scheme.

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.

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