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Article

Is Active Mobility Associated with Increased Levels of Perceived Well-Being? The Role of Perceived Constraints

by
Apostolia Ntovoli
1,2,*,
Evmorfia Giannakou
2,
Georgia Stavropoulou
2,
Thomas Karagiorgos
2,
Afroditi Lola
2,
Eleni Anoyrkati
3 and
Kostas Alexandris
2,*
1
Department of Physical Education and Sport Science, School of Health Sciences, Frederick University, Nicosia 1036, Cyprus
2
Department of Physical Education and Sport Science, Aristotle University of Thessaloniki, Campus Thermi, 57001 Thessaloniki, Greece
3
Department of Enterprise and Innovation, Coventry University, Coventry CV1 5FB, UK
*
Authors to whom correspondence should be addressed.
Sustainability 2026, 18(6), 3014; https://doi.org/10.3390/su18063014
Submission received: 1 February 2026 / Revised: 7 March 2026 / Accepted: 12 March 2026 / Published: 19 March 2026
(This article belongs to the Section Health, Well-Being and Sustainability)
Browse Figure
Versions Notes

Abstract

Physical activity is today a major global problem, since it is associated with physical, psychological, and social health risks. Promoting active mobility by using walking and cycling as modes of transportation has been proposed as one of the strategies to promote physical activity in urban areas while also addressing several of the United Nations’ Sustainable Development Goals. This study aimed to examine whether the adoption of active mobility behavior contributes to individual well-being and further test which constraints individuals face when adopting it. The sample of the study consisted of 294 citizens from Enterprise and Innovation a metropolitan area in Greece. The factorial analysis of the constraints active mobility scale confirmed the five dimensions: environmental, psychological, individual, social, and interest. The results indicated that citizens who reported the use of active mobility were more likely to report higher levels. Furthermore, lack of interest was not the main reason for not using active mobility. Instead, most of the reported constraints were directly or indirectly related to the inadequate, unfriendly, and unsafe urban infrastructure, which creates concerns about individual safety. The implications of these results are discussed.

1. Introduction

Physical inactivity is one of the major problems globally today, since it is associated with multiple negative physical, psychological, and social consequences for individuals and collectively for societal well-being [1]. About 1.4 billion adults worldwide, representing 27.5% of the world’s adult population, are not active enough to meet the physical activity levels recommended by the World Health Organization to improve and protect their health [1]. Interestingly, in high-income countries, the percentage of inactive individuals is almost double (36.8%) when compared with the low-income countries (16.2%). According to the latest Eurobarometer [2], Portugal (73%), Greece (68%), and Poland (65%) have the highest inactivity rates [3].
There have been several approaches to address physical inactivity, aiming to motivate individuals to change their behavior, be more active, and be healthier. Promoting active mobility, by using walking and cycling as modes of transportation, has recently become a popular policy strategy to promote physical activity in urban places [4]. This is in line with several of the UN sustainable development goals (SDGs), such as SDG 3: “ensure healthy lives and promote well-being for all at all ages”, SDG 16: “promote peaceful and inclusive societies”. The concept of “nudging” is one of the approaches that have been introduced to change travel behavior by providing governments with softer policy options to reduce the negative impacts of private vehicles in urban areas [5].
Despite the well-documented evidence of the positive outcomes of adopting active mobility behavior, several individual and structural constraints exist. Changing citizens’ behavior depends on multiple factors, such as the level of active mobility already existing in a country, the active mobility infrastructure, and cultural and individual aspects [6,7,8]. In this line, ref. [9] (p. 806) emphasized that there is still a need for studies to better understand the determinants of active mobility behavior. They also added that more research is required on the cultural aspects, considering factors and evaluation elements that should be considered for the design of future policies.
The present study sought to investigate whether, and through which mechanisms, active mobility behavior is associated with higher levels of perceived well-being, while also identifying the individual and structural constraints that impede the adoption of such behaviors among Greek citizens residing in an urban environment. Active mobility—typically defined as walking, cycling, and other forms of human-powered transportation—has increasingly been recognized as a multidimensional behavior with implications that extend beyond physical activity alone [10,11]. Contemporary research underscores that individual well-being is closely linked with physical and psychological health, encompassing elements such as emotional stability, life satisfaction, social integration, and subjective happiness [12,13]. These dimensions of well-being are further reinforced by engagement in health-promoting behaviors, ultimately contributing to the development of healthier and more resilient societies [14,15,16,17,18,19].
Despite this growing recognition, empirical evidence specifically examining the relationship between active mobility and individual well-being remains relatively limited. Much of the existing literature has focused on environmental or infrastructural determinants of active mobility—such as walkability, safety, and urban design—while fewer studies have explored its psychological or health-related outcomes [20,21]. As highlighted by [22] (p. 157), “a notable gap persists in the empirical literature regarding the extent to which active mobility influences health-related indicators, including perceived well-being”. This gap is particularly evident in Southern European contexts, such as Greece, where cultural norms, climatic conditions, urban density, and mobility infrastructures may shape both the feasibility and the perceived value of adopting active mobility behaviors [23,24]. Addressing this gap is essential for informing evidence-based policies and interventions that promote sustainable mobility, enhance public well-being, and support the transition toward healthier urban environments.

2. Theoretical Background

2.1. Definition of Active Mobility

Active Mobility (AM) refers to a person’s ability to access relevant activities in urban settings, such as shopping, visiting friends, or getting to her/ his work by walking or cycling [19,25,26]. AM emphasizes purposeful movement and offers a broader and more inclusive way to describe physical activity used for transportation [19,27]. Walking and cycling are the main modes of transportation, promoting at the same time physical activity [19,28,29]. In this line, the term AM is dissociated from leisure, exercise, and sports, and relates to active transport, active travel, and sustainable transport [19]. Incorporating AM into daily routines, such as commuting to work or school, walking, and cycling, enhances physical activity and, as a mode for transportation, can be independent or can be combined with public transportation [25].

2.2. Health-Related Outcomes of Active Mobility

Walking and cycling, as the primary modes of active mobility (AM), can effectively substitute motorized transport, mitigate car dependency, and contribute to improving overall quality of life [7,9,18,19,20,21,22,29]. Because AM inherently increases levels of physical activity, it can generate substantial benefits for both individual and public health [15,18,19]. Empirical evidence links AM to improvements in physical health—such as enhanced cardiovascular function—and mental health, including reductions in stress, anxiety, and other mental health disorders [7,9]. Beyond its health-related advantages, AM is also environmentally sustainable. As a carbon-neutral form of movement, AM contributes to reducing emissions and improving urban environmental conditions. This is particularly relevant given that public transport systems and motorized mobility more broadly play a central role in global greenhouse gas emissions [7,8,22].
AM encompasses walkability and cycling, both of which align with broader sustainability objectives. These behaviors support several goals outlined in the WHO Agenda 2030, including Sustainable Development Goal 11, “Sustainable Cities and Communities,” which emphasizes inclusive, safe, resilient, and environmentally responsible urban development [22,30]. Despite these recognized benefits, the empirical evidence base remains incomplete. As [22] (p. 157) noted, further empirical research is required to better understand the relationship between active mobility and health-related outcomes, particularly those associated with mental health. Following this discussion, the first hypothesis was formulated:
Hypothesis 1 (H1). 
Citizens who use and adopt active mobility behavior will exhibit higher levels of perceived well-being.

2.3. Leisure Constraints on Active Mobility

The literature on leisure constraints is extensive, with the hierarchical model of leisure constraints emerging as the dominant theoretical framework guiding research over the past three decades [31]. According to this model, constraints are categorized into intrapersonal, interpersonal, and structural constraints, each influencing an individual’s decision-making process at different stages of leisure participation. Intrapersonal constraints refer to internal psychological factors such as cultural norms, negative self-perceptions regarding body image or physical appearance, low self-efficacy, and lack of interest or motivation. Interpersonal constraints arise from social interactions and include the absence of partners with whom to participate, social isolation, or incompatible schedules. Structural constraints are external barriers related to financial limitations, environmental conditions, accessibility issues, and deficiencies in service provision or infrastructure. As noted by [31] (pp. 309–320), intrapersonal constraints are often the most powerful because they can prevent participation altogether, whereas structural constraints can frequently be negotiated or overcome, potentially leading to modified or alternative forms of participation [16,32].
Although the hierarchical model has been widely applied in sport and leisure contexts, its direct applicability to active mobility (AM) remains open to debate. AM—encompassing walking, cycling, and other non-motorized forms of transportation—appears to involve a broader and more complex set of constraints. Several factors discourage individuals from adopting AM, reinforcing reliance on private car use. These constraints include structural and environmental barriers, such as inadequate or unsafe urban infrastructure, poor walkability, limited cycling networks, and concerns related to traffic safety and air pollution. They also include cultural and societal factors, such as strong car dependency norms and perceptions of AM as inconvenient or socially undesirable [33]. Consequently, AM constraints span personal, social, economic, and urban-planning dimensions [9,30].
Despite these parallels, the hierarchical influence of intrapersonal, interpersonal, and structural constraints—central to the leisure constraints model—has not been systematically examined within the active mobility (AM) literature. While it is conceptually reasonable to map personal constraints onto intrapersonal ones, social constraints onto interpersonal ones, and economic or planning constraints onto structural ones, empirical evidence confirming this three-dimensional structure in the context of AM remains limited. Existing studies tend to examine constraints in isolation or focus predominantly on infrastructural and environmental barriers, such as inadequate cycling lanes, unsafe pedestrian environments, and limited accessibility [9,30,33]. Other research highlights the role of cultural norms, car dependency, and perceptions of convenience, which may function as powerful intrapersonal and interpersonal constraints that discourage AM adoption [7,22,33]. However, few studies have explored how these constraints interact hierarchically or how they collectively shape individuals’ attitudes and behaviors toward AM. This gap underscores the need for further research that applies or tests the hierarchical model within AM settings, particularly given the complex interplay of psychological, social, cultural, and environmental factors influencing mobility choices [30,31,32]. Following this discussion, and in line with the hierarchical model of leisure constraints, the second hypothesis was formulated:
Hypothesis 2 (H2). 
Users and non-users of active mobility behavior will report different levels of constraints, with intrapersonal (individual, psychological, and lack-of-interest) constraints being the highest among non-users.
A brief of the constraining factors that have been identified in the literature follows.

2.4. Urban Infrastructure

Structural constraints are among the main factors that discourage individuals from engaging in active mobility [9,34]. A case study conducted in Flanders, Belgium, in 2025, showed that most of the participants reported that structural barriers were the most common in rural areas [33]. Structural constraints create concerns about individual safety in the cases of poorly maintained urban infrastructure [35,36,37,38]. Examples of infrastructure problems include inadequate or narrow sidewalks, the presence of stairs or steps, objects blocking walking/cycling routes (e.g., street furniture, stairs, ramps), and whatever hinders walkability and undermines AM [39,40]. The absence of adequate and continuous paths to lead to “key locations” in the urban planning and transport network, the unavailability of alternative routes that bypass hazardous areas collectively, and the lack of traffic-calming measures can hinder the promotion of safe and accessible active mobility [38]. The lack of cycling infrastructure (bike lanes), combined with the poor quality of public transportation, makes individuals rely more on private cars rather than opting for alternative modes of travel [34]. A final aspect of urban planning infrastructure in urban environments relates to the lack of supportive features, such as rest areas on the sidewalk, which create a particularly unfriendly terrain [41]. Lack of rest areas for elderly people in combination with the fear of falling while walking, increases the hesitation to use walking as a mode of transport, especially among individuals above the age of seventy [39,41]. A systematic literature review concluded that the most cited constraints for individuals above the age of seventy years old to walk were concerns that refer to physical health, such as poor fatigue, pain, and shortness of breath, or fear of falling and lack of motivation [41].
The reason behind the lack of friendly urban planning towards pedestrians and cyclists is often attributed to funding limitations, regulatory barriers, and slow implementation of constructions [30,38]. Stakeholders often appear to have different goals and conflicting interests regarding urban planning and decision-making [30].

2.5. Perception About Safety

Citizens’ perception of walking or cycling as an unsafe transport mode is an important constraint to active mobility [9,42,43]. In general, pedestrians and cyclists might feel unsafe due to a lack of adequate infrastructure in combination with traffic congestion issues, poor route network cohesion, and the behavior of drivers in urban environments [42,44]. Negative consequences of this combination are the sense of insecurity and the fear of getting into an accident when engaging with active mobility [9,25,43].
Pedestrians’ need to safely cross the street is often treated as secondary to maintaining the traffic flow, as evidenced by the long distances frequently found between designated crosswalks reported in Malta’s case study [30]. In addition, poor traffic management, such as limited parking in combination with stationary vehicles that usually occupy a significant amount of urban space, like pavements and bike lanes, prevents pedestrians and cyclists from passing [8,22].
A final note should be made about the weather conditions as a constraint to active mobility. Cities with severe weather conditions, such as cold waves or intense heat, heavy rainfall, or even hilly terrain, are factors that prevent people from engaging with active mobility in their daily routine. An example can be the city of Helsinki, which has heavy and long-lasting winters [8,42]. Environmental or geographical factors, such as hilly topography, high slope variability, steep terrain, and rivers or canals without adequate crossing infrastructure, which can make walking and cycling physically demanding and discomforting, further discourage individuals from engaging with active mobility (walking or cycling) [8,42].

2.6. Car Dependency

On average, local trips by car cover areas of approximately 20 km distance, whereas walking trips are only around 3 km long, suggesting that people usually use cars to travel long distances because of time constraints [7,34]. The choice of transport mode can vary according to the level of car ownership in each country and the infrastructure accessibility, quality, and vehicle availability, but also cultural reasons [34]. In Northern European countries, cycling ranks as the second most preferred mode of transport [34], and this also shows the influence of culture.
The car-oriented culture has been reported as an important constraint that affects people’s behavior towards active mobility [7,8,9]. A study on twenty European countries reported highly car-dependent levels among citizens, which is attributed both to the car-oriented culture and to the limited infrastructure, as discussed in the previous section [9]. Similar results were reported in Malta and Belgium by [30] and Scotland [8], who found that car-centric culture, but also the limited effectiveness of public transit, are cultural and structural barriers.
An additional constraint related to car dependency is the availability of job opportunities and the cost of housing in determining where individuals choose to live and work. Long distances between house and work play a key role in constraining people from using bicycles or walking to their work [34]. Furthermore, the lack of alternative transportation, even in rural areas, in combination with poor connectivity, makes public transport unreliable and impedes the use of multiple modes of transport within a single journey [8,9,33].
It is worth noting that individuals with low income display a higher frequency of walking while covering longer walking distances. However, this is not a conscious option but rather a necessity driven by low accessibility to other modes of transport [43,44,45]. Cost also appears to be a significant constraint regarding the aspect of cycling as a transport mode, particularly when considering families with multiple members, where each would need to own a bicycle to travel [33].
As previously noted, due to the increased levels of physical activity, AM is associated with multiple health-related benefits. However, empirical research on the influence of AM on individuals’ well-being is still limited. Moreover, while several constraints have been examined in the literature, their influence on actual behavior (adoption of AM) and in relation to the perception of constraints is still limited. In this line and considering also the two hypotheses that were discussed before, the objective of this study was also to explore the constraints that citizens face in adopting active mobility.

3. Materials and Methods

The study adopted a quantitative approach, with the use of an online questionnaire. Constraints to active mobility were measured with adjusted versions of [16,29,46,47] questionnaires, to combine context-based constraints (active mobility constraints) and more general ones (leisure constraints). The scale includes both environmental/structural and psycho-social barriers to active mobility, as proposed by Palma-Leal et al. [46]. This is a fourteen-item context-based scale when compared with the leisure constraints scales that have been used in exercise and other leisure settings (e.g., [16,32]). The final scale used included 20 items including the two dimensions proposed by Palma-Leal et al. [46] (environmental and psychosocial) plus six items form the general leisure constraint scales [16,32] in order to be more detailed in the psychological and lack of interest constraints, covering the intrapersonal constraints, as they have been defined in the hierarchical model of leisure constraints [16,32]. To confirm the factor structure of the scale, a Confirmatory Factor Analysis (CFA) was conducted. Confirmatory factor analysis is a statistical technique used to test whether the data fit a hypothesized measurement model based on prior theory or research. Unlike exploratory factor analysis (EFA), where the factor structure emerges from the data, CFA allows researchers to specify in advance the number of factors and the relationships between observed items and latent constructs and then evaluate how well the proposed model fits the observed data. The model indicated five factors with a very good fit to the data, χ2(160) = 275.67, p < 0.001, CFI = 0.97, TLI = 0.96, IFI = 0.97, RMSEA = 0.05, SRMR = 0.08. The internal consistency was good α = 0.82 for Psychological Constraint, α = 0.92 for Environmental Constraint, α = 0.89 for Social Constraint, α = 87 for Interest Constraint and α = 0.75 for Individual Constraint. Subjective well-being was measured with the 5-item World Health Organization Well-Being Index (WHO-5) [48]. This short scale has been extensively used in the literature and has been proven to be valid and reliable. The internal consistency was good α = 0.87 for Well-Being. Considering that our online questionnaire targeted the general population, a short scale was highly desirable to increase our response rate. A macro-level well-being index like the WHO-5 can still pick up differences between travel modes because—even though it is broad—it taps into psychological processes that are systematically influenced by everyday mobility. In other words, travel is a small part of life, but it reliably shapes the very dimensions the WHO-5 measures. The sample of the study was a convenience one, consisting of citizens of the metropolitan area of Thessaloniki, Greece. This paper is part of a larger European Sport Erasmus project that includes data collection in five European cities. Only the results of Greece will be presented in this study. An e-questionnaire was posted to the social media of the research group (Facebook, WhatsApp, and blogs), inviting adult citizens to fill in the questionnaire. Consent forms were obtained from participants before completing the anonymous questionnaire, following the institution’s ethical procedures. Ethical approval was also obtained from the host institution before the data collection. The convenience sample is obviously a limitation of the research design. Results cannot be considered representative of the population of the metropolitan area. They are only indicative, and generations should be made cautious. The sample consisted of 294 participants, which was predominantly females (68.0%), while males accounted for 32%. The mean age of the participants was 44.10 (SD = 10.94). Regarding marital status, just over half of the participants were married (52.4%), whereas 41.2% were single, and 6.5% preferred not to disclose their marital status. In terms of education level, most participants held post-secondary qualifications, with 40.5% reporting a master’s degree, 36.1% a bachelor’s degree, and 11.6% a Doctoral degree, while lower educational levels were less common. Concerning occupation, the majority were employees (69.4%), followed by self-employed individuals (16.3%) and entrepreneurs (8.2%), with smaller proportions being retired, students, engaged in household work, or unemployed. With respect to commuting behaviors, most participants reported never walking to work or using a bike (54.6%), whereas 45.2% reported active commuting to work (via bike or walking to work). Regarding the frequency of using a car or motorbike to commute to work, more than half of the participants reported using a car/motorbike almost every day (55.1%). A total of 20.2% indicated that they never use a car or motorbike for commuting, while 11.3% reported using it less than once per week. Additionally, 7.9% stated that they use a car or motorbike 1–2 times per week, and 5.5% reported using it 3–4 times per week. It should be noted that the sample was not demographically balanced, as participants with higher educational backgrounds and female athletes were overrepresented, whereas individuals with lower educational levels and male athletes were underrepresented. This imbalance may limit the generalizability of the findings.

Statistical Analyses

Initially, descriptive statistics were used to describe the variables, highlighting means, standard deviations, 95% confidence intervals, minimum, and maximum values. Descriptive statistics were employed to provide an overall summary of the data distribution and central tendencies prior to inferential analyses. Νormality was examined using skewness and kurtosis indices, in order to assess whether the variables approximated a normal distribution, a key assumption for parametric tests (all values within acceptable ranges). Homogeneity of variances was assessed using Levene’s test, which was non-significant, indicating that the assumption was met. This test was used to determine whether group variances were equal, as required for MANOVA comparisons. Multicollinearity was examined through correlation coefficients and variance inflation factors (VIF), to ensure that independent variables were not excessively correlated and did not distort regression estimates. The values were below the recommended threshold; these procedures were conducted to detect extreme values that could bias parametric analyses and indicate no multicollinearity concerns. Additionally, boxplots and standardized residuals were inspected to identify potential outliers; no extreme outliers were detected that warranted removal. A one-way multivariate analysis of variance (MANOVA) was conducted to examine whether walking to work or using a bike is associated with planning, individual, environmental, social, interest, and psychosocial constraints, and well-being. MANOVA was selected because it allows the simultaneous examination of group differences across multiple correlated dependent variables while controlling for Type I error inflation. Pairwise comparisons using the Least Significant Difference (LSD) method were conducted to investigate specific group differences. The Least Significant Difference (LSD) test is a post hoc statistical method used after a significant ANOVA result to determine which specific group means differ by calculating the minimum difference required for two means to be considered statistically different. It is one of the simplest pairwise comparison methods and is widely used in experimental research. Regarding the post hoc procedure, the Least Significant Difference (LSD) test was selected because the analysis involved a limited number of comparisons within a theoretically driven design. Given the small number of groups and the presence of a significant overall F-test, the LSD test was considered appropriate due to its greater statistical power while maintaining adequate control of Type I error under these conditions. Effect sizes were reported using partial eta squared to estimate the magnitude of group differences beyond statistical significance. A multiple linear regression was conducted to investigate whether the Constraints (Psychological, Environmental, Social, Interest, and Individual) predicted Well-being. Multiple regression was used to examine the unique contribution of each predictor while controlling for the others. The model was conducted using the Stepwise method. This approach allows identification of the most parsimonious set of significant predictors while excluding variables that do not contribute meaningfully to the prediction of well-being. The statistical analyses were carried out using IBM SPSS Statistics version 26.

4. Results

4.1. Descriptive Statistics

Descriptive statistics for the variables under investigation are reported in Table 1. Well-being showed a moderate-to-high mean score (M = 3.79, SD = 1.06), indicating generally positive perceived well-being among participants. Environmental constraints also demonstrated relatively high values (M = 3.81, SD = 1.25), followed by individual constraints (M = 2.90, SD = 1.03) and psychological constraints (M = 2.70, SD = 1.08), suggesting moderate perceived influence of these factors. In contrast, social constraints (M = 1.80, SD = 1.09) and interest-related constraints (M = 1.82, SD = 1.11) presented the lowest mean scores, indicating that these factors were perceived as less restrictive.

4.2. Testing Differences Among Active Mobility Users and Non-Users in Well-Being and Constraints (MANOVA)

A one-way multivariate analysis of variance (MANOVA) was conducted to examine whether walking to work or using a bike affects Constraints (Psychological, Environmental, Social, Interest, and Individual) and Well-being. MANOVA (Multivariate Analysis of Variance) is a statistical test that examines whether groups differ across multiple dependent variables at the same time, making it a more powerful extension of ANOVA when outcomes are related. It helps researchers understand how independent variables jointly influence several continuous outcomes rather than analyzing each one separately. The multivariate test revealed a statistically significant overall effect of walking to work or using a bike on the combined dependent variables. Using Pillai’s Trace, the effect was significant, V = 0.24, F (6, 243) = 12.76, p < 0.001, pη2 = 0.24. This result indicates that active commuting is associated with systematic differences in well-being, which was significantly higher in the active commuting group compared to the other group (M difference = −0.41, p < 0.01). These results support our first hypothesis, as it was hypothesized that citizens who use and adopt active mobility behavior will exhibit higher levels of perceived well-being. The results also indicated that active commuting is associated with systematic differences in perceived constraints. Tests of Between-Subjects Effects showed the univariate follow-up analyses that examined the effect of walking to work or using a bike on each variable. The results showed statistically significant results with Psychological Constraint, F(1, 248) = 56.31, p < 0.001, pη2= 0.19, Environmental Constraints, F(1, 248) = 26.04, p < 0.001, pη2= 0.10, Individual Constraints, F (1, 248) = 29.54, p < 0.001, pη2= 0.11. No significant differences were observed for Social Constraints (F (1, 248) = 1.34, p = 0.248) or Interest constraints (F (1, 248) = 2.68, p = 0.103), indicating that these factors are relatively unaffected by commuting behavior. Pairwise comparisons showed that participants who do not report active commuting to work reported significantly higher scores than those who walk to work or use a bike in Psychological Constraint (MD = 0.92, p < 0.001), in Environmental Constraint (MD = 0.78, p < 0.01), and in Individual Constraint (MD = 0.67, p < 0.001). These results offer partial support of the second hypothesis, since significant differences were found in the psychological—intrapersonal—and environmental—structural—constraints, while it had been hypothesized that users and non-users of active mobility behavior will report different levels of constraints, with intrapersonal (individual, psychological, and lack-of-interest) constraints being the highest among non-users. This information is shown in Table 2.

4.3. Testing the Relationship Between Constraints and Well-Being (Multiple Regression)

A multiple linear regression was conducted to examine whether Constraints predict Well-Being via the Stepwise Method. Multiple linear regression is a statistical method used to model how several independent variables together predict a single continuous dependent variable. It extends simple linear regression by allowing more than one predictor, making it useful when real-world outcomes depend on multiple factors. The resulting model showed that Psychological Constraint influenced Well-Being, F (1, 249) = 8.45, p < 0.01, and explained a small proportion of the variance in well-being (AdjR2 = 2.9%). Specifically, Psychological Constraint was a negative predictor of Well-Being (β = −0.18, t = −2.91, p < 0.01). Adjusted R2 (AdjR2) is a refined version of R2 that tells you how much variance in the dependent variable is explained by your regression model after correcting for the number of predictors. This information is shown in Figure 1.

5. Discussion

As previously noted, this study aimed to examine active mobility behavior, constraints, and well-being by drawing on both the leisure constraints literature and contemporary active mobility research. Regarding the factorial structure of the active mobility constraints scale, the results of the confirmatory factor analysis supported the factorial validity and internal consistency reliability of the five dimensions: Structural, Psychological, Individual, Social, and Interest. This structure is grounded in the hierarchical model of leisure constraints, which differentiates between intrapersonal, interpersonal, and structural barriers to participation. Compared with the questionnaire used in previous studies [16,46]—which primarily distinguished between environmental/structural and psycho-social barriers—the present study offers a more nuanced and analytically rich categorization. Specifically, the broad psycho-social constraints identified in earlier research were further differentiated into four separate dimensions in the current study: Psychological, Individual, Interest, and Social. This refined structure provides a more detailed understanding of the diverse factors that may hinder individuals from adopting active mobility behaviors. By distinguishing between psychological barriers (e.g., fear, low confidence), individual constraints (e.g., lack of skills or physical limitations), interest-related constraints (e.g., low motivation or preference for motorized transport), and social constraints (e.g., lack of companions or social support), the study advances the conceptualization of active mobility constraints beyond existing frameworks. This expanded factorial structure not only aligns with the hierarchical model of leisure constraints but also contributes to the active mobility literature by offering a more comprehensive and theoretically grounded approach to understanding the barriers that shape mobility choices.
In terms of the descriptive statistics, the results indicated that Environmental constraints were those with the highest mean scores, followed by the Individual ones. These results confirm previous studies, which were conducted in other countries [9,29,34], in which the structural/environmental constraints were reported as the most important ones in individuals’ decision to adopt active mobility. Limited and not well-maintained walking/bike lanes, non-safe walking/bike routes, too much traffic, and lanes occupied by cars were reported as the most important structural constraints, supporting previous studies [39,40]. Structural constraints were originally conceptualized within the hierarchical model of leisure constraints [31], which has been widely applied in leisure and exercise settings (e.g., [29,49,50]). Within this framework, structural constraints are considered the least influential in determining whether an individual decides to participate in sport or leisure activities, as they tend to modify participation rather than block it. In other words, individuals can often negotiate structural barriers by adjusting the type, frequency, or context of their participation. This assumption, however, does not fully apply to active mobility (AM). In the context of AM, structural constraints—such as inadequate infrastructure, unsafe road conditions, poor maintenance, and lack of dedicated walking or cycling networks—are often internalized by individuals because they directly shape perceptions of safety and personal risk. As a result, these constraints function more like intrapersonal barriers, influencing attitudes, perceived behavioral control, and ultimately the willingness to engage in AM. Previous studies have consistently shown that poorly designed or poorly maintained infrastructure generates significant safety concerns, discouraging individuals from walking or cycling and reinforcing reliance on motorized transport [35,37,38]. According to the hierarchical model, individuals can typically negotiate structural constraints, especially when they are intrinsically motivated, leading to modified participation (e.g., choosing alternative activities or reducing participation time) [16,31]. However, in the case of AM, such negotiation is often not possible. If the built environment is not walkable, cycling infrastructure is unsafe, or traffic conditions are hazardous, individuals have no viable alternatives for engaging in AM. Unlike leisure or sport activities—where one can switch to a different activity or setting—active mobility is inherently tied to the physical environment. When infrastructure is unfriendly or unsafe, participation is effectively blocked rather than modified.

5.1. Active Mobility and Well-Being

By comparing individuals who adopt active mobility behavior with those who do not in the well-being scores, with the use of the MANOVA, several clear and meaningful patterns emerged. First, the results indicated statistically significantly higher well-being mean scores among active mobility users, supporting our first hypothesis. This finding is particularly noteworthy, as it provides strong empirical support for investing in policies, infrastructure, and community initiatives that promote active mobility in urban environments. As previously discussed, higher levels of well-being are closely associated with a range of health-enhancing behaviors, including social integration, improved physical and mental health, and overall quality of life [14,16,51]. Active mobility contributes directly to increased levels of physical activity, which collectively enhance public health outcomes and reduce the burden of non-communicable diseases [15,18,19]. Given that individual well-being is widely recognized as a foundational component of healthy and resilient societies [52,53], the present findings highlight the potential of active mobility to serve as a meaningful lever for improving both personal and societal well-being.
The positive association observed aligns with previous research suggesting that individuals with higher levels of subjective well-being are more likely to participate in leisure and physical activities [54], implying that well-being may act as a driver of active behavior. Conversely, active mobility itself generates physical, social, and psychological benefits—such as improved cardiovascular health, enhanced social connectedness, and reduced stress—which are well-established antecedents of life satisfaction and subjective well-being. Taken together, these findings point toward a potentially reciprocal relationship between active mobility and well-being, in which each reinforces the other. This dynamic interplay underscores the need for longitudinal, experimental, or mixed-methods research designs that can more precisely disentangle causal pathways and deepen our understanding of how mobility behaviors and well-being coevolve over time. At the same time, the results raise important questions regarding the directionality of the relationship between active mobility and well-being. The cross-sectional nature of the study precludes any causal inference regarding the direction of the relationship between active mobility and well-being; thus, it remains unclear whether well-being results from active mobility or whether individuals with higher well-being are more inclined to engage in such behaviors [55].

5.2. Active Mobility and Constraints

By comparing individuals who adopt active mobility behavior with those who do not in the constraint scores, with the use of the MANOVA, several clear and meaningful patterns emerged. Comparison between those who adopt and do not adopt active mobility behavior in terms of constraints also revealed some clear patterns. Higher mean scores in the perception of constraints among those who do not adopt active mobility behavior were revealed in the Environmental, Individual, and Psychological Dimensions; this is an indication that these might be the blocking constraints for adopting active mobility behavior. It is worth noting that no statistically significant differences were found in the Lack of interest for adopting active mobility behavior, which is an indication that there is a latent demand to do so. These results offer partial support for our second hypothesis. As previously noted, Individual and Psychological constraints can be classified as Interpersonal ones in the hierarchical model of leisure constraints. On the other hand, the Environmental constraints are external to an individual, but, as previously noted, in the case of active mobility, they are internalized since they still influence safety issues. These results show that the hierarchical model of leisure constraints is not fully applicable in the case of active mobility.
Alleviating or removing structural constraints is on the side of local authorities, city planners, and related stakeholders. It has been proposed that among the reasons behind the lack of friendly and safe urban planning for pedestrians and cyclists are often attributed to funding limitations, regulatory barriers, and slow implementation of constructions [30,38]. Indeed, stakeholders often have different goals and conflicting interests regarding urban planning when strategic decisions are made [30]. Local authorities and stakeholders should realize that active mobility has multiple positive effects on citizens’ physical and psychological health, as the results of the present study showed, which collectively reflect societal well-being. Active mobility also aligns with several of the Sustainable Development Goals [56], such as SDG 3: “ensure healthy lives and promote well-being for all at all ages”, SDG 16: “promote peaceful and inclusive societies, and SDG 12: “Ensure sustainable consumption and production patterns. SDG 13 “take urgent action to combat climate change and its impacts” and SDG 15 “Protect, restore and promote sustainable use of terrestrial ecosystems and halt biodiversity loss”.
Individual and psychological constraints are obviously more difficult to remove since they are not absolutely under the control of local authorities and planners. They relate to health and fitness-related perceptions, but they might also be related to car-oriented culture, which has been reported as an important constraint in previous studies [7,8,9]. This culture can be changed only with education, promotional campaigns, and offering incentives to the citizens [5]. However, it should be noted that some of these constraints might still be removed if the infrastructure is improved and citizens are reassured that it is safe, convenient, and healthy to adopt active mobility behavior. The lack of supportive features, such as rest areas on the sidewalk, which create a particularly unfriendly terrain [41], is one more aspect that also relates to individual and psychological constraints and perceptions about health, and low fitness levels. Some of these constraints relate obviously to socio-demographic variables such as age and gender. Older individuals need more support services such as rest areas [39,41], and more friendly walking routes. Sociodemographic differences were not examined in this paper, and this is a topic for future research.

5.3. Constraints and Well-Being

The multiple linear regression findings indicate that psychological constraint is the only constraint that significantly predicts well-being. Although the explained variance is modest, the negative association suggests that individuals who experience stronger internal barriers tend to report lower well-being. This aligns with research showing that psychological factors—such as self-efficacy, perceived control, and internalized barriers—play a central role in shaping both behavior and subjective well-being [57,58]. The absence of other constraints in the final model suggests that external barriers may be less directly linked to well-being in the context of active mobility or may exert their influence through psychological processes, a pattern consistent with prior work on leisure constraints and participation [31]. These findings highlight the importance of addressing internal, perception-based barriers when aiming to support individuals’ well-being in the context of active mobility. Future longitudinal research would help clarify whether reducing psychological constraints leads to measurable improvements in well-being over time [12].

6. Conclusions

This study is among the very few that provided empirical evidence for the role of active mobility behavior in citizens’ well-being. Although the results were based on a cross-cultural design, they provided some evidence that citizens who adopt active mobility behavior are more likely to report higher well-being levels. The study also provided evidence that not adopting active mobility behavior is not attributed to the lack of interest but to the several constraints that citizens face. Most of them are directly or indirectly related to the unfriendly or inadequate urban infrastructure for active mobility, which creates concerns about individual safety. Considering, however, that individual and collective social well-being are important factors today for healthy societies and considering also the SDGs as developed by the UN, it is crucial today for local/regional authorities and related stakeholders to invest in promoting city active mobility.

7. Study Limitations and Future Research

This paper is based on a cross-cultural research design. Subsequently, and considering also the statistical analysis applied (MANOVA), the results cannot be used to support causal relationships. A longitudinal research method should be used for studying causal relationships among the study variables. As previously noted, walking and biking are modes of active mobility behavior. Since in our study those who used biking were a few, the two groups were merged into one, as has been done in previous studies as well [59], studying active mobility behavior as one construct. It will be interesting for future studies to compare walkers and bikers or those who do both, in terms of well-being and constraints, to examine possible differences. Furthermore, it has to be noted that active behavior has been seen as both a cause and an effect of perceived well-being [e.g., 46]. In this line, it is possible that individuals with higher well-being might be more inclined to adopt active mobility behavior. This is an issue that needs further research. Finally, this study did not examine any socio-demographic differences in active mobility behavior, well-being, and perception of constraints. As previous studies reported, there might be differences in relation to the age of the individuals, the gender, the socioeconomic status, and even the professional status. Finally, it should be noted that the study reports results from data collected in one country (Greece). Cross-cultural comparisons among citizens of different countries could show possible culturally related differences in adopting active mobility behavior.
Finally, it should be noted that the study reports findings derived from data collected in a single national context (Greece). While this provides valuable insights into active mobility behavior within a specific cultural and urban environment, it also limits the generalizability of the results. Active mobility is shaped not only by individual preferences and infrastructural conditions but also by broader cultural norms, mobility traditions, and societal attitudes toward walking, cycling, and car use. Therefore, cross-cultural comparisons involving citizens from different countries could reveal important culturally driven differences in the adoption of active mobility behaviors. Such comparative research would allow scholars to examine how variations in urban design, transport policies, social norms, and environmental values influence individuals’ mobility choices, thereby offering a more comprehensive understanding of the factors that facilitate or hinder active mobility across diverse contexts.
A final note should be made about the use of the quantitative methodology. Their fixed-response formats often restrict the depth of information obtained, producing relatively superficial accounts of attitudes or experiences. They are also prone to low response rates, which can introduce non-response bias and undermine the representativeness of the sample. Furthermore, the questionnaire used in the present study was adjusted from previously published contextual ones. Subsequently, efforts should be made in the future to standardize a widely used active mobility constraints questionnaire.

Author Contributions

Conceptualization, A.N. and K.A.; Methodology, A.N., G.S. and K.A.; Software, G.S. and T.K.; Validation, E.G. and T.K.; Formal analysis, G.S.; Investigation, E.G., T.K., A.L. and E.A.; Resources, A.N.; Data curation, A.N. and T.K.; Writing—original draft, A.N. and E.G.; Writing—review and editing, A.N., A.L., E.A. and K.A.; Visualization, A.N. and K.A.; Supervision, K.A.; Project administration, K.A.; Funding acquisition, A.L. and E.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the project Nudgin2Move, which was co-funded from the Erasmus Sport programme, with the contract number 101184539.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of the School of Physical Education and Sport Science at Thessaloniki (protocol code 287/2025 and 30 October 2025).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author due to the ethics committee process.

Conflicts of Interest

The authors declare that this study received funding from project Nudging2Move and co-funded from the Erasmus Sport +. The funders were not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication.

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Sustainability 18 03014 g001
Figure 1. Psychological Constraint Predicting Well-being. Note: ** p < 0.01.
Figure 1. Psychological Constraint Predicting Well-being. Note: ** p < 0.01.
Sustainability 18 03014 g001
Table 1. Mean, Standard Deviation, 95% Confidence Interval, Minimum, and Maximum Values for Well-Being, Planning, Individual, Environmental, Social, Interest, and Psychosocial as Constraints.
Table 1. Mean, Standard Deviation, 95% Confidence Interval, Minimum, and Maximum Values for Well-Being, Planning, Individual, Environmental, Social, Interest, and Psychosocial as Constraints.
VariablesΜ ± SD95% CI Mean Upper95% CI Mean
Lower		MinMax
Well-Being3.79 ± 1.063.913.661.46
Psychological_Constraint 2.70 ± 1.082.822.5715
Environmental_Constraint3.81 ± 1.253.963.6615
Social_Constraint1.80 ± 1.091.921.6715
Interest_Constraint1.82 ± 1.111.951.6915
Individual_Constraint2.90 ± 1.033.022.7815
Table 2. Multivariate Test and Test of Between-Subjects Effects between walking to work, Psycological Constraint, Environmental Constraint, Individual Constraint, and Well-Being.
Table 2. Multivariate Test and Test of Between-Subjects Effects between walking to work, Psycological Constraint, Environmental Constraint, Individual Constraint, and Well-Being.
Multivariate Test
Pillai’s Trace (V)Fdfppη2
0.2412.766, 243<0.0010.24
Test of Between-Subjects Effects
Dependent VariablesFdfppη2Means
Psychological Constraint56.311, 248<0.0010.19MAC < MNAC
Environmental Constraint26.041, 248<0.0010.10MAC < MNAC
Individual Constraint29.541, 248<0.0010.11MAC < MNAC
Well-Being9.661, 2480.0020.04MAC > MNAC
Note: AC = Active Commuting; NAC = Non-Active Commuting.
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Ntovoli, A.; Giannakou, E.; Stavropoulou, G.; Karagiorgos, T.; Lola, A.; Anoyrkati, E.; Alexandris, K. Is Active Mobility Associated with Increased Levels of Perceived Well-Being? The Role of Perceived Constraints. Sustainability 2026, 18, 3014. https://doi.org/10.3390/su18063014

AMA Style

Ntovoli A, Giannakou E, Stavropoulou G, Karagiorgos T, Lola A, Anoyrkati E, Alexandris K. Is Active Mobility Associated with Increased Levels of Perceived Well-Being? The Role of Perceived Constraints. Sustainability. 2026; 18(6):3014. https://doi.org/10.3390/su18063014

Chicago/Turabian Style

Ntovoli, Apostolia, Evmorfia Giannakou, Georgia Stavropoulou, Thomas Karagiorgos, Afroditi Lola, Eleni Anoyrkati, and Kostas Alexandris. 2026. "Is Active Mobility Associated with Increased Levels of Perceived Well-Being? The Role of Perceived Constraints" Sustainability 18, no. 6: 3014. https://doi.org/10.3390/su18063014

APA Style

Ntovoli, A., Giannakou, E., Stavropoulou, G., Karagiorgos, T., Lola, A., Anoyrkati, E., & Alexandris, K. (2026). Is Active Mobility Associated with Increased Levels of Perceived Well-Being? The Role of Perceived Constraints. Sustainability, 18(6), 3014. https://doi.org/10.3390/su18063014

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