Multimodal trip planning app for college student commuters enrolled in a suburban Florida public university: Feasibility Cluster Randomized Controlled Trial

Multimodal trip planning app for college student commuters enrolled in a suburban Florida public university: Feasibility Cluster Randomized Controlled Trial | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Multimodal trip planning app for college student commuters enrolled in a suburban Florida public university: Feasibility Cluster Randomized Controlled Trial Katherine Freeman, Louis Merlin, John Renne, Serena Hoermann This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6605622/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract A significant challenge student commuters face in transitioning to college is navigating transportation choices and expenses. Costs of driving often necessitate students work long hours to pay to drive. Time spent working can increase stress, and reduce time for studying or engaging in extracurricular activities. We designed a two-arm cluster-randomized controlled trial (cRCT) evaluating if a multimodal trip planning (MTP) app would benefit undergraduate student commuters at a large public university located in a Southeast Florida suburban area. The research focuses on addressing transportation barriers to improve academic and health outcomes among college students who are disproportionately first-generation, low-income and persons of color. Of the 427 students randomized, 106 completed both baseline (early semester) and follow-up (end of semester) surveys. Differences in travel behaviors and academic achievement between intervention and control groups were not significant. The number of days participants felt worried, tense or anxious was significantly greater in the control group (p = .0420). A MTP app is one promising option for promoting students' use of alternative travel choices. More studies are needed to understand the potential influence of MTP app features on student travel behaviors and overcome barriers to the adoption of such new technologies, while informing universities and transportation providers how to best meet the needs of students. In particular, a fully integrated Mobility as a Service (MaaS) app that includes ticketing and payment may offer a more compelling option than what we were able to develop for this study, given the budgetary and time limitations. Multimodal trip planning app for college student commuters enrolled in a suburban Florida public university: Feasibility Cluster Randomized Controlled Trial Cluster randomized controlled trial Mobility-as-a-service Transportation planning Health outcomes Public Florida University Multimodal trip planning app Figures Figure 1 Figure 2 Introduction Currently, obtaining a college degree is increasingly vital for career success and upward mobility. However, for many students, especially those from low-income households inclusive of families of color and first-generation college students, the path to obtaining a college degree can be fraught with obstacles which can result in educational disparities (US Census Bureau 2022 ). One of the most significant barriers these students face is transportation, particularly those who commute to campus (Allen and Farber 2018 ). According to the Urban Institute (Baum 2016), most students (64%) enrolled in public four-year colleges commute to college, thus making transportation to and from campus a notable concern. Among students enrolled in public four-year in-state colleges, the average cost for transportation was $ 1250 in 2021 (College Board 2022 ). Assuming travel costs are negligible for the 36% living on campus, a more accurate estimate for those living off campus is $ 1950 or roughly 20% of tuition costs (College Board 2022 ). With 40% of commuter students from public colleges living with one or more parents to save money, transit choices are often limited (Kelchen 2018 ). The remaining students end up weighing travel costs with rental costs. Living in a rural county puts students who reside there at a further disadvantage, given that only 32% of all rural counties have full access to public transportation (NASCSP 2008). The catchment area for the University students in our study encompasses the eastern region of South Florida, which includes both suburban and rural areas. Also, the University is in a county in South Florida in which the cost of living (CoL) is 14.1% higher than the national average (BestPlaces 2025). Furthermore, the University is the most racially, culturally, and ethnically diverse public university in Florida (U.S. News & World Report 2023), and among its almost 25,000 undergraduate students, 54% are minority, 30% are first generation, 19% receive income-based Federal Pell Grants, 39% are designated as low-income, and 78% commute. Thus, costs associated with driving can be significant, often necessitating students work long hours to pay for the ability to drive. Using $ 1426 per year for transportation costs (extrapolated from a 14.1% CoL increase), then given that the annual in-state tuition is $ 5,952, transportation costs comprise a conservative 20% above the estimated tuition costs for the University. Thus, transportation expenses place a significant financial burden on college students which increases the risk that they may not graduate. Time spent working can impede educational performance and achievement. For both low-income students and students of color who may already be facing additional obstacles such as financial insecurity and cultural acclimation, transportation barriers can be particularly debilitating and further exacerbate disparities. For example, students who spend a significant portion of their limited income on car payments, gas, insurance, and vehicle maintenance may struggle to afford other necessities such as tuition, textbooks and healthcare. In addition to employment, many commuter students also juggle family responsibilities which can impact their education negatively, thus inhibiting longer-term career mobility (Clay and Valentine 2021 ). Many students in the catchment area have never taken public transit, and because the built environment does not support walking, bicycling, or transit use, they subscribe to an auto-centric culture. Moreover, infrequent transit services and a lack of network connectivity can make using buses or commuter rail nearly impossible when students have multiple destinations in a day, such as school, work and home. Thus, having to trip chain can be nearly impossible for those who rely on transit modes other than a car. Prior Randomized Controlled Trials (RCT) to Address College Student Travel Behaviors Because randomized trials represent an optimal method for comparing interventions on human behavior, we reviewed all existing trials related to college students and transportation. We found that there were no RCTs that assessed digital interventions. Grimes and Baker ( 2020 ) conducted an RCT involving college and graduate students who lived within 5-miles of an urban campus, in which 28 students (intervention group) were offered a free one-month membership to a bike share system, and 25 (control group) received no intervention. The outcome was change in steps and biking events recorded over one-month from activity trackers. No power analysis was performed, and there were no significant differences between groups. Ralph and Brown ( 2019 ) conducted an RCT in an urban area involving incoming graduate students. The intervention included a transportation map of the campus and surrounding neighborhoods to facilitate apartment searches, and information on student biking, parking and transit passes; the control group received no information. Of the 3166 in the study, 810 attempted to complete the surveys (25.9%), and 561 (17.7%) had complete data for analysis. Investigators were unable to track randomization assignment, and relied on participant self-report. Outcomes evaluated at three months included modes of travel and distance to school, and choice of residential location. Results were inconclusive due to design limitations and missing data. The last of the three trials (Rodriguez and Rogers 2014 ) involved two university towns in North Carolina (one more urban) that included all newly enrolled undergraduate transfer and graduate students. Participants were randomly assigned on a 1:1 basis to the intervention (bundled travel and residential information provided in May prior to selecting residence), and control group (no information provided). Both groups were contacted five months later to complete online surveys. Outcomes encompassed both travel behaviors and housing. A power analysis was performed indicating that 3200 students were to be recruited; however, only 273 responded; of these, 189 qualified (5.9%). Results indicated that at the more urban university only, students in the intervention group were more likely to move closer to campus, reside in areas with more transit stops, and reduce their daily vehicle mileage by at least 50% relative to controls. Multimodal Trip Planning Application to Further Travel Behavior Change With college students more likely to embrace technology, having a phone app to help plan lower-cost transportation options could be helpful. In addition, it could help foster inclusive transportation among diverse lower-income college students by saving money and time (Dziuban and Shea 2007 ). Mobility as a Service, or MaaS, integrates existing mobility services into a smartphone app that allows travelers to search, book, use, and pay for all their transport needs (Ho and Tirachini 2024 ). Using existing trial data, Militao, Ho, and Nelson ( 2024 ) demonstrated for the first time that a mix of transport modes bundled into a MaaS app can alter travel behavior by lowering the probability of choosing private cars, and by increasing the probability of choosing more energy efficient travel modes displayed within the MaaS app. Houda et al. ( 2024 ) identified several key factors in user acceptance of a MaaS app; these include being younger, facile with changing technologies, using ride-sourcing apps, traveling more frequently, residing in a dense population which often reflects greater efficiency of a transportation infrastructure, no prior car ownership, use of only one mode of transport for a single trip, cost of the app, compatibility with phone platform, and quality of app support. Because students are generally younger, travel more frequently to school and work, are more comfortable with technology, and may be more conscious of greener alternatives, we developed a multimodal trip planning (MTP) app as the intervention to evaluate as part of a cluster RCT to assess its acceptance and effectiveness relative to standard commuting practice. Notably, our MTP app did not include all the desirable features of a full-featured MaaS app, because it did not include mechanisms for booking and payment. However, the MTP app did deliver real-time travel options for transit, walking, biking, and ride-hailing including employing combinations of these aforementioned modes. The MTP app also included customization, such as the ability to remember favorite stops or routes. Also of note is that the features of our MTP app resembled similar features of the leading MaaS apps on the market, including the Transit app and Moovit apps. During the time of the study, the leading MaaS apps had the capability to provide booking and payment options but rarely did so due to the complexity of agreements needed with transit agencies. The concept behind the intervention is to leverage information and marketing to shift students’ intentions regarding their travel to campus, following Azjen’s Theory of Planned Behavior (1991). We recognize that some generic travel/mapping apps are preinstalled on smartphones, and thus readily accessible to students. However, we introduce an innovative MTP application that is tailored to students enrolled in the University’s campuses. The MTP provides users with information about multimodal travel options from origin to destination, user-friendly mapping interfaces, travel time and user cost comparisons across multiple options, and information about the exact location of transit vehicles in real time utilizing a general transit feed specification (GTFS). For college students looking to save money on travel expenses, our MTP app could provide them the ability to replace some or all their trips with lower-cost transit options, potentially saving them hundreds of dollars a year and possibly freeing up time from the need to work to pay for a vehicle (Butler et al. 2021 ). Having an app tailored to the South Florida environment is particularly important given the significant challenges students face in the region such as expensive housing, land uses that do not readily support public transit, bicycling, or walking, considerable traffic congestion, and high auto expenses that include car insurance rates among the highest in the nation (Sleight 2023 ). Also, South Florida typically has the highest gasoline prices in the state. Using forms of transit other than cars has the potential for students to study or engage in social connections, instead of concentrating on driving (Gripsrud and Hjorthol 2012 ). Therefore, providing information on alternatives to driving streamlines decision-making, and may benefit students’ educational achievement and health, as well as the environment. To summarize, our primary research question concerns whether the provision of travel information through a MTP app can shift travel behavior among students who commute regularly to campus. Other travel behavior interventions such as free transit passes or improved transit services, should also be part of a broader campus transit promotion strategy (Toor and Havlick 2004 ). This study builds on previous work, known as TravelSmart, that sought to inform individuals about nondriving travel options and demonstrated that information contributed to attitude change regarding willingness to use public transit; it is of note that TravelSmart was conducted as a program before the use of smartphones (Zhang et al. 2013 ). Potential Outcomes Academic Benefits : Transportation services can be an important contributor (or obstacle) to the academic success of college students. The provision of free metro cards at City College of New York contributed to higher graduation rates -- three times that of the national average, thus lowering educational costs and expediting entry into the workforce (Move Minnesota 2021). Overcoming such transportation-related barriers such as cost, inadequate knowledge about alternatives to personal vehicles, inconveniently located stops or stations, schedules incompatible with need, and unreliable transit may provide needed support for some college students to finish their degrees (Price and Curtis 2018 ). Health Benefits : Public transportation benefits students’ health because transit riders must walk to and from their transit stop, likely achieving the 30 minutes of daily physical activity five times per week recommended by the US Department of Health and Human Services (2015). Those engaged in active travel to campus may experience even greater benefits, including more optimal mental health (St-Louis et al. 2014 ). Also, with reduced automobile use, people are exposed to fewer triggers for respiratory ailments (Remix 2021 ). Additionally, public transportation facilitates more equitable access to food markets, healthcare services and social networks, all of which impact public health (Remix 2021 ). In a survey of students living off campus at a northeastern university, those using more active means of travel had significantly greater cardiovascular fitness, were more flexible and had lower systolic blood pressure than nonactive travelers (Bopp et al. 2015 ). Finally, because vehicular accidents are the leading cause of death among college students, with a fatality rate of 6.88 per 100,000 (Turner et al. 2013 ), the use of public transportation can help reduce mortality and morbidity rates. Other Benefits : Commuting by means other than driving an automobile has many other benefits. Regarding the environment, decreasing the number of cars on the road improves air quality. Choosing alternatives to driving helps ease road congestion which results in fewer road repairs and lower noise levels (Pei 2021 ). Using public transit yields economic gains for individuals and communities and provides time to relax or be more productive during the commute (Frei et al. 2015 ; Remix 2021 ). Regarding universities’ infrastructures, building parking lots is not only costly but may encourage students to drive. Large parking lots built on many commuter campuses are substantial barriers for walkable campus land-use patterns. Finally, active commutes have been repeatedly shown to improve commuter satisfaction relative to driving. (Pritchard et al. 2021 ; St-Louis et al. 2014 ). Methodology Study Framework The full description of the protocol including study design, participants with inclusion/exclusion criteria, settings, randomization, and definitions of Mobility-as-a-Service smartphone App, intervention and control groups, outcomes and statistical methods has been published previously, and thus we provide a brief summary here (Merlin et al. 2022 ). The protocol and amendments were approved by the University’s Institutional Review Board. Objectives The primary research question is whether the introduction of a multimodal travel planning app can result in change in travel behavior relative to a control group. The primary outcomes are change in students’ travel behavior away from personal vehicles and towards alternative modes that include public transit, biking, and walking. These outcomes could serve as mediating factors for improvements in the environment, academic performance and self-perceived physical and mental health. Our broad research objective is to investigate how to best promote alternative mode usage among college students to alleviate the financial burden of transportation, and improve health and academic performance by reducing stress and time associated with car ownership and driving. Setting and Population The University, with an enrollment of over 25,000, has the most diverse student body of all public universities in Florida, and ranks nationally as one of the most ethnically diverse universities. It serves a predominantly local population with a high share of Pell Grant recipients, and is a Hispanic-Serving Institution (HSI) designated by the U.S. Department of Education as having at least 25 percent Hispanic undergraduates. In this suburban setting, three separate campuses were included in the original study design which were moderately or poorly served by transit that included Palm Tran (bus), Broward County Transit (bus), and Tri-Rail. Most of the rail and bus services run on hourly schedules. Train stations are located beyond walking distance from the campuses, making the last-mile connection a significant barrier, though there are bus services connecting each commuter rail station to each campus. As a result, students who utilize transit often experience lengthy travel and wait times, inflexible scheduling and sometimes unreliable service. Eligible students included undergraduates who reside off campus and intended to continue their education at the same institution through the fall of that year, and provided informed consent. Design : The study was designed as a two-arm cluster randomized controlled trial (cRCT) in which participants were assigned to either the MTP app (intervention) or the control group. The participant’s residence was considered the clustering unit which was randomized 1:1 to intervention and control groups; incorporating clustering into the design accounts for possible contamination across students who live in the same household who are expected to have more similar patterns of commuting. Participants who lived at unique addresses were randomized as a unique ‘cluster’. Randomization was stratified by campus to account for differences in transit resources and population density, and by whether the student was enrolled full or part-time. Completion of two questionnaires (baseline and at the end of the corresponding semester) in Qualtrics was requested as part of the informed consent process. Outcomes included changes in commuting to campus via driving alone, carpooling, ride-share, public transit and active transit; semester grade point average and credits completed; a subset of the SF-36 quality of life instrument to assess overall physical and mental health. To augment existing strategies for recruitment and retention, we selected and trained peer “Student Ambassadors” who mirrored the demographics of the student population; these peers were assembled at central locations on the campuses to engage other students to participate in the study (Hoermann et al. 2024 ). Intervention and Control Groups Students randomized to the intervention received the MTP app which was designed to facilitate students’ use of alternative transportation modes for each leg of their journey. Participants in the control group had not received the resources provided to the intervention group but have received the same contact from investigators requesting completion of baseline and follow-up forms. The MTP app includes real-time information about public transit and routing services, costs, wait times; more specifically it includes the location of nearby stops, expected travel times, transit fare costs, and various details of recommended routes, including boarding locations, routes, transfers, wait times, alighting locations, and recommended walk routes. The intervention is somewhat similar to previous studies of commuter students in that we focused on the provision of information to alter student travel behavior and other outcomes; however, we deliver information on transit through the MTP app and electronic delivery of campus and housing maps rather than through physical materials. The app differs from existing routing apps such as Google maps in several respects: 1) the app is branded in association with students’ colleges to create an affiliation with the app, and 2) the app provides personalization options for students to match their travel patterns. Randomization : A list of random numbers from 0 to 1.0 was generated using the R statistical package (rand() procedure) and merged with the weekly list of participants newly enrolled in the trial by stratum (campus, enrollment status: full or part-time). When each batch of survey data came in (weekly), the participants’ data were first matched by address with any previously enrolled participants who already had an assignment. If a new participant came in with the same address as one in a prior batch, they were given the same assignment which defined their ‘cluster’. Otherwise, within each stratum, participants were sorted by random number with the top 50% (random number > = .5) being assigned to intervention and the bottom 50% (random number < .5) assigned to the control group. Retention After contacting students through various forms of media and student ambassadors, and obtaining one or more email addresses from each student, all interested students were contacted by email first. To obtain both baseline and follow-up information, we contacted participants initially by email, and if they did not respond, then subsequently by text or by phone if they provided their phone number at baseline. We contacted participants up to three times for each survey instrument if they had not responded to a prior contact. Subsequent Changes in Design After the baseline data were collected, two changes in the design were implemented upon reviewing both the frequencies of participants across campuses and the frequencies of participants living in the same residence (cluster). First, we observed too few participants from campuses other than the University’s Boca Raton campus to yield reliable results and preserve the confidentiality of student data; thus, we limited analyses to data from participants from the Boca Raton campus. Second, we observed only two students living in the same residence that formed a cluster; neither student had follow up data and thus, the data were analyzed as per an RCT rather than a cRCT. :Statistical Methods: Baseline descriptive statistics included data for the 1 cluster; to avoid complexity in interpretation, each student in the pair that comprised the cluster was considered independent. Because there were no clusters for the statistical analyses of longitudinal data, analyses were performed without consideration of clustering. For continuous variables, descriptive statistics are presented as means and standard deviations for normally distributed data, and medians and ranges otherwise. For categorical or short scale ordinal variables, relative frequencies are presented. For each analysis, descriptive statistics are tabulated by group (intervention and control) and for the total. Analysis I (Database Integrity) included examining the distributions of participants across campuses, within Cluster IDs, by group assignment, and checking whether all participants met inclusion/exclusion criteria; additionally, the date the first participant enrolled was recorded. Analysis II (Baseline Comparison of Treatment and Control Groups) was performed to assess whether the randomization resulted in reasonably balanced groups for known baseline characteristics; it involved determining the significance of differences between groups for all enrollees (those randomized). For categorical variables, the significance of differences between groups were tested using chi-square tests if assumptions were met or otherwise Exact tests, or using Mantel-Haenszel chi-square for short-scale ordinal variables. Differences between groups for continuous normally distributed variables were tested with t-tests, or otherwise, Wilcoxon Rank Sum tests as appropriate. Analysis III (Comparison of Participants at Baseline Between Those Without Follow-up and Those With Follow-up Data) was performed to assess whether results from the final sample that included participants with both baseline and follow-up data were generalizable to the sample of participants randomized. The analysis involved assessing the significance of differences in baseline characteristics between the subset of participants with baseline data only and the complement subset with both baseline and follow-up data. Tests of statistical significance were similar to those described for Analysis II above. Also, we noted the date at which the last participant completed the follow-up survey. Analysis IV (Comparison Between Intervention and Control Groups Regarding Baseline Characteristics, and Travel Behavior, Academic Achievement, and Mental Health Outcomes) involved data from the subset of participants with both baseline and follow-up data (final sample) to compare the groups regarding specified characteristics at baseline, and to compare the groups regarding outcomes. Bivariate tests of the significance of differences between the groups are described in Analysis II. For each outcome, those bivariate associations resulting in p-values < .20 identified independent variables that were potential candidates for the initial mixed effects model to assess longitudinal differences accounting for selected baseline characteristics. Iterative models were derived in which the independent variable with the largest p-value > .05 was deleted until the final model included only significant independent variables. Missing data were not imputed due to a large proportion of missing data (Jakobsen et al. 2017 ), most of which occurred at follow up. Analysis V (Post-hoc Analysis to Identify Who Opened the App) was conducted using data from all participants randomized to the intervention group to identify baseline variables associated with whether or not the participant opened the app. (Note: data for those who opened the app were provided by the app developer). Bivariate analyses were performed using Wilcoxon Rank Sum tests for continuous variables, and chi-square, Exact or Mantel-Haenszel chi-square for categorical or short-scale ordinal variables as appropriate using all demographic and travel behavior variables at baseline. For those baseline characteristics yielding p-values < .20, an initial logistic regression model was constructed. A monitored iterative procedure was performed in which the predictor variable with the greatest non-significant p-value was eliminated, and the model was rerun until all remaining predictor variables were significant. Odds ratios and 95% confidence intervals were derived. Note analyses II through V were performed using data from the Boca Raton campus only. The trial was registered with Clinicaltrials.gov NCT04720300. The manuscript is reported according to the Consolidated Standards of Reporting Trials (CONSORT) 2010 statement: extension to cluster randomized trials, which provides a checklist and flow diagram to standardize reporting of the design, conduct, analysis, results and interpretation of findings from the trial. Adherence to CONSORT helps insure completeness of reporting and transparency. All tests of hypotheses were two-tailed and conducted at α = .05 (SAS Version 9.4, Cary, NC). Results A total of 1,228 students were screened (see Fig. 1 ). Screening of the first student occurred on January 11, 2022, with the last baseline questionnaire completed on March 13, 2022. Completion of the follow-up questionnaire began on April 25, 2022 and the last was completed on July 7, 2022. A total of 427 participants met all inclusion criteria, had sufficient data and were enrolled as participants in the cRCT, with 214 randomized to the intervention (MaaS) and 213 randomized to the control groups. All participants indicated they lived off campus, had a smartphone capable of running apps, planned to study at their current institution through the summer of 2022 and were enrolled at the University as either full or part-time students. Only two students lived at the same address and comprised one cluster. Among the 427 participants randomized, descriptive statistics for baseline characteristics by group and for the total are shown in Table 1 . In addition, Table 1 provides the significance of differences between randomization groups at baseline (Analysis II), as well as the significance of differences in baseline characteristics between those with and without follow-up data (Analysis III). Regarding differences at baseline between groups, differences for two of 33 variables in Table 1 were significant (p < .05): relatively more first-generation participants were randomized to the App group (p = .0133), and relatively more with iPhones to the control group (p = .0338). Table 1. Baseline Characteristics of Participants Randomized by Group with Bivariate Analysis Results Characteristic INTERVENTION (n=214) CONTROL (n=213) TOTAL (N=427) P-value* BL Differences II P-value** FU vs. BL III n % n % n % COLLEGE LOAD (Full-Time) 152 71.0 150 70.4 302 70.7 0.8906 0.0141 RACE 0.3478 0.4948 Asian 6 5.7 13 10.7 19 8.4 Black 33 31.4 35 28.9 68 30.1 Hawaiian/Pacific Islander 1 1.0 0 0.0 1 .4 Multiracial 7 6.7 6 5.0 13 5.8 Native American 2 1.9 0 0.0 2 .9 White 56 53.3 67 55.4 123 54.4 ETHNICITY (Hispanic or Latinx) 69 32.6 54 25.4 123 28.9 0.1020 0.0640 GENDER (Female)*** 124 62.9 134 67.7 258 65.3 0.3231 0.5095 RECEIVED PELL GRANT (Yes) 8 4.1 12 6.1 20 5.1 0.3647 0.2202 FIRST GENERATION (Yes) 79 40.1 56 28.3 135 34.2 0.0133 0.8533 CAMPUS MOST FREQUENTED (BOCA) 198 92.5 206 96.7 404 94.6 0.0844 0.2210 SMARTPHONE OPERATING SYSTEM 0.0338 0.0068 IPhone 153 71.8 173 82.4 326 77.1 Android 59 27.7 36 17.1 95 22.5 Other Phone 1 0.5 1 0.5 2 0.5 HEAR ABOUT STUDY 0.9534 0.9332 Ambassadors 8 5.9 10 6.9 18 6.4 Other 55 40.4 66 45.8 121 43.2 Poster 24 17.7 21 14.6 45 16.1 Social Media 3 2.2 6 4.2 9 3.2 Website 44 32.4 39 27.1 83 29.6 Word of Mouth 2 1.5 2 1.4 4 1.4 LICENSED DRIVER (Yes) 183 86.7 189 90.0 372 88.4 0.2955 0.1844 ACCESS TO AUTO MOST DAYS (Yes) 181 85.8 178 85.6 359 85.7 0.9522 0.9908 COVER ALL AUTO COSTS (Yes) 108 51.2 109 51.7 217 51.4 0.5822 0.6681 DO YOU WORK 0.4425 0.2447 Yes: > 30 hours/week 56 26.5 52 24.6 108 25.6 Yes: <30 hours/week 99 46.9 118 55.9 217 51.4 No 56 26.5 41 19.4 97 23.0 WORK ON CAMPUS (Yes) 32 20.8 39 23.1 71 22.0 0.6185 0.0003 PARENT/GUARDIAN OF CHILDREN (Yes) 13 6.3 14 6.9 27 6.6 0.8011 0.3673 ADULT CAREGIVER (Yes) 38 18.7 44 22.0 82 20.4 0.4134 0.3348 RESIDE WITH PARENT/GUARDIAN (Yes) 129 60.3 129 60.6 258 60.4 0.9523 0.6392 NEVER REGULARLY COMMUTED TO CAMPUS (Yes) 15 7.7 9 4.6 24 6.1 0.2062 0.1081 ACCIDENT DRIVING IN LAST 4 MONTHS (Yes) 9 6.6 13 8.3 22 7.5 0.5795 0.3396 ACCIDENT AS PASSENGER IN AUTO IN LAST 4 MONTHS (Yes) 2 1.5 2 1.3 4 1.4 0.8900 0.7672 ACCIDENT RIDING IN BUS/TRAIN IN LAST 4 MONTHS (Yes) 1 0.7 1 0.7 2 0.7 0.9260 0.5830 ACCIDENT RIDING BICYCLE IN LAST 4 MONTHS (Yes) 1 0.7 0 0.0 1 0.3 0.2849 0.4924 ACCIDENT WHILE WALKING IN LAST 4 MONTHS (Yes) 1 0.7 0 0.0 1 0.3 0.2833 0.4935 DISTANCE LEARNING ( > 1 CREDIT) 142 72.1 115 58.1 257 65.1 0.3316 0.0364 DRIVE DISTANCE ( > 30000 METERS) 99 46.7 104 50.7 203 48.7 0.2997 0.1844 DRIVE TIME ( > 1800 SECONDS) 81 38.2 90 43.9 171 41.0 0.3043 0.1577 BICYCLE DISTANCE ( > 30000 METERS) 102 48.6 106 52.0 208 50.2 0.2636 0.3864 BICYCLE TIME ( > 6000 SECONDS) 96 45.7 101 49.5 197 47.6 0.2386 0.3818 WALK DISTANCE ( > 30000 METERS) 85 40.5 94 46.1 179 43.2 0.1982 0.3581 WALK TIME ( > 1800 SECONDS) 111 52.9 119 58.3 230 55.6 0.1988 0.3586 TRANSIT DISTANCE ( > 30000 METERS) 101 55.5 107 60.8 208 58.1 0.1756 0.4128 TRANSIT TIME ( > 6000 SECONDS) 92 50.6 99 56.3 191 53.4 0.5573 0.0827 CREDIT HOURS ( > 12) 113 58.0 108 55.4 221 56.7 0.9108 0.1646 GPA (>=3.5) 56 40.0 70 46.7 126 43.5 0.2472 0.0667 BL=Baseline FU=Follow-up *P-value for the difference in baseline characteristics between the intervention and control groups (Analysis II). **P-value for the difference in baseline characteristics between those with baseline data and no follow up data and the complement with both baseline and followup data (Analysis III). ***Refers to cisgender pants Randomized by Group with Bivariate Analysis Results Participants enrolled were categorized according to whether they had both baseline and follow-up data vs. baseline data without follow-up to evaluate possible limitations in generalizability. Significance levels of differences between these subsets are shown in Table 1 within the column for Analysis III. The subset with both baseline and follow-up data differed significantly from its complement regarding college load (part-time participants more often completed both surveys; p = .0141), smartphone operating system (those with Android more likely to complete both surveys; p = .0068), whether they worked on campus (those working on campus were more likely to complete both surveys; p = .0003), and whether they had taken more credits in distance learning (those with fewer credits in distance learning were more likely to complete both surveys; p = .0364). Table 2 provides descriptive statistics for baseline characteristics for participants with both baseline and follow-up data. Table 2 Baseline Characteristics of Participants With Data From Both Baseline and Followup Assessments by Group Characteristic INTERVENTION (n = 38) CONTROL (n = 68) TOTAL (N = 106) P-value* BL Differences IV n % n % n % COLLEGE LOAD (Full-Time) 23 60.5 42 61.8 65 61.3 1.0000 RACE 0.4690 Asian 1 4.0 5 10.9 6 8.5 Black 5 20.0 13 28.3 18 25.4 Hawaiian/Pacific Islander 0 0.0 0 0.0 0 0.0 Multiracial 1 4.0 1 2.2 2 2.8 Native American 1 4.0 0 0.0 1 1.4 White 17 68.0 27 58.7 44 58.7 ETHNICITY (Hispanic or Latinx) 8 21.1 15 22.1 1.0000 GENDER (Female) 29 76.3 43 63.2 72 67.9 0.1665 RECEIVED PELL GRANT (Yes) 36 94.7 67 98.5 103 97.2 0.2588 FIRST GENERATION (Yes) 17 44.7 52 76.5 69 65.1 0.0010 CAMPUS MOST FREQUENTED (BOCA) 24 63.2 49 72.1 73 68.9 0.4538 SMARTPHONE OPERATING SYSTEM (IPhone) 17 44.7 53 79.1 70 66.7 0.0003 HEAR ABOUT STUDY 0.8635 Ambassadors 0 0.0 3 6.0 3 4.1 Other 10 43.5 22 44.0 32 43.8 Poster 4 17.4 7 14.0 11 15.1 Social Media 0 0.0 2 4.0 2 2.7 Website 9 39.1 15 30.0 24 32.9 Word of Mouth 0 0.0 1 2.0 1 1.4 LICENSED DRIVER (Yes) 30 79.0 59 88.1 89 84.8 0.2118 ACCESS TO AUTO MOST DAYS (Yes) 32 84.2 58 86.6 90 85.7 0.7402 COVER ALL AUTO COSTS (Yes) 21 55.3 31 45.6 52 49.1 0.1583 DO YOU WORK 0.5017 Yes: ≥ 30 hours/week 8 21.1 20 29.4 28 26.4 Yes: <30 hours/week 17 44.7 31 45.6 48 45.3 No 13 34.2 17 25.0 30 28.3 WORK ON CAMPUS (Yes) 8 32.0 20 39.2 28 36.8 0.5401 PARENT/GUARDIAN OF CHILDREN (Yes) 5 13.2 4 5.9 9 8.5 0.1975 ADULT CAREGIVER (Yes) 7 18.4 17 26.5 25 23.6 0.3492 RESIDE WITH PARENT/GUARDIAN (Yes) 19 50.0 43 63.2 62 58.5 0.1848 NEVER REGULARLY COMMUTED TO CAMPUS (Yes) 3 8.1 0 0.0 3 2.9 0.0427 ACCIDENT DRIVING IN LAST 4 MONTHS (Yes) 0 0.0 5 8.1 5 5.4 0.1653 ACCIDENT AS PASSENGER IN AUTO IN LAST 4 MONTHS (Yes) 0 0.0 1 1.6 1 1.1 1.0000 ACCIDENT RIDING IN BUS/TRAIN IN LAST 4 MONTHS (Yes) 0 0.0 1 1.6 1 1.1 1.0000 ACCIDENT RIDING BICYCLE IN LAST 4 MONTHS (Yes) 0 0.0 0 0.0 0 0.0 1.0000 ACCIDENT WHILE WALKING IN LAST 4 MONTHS (Yes) 0 0.0 0 0.0 0 0.0 1.0000 DISTANCE LEARNING ( ≥ 1 CREDIT) 26 68.4 33 48.5 59 55.7 0.0480 DRIVE DISTANCE ( ≥ 30000 METERS) 18 47.4 29 43.3 47 44.8 0.6858 DRIVE TIME ( ≥ 1800 SECONDS) 15 39.5 26 38.8 41 39.1 0.9463 BICYCLE DISTANCE ( ≥ 30000 METERS) 20 52.6 30 44.8 50 47.6 0.4386 BICYCLE TIME ( ≥ 6000 SECONDS) 20 52.6 29 43.3 49 46.7 0.3562 WALK DISTANCE ( ≥ 30000 METERS) 18 47.4 29 43.3 47 44.8 0.6858 WALK TIME ( ≥ 1800 SECONDS) 21 55.3 33 49.3 54 51.4 0.5538 TRANSIT DISTANCE ( ≥ 30000 METERS) 19 57.6 34 55.7 53 56.4 0.8638 TRANSIT TIME ( ≥ 6000 SECONDS) 9 27.3 32 52.5 41 43.6 0.0188 CREDIT HOURS ( ≥ 12) 21 55.3 35 51.5 56 52.8 0.7076 GPA ( > = 3.5) 13 56.5 26 55.3 39 55.7 0.6582 BL = Baseline *Among those with both baseline and followup data, comparison between assigned groups for baseline characteristics (Analysis IV). Regarding changes in outcomes from baseline to follow-up (Table 3 ), the only outcome that was significantly different between the groups was the number of days participants felt worried, tense or anxious. For those in the App group, the average decreased whereas in the control group the average increased (p = .0420). Although not significant (p = .0560), it should be noted that the change in the number of minutes to travel to campus increased in both groups, with a minimal mean change in the App group vs. a larger one in the control group. Table 3 Differences between Intervention and Control Groups Regarding Changes in Outcomes from Baseline to Followup (Bivariate) Outcomes: transportation, health, academic INTERVENTION (N = 38) CONTROL (N = 68) TOTAL (N = 106) P-value* Mean STD Mean STD Mean STD TYPICAL WEEK CAMPUS COMMUTE: Drive alone -0.28 1.05 -0.17 1.34 -0.21 1.24 0.3958 Carpool -0.31 1.54 -0.42 2.48 -0.38 2.18 0.4758 Ride-hailing (Uber or Lyft) -0.79 2.50 -0.09 1.78 -0.34 2.07 0.6541 Transit (bus/train) + walk, bike, drive, etc.to/from station 0.47 2.01 -0.27 2.20 -0.01 2.15 0.1223 Walk, bike, skate only -0.37 1.45 -0.26 1.81 -0.30 1.68 0.9025 Bike/electric scooter share only 0.10 0.56 0.00 1.44 0.04 1.19 0.4826 # MINUTES USUAL TRAVEL TO CAMPUS 0.97 11.34 6.12 18.81 4.38 16.78 0.0560 FREQUENCY TAKE TRANSIT TO CAMPUS/SHOP/COMMUTE/PLAY 0.00 1.15 -0.08 0.46 -0.05 0.76 0.9150 HEALTH WITHIN PAST 30 DAYS What is your general health now -0.03 0.57 -0.02 0.64 -0.02 0.61 0.9442 # days your physical health was not good -0.61 3.74 -0.96 4.86 -0.86 4.53 0.6259 # days your mental health was not good 0.73 7.01 0.60 6.66 0.64 6.71 0.7796 # days pain impaired usual activities -0.26 3.25 0.27 5.27 0.10 4.71 0.5332 # days felt sad, blue, or depressed -0.36 4.07 0.77 6.58 0.38 5.84 0.8934 # days felt worried, tense, or anxious -1.00 6.10 1.71 6.07 0.80 6.17 0.0420 # days felt not enough rest/sleep 0.28 7.53 -0.71 8.97 -0.38 8.47 0.6928 # days felt very healthy and full of energy -0.56 7.96 -1.02 7.55 -0.87 7.64 0.7957 # days poor mental/physical health impaired usual activities -1.04 5.46 -0.90 5.24 -0.95 5.27 0.8712 GPA -0.02 0.45 -0.04 0.74 -0.04 0.66 0.8272 *P-value for differences in outcomes between groups regarding changes from baseline to follow-up (Analysis IV). Feasibility : A total of 50 of the 214 participants randomized to the App group opened the App (23.4%). For Analysis V, bivariate and multivariate analyses are presented in Table 4 to identify factors associated with opening the App. Among the 33 baseline variables in Table 1 , associations with opening the App yielded bivariate p-values less than .20 for 14 variables; these comprised the set of independent variables in the initial multiple logistic regression model. In the final model, three variables were significant, indicating that those who opened the App: a) were less likely to have ever taken transit (p = .0014), b) traveled for longer periods of time (p = .0076), and c) were less likely to be responsible for caring for an adult (p = .0083). Table 4 Factors associated with opening the App among those randomized to the Intervention group (Bivariate & Multivariate Models: Analysis IV). BASELINE VARIABLE LABELS BIVARIATE P-value FINAL MULTIVARIATE MODEL P-value < .05 * Odds Ratio [95% Confidence Limits] How often take transit, such as bus/train (TOTAL) (to campus/shopping/commuting/leisure, etc.)? 0.0014 0.0010 0.659 [0.514, 0.845] During past 30 days, for about # days have you felt you did not get enough rest or sleep? 0.0985 -- -- Commuting to CAMPUS on typical week:Drive alone 0.0516 -- -- Commuting to CAMPUS typical week:Use Ride-hailing(Uber or Lyft) 0.0727 -- -- Commuting to CAMPUS typical week:Use transit(bus/train) + walk, bike, drive, etc.to/from transit station 0.0654 -- -- Number of minutes does your trip to campus usually take? 0.0166 -- -- Driving commute in meters 0.0401 -- -- Driving commute in seconds ** 0.0406 0.0076 1.001 [1.000, 1.001] During past four MONTHS Have you been in a crash or accident While driving? Y/N 0.0569 -- -- On most days, do you have access to a motor vehicle you can use to campus? 0.0234 -- -- Do you cover the costs of a motor vehicle? 0.0836 -- -- Are you responsible for care/needs of an adult? (i.e., health care/shopping/errands/other routine activities) 0.0015 0.0083 2.803 [1.304, 6.023] During past four MONTHS Have you been in a crash or accident Yes - While driving? 0.0729 -- -- During past four MONTHS Have you been in a crash or accident Yes - While a passenger in a car? 0.1369 -- -- * Full multivariate model not shown due to multicolinearity. ** Variable ‘Driving Commute’ appeared to have an outlier (210 minutes); sensitivity analysis was performed deleting this observation and the resulting P-value was .0078. Figure 2 describes aggregate data on frequency of app openings with a 30-day lagging average from January 11, 2022 (when the first student completed the baseline survey) until July 7, 2022 (when the last student completed the follow-up survey). App usage appeared to peak in early February 2022 and declined through the end of May approaching the end of the survey period. Discussion This is the first randomized controlled trial of undergraduate college students to evaluate a digital intervention designed to change travel behaviors. It is also the first cRCT to assess the additional outcomes of changes in physical and mental health indicators and academic performance. The cRCT design provided a University-specific App to the Intervention group which promotes travel alternatives to campus other than driving. We found that participants randomly assigned to the App group had on average significantly fewer days in which they felt worried, tense or anxious. This may contrast with results from a prior study which found that college students trying new modes of travel tended to have more anxiety and negative experiences compared with those whose travel is familiar (Mallari and Delariarte 2021 ). One contributing factor to anxiety and negative experiences during unfamiliar travel may be the fear of arriving late to class or work. Therefore, methods to reduce anxiety such as wayfinding and information about schedules may reduce anxiety and increase the potential for transit usage among college students. Generally, if MaaS App information can reduce the fear associated with first-time transit usage, the technology could help students overcome barriers in trying new transit modes and using it on a regular basis. Another potential fear of students in the post-Pandemic era may involve preference for contactless and cashless payments (Nelnet 2023). Unfortunately, transit systems have been notoriously slow to adopt cashless payment systems. It should be noted that the App used in our study did not have a mobile payment option; such an option could help improve the effectiveness of the App by reducing fear about how to pay for travel. Gonzales-Sanchez et al. (2024) report that technophilia, perceived usefulness and perceived ease of use are determining factors in MaaS adoption, along with inclusion of a gender perspective in the design—our MTP app had input from numerous male and female stakeholders. There were no significant differences between groups regarding changes in travel behaviors or academic performance, albeit the sample size was low and the period of the study was one semester only. The study did find that the number of minutes commuting to campus increased in both groups, with greater increases on average for the control group. In the spring of 2022 while the study was conducted, traffic began increasing as society returned to a post-COVID new normal and notable numbers of people moved to South Florida from other states, possibly explaining the increases in average travel times. The App group had a smaller increase in average number of minutes to travel to campus–an encouraging sign that MTP may have prompted a more thoughtful approach to travel that considered trip planning and avoidance of non-essential excursions. Because of the COVID-19 pandemic, recruitment efforts were delayed until 2022 to allow students to return to traditional in-class education. On-campus attempts to recruit a representative population of students who lived off campus included “Student Ambassadors” and “micro-influencers”; however, this creative approach may not have reached those students who elected remote or hybrid learning, which may have limited their participation in the initial (baseline) survey. Also, adoption of the intervention was a challenge, with less than 25% of those randomized to the App group opening the App. It is of note that those who opened the App were less likely to have taken transit and their trips to campus were longer in terms of time. This could indicate that the App provided a resource to students looking for new travel options, especially among those burdened with long commutes. That is, the app appeared to be most attractive among students who were looking for alternatives to their current commute. One benefit of using an app and taking transit to campus may reduce the likelihood of students being in a crash while driving, which we found to be a frequent occurrence among our survey participants, as reported in an ancillary study (Merlin et al. 2025 ). Unfortunately, transit services in Palm Beach and Broward Counties are not as extensive compared to those in cities with more robust and frequent services that encompass broader hours. Additionally, micromobility and bicycle options are not well supported in the community. Thus, given the suburban/rural environments we would not expect as many student commuters in South Florida to switch from driving to transit compared with students in cities and regions with more accessible transit systems. Although the state university campuses originally recruited for the study showed great interest in the topic, the campus transportation departments in these schools were mostly focused on managing on-campus parking and they did not have robust strategic campus planning teams which could support the study and promote alternative modes beyond operating some shuttle buses. For example, early in the project we held meetings with key stakeholders, including representatives from the campuses and transit providers. Despite seeking to incorporate staff from the campus transportation departments, the team was not successful in getting representation to attend the meetings. Therefore, the meetings consisted mainly of researchers, academic staff, and representatives from transit agencies. Despite good intentions, the research and academic staff did not have the power to implement campus-wide travel demand management plans. An MTP or MaaS app solution should be a component of a larger multifocal transportation strategy; perhaps a technology-only solution for students enmeshed with a more costly buy-in from universities may be more optimal. Prior RCT’s present several limitations that motivated design features in our current study. In one study, randomization was compromised due to the inability to validate random assignment (Ralph and Brown 2019 ), and in the other, groups had different follow-up times which increases the risk of differential drop-out rates between groups (Rodriguez and Rogers 2014 ) and decreases the validity of the comparison due to differences in environmental exposure. Furthermore, none of the three RCT’s considered the non-independence of travel behaviors among participants living in the same household, and thus, our group assignment was designed to randomize clusters of participants’ residences. Our methodology involved conducting a power analysis, and multiple attempts at student follow-up to reach the targeted sample size. Also, the observation period in our study was framed around the semester which was longer than those in prior RCTs. To assess potential bias, as part of our design we checked whether the resulting sample was representative of the University population, that participants in the two groups were comparable regarding baseline characteristics, and that results of follow-up assessments were generalizable. Limitations In assessing the feasibility and effectiveness of the trial, we identified several limitations. First , the study was conducted in the winter/spring of 2022 during which time many classes were still hybrid with remote learning. During this time, ridership on public transportation had decreased and the motivation of students to commute to campus may not reflect pre-COVID travel, which may have affected motivation to use the MTP app. Given the context of the COVID-19 pandemic which contributed potentially to low sample size and biased estimates, our methodology demonstrated feasibility. Second , we did not evaluate the extent to which other travel/mapping apps were used in both control and intervention groups; overall use by the two groups and differential use between the two groups of existing apps may have compromised observed differences between groups and biased overall results. However, the randomization resulted in reasonably balanced groups at baseline for known factors, which supports the notion that the two groups would likely have similar travel motivations and use of existing travel/mapping apps. Third , we observed a relatively low engagement rate defined as opening the MTP app (23.4%), not unlike other RCTs conducted among college students. For example, only 7.4% of those in the Grimes and Baker’s ( 2020 ) RCT redeemed their free bike memberships. Neither Ralph and Brown ( 2019 ) nor Rodriguez and Rogers ( 2014 ) report the percentage of students who opened emails containing educational materials. Also, we preserved randomization, not relying on student recall as per Ralph and Brown ( 2019 ). Fourth , although the study was designed as a cluster randomized trial, only one cluster was identified at baseline, and participants in this cluster did not have follow-up data; thus, longitudinal analyses were performed without regard to clusters. The design of this trial as a cRCT was supported by the investigators’ External Advisory Committee. Fifth , regarding generalizability, there were three concerns: 1) selection bias is possible given that some students may not have been interested in participating in the survey; in an attempt to assess this, we were only able to obtain the racial distribution of participants for comparison with that of the university’s student population; 2) regarding generalizability of results to external universities, as stated earlier, the campus location is in Boca Raton, Florida within Palm Beach County which is considered suburban, and incurs a higher cost of living than that found nationally. Also, the University is one of the most diverse public universities in the country, and the most diverse in Florida. However, the campus is typical of many suburban campuses throughout the country in that most commuter students drive to campus. The campus is well served by major arterials running on the northern and southern boundaries of campus, both of which have access to interstate exits nearby. The campus has access to some shuttles, buses, and train routes, but services are typically limited to hourly frequencies. The Walk Score of the main Boca Raton Campus is a 28, which means that most trips require a car, whereas those of the University of North Carolina at Chapel Hill and University of California, Los Angeles have walk scores of 52 and 91, respectively (Walk Score 2023); 3) differential response rates in the two groups limit generalizability: only 25% of participants completed follow-up surveys, and the difference in follow-up survey completion rates between the intervention and control groups was significant (p = .0003), with those in the control group almost twice as likely to complete the survey. A possible explanation is that those in the control group were more motivated to stay in the study to obtain the App after study closure. Sixth , because the cRCT was unblinded, our study shares the increased risk of cross-contamination found in other studies in that students randomized to the App group could have shared information with others in the control group, thus underestimating differences; however, the App is specific to the student’s personal email address (for login) which may have curtailed sharing of information. To minimize cross-contamination, we limited download of the App to students assigned to the intervention by requiring that the students’ official email be used to request App download. Seventh , although the randomization appeared to result in reasonably balanced groups for prespecified characteristics identified by content experts, differences in unspecified characteristics between the intervention and control groups may explain results. For known characteristics, there were some differences at baseline; however, given the large number of variables assessed and participants randomized carefully using campus and enrollment status strata, we expect chance differences to occur. Eighth , among those in the App group, we did not capture travel behavior change related to whether there was a change in residence after receiving the App. However, because the App was delivered early in the semester, almost all these students would likely have established their residence location before receiving the App. Ninth , two data concerns limited statistical power. Because the proportion of missing follow-up data is quite large (approximately 75% of those who completed baseline assessments had no follow-up), no multiple imputation procedures were used, and analyses were performed on the subset of cases with both baseline and follow-up data only. Thus, coupled with the fact that we did not reach our target sample size–reducing the ability to detect a difference as significant, the trial results are considered hypothesis-generating and not definitive (Jakobsen et al. 2017 ). Despite these limitations, the randomization process was conducted carefully and resulted in reasonably balanced groups for the characteristics specified. All participants were observed at the same timepoints chronologically. We examined potential biases regarding differences between the derived sample with both baseline and follow-up data, and the complement with no subsequent follow-up, as well as differences in proportions of participants in the two groups who had follow-up data. Methodologically, this study will provide important design features for planning future studies such as empirical data to compute effect sizes, refinement of inclusion/exclusion criteria to encompass characteristics of participants who may be more likely to open the App to use for possible stratification, and augmenting outcomes of transportation interventions to encompass physical and mental health criteria. We also found that although the study was designed as a cRCT, there was virtually no clustering at this University. Results of this study inform efforts toward inclusive transportation in that MTP has been adopted by college students from diverse backgrounds to facilitate transportation, regardless of their circumstances (Davis and Butler 2023 ). Conclusions Results of this feasibility study suggest that promoting a multimodal trip planning app may help decrease feelings of worry, tension or anxiety; we did not find significant differences in travel patterns or academic achievement between groups. Given design limitations and the fact that the study was conducted just after the height of the COVID-19 pandemic when ridership was low, the study should be replicated during a time of relative stability in population health. We found that students who opened the app were significantly more likely to: 1) not take transit, 2) have long commutes, and 3) not provide care for an adult. Study results also indicate that less than 25% of the intervention group opened the app, suggesting possible barriers to adoption; from our study, these barriers could include preference for an existing travel app on their phone, overcoming inexperience in using transit, and family-related time constraints that render alternatives to private vehicle use unwieldy. Further research is needed to identify what these barriers are and ways to address them to increase engagement with such tools in the future. Ultimately, a MaaS app is a tool for transacting information and payments regarding alternative modes of travel. A MaaS app could also be used as a tool for regional planners and officials to better understand the mismatch between student requirements for travel and the current transportation network, underscoring the tool’s utility in the strategic planning effort. However, the best app cannot overcome shortcomings in a deficient multimodal transportation network that is less accessible in rural areas. Moreover, strategic campus planning for sustainable travel is also an important component lacking in our study. As technology continues to improve and a larger share of college students turn to apps to facilitate travel, more research will be needed to study how MaaS apps can benefit students and simultaneously inform universities and transportation providers how to best meet their needs. There are many mechanisms for improving students' travel options to campus, including providing more frequent and reliable transit service, providing free transit passes, and offering technology options to facilitate transportation planning. A MaaS app is one promising option for promoting students' use of alternative transportation to reach campus, but more studies are needed to understand the potential influence of MaaS features on student travel behaviors and overcome barriers to the adoption of such new technologies. A fully integrated MaaS app that includes ticketing and payment may offer a more compelling option than what we were able to develop for this study, given budgetary and time limitations. Declarations Declaration of Competing Interest The authors declare that we have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The content of this manuscript has not been published elsewhere, and is not under consideration by any other journal at this time. All authors have reviewed and approved the manuscript in its entirety. If this manuscript is accepted it will not be published elsewhere in the same form or in various languages or formats. Declaration of Generative AI and AI-assisted technologies: No AI and AI-assisted technologies were used in the writing process or for analyses of data used in this manuscript. CRediT authorship contribution statement: Katherine Freeman: Conceptualization, Formal statistical analysis, Methodology, Writing – original draft. Louis A. Merlin: Conceptualization, Funding acquisition, Methodology, Writing – review & editing. John Renne: Conceptualization, Funding acquisition, Supervision, Writing – review & editing. Serena Hoermann: Conceptualization, Funding acquisition, Project administration, Writing – review & editing. Vitae/biography Katherine Freeman, DrPH currently serves as the Biostatistics Collaborative Core Leader for Florida Atlantic University (FAU), and Professor, Charles E. Schmidt College of Medicine. She received her doctorate in biostatistics from the Columbia School of Public Health, and is an expert in the design and statistical analysis of multicenter randomized clinical trials (RCT) and observational studies. She has written or coauthored several successful NIH and foundation research grant applications for which she has been the Principal or Co-Investigator. Collaborators include FAU and other faculty from varied disciplines including medicine, nursing, behavioral health, education, bioengineering, health economics, urban planning, transportation and biology which have resulted in nearly 150 peer-reviewed publications. She has taught biostatistics, epidemiology, critical review of the literature, clinical trials methodology, and meta-analysis. Dr. Freeman spent most of her career at the Albert Einstein College of Medicine, and had been a member of Montefiore Medical Center’s Institutional Review Board for 14 years. She has Chaired several NIH and industry sponsored Data and Safety Monitoring Boards, and has served as a reviewer for both NIH and PCORI. She has been a Co-Investigator on the Kresge Foundation transportation award, having collaborated on the design and analysis of its cluster randomized controlled trial. Presently, Dr. Freeman is PI of the NIH R25 5-year Florida Summer Institute in Biostatistics and Data Science to train undergraduate students and early graduates in these fields. Recent Publications Merlin LA, Freeman K , Renne J, Hoermann S. Clustered randomized controlled trial protocol of a Mobility-as-a-Service app for College campuses. TRIP 14 (2022): 1-13. Hoermann S, Renne JL, Freeman K , Merlin LA, Dzhurova A, Lopez P. Peer Engagement: On Reflecting Student Diversity in a Research Trial. International Journal of Qualitative Methods 2024; 23 : 1–14. Merlin LA, Simpson DA, Freeman K , Hoermann S, Renne J. Driver vehicle crashes and mental health challenges among commuter college students. J Transport & Health 2025; 40 . Author Contributions All authors were involved in the conceptualization/design, JR, LM with funding acquisition; LM, SH with data curation; LM with randomization; KF with statistical analyses; KF with original and continued drafts; all with writing and editing. Acknowledgements This work was supported by the Kresge Foundation [grant number: R-1905-283549]. The Sponsor had no involvement in the design, analysis, interpretation and manuscript preparation, or the distribution of funds for research activities. We thank Lindsay Paige, James Sullivan, and Michael Gottfried as External Advisory Board Members for their guidance on our Cluster Randomized Controlled Trial research design. We also wish to thank the administrations of Florida Atlantic University, Broward College, and Palm Beach State College for supporting this campus-based research effort. References Ajzen, I.: The theory of planned behavior. OBHDP Process. 50 , 179–211 (1991) Allen, J., Farber, S.: How time-use and transportation barriers limit on-campus participation of university students. Travel Behav. Soc. 13 , 174–182 (2018) Baum, S., Johnson, M., Lee, V.: Understanding College Affordability: Room and Board. Urban Institute. (2018). https://collegeaffordability.urban.org/prices-and-expenses/room-and-board/ Accessed 16, December 2024 Bestplaces: (2024). https://www.bestplaces.net/cost_of_living/county/florida/palm_beach . Accessed 16, December 2024 Bopp, M., Bopp, C., Schucher, M.: Active Transportation to and on Campus is Associated With Objectively Measured Fitness Outcomes Among College Students. J. Phys. Act. Health. 12 (3), 418–423 (2015) Butler, L., Tan, Y., Paz, A.: Barriers and risks of Mobility-as-a-Service (MaaS) adoption in cities: A systematic review of the literature. Cities. 109 , 103036 (2021) Centers for Disease Control and Prevention (CDC): (2023). https://www.cdc.gov/physicalactivity/basics/adults/index.htm Accessed 8 January 2024 Clay, J.R., Valentine, J.L.: Impact of Transportation Supports on Students’ Academic Outcomes: A Quasi-Experimental Study of the U-Pass at Rio Hondo College. Faculty/Researcher Works. (2021). https://scholarshare.temple.edu/bitstream/handle/20.500.12613/6951/HopeCenter-Report-2021-09.pdf?sequence=1&isAllowed=y Accessed 16, December 2024 College Board: Trends in College Pricing. Trends in Higher Education Series. (2022). https://research.collegeboard.org/media/pdf/trends-college-pricing-presentation-2022 (2022). Accessed 16, December 2024 Consort: extension to cluster randomised trials BMJ; 345 (2012) (2010) statement Davis, V., Butler, T.L.: Inclusive Transportation: A Manifesto for Repairing Divided Communities. Island Press. Jul 13 , (2023) Dziuban, C., Shea, P.: Technology-enhanced education and millennial students in higher education. Metropolitan Universities, 18(3), 75–90. (2007). https://journals.iupui.edu/index.php/muj/article/view/20317 Accessed 16, December 2024 Frei, C., Mahmassani, H.S., Frei, A.: Making time count: Traveler activity engagement on urban transit. Transp. Res. Policy Pract. 76 , 58–70 (2015) General Transit Feed Specification: (n.d.). GTFS: Making Public Transit Data Universally Accessible. (2024). https://gtfs.org / Accessed 16, December 2024 González-Sánchez, G., Maeso-González, E., López, E., Aguiar, I.: Exploring determining factors of MaaS app use and its potential effects on mobility behavior: Keys to gender-sensitive planning and management. Transp. Policy. 158 , 175–195 (2024) Grimes, A., Baker, M.: The Effects of a Citywide Bike Share System on Active Transportation Among College Students: A Randomized Controlled Pilot Study, vol. 47, pp. 412–418. HE&B (2020). 3 Gripsrud, M., Hjorthol, R.: Working on the train: from ‘dead time’ to productive and vital time. Transp. 39 , 941–956 (2012) Ho, C.Q., Tirachini, A.: Mobility-as-a-Service and the role of multimodality in the sustainability of urban mobility in developing and developed countries. Transp. Policy. 145 , 161–176 (2024) Hoermann, S., Renne, J.L., Freeman, K., Merlin, L.A., Dzhurova, A., Lopez, P.: Peer Engagement: On Reflecting Student Diversity in a Research Trial. Int. J. Qual. Methods, 23 (2024) Houda, E.M., Ozkan, B., Turetken, O.: Acceptance of Mobility-as-a-Service: Insights from empirical studies on influential factors. COMMTR. 4 , 100119 (2024) Jakobsen, J.C., Gluud, C., Wetterslev, J., Winkel, P.: When and how should multiple imputation be used for handling missing data in randomised clinical trials – a practical guide with flowcharts. BMC Med. Res. Methodol. 17 , 162 (2017) Kelchen, R.A.: Look at College Students' Living Arrangements. Kelchen on Education [blog]. (2018). https://robertkelchen.com/2018/05/28/a-look-at-college-students-living-arrangements/#:~:text=About%2040%25%20of%20community%20college,when%20we%20went%20to%20college Accessed 16, December 2024 Mallari, E.F.I., Delariarte, C.F.: Academic Stress Mediates the Relationship Between Satisfaction with Travel and Psychological Well-being. N Am. J. Psychol. 23 (3), 397–414 (2021) Merlin, L.A., Freeman, K., Hoermann, S., Renne, J.: Clustered Randomized Controlled Trial Protocol of a Mobility-as-a-Service App for College Campuses. TRIP Adv. online publication. 14 , 1–13 (2022) Merlin, L.A., Simpson, D.A., Freeman, K., Hoermann, S., Renne, J.: Driver vehicle crashes and mental health challenges among commuter college students. J. Transp. Health. 40 , 1–9 (2025) Militao, A.M., Ho, C., Nelson, J.: Mobility-as-a-Service (MaaS) and the potential of multiservice, Conference Paper: The University of Sydney Business School, Institute of Transport and Logistics Studies (ITLS) December 6, (2024) Move Minnesota: https://www.movemn.org/NASCSP, National Association for State Community Services Programs: (). The Stranded Poor: Recognizing the Importance of Public Transportation for Low-Income Households. (2008). https://nascsp.org/wp-content/uploads/2018/02/issuebrief-benefitsofruralpublictransportation.pdf Accessed 16, December 2024 Nelnet: (2024). https://nelnetinc.com/capabilities/payment-technology/payment-processing/payment-processing-solutions/ Accessed 16, December 2024 Pei, A.: 5 Environmental Benefits of Sustainable Transportation. UCLA Transportation. (2021). https://transportation.ucla.edu/blog/5-environmental-benefits-sustainable-transportation#:~:text=Less%20Pollution%20and%20Clearer%20Skies,atmosphere%20and%20improving%20air%20quality Accessed 16, December 2024 Price, D.V., Curtis, D.: Overcoming Transportation Barriers to Improve Postsecondary Student Success. DVD-PRAXIS|Strategic Thinking for Action-Oriented Organizations. (2018). https://www.dvp-praxis.org/wp-content/uploads/2018/02/Kresge-Higher-Education-and-Transportation-Brief.pdf Accessed 16, December 2024 Pritchard, J.P., Geurs, K., Tomasiello, D.B., Slovic, A., Nardocci, A., Kumar, P., Giannotti, M., Hagen-Zanker, A.: Satisfaction with travel, ideal commuting, and accessibility to employment. JTLU. 14 (1), 995–1017 (2021) Ralph, K.M., Brown, A.E.: The role of habit and residential location in travel behavior change programs, a field experiment. Transp. 46 , 719–734 (2019) Remix: 8 Benefits of Public Transportation. (2021). https://www.remix.com/blog/8-benefits-of-public-transportation Accessed 16, December 2024 Rodriguez, D.A., Rogers, J.: Can housing and accessibility information influence residential location choice and travel behavior? An experimental study. Environ. Plann. B Plann. Des. 41 , 534–550 (2014) SF-36: (36-Item Short Form Survey) (1992). https://www.rand.org/health-care/surveys_tools/mos/36-item-short-form.html Accessed 15, October 2020 Sleight, M.: Average cost of car insurance in Florida in 2023. Bankrate. (2023). https://www.bankrate.com/insurance/car/average-cost-of-car-insurance-in-florida/ Accessed 16, December 2024 St-Louis, E., Manaugh, K., van Lierop, D., El-Geneidy, A.: The happy commuter: A comparison of commuter satisfaction across modes. Transp. Res. F: Traffic Psychol. Behav. 26 , 160–170 (2014) Toor, W., Havlick, S.: Transportation and Sustainable Campus Communities: Issues, Examples, Solutions. Island, Washington, D.C. (2004) Turner, J.C., Leno, E.V., Keller, A.: Causes of Mortality Among American College Students: A Pilot Study. J. Coll. Stud. Psychother. 27 (1), 31–42 (2013) US Census Bureau: Census Bureau Releases New Educational Attainment Data. (2022). https://www.census.gov/newsroom/press-releases/2022/educational-attainment.html Accessed 15 September 2022 US Dept. of Health and Human Services. Step It Up! The Surgeon General’s Call To Action To Promote Walking And Walkable Communities U.S. Department Of Health And Human Services. www.surgeongeneral.gov: Accessed 15 September 2022 (2015) US News and World Report: Campus Ethnic Diversity (2023). https://www.usnews.com/best-colleges/rankings/national-universities/campus-ethnic-diversity (2023). Accessed 16 December 2024 Walk Score: (2025). https://www.walkscore.com / Accessed 15 January 2025 Zhang, Y., Stopher, P., Halling, B.: Evaluation of south-Australia’s TravelSmart project: Changes in community’s attitudes to travel. Transp. Policy. 26 , 12–22 (2013) Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 07 Jan, 2026 Reviews received at journal 11 Nov, 2025 Reviewers agreed at journal 12 Oct, 2025 Reviews received at journal 28 Aug, 2025 Reviewers agreed at journal 03 Aug, 2025 Reviewers invited by journal 03 Aug, 2025 Editor assigned by journal 21 May, 2025 Submission checks completed at journal 10 May, 2025 First submitted to journal 06 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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1","display":"","copyAsset":false,"role":"figure","size":672651,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow Chart for Cluster Randomized Trial\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6605622/v1/74ce40b7869c208946809dc7.jpeg"},{"id":91827047,"identity":"d9278710-545f-4a6a-a6ec-a933250b610a","added_by":"auto","created_at":"2025-09-22 08:45:14","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":265120,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMTP App Usage: 30 Day Average Opens\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6605622/v1/4cb55b377a2f3057ccfcbc47.jpeg"},{"id":91830204,"identity":"41fa2fe8-a1a2-4e0d-bec5-0304c1a6ed73","added_by":"auto","created_at":"2025-09-22 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However, for many students, especially those from low-income households inclusive of families of color and first-generation college students, the path to obtaining a college degree can be fraught with obstacles which can result in educational disparities (US Census Bureau \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). One of the most significant barriers these students face is transportation, particularly those who commute to campus (Allen and Farber \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). According to the Urban Institute (Baum 2016), most students (64%) enrolled in public four-year colleges commute to college, thus making transportation to and from campus a notable concern. Among students enrolled in public four-year in-state colleges, the average cost for transportation was \u003cspan\u003e$\u003c/span\u003e1250 in 2021 (College Board \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Assuming travel costs are negligible for the 36% living on campus, a more accurate estimate for those living off campus is \u003cspan\u003e$\u003c/span\u003e1950 or roughly 20% of tuition costs (College Board \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). With 40% of commuter students from public colleges living with one or more parents to save money, transit choices are often limited (Kelchen \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The remaining students end up weighing travel costs with rental costs. Living in a rural county puts students who reside there at a further disadvantage, given that only 32% of all rural counties have full access to public transportation (NASCSP 2008). The catchment area for the University students in our study encompasses the eastern region of South Florida, which includes both suburban and rural areas. Also, the University is in a county in South Florida in which the cost of living (CoL) is 14.1% higher than the national average (BestPlaces 2025). Furthermore, the University is the most racially, culturally, and ethnically diverse public university in Florida (U.S. News \u0026amp; World Report 2023), and among its almost 25,000 undergraduate students, 54% are minority, 30% are first generation, 19% receive income-based Federal Pell Grants, 39% are designated as low-income, and 78% commute.\u003c/p\u003e\u003cp\u003eThus, costs associated with driving can be significant, often necessitating students work long hours to pay for the ability to drive. Using \u003cspan\u003e$\u003c/span\u003e1426 per year for transportation costs (extrapolated from a 14.1% CoL increase), then given that the annual in-state tuition is \u003cspan\u003e$\u003c/span\u003e5,952, transportation costs comprise a conservative 20% above the estimated tuition costs for the University. Thus, transportation expenses place a significant financial burden on college students which increases the risk that they may not graduate. Time spent working can impede educational performance and achievement. For both low-income students and students of color who may already be facing additional obstacles such as financial insecurity and cultural acclimation, transportation barriers can be particularly debilitating and further exacerbate disparities. For example, students who spend a significant portion of their limited income on car payments, gas, insurance, and vehicle maintenance may struggle to afford other necessities such as tuition, textbooks and healthcare. In addition to employment, many commuter students also juggle family responsibilities which can impact their education negatively, thus inhibiting longer-term career mobility (Clay and Valentine \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Many students in the catchment area have never taken public transit, and because the built environment does not support walking, bicycling, or transit use, they subscribe to an auto-centric culture. Moreover, infrequent transit services and a lack of network connectivity can make using buses or commuter rail nearly impossible when students have multiple destinations in a day, such as school, work and home. Thus, having to trip chain can be nearly impossible for those who rely on transit modes other than a car.\u003c/p\u003e\n\u003ch3\u003ePrior Randomized Controlled Trials (RCT) to Address College Student Travel Behaviors\u003c/h3\u003e\n\u003cp\u003eBecause randomized trials represent an optimal method for comparing interventions on human behavior, we reviewed all existing trials related to college students and transportation. We found that there were no RCTs that assessed digital interventions. Grimes and Baker (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) conducted an RCT involving college and graduate students who lived within 5-miles of an urban campus, in which 28 students (intervention group) were offered a free one-month membership to a bike share system, and 25 (control group) received no intervention. The outcome was change in steps and biking events recorded over one-month from activity trackers. No power analysis was performed, and there were no significant differences between groups. Ralph and Brown (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) conducted an RCT in an urban area involving incoming graduate students. The intervention included a transportation map of the campus and surrounding neighborhoods to facilitate apartment searches, and information on student biking, parking and transit passes; the control group received no information. Of the 3166 in the study, 810 attempted to complete the surveys (25.9%), and 561 (17.7%) had complete data for analysis. Investigators were unable to track randomization assignment, and relied on participant self-report. Outcomes evaluated at three months included modes of travel and distance to school, and choice of residential location. Results were inconclusive due to design limitations and missing data. The last of the three trials (Rodriguez and Rogers \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) involved two university towns in North Carolina (one more urban) that included all newly enrolled undergraduate transfer and graduate students. Participants were randomly assigned on a 1:1 basis to the intervention (bundled travel and residential information provided in May prior to selecting residence), and control group (no information provided). Both groups were contacted five months later to complete online surveys. Outcomes encompassed both travel behaviors and housing. A power analysis was performed indicating that 3200 students were to be recruited; however, only 273 responded; of these, 189 qualified (5.9%). Results indicated that at the more urban university only, students in the intervention group were more likely to move closer to campus, reside in areas with more transit stops, and reduce their daily vehicle mileage by at least 50% relative to controls.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eMultimodal Trip Planning Application to Further Travel Behavior Change\u003c/h2\u003e\u003cp\u003eWith college students more likely to embrace technology, having a phone app to help plan lower-cost transportation options could be helpful. In addition, it could help foster inclusive transportation among diverse lower-income college students by saving money and time (Dziuban and Shea \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Mobility as a Service, or MaaS, integrates existing mobility services into a smartphone app that allows travelers to search, book, use, and pay for all their transport needs (Ho and Tirachini \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Using existing trial data, Militao, Ho, and Nelson (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) demonstrated for the first time that a mix of transport modes bundled into a MaaS app can alter travel behavior by lowering the probability of choosing private cars, and by increasing the probability of choosing more energy efficient travel modes displayed within the MaaS app. Houda et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) identified several key factors in user acceptance of a MaaS app; these include being younger, facile with changing technologies, using ride-sourcing apps, traveling more frequently, residing in a dense population which often reflects greater efficiency of a transportation infrastructure, no prior car ownership, use of only one mode of transport for a single trip, cost of the app, compatibility with phone platform, and quality of app support.\u003c/p\u003e\u003cp\u003eBecause students are generally younger, travel more frequently to school and work, are more comfortable with technology, and may be more conscious of greener alternatives, we developed a multimodal trip planning (MTP) app as the intervention to evaluate as part of a cluster RCT to assess its acceptance and effectiveness relative to standard commuting practice. Notably, our MTP app did not include all the desirable features of a full-featured MaaS app, because it did not include mechanisms for booking and payment. However, the MTP app did deliver real-time travel options for transit, walking, biking, and ride-hailing including employing combinations of these aforementioned modes. The MTP app also included customization, such as the ability to remember favorite stops or routes. Also of note is that the features of our MTP app resembled similar features of the leading MaaS apps on the market, including the Transit app and Moovit apps. During the time of the study, the leading MaaS apps had the capability to provide booking and payment options but rarely did so due to the complexity of agreements needed with transit agencies.\u003c/p\u003e\u003cp\u003eThe concept behind the intervention is to leverage information and marketing to shift students\u0026rsquo; intentions regarding their travel to campus, following Azjen\u0026rsquo;s Theory of Planned Behavior (1991). We recognize that some generic travel/mapping apps are preinstalled on smartphones, and thus readily accessible to students. However, we introduce an innovative MTP application that is tailored to students enrolled in the University\u0026rsquo;s campuses. The MTP provides users with information about multimodal travel options from origin to destination, user-friendly mapping interfaces, travel time and user cost comparisons across multiple options, and information about the exact location of transit vehicles in real time utilizing a general transit feed specification (GTFS). For college students looking to save money on travel expenses, our MTP app could provide them the ability to replace some or all their trips with lower-cost transit options, potentially saving them hundreds of dollars a year and possibly freeing up time from the need to work to pay for a vehicle (Butler et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Having an app tailored to the South Florida environment is particularly important given the significant challenges students face in the region such as expensive housing, land uses that do not readily support public transit, bicycling, or walking, considerable traffic congestion, and high auto expenses that include car insurance rates among the highest in the nation (Sleight \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Also, South Florida typically has the highest gasoline prices in the state. Using forms of transit other than cars has the potential for students to study or engage in social connections, instead of concentrating on driving (Gripsrud and Hjorthol \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Therefore, providing information on alternatives to driving streamlines decision-making, and may benefit students\u0026rsquo; educational achievement and health, as well as the environment.\u003c/p\u003e\u003cp\u003eTo summarize, our primary research question concerns whether the provision of travel information through a MTP app can shift travel behavior among students who commute regularly to campus. Other travel behavior interventions such as free transit passes or improved transit services, should also be part of a broader campus transit promotion strategy (Toor and Havlick \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). This study builds on previous work, known as TravelSmart, that sought to inform individuals about nondriving travel options and demonstrated that information contributed to attitude change regarding willingness to use public transit; it is of note that TravelSmart was conducted as a program before the use of smartphones (Zhang et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePotential Outcomes\u003c/h3\u003e\n\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eAcademic Benefits\u003c/span\u003e: Transportation services can be an important contributor (or obstacle) to the academic success of college students. The provision of free metro cards at City College of New York contributed to higher graduation rates -- three times that of the national average, thus lowering educational costs and expediting entry into the workforce (Move Minnesota 2021). Overcoming such transportation-related barriers such as cost, inadequate knowledge about alternatives to personal vehicles, inconveniently located stops or stations, schedules incompatible with need, and unreliable transit may provide needed support for some college students to finish their degrees (Price and Curtis \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eHealth Benefits\u003c/span\u003e: Public transportation benefits students\u0026rsquo; health because transit riders must walk to and from their transit stop, likely achieving the 30 minutes of daily physical activity five times per week recommended by the US Department of Health and Human Services (2015). Those engaged in active travel to campus may experience even greater benefits, including more optimal mental health (St-Louis et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Also, with reduced automobile use, people are exposed to fewer triggers for respiratory ailments (Remix \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Additionally, public transportation facilitates more equitable access to food markets, healthcare services and social networks, all of which impact public health (Remix \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In a survey of students living off campus at a northeastern university, those using more active means of travel had significantly greater cardiovascular fitness, were more flexible and had lower systolic blood pressure than nonactive travelers (Bopp et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Finally, because vehicular accidents are the leading cause of death among college students, with a fatality rate of 6.88 per 100,000 (Turner et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), the use of public transportation can help reduce mortality and morbidity rates.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eOther Benefits\u003c/span\u003e: Commuting by means other than driving an automobile has many other benefits. Regarding the environment, decreasing the number of cars on the road improves air quality. Choosing alternatives to driving helps ease road congestion which results in fewer road repairs and lower noise levels (Pei \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Using public transit yields economic gains for individuals and communities and provides time to relax or be more productive during the commute (Frei et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Remix \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Regarding universities\u0026rsquo; infrastructures, building parking lots is not only costly but may encourage students to drive. Large parking lots built on many commuter campuses are substantial barriers for walkable campus land-use patterns. Finally, active commutes have been repeatedly shown to improve commuter satisfaction relative to driving. (Pritchard et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; St-Louis et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003e\u003cstrong\u003eStudy Framework\u003c/strong\u003e\u003cp\u003eThe full description of the protocol including study design, participants with inclusion/exclusion criteria, settings, randomization, and definitions of Mobility-as-a-Service smartphone App, intervention and control groups, outcomes and statistical methods has been published previously, and thus we provide a brief summary here (Merlin et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The protocol and amendments were approved by the University\u0026rsquo;s Institutional Review Board.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eObjectives\u003c/strong\u003e\u003cp\u003eThe primary research question is whether the introduction of a multimodal travel planning app can result in change in travel behavior relative to a control group. The primary outcomes are change in students\u0026rsquo; travel behavior away from personal vehicles and towards alternative modes that include public transit, biking, and walking. These outcomes could serve as mediating factors for improvements in the environment, academic performance and self-perceived physical and mental health. Our broad research objective is to investigate how to best promote alternative mode usage among college students to alleviate the financial burden of transportation, and improve health and academic performance by reducing stress and time associated with car ownership and driving.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eSetting and Population\u003c/strong\u003e\u003cp\u003eThe University, with an enrollment of over 25,000, has the most diverse student body of all public universities in Florida, and ranks nationally as one of the most ethnically diverse universities. It serves a predominantly local population with a high share of Pell Grant recipients, and is a Hispanic-Serving Institution (HSI) designated by the U.S. Department of Education as having at least 25 percent Hispanic undergraduates. In this suburban setting, three separate campuses were included in the original study design which were moderately or poorly served by transit that included Palm Tran (bus), Broward County Transit (bus), and Tri-Rail. Most of the rail and bus services run on hourly schedules. Train stations are located beyond walking distance from the campuses, making the last-mile connection a significant barrier, though there are bus services connecting each commuter rail station to each campus. As a result, students who utilize transit often experience lengthy travel and wait times, inflexible scheduling and sometimes unreliable service. Eligible students included undergraduates who reside off campus and intended to continue their education at the same institution through the fall of that year, and provided informed consent.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eDesign\u003c/em\u003e: The study was designed as a two-arm cluster randomized controlled trial (cRCT) in which participants were assigned to either the MTP app (intervention) or the control group. The participant\u0026rsquo;s residence was considered the clustering unit which was randomized 1:1 to intervention and control groups; incorporating clustering into the design accounts for possible contamination across students who live in the same household who are expected to have more similar patterns of commuting. Participants who lived at unique addresses were randomized as a unique \u0026lsquo;cluster\u0026rsquo;. Randomization was stratified by campus to account for differences in transit resources and population density, and by whether the student was enrolled full or part-time. Completion of two questionnaires (baseline and at the end of the corresponding semester) in Qualtrics was requested as part of the informed consent process. Outcomes included changes in commuting to campus via driving alone, carpooling, ride-share, public transit and active transit; semester grade point average and credits completed; a subset of the SF-36 quality of life instrument to assess overall physical and mental health. To augment existing strategies for recruitment and retention, we selected and trained peer \u0026ldquo;Student Ambassadors\u0026rdquo; who mirrored the demographics of the student population; these peers were assembled at central locations on the campuses to engage other students to participate in the study (Hoermann et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eIntervention and Control Groups\u003c/strong\u003e\u003cp\u003eStudents randomized to the intervention received the MTP app which was designed to facilitate students\u0026rsquo; use of alternative transportation modes for each leg of their journey. Participants in the control group had not received the resources provided to the intervention group but have received the same contact from investigators requesting completion of baseline and follow-up forms. The MTP app includes real-time information about public transit and routing services, costs, wait times; more specifically it includes the location of nearby stops, expected travel times, transit fare costs, and various details of recommended routes, including boarding locations, routes, transfers, wait times, alighting locations, and recommended walk routes. The intervention is somewhat similar to previous studies of commuter students in that we focused on the provision of information to alter student travel behavior and other outcomes; however, we deliver information on transit through the MTP app and electronic delivery of campus and housing maps rather than through physical materials.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eThe app differs from existing routing apps such as Google maps in several respects: 1) the app is branded in association with students\u0026rsquo; colleges to create an affiliation with the app, and 2) the app provides personalization options for students to match their travel patterns.\u003c/p\u003e\u003cp\u003e\u003cem\u003eRandomization\u003c/em\u003e: A list of random numbers from 0 to 1.0 was generated using the R statistical package (rand() procedure) and merged with the weekly list of participants newly enrolled in the trial by stratum (campus, enrollment status: full or part-time). When each batch of survey data came in (weekly), the participants\u0026rsquo; data were first matched by address with any previously enrolled participants who already had an assignment. If a new participant came in with the same address as one in a prior batch, they were given the same assignment which defined their \u0026lsquo;cluster\u0026rsquo;. Otherwise, within each stratum, participants were sorted by random number with the top 50% (random number\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;.5) being assigned to intervention and the bottom 50% (random number\u0026thinsp;\u0026lt;\u0026thinsp;.5) assigned to the control group.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eRetention\u003c/strong\u003e\u003cp\u003eAfter contacting students through various forms of media and student ambassadors, and obtaining one or more email addresses from each student, all interested students were contacted by email first. To obtain both baseline and follow-up information, we contacted participants initially by email, and if they did not respond, then subsequently by text or by phone if they provided their phone number at baseline. We contacted participants up to three times for each survey instrument if they had not responded to a prior contact.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eSubsequent Changes in Design\u003c/strong\u003e\u003cp\u003e After the baseline data were collected, two changes in the design were implemented upon reviewing both the frequencies of participants across campuses and the frequencies of participants living in the same residence (cluster). First, we observed too few participants from campuses other than the University\u0026rsquo;s Boca Raton campus to yield reliable results and preserve the confidentiality of student data; thus, we limited analyses to data from participants from the Boca Raton campus. Second, we observed only two students living in the same residence that formed a cluster; neither student had follow up data and thus, the data were analyzed as per an RCT rather than a cRCT.\u003c/p\u003e\u003c/p\u003e\n\u003ch3\u003e:Statistical Methods:\u003c/h3\u003e\n\u003cp\u003eBaseline descriptive statistics included data for the 1 cluster; to avoid complexity in interpretation, each student in the pair that comprised the cluster was considered independent. Because there were no clusters for the statistical analyses of longitudinal data, analyses were performed without consideration of clustering. For continuous variables, descriptive statistics are presented as means and standard deviations for normally distributed data, and medians and ranges otherwise. For categorical or short scale ordinal variables, relative frequencies are presented. For each analysis, descriptive statistics are tabulated by group (intervention and control) and for the total.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eAnalysis I (Database Integrity)\u003c/span\u003e included examining the distributions of participants across campuses, within Cluster IDs, by group assignment, and checking whether all participants met inclusion/exclusion criteria; additionally, the date the first participant enrolled was recorded.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eAnalysis II\u003c/span\u003e (Baseline Comparison of Treatment and Control Groups) was performed to assess whether the randomization resulted in reasonably balanced groups for known baseline characteristics; it involved determining the significance of differences between groups for all enrollees (those randomized). For categorical variables, the significance of differences between groups were tested using chi-square tests if assumptions were met or otherwise Exact tests, or using Mantel-Haenszel chi-square for short-scale ordinal variables. Differences between groups for continuous normally distributed variables were tested with t-tests, or otherwise, Wilcoxon Rank Sum tests as appropriate.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eAnalysis III\u003c/span\u003e (Comparison of Participants at Baseline Between Those Without Follow-up and Those With Follow-up Data) was performed to assess whether results from the final sample that included participants with both baseline and follow-up data were generalizable to the sample of participants randomized. The analysis involved assessing the significance of differences in baseline characteristics between the subset of participants with baseline data only and the complement subset with both baseline and follow-up data. Tests of statistical significance were similar to those described for Analysis II above. Also, we noted the date at which the last participant completed the follow-up survey.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eAnalysis IV\u003c/span\u003e (Comparison Between Intervention and Control Groups Regarding Baseline Characteristics, and Travel Behavior, Academic Achievement, and Mental Health Outcomes) involved data from the subset of participants with both baseline and follow-up data (final sample) to compare the groups regarding specified characteristics at baseline, and to compare the groups regarding outcomes. Bivariate tests of the significance of differences between the groups are described in Analysis II. For each outcome, those bivariate associations resulting in p-values\u0026thinsp;\u0026lt;\u0026thinsp;.20 identified independent variables that were potential candidates for the initial mixed effects model to assess longitudinal differences accounting for selected baseline characteristics. Iterative models were derived in which the independent variable with the largest p-value\u0026thinsp;\u0026gt;\u0026thinsp;.05 was deleted until the final model included only significant independent variables. Missing data were not imputed due to a large proportion of missing data (Jakobsen et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), most of which occurred at follow up.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eAnalysis V\u003c/span\u003e (Post-hoc Analysis to Identify Who Opened the App) was conducted using data from all participants randomized to the intervention group to identify baseline variables associated with whether or not the participant opened the app. (Note: data for those who opened the app were provided by the app developer). Bivariate analyses were performed using Wilcoxon Rank Sum tests for continuous variables, and chi-square, Exact or Mantel-Haenszel chi-square for categorical or short-scale ordinal variables as appropriate using all demographic and travel behavior variables at baseline. For those baseline characteristics yielding p-values\u0026thinsp;\u0026lt;\u0026thinsp;.20, an initial logistic regression model was constructed. A monitored iterative procedure was performed in which the predictor variable with the greatest non-significant p-value was eliminated, and the model was rerun until all remaining predictor variables were significant. Odds ratios and 95% confidence intervals were derived.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e\u003cp\u003eanalyses II through V were performed using data from the Boca Raton campus only. The trial was registered with Clinicaltrials.gov NCT04720300. The manuscript is reported according to the Consolidated Standards of Reporting Trials (CONSORT) 2010 statement: extension to cluster randomized trials, which provides a checklist and flow diagram to standardize reporting of the design, conduct, analysis, results and interpretation of findings from the trial. Adherence to CONSORT helps insure completeness of reporting and transparency. All tests of hypotheses were two-tailed and conducted at α\u0026thinsp;=\u0026thinsp;.05 (SAS Version 9.4, Cary, NC).\u003c/p\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 1,228 students were screened (see Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Screening of the first student occurred on January 11, 2022, with the last baseline questionnaire completed on March 13, 2022. Completion of the follow-up questionnaire began on April 25, 2022 and the last was completed on July 7, 2022. A total of 427 participants met all inclusion criteria, had sufficient data and were enrolled as participants in the cRCT, with 214 randomized to the intervention (MaaS) and 213 randomized to the control groups. All participants indicated they lived off campus, had a smartphone capable of running apps, planned to study at their current institution through the summer of 2022 and were enrolled at the University as either full or part-time students. Only two students lived at the same address and comprised one cluster. Among the 427 participants randomized, descriptive statistics for baseline characteristics by group and for the total are shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. In addition, Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e provides the significance of differences between randomization groups at baseline (Analysis II), as well as the significance of differences in baseline characteristics between those with and without follow-up data (Analysis III). Regarding differences at baseline between groups, differences for two of 33 variables in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e were significant (p\u0026thinsp;\u0026lt;\u0026thinsp;.05): relatively more first-generation participants were randomized to the App group (p\u0026thinsp;=\u0026thinsp;.0133), and relatively more with iPhones to the control group (p\u0026thinsp;=\u0026thinsp;.0338).\u003c/p\u003e\n\u003cp\u003eTable 1. Baseline Characteristics of Participants Randomized by Group with Bivariate Analysis Results\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"666\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eINTERVENTION\u003c/p\u003e\n \u003cp\u003e(n=214)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eCONTROL\u003c/p\u003e\n \u003cp\u003e(n=213)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eTOTAL\u003c/p\u003e\n \u003cp\u003e(N=427)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eP-value*\u003c/p\u003e\n \u003cp\u003eBL Differences\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eII\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eP-value**\u003c/p\u003e\n \u003cp\u003eFU vs. BL\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eIII\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003eCOLLEGE LOAD (Full-Time)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e71.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e70.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e70.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.8906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0141\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003eRACE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.3478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.4948\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003e\u0026nbsp; Asian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e5.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e10.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e8.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003e\u0026nbsp; Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e31.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e28.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e30.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003e\u0026nbsp; Hawaiian/Pacific Islander\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003e\u0026nbsp; Multiracial\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e5.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003e\u0026nbsp; Native American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003e\u0026nbsp; White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e53.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e55.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e54.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003eETHNICITY (Hispanic or Latinx)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e32.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e25.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e28.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.1020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0640\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003eGENDER (Female)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e62.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e67.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e65.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.3231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.5095\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003eRECEIVED PELL GRANT (Yes)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e5.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.3647\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.2202\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003eFIRST GENERATION (Yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e40.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e28.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e34.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.0133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.8533\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003eCAMPUS MOST FREQUENTED (BOCA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e92.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e96.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e94.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.0844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.2210\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003eSMARTPHONE OPERATING SYSTEM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.0338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0068\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003e\u0026nbsp; IPhone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e71.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e82.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e77.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003e\u0026nbsp; Android\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e27.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e17.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e22.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003e\u0026nbsp; Other Phone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003eHEAR ABOUT STUDY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.9534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.9332\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003e\u0026nbsp; Ambassadors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003e\u0026nbsp; Other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e40.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e45.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e43.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003e\u0026nbsp; Poster\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e17.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e14.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e16.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003e\u0026nbsp; Social Media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003e\u0026nbsp; Website\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e32.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e27.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e29.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003e\u0026nbsp; Word of Mouth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003eLICENSED DRIVER (Yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e86.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e90.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e88.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.2955\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.1844\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003eACCESS TO AUTO MOST DAYS (Yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e85.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e85.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e85.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.9522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.9908\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003eCOVER ALL AUTO COSTS (Yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e51.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e51.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e51.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.5822\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.6681\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003eDO YOU WORK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.4425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.2447\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Yes: \u003cu\u003e\u0026gt;\u003c/u\u003e30 hours/week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e26.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e24.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e25.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Yes: \u0026lt;30 hours/week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e46.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e55.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e51.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e26.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e19.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e23.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003eWORK ON CAMPUS (Yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e20.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e23.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e22.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.6185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003ePARENT/GUARDIAN OF CHILDREN (Yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.8011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.3673\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003eADULT CAREGIVER (Yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e18.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e22.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e20.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.4134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.3348\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003eRESIDE WITH PARENT/GUARDIAN (Yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e60.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e60.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e60.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.9523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.6392\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003eNEVER REGULARLY COMMUTED TO CAMPUS (Yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e7.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.2062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.1081\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003eACCIDENT DRIVING IN LAST 4 MONTHS (Yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e8.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.5795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.3396\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003eACCIDENT AS PASSENGER IN AUTO IN LAST 4 MONTHS (Yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.8900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.7672\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003eACCIDENT RIDING IN BUS/TRAIN IN LAST 4 MONTHS (Yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.9260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.5830\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003eACCIDENT RIDING BICYCLE IN LAST 4 MONTHS (Yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.2849\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.4924\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003eACCIDENT WHILE WALKING IN LAST 4 MONTHS (Yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.2833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.4935\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003eDISTANCE LEARNING (\u003cu\u003e\u0026gt;\u003c/u\u003e1 CREDIT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e72.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e58.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e65.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.3316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0364\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003eDRIVE DISTANCE (\u003cu\u003e\u0026gt;\u003c/u\u003e30000 METERS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e46.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e50.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e48.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.2997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.1844\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003eDRIVE TIME (\u003cu\u003e\u0026gt;\u003c/u\u003e1800 SECONDS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e38.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e43.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e41.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.3043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.1577\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003eBICYCLE DISTANCE (\u003cu\u003e\u0026gt;\u003c/u\u003e30000 \u0026nbsp;METERS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e48.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e52.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e50.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.2636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.3864\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003eBICYCLE TIME (\u003cu\u003e\u0026gt;\u003c/u\u003e6000 SECONDS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e45.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e49.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e47.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.2386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.3818\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003eWALK DISTANCE (\u003cu\u003e\u0026gt;\u003c/u\u003e30000 \u0026nbsp;METERS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e40.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e46.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e43.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.1982\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.3581\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003eWALK TIME (\u003cu\u003e\u0026gt;\u003c/u\u003e1800 SECONDS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e52.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e58.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e55.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.1988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.3586\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003eTRANSIT DISTANCE (\u003cu\u003e\u0026gt;\u003c/u\u003e30000 \u0026nbsp;METERS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e55.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e60.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e58.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.1756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.4128\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003eTRANSIT TIME (\u003cu\u003e\u0026gt;\u003c/u\u003e6000 SECONDS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e50.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e56.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e53.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.5573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0827\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003eCREDIT HOURS (\u003cu\u003e\u0026gt;\u003c/u\u003e 12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e58.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e55.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e56.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.9108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.1646\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003eGPA (\u0026gt;=3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e40.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e46.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e43.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.2472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0667\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eBL=Baseline\u003c/p\u003e\n\u003cp\u003eFU=Follow-up\u003c/p\u003e\n\u003cp\u003e*P-value for the difference in baseline characteristics between the intervention and control groups (Analysis II).\u003c/p\u003e\n\u003cp\u003e**P-value for the difference in baseline characteristics between those with baseline data and no follow up data and the complement with both baseline and followup data (Analysis III).\u003c/p\u003e\n\u003cp\u003e***Refers to cisgender pants Randomized by Group with Bivariate Analysis Results\u003c/p\u003e\n\u003cp\u003eParticipants enrolled were categorized according to whether they had both baseline and follow-up data vs. baseline data without follow-up to evaluate possible limitations in generalizability. Significance levels of differences between these subsets are shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e within the column for Analysis III. The subset with both baseline and follow-up data differed significantly from its complement regarding college load (part-time participants more often completed both surveys; p\u0026thinsp;=\u0026thinsp;.0141), smartphone operating system (those with Android more likely to complete both surveys; p\u0026thinsp;=\u0026thinsp;.0068), whether they worked on campus (those working on campus were more likely to complete both surveys; p\u0026thinsp;=\u0026thinsp;.0003), and whether they had taken more credits in distance learning (those with fewer credits in distance learning were more likely to complete both surveys; p\u0026thinsp;=\u0026thinsp;.0364). Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e provides descriptive statistics for baseline characteristics for participants with both baseline and follow-up data.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBaseline Characteristics of Participants With Data From Both Baseline and Followup Assessments by Group\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eINTERVENTION\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;38)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCONTROL\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;68)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTOTAL\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;106)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value*\u003c/p\u003e\n \u003cp\u003eBL Differences\u003c/p\u003e\n \u003cp\u003eIV\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCOLLEGE LOAD (Full-Time)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRACE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4690\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHawaiian/Pacific Islander\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMultiracial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNative American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eETHNICITY (Hispanic or Latinx)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGENDER (Female)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1665\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRECEIVED PELL GRANT (Yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2588\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFIRST GENERATION (Yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCAMPUS MOST FREQUENTED (BOCA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4538\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSMARTPHONE OPERATING SYSTEM (IPhone)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHEAR ABOUT STUDY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8635\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAmbassadors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePoster\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSocial Media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWebsite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWord of Mouth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLICENSED DRIVER (Yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2118\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eACCESS TO AUTO MOST DAYS (Yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7402\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCOVER ALL AUTO COSTS (Yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1583\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDO YOU WORK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes: \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e30 hours/week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes: \u0026lt;30 hours/week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWORK ON CAMPUS (Yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5401\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePARENT/GUARDIAN OF CHILDREN (Yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1975\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eADULT CAREGIVER (Yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3492\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRESIDE WITH PARENT/GUARDIAN (Yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1848\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNEVER REGULARLY COMMUTED TO CAMPUS (Yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0427\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eACCIDENT DRIVING IN LAST 4 MONTHS (Yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1653\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eACCIDENT AS PASSENGER IN AUTO IN LAST 4 MONTHS (Yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eACCIDENT RIDING IN BUS/TRAIN IN LAST 4 MONTHS (Yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eACCIDENT RIDING BICYCLE IN LAST 4 MONTHS (Yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eACCIDENT WHILE WALKING IN LAST 4 MONTHS (Yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDISTANCE LEARNING (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;1 CREDIT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0480\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDRIVE DISTANCE (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;30000 METERS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6858\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDRIVE TIME (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;1800 SECONDS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.9463\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBICYCLE DISTANCE (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;30000 METERS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4386\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBICYCLE TIME (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;6000 SECONDS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3562\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWALK DISTANCE (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;30000 METERS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6858\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWALK TIME (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;1800 SECONDS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5538\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTRANSIT DISTANCE (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;30000 METERS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8638\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTRANSIT TIME (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;6000 SECONDS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0188\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCREDIT HOURS (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7076\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGPA (\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6582\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eBL\u0026thinsp;=\u0026thinsp;Baseline\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e*Among those with both baseline and followup data, comparison between assigned groups for baseline characteristics (Analysis IV).\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eRegarding changes in outcomes from baseline to follow-up (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e), the only outcome that was significantly different between the groups was the number of days participants felt worried, tense or anxious. For those in the App group, the average decreased whereas in the control group the average increased (p\u0026thinsp;=\u0026thinsp;.0420). Although not significant (p\u0026thinsp;=\u0026thinsp;.0560), it should be noted that the change in the number of minutes to travel to campus increased in both groups, with a minimal mean change in the App group vs. a larger one in the control group.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDifferences between Intervention and Control Groups Regarding Changes in Outcomes from Baseline to Followup (Bivariate)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOutcomes: transportation, health, academic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eINTERVENTION\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;38)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCONTROL\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;68)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTOTAL\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;106)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value*\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSTD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSTD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSTD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTYPICAL WEEK CAMPUS COMMUTE:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDrive alone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3958\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCarpool\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4758\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRide-hailing (Uber or Lyft)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6541\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTransit (bus/train)\u0026thinsp;+\u0026thinsp;walk, bike,\u003c/p\u003e\n \u003cp\u003edrive, etc.to/from station\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1223\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWalk, bike, skate only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.9025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBike/electric scooter share only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4826\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e# MINUTES USUAL TRAVEL TO CAMPUS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0560\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFREQUENCY TAKE TRANSIT TO CAMPUS/SHOP/COMMUTE/PLAY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.9150\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHEALTH WITHIN PAST 30 DAYS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWhat is your general health now\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.9442\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e# days your physical health was not good\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6259\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e# days your mental health was not good\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7796\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e# days pain impaired usual activities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5332\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e# days felt sad, blue, or depressed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8934\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e# days felt worried, tense, or anxious\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0420\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e# days felt not enough rest/sleep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6928\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e# days felt very healthy and full of energy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7957\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e# days poor mental/physical health\u003c/p\u003e\n \u003cp\u003eimpaired usual activities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8712\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8272\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e*P-value for differences in outcomes between groups regarding changes from baseline to follow-up (Analysis IV).\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eFeasibility\u003c/em\u003e: A total of 50 of the 214 participants randomized to the App group opened the App (23.4%). For Analysis V, bivariate and multivariate analyses are presented in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e to identify factors associated with opening the App. Among the 33 baseline variables in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, associations with opening the App yielded bivariate p-values less than .20 for 14 variables; these comprised the set of independent variables in the initial multiple logistic regression model. In the final model, three variables were significant, indicating that those who opened the App: a) were less likely to have ever taken transit (p\u0026thinsp;=\u0026thinsp;.0014), b) traveled for longer periods of time (p\u0026thinsp;=\u0026thinsp;.0076), and c) were less likely to be responsible for caring for an adult (p\u0026thinsp;=\u0026thinsp;.0083).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eFactors associated with opening the App among those randomized to the Intervention group (Bivariate \u0026amp; Multivariate Models: Analysis IV).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBASELINE VARIABLE LABELS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBIVARIATE\u003c/p\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFINAL MULTIVARIATE MODEL P-value\u0026thinsp;\u0026lt;\u0026thinsp;.05\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOdds Ratio\u003c/p\u003e\n \u003cp\u003e[95% Confidence Limits]\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHow often take transit, such as bus/train (TOTAL) (to campus/shopping/commuting/leisure, etc.)?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.659 [0.514, 0.845]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDuring past 30 days, for about # days have you felt you did not get enough rest or sleep?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e--\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e--\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCommuting to CAMPUS on typical week:Drive alone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e--\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e--\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCommuting to CAMPUS typical week:Use Ride-hailing(Uber or Lyft)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0727\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e--\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e--\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCommuting to CAMPUS typical week:Use transit(bus/train)\u0026thinsp;+\u0026thinsp;walk, bike, drive, etc.to/from transit station\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0654\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e--\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e--\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of minutes does your trip to campus usually take?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e--\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e--\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDriving commute in meters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e--\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e--\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDriving commute in seconds\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0406\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.001 [1.000, 1.001]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDuring past four MONTHS Have you been in a crash or accident While driving? Y/N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e--\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e--\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOn most days, do you have access to a motor vehicle you can use to campus?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e--\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e--\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDo you cover the costs of a motor vehicle?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e--\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e--\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAre you responsible for care/needs of an adult? (i.e., health care/shopping/errands/other routine activities)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.803 [1.304, 6.023]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDuring past four MONTHS Have you been in a crash or accident Yes - While driving?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e--\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e--\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDuring past four MONTHS Have you been in a crash or accident Yes - While a passenger in a car?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e--\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e--\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\u003csup\u003e*\u003c/sup\u003e Full multivariate model not shown due to multicolinearity.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\u003csup\u003e**\u003c/sup\u003e Variable \u0026lsquo;Driving Commute\u0026rsquo; appeared to have an outlier (210 minutes); sensitivity analysis was performed\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003edeleting this observation and the resulting P-value was .0078.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e describes aggregate data on frequency of app openings with a 30-day lagging average from January 11, 2022 (when the first student completed the baseline survey) until July 7, 2022 (when the last student completed the follow-up survey). App usage appeared to peak in early February 2022 and declined through the end of May approaching the end of the survey period.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis is the first randomized controlled trial of undergraduate college students to evaluate a digital intervention designed to change travel behaviors. It is also the first cRCT to assess the additional outcomes of changes in physical and mental health indicators and academic performance. The cRCT design provided a University-specific App to the Intervention group which promotes travel alternatives to campus other than driving. We found that participants randomly assigned to the App group had on average significantly fewer days in which they felt worried, tense or anxious. This may contrast with results from a prior study which found that college students trying new modes of travel tended to have more anxiety and negative experiences compared with those whose travel is familiar (Mallari and Delariarte \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). One contributing factor to anxiety and negative experiences during unfamiliar travel may be the fear of arriving late to class or work. Therefore, methods to reduce anxiety such as wayfinding and information about schedules may reduce anxiety and increase the potential for transit usage among college students. Generally, if MaaS App information can reduce the fear associated with first-time transit usage, the technology could help students overcome barriers in trying new transit modes and using it on a regular basis. Another potential fear of students in the post-Pandemic era may involve preference for contactless and cashless payments (Nelnet 2023). Unfortunately, transit systems have been notoriously slow to adopt cashless payment systems. It should be noted that the App used in our study did not have a mobile payment option; such an option could help improve the effectiveness of the App by reducing fear about how to pay for travel.\u003c/p\u003e\u003cp\u003eGonzales-Sanchez et al. (2024) report that technophilia, perceived usefulness and perceived ease of use are determining factors in MaaS adoption, along with inclusion of a gender perspective in the design\u0026mdash;our MTP app had input from numerous male and female stakeholders. There were no significant differences between groups regarding changes in travel behaviors or academic performance, albeit the sample size was low and the period of the study was one semester only. The study did find that the number of minutes commuting to campus increased in both groups, with greater increases on average for the control group. In the spring of 2022 while the study was conducted, traffic began increasing as society returned to a post-COVID new normal and notable numbers of people moved to South Florida from other states, possibly explaining the increases in average travel times. The App group had a smaller increase in average number of minutes to travel to campus\u0026ndash;an encouraging sign that MTP may have prompted a more thoughtful approach to travel that considered trip planning and avoidance of non-essential excursions.\u003c/p\u003e\u003cp\u003eBecause of the COVID-19 pandemic, recruitment efforts were delayed until 2022 to allow students to return to traditional in-class education. On-campus attempts to recruit a representative population of students who lived off campus included \u0026ldquo;Student Ambassadors\u0026rdquo; and \u0026ldquo;micro-influencers\u0026rdquo;; however, this creative approach may not have reached those students who elected remote or hybrid learning, which may have limited their participation in the initial (baseline) survey. Also, adoption of the intervention was a challenge, with less than 25% of those randomized to the App group opening the App. It is of note that those who opened the App were less likely to have taken transit and their trips to campus were longer in terms of time. This could indicate that the App provided a resource to students looking for new travel options, especially among those burdened with long commutes. That is, the app appeared to be most attractive among students who were looking for alternatives to their current commute. One benefit of using an app and taking transit to campus may reduce the likelihood of students being in a crash while driving, which we found to be a frequent occurrence among our survey participants, as reported in an ancillary study (Merlin et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eUnfortunately, transit services in Palm Beach and Broward Counties are not as extensive compared to those in cities with more robust and frequent services that encompass broader hours. Additionally, micromobility and bicycle options are not well supported in the community. Thus, given the suburban/rural environments we would not expect as many student commuters in South Florida to switch from driving to transit compared with students in cities and regions with more accessible transit systems.\u003c/p\u003e\u003cp\u003eAlthough the state university campuses originally recruited for the study showed great interest in the topic, the campus transportation departments in these schools were mostly focused on managing on-campus parking and they did not have robust strategic campus planning teams which could support the study and promote alternative modes beyond operating some shuttle buses. For example, early in the project we held meetings with key stakeholders, including representatives from the campuses and transit providers. Despite seeking to incorporate staff from the campus transportation departments, the team was not successful in getting representation to attend the meetings. Therefore, the meetings consisted mainly of researchers, academic staff, and representatives from transit agencies. Despite good intentions, the research and academic staff did not have the power to implement campus-wide travel demand management plans. An MTP or MaaS app solution should be a component of a larger multifocal transportation strategy; perhaps a technology-only solution for students enmeshed with a more costly buy-in from universities may be more optimal.\u003c/p\u003e\u003cp\u003ePrior RCT\u0026rsquo;s present several limitations that motivated design features in our current study. In one study, randomization was compromised due to the inability to validate random assignment (Ralph and Brown \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and in the other, groups had different follow-up times which increases the risk of differential drop-out rates between groups (Rodriguez and Rogers \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and decreases the validity of the comparison due to differences in environmental exposure. Furthermore, none of the three RCT\u0026rsquo;s considered the non-independence of travel behaviors among participants living in the same household, and thus, our group assignment was designed to randomize clusters of participants\u0026rsquo; residences. Our methodology involved conducting a power analysis, and multiple attempts at student follow-up to reach the targeted sample size. Also, the observation period in our study was framed around the semester which was longer than those in prior RCTs. To assess potential bias, as part of our design we checked whether the resulting sample was representative of the University population, that participants in the two groups were comparable regarding baseline characteristics, and that results of follow-up assessments were generalizable.\u003c/p\u003e\n\u003ch3\u003eLimitations\u003c/h3\u003e\n\u003cp\u003eIn assessing the feasibility and effectiveness of the trial, we identified several limitations. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eFirst\u003c/span\u003e, the study was conducted in the winter/spring of 2022 during which time many classes were still hybrid with remote learning. During this time, ridership on public transportation had decreased and the motivation of students to commute to campus may not reflect pre-COVID travel, which may have affected motivation to use the MTP app. Given the context of the COVID-19 pandemic which contributed potentially to low sample size and biased estimates, our methodology demonstrated feasibility. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSecond\u003c/span\u003e, we did not evaluate the extent to which other travel/mapping apps were used in both control and intervention groups; overall use by the two groups and differential use between the two groups of existing apps may have compromised observed differences between groups and biased overall results. However, the randomization resulted in reasonably balanced groups at baseline for known factors, which supports the notion that the two groups would likely have similar travel motivations and use of existing travel/mapping apps. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eThird\u003c/span\u003e, we observed a relatively low engagement rate defined as opening the MTP app (23.4%), not unlike other RCTs conducted among college students. For example, only 7.4% of those in the Grimes and Baker\u0026rsquo;s (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) RCT redeemed their free bike memberships. Neither Ralph and Brown (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) nor Rodriguez and Rogers (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) report the percentage of students who opened emails containing educational materials. Also, we preserved randomization, not relying on student recall as per Ralph and Brown (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eFourth\u003c/span\u003e, although the study was designed as a cluster randomized trial, only one cluster was identified at baseline, and participants in this cluster did not have follow-up data; thus, longitudinal analyses were performed without regard to clusters. The design of this trial as a cRCT was supported by the investigators\u0026rsquo; External Advisory Committee. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eFifth\u003c/span\u003e, regarding generalizability, there were three concerns: 1) selection bias is possible given that some students may not have been interested in participating in the survey; in an attempt to assess this, we were only able to obtain the racial distribution of participants for comparison with that of the university\u0026rsquo;s student population; 2) regarding generalizability of results to external universities, as stated earlier, the campus location is in Boca Raton, Florida within Palm Beach County which is considered suburban, and incurs a higher cost of living than that found nationally. Also, the University is one of the most diverse public universities in the country, and the most diverse in Florida. However, the campus is typical of many suburban campuses throughout the country in that most commuter students drive to campus. The campus is well served by major arterials running on the northern and southern boundaries of campus, both of which have access to interstate exits nearby. The campus has access to some shuttles, buses, and train routes, but services are typically limited to hourly frequencies. The Walk Score of the main Boca Raton Campus is a 28, which means that most trips require a car, whereas those of the University of North Carolina at Chapel Hill and University of California, Los Angeles have walk scores of 52 and 91, respectively (Walk Score 2023); 3) differential response rates in the two groups limit generalizability: only 25% of participants completed follow-up surveys, and the difference in follow-up survey completion rates between the intervention and control groups was significant (p\u0026thinsp;=\u0026thinsp;.0003), with those in the control group almost twice as likely to complete the survey. A possible explanation is that those in the control group were more motivated to stay in the study to obtain the App after study closure. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSixth\u003c/span\u003e, because the cRCT was unblinded, our study shares the increased risk of cross-contamination found in other studies in that students randomized to the App group could have shared information with others in the control group, thus underestimating differences; however, the App is specific to the student\u0026rsquo;s personal email address (for login) which may have curtailed sharing of information. To minimize cross-contamination, we limited download of the App to students assigned to the intervention by requiring that the students\u0026rsquo; official email be used to request App download. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSeventh\u003c/span\u003e, although the randomization appeared to result in reasonably balanced groups for prespecified characteristics identified by content experts, differences in unspecified characteristics between the intervention and control groups may explain results. For known characteristics, there were some differences at baseline; however, given the large number of variables assessed and participants randomized carefully using campus and enrollment status strata, we expect chance differences to occur. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eEighth\u003c/span\u003e, among those in the App group, we did not capture travel behavior change related to whether there was a change in residence after receiving the App. However, because the App was delivered early in the semester, almost all these students would likely have established their residence location before receiving the App. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eNinth\u003c/span\u003e, two data concerns limited statistical power. Because the proportion of missing follow-up data is quite large (approximately 75% of those who completed baseline assessments had no follow-up), no multiple imputation procedures were used, and analyses were performed on the subset of cases with both baseline and follow-up data only. Thus, coupled with the fact that we did not reach our target sample size\u0026ndash;reducing the ability to detect a difference as significant, the trial results are considered hypothesis-generating and not definitive (Jakobsen et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e Despite these limitations, the randomization process was conducted carefully and resulted in reasonably balanced groups for the characteristics specified. All participants were observed at the same timepoints chronologically. We examined potential biases regarding differences between the derived sample with both baseline and follow-up data, and the complement with no subsequent follow-up, as well as differences in proportions of participants in the two groups who had follow-up data.\u003c/p\u003e\u003cp\u003eMethodologically, this study will provide important design features for planning future studies such as empirical data to compute effect sizes, refinement of inclusion/exclusion criteria to encompass characteristics of participants who may be more likely to open the App to use for possible stratification, and augmenting outcomes of transportation interventions to encompass physical and mental health criteria. We also found that although the study was designed as a cRCT, there was virtually no clustering at this University. Results of this study inform efforts toward inclusive transportation in that MTP has been adopted by college students from diverse backgrounds to facilitate transportation, regardless of their circumstances (Davis and Butler \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eResults of this feasibility study suggest that promoting a multimodal trip planning app may help decrease feelings of worry, tension or anxiety; we did not find significant differences in travel patterns or academic achievement between groups. Given design limitations and the fact that the study was conducted just after the height of the COVID-19 pandemic when ridership was low, the study should be replicated during a time of relative stability in population health. We found that students who opened the app were significantly more likely to: 1) not take transit, 2) have long commutes, and 3) not provide care for an adult.\u003c/p\u003e\u003cp\u003eStudy results also indicate that less than 25% of the intervention group opened the app, suggesting possible barriers to adoption; from our study, these barriers could include preference for an existing travel app on their phone, overcoming inexperience in using transit, and family-related time constraints that render alternatives to private vehicle use unwieldy. Further research is needed to identify what these barriers are and ways to address them to increase engagement with such tools in the future.\u003c/p\u003e\u003cp\u003eUltimately, a MaaS app is a tool for transacting information and payments regarding alternative modes of travel. A MaaS app could also be used as a tool for regional planners and officials to better understand the mismatch between student requirements for travel and the current transportation network, underscoring the tool\u0026rsquo;s utility in the strategic planning effort. However, the best app cannot overcome shortcomings in a deficient multimodal transportation network that is less accessible in rural areas. Moreover, strategic campus planning for sustainable travel is also an important component lacking in our study. As technology continues to improve and a larger share of college students turn to apps to facilitate travel, more research will be needed to study how MaaS apps can benefit students and simultaneously inform universities and transportation providers how to best meet their needs.\u003c/p\u003e\u003cp\u003eThere are many mechanisms for improving students' travel options to campus, including providing more frequent and reliable transit service, providing free transit passes, and offering technology options to facilitate transportation planning. A MaaS app is one promising option for promoting students' use of alternative transportation to reach campus, but more studies are needed to understand the potential influence of MaaS features on student travel behaviors and overcome barriers to the adoption of such new technologies. A fully integrated MaaS app that includes ticketing and payment may offer a more compelling option than what we were able to develop for this study, given budgetary and time limitations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u0026nbsp;\u003c/strong\u003eThe authors declare that we have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The content of this manuscript has not been published elsewhere, and is not under consideration by any other journal at this time. All authors have reviewed and approved the manuscript in its entirety. If this manuscript is accepted it will not be published elsewhere in the same form or in various languages or formats.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Generative AI and AI-assisted technologies:\u0026nbsp;\u003c/strong\u003eNo AI and AI-assisted technologies were used in the writing process or for analyses of data used in this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCRediT authorship contribution statement:\u0026nbsp;\u003c/em\u003e\u003cstrong\u003eKatherine Freeman:\u0026nbsp;\u003c/strong\u003eConceptualization, Formal statistical analysis, Methodology, Writing \u0026ndash; original draft.\u003cstrong\u003e\u0026nbsp;Louis A. Merlin:\u0026nbsp;\u003c/strong\u003eConceptualization, Funding acquisition, Methodology, Writing \u0026ndash; review \u0026amp; editing. \u003cstrong\u003eJohn Renne:\u0026nbsp;\u003c/strong\u003eConceptualization, Funding acquisition, Supervision, Writing \u0026ndash; review \u0026amp; editing. \u003cstrong\u003eSerena Hoermann:\u0026nbsp;\u003c/strong\u003eConceptualization, Funding acquisition, Project administration, Writing \u0026ndash; review \u0026amp; editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVitae/biography\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKatherine Freeman, DrPH\u0026nbsp;\u003c/strong\u003ecurrently\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eserves as the Biostatistics Collaborative Core Leader for Florida Atlantic University (FAU), and Professor, Charles E. Schmidt College of Medicine. She received her doctorate in biostatistics from the Columbia School of Public Health, and is an expert in the design and statistical analysis of multicenter randomized clinical trials (RCT) and observational studies. She has written or coauthored several successful NIH and foundation research grant applications for which she has been the Principal or Co-Investigator. Collaborators include FAU and other faculty from varied disciplines including medicine, nursing, behavioral health, education, bioengineering, health economics, urban planning, transportation and biology which have resulted in nearly 150 peer-reviewed publications. \u0026nbsp;She has taught biostatistics, epidemiology, critical review of the literature, clinical trials methodology, and meta-analysis. Dr. Freeman spent most of her career at the Albert Einstein College of Medicine, and had been a member of Montefiore Medical Center\u0026rsquo;s Institutional Review Board for 14 years. She has Chaired several NIH and industry sponsored Data and Safety Monitoring Boards, and has served as a reviewer for both NIH and PCORI. She has been a Co-Investigator on the Kresge Foundation transportation award, having collaborated on the design and analysis of its cluster randomized controlled trial. Presently, Dr. Freeman is PI of the NIH R25 5-year Florida Summer Institute in Biostatistics and Data Science to train undergraduate students and early graduates in these fields.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRecent Publications\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eMerlin LA, \u003cstrong\u003eFreeman K\u003c/strong\u003e, Renne J, Hoermann S. Clustered randomized controlled trial protocol of a Mobility-as-a-Service app for College campuses. \u003cem\u003eTRIP\u0026nbsp;\u003c/em\u003e\u003cstrong\u003e14\u003c/strong\u003e(2022): 1-13.\u003c/p\u003e\n\u003cp\u003eHoermann S, Renne JL, \u003cstrong\u003eFreeman K\u003c/strong\u003e, Merlin LA, Dzhurova A, Lopez P. Peer Engagement: On Reflecting Student Diversity in a Research Trial. \u003cem\u003eInternational Journal of Qualitative Methods\u003c/em\u003e 2024;\u003cstrong\u003e23\u003c/strong\u003e: 1\u0026ndash;14.\u003c/p\u003e\n\u003cp\u003eMerlin LA, Simpson DA, \u003cstrong\u003eFreeman K\u003c/strong\u003e, Hoermann S, Renne J. Driver vehicle crashes and mental health challenges among commuter college students. \u003cem\u003eJ Transport \u0026amp; Health\u003c/em\u003e 2025;\u003cstrong\u003e40\u003c/strong\u003e.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e All authors were involved in the conceptualization/design, JR, LM with funding acquisition; LM, SH with data curation; LM with randomization; KF with statistical analyses; KF with original and continued drafts; all with writing and editing.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgements\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Kresge Foundation [grant number: R-1905-283549]. The Sponsor had no involvement in the design, analysis, interpretation and manuscript preparation, or the distribution of funds for research activities.\u003c/p\u003e\n\u003cp\u003eWe thank Lindsay Paige, James Sullivan, and Michael Gottfried as External Advisory Board Members for their guidance on our Cluster Randomized Controlled Trial research design. We also wish to thank the administrations of Florida Atlantic University, Broward College, and Palm Beach State College for supporting this campus-based research effort.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAjzen, I.: The theory of planned behavior. OBHDP Process. \u003cb\u003e50\u003c/b\u003e, 179\u0026ndash;211 (1991)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAllen, J., Farber, S.: How time-use and transportation barriers limit on-campus participation of university students. Travel Behav. 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Accessed 16 December 2024\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWalk Score: (2025). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.walkscore.com\u003c/span\u003e\u003cspan address=\"https://www.walkscore.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e/ Accessed 15 January 2025\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang, Y., Stopher, P., Halling, B.: Evaluation of south-Australia\u0026rsquo;s TravelSmart project: Changes in community\u0026rsquo;s attitudes to travel. Transp. Policy. \u003cb\u003e26\u003c/b\u003e, 12\u0026ndash;22 (2013)\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"transportation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"port","sideBox":"Learn more about [Transportation](http://link.springer.com/journal/11116)","snPcode":"11116","submissionUrl":"https://submission.nature.com/new-submission/11116/3","title":"Transportation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Cluster randomized controlled trial, Mobility-as-a-service, Transportation planning, Health outcomes, Public Florida University, Multimodal trip planning app","lastPublishedDoi":"10.21203/rs.3.rs-6605622/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6605622/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eA significant challenge student commuters face in transitioning to college is navigating transportation choices and expenses. Costs of driving often necessitate students work long hours to pay to drive. Time spent working can increase stress, and reduce time for studying or engaging in extracurricular activities. We designed a two-arm cluster-randomized controlled trial (cRCT) evaluating if a multimodal trip planning (MTP) app would benefit undergraduate student commuters at a large public university located in a Southeast Florida suburban area. The research focuses on addressing transportation barriers to improve academic and health outcomes among college students who are disproportionately first-generation, low-income and persons of color. Of the 427 students randomized, 106 completed both baseline (early semester) and follow-up (end of semester) surveys. Differences in travel behaviors and academic achievement between intervention and control groups were not significant. The number of days participants felt worried, tense or anxious was significantly greater in the control group (p\u0026thinsp;=\u0026thinsp;.0420). A MTP app is one promising option for promoting students' use of alternative travel choices. More studies are needed to understand the potential influence of MTP app features on student travel behaviors and overcome barriers to the adoption of such new technologies, while informing universities and transportation providers how to best meet the needs of students. In particular, a fully integrated Mobility as a Service (MaaS) app that includes ticketing and payment may offer a more compelling option than what we were able to develop for this study, given the budgetary and time limitations.\u003c/p\u003e\u003cp\u003eMultimodal trip planning app for college student commuters enrolled in a suburban Florida public university: Feasibility Cluster Randomized Controlled Trial\u003c/p\u003e","manuscriptTitle":"Multimodal trip planning app for college student commuters enrolled in a suburban Florida public university: Feasibility Cluster Randomized Controlled Trial","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-22 08:45:09","doi":"10.21203/rs.3.rs-6605622/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-08T03:39:02+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-11T21:24:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"200753488380123715909416165131792202817","date":"2025-10-12T16:10:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-28T04:02:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"84663499161309377696185946491016426047","date":"2025-08-03T22:18:34+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-03T19:07:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-21T06:45:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-10T11:46:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"Transportation","date":"2025-05-06T18:01:16+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"transportation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"port","sideBox":"Learn more about [Transportation](http://link.springer.com/journal/11116)","snPcode":"11116","submissionUrl":"https://submission.nature.com/new-submission/11116/3","title":"Transportation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"80eb5719-4402-4034-899d-2cb013d7bc04","owner":[],"postedDate":"September 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-29T01:23:23+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-22 08:45:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6605622","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6605622","identity":"rs-6605622","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}