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Article

Are New Campus Mobility Trends Causing Health Concerns?

by
Zeenat Kotval-K
*,
Shruti Khandelwal
,
Eva Kassens-Noor
,
Tongbin Teresa Qu
and
Mark Wilson
School of Planning, Design & Construction, Michigan State University, 552 W. Circle Dr., East Lansing, MI 48840, USA
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(6), 2249; https://doi.org/10.3390/su16062249
Submission received: 3 January 2024 / Revised: 29 February 2024 / Accepted: 1 March 2024 / Published: 7 March 2024
Versions Notes

Abstract

:
An influx of new mobility trends such as fare-free bus transportation, ride hail, and e-scooter services to improve access and affordability of transportation on campus may be shifting the travel behavior of campus patrons such that it affects their long-term health outcomes. The main research questions explored in this study are as follows: (1) why university patrons choose new modes of travel?; (2) what existing mode did the new modes of travel replace for the riders?; and (3) is the average body mass index (BMI) of users primarily using non-motorized transit options lower than those using motorized or both (referred to as hybrid) for on-campus travel needs? An online survey was administered to a campus community (n = 3309) including students (48%), faculty (15%), and staff (37%) in fall of 2018 when fare-free bus transportation and e-scooters became available on campus, and a gradual increase in ridership of ride-hail services was simultaneously observed. This study found that campus patrons were more inclined to replace active modes of travel with affordable and accessible modes of transportation, thereby substituting their walking or biking routine with app-based transportation services. The mean BMI among travelers who chose motorized transportation modes was more than active travelers, and the BMI was statistically significantly associated with age, gender, race, class standing (undergraduate/graduate), and residence on/off campus. This study concludes with suggestions to prevent substitution of active with non-active transport choices and provides policy guidelines to increase awareness on achieving physical activity levels through active modes of travel for university patrons.

1. Introduction

Universities tend to promote active mobility on campus that leads to safer access and convenience and promotes health for its patrons. However, travel time and affordability have also been researched to promote sustainable transit behavior on campus [1,2]. Transit policies of providing fare-free bus routes, also known as “unlimited access”, provide an alternative mode of travel to transit non-users while adding no cost to its current patrons [2,3]. While providing better transit infrastructure, university environments shape the mobility behavior for young adults studying and residing on campus [4]. The university discussed in this paper is a higher education institution located in the mid-western region of the United States and is referred to as ‘university’ or ‘campus’ henceforth.
The university introduced ‘unlimited access’ to its campus patrons on the transit bus service on six routes plying in and around the campus. This was initiated in the fall of 2018 and aimed to improve campus mobility by making it more efficient, safe, and convenient for campus patrons [5]. Simultaneously, the campus was also inundated with single-rider e-scooters from Bird and Lime companies, which promote micromobility and last-mile connectivity for its users [6]. Lastly, ride-hail ridership has been gradually increasing since 2014 in this university city [7]. With all these shifts in transit availability on campus, the research on campus patrons’ travel behavior and preferences became warranted.

1.1. Travel Behavior for the Campus Community

Campus mobility has an important role to frame travel behaviors and health outcomes for young adults [2,8,9] especially considering that students’ travel behaviors have gradually shifted based on their needs to travel and available transportation expenditure. Eboli and colleagues [10] discussed that both ‘patience’ and ‘laziness’ can contribute to waiting for public transportation rather than walking. Socioeconomic factors related to travel by a personal vehicle suggest that males, those over 55 years of age, and university staff are more likely to have higher VMTs compared with females, younger university patrons, and students [11]. However, low preference for bus transportation is due to low frequency and longer wait times [12,13], which provides preference for ride hail and electric mobiles for travel [14]. Along the same line of thought, Hamad et al. [15] found that weather played a significant role in avoiding public transit as a mode to travel to the campus by university patrons. Moreover, Allen and Farber [16] studied student mobility across six postsecondary institutes in Canada and found that students who use a personal vehicle commute less often than those who walk and students with a transit pass commute more often supporting the evidence that ‘unlimited access’ improves student commute corroborating the research by several researchers who look not only at student travel but also at other patrons’ travel behavior such as staff and faculty [3,17,18]. Research by Shannon et al. [18] also stressed on the premise that promoting active travel would be effective to not only students but also staff as they found that 30% of the students and staff at a university in Australia were open to switching to active modes of travel. A Shared Micromobility in the US:2018 report states that e-scooters provide the element of fun for young adults and for them to get around faster and is, therefore, increasing in ridership [19]. Even though e-scooters are associated with public safety issues [20], they address the convenience [21], rider enjoyment [22], and affordability [23] criteria in some students’ mobility decisions. They have also been researched to find better gender parity and also provide better patronage among women and low-income populations [24].
Micromobility and ride-hail services have become ubiquitous on campus due to improved connectivity, perception of better transport solutions for the environment, and less burden of car ownership [14,25,26]. The new modes can lead to healthier benefits such as a smaller number of vehicles and miles travelled leading to less congestion and air pollution, creating a need for more open spaces, which otherwise would be covered with traffic and parking lots [27,28]. Micromobility and ride hail are emerging among young adults whose travel behavior is dependent on fast and cheap travel modes [23,29,30]. However, these new trends have also been dubbed ‘disruptive’ by researchers and planning and policy experts due to transforming mobility behavior, economic and policy impacts on the existing modes, and impacts on health determinants related to transport [23,27].

1.2. Travel Patterns on Campus

Research on students’ travel patterns shows that instead of them being concentrated around a few peak hours of traffic, students and faculty mobility patterns are spread largely according to weekday class schedules and are inter-dependent on complementary transit routes, proximity to transit hubs, environmental conditions, and personal barriers [31,32]. Students living on campus have less distance to travel and traditionally have relied on walking, biking, skateboarding, or campus shuttles to access different parts of the campus. In contrast, students living off campus and those not living within walking distance depend on biking, bus transport, or personal vehicles [2,12].

1.3. Using a Health Indicator: Body Mass Index with Travel Preferences

Active and public transportation is significantly associated with lower BMI among both men and women when compared with private transport modes [33,34]. Non-motorized transport (NMT) such as walking, cycling, and skateboarding improves physical activity levels [35,36] and is associated with lower body mass index (BMI) in comparison with car, taxi, or motorcycle riders [33,37], and bicycle users have overall better fitness results in comparison with pedestrians and other transport modes in different environments [38,39]. Therefore, the thrust of enhancing mobility on university campuses has been shown to take the form of overall improvement in active mobility infrastructure and bike-share programs, reducing travel times and promoting mass-transit modes [2,8,40,41,42]. Crist et al. [37] discussed that transit commuters accomplish more physical activity than drivers and, therefore, should be considered as an active form of travel, while Tribby, Graubard, & Berrigan [43] found no association between ridesharing and changes in travel-related walking in a national and a metropolitan area survey using the National Household Travel Survey of 2009 and 2017.
Along with the research supporting the use of the BMI as a health indicator, there has also been research stating some limitations of using the BMI as a health indicator, and specifically for using it for certain conditions and illnesses. Rothman [44] stressed on the facts that BMI metrics to assess health are flawed as they do not take into consideration “age, sex, bone structure, fat distribution or muscle mass” (p. S56). They suggest using objective measures to obtain the total body fat content, densitometry, and body fat percentages. Shields et al. [45] specified additional measures such as abdominal obesity to assess obesity as a risk factor for cardiovascular disease risk. Similarly, Hartanto & Yong [46] showed that the waist-to-height ratio (WHR) was a superior measure of the adverse impacts of obesity on memory and executive functions compared with the regular BMI calculation. Charbonneau-Roberts et al. [47] stressed on revisions to using the standing BMI as an indicator of health risk, especially when it comes to Inuit and Far East Asian populations, and thus suggested the use of sitting BMI calculations.
There is limited research for a university campus evaluation of a newly implemented transit policy in comparison with new private modalities of travel such as e-scooters and ride-hail services. This research highlights the needs for evaluating transit policies in light of shifting travel behavior for young adults and discusses policy recommendations to avoid substitution of active mobility such that transit on campus promotes an active lifestyle and a healthy behavior for young adults to pursue after graduation.

2. Methods

The main research questions explored in this study are as follows: (1) why university patrons choose new modes of travel?; (2) what existing mode did the new modes of travel replace for the riders?; and (3) what is the average body mass index of users using primarily non-motorized transit options compared with those using motorized or both (referred to as hybrid) for on-campus travel needs?

2.1. Study Framework

The aim is to understand if campus mobility has a relationship with health outcomes for university patrons and what factors can influence their travel behavior using three research questions. The first question investigates whether respondents select new modes of travel on and to campus, and the reason for it. The second question asks for substituted modes of travel if the new modes were not available. The third question asks about the BMI of users travelling on campus (a) for different frequency of unlimited access to bus transportation and (b) based on the three different categories of travel: active (non-motorized), motorized, and hybrid (chose one of each)? The respondents reported their own weight and height, and the BMI was calculated using the standard formula. Two BMI categories were created: (a) underweight and normal (mean BMI < 25 lbs/inches2) and (b) overweight and obese (mean BMI ≥ 25 lbs/inches2); and the categorical variable was used in the analysis. The BMI was used as an indicator of health in this study as other alternatives to inactivity and obesity would have been more of an invasion of privacy and would have prompted more respondents to not answer those questions at all. Asking about height and weight provided the least invasion into their privacy and was, therefore, chosen as the assessment metric. Even though this paper included transit modal options of e-scooters and ride hail, there are fourteen choices provided that were categorized based on physical activity levels and travel modes. ‘Walking’, ‘biking’, and ‘skateboarding’ were categorized as non-motorized transportation (NMT) modes due to a relatively higher level of physical activity involved in commuting. Wheelchair was also provided as an option, but no responses were recorded from the participants. ‘CATA’, ‘driving alone’, ‘carpooling’, ‘motorcycle, motorized bike or moped’, ‘uber or lyft’, ‘taxi’, ‘zipcar’, ‘electric scooter’, and ‘special transit’ were categorized under motorized transportation (MT). Respondents who chose ‘other’ were recategorized under the NMT or MT categories based on their responses. As the question allowed for multiple selections, respondents who selected at least one NMT and one MT option were categorized as a hybrid category. The last question was further analyzed if there was any statistical difference in the BMI with the mode of travel reported by the users.

2.2. Data Collection

This study was approved by the University Institutional Review Board (IRB) as exempt: STUDY00002374. The cross-sectional study was pilot-tested with a few students. After reviewing the pilot-test responses, the survey was administered online using Qualtrics to collect data during the month of March 2019. The participants were recruited through the online listserv for the campus community, which included students, faculty, and staff. A total of 3309 survey responses were collected.

2.3. Data Analysis

The preliminary examination of sociodemographic variables illustrates the preferences of travel modes of the respondents for this study. Thereafter, the results of the three research questions are presented. Logistic regression is conducted for the last research question as the dependent variable, and four of the six independent variables are categorical [48,49] to test if the mean BMI is significantly associated with travel patterns, as well as age, undergrad/graduate, gender, race, and living residence variables (p < 0.05).

3. Findings

3.1. Preliminary Results

Table 1 shows the travel to and on campus for various university patron categories. This table breaks down travel modes used into three categories: non-motorized (NMT), motorized (MT), and hybrid (HYB). While the NMT and MT modes are intuitive, the HYB mode involves a combination of an NMT and an MT mode for travel. Overall, a majority of the campus population uses MT modes to get to the campus and a combination of NMT and HYB modes for their on-campus travel needs. The only exceptions to the majority who use MT modes to get to the campus are those who live on campus (as this variable would not be calculated for them) and those without access to a car, a majority of which use NMT modes to get to the campus. These patrons probably live closer to the campus since they do not have access to a car and can probably just walk/bike/skateboard to the campus. For the modes used on campus, a majority of males used NMT modes while females used HYB, undergraduates were more likely to use HYB modes while graduate students used NMT, those living on campus were more likely to use HYB modes while those living off campus were more likely to use NMT modes for their on-campus travel, and those without access to a car used NMT more compared with those with access to at least one car as they were more likely to use HYB modes for their on-campus travel needs. There were no differences in the majority of different races and those with or without disabilities as they were more likely to use HYB modes for their on-campus travel needs. A brief explanation on the disability variable shown in multiple analyses throughout is in order. This variable is made up of about six types of disabilities (namely hearing, vision, cognitive, ambulatory, self-care, and independent living). Most of the respondents in this study (over 52%) said they do not have any disability, while less than 2% (the next highest chosen option) mentioned they had difficulty with hearing or vision.

3.2. Factors for Choosing Selected Mobility Options

Answering the first research question, Table 2 shows the reasons behind respondents’ travel behavior preferences (multiple selection) for choosing newer transit modes such as the bus with unlimited access on campus, ride hail, or e-scooters; it shows that the highest number of respondents selected ‘bad weather’ (65%) and ‘convenience and easy to use’ (60%) as their reason to use the bus showing the likelihood of using public transportation due to external factors; while 48% selected to ‘save money on gas and parking’, 47% ‘didn’t own or have access to a car’, and 41% selected ‘faster than alternatives’. Respondents’ preferences for choosing ride hail were that they do not own or have access to a car, bad weather, and convenience (over 60% of the respondents chose these reasons), while 51% chose that it is faster than alternative options. For the respondents who chose to take e-scooter trips, 78% and 69% selected them for fun and to get around easier and faster, respectively, and less than 25% selected them to save money over other transportation options and because it is good for the environment. Therefore, based on these results, the travel behavior on campus can be inferred to be influenced by access to a personal vehicle, weather, and convenience for transitioning to newer modes of travel.

3.3. Substitution Effect

Answering the second research question, findings shown in Table 3 indicate that free bus rides and e-scooter rides have replaced travel modes for almost 54% and 38% of active mobility travelers (or NMT travelers), respectively, and ride hail has replaced almost 71% of hybrid travel modes (HYB) (assuming that active mobility will be less preferred as a trip mode). On the other hand, ride hail has been chosen over other motorized modes (23.5%) of travel, and e-scooters have been chosen over hybrid modes of travel, which are both positive trends to reduce the need for parking on campus and improved accessibility. However, the affordability of ride hail, safety concerns and parking for e-scooters, and congestion on campus roads are still problematic at peak times during the day.
The trend for choosing ride hail for campus patrons is due to the inaccessibility of personal vehicles, and it is faster than alternative routes such as public transportation [23]. E-scooter riders chose to use this mode for fun and to get around faster. This trend is more prevalent in younger adults and gradually increasing as seen from a report on Shared Micromobility in the US: 2018 [19]. However, the number of accidents and major injuries reported with e-scooter riders should caution young and mature adults to follow the road safety rules and riding responsibly and for planners to enforce traffic rules that safeguard other riders.

3.4. Association with BMI

Before answering the third research question, the mean BMI was calculated for respondents with various sociodemographic characteristics. Table 4 provides an overview of the BMI for various respondent sub-groups.

3.4.1. BMI for Different Modalities Used on Campus

Table 5 shows the mean BMIs by selected modes of travel for various frequencies of travel. Respondents who used the bus less than once per week had the highest mean BMI of 27.18 lbs/inches2, which is considered overweight in comparison with those who used it more frequently. This trend is evident for the ride-hail and e-scooter modes as well. The number of respondents for ride-hail frequencies is few, so those results should be read with caution. However, those responding frequencies for the bus system and e-scooters indicate that these modes require some form of active travel, and, therefore, the less the people use them, the more likely they are to be overweight.

3.4.2. BMI Comparisons for Alternative Modalities

This study asked for alternative modes to the bus, ride hail, and e-scooters if these modes were not present. Respondents who would have substituted their travel with non-motorized travel were grouped as NMT, respondents who would have used motorized travel were placed in MT, and those who would have used at least one of each were grouped as hybrid (HYB). Table 6 shows a majority of the respondents who rode the bus said that if the bus was not available, they would have used other non-motorized travel modes (54%). The average BMI for this group of respondents was the lowest at 24.18 when compared with those that said they would have used some motorized mode of travel (14% with a mean BMI of 25.83) and those that would have used a hybrid mode of travel (32% with a mean BMI of 25.14). This result is understandable as on-campus travel is usually by walking/biking for most respondents, and unlimited fare-free access to the bus system would be attractive to this group. It is also noteworthy that the BMI for those that would have used a motorized form of travel if the bus was not available is the highest of the three alternative travel groups. The group of respondents who used ride hail was considerably small at 34 people, so the categorization into the NMT, MT, and hybrid alternative modes must be read with caution. However, a large majority (70%) of respondents said that they would have used a hybrid form of travel if ride hail was not available, and this group has the lowest BMI at 22.73. Again, those who said that they would have used a motorized form of travel if the ride hail was not available had the highest BMI at 26.99 compared with the other two alternative travel modes. Lastly, the majority of the e-scooter users (49%) said that they would use a hybrid form of travel if the e-scooters would not be available. Similar to the other modes, those who would have used a motorized form of travel if the e-scooters would not be available to them had the highest BMI at 25.39 when compared with the other alternative modes.
Logistic regressions are conducted as the dependent variable, and eight of the nine independent variables are categorical [48,49]. The likelihood of being overweight or obese is significantly associated with age, undergrad/graduate, gender, race, and residence variables and is statistically significant (p < 0.05) (see Table 7). This also indicates that males are 1.5 times and White respondents are 1.3 times more likely to be overweight and/or obese than females and non-White respondents. Undergraduates are 0.6 times and on-campus residents are 0.5 times less likely to be overweight or obese than graduates and off-campus residents. The associations between having a disability, or having access to a vehicle, and BMI were not statistically significant.

4. Discussion

Results to the first research question on why people choose different modes of travel on campus pointed to issues of access to a personal vehicle, weather, and convenience. Results for the second research question on what existing modes did the new modes replace provided some nuances. Non-motorized mode users switched to the bus because of the free bus rides on campus and to e-scooters due to the availability on campus. Hybrid mode users switched to ride hail due to the inaccessibility of a personal vehicle and due to this mode being faster than the alternative modes. Lastly, results to the third research question on the association between socioeconomic characteristics, various modes used, and BMI suggested that males, White, graduates, and those living off campus were more likely to be overweight/obese compared with females, non-White, undergraduates, and those living on campus. Those respondents who would have chosen motorized transport modes if the bus was not available had the highest BMI, while those who would have chosen motorized modes if the e-scooters were not available had the lowest BMI.

4.1. Policy Recommendations: Institutional Transport Policies and Programs

Changing mobility to a less active mode is a by-product of improving accessibility and convenience for travelers. These trends are concerning as this is devaluing the active lifestyle for primarily walkers, cyclists, and skateboarders, and indicating travel time and convenience can lead to less physical activity if they choose to travel by motorized modes. While free and easy access to transportation assists residents, especially those on low incomes, planning needs to consider the health implications of modal choice. This study corroborates research showing that active mobility users have, on average, a lower mean BMI than those using hybrid and motorized modes [22,50]. Accessing public transportation does encounter more physical activity as it requires walking or biking for access and egress purposes. Therefore, a slight trend of normal and marginally overweight BMI among those frequently travelling using public transportation than the higher BMI for those who rarely use bus services (less than once per week) is understandable. Encouraging more use of the bus system on campus by giving free rides is a step in the right direction. Policies coupling this mode with other active and sustainable modes like e-scooters and walking/biking would enhance the overall health of these riders. Students, both graduates and undergraduates, and those residing on campus tend to rely more on hybrid modes of travel on campus, which can be attributed to less travel time to attend classes and convenience factors. Students residing on campus tend to have lesser travel distances [29,51] and are, therefore, negatively influenced by the addition of convenient modes of travel that reduces their active mobility.
A bicycle share program can also benefit campus patrons [52], especially those residing on campus to prevent them from shifting toward motorized transport. University patrons living off campus who drive or use hybrid modes to travel to attend classes can benefit from accessible alternate modes of travel that reduce the need for more parking spaces and noise and air pollution in the area. Even though bus rides for all on campus are free, reduced bus fares or bus passes for students living further away can be provided rather than have them depend on their personal vehicles. However, preference for non-motorized travel behavior requires conducive environmental conditions, safe infrastructural amenities, and efficient travel time that can be benefitted with integrated and real-time management of transit services for accessing alternative modes of travel [21,26].

4.2. Policy Recommendations: Enable Safe Transit Infrastructure

While the trends of new modalities are surging, the number of accidents and major injuries reported in e-scooter accidents should caution young and mature adults to follow the road safety rules and riding responsibly and for planners to enforce traffic rules that safeguard other riders. Moving toward shared micromobility, which includes the use of bikes, e-bikes, e-scooters, and autonomous mobility in the future, university campuses can integrate transport infrastructure to safely promote active mobility and accessibility for all patrons.
Improving campus mobility for all student classes such as undergraduates, those with no access to a vehicle, and students who may be differently abled is vital to promote healthy lifestyles. Transportation for people with disabilities requires nuanced and safe approaches to promote active mobility for them that will reduce their dependency on motorized mechanisms and improve travelers’ health [53]. Few infrastructural improvements such as segregated pathways for cyclists, skateboarders, and shared micromobility riders with designated parking stops and cautionary intersections where high pedestrian traffic flows are encountered can ease the movement/traffic flow while preventing accidents from occurring.

4.3. Policy Recommendations: Encourage Non-Motorized Transportation

According to these results, the campus environment, weather conditions, and convenience are primary factors that influence travel behavior for campus patrons, while secondary factors include expenditure on car and parking, access to a vehicle, and parking availability, as also reported in previous studies [29,54]. While unlimited access for campus patrons may have improved mobility and affordability of transit on campus, substitution of walking, biking, or skateboarding may affect the long-term health and behavior for a young individual. Policies that encourage the use of e-scooters and transit focus on the middle ground between motorized and non-motorized modes of travel. There is some aspect of physical activity involved with both modes, and they are far more sustainable than motorized modes of transportation, especially when results show that convenience, weather, and access to a vehicle are the biggest issues affecting the shift to motorized modes.

4.4. Limitations

This study has common limitations seen in qualitative surveys. When it came down to answering questions on height and weight, the responses to those questions dropped considerably showing that people did not want to answer questions of a personal or private nature. There may be response bias while reporting height and weight by respondents, which is used to determine the mean BMI of respondents as the dependent variable. This study is conducted in a region with extreme winter conditions that can prevent users from active travel due to weather conditions and, therefore, limits its generalizability to other US university campuses or similar-sized cities.
Future research can incorporate additional health indicators such as waist circumference, level of physical activity, and travel satisfaction as evidence for assessing health relationships with travel behavior. A better sampling strategy would allow for creation of quotas in order to have representation from each group, students, faculty, and staff. And finally, an activity tracker and a travel diary that participants in this study agree to use for a certain number of weeks would give a more accurate picture of the travel routes and physical activity during a specified time period.

5. Conclusions

Actions within a microcosm, such as a university campus, can often reflect behaviors, opportunities, and outcomes in a small city. While unlimited access for campus patrons may have improved mobility and affordability of transit on campus, substitution of walking, biking, or skateboarding may affect the long-term health and behavior for a young individual. Active mobility is a choice that promotes physical activity for campus patrons, and, therefore, faculty and staff (adults) can be role models to drive healthy lifestyle habits to influence young minds. Incentives to promote the use of active transportation among faculty and staff and discourage single-occupancy vehicle commutes may promote a behavioral shift for students to engage in active commuting as well [37]. The university’s sustainable mobility initiatives can prioritize active mobility integrated with public transit opportunities beyond campus boundaries, which limits students’ use. Ride share and electric mobility can be enhanced for off-campus riders and make it more accessible for vulnerable students including those with disabilities. From the literature, we see that ride sharing and e-mobility may offer improved air quality, traffic noise reduction, and less traffic congestion and, therefore, can enhance health benefits [27], but land-use planning and transportation planning need to assess infrastructural improvements and active commuting programs for university patrons and safety guidelines for all riders. Active transportation should continue to be promoted through engaging active groups on campus that can spread awareness about substituting their physical activity and combining travel routes with micromobility or autonomous vehicles [55]. After all, travel behaviors and habits cultivated as young adults are likely to shape behaviors as they move into their mature adult lives, and this can affect health outcomes.

Author Contributions

Conceptualization, Z.K.-K.; methodology, Z.K.-K. and E.K.-N.; formal analysis, S.K.; data curation, S.K. and Z.K.-K.; writing—original draft preparation, S.K.; writing—review and editing, Z.K.-K. and E.K.-N.; project administration, E.K.-N., T.T.Q. and M.W.; funding acquisition, E.K.-N., Z.K.-K., T.T.Q. and M.W. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Center for Business and Social Analytics at the Michigan State University.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board (or Ethics Committee) of Michigan State University (protocol code STUDY00002374, approved in November 2018).

Informed Consent Statement

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

Data Availability Statement

The datasets presented in this article are not readily available because the data are part of an ongoing study.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Black, W.R. North American transportation: Perspectives on research needs and sustainable transportation. J. Transp. Geogr. 1997, 5, 12–19. [Google Scholar] [CrossRef]
  2. Balsas, C. Sustainable transportation planning on college campuses. Transp. Policy 2003, 10, 35–49. [Google Scholar] [CrossRef]
  3. Brown, J.; Hess, D.B.; Shoup, D. Unlimited access. Transportation 2001, 28, 233–267. [Google Scholar] [CrossRef]
  4. Ahlers, D.; Aulie, K.; Eriksen, J.; Krogstie, J. Visualizing a City Within a City—Mapping Mobility within a University Campus. In Smart City 360°. SmartCity 360 2016, SmartCity 360 2015; Leon-Garcia, A., Lenort, R., Holman, D., Staš, D., Krutilova, V., Wicher, P., Cagáňová, D., Špirková, D., Golej, J., Nguyen, K., Eds.; Springer: Cham, Switzerland, 2016; Volume 166. [Google Scholar]
  5. General Media. Agreement with CATA Means Free On-Campus Bus Rides. 2019. Available online: https://msutoday.msu.edu/news/2019/agreement-with-cata-means-free-on-campus-bus-rides/#:~:text=Michigan%20State%20University%20and%20CATA,faculty%20for%20years%20to%20come (accessed on 30 November 2019).
  6. Lacy, E. How Long Will Bird, Lime Scooters Be Left on Lansing Area Streets? 2018. Available online: https://www.lansingstatejournal.com/story/news/2018/11/14/bird-lime-scooters-transportation-lansing-eastlansing-michiganstateuniversity-spartans-police-ingham/1986479002/ (accessed on 30 November 2019).
  7. Alusheff, A. Uber Competitor Lyft Launches in Lansing Today. 2016. Available online: https://www.lansingstatejournal.com/story/news/local/2016/08/11/uber-competitor-lyft-launches-lansing-today/88559056/ (accessed on 1 November 2019).
  8. Tolley, R. Green campuses: Cutting the environmental cost of commuting. J. Transp. Geogr. 1996, 4, 213–217. [Google Scholar] [CrossRef]
  9. Orr, D.W. The Problem of Education. New Dir. High. Educ. 1992, 1992, 3–8. [Google Scholar] [CrossRef]
  10. Eboli, L.; Mazzulla, G.; Salandria, A. Sustainable Mobility at a University Campus: Walking Preferences and the Use of Electric Minibus. Int. J. Transp. 2013, 1, 21–34. [Google Scholar] [CrossRef]
  11. Soria-Lara, J.A.; Marquet, O.; Miralles-Guasch, C. The influence of location, socioeconomics, and behaviour on travel-demand by car in metropolitan university campuses. Transp. Res. Part D Transp. Environ. 2017, 53, 149–160. [Google Scholar] [CrossRef]
  12. Limanond, T.; Butsingkorn, T.; Chermkhunthod, C. Travel behavior of university students who live on campus: A case study of a rural university in Asia. Transp. Policy 2011, 18, 163–171. [Google Scholar] [CrossRef]
  13. Dong, X. Trade Uber for the Bus? J. Am. Plan. Assoc. 2020, 86, 222–235. [Google Scholar] [CrossRef]
  14. Gössling, S. Integrating e-scooters in urban transportation: Problems, policies, and the prospect of system change. Transp. Res. Part D Transp. Environ. 2020, 79, 102230. [Google Scholar] [CrossRef]
  15. Hamad, K.; Htun, P.T.T.; Obaid, L. Characterization of travel behavior at a university campus: A case study of Sharjah University City, UAE. Transp. Res. Interdiscip. Perspect. 2021, 12, 100488. [Google Scholar] [CrossRef]
  16. Allen, J.; Farber, S. How time-use and transportation barriers limit on-campus participation of university students. Travel Behav. Soc. 2018, 13, 174–182. [Google Scholar] [CrossRef]
  17. Duque, R.B.; Gray, D.; Harrison, M.; Davey, E. Invisible commuters: Assessing a university’s eco-friendly transportation policies and commuting behaviours. J. Transp. Geogr. 2014, 38, 122–136. [Google Scholar] [CrossRef]
  18. Shannon, T.; Giles-Corti, B.; Pikora, T.; Bulsara, M.; Shilton, T.; Bull, F. Active commuting in a university setting: Assessing commuting habits and potential for modal change. Transp. Policy 2006, 13, 240–253. [Google Scholar] [CrossRef]
  19. NACTO. Shared Micromobility in the U.S.: 2018. National Association of City Transportation Officials. 2019. Available online: https://nacto.org/shared-micromobility-2018/ (accessed on 1 December 2019).
  20. Choron, R.L.; Sakran, J.V. The Integration of Electric Scooters: Useful Technology or Public Health Problem? Am. J. Public Health 2019, 109, 555–556. [Google Scholar] [CrossRef] [PubMed]
  21. Tuncer, S.; Brown, B. E-Scooters on the Ground: Lessons for Redesigning Urban Micro-Mobility. In Proceedings of the CHI ’20: 2020 CHI Conference on Human Factors in Computing Systems, Honolulu, HI, USA, 25–30 April 2020; pp. 1–14. [Google Scholar]
  22. Langford, B.C.; Cherry, C.R.; Bassett, D.R.; Fitzhugh, E.C.; Dhakal, N. Comparing physical activity of pedal-assist electric bikes with walking and conventional bicycles. J. Transp. Health 2017, 6, 463–473. [Google Scholar] [CrossRef]
  23. Nielsen, J.R.; Hovmøller, H.; Blyth, P.-L.; Sovacool, B.K. Of “white crows” and “cash savers:” A qualitative study of travel behavior and perceptions of ridesharing in Denmark. Transp. Res. Part A Policy Pract. 2015, 78, 113–123. [Google Scholar] [CrossRef]
  24. Clewlow, R. The Micro-Mobility Revolution: The Introduction and Adoption of Electric Scooters in the United States. In Proceedings of the Transportation Research Board 98th Annual Meeting, Washington, DC, USA, 13–17 January 2019; The National Academies of Sciences, Engineering, and Medicine: Washington, DC, USA, 2019. [Google Scholar]
  25. Chan, N.D.; Shaheen, S.A. Ridesharing in North America: Past, Present, and Future. Transp. Rev. 2012, 32, 93–112. [Google Scholar] [CrossRef]
  26. Tirachini, A. Ride-hailing, travel behaviour and sustainable mobility: An international review. Transportation 2020, 47, 2011–2047. [Google Scholar] [CrossRef]
  27. Rojas-Rueda, D. Chapter nine—New transport technologies and health. In Advances in Transportation and Health-Tools, Technologies, Policies, and Developments; Nieuwenhuijsen, M., Khreis, H., Eds.; Elsevier: Amsterdam, The Netherlands, 2020; pp. 225–237. [Google Scholar] [CrossRef]
  28. Brown, A. Redefining Car Access. J. Am. Plan. Assoc. 2019, 85, 83–95. [Google Scholar] [CrossRef]
  29. McLoughlin, I.V.; Narendra, I.K.; Koh, L.H.; Nguyen, Q.H.; Seshadri, B.; Zeng, W.; Yao, C. Campus Mobility for the Future: The Electric Bicycle. J. Transp. Technol. 2012, 2, 1–12. [Google Scholar] [CrossRef]
  30. Clewlow, R.R.; Mishra, G.S. Disruptive Transportation: The Adoption, Utilization, and Impacts of Ride-Hailing in the United States. Institute of Transportation Studies, UC Davis. University of California, Davis. 2017. Available online: https://escholarship.org/uc/item/82w2z91j (accessed on 1 November 2019).
  31. Aliari, S.; Nasri, A.; Nejad, M.M.; Haghani, A. Toward sustainable travel: An analysis of campus bikeshare use. Transp. Res. Interdiscip. Perspect. 2020, 6, 100162. [Google Scholar] [CrossRef]
  32. Maiti, A.; Vinayaga-Sureshkanth, N.; Jadliwala, M.; Wijewickrama, R.; Griffin, G.P. Impact of E-Scooters on Pedestrian Safety: A Field Study Using Pedestrian Crowd-Sensing. 2020. pp. 1–15. Available online: https://sprite.utsa.edu/publications/reports/maitiarXiv19-BirdWatch.pdf (accessed on 15 May 2020).
  33. Flint, E.; Cummins, S.; Sacker, A. Associations between active commuting, body fat, and body mass index: Population based, cross sectional study in the United Kingdom. BMJ 2014, 349, g4887. [Google Scholar] [CrossRef] [PubMed]
  34. Audrey, S.; Procter, S.; Cooper, A.R. The contribution of walking to work to adult physical activity levels: A cross sectional study. Int. J. Behav. Nutr. Phys. Act. 2014, 11, 37. [Google Scholar] [CrossRef] [PubMed]
  35. Costa, S.; Ogilvie, D.; Dalton, A.; Westgate, K.; Brage, S.; Panter, J. Quantifying the physical activity energy expenditure of commuters using a combination of global positioning system and combined heart rate and movement sensors. Prev. Med. 2015, 81, 339–344. [Google Scholar] [CrossRef] [PubMed]
  36. Ek, A.; Alexandrou, C.; Nyström, C.D.; Direito, A.; Eriksson, U.; Hammar, U.; Henriksson, P.; Maddison, R.; Lagerros, Y.T.; Löf, M. The Smart City Active Mobile Phone Intervention (SCAMPI) study to promote physical activity through active transportation in healthy adults: A study protocol for a randomised controlled trial. BMC Public Health 2018, 18, 880. [Google Scholar] [CrossRef] [PubMed]
  37. Crist, K.; Brondeel, R.; Tuz-Zahra, F.; Reuter, C.; Sallis, J.F.; Pratt, M.; Schipperijn, J. Correlates of active commuting, transport physical activity, and light rail use in a university setting. J. Transp. Health 2021, 20, 100978. [Google Scholar] [CrossRef]
  38. Andersen, L.B.; Lawlor, D.A.; Cooper, A.R.; Froberg, K.; Anderssen, S.A. Physical fitness in relation to transport to school in adolescents: The Danish youth and sports study. Scand. J. Med. Sci. Sports 2009, 19, 406–411. [Google Scholar] [CrossRef]
  39. Shephard, R.J. Is active commuting the answer to population health? Sports Med. 2008, 38, 751–758. [Google Scholar] [CrossRef]
  40. Longo, G.; Medeossi, G.; Padoano, E. Multi-criteria Analysis to Support Mobility Management at a University Campus. Transp. Res. Procedia 2015, 5, 175–185. [Google Scholar] [CrossRef]
  41. Miralles-Guasch, C.; Domene, E. Sustainable transport challenges in a suburban university: The case of the Autonomous University of Barcelona. Transp. Policy 2010, 17, 454–463. [Google Scholar] [CrossRef]
  42. Stein, P.P.; da Silva, A.N.R. Barriers, motivators and strategies for sustainable mobility at the USP campus in São Carlos, Brazil. Case Stud. Transp. Policy 2018, 6, 329–335. [Google Scholar] [CrossRef]
  43. Tribby, C.P.; Graubard, B.I.; Berrigan, D. National and metropolitan trends in public transit use, transit-related walking, and ridesharing between 2009 and 2017. J. Transp. Health 2020, 19, 100918. [Google Scholar] [CrossRef]
  44. Rothman, K.J. BMI-related errors in the measurement of obesity. Int. J. Obes. 2008, 32, S56–S59. [Google Scholar] [CrossRef] [PubMed]
  45. Shields, M.; Tremblay, M.S.; Gorber, S.C.; Janssen, I. Abdominal obesity and cardiovascular disease risk factors within body mass index categories. Health Rep. 2012, 23, 7–15. [Google Scholar] [PubMed]
  46. Hartanto, A.; Yong, J.C. Measurement matters: Higher waist-to-hip ratio but not body mass index is associated with deficits in executive functions and episodic memory. PeerJ 2018, 6, e5624. [Google Scholar] [CrossRef] [PubMed]
  47. Charbonneau-Roberts, G.; Saudny-Unterberger, H.; Kuhnlein, H.V.; Egeland, G.M. Body mass index may overestimate the prevalence of overweight and obesity among the Inuit. Int. J. Circumpolar Health 2005, 64, 163–169. [Google Scholar] [CrossRef]
  48. Anderson, J.A. Regression and ordered categorical variables. J. R. Stat. Soc. Ser. B 1984, 46, 1–22. [Google Scholar] [CrossRef]
  49. Peng, C.-Y.J.; Lee, K.L.; Ingersoll, G.M. An introduction to logistic regression analysis and reporting. J. Educ. Res. 2002, 96, 3–14. [Google Scholar] [CrossRef]
  50. Rojas-Rueda, D. Autonomous vehicles and mental health. J. Urban Des. Ment. Health 2017, 3. Available online: https://www.urbandesignmentalhealth.com/journal3-autonomous-vehicles.html (accessed on 15 November 2019).
  51. Toor, W. Transportation & Sustainable Campus Communities: Issues, Examples, Solutions; Island Press: Washington, DC, USA, 2004. [Google Scholar]
  52. Kellstedt, D.; Spengler, J.O.; Bradley, K.; Maddock, J.E. Evaluation of free-floating bike-share on a university campus using a multi-method approach. Prev. Med. Rep. 2019, 16, 100981. [Google Scholar] [CrossRef]
  53. Cochran, A.L. Understanding the role of transportation-related social interaction in travel behavior and health: A qualitative study of adults with disabilities. J. Transp. Health 2020, 19, 100948. [Google Scholar] [CrossRef]
  54. Brown, A.; Klein, N.J.; Thigpen, C.; Williams, N. Impeding access: The frequency and characteristics of improper scooter, bike, and car parking. Transp. Res. Interdiscip. Perspect. 2020, 4, 100099. [Google Scholar] [CrossRef]
  55. Singleton, P.A.; De Vos, J.; Heinen, E.; Pudane, B. Potential health and well-being implications of autonomous vehicles. Adv. Transp. Policy Plan. 2020, 5, 163–190. [Google Scholar] [CrossRef]
Table 1. Frequencies for modes used to and on campus.
Table 1. Frequencies for modes used to and on campus.
VariableMode to CampusMode on Campus
nNMTMTHYBnNMTMTHYB
Population18657.40%78.39%14.21%231241.83%12.02%46.15%
Mean BMI 24.5727.6925.32 25.628.3327.06
Gender
 Male57310.12%71.20%18.67%70845.34%13.98%40.68%
 Female10695.61%83.16%11.23%133739.34%10.47%50.19%
Student
 Undergrad40116.21%59.85%23.94%82642.98%7.26%49.76%
 Graduate2487.66%72.98%19.35%27157.93%5.90%36.16%
Residence
 On campus0---44340.86%8.35%50.79%
 Off campus65312.86%65.08%22.05%65051.23%5.85%42.92%
Car
 No Car1050.00%20.00%30.00%1070.00%0.00%30.00%
 >1 car6784.57%85.10%10.32%67737.37%16.10%46.53%
Race
 White13537.46%78.79%13.75%166342.57%11.06%46.36%
 Non-White2656.04%78.87%15.09%33637.20%13.10%49.70%
Disability
 No disability13607.79%77.57%14.63%169642.98%9.55%47.46%
 >1 disability2425.37%83.47%11.16%30235.10%18.87%46.03%
Table 2. Top choices for why respondents chose a certain mode.
Table 2. Top choices for why respondents chose a certain mode.
Mode% of Responses
Bus
  Bad weather65%
  Convenience60%
  Saves money48%
  No access to a vehicle47%
  Faster than alternative modes41%
Ride Hail
  No access to a vehicle69%
  Bad weather63%
  Convenience60%
  Faster than alternative modes51%
  Difficult to find parking17%
E-scooter
  It is fun78%
  Get around easily and faster69%
  Saves money21%
  Good for the environment21%
Table 3. Substitution Effect and Comparative BMIs.
Table 3. Substitution Effect and Comparative BMIs.
VariablenMean BMI
BUS342
NMT54.10%24.18
MT14.00%25.83
HYB31.90%25.14
HAIL34
NMT5.90%25.81
MT23.50%26.99
HYB70.60%22.73
ESCOOT239
NMT38.00%25.39
MT13.00%22.94
HYB49.00%25.19
Table 4. Demographic frequencies and comparative mean BMIs.
Table 4. Demographic frequencies and comparative mean BMIs.
VariablesOptions%Mean BMI
(lbs/in2)
Age (in years)Up to 2438.6424.57
 (n = 2143)25–3418.5326.87
35 to 6439.6628.50
65 and up3.1726.90
GenderMale34.6926.88
 (n = 2081)Female65.3126.41
Type of respondentStudent48.0424.99
 (n = 3227)Non-students51.9628.14
Grad-UndergradUndergraduate75.2124.58
 (n = 1147)Graduate24.7926.24
ResidenceOn campus40.2724.4
 (n = 1140)Off campus59.7325.45
CarNo car26.5030.08
 (n = 1000)At least one car73.5027.5
RaceWhite80.4026.73
 (n = 2097)Non-White *19.6025.8
DisabilityNo disability86.3126.28
 (n = 2030)At least one or more disability13.6927.85
BMI lbs/inches2
n = 1954		Underweight (<18. lbs/inches2)2.45
Normal (18.5–24.9 lbs/inches2)47.28
Overweight (25–29.9 lbs/inches2)27.68
Obese (30+ lbs/inches2)22.59
* Non-White category includes African American, Asian, American Indian and Alaskan Native, Native Hawaiian and other Pacific Islander, and others.
Table 5. Mean BMI for various mode frequencies.
Table 5. Mean BMI for various mode frequencies.
VariableOptionsNumber of RespondentsPercentageMean BMI
BUS frequencyAt least once per day226.0051.95%24.89
n = 4354–6 times per week58.0013.33%25.54
1–3 times per week46.0010.57%24.50
Occasionally, but less than once per week78.0017.93%27.18
RIDE-HAIL frequencyAt least once per day0.000.00%-
n = 264–6 times per week2.007.69%20.28
1–3 times per week10.0038.46%22.81
Occasionally, but less than once per week14.0053.85%25.10
ESCOOTER frequencyAt least once per day19.008.88%23.89
n = 2144–6 times per week28.0013.08%23.40
1–3 times per week54.0025.23%25.05
Occasionally, but less than once per week113.0052.80%25.47
Table 6. Comparing BMI among Alternative Group Modes.
Table 6. Comparing BMI among Alternative Group Modes.
VariablenMean BMI
Public Transit (Bus)342
Non-Motorized Transport54.10%24.18
Motorized Transport14.00%25.83
Hybrid Mode of Transport31.90%25.14
Ride-hail34
Non-Motorized Transport5.90%25.81
Motorized Transport23.50%26.99
Hybrid Mode of Transport70.60%22.73
E-Scooter239
Non-Motorized Transport37.70%25.39
Motorized Transport13.00%22.94
Hybrid Mode of Transport49.00%25.19
Table 7. Logistic Regression with Dependent Variable BMI, Odd’s Ratio = exp(B).
Table 7. Logistic Regression with Dependent Variable BMI, Odd’s Ratio = exp(B).
VariableWaldS.E.Exp(B)
Age (Average) (n= 1949, R2 = 0.09)121.3200.0031.038 ***
Gender (Ref Female; n = 1901, R2 = 0.01)16.7420.0961.486 ***
Grad/Undergrad (Ref Grad; n = 954, R2 = 0.01)10.0970.1520.616 **
Race (Ref Non-White; n = 1874, R2 = 0.003)3.8540.1281.286 *
Disability (Ref No disability; n = 1872, R2 = 0.002)3.3230.1351.278
Residence (Ref Off Campus; n = 950, R2 = 0.02)15.1540.1400.576 ***
Cars (Ref No Car; n = 607, R2 = 0.00)0.0120.7350.923
Mode On Campus (Ref Hybrid; n = 1920, R2 = 0.01)
Non-Motorized Transport5.9090.0970.790 *
Motorized Transport5.1180.1571.428 *
Mode to Campus (Ref Hybrid; n = 1530, R2 = 0.03)
Non-Motorized Transport0.0010.2330.993
Motorized Transport23.9970.1502.088 ***
* p < 0.05, ** p < 0.01; *** p < 0.001 Ref = Reference Category; R2 = Negelkerke R2; logistic regression is testing likelihood of being overweight/obese.
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Kotval-K, Z.; Khandelwal, S.; Kassens-Noor, E.; Qu, T.T.; Wilson, M. Are New Campus Mobility Trends Causing Health Concerns? Sustainability 2024, 16, 2249. https://doi.org/10.3390/su16062249

AMA Style

Kotval-K Z, Khandelwal S, Kassens-Noor E, Qu TT, Wilson M. Are New Campus Mobility Trends Causing Health Concerns? Sustainability. 2024; 16(6):2249. https://doi.org/10.3390/su16062249

Chicago/Turabian Style

Kotval-K, Zeenat, Shruti Khandelwal, Eva Kassens-Noor, Tongbin Teresa Qu, and Mark Wilson. 2024. "Are New Campus Mobility Trends Causing Health Concerns?" Sustainability 16, no. 6: 2249. https://doi.org/10.3390/su16062249

APA Style

Kotval-K, Z., Khandelwal, S., Kassens-Noor, E., Qu, T. T., & Wilson, M. (2024). Are New Campus Mobility Trends Causing Health Concerns? Sustainability, 16(6), 2249. https://doi.org/10.3390/su16062249

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