*Article* **Why Do Students Walk or Cycle for Transportation? Perceived Study Environment and Psychological Determinants as Predictors of Active Transportation by University Students**

**Monika Teuber \* and Gorden Sudeck**

Institute of Sports Science, University of Tübingen, 72074 Tübingen, Germany; gorden.sudeck@uni-tuebingen.de **\*** Correspondence: monika.teuber@uni-tuebingen.de

**Abstract:** University students are particularly at risk to suffer from physical and psychological complaints and for not fulfilling health-oriented physical activity (PA) recommendations. Since PA is linked with various benefits for health and educational outcomes, the group of students is of particular interest for PA promotion. Although active commuting has been identified as a relevant domain of PA in order to gain the various benefits of PA, little knowledge is available with respect to university students. This study tested conditions in the study environment, as well as personal motivators and barriers, as determinants for the active transportation of university students. Using a cross-sectional convenience sample of a university in the southwest of Germany (*n* = 997), we applied factor analyses to bundle relevant information on environmental and psychological determinants (adapted NEWS-G; adapted transport-related items from an Australian university survey) and blockwise hierarchical regressions. The objective was to analyze associations between the bundled determinants and self-reports on PA for transport-related walking and cycling (measured by the EHIS-PAQ). Results revealed associations between transport-related cycling and the perceived study environment (e.g., high automobile traffic) as well as certain personal motivators and barriers (e.g., time effort or weather conditions). The study contributes to the knowledge about determinants that are important for the development and improvement of public health interventions for students in a university setting.

**Keywords:** active transportation; physical activity; perceived study environment; psychological determinants; motivators; barriers; university students; socio-ecological approaches

#### **1. Introduction**

Academic studies often impose high demands on university students, which can be associated with negative effects on health. Students suffer more often from perceived stress [1] and from physical and psychological complaints than their peers [1–4]. As health is positively related with physical activity (PA) and less sedentary behavior, these behaviors can provide starting points for improving the students' health: because students who are more physically active through sports or everyday activities have fewer complaints and a greater sense of well-being than inactive students [2,4–6]. For the same reason, active transportation is associated with less obesity, less cardiovascular risk factors, and higher physical fitness for students [7,8].

Since the transition from school to university often marks a particular risk for becoming physically inactive [9], the group of students is of particular interest for PA promotion in order to gain health benefits. According to current guidelines for health-enhancing PA, about half of the students in the United States, Canada, and China, 40% in Australia, and 67% in Europe are not sufficiently physically active [10]. Reasons for students' physical inactivity are increasing self-employment, increasing academic workload with resulting problems in time management regarding work and social demands [8], and an increasing distance from home to university [11].

**Citation:** Teuber, M.; Sudeck, G. Why Do Students Walk or Cycle for Transportation? Perceived Study Environment and Psychological Determinants as Predictors of Active Transportation by University Students. *IJERPH* **2021**, *18*, 1390. https://doi.org/10.3390/ ijerph18041390

Academic Editor: Paul B. Tchounwou Received: 21 December 2020 Accepted: 28 January 2021 Published: 3 February 2021

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**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

To counteract this, the promotion of PA in university settings is necessary. Due to the increasing number of people who will study, universities have a growing potential to reach a large mass of young adults in order to promote positive PA behavior, which will last in later life. However, in contrast to school settings, the promotion of PA is not yet widespread in university settings, which leads to a gap between school-based and workplace-oriented approaches of PA promotion. Moreover, the knowledge about determinants of PA in university students is scarce, but this knowledge is necessary to guide evidence-based PA promotion in university settings [12].

Since the 2000s, PA promotion research has emphasized that the physical and social environment play an important role for PA behavior. Socio-ecological approaches increasingly have taken this into account and complement individually-focused approaches [12]. For example, Bauman and colleagues [12] as well as Bucksch and colleagues [13] differ between personal/individual and contextual/environmental factors that contribute to differences in PA behaviors. According to these basic ideas, Figure 1 schematically depicts individual and contextual factors of students' PA behavior which are important to understand in order to develop and improve interventions for active transportation, which can lead to an higher level of physical activity and in turn to a better health status [12]. Adapted to the university setting, the perspective of students' individual conditions is integrated into the perspective of the surrounding conditions of the study environment, increasing the extent of the effect radius of the PA promotion when regarded together [14–17]. Hence, this adaption follows public health and socio-ecological approaches [13,15,18].

**Figure 1.** Schematic representation of the socio-ecological approach of PA promotion adapted to the university setting (own presentation based on Bucksch et al. (2012) [13] and Bauman et al. (2012) [12]).

Some empirical studies in the university setting already exist, which have revealed several factors important for the active transportation behavior of students [19–30]. The results show basically that encouraging students to commute to university by bicycle or by foot is linked with the learning environment as well as with the campus environment, which deliver more or less activity-friendly physical environments.

The connectivity of the street network has been identified as an important determinant for the cycling behavior of students [22,24,27,28,30]. This refers, for example, to intersection density [28], street connectivity [24], and bicycle racks installed on buses to extend the commuting distance [20]. Such improvements to the cycling infrastructure reduce effort and time demands, which in turn mitigate the negative impact of distance [21,22,26,28,29] and increase the likelihood of cycling for commuting reasons [30].

In addition, the availability and proximity of walking or cycling facilities encourage students to cycle more [19–21,23,25,27,28,30]. However, also in terms of active commuting in general, the perception of walking and cycling facilities are positively associated with active commuting to university [23].

The feeling of safety also contributes to increased active transportation of students. Traffic safety, for example, based on traffic-calming measures [22], has been shown to be important for the active commuting of students [19,22,27]. On the other hand, safety concerns can lead to avoidance of active commuting. Such is the case, for example, with high automobile traffic including sharing the roadway with automobile traffic [19]. Moreover, crime issues are related to students' active transportation behavior [21,25,27,28]; this refers to personal safety as well as to bicycle security such as secure bicycle-parking racks and lockers, and a high degree of safety against bicycle theft [22,25,27].

Finally, there are the aesthetic aspects, which are positively related to active transportation and are expressed, for example, by the "attractiveness of the surroundings" [27] (p. 72).

In addition to environmental conditions, potential personal motivators, and barriers among students' active forms of transportation are also known from empirical studies [19,20,22,23,25,26,28]. For example, motivators such as concerns for the environment increase the probability of choosing bicycles [28]. Barriers such as travel costs [26,28] or inclement weather [25] prevent students from active transportation. In addition to the barrier of time effort [26], there are other types of effort that prevent students from active transportation such as planning [23], inconvenience, time constraints [21], or physiological discomfort [27].

The current state of research leaves open questions regarding the environmental and personal determinants of active transportation behavior in university settings. So far, only a few studies have dealt with such questions by considering environmental and psychological determinants together. Especially the environmental variables have been less studied, but are thought to have widespread effects for active transportation behavior [12]. Furthermore, there is less known about the differentiation in various modes of active transportation, as most often only general PA or a specific mode of transportation is considered. In addition, there is a lack of consistent measurement methods. Since the relationship of the environment to physically active behavior has also been studied in the community neighborhood, various survey instruments have been established in the communities for assessing the neighborhood environment [17,18,31–41]. None are yet available for the study environment. Therefore, there is a lack of both a general more extensive survey procedure of the PA-friendliness of the study environment and investigations on how this relates to the two transport-related modes of PA, walking and cycling.

#### **2. Purpose of the Study**

The present study addresses the question of which conditions of the study environment as well as personal motivators and barriers are related to the active transportation behavior of university students. Relationships are considered separately for transportrelated walking and cycling because different modes of transportation can have different interactions with the environment.

This question is addressed because students are particularly at risk of not fulfilling health-oriented PA recommendations and active transportation has been shown to be a relevant domain of PA that brings various health-promoting benefits of PA. As students suffer from physical and psychological complaints, promoting active transportation could counteract this. This requires specific knowledge of socio-ecological determinants. So far only a few studies have considered both personal and environmental determinants together [22,23,27].

By this study, additional specific knowledge about why students walk or cycle will be gained to support the implementation of specific interventions in the university setting to improve personal health and, beyond that, public health.

To achieve the purpose of the study, two steps are carried out. Since there is no established survey instrument available for the study environment, in a first step the questionnaire for the Neighborhood Environment Walkability Scale for Germany (NEWS-G) was adapted to the study environment. NEWS-G offers a comprehensive collection of environmental characteristics across several sub-chapters and is one of the most widespread measurement procedures of the perceived PA environment [31]. The adapted version should represent a coherent construct for friendliness of the study environment regarding transport-related PA. For the individual perspective, motivators/barriers that are interrelated will be exploratively clustered so that the relationships between them are considered. In the second step, regression analyses will be conducted to empirically identify associations between individual as well as environmental factors and the two separate outcomes of transport-related walking and cycling in terms of health-enhancing activity. We assume that, in the context of the socio-economic approach, both psychological and environmental factors are related to the active transportation behavior of university students.

#### **3. Materials and Methods**

#### *3.1. Design, Setting, and Sample*

The cross-sectional convenience sample for this study (*n* = 997) was formed by students at the University of Tübingen in southwestern Germany who completed the survey. The University of Tübingen represents an urban university setting integrated into an urban hilly landscape. It consists of eight faculties plus a further five interfaculty institutes. More than 200 courses of study are offered. The cross-sectional study was conducted as part of the PA promoting project "BeTa*Balance*" of the university sports organization at the Institute of Sports Science of the University of Tübingen. The study was performed online for three weeks during the end of the university time of the summer semester in 2018 and addressed all students at the university. The online questionnaire was distributed via the university's mailing list and Facebook posts, as well as outreach campaigns in cafeterias and at university meeting points (e.g., the University library and lecture halls) using flyers with a QR code leading to the link for the online questionnaire. The Ethics Commission of the Faculty of Economics and Social Sciences at the University of Tübingen gave a positive vote for the study procedures.

Of a total of 999 returned questionnaires, 997 form the sample of this study, since two cases were not usable due to incomplete answers. Among the participants were 718 female students (72%), 232 male students (23.3%), and 47 (4.7%) did not provide any gender information. The average age of the surveyed students was 23.4 years (SD = 3.45), with an age range from 18 to 42 years. A total of 224 students (22.5%) of the sample reported that they are not living in town and commute to the university town (Table 1). In total, 26,151 students were registered in the summer semester 2018 of the University of Tübingen (state 15 May 2018), resulting in a response rate of 3.8%. Of these, 58.2% were female and 42.8% were male. The sample in this study showed a shift toward more female students.

#### *3.2. Measures*

#### 3.2.1. Physical Activity: Transport-Related Walking and Cycling

To assess PA, the instrument from the European Health Interview Survey (EHIS-PAQ) was used, which records domain-specific information on PA for transport-related walking and transport-related cycling [42]. The questionnaire enables the determination of activity volume in different activity domains. In the domain of active transportation, the participants answered the following questions, each worded in the same way, but separately for walking and cycling in relation to a typical week: (a) "In a typical week, on how many days do you walk/bicycle for at least 10 min continuously to get to and from places?" and (b) "How much time do you spend walking/bicycling in order to get to and from places on a typical day?" Activity volumes were determined in accordance with the procedure in the validation studies [42] and were subsequently indicated as duration per week (minutes or hours). Time values were transformed in metabolic equivalent (MET) values using 3.3 as a factor for computing MET-minutes for walking and 6.0 for cycling, which corresponds to the procedure in the validation studies [42]. As a guideline, 1 MET equals the energy expenditure in the state of complete rest [43].

**Table 1.** Overview of the sample as well as of the study variables according to physical activity (dependent variables), perceived study environment, and personal motivators for and barriers to PA.


<sup>1</sup> Factor 3.3 for computing MET-minutes for walking. <sup>2</sup> Factor 6.0 for computing MET-minutes for cycling.

#### 3.2.2. Contextual Conditions: Perceived PA-Friendliness of the Study Environment

For the assessment of the perceived study environment, the German version of the Neighborhood Environment Walkability Scale (NEWS-G) [44] was contextualized to the university setting. Thus, it can be applied to everyday study life and it records the PA friendliness of the study environment. The following two sections were relevant for this article: (1) opportunities for walking and cycling (including land use mix–access, street connectivity, walking/cycling facilities, and environmental design) and (2) (traffic) safety (including crime) (Appendix A Table A1). Both areas consider relevant factors of leisure-related resources, appearance, and land use, and they are also congruent with the categories of the instrument "Neighborhood Active Living Potential" [45]. Both sections of the adapted version of NEWS-G consist of different statements for which the participants had to indicate their degree of agreement: 1 = totally disagree; 2 = more likely to disagree; 3 = more likely to agree; 4 = totally agree. For the purpose of the main analysis, the items of the study environment were bundled to main factors in the pre-analysis to get a dimensionally reduced yet statistically coherent measurement of the perceived PAfriendliness of the study environment (see Section 4.1.1).

3.2.3. Individual Conditions: Psychological Determinants of Active Transportation—Motivators and Barriers

The survey instrument of Shannon et al. (2006) guided the measurement of motivators and barriers. This instrument was used in a study with university students in order to

analyze motivators and barriers for active commuting [26]. All items include statements, which should be rated according to the extent to which they either motivate or prevent one toward or from engaging in active transportation behavior (e.g., Mot1, "Potential to save money", or Bar1, "Inappropriate weather" with the answer choices: 1 = not at all, 2 = a little, 3 = strong, 4 = very strong). There was also an option, "I cannot judge", which was included in this study for further analysis as an item-nonresponse, since a simultaneous increase in the proportion of people who claim to have no opinion does not allow for the attribution of an actual lack of opinion [46–48].

In addition, two items were added (Mot5, and Mot8) which describe study-related motivator-items. Both were supplemented by the reasons for doing sports or PA, which were asked in the questionnaire for the study "GEDA 2014/2015-EHIS" [49] (p. 118) (Appendix A Table A2).

For barriers, three items were added which should account for the hilly conditions of the university setting (Bar2), study-related barriers (Bar5), and barriers relating to mood or desire (Bar12). Here, concerns of "GEDA 2014/2015-EHIS" [49] (p. 118) and the study by Krämer and Fuchs [50] (p. 174) were used to complement the specific areas (Appendix A Table A3).

Again, factor analyses were applied to obtain dimensionally reduced data on barriers and motivators for active transportation. This was done in the pre-analysis in order to bundle relevant information to be considered later in the main analysis (see Section 4.1.2).

#### *3.3. Statistical Analysis*

In a first step (pre-analyses), exploratory factor analyses (EFA) were conducted separately for the items measuring the study environment as well as for the psychological determinants of active transportation (in terms of motivators and barriers). Therefore, the IBM SPSS 25 software package was used. The decision regarding the number of factors extracted was based on both statistical indices (eigenvalue scree plot, commonalities *h*2, factor loadings, and internal consistency of bundled items *r/α*) as well as content-related fit with the literature and the dimensions measured by the NEWS-G.

In a second step (main analyses), blockwise hierarchical regression analyses using IBM SPSS AMOS 25 software was applied. This was done in order to analyze associations between self-reports on PA, the perceived study environment, and psychological determinants of active transportation. Dependent variables were, separately, transport-related walking (A) and cycling (B). Firstly, in the blockwise procedure, sociodemographic factors were included as predictors (sex, age, and whether or not a resident in the university town). Secondly, determinants of the perceived study environment were included, and thirdly the psychological determinants of motivators and barriers were added as predictors in the regression model.

Missing values were estimated in the main analysis using the full information maximum likelihood (FIML) method implemented in AMOS 25. This was done in the case when at least one item of a scale measure was missing. In those cases, no mean value was calculated for the respective scale measure that could be included in the main analysis. Accordingly, the number of cases *n* for certain scales was reduced due to missing scale mean values.

For the evaluation of the global model fit, the root mean square error of approximation (RMSEA) [51], the comparative fit index (CFI) [52], and the minimum of discrepancy in relation to the degrees of freedom (CMIN/DF) were used [51,52]. In order to compare the different models within the hierarchical blockwise approach, information on the determination of variance (R2) and the change of R2 compared to the previous model were calculated.

Furthermore, the regression models were specified in a way that included significant correlations between the predictors. This led to an acceptable and good model fit, which ensures the model-based estimation of missing values. In addition to tests for statistical significance (*α* < 0.05), effect sizes were determined and interpreted—according to small effects in agreement with Cohen (1988) [53]—if the standardized regression coefficient is equal to or higher than *β* ≥ 0.10.

#### **4. Results**

#### *4.1. Pre-Analyses: Exploratory Factor Analyses*

4.1.1. Contextual Conditions: Perceived PA-Friendliness of the Study Environment

The bundling of the 14 initial items for the perceived study environment resulted in a differentiation of seven factors. A comparison of the finally derived factors and their content fit with the categories of the NEWS-G [44] and were adapted for the study environment as reported in Appendix A (Table A1). In this bundling process, we considered the subsequent categories of the NEWS-G: (C) "Land use mix–access", (D) "Street connectivity", (E) "Walking/Cycling facilities", (F) "Aesthetics", (G) "Pedestrian/automobile traffic safety", (H) "Crime safety". The EFA with 14 items initially proposed a five-factor solution, however, the results of the EFA led to the elimination of two items in order to improve the reliability of the factor regarding traffic safety. This concerns two items of category G, pedestrian/automobile traffic safety (Item G3, "The traffic speed in most surrounding streets is normally low (30 km/h or less)", and item G6, "There are crosswalks and pedestrian signals to help walkers cross busy streets in my study environment"). The item wordings, item descriptives, factor loadings, and communalities are reported in Appendix A (Table A4).

For the remaining items, statistical- and content-based criteria indicated a preference for a more differentiated seven-factor solution after keeping individual items that could not be bundled well to one factor, but which contributed to a variance explanation with reasonable communalities. They were either included as a single item in the further analyses after a content-wise comparison with the categories of the NEWS-G, or were assigned to other content-wise suitable factors after statistical verification by reliability analyses. The derived seven factors reflect the following areas of the perceived PA-friendliness of the study environment:


4.1.2. Individual Conditions: Psychological Determinants of Active Transportation—Motivators and Barriers

The analyses to bundle psychological determinants of active transportation resulted in a differentiation of four factors for motivators and three factors for barriers. To arrive at these results, the following steps were taken. The EFA for motivators suggested the formation of two factors, after having previously removed item Mot6 (opportunity to socialize) due to a very low communality value (*h*<sup>2</sup> = 0.23). Mainly based on content-related considerations as well as on information of internal consistency statistics, and inter-itemcorrelations, we decided to split up two factors and preferred a four-factor solution. The item wordings, item descriptives, factor loadings, and communalities of motivators are reported in Appendix A (Table A5).


The EFA for barriers to PA suggested using three factors, after having previously removed two items due to overlap with items of the perceived study environment regarding the hilly landscape and the lack of secure bicycle parking facilities. Regarding the results of the EFA, two more items were excluded that could not be satisfactorily assigned to one factor due to statistical reasons (Bar3, and Bar10). Additionally, item Bar10, which refers to the lack of knowledge of the quickest and easy routes, had the lowest communality (*h*<sup>2</sup> = 0.34). The item wordings, item descriptives, factor loadings, and communalities are reported in Appendix A (Table A6). For the remaining items, the following three factors were considered:


Tables A2 and A3 in Appendix A summarize the results regarding the motivators and barriers for PA. Moreover, Table 1 gives an overview for the finally considered determinants of active transportation behavior and provides descriptive information.

#### *4.2. Main Results: Regression Models*

The main analysis consisted of two separate analyses for the respective dependent variables of transport-related walking (A) and transport-related cycling (B). For each type of transportation mode, bivariate correlations and a separate regression model were calculated. In the basis model, we included the three sociodemographic indicators (sex, age, resident in university town; Model A0 and Model B0). We then added blockwise the indicators of the perceived study environment (models A1 and B1) and the indicators for personal motivators and barriers (models A2 and B2) (see Tables 2 and 3).

**Table 2.** Results of the blockwise multivariate regression models A1 (predictors—sociodemographics, and perceived study environment) and A2 (A1 plus motivators and barriers) for the active transportation by walking.


RMSEA: Root Mean Square Error of Approximation; CFI: Comparative Fit Index; CMIN/DF: ratio of Chi-square (minimum discrepancy) to its Degrees of Freedom; \* The probability of error is less than 5%.

#### 4.2.1. Regression Analyses for Walking

The regression models for active transportation by walking showed good global fit indices (CMIN/DF = 1.52–2.40; RMSEA = 0.023–0.037). There was also an improvement of variance clarification by the number of predictors added to the model (R2 = 0.005–0.032). Altogether five of 17 predictors in the model showed associations with the weekly amount of walking, all of which had a standardized regression coefficient *β* lower than 0.10. Most associations were found among the motivator predictors (see Appendix A Table A7).

In the multivariate Model A1 including sociodemographic variables and determinants of the perceived study environment, only aesthetics showed a significant regression coefficient, which was lower than 0.10 (*β* = 0.07). When adding psychological determinants in Model A2, this association disappeared, but three other associations were statistically significant: not living in the university town (*β* = −0.07), bicycle-related crime (*β* = 0.07), and the barrier related to discomfort with study life (*β* = −0.08). All of them showed regression coefficients smaller than *β* < 0.10.


**Table 3.** Results of the blockwise multivariate regression models B1 (predictors—sociodemographics, and perceived study environment) and B2 (A1 plus motivators & barriers) for the active transportation by cycling.

\* The probability of error is less than 5%. \*\* The probability of error is less than or equal to 1%. \*\*\* The probability of error is less than or equal to 0.1%.

#### 4.2.2. Regression Analyses for Cycling

The regression models for active transportation by cycling showed adequate to good global fit indices (CMIN/DF = 1.52–2.41; RMSEA = 0.023–0.038). There was also an improvement of variance clarification reached by blockwise including the sets of different predictors to the model (R<sup>2</sup> = 0.05–0.24). Altogether, 12 of 17 predictors showed associations with the weekly amount of cycling, whereas all of the psychological determinants were present. For most predictors, the standardized regression coefficients were considered small to medium size (|0.06| < *r* < |0.38|) (see Appendix A Tables A8 and A9).

In the multivariate Model B1 including sociodemographic variables and variables of the perceived study environment, five predictors showed a significant regression coefficient, but for two of them it was lower than 0.10. The highest regression coefficient was found for the predictor of resident in the university town (*β* = 0.20), followed by bicycle-related crime (*β* = −0.14), and high automobile traffic (*β* = 0.118). When adding psychological determinants in Model B2, all associations became smaller or, in the case of high automobile traffic, showed a regression coefficient smaller than *β* < 0.10 (*β* = 0.08). The following associations remained with a small to medium regression coefficient: resident in the

university town (*β* = 0.14) and bicycle-related crime (*β* = −0.13). While the association with "active transportation: uphill" disappeared, "walking/cycling facilities" were statistically significant but with a regression coefficient smaller than *β* < 0.10. Additionally, three other associations were statistically significant: personal barriers (*β* = −0.24), external barriers (*β* = −0.23), and personal benefits (*β* = 0.13).

#### **5. Discussion**

Using a socio-ecological approach in a university setting, the present study addresses the question of which conditions of the study environment as well as individual motivators and barriers are related to students' transport-related walking and cycling. Results show that there were no relevant predictors associated with the amount of transport-related walking: neither sex, age, and place of living nor the study environment or personal motivators and barriers were substantially linked with transport-related walking. In contrast, transport-related cycling was associated with predictors from both depicted conditions of students' PA behavior, which are important to understand for developing and improving public health interventions: resident in university town, personal benefits, personal barriers, and external barriers relying on individual conditions and high automobile traffic, and bicycle-related crime relying on contextual conditions. Bearing in mind the social-ecological approach of the study, the results reveal multivariate relationships between the level of cycling for transportation and both environmental and individual conditions.

To investigate these relationships, the present study has firstly bundled factors for the perceived study environment regarding the established survey instruments for neighborhood environment NEWS-G and statistical indices of EFA. The same was done for psychological determinants of students for active transportation regarding the study of Shannon et al., (2006) [26]. This procedure has enabled us to link the study environment based upon an adaption of the NEWS-G as well as psychological determinants with the active transport behavior of students, something that has not yet been investigated much in German-speaking countries. So far, only Molina-Gracia et al., (2010) in Spain have used parts of the NEWS besides other aspects to analyze the active commuting of students to university, namely "walking/cycling facilities" (E) [23]. A short version without adaption was used by Peachey and Baller (2015) in a mid-Atlantic undergraduate university with the NEWS-Abbreviate to distinguish environmental characteristics of the living environment between on-campus neighborhoods and off-campus neighborhoods, and to bring this into connection with general PA [54]. While the NEWS assesses the environment of the neighborhood, none of the previous studies used an adaption to access the environment of the study area. Titze et al., (2007) developed a questionnaire based on the literature and focus groups with a special relation to cycling for transportation and the environment along the transport route of students [27]. With the adaption of NEWS-G to the study environment in this study, we wanted to rely on an established survey procedure of the perceived environment and bring it together with the PA-friendliness of the study environment for transport-related PA. The conceptually and empirically derived factors covered areas of the environmental conditions in relation to the study environment: land use mix–access, connectivity, walking/cycling facilities, aesthetics, automobile traffic, and crime safety. The last two factors showed significant correlations for the convenience sample with students' cycling for transportation, but none showed associations with walking.

That "high automobile traffic" is positively associated with cycling is contrary to the expected result. This association was slightly weakened by adding psychological determinants into the regression model. It seems paradoxical that sampled students' perceived difficulties, unpleasantness, or insecure feeling when active traveling due to much traffic and noticeable exhaust fumes from cars or buses, is positively related to cycling for transportation. The same contrary effect was found in multinomial regression analysis from Titze et al., (2007) [27] for regular cyclists, who cycle more than three times a week. For irregular cyclists, the perception of traffic did not show any effect at all. One possible explanation is that cyclists are more exposed to the problem and therefore more

likely to report it [27]. Further studies should investigate moderation analyses based on a representative sample, whereby psychological determinants should be integrated as moderators between the study environment and active commuting—especially cycling for transportation.

There is a negative correlation between bicycle-related crime and cycling. Students' unsafe feeling for leaving even a locked bicycle in the study environment is negatively related to cycling for transportation. This association has repeatedly been reported in the literature [22,25,27]. For example, Rybarczyk and Gallagher (2014) [25] showed that general crime was the strongest barrier for cycling among students and staff of the university, but also bicycle theft was represented under the three most highly ranked barriers. Rybarczyk and Gallagher concluded that the implementation of law enforcement and safe bicycle facility may promote cycling. This was also suggested by Shannon et al., (2006) [26].

Regarding individual conditions, personal barriers showed the strongest associations with cycling. This is in line with the conclusion of Shannon et al., (2006) that reducing barriers to using active transportation modes is likely to be more effective than promoting the benefits of active modes [26]. Further, Rybarczuk and Gallagher (2014) showed that students indicated that any bicycle barrier would cause a decrease in cycling [25]. Our study results reinforce the premise that students' personal barriers such as physical effort, time effort, and bad mood are negatively related to cycling for transportation. Such personal barriers of time constrains, inconvenience, or physiological discomfort are in accordance with previous findings [21,27]. The same applies to students' external barriers such as the weather or the time of day. These external inhibiting factors were also found in previous studies [25,26,28]. Nordfjærn et al., (2019) [55] recently showed that those who strongly prioritized convenience tended to use a car for transportation modes. However, the increased awareness of the negative consequences was related to a more use of active transportation and less car use. A positive association with cycling for transportation applies to students' personal benefits for active transportation such as joy, health, and fitness. This finding is also in line with the positive relation between emotional satisfaction and regular cycling as found by Titze et al., (2007) [27]. It is also in accordance with the association between strong priorities of PA and less public transportation mode use and more use of active transportation found by Nordfjærn et al., (2019) [55]. Overall, the inclusion of the set of psychological factors in the model improved the variance explanation for the cycling behavior of university students, indicating their important role for individual decisions related to transport-related cycling. However, Nordfjærn et al., (2019) showed that besides psychological variables, situational constraints were more important for mode use than psychological variables and are important to consider as well, for example, car ownership or longer walking time [55].

Regarding sociodemographic variables of the sampled students, the association between residence in the university town and cycling was slightly weakened by adding psychological determinants into the regression model but was still significant at medium level. Students' residence in the university town was positively associated with cycling for transportation. This is in line with the negative impact of distance found in previous studies [21,22,26,28,29] and also with the association between longer walking time from students' residence to university and the more use of public transportation for less active transportation recently showed by Nordfjærn et al., (2019) [55]. Moreover, Zannat et al., (2020) [56] revealed in terms of city planning the travel time besides the provision of infrastructure as influencing factors for active and public transportation of university students. Furthermore, the factor "personal barriers" of our study, which covers the barrier of time effort, is negatively associated with cycling on a medium level and reinforces this interpretation.

The result that there were no relevant contextual and individual predictors for students' transport-related walking has already been shown in both the university and community setting. Missing statistical significance for the probability of use of walking for students with environmental incentives was also the case in the results of Rybarczuk and

Gallagher (2014) [25]. In communal settings, walking for transportation shows a different association than walking for leisure, which is associated with recreation facilities and aesthetics and green spaces [13,17,36,37]. That the results of this study, which investigated only the domain of active transportation, did not show such correlations, suggests that students were not likely to choose walking as an active mode of transportation for contextual or individual reasons, but rather that it was purely a means of getting from point A to point B. However, in terms of active commuting by students in general, positive associations with the perception of walking and cycling facilities [23], traffic and crime safety [19,21,22,25,27,28], and aesthetic aspects such as the "attractiveness of the surroundings" [27] (p. 72) exist, which could not be shown in this study for walking.

Furthermore, active transportation cannot only be considered in the perspective of promoting PA but also in the perspective of promoting more sustainable modes of transport which in turn has effects on the environment, on the economy, and on the health of people [57]. Some recent studies have dealt with the importance of using sustainable means of transport by the university community [56,57]. The authors of these studies also showed that the mode of transportation is conditioned by particularities of university campuses such as bike share systems [58], tailored and strategically-placed point-of-choice prompts, through which students should switch to active transportation [59], or the distribution of the university scheduled classes on the days of the week [60]. However, in order to make use of the potential to increase cycling among students Grimes and Baker (2020) [58] revealed that bike share systems conditions in university settings must be tailored to the target group, Chim et al., (2020) [60] pointed out that there is only a positive association of university courses on weekdays with more time spent cycling if students cycle to classes anyway, and Irwin (2019) [61] showed that uncontrollable factors for example time, built environment, and weather affected the participation in activities. Thus, just like the results of our study, these findings show that the combination of environmental conditions and personal psychological determinants is important to consider. In addition to tailored measures offered by the university to promote sustainable and active transportation, also competing modes of transportation bring further psychological factors into play. Cruz-Rodriguez et al., (2020) [57] analyzes students' feelings and emotions provoked by alternative means of transport. In addition to various electric means of transportation, only the use of bicycles showed associations with the possibility of PA, but, for example, the feeling of freedom or getting around quickly in the city or avoiding traffic jams were also present for scooters and motorcycles [57]. Further studies should include deeper psychological backgrounds of transportation choice. To take advantage of the synergies between promoting PA and sustainability, further studies should additionally compare competing modes of transportation such as scooters and motorcycles.

#### *Strengths and Limitations*

Certain limitations must be considered when interpreting the results. Due to the crosssectional study design, we could not identify causal associations. In addition, the study was conducted in the summertime, which could have an influence on the reported active commuting information due to better weather [19]. Furthermore, regarding the shift toward more female students in the convenience sample of the study, possible sampling bias cannot be excluded. Some studies report a gender difference in favor of male students with regard to the use of bicycles for active transportation [28,30], but other studies did not found different travel patterns between male and female students [23,62]. Agarwal and North (2012) [19] found some gender differences regarding the perception of barriers to cycling. Accordingly, generalizability of the associations would still need to be empirically verified.

The measuring instrument for the study environment was empirically used for the first time. Although the study has attempted to bundle information for both study environment and psychological determinants to better account for psychometric properties of the factors, some variables were measured as single items. For study environment the categories "land use mix–access", "connectivity," "general crime", and "bicycle-related crime" were only

covered with one item each. For psychological determinants, the motivator item "avoid air pollution" was considered separately due to content and statistical indices. It is possible that the single items contributed to the absence of associations due to their lower variance. However, it has not been uncommon to include single items in this area of research to date [19,21,26]. Further development is thus needed for measurement procedures. For some areas, the present study provides indications. Our study did form a factor, which dealt with study-related psychological determinants. Furthermore, factors relying on personal benefits, on instrumental extrinsic benefits, and on avoiding air pollution were formed for motivators. Factors for barriers were discomfort with study life, personal barriers, and external barriers. Overall, further surveys in other universities are necessary to concretize and validate the adapted NEWS-G for the study environment as well as to confirm the factors formed.

In addition, the measuring instrument for the study environment captures the selfassessed perception of the students and thus does not provide an objective measure of the survey. This can lead to distortions, for example, as people who frequently walk or cycle outside might perceive traffic more strongly [40]. The importance of perception can only be filtered out and captured through a combination of objective and self-assessed measurement of physical environmental characteristics [41].

Despite the limitations, this study provides some strengths. It tried for the first time to assess not the living environment but the specific study environment with reference to an established survey instrument, so it can be used for campus as well as urban universities. This is important due to the fact that the transfer of results from campus universities is difficult to universities, which are not structured as closed geographical spaces, but the urban university is integrated into urban landscape [24,63].

In addition, referring to socio-ecological approaches could confirm the relationship between transport-related PA and both contextual as well as individual determinants. Further, it provides initial multivariate results on active transportation and its relation to contextual and individual determinants from Germany. Furthermore, since this study differentiated the PA domains into the different modes of transportation, walking and cycling, it could show that the compositional and contextual conditions are different for both modes. So for promoting PA it is important to distinguish between the needs of pedestrians and cyclists [20].

To sum up, in relation to other studies with respect of university students which considered both personal and environmental determinants together in relation with active transportation, the scientific value of the presented study lies in the insights into the contextual conditions of the study environment, the consideration of associated correlates through the factor bundling, and separate information for transport-related cycling and transport-related walking.

#### **6. Conclusions**

Current findings confirm on a regression-analytical basis the postulated socio-ecological relationships between both contextual as well as individual factors and transport-related cycling, but not with transport-related walking. In total, the students' amount of cycling a week is positively associated with the students' residence in the university town, high automobile traffic, and personal benefits such as joy and health, and negatively associated with bicycle-related crime, personal barriers such as physical or time effort, and external barriers such as weather conditions. It should be noted that there might be a partial correlation between "high automobile traffic" and psychological determinants which indicate a moderation role of psychological determinants.

Possible strategies leading to an adequate infrastructure for universities may be the implementation of safe bicycle racks, bicycle routes, or more student residences in town. Additionally, academic training programs that indicate the benefits of transport-related cycling may students help to understand the associations between cycling and health, environment, sports and recreation. This can increase motivation to use the bicycle for

transportation and lead to consolidate the bicycle culture in transportation in the university community. Given the current climate change and the increasing physical inactivity of society, a cycling culture can advance alternative means of transportation and thus have positive effects on the economy, environment, and health. As PA is linked with various benefits for health and educational outcomes, the results contribute to the understanding of the correlates of active commuting. This is important especially for university students who are particularly at risk of not fulfilling health-oriented PA recommendations. Therefore, the present study supplements specific knowledge about determinants that are important for developing and improving public health interventions for students in a university setting.

**Author Contributions:** Conceptualization, M.T. and G.S.; methodology, M.T. and G.S.; formal analysis, M.T.; investigation, M.T. and G.S.; resources, G.S.; data curation, M.T.; writing—original draft preparation, M.T.; writing—review and editing, M.T. and G.S.; supervision, G.S.; project administration, M.T. and G.S.; funding acquisition, G.S. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Allgemeiner Deutscher Hochschulsportverband (adh) (German University Sports Federation) and the Techniker Krankenkasse, health insurance fund.

**Institutional Review Board Statement:** Translation of the Ethics Committee Statement of the Faculty of Economics and Social Sciences of the University of Tübingen (original version: German): "From our point of view, there are no concerns for the project as you have presented it, since you inform the participating persons in detail about the purpose of the study, explicitly assure the voluntary nature of the survey, obtain a declaration of consent for the storage and analysis of the data, and the data are collected and processed anonymously." The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee of the Faculty of Economics and Social Sciences of the University of Tübingen (protocol code A2.54-077\_aa, 26 June 2018).

**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.

**Acknowledgments:** We would like to thank Ingrid Arzberger, Head of University Sports at the University of Tübingen, for providing the resources and co-applying for the funding. We would like to thank the participating students in the master course of the sports science program at the University of Tübingen and again Ingrid Arzberger for their support during the project conduction. We acknowledge support by Open Access Publishing Fund of University of Tübingen.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

#### **Appendix A**


**Table A1.** Comparison of the finally selected items and their category bundled by the factor analysis and content fit in this article with the adapted categories of the NEWS-G 1 [44] for the study environment asked in the survey.


<sup>1</sup> Neighborhood Environment Walkability Scale—Germany. <sup>2</sup> adapted categories of the NEWS-G for the study environment asked in the survey. <sup>3</sup> newly formed by factor analysis and content fit in this article. <sup>4</sup> for reasons of condensation, a summarized generalized statement of the respective category of the NEWS-G was newly formed. <sup>5</sup> due to previous evidence on the relationship between bicycle thefts and active camouflage transport behavior of students, a new specific item on crime was formed.

**Table A2.** Comparison of the final construct of motivator items newly formed by the factor analysis and content fit in this article.



**Table A2.** *Cont.*

<sup>1</sup> Newly formed by factor analysis and content fit in this article. <sup>2</sup> Own items according to the study "GEDA 2014/2015-EHIS" [44] (p. 118). <sup>3</sup> Motivator items (Shannon et al., 2006) [26].

**Table A3.** Comparison of the final construct of barrier items newly formed by the factor analysis and content fit in this article.


<sup>1</sup> Newly formed by factor analysis and content fit in this article. <sup>2</sup> Own items according to the study "GEDA 2014/2015-EHIS" [44] (p. 119). <sup>3</sup> Barrier items (Shannon et al., 2006) [26]. <sup>4</sup> Krämer & Fuchs (2010) [50] (p. 174). <sup>5</sup> Supplemented due to site-specific hilly conditions of the University of Tübingen.



The backgrounds highlight the largest part of the frequency distribution of the scale in each case. The bolds highlight the largest rotated factor loading in each case.

**Table A5.** Item's descriptives (frequencies in %; 1 = totally disagree; 2 = more likely to disagree; 3 = more likely to agree; 4 = totally agree), factor loadings, and communalities (*h*2) of the explorative factor analysis (EFA) of motivator items.


The backgrounds highlight the largest part of the frequency distribution of the scale in each case. The bolds highlight the largest rotated factor loading in each case.

**Table A6.** Item's descriptives (frequencies in %; 1 = totally disagree; 2 = more likely to disagree; 3 = more likely to agree; 4 = totally agree), factor loadings and communalities (*h*2) of the explorative factor analysis (EFA) of barrier items.


The backgrounds highlight the largest part of the frequency distribution of the scale in each case. The bolds highlight the largest rotated factor loading in each case.

**Table A7.** Results (inter-item-correlation-coefficients r, β-coefficients and significant *p*-value) for the bivariate correlation and multivariate regression models A1 (sociodemographics and perceived study environment) and A2 (A1 plus Motivators & Barriers) for the transportation mode "walk".



**Table A7.** *Cont.*

\* The probability of error is less than 5%. \*\* The probability of error is less than or equal to 1%.

**Table A8.** Results (inter-item-correlation-coefficients r, β-coefficients and significant *p*-value) for the bivariate correlation and multivariate regression models B1 (sociodemographics and perceived study environment) and B2 (B1 plus Motivators & Barriers) for the transportation mode "cycle".


\* The probability of error is less than 5%. \*\* The probability of error is less than or equal to 1%. \*\*\* The probability of error is less than or equal to 0.1%.




**Table A9.** *Cont.* probability of error is less than 5%. \*\* The probability of error is less than or equal

#### **References**


### *Article* **Environmental and Psychosocial Barriers Affect the Active Commuting to University in Chilean Students**

**Antonio Castillo-Paredes 1,\*, Natalia Inostroza Jiménez 2,3, Maribel Parra-Saldías 4, Ximena Palma-Leal 4, José Luis Felipe 5, Itziar Págola Aldazabal 5, Ximena Díaz-Martínez <sup>6</sup> and Fernando Rodríguez-Rodríguez <sup>4</sup>**


AC and encourage personal organization to travel more actively.

**Abstract:** Biking and walking are active commuting, which is considered an opportunity to create healthy habits. Objective: The purpose of this study was to determine the main environmental and psychosocial barriers perceived by students, leading to less Active Commuting (AC) to university and to not reaching the Physical Activity (PA) recommendations. Material and Methods: In this crosssectional study, 1349 university students (637 men and 712 women) were selected. A self-reported questionnaire was applied to assess the mode of commuting, PA level and barriers to the use of the AC. Results: Women presented higher barriers associated with passive commuting than men. The main barriers for women were "involves too much planning" (OR: 5.25; 95% CI: 3.14–8.78), "It takes too much time" (OR: 4.62; 95% CI: 3.05–6.99) and "It takes too much physical effort " (OR: 3.18; 95% CI: 2.05–4.94). In men, the main barriers were "It takes too much time" (OR: 4.22; 95% CI: 2.97–5.99), "involves too much planning" (OR: 2.49; 95% CI: 1.67–3.70) and "too much traffic along the route" (OR: 2.07; 95% CI: 1.47–2.93). Psychosocial barriers were found in both sexes. Conclusions: Psychosocial and personal barriers were more positively associated with passive commuting than

**Keywords:** active; commuting; active transport; physical activity; active behavior; college

environmental barriers. Interventions at the university are necessary to improve the perception of

#### **1. Introduction**

Sedentary lifestyle represents an important risk factor for health, since it participates in the development of chronic non-communicable diseases such as cardiovascular diseases, type 2 diabetes and some types of cancer [1,2]. Sedentarism constitutes one of the main causes of mortality worldwide, especially among those who fail to comply with the recommendations of physical activity (PA) for the adult population [3].

This decrease in PA has not only been related to weight gain and worse psychological well-being [4], but also contributes to an increased risk of developing non-communicable diseases and lower life expectancy [5].

Active commuting (walking or cycling from one place to another), is considered an opportunity to create healthy habits, which improve PA levels, decrease cardiovascular

**Citation:** Castillo-Paredes, A.; Inostroza Jiménez, N.; Parra-Saldías, M.; Palma-Leal, X.; Felipe, J.L.; Págola Aldazabal, I.; Díaz-Martínez, X.; Rodríguez-Rodríguez, F. Environmental and Psychosocial Barriers Affect the Active Commuting to University in Chilean Students. *IJERPH* **2021**, *18*, 1818. https://doi.org/10.3390/ ijerph18041818

Academic Editors: Gregory Heath and Adilson Marques Received: 22 December 2020 Accepted: 2 February 2021 Published: 13 February 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

risk [6,7] and help achieve a healthier body composition in the young and adult population [8]. Although there is limited evidence in developing countries about the association between active commuting (AC) and health benefits [9,10], Steell et al. [11] showed that, in Chile, 30 min of AC was associated with less adiposity and a healthier metabolic profile that includes a lower risk of obesity, diabetes and metabolic syndrome. Another prospective study, which included a cohort of 263,540 participants from the United Kingdom (Biobank), reported that AC on a bicycle was associated with a lower risk of cardiovascular disease, cancer and all-cause mortality [12].

The university stage is a period of transition from adolescence to adulthood, which is characterized by long days of study, a high sitting time [13], little time for PA [14] and generally bad eating habits [15]. The decrease in PA [16] is mainly due to the fact that the subject of physical education is not mandatory in university, as it is at school [17]. Likewise, sports practice improves physical self-concept, which improves physical appearance, physical ability and weight control behaviors [18]. In Chile, it has been shown that men and women who perform sports activities have a more positive self-concept as compared to men and women who do not perform sports activities [14]. This positive physical self-concept could be associated with lower barriers to PA and AC, but this has not been studied to date.

Studies have shown that the main barriers to PA among university students are related to a lack of time, lack of social support, lack of motivation/enjoyment and economic reasons [19–21]. PA behaviors are influenced by personal (knowledge, skills, attitudes, and self-esteem) and environmental factors (social support, institutional characteristics and built environment [22,23]. However, there is no clarity as t which barriers affect PA in university students, especially considering the different dynamics of different universities [24].

In the same way, the choice of AC, such as walking, can be influenced by several barriers, such as distance and socioeconomic status. It was observed in a study in Spanish university students that those who lived less than 2 km (km) away from the university and those who had a low socioeconomic status used to walk [25]. A short travel distance, high connectivity on the streets, living in an urban area and high density on the roads have been positively associated with higher levels of AC [26,27].

In order to follow the World Health Organization (WHO) recommendation that urban planning in cities promote PA and AC through the design of urban spaces [28], it is important to analyze the previous patterns of population commuting [29,30] to achieve the implementation of promotion programs, improvements in bikeway and walking infrastructure, and road safety [31].

According to the evidence presented and the importance of increasing the level of PA in university students by active commuting, it is necessary to get an idea of the mode of commuting in Chilean university students. The objective of this study was to determine the main environmental and psychosocial barriers perceived by students and associated with less AC to university and not reaching the PA recommendations.

#### **2. Materials and Methods**

#### *2.1. Study Design and Participants*

This cross-sectional study had a non-probabilistic sample (intentional) and had a descriptive and correlational analysis in university students from three Chilean regions.

A total of 1349 students (637 men and 712 women), with an average age of 22.7 ± 5.8 years, from three public (two in Valparaíso, Viña del Mar and one in Chillán) and one private university (Santiago) were selected. They all agreed to participate voluntarily in the study. They were regular students from the first to fifth academic year studying in fields such as Education (*n* = 265), Health (*n* = 308), Engineering (*n* = 716) and Social Sciences (*n* = 60). More information and inclusion criteria are shown in Figure 1.

**Figure 1.** Methodological diagram to explain the sample and instrument.

#### *2.2. Instruments*

A self-reported questionnaire was applied to assess the mode of commuting [32], PA level [33] and barriers to the use of the AC in university students [34]. The instrument consisted of five items: sociodemographic characteristics (14 questions); mode of commuting (14 questions); barriers to AC (14 questions); PA level (4 questions) and a self-assessment questionnaire on physical condition—IFIS (International Fitness Scale) (not included in present analysis). This instrument was previously subjected to a specific reliability process for Chilean university students [35] In order to evaluate the reliability of this questionnaire, a test–retest process was performed. Kappa coefficient and Intraclass Correlation Coefficient (ICC) were calculated. Commuting to and from university was found to be in

almost perfect agreement, with Kappa coefficient values of 0.882 and 0.822, respectively. ICC scores on distance to and from university and time to and from university showed good reliability in all its items, with high according values.

The sociodemographic characteristics item was sex, age, field of study (*health*, *education*, *social science*, *engineer*), university (*public*, *private*), residence area (*urban*, *rural*), live with family and socioeconomic level. To determine socioeconomic level, the Family Affluence Scale (FAS) was used, with the following questions: *(1) "Does your family own a car?"* (No = 0; Yes, one = 1; Yes, two or more = 2), *(2) "How many computers does your family own?"* (None = 0; One = 1; Two = 2; More than two = 3), *(3) "Do you have your own bedroom for yourself?"* (No = 0; Yes = 1) and *(4) "Do you have internet access?"* (No = 0; Yes = 1). Each answer was summed to obtain the total points. A score was assigned, and participants were classified into three categories regarding the FAS: low (0–3 points), medium (4–5 points) and high (6–7 points) [36].

The mode of commuting to university was defined with the questions: *(1) "How do you usually get to university?"* and *(2) "How do you usually get home from university?*", with a choice of answer options such as walking, bicycle, car, motorcycle, public bus, metro/train and other modes. Walking and bicycle were categorized as "active" modes and other motorized modes as "passive" commuting. The items pertaining to mode of commuting and barriers to the use of AC were used.

The barriers to AC were indicated, such as "There are no sidewalk or bikeways"; "Bikeways occupied by people who walk"; "There is too much traffic along the route"; "Are dangerous crossing along the way"; "Walking or biking is insecure due to crime"; "Are no places to leave the bicycle safely"; "Streets are dangerous because of the cars"; "I get hot and sweat when I'm walking or biking"; "I'm too loaded to go walking or cycling", "It is easier to move with car or motorcycle"; "Walk or biking involves too much planning"; "It takes too much time"; "It takes too much physical effort"; "I need the car or the motorcycle to university". These questions had a categorical like-type response with the alternatives: totally agree = 1; agree = 2; disagree = 3; and strongly disagree=4[34].

The International Physical Activity Questionnaire (IPAQ, short version) was used to determine PA levels [33,37]. The PA was classified into sedentary time (min/day), light PA (min/week), moderate PA (min/week), vigorous PA (min/week) and moderatevigorous (MVPA) as has been reported in a previous study [16]. Regarding the MVPA recommendations for adults (≥150 min/week) [38], students were classified as "Reaching" or "Not reaching" the weekly recommendations.

#### *2.3. Procedure*

With the authorization of the academic direction of each university and the knowledge of the career directors, the questionnaire was applied in paper format.

The application of the questionnaire was carried out from Monday to Friday in the day, evening and executive programs, in the same classroom. The application was carried out in 15–20 min and between the months of April and July 2017. Informed consent was obtained from each student before their participation, which requested the authorization to participate in the research project and explained the objectives and that the collected data were anonymous, private, confidential and for exclusive use in the study.

All the participants voluntarily agreed to participate in the study, which was approved by the Ethics Committee of the corresponding university (Code: CCF02052017) and governed by the Declaration of Helsinki 2013 [39].

#### *2.4. Statistical Analysis*

The results are presented in frequencies for categorical variables and for continuous variables, in means and standard deviations (M ± SD). To establish the associations between barriers and mode of commuting and barriers and compliance with MVPA, a binary logistic regression was applied to obtain the Odds Ratio (OR) and Confidence Interval (95% CI). Mode of commuting was included in the model as the dependent variable and barriers

were included individually as independent variables. The score was also calculated for the barriers, which were grouped into the categories of Environment and Psychosocial. Significant values of *p* < 0.05 were considered. For the analyses, the statistical software IBM SPSS, version 26, was used.

#### **3. Results**

Table 1 present the description and sociodemographic characteristics of the participants. The distribution of the sample by sex was similar (52.8% women and 47.2% men). In both sexes, there was a higher percentage of students in the age range from 18 to 24 years (79.2%), belonging to the engineering study field (53.1%), who reside in urban areas (96.1%), live with their families (73.41%), and have a medium socioeconomic level (52.9%).


**Table 1.** Sociodemographic characteristics of the participants by sex.

Table 2 shows the mode of commuting of university students according to sex. A total of 82.2% commuters were passive commuters and 17.8% were active commuters, with men (33.8%) forming a higher percentage of those who moved actively. In both sexes, the main mode of commuting was by public bus. The proportion of women and men who reported walking to university was 16.9% and 32%, respectively, while bicycle use was 1% and 1.7%, respectively.


**Table 2.** Mode of commuting for the university students by sex.

Table 3 exhibits the PA of university students according to sex. According to the PA, the mean sitting min/day for the entire sample was 495.7 ± 696.9. Similar data were found in both sexes for minutes/day sitting (women 508.4 ± 751.7 and men 481.6 ± 630.2). Most of the sample (83.5%) did not comply with the recommendation of MVPA for 150 min a week.

**Table 3.** Physical activity and recommendations of the university students by sex.


Abbreviations: (mean ± SD) Mean ± Standard Deviation; (PA) Physical Activity (MVPA) Moderate-Vigorous Physical Activity.

Table 4 shows the association between barriers and passive commuting by sex. In women, the barriers *"Walk or biking involves too much planning"* (OR: 5.25; 95% CI: 3.14–8.78; *p* < 0.001), *"It takes too much time"* (OR: 4.62; 95% CI: 3.05–6.99; p < 0.001), *"It takes too much physical effort"* (OR: 3.18, 95% CI: 2.05–4.94, p < 0.001), were the main barriers. The perception of barriers *"The bikeways are occupied by people who walk"* (OR: 2.54; 95% CI: 1.69–3.84; *p* < 0.001), and *"I'm too loaded to go walking or cycling"* (OR: 2.44; 95% CI: 1.65–3.61; *p* < 0.001), is associated with the probability of passive commuting increasing by two times. In men, *"It takes too much time"* (OR: 4.22; 95% CI: 2.97–5.99; *p* < 0.001) increases the probability of choosing passive commuting four times, while *"Walk or biking involves too much planning"* (OR: 2.49; 95% CI: 1.67–3.70, *p* < 0.001) and *"There is too much traffic along the route"* (OR: 2.07; 95% CI: 1.47–2.93; *p* < 0.001) increases the probability of passive commuting two times. In general, women presented a greater association between barriers and passive commuting than men.


**Table 4.** Association between barriers and passive commuting by sex.

Significant association in bold as *p* < 0.05 and *p* < 0.001.

Table 5 indicates the association between barriers to AC and complying with the MVPA recommendation by sex. The statement "There are no sidewalk or bikeways" (OR: 1.81; 95% CI: 1.02–3.19; *p* < 0.05) was positively associated with non-compliance with the recommendations for MVPA in women. In addition, women who reported being "I get hot and sweat when I'm walking or biking" (OR: 0.56; 95% CI: 0.35–0.89; *p* < 0.05) as a barrier, and men who referred to "It takes too much physical effort" (OR: 0.66; 95% CI: 0.44–0.99; *p* < 0.05) as a barrier, are less likely to comply with the recommendations for MVPA, since the perception of these barriers is negatively associated with compliance with the recommendations.

**Table 5.** Association between barriers to active commuting and compliance with the MVPA recommendation by sex.


Significant differences in bold were set at *p* < 0.05.

#### **4. Discussion**

The main objective was to determine the barriers perceived by students to active commuting to university and the association with physical activity. The main findings were that the passive commuting was the most-used commuting mode to university in Chilean students. The most common barriers associated with passive commuting were "walk or biking involves too much planning" and "it takes too much time" in both sexes.

#### *4.1. Mode of Commuting*

The mode of commuting to university in the current study was mainly non-active commuting (82.2%). Similar results were reported in Spain, where a study of 518 students from two universities revealed that 65.1% of participants engaged in non-active commuting [19]. On the other hand, a study conducted at Kansas State University found a prevalence of 34.7% for non-active commuting and 65.3% for active commuting [40], a higher percentage compared to our study. In this regard, a study carried out by the Autonomous University of Barcelona justified the use of the non-active commuting mode for long distances between the university campuses, because the infrastructure was only available for motorized transportation [41]. In the United States, in a study conducted by the University of Kent, students were classified according to their place of residence (on the university campus or outside of it), revealing that only 4% of the students who lived off-campus walked, compared to 42% of students who lived on campus that walked, and 3% who cycled, highlighting the importance of distance in the choice of commuting mode [42]. In another study, also conducted in the United States, 76.1% of students actively moved [43]. These are high figures compared to this Chilean study, because students reside in different districts of the cities, since universities do not have on-campus housing. Thus, in our study, the main passive mode of commuting was public bus, with 50.5%, which was higher in women than in men. A Spanish study done on university students defined the use of the metro/train (31.1%) as the main non-active commuting mode [19], revealing the difference that exists in commuting modes compared with Chilean students. Public transport is more often classified as passive commuting [44]. However, there could be a small benefit associated with its use, since students usually have to walk to public bus stops [45]. In this sense, the choice of the mode of commuting to university is extremely important, not only for the benefits of AC, but also for the increase in daily PA.

#### *4.2. Barriers Perception for Active Commuting*

The perception of barriers was divided into "Environmental" and "Psychosocial", and both variables had a significant association with the choice of commuting to university. In both sexes, the most often perceived barriers were "Walk or biking involves too much planning" and "It takes too much time". In an Australian study of AC to and from university, travel time was the most important barrier to AC [46], which is consistent with our study. Another study conducted in university students in Ireland showed that an increase in travel time to university decreased the probability of being classified in a group containing AC and recreational PA at university [47]. This could indicate that students seek to minimize their commuting time, and that it is necessary to provide advice on travel planning and promote walking and cycling, especially for those who live near the university.

In our study, it was possible to appreciate that there were more barriers caused by the personal (psychosocial) compared to environmental barriers. A study carried out on university students from Spain showed that both the psychosocial and environmental variables had a significant correlation with AC to the university [48]. Another study in Spain on AC showed that socioeconomic factors are the most decisive with respect to the use of passive commuting, followed by social behavior variables [41]. A study in Africa on the effect of various motivators and barriers in cycling showed that addressing physical or environmental barriers individually has little impact on promoting cycling, as the perceived motivating variables were more personal [49]. In this sense, and according to the results of our study, it is important to take a comprehensive approach to psychosocial and environmental barriers, since the understanding of psychosocial barriers provides a useful framework to understand the mindset of travelers when designing policies that promote AC.

On the other hand, women showed a greater negative association between AC and the perception of environmental barriers compared to men. In a study on the influence of AC in college students, it was observed that men were more likely to use AC than women, and that, among women, there was a relationship between appearance (e.g., being sweaty) and AC [50]. In addition, this is in agreement with other studies that indicate that women are more concerned about access to services such as showers [51], and that clothing can play an important role in travel decision [52], which coincides with some perceived psychosocial barriers in this study. Another study on the sex gap in choosing to use bicycles showed that women choose bicycles 30% less often than men for their trips to campus and that there are various factors for not commuting by bicycle, such as an unsafe environment [53], which is also consistent with several environmental barriers perceived in this study by women. These findings reinforce the idea that the design of future interventions to promote AC should consider the specific barriers of women and men. Looking at the environmental and social factors that affect the perspective of women and men could directly contribute to increasing rates of AC.

In our study, it was possible to appreciate that there is a perception of both psychosocial and environmental barriers that would affect the use of bicycles as an active means of transport, with the former being the ones that have the greatest influence. Although it has been described that bicycle use not only depends on the individual mobility behaviors of the user but is also associated with the environment, urban cycling interacts with other modes of travelling, such as public buses, cars, motorcycles and pedestrians [54]. The use of a bicycle is associated more with the cyclist, as long as he/she controls the conditions of the trip, such as the distance travelled [55], physical effort and greater exposure to the weather, which conditions the mobility behaviors that stand out as individual, sociodemographic and psychological factors [56].

A study carried out in Argentina showed a lack of road safety when sharing the road with motorized vehicles as a main barrier to bicycle use [57]. In Chile, a study showed that the bicycle is used downtown through the streets, since bikeways are located in certain sectors of the metropolitan region (137 bikeways in 14 districts), which are mainly concentrated in districts of higher socioeconomic status and greater motorization [58]. As safety is an important factor in the choice of the bicycle as a means of AC, it would be important to promote changes in the cycling infrastructure that would make students perceive cycling as safer, such as improvements to local bike routes and the creation of more off-street bikeways. It is fundamental to improve the behaviors of PA practices through programs aimed at people that decrease the personal barriers to the use of this means of AC and to invest in road education.

In relation to the use of vehicles, an investigation showed that postgraduate students have a greater tendency to use passive modes of commuting (bus or car) due to their work or the possibility of acquiring a vehicle [59]. In Chile, the statistics show that the automotive fleet continues to increase [60], which could become a barrier with greater weight for AC. In our study, car use was only a barrier in men. However, in this university stage, students have low purchasing power; young people do not own cars and it is not presented as a significant barrier.

#### *4.3. Barriers Perception for MVPA Recommendation*

The barriers associated with non-compliance with MVPA are greater in women than in men. In women, in terms of environmental barriers, we find "There are no sidewalk or bikeways" and in the psychosocial barrier is "I get hot and sweat when I'm walking or biking", compared to men who only have the psychosocial barrier "It takes too much physical effort". It is important, at this point, to emphasize that the perceived barriers associated with MVPA coincide with AC, that is, a lot of effort and sweating are repeated reasons for women for not doing PA in this study.

In the literature, it was found that the barriers to compliance with the MVPA were economic levels, interest in the use of sports facilities, residence, intrapersonal and interpersonal barriers [22], distance [61] and psychosocial factors [47]. In the other study, carried out in university students, it is mentioned that the barriers to compliance with PA are economic levels, health, peer support, self-efficacy and effect related to the practice, which were increased in men compared to women [62]. In comparison with other investigations, women face greater barriers to the practice of PA [63].

On the other hand, Sevil et al. [64] analyzed the relationships between physical activity and the perceived barriers to physical activity, motivation and stages of change in Spanish university students, where they found that the barriers to participation were related negatively to the levels of PA and more self-determined forms of motivation. Recommendations include intervention from a medical area to help to comply with the recommendations of physical activity for optimal health [65], due to the high incidence of sedentary lifestyle in university students [66]. A study of Peruvian medical college students from a private university indicates that of the 312 students, just under a third performed MVPA for ≥150 min/week, and slightly more than a third performed MVPA for ≤30 min/week [67]. In turn, a study carried out with 244 adults mentions environmental barriers, where they indicate that cold days with little light are a barrier to compliance with the MVPA, because the increase of 10◦ is associated with an additional increase of 1.5 min/day and every extra hour of light in the day adds 2.23 min to the practice of PA [68]. Moreover, in an investigation in 507 adolescents, they mention internal and external barriers, such as "I am not interested in physical activity" and "I need equipment I don't have", and no significant differences between sex were found [69]. In this sense, a better understanding of the barriers that prevent compliance with the MPVA between both sexes is essential to minimize passive commuting and obtain the variety of benefits associated with AC.

#### *4.4. Limitations and Implications*

Despite the numerous significant findings in our study, there were some limitations, including that the student questionnaire was self-reported, and therefore could be subject to bias. On the other hand, the data used were only related to travel between home and university, and cannot be extrapolated to other environments. Our study only recorded the main mode used for commuting, so we did not consider mixed modes. Finally, only four universities were consulted in the research and the geographical characteristics of the three cities where they are located are heterogeneous, and a representative sample per university was not calculated. Therefore, precaution must be taken when generalizing the current results. However, this study makes a significant contribution to the literature on the variety of influences that can affect active commuting. A change in the culture of mobility must begin from within communities and in this sense, university students can be an important target group, especially women, who show a greater negative association between the perception of environmental barriers and AC, as well as with non-compliance with MVPA. Therefore, these findings, in combination with existing research, provide a solid foundation for future studies in this area and the development of policies and programs to improve AC in the university, suggesting that a strong association between the government and university organizations can produce positive results.

#### **5. Conclusions**

This study provided important information on perceived barriers to AC and MVPA compliance in Chilean university students. The results of the present study suggest that psychosocial or personal barriers were more positively associated with passive commuting to university than environmental barriers. In other words, aspects based on personal decision to commute actively intervene to a greater extent than the barriers imposed by the

environment. Therefore, implementing policies that address the psychosocial factors more, but environmental factors as well, are necessary to increase AC rates, and thus achieve an impact in terms of both health and PA in university students. In addition, our study suggests that it is necessary to target women over men for AC and PA interventions, since women present more barriers and less active commuting. In this way, the measures should not only be applied by the government, but should mainly include universities as the lead actor for the development of educational strategies that promote and increase AC.

**Author Contributions:** Data collection, conceptualization, methodology, formal analysis, writing, original draft, visualization, A.C.-P.; conceptualization, formal analysis, writing, N.I.J.; review and editing, M.P.-S., J.L.F. and I.P.A.; data collection—review and editing, X.P.-L. and X.D.-M.; conceptualizatión, writing—review and editing, supervision and project administration, F.R.-R.; critical review, A.C.-P., N.I.J., F.R.-R., X.P.-L., M.P.-S., X.D.-M., J.L.F. and I.P.A. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee of Pontificia Universidad Católica de Valparaíso (Code: CCF02052017).

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Acknowledgments:** We appreciate the university students who participated in and the academics who supported this project and to Universidad de Las Américas, Universidad Técnica Federico Santa María, Universidad del BioBio and Research Unit of Pontificia Universidad Católica de Valparaíso.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

