Urban Active Transportation Behaviour is Sensitive to the Fresh Start Effect: Triangulating observational evidence from real world data

Urban Active Transportation Behaviour is Sensitive to the Fresh Start Effect: Triangulating observational evidence from real world data | 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 Urban Active Transportation Behaviour is Sensitive to the Fresh Start Effect: Triangulating observational evidence from real world data Jonathan McGavock, Isaak Fast, Shamsia Siobhan, Nika Klaprat, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5375352/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Jun, 2025 Read the published version in International Journal of Behavioral Nutrition and Physical Activity → Version 1 posted 9 You are reading this latest preprint version Abstract Objective: This study was designed to determine if active transportation (AT) was sensitive to the behavioural economics heuristic “The Fresh Start Effect”, with a temporal peak in traffic at the beginning of a work week, that declined by the end of the week. Design and Methods: We triangulated data from five data sources to test the study hypothesis. First, we categorized 5 urban trails as “AT” or “leisure” based on hourly traffic patterns collected from May to September between 2014 to 2019 using electromagnetic counters (EcoCounter Inc, Montreal Qc.). Daily trends in cycling traffic were then compared with daily trends bicycle parking (n=56,307 counts), vehicular traffic (n=6.2M counts), fitness centre attendance (n=563,290 counts) and sales from a local coffee shop (n=166,753 counts). Results: We found a significant ~22% decline in cycling traffic on both AT (-147 cyclists/day; 95% CI: -199.0 to -95 cyclists/day) and leisure trails (-22 cyclists/day; 95% CI: -59 to +15 cyclists/day) over the course of a work week. The relative decline in AT-based cycling traffic was similar to the decline in bicycle parking (~14%; -12 cyclists/day; 95% CI: -17 to -7 cyclists/day). The relative effect size of this trend was nearly identical to the weekly decline in fitness centre attendance (~21%; -592 visits/day; 95% CI: -759 visits/day to -425 visits/day), replicating the original Fresh Start Effect. In contrast, to the decline in AT-based cycling traffic, daily vehicular traffic (+2248 cars/day; 95% CI: 2022 to +3674 cars/day) and coffee sales (+31 units/day; 95% CI: +22 to +42 units/day) increased ~7% from the beginning to the end of a work week. Conclusions: Weekly patterns of leisure and AT-based cycling are sensitive to the Fresh Start Effect and could be used to inform policies for increasing cycling rates in urban centres. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Populations that engage in regular physical activity experience lower rates of non-communicable diseases compared to populations that do not 1,2 . Promoting active transportation (AT) is a common public health strategy for increasing population-level physical activity in urban areas 3,4 . Municipal governments in most high-income countries are investing millions of dollars annually to remodel the built environment to support AT, particularly by creating protected spaces for cycling 5-7 . There is growing evidence that this infrastructure 8 , and subsequent use for AT 9 is associated with reduced rates of various chronic diseases in neighbourhoods where they are constructed. Despite the rapid growth in urban cycling infrastructure over the past decade, the primary determinants AT-based cycling behaviour is poorly understood. Behavioural economics is an area of behavioural science that describes how individual behaviours are governed by heuristics 10 . Several behavioural economic heuristics govern lifestyle behaviours, and are being leveraged to support behaviour change of entire populations 11 . One of these heuristics, The Fresh Start Effect, describes the trend of adopting a new behaviour during a temporal landmark 12 . For example, individuals are more likely to engage in physical activity behaviours at the beginning of a calendar year (New Year’s Resolutions) 13,14 , academic semester 12 , birthday, or the beginning of a week 15 compared to days without a distinct temporal landmark. In each case, the modifiable lifestyle behaviours, like leisure physical activity, decline by the end of the month or week. While the Fresh Start effect appears to govern leisure-type physical activity behaviours, it is unclear if it also governs AT behaviours. We triangulated real-world data from five different urban contexts (Figure 1) to determine if urban AT-based cycling behaviours follow a distinct Fresh Start Effect behavioural pattern, characterized by peak traffic at the beginning of a work week and declining thereafter 12 . The primary hypothesis was that cycling traffic along trails characterized by a distinct AT pattern would not exhibit a “Fresh Start Effect”, compared to cycling traffic along trails characterized by leisure-type patterns of use. Study Design and Methods Triangulation of Data from Multiple Sources We applied the concept of epidemiological triangulation 16 to test the study hypothesis that AT-based cycling does not follow the Fresh Start Effect behavioural heuristic. Specifically, we compared and contrasted weekly trends in (A) leisure-based and AT-based cycling patterns along urban trails; (B) daily rates of use of a protected bicycle parking facility at one of the city’s largest public employers office buildings, (C) weekly visits to a local fitness centre; (D) daily vehicle counts along one of the city’s busiest road ways; and (E) coffee purchases at a local coffee shop (Figure 1). Three datasets were selected specifically to capture and replicate weekly patterns of both AT and leisure-time physical activity. The fourth (coffee sales) and fifth datasets (vehicular traffic counts) were obtained to estimate trends in working from home and a shift towards non-active transportation. All datasets were collected within the City of Winnipeg, Canada’s 6 th largest city, with a population of ~841,000. We secured data that included 1.22M cycling counts collected over 634 days within 126 weeks along urban trails (2014-2019), 87,794 counts of bicycle parking obtained from 2602 days, within 411 weeks from a large public corporation (2012-2019); 1.95M individual visits over 839 days within 123 weeks from the local University-based fitness centres (2017-2019), 6,193,449 vehicle counts from 153 days within 22 weeks along one of the city’s busiest roadways (May-Sept 2019) and finally, 366,000 individual coffee sales, from 1504 days, within 313 weeks from the local coffee shop (2012-2019). Research Question #1: Is the Fresh Start Effect evident for weekly patterns of AT-based cycling behaviours? Patterns of AT and leisure-specific cycling behaviours were quantified using open access cycling traffic data collected on five multi-use urban trails from 2014 to 2019 provided to our team from the Active Transportation Department at the City of Winnipeg. In 2014, the department embedded several automatic inductive loop detectors (Zelt 2, Eco-counter, Montreal Qc) along 5 large urban trails: Awasisak Mēskanow (AM), Northeast Pioneers Greenway (NPG), Transcona Trail (TRT), Yellow Ribbon Greenway (YRG) and the Harte Trail (HRT) (Figure 2) to quantify patterns and trends in cycling traffic. Details for the multi-use trails on which cycling count data were collected and the population living within 400m of the trails are provided in Table 1. Each urban trail is at least 4km in length. There are ~52,000 individuals living within ~100 neighbourhoods within 400m of the five trails. Quantification of “real world” population-based trends in active transportation and leisure cycling: The automatic inductive loop detectors embedded in all five trails quantify individual bicycle counts from electromagnetic signature of the two wheels 8,17 , registering an individual count with an accuracy between 90 and 95% 18 , and data are recorded and logged hourly. Hourly cycling counts were collected every day of the year from 2014 to 2019, however we restricted analyses to weeks between May 1 st to September 30 th as cycling rates decline by ~90% during winter months in Winnipeg 8 . To classify trails as “AT” or “leisure”, we stratified hourly cycling counts into windows of active transportation (6h00-9h00 and 15h00-18h00) and leisure time cycling (9h00 to 15h00 and 18h00 to 22h00). Trails that displayed a distinct bi-phasic increase in cycling counts during the 6h00-9h00 and 15h00-18h00 windows were classified as AT trails. Trails without the bi-phasic increase in cycling counts during active transportation windows of time were classified as leisure or primarily recreational (Figure 3). To determine if the Fresh Start Effect was evident within daily cycling patterns, we compared daily cycling counts from Monday through Friday on both AT and leisure trails. To increase the resolution for active transportation-based cycling pattern, we also conducted sensitivity analyses restricted to counts restricted to the AT windows of time. Research Question #2: How do weekly trends observed on active transportation trails compare to actual active transportation at a large company? The two main limitations of the population-based real-world data used to answer research question #1 are: (1) information on origin and destination of each cycling trip were unavailable and (2) data for individual cyclists were not available, therefore it was unclear if the hourly patterns in trail use observed on the trails were for individual-level AT. To overcome these limitations, we obtained counts for daily use of a protected bicycle parking space located at Manitoba Hydro, the largest provider of electricity in the province and one of the largest employers in the City of Winnipeg with a staff of 6,463 employees. The head offices for the corporation are in downtown Winnipeg and have offered secure bicycle parking for all employees since 2012. An employee-driven AT committee provided access to a de-identified database for daily bicycle parking rates for employees that cycled to work and used the secure facility since 2012. Using this database, we tested for a Fresh Start Effect pattern of active transportation by comparing daily counts for individual use of the secured bicycle parking space from Tuesday to Friday, with data restricted to counts between May 1 st to September 30 th . An advantage of using this data is that the temporal landmark for these trends was Tuesday, rather than Monday. A corporate policy/benefit called “Hydro Mondays” is a 9-day bi-weekly work schedule which provides most employees with a non-work day every second Monday. These data therefore provide additional information to support the concept that the temporal landmark of interest for trends in AT is the start of a work week. Research Question #3: How do weekly patterns of cycling behaviours compare to the original description of the Fresh Start Effect? Research questions #1 and #2 were designed to detect a Fresh Start Effect for cycling-specific behaviours. The original experiment that described the weekly Fresh Start Effect relied on data for individual access to a university-based fitness centre, comparing rates of attendance from Monday to Friday 12 . To replicate the original Fresh Start Effect and to compare the effect sizes we observed in daily patterns of AT-based cycling behaviour, we secured individual swipe card access to two fitness centres at the University of Manitoba from 2017 to 2019. To gain access to each fitness centre, attendants must swipe their access card, and the fitness centre records individual identification numbers, as well as the date and time of each attendance. The University of Manitoba supports 9421 staff/faculty and 31,020 students. Fitness centre access is provided free of charge to students and at discounted rates to staff and faculty. To answer research question #3 and compare the effect sizes observed in questions #1 and 2, we calculated absolute and relative decline in daily attendance rates at the fitness centres from Monday to Friday between 2017 and 2019. Research question #4: Could daily trends in active transportation be explained by trends in office-based occupational attendance? It is possible that the daily trends in AT-based cycling traffic over the course of a work week were related to trends in working from home or reducing work weeks during summer months. Although we were unable to obtain data for employee attendance rates in large corporate organizations, we did obtain data for vehicle traffic along one of the city’s busiest roads from May 1 to September 30 th 2019. Data for other years or other roads were not available prior to 2020. Vehicular traffic in both north and southbound directions were counted using dual side-fire radar technology (Speedlane Pro, Houston Radar Inc, Sugar Land, TX). In addition to vehicle traffic, we also obtained data from coffee sales at a local provider within downtown Winnipeg, the city’s primary business sector. Parlour Coffee provided their daily sales of all types of coffee from 2014 to 2019. Parlour coffee was open Mondays through Saturdays from 7am to 3pm and is not open in the evenings. The validity of daily coffee sales as a surrogate of weekly work/occupational attendance was based on two assumptions. First, Parlour coffee serves largely a business and student clientele which is reinforced by the hours of operation and location. Second, individuals often purchase coffee prior to or during their workday. Similar to, questions 1 to 3, we compared daily coffee sales and vehicular traffic from Monday to Friday to determine if sales and traffic declined on Fridays due to fewer individuals travelling to work. Statistical Analyses After classifying trail type, descriptive statistics were used to compare daily trends in each year that data we available for all four datasets. For each dataset, unadjusted analysis of variance (ANOVA) was used to compare daily counts across all five days of the week without adjusting for co-variates. Finally, a linear regression model with repeated measures, was used to compare daily counts between the beginning of the work week (control day) to the other days of the week, controlling for year of measurement. To test the main hypothesis (research question #1), the model included an interaction term of trail type and day of the week, to determine if the daily trends over the week were different between AT and leisure type trails. A significant interaction between trail type and day of the week would reflect differences in trends in cycling behaviour over the course of the week. Data are presented as counts with confidence intervals, and differences in counts between Mondays and other days of with week with 95% confidence intervals. Differences in daily counts were considered significant if the 95% confidence intervals do not include zero. All analyses were conducted in RStudio 4.3.0. R code for analyses is provided in the appendix. Results Weekly trends in active transportation and leisure cycling are characterized by a Fresh Start Effect behavioural pattern. Of the five urban multi-use trails studied, three displayed distinct daily AT profiles with bi-phasic peaks during the windows of time when AT is more common 19 (Figure 3A) while two displayed a leisure-type cycling profile (Figure 3B). In addition to a distinct hourly AT profile, these trails also had more cycling traffic than trails defined as leisure-type (910 270 total bicycle counts vs 313 632 total bicycle counts from 2014 to 2019). Between 2014 and 2019, weekly cycling traffic remained similar on all five trails (eFigure 1). Trails with a distinct AT profile were built in neighbourhoods with more diverse populations, greater trail connectivity and more destination points, compared to trails with a leisure-type hourly profile (Table 1). In a fully adjusted linear mixed effects model, we found that the beginning of the work week was a distinct temporal landmark for AT-based cycling, after which daily rates of cycling traffic declined progressively to the end of a work week (Figure 4A). Trails with an AT profile observed a 22% decline cycling traffic by Fridays compared to Mondays (-147 cyclists/day; 95% CI: -199.0 to -94.6 cyclists/day). Urban trails defined as leisure type, were not characterized by a decline in cycling traffic between the start and end of a work week (-22.1 cyclists/day; 95% CI: -59.1 to +15.0 cyclists/day). To increase the resolution of these trends, we repeated comparisons of daily cycling counts restricted to windows of time characterised by AT. We found that counts per hour during AT windows declined progressively through the week, with 5 fewer cyclists per hour (95% CI: -7.75 to -2.04) on Fridays compared to Mondays (eFigure 2). These trends during commuting windows were evident on trails defined as AT and leisure, suggesting they are robust to AT-specific windows of time (Figure 4B). The trend that the beginning of each week was a temporal landmark for AT-based cycling behaviour were consistent across all five years of data collection and across all AT trails (eFigure 3). Daily trends in cycling on active transportation trails are similar to those observed at a large corporate bicycle parking space. To validate and replicate the trends observed in AT-based cycling along urban multi-use trails, we tested for differences in individual access to a protected bicycle parking space between the beginning and end of the work week. From 2012 and 2019, there were 56,307 unique accesses to the protected bicycle parking space, with annual use increasing nearly three-fold from 3640 accesses in 2012 to 9754 accesses in 2019. The temporal landmark for this dataset was Tuesday, as there is a corporate policy for a statutory holiday every other Monday for most employees of the company. Similar to the trends observed along urban multi-use trails, daily rates of bicycle parking declined by ~20% (-12 cyclists/day 95% CI: -17 to -7 cyclists) between Tuesday and Friday (Figure 5a). The decline in the use of protected space for bicycle parking over a work week was evident in all 7 years of data collection (eFigure 4). Daily trends in cycling traffic on active transportation trails is similar to daily trends in fitness centre attendance. To determine if the magnitude of the decline AT-based cycling behaviours over the course of a work week was similar relative decline in PA described in the original Fresh Start effect 12 . Like the daily trends we observed for AT-based cycling traffic, visits to the fitness centre were highest on at the beginning of the work/academic week, with an average of 2,833 visits per day (95% CI: 2740 – 2926 visits per day) and declined progressively through the week, with the lowest visits on Fridays (Figure 5b). The relative decline in daily visits to the fitness centres between Mondays and Fridays was 21%, with an absolute difference of -592 visits per day (95% CI: -759 to -425 visits per day). These data replicate the original findings that were used to define the Fresh Start Effect heuristic 12 and mirror the trends observed for AT-based cycling along urban multi-use trails. The decline in fitness centre attendance from the beginning to the end of the work week was evident in all three years data were available (eFigure 5). Trends in active transportation do not appear to be driven by daily patterns in working from home or four-day work weeks. In contrast to the trends observed in AT-based cycling behaviour along urban multi-use trails and corporate bicycle parking spaces, vehicular traffic increased 7% (+2848 cars/day; 95% CI: +2202 to +3674 cars/day) from the beginning of a work week (Monday) to the end of the work week (Figure 6a). Similar to vehicular traffic patterns, coffee sales were lowest at the beginning of a work week (245 units per day; 95% CI: 234-253 units per day) and increased incrementally through the work week to a peak of 275 (95% CI: 266-286) units of coffee sold on Fridays (Figure 6b). The relative increase in coffee sales (~10%; 31.9 units sales per day; 95% CI: 22.0-41.7 unit sales per day) over the course of a work week was similar to the increase in vehicular traffic and was evident in all 5 years that data were available. Discussion Using real-world population-level data, we find that AT-based cycling behaviour in urban cities displays a distinct weekly pattern indicative of a Fresh Start effect. Specifically, we find that the beginning of a work week is a temporal landmark for AT-based cycling behaviour that declines approximately 20% over the course of a work week. These weekly trends in AT-based cycling are particularly robust for cycling traffic between the hours of 6h00 and 9h00 and 15h00 and 18h00. This decline in cycling traffic by the end of a work week is mirrored by an increase in vehicular traffic and coffee sales at a local coffee parlour. The relative decline in AT-based cycling was similar to the relative decline in fitness centre attendance over the course of a work week. Taken together, the trends observed across all five datasets strongly suggest that AT-based cycling patterns follow a distinct Fresh Start Effect heuristic, with the beginning of the work week acting as a temporal landmark. The promotion of AT is a growing public health strategy for reducing carbon emissions and the burden of chronic diseases in large urban centres within high income countries 20,21 . The proportion of urban residents that engage in AT varies considerably within and between cities 8,19,22-27 . This variability in AT has been attributed to availability of infrastructure, localized mix land use, perceived safety and local culture for cycling 28,29 . The data presented here add a novel behavioural driver of AT-based cycling behaviour. At a population level, a decline in AT-based cycling of ~20% over the course of a week equates to ~10,000-50,000 fewer individuals travelling by bicycle in cities with populations of 1 to 5M residents. Understanding this trend in AT-based behaviours provides a novel lever for urban public health officials to nudge active commuters to sustain behaviours they adopted early in the week. Additionally, the observation that the beginning of the week is a distinct temporal landmark for people to engage in AT-based cycling, it could also serve as an urban public health strategy to engage large segments of the population to adopt AT. Various levels of government are using principles of behavioural economics to guide policy decisions to improve the health and well-being of its citizens 30,31 . Using real world data, organizations can detect, track and experiment with approaches to change the behaviours of large segments of a population. As cities are generating and sharing large amounts of population-level data 32 , the opportunities for urban policy making using principles of behavioural economics are increasing 33 . Using this urban data we are able to advance previous work that focused on intended behaviours (google searches, enrollment in programs) 34,35 and demonstrate that across a large urban population, objectively measured AT-based cycling and vehicle driving are both sensitive to temporal landmarks, and the uptake of these behaviours progressively wanes over the course of a work week. The observation that these trends are detectable at the population level and were consistent over 5 years, suggests that temporal landmarks are a robust driver of AT-based cycling behaviour. This information could inform municipal governments when implementing behavioural economic strategies to combat climate change and non-communicable diseases. The study is strengthened by a large effect size observed at the population-level using real world objectively-measured trends in cycling patterns along urban trails that were consistently replicated, over multiple years. Additionally, triangulating observations from real-world cycling patterns with data collected from other sources, with different limitations, enhances the interpretation of our findings. Despite these strengths, this study has several limitations. First, the data presented are descriptive in nature and we cannot infer that there is a causal association between temporal landmarks and AT behaviours. Second, individual-level cycling data were not available to test the study hypothesis. Therefore, the weekly trends described here cannot be attributed directly to individual behaviours, rather we infer that similar trends would be observed if individual data were available. Third, we were able to control for several factors including yearly trends, weather patterns and holiday days, however several co-variates were not measured including origin and destination points, sex, gender, race, ethnicity and age. Data were collected in a medium sized urban setting in a northern climate, therefore the generalizability of findings may be limited. This demographic information was not available to determine if these trends were evident for individuals from different genders, ages and structurally oppressed, racialized groups. Future research using data from wearable technology may be used to overcome these limitations. Conclusion In conclusion, in a large urban city, weekly trends in AT-based cycling display a Fresh Start effect with the beginning of each work week serving as an important temporal landmark to engage in this behaviour. The trends in AT are similar to trends used to define the original Fresh Start Effect. These behavioural patterns could be used by municipal policy makers to tailor public health messages to increase rates of AT. Declarations Ethics approval and consent to participate: This study did not require ethics approval or consent for participation. Consent for publication: Not applicable. Data availability: Geospatial data for cycling infrastructure, cycling traffic counts, vehicular traffic counts are all available within the City of Winnipeg Open Data Portal: https://data.winnipeg.ca/browse?category=Transportation+Planning+%26+Traffic+Management&limitTo=datasets%2Cmaps&sortBy=newest Data for corporate bicycle parking access, university of Manitoba Recreation Centre accesses and coffee sales at Parlour coffee can be made available upon request to stewards of the data. Competing Interests: The authors declare that they have no competing interests to declare. Funding: Funding for this project was provided by operating grants from the Heart and Stroke Foundation of Canada (G-17-0018638) and the Canadian Institutes of Health Research (PJT-153449; CPP-137910). Role of funders/sponsors: Funding bodies were not involved in the study design, conduct, interpretation or manuscript preparation for this project. Scientists involved in this study had no relationship with funding agencies and conducted the study independent of funders. Author Contributions: All authors contributed to the study and manuscript in alignment with current ICMJE guidelines. The study was conceived by JM and NK. JM is the principal investigator on the original funded grant. JM, and NK participated in designing the study and collecting data. JM and CN are involved in data cleaning and verification and conducted the statistical analyses. All authors contributed to the writing of the final document. NK, IF and JM drafted the original manuscript. NV, TG, DP and JC collected data for the study. SS analyzed data. All authors contributed to critically revising the manuscript for important intellectual content, gave their final approval and agreed to be accountable for all aspects of the work, and they will participate in future interpretation of the data and drafting of further manuscripts arising from this work. 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Behavioural insights teams in practice: nudge missions and methods on trial. Policy Press 49 , 105-120 (2021). Han, Z., Xia, T., Xi, Y. & Li, Y. Healthy Cities, A comprehensive dataset for environmental determinants of health in England cities. Sci Data 10 , 165, doi:10.1038/s41597-023-02060-y (2023). Boeing, G. et al. Using open data and open-source software to develop spatial indicators of urban design and transport features for achieving healthy and sustainable cities. Lancet Glob Health 10 , e907-e918, doi:10.1016/S2214-109X(22)00072-9 (2022). Wang, S. Y. & Groene, O. The effectiveness of behavioral economics-informed interventions on physician behavioral change: A systematic literature review. PLoS One 15 , e0234149, doi:10.1371/journal.pone.0234149 (2020). Forberger, S., Reisch, L., Kampfmann, T. & Zeeb, H. Nudging to move: a scoping review of the use of choice architecture interventions to promote physical activity in the general population. Int J Behav Nutr Phys Act 16 , 77, doi:10.1186/s12966-019-0844-z (2019). Tables Table 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1.docx AppendixFSeffectATv1012324.docx Cite Share Download PDF Status: Published Journal Publication published 19 Jun, 2025 Read the published version in International Journal of Behavioral Nutrition and Physical Activity → Version 1 posted Editorial decision: Revision requested 13 Feb, 2025 Reviews received at journal 12 Feb, 2025 Reviews received at journal 30 Jan, 2025 Reviewers agreed at journal 21 Jan, 2025 Reviewers agreed at journal 08 Jan, 2025 Reviewers invited by journal 17 Nov, 2024 Editor assigned by journal 12 Nov, 2024 Submission checks completed at journal 12 Nov, 2024 First submitted to journal 01 Nov, 2024 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5375352","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":385008084,"identity":"1d0f8d69-fe34-4b93-9e5e-ca0580b3635a","order_by":0,"name":"Jonathan 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17:13:48","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":398547,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixFSeffectATv1012324.docx","url":"https://assets-eu.researchsquare.com/files/rs-5375352/v1/37d42f01b68315980a63df3b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Urban Active Transportation Behaviour is Sensitive to the Fresh Start Effect: Triangulating observational evidence from real world data","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePopulations that engage in regular physical activity experience lower rates of non-communicable diseases compared to populations that do not\u003csup\u003e1,2\u003c/sup\u003e. Promoting active transportation (AT) is a common public health strategy for increasing population-level physical activity in urban areas\u003csup\u003e3,4\u003c/sup\u003e. Municipal governments in most high-income countries are investing millions of dollars annually to remodel the built environment to support AT, particularly by creating protected spaces for cycling\u003csup\u003e5-7\u003c/sup\u003e. There is growing evidence that this infrastructure\u003csup\u003e8\u003c/sup\u003e, and subsequent use for AT\u003csup\u003e9\u003c/sup\u003e is associated with reduced rates of various chronic diseases in neighbourhoods where they are constructed. Despite the rapid growth in urban cycling infrastructure over the past decade, the primary determinants AT-based cycling behaviour is poorly understood.\u003c/p\u003e\n\u003cp\u003eBehavioural economics is an area of behavioural science that describes how individual behaviours are governed by heuristics\u003csup\u003e10\u003c/sup\u003e. Several behavioural economic heuristics govern lifestyle behaviours, and are being leveraged to support behaviour change of entire populations\u003csup\u003e11\u003c/sup\u003e. One of these heuristics, The Fresh Start Effect, describes the trend of adopting a new behaviour during a temporal landmark\u003csup\u003e12\u003c/sup\u003e. For example, individuals are more likely to engage in physical activity behaviours at the beginning of a calendar year (New Year\u0026rsquo;s Resolutions)\u003csup\u003e13,14\u003c/sup\u003e, academic semester\u003csup\u003e12\u003c/sup\u003e, birthday, or the beginning of a week\u003csup\u003e15\u003c/sup\u003e compared to days without a distinct temporal landmark. In each case, the modifiable lifestyle behaviours, like leisure physical activity, decline by the end of the month or week. While the Fresh Start effect appears to govern leisure-type physical activity behaviours, it is unclear if it also governs AT behaviours. We triangulated real-world data from five different urban contexts (Figure 1) to determine if urban AT-based cycling behaviours follow a distinct Fresh Start Effect behavioural pattern, characterized by peak traffic at the beginning of a work week and declining thereafter\u003csup\u003e12\u003c/sup\u003e. The primary hypothesis was that cycling traffic along trails characterized by a distinct AT pattern would not exhibit a \u0026ldquo;Fresh Start Effect\u0026rdquo;, compared to cycling traffic along trails characterized by leisure-type patterns of use.\u003c/p\u003e"},{"header":"Study Design and Methods","content":"\u003cp\u003e\u003cstrong\u003eTriangulation of Data from Multiple Sources\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe applied the concept of epidemiological triangulation\u003csup\u003e16\u003c/sup\u003e to test the study hypothesis that AT-based cycling does not follow the Fresh Start Effect behavioural heuristic. Specifically, we compared and contrasted weekly trends in (A) leisure-based and AT-based cycling patterns along urban trails; (B) daily rates of use of a protected bicycle parking facility at one of the city\u0026rsquo;s largest public employers office buildings, (C) weekly visits to a local fitness centre; (D) daily vehicle counts along one of the city\u0026rsquo;s busiest road ways; and (E) coffee purchases at a local coffee shop (Figure 1). Three datasets were selected specifically to capture and replicate weekly patterns of both AT and leisure-time physical activity. The fourth (coffee sales) and fifth datasets (vehicular traffic counts) were obtained to estimate trends in working from home and a shift towards non-active transportation. All datasets were collected within the City of Winnipeg, Canada\u0026rsquo;s 6\u003csup\u003eth\u003c/sup\u003e largest city, with a population of ~841,000.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe secured data that included 1.22M cycling counts collected over 634 days within 126 weeks along urban trails (2014-2019), 87,794 counts of bicycle parking obtained from 2602 days, within 411 weeks from a large public corporation (2012-2019); 1.95M individual visits over 839 days within 123 weeks from the local University-based fitness centres (2017-2019), 6,193,449 vehicle counts from 153 days within 22 weeks along one of the city\u0026rsquo;s busiest roadways (May-Sept 2019) and finally, 366,000 individual coffee sales, from 1504 days, within 313 weeks from the local coffee shop (2012-2019).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch Question #1: Is the Fresh Start Effect evident for weekly patterns of AT-based cycling behaviours?\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatterns of AT and leisure-specific cycling behaviours were quantified using open access cycling traffic data collected on five multi-use urban trails from 2014 to 2019 provided to our team from the Active Transportation Department at the City of Winnipeg. In 2014, the department embedded several automatic inductive loop detectors (Zelt 2, Eco-counter, Montreal Qc) along 5 large urban trails: Awasisak Mēskanow (AM), Northeast Pioneers Greenway (NPG), Transcona Trail (TRT), Yellow Ribbon Greenway (YRG) and the Harte Trail (HRT) (Figure 2) to quantify patterns and trends in cycling traffic. Details for the multi-use trails on which cycling count data were collected and the population living within 400m of the trails are provided in Table 1. Each urban trail is at least 4km in length. There are ~52,000 individuals living within ~100 neighbourhoods within 400m of the five trails.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuantification of \u0026ldquo;real world\u0026rdquo; population-based trends in active transportation and leisure cycling:\u0026nbsp;\u003c/strong\u003eThe automatic inductive loop detectors embedded in all five trails quantify individual bicycle counts from electromagnetic signature of the two wheels\u003csup\u003e8,17\u003c/sup\u003e, registering an individual count with an accuracy between 90 and 95%\u003csup\u003e18\u003c/sup\u003e, and data are recorded and logged hourly. Hourly cycling counts were collected every day of the year from 2014 to 2019, however we restricted analyses to weeks between May 1\u003csup\u003est\u003c/sup\u003e to September 30\u003csup\u003eth\u003c/sup\u003e as cycling rates decline by ~90% during winter months in Winnipeg\u003csup\u003e8\u003c/sup\u003e. To classify trails as \u0026ldquo;AT\u0026rdquo; or \u0026ldquo;leisure\u0026rdquo;, we stratified hourly cycling counts into windows of active transportation (6h00-9h00 and 15h00-18h00) and leisure time cycling (9h00 to 15h00 and 18h00 to 22h00). Trails that displayed a distinct bi-phasic increase in cycling counts during the 6h00-9h00 and 15h00-18h00 windows were classified as AT trails. Trails without the bi-phasic increase in cycling counts during active transportation windows of time were classified as leisure or primarily recreational (Figure 3). To determine if the Fresh Start Effect was evident within daily cycling patterns, we compared daily cycling counts from Monday through Friday on both AT and leisure trails. To increase the resolution for active transportation-based cycling pattern, we also conducted sensitivity analyses restricted to counts restricted to the AT windows of time.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch Question #2: How do weekly trends observed on active transportation trails compare to actual active transportation at a large company?\u0026nbsp;\u003c/strong\u003eThe two main limitations of the population-based real-world data used to answer research question #1 are: (1) information on origin and destination of each cycling trip were unavailable and (2) data for individual cyclists were not available, therefore it was unclear if the hourly patterns in trail use observed on the trails were for individual-level AT. To overcome these limitations, we obtained counts for daily use of a protected bicycle parking space located at Manitoba Hydro, the largest provider of electricity in the province and one of the largest employers in the City of Winnipeg with a staff of 6,463 employees. The head offices for the corporation are in downtown Winnipeg and have offered secure bicycle parking for all employees since 2012. An employee-driven AT committee provided access to a de-identified database for daily bicycle parking rates for employees that cycled to work and used the secure facility since 2012. Using this database, we tested for a Fresh Start Effect pattern of active transportation by comparing daily counts for individual use of the secured bicycle parking space from Tuesday to Friday, with data restricted to counts between May 1\u003csup\u003est\u003c/sup\u003e to September 30\u003csup\u003eth\u003c/sup\u003e. An advantage of using this data is that the temporal landmark for these trends was Tuesday, rather than Monday. A corporate policy/benefit called \u0026ldquo;Hydro Mondays\u0026rdquo; is a 9-day bi-weekly work schedule which provides most employees with a non-work day every second Monday. These data therefore provide additional information to support the concept that the temporal landmark of interest for trends in AT is the start of a work week.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch Question #3: How do weekly patterns of cycling behaviours compare to the original description of the Fresh Start Effect?\u0026nbsp;\u003c/strong\u003eResearch questions #1 and #2 were designed to detect a Fresh Start Effect for cycling-specific behaviours. The original experiment that described the weekly Fresh Start Effect relied on data for individual access to a university-based fitness centre, comparing rates of attendance from Monday to Friday\u003csup\u003e12\u003c/sup\u003e. To replicate the original Fresh Start Effect and to compare the effect sizes we observed in daily patterns of AT-based cycling behaviour, we secured individual swipe card access to two fitness centres at the University of Manitoba from 2017 to 2019. To gain access to each fitness centre, attendants must swipe their access card, and the fitness centre records individual identification numbers, as well as the date and time of each attendance. The University of Manitoba supports 9421 staff/faculty and 31,020 students. Fitness centre access is provided free of charge to students and at discounted rates to staff and faculty. To answer research question #3 and compare the effect sizes observed in questions #1 and 2, we calculated absolute and relative decline in daily attendance rates at the fitness centres from Monday to Friday between 2017 and 2019.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch question #4: Could daily trends in active transportation be explained by trends in office-based occupational attendance?\u0026nbsp;\u003c/strong\u003eIt is possible that the daily trends in AT-based cycling traffic over the course of a work week were related to trends in working from home or reducing work weeks during summer months. Although we were unable to obtain data for employee attendance rates in large corporate organizations, we did obtain data for vehicle traffic along one of the city\u0026rsquo;s busiest roads from May 1 to September 30\u003csup\u003eth\u003c/sup\u003e 2019. Data for other years or other roads were not available prior to 2020. Vehicular traffic in both north and southbound directions were counted using dual side-fire radar technology (Speedlane Pro, Houston Radar Inc, Sugar \u0026nbsp;Land, TX). \u0026nbsp; In addition to vehicle traffic, we also obtained data from coffee sales at a local provider within downtown Winnipeg, the city\u0026rsquo;s primary business sector. Parlour Coffee provided their daily sales of all types of coffee from 2014 to 2019. Parlour coffee was open Mondays through Saturdays from 7am to 3pm and is not open in the evenings. The validity of daily coffee sales as a surrogate of weekly work/occupational attendance was based on two assumptions. First, Parlour coffee serves largely a business and student clientele which is reinforced by the hours of operation and location. Second, individuals often purchase coffee prior to or during their workday. Similar to, questions 1 to 3, we compared daily coffee sales and vehicular traffic from Monday to Friday to determine if sales and traffic declined on Fridays due to fewer individuals travelling to work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter classifying trail type, descriptive statistics were used to compare daily trends in each year that data we available for all four datasets. \u0026nbsp; For each dataset, unadjusted analysis of variance (ANOVA) was used to compare daily counts across all five days of the week without adjusting for co-variates. Finally, a linear regression model with repeated measures, was used to compare daily counts between the beginning of the work week (control day) to the other days of the week, controlling for year of measurement. To test the main hypothesis (research question #1), the model included an interaction term of trail type and day of the week, to determine if the daily trends over the week were different between AT and leisure type trails. A significant interaction between trail type and day of the week would reflect differences in trends in cycling behaviour over the course of the week. Data are presented as counts with confidence intervals, and differences in counts between Mondays and other days of with week with 95% confidence intervals. Differences in daily counts were considered significant if the 95% confidence intervals do not include zero. All analyses were conducted in RStudio 4.3.0. R code for analyses is provided in the appendix.\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eWeekly trends in active transportation and leisure cycling are characterized by a Fresh Start Effect behavioural pattern.\u003c/strong\u003e Of the five urban multi-use trails studied, three displayed distinct daily AT profiles with bi-phasic peaks during the windows of time when AT is more common\u003csup\u003e19\u003c/sup\u003e (Figure 3A) while two displayed a leisure-type cycling profile (Figure 3B). In addition to a distinct hourly AT profile, these trails also had more cycling traffic than trails defined as leisure-type (910 270 total bicycle counts vs 313 632 total bicycle counts from 2014 to 2019). Between 2014 and 2019, weekly cycling traffic remained similar on all five trails (eFigure 1). Trails with a distinct AT profile were built in neighbourhoods with more diverse populations, greater trail connectivity and more destination points, compared to trails with a leisure-type hourly profile (Table 1).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;In a fully adjusted linear mixed effects model, we found that the beginning of the work week was a distinct temporal landmark for AT-based cycling, after which daily rates of cycling traffic declined progressively to the end of a work week (Figure 4A). Trails with an AT profile observed a 22% decline cycling traffic by Fridays compared to Mondays (-147 cyclists/day; 95% CI: -199.0 to -94.6 cyclists/day). Urban trails defined as leisure type, were not characterized by a decline in cycling traffic between the start and end of a work week (-22.1 cyclists/day; 95% CI: -59.1 to +15.0 cyclists/day). To increase the resolution of these trends, we repeated comparisons of daily cycling counts restricted to windows of time characterised by AT. We found that counts per hour during AT windows declined progressively through the week, with 5 fewer cyclists per hour (95% CI: -7.75 to -2.04) on Fridays compared to Mondays (eFigure 2). \u0026nbsp; These trends during commuting windows were evident on trails defined as AT and leisure, suggesting they are robust to AT-specific windows of time (Figure 4B). The trend that the beginning of each week was a temporal landmark for AT-based cycling behaviour were consistent across all five years of data collection and across all AT trails (eFigure 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDaily trends in cycling on active transportation trails are similar to those observed at a large corporate bicycle parking space.\u0026nbsp;\u003c/strong\u003eTo validate and replicate the trends observed in AT-based cycling along urban multi-use trails, we tested for differences in individual access to a protected bicycle parking space between the beginning and end of the work week. From 2012 and 2019, there were 56,307 unique accesses to the protected bicycle parking space, with annual use increasing nearly three-fold from 3640 accesses in 2012 to 9754 accesses in 2019. The temporal landmark for this dataset was Tuesday, as there is a corporate policy for a statutory holiday every other Monday for most employees of the company. Similar to the trends observed along urban multi-use trails, daily rates of bicycle parking declined by ~20% (-12 cyclists/day 95% CI: -17 to -7 cyclists) between Tuesday and Friday (Figure 5a). The decline in the use of protected space for bicycle parking over a work week was evident in all 7 years of data collection (eFigure 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDaily trends in cycling traffic on active transportation trails is similar to daily trends in fitness centre attendance.\u0026nbsp;\u003c/strong\u003eTo determine if the magnitude of the decline AT-based cycling behaviours over the course of a work week was similar relative decline in PA described in the original Fresh Start effect\u003csup\u003e12\u003c/sup\u003e. Like the daily trends we observed for AT-based cycling traffic, visits to the fitness centre were highest on at the beginning of the work/academic week, with an average of 2,833 visits per day (95% CI: 2740 \u0026ndash; 2926 visits per day) and declined progressively through the week, with the lowest visits on Fridays (Figure 5b). The relative decline in daily visits to the fitness centres between Mondays and Fridays was 21%, with an absolute difference of -592 visits per day (95% CI: -759 to -425 visits per day). These data replicate the original findings that were used to define the Fresh Start Effect heuristic\u003csup\u003e12\u003c/sup\u003e and mirror the trends observed for AT-based cycling along urban multi-use trails. The decline in fitness centre attendance from the beginning to the end of the work week was evident in all three years data were available (eFigure 5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrends in active transportation do not appear to be driven by daily patterns in working from home or four-day work weeks.\u003c/strong\u003e In contrast to the trends observed in AT-based cycling behaviour along urban multi-use trails and corporate bicycle parking spaces, vehicular traffic increased 7% (+2848 cars/day; 95% CI: +2202 to +3674 cars/day) from the beginning of a work week (Monday) to the end of the work week (Figure 6a). \u0026nbsp;Similar to vehicular traffic patterns, coffee sales were lowest at the beginning of a work week (245 units per day; 95% CI: 234-253 units per day) and increased incrementally through the work week to a peak of 275 (95% CI: 266-286) units of coffee sold on Fridays (Figure 6b). The relative increase in coffee sales (~10%; \u0026nbsp;31.9 units sales per day; 95% CI: 22.0-41.7 unit sales per day) over the course of a work week was similar to the increase in vehicular traffic and was evident in all 5 years that data were available.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eUsing real-world population-level data, we find that AT-based cycling behaviour in urban cities displays a distinct weekly pattern indicative of a Fresh Start effect. Specifically, we find that the beginning of a work week is a temporal landmark for AT-based cycling behaviour that declines approximately 20% over the course of a work week. These weekly trends in AT-based cycling are particularly robust for cycling traffic between the hours of 6h00 and 9h00 and 15h00 and 18h00. This decline in cycling traffic by the end of a work week is mirrored by an increase in vehicular traffic and coffee sales at a local coffee parlour. The relative decline in AT-based cycling was similar to the relative decline in fitness centre attendance over the course of a work week. Taken together, the trends observed across all five datasets strongly suggest that AT-based cycling patterns follow a distinct Fresh Start Effect heuristic, with the beginning of the work week acting as a temporal landmark.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;The promotion of AT is a growing public health strategy for reducing carbon emissions and the burden of chronic diseases in large urban centres within high income countries\u003csup\u003e20,21\u003c/sup\u003e. The proportion of urban residents that engage in AT varies considerably within and between cities\u003csup\u003e8,19,22-27\u003c/sup\u003e. This variability in AT has been attributed to availability of infrastructure, localized mix land use, perceived safety and local culture for cycling\u003csup\u003e28,29\u003c/sup\u003e. The data presented here add a novel behavioural driver of AT-based cycling behaviour. At a population level, a decline in AT-based cycling of ~20% over the course of a week equates to ~10,000-50,000 fewer individuals travelling by bicycle in cities with populations of 1 to 5M residents. Understanding this trend in AT-based behaviours provides a novel lever for urban public health officials to nudge active commuters to sustain behaviours they adopted early in the week. Additionally, the observation that the beginning of the week is a distinct temporal landmark for people to engage in AT-based cycling, it could also serve as an urban public health strategy to engage large segments of the population to adopt AT.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Various levels of government are using principles of behavioural economics to guide policy decisions to improve the health and well-being of its citizens\u003csup\u003e30,31\u003c/sup\u003e. Using real world data, organizations can detect, track and experiment with approaches to change the behaviours of large segments of a population. As cities are generating and sharing large amounts of population-level data\u003csup\u003e32\u003c/sup\u003e, the opportunities for urban policy making using principles of behavioural economics are increasing\u003csup\u003e33\u003c/sup\u003e. Using this urban data we are able to advance previous work that focused on intended behaviours (google searches, enrollment in programs)\u003csup\u003e34,35\u003c/sup\u003e and demonstrate that across a large urban population, objectively measured AT-based cycling and vehicle driving are both sensitive to temporal landmarks, and the uptake of these behaviours progressively wanes over the course of a work week. The observation that these trends are detectable at the population level and were consistent over 5 years, suggests that temporal landmarks are a robust driver of AT-based cycling behaviour. This information could inform municipal governments when implementing behavioural economic strategies to combat climate change and non-communicable diseases.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;The study is strengthened by a large effect size observed at the population-level using real world objectively-measured trends in cycling patterns along urban trails that were consistently replicated, over multiple years. Additionally, triangulating observations from real-world cycling patterns with data collected from other sources, with different limitations, enhances the interpretation of our findings. Despite these strengths, this study has several limitations. First, the data presented are descriptive in nature and we cannot infer that there is a causal association between temporal landmarks and AT behaviours. Second, individual-level cycling data were not available to test the study hypothesis. Therefore, the weekly trends described here cannot be attributed directly to individual behaviours, rather we infer that similar trends would be observed if individual data were available. Third, we were able to control for several factors including yearly trends, weather patterns and holiday days, however several co-variates were not measured including origin and destination points, sex, gender, race, ethnicity and age. Data were collected in a medium sized urban setting in a northern climate, therefore the generalizability of findings may be limited. This demographic information was not available to determine if these trends were evident for individuals from different genders, ages and structurally oppressed, racialized groups. Future research using data from wearable technology may be used to overcome these limitations.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, in a large urban city, weekly trends in AT-based cycling display a Fresh Start effect with the beginning of each work week serving as an important temporal landmark to engage in this behaviour. The trends in AT are similar to trends used to define the original Fresh Start Effect. These behavioural patterns could be used by municipal policy makers to tailor public health messages to increase rates of AT.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e This study did not require ethics approval or consent for participation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u0026nbsp;\u003c/strong\u003eGeospatial data for cycling infrastructure, cycling traffic counts, vehicular traffic counts are all available within the City of Winnipeg Open Data Portal: https://data.winnipeg.ca/browse?category=Transportation+Planning+%26+Traffic+Management\u0026amp;limitTo=datasets%2Cmaps\u0026amp;sortBy=newest\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData for corporate bicycle parking access, university of Manitoba Recreation Centre accesses and coffee sales at Parlour coffee can be made available upon request to stewards of the data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u003c/strong\u003e The authors declare that they have no competing interests to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e Funding for this project was provided by operating grants from the Heart and Stroke Foundation of Canada (G-17-0018638) and the Canadian Institutes of Health Research (PJT-153449; CPP-137910).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRole of funders/sponsors:\u003c/strong\u003e Funding bodies were not involved in the study design, conduct, interpretation or manuscript preparation for this project. Scientists involved in this study had no relationship with funding agencies and conducted the study independent of funders.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e All authors contributed to the study and manuscript in alignment with current ICMJE guidelines. The study was conceived by JM and NK. JM is the principal investigator on the original funded grant. JM, and NK participated in designing the study and collecting data. JM and CN are involved in data cleaning and verification and conducted the statistical analyses. All authors contributed to the writing of the final document. NK, IF and JM drafted the original manuscript. NV, TG, DP and JC collected data for the study. SS analyzed data. All authors contributed to critically revising the manuscript for important intellectual content, gave their final approval and agreed to be accountable for all aspects of the work, and they will participate in future interpretation of the data and drafting of further manuscripts arising from this work. JM takes full responsibility for the work, conduct of the study, had access to the data and controlled the decision to publish.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e: Not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKatzmarzyk, P. T., Friedenreich, C., Shiroma, E. J. \u0026amp; Lee, I. M. Physical inactivity and non-communicable disease burden in low-income, middle-income and high-income countries. \u003cem\u003eBr J Sports Med\u003c/em\u003e\u003cstrong\u003e56\u003c/strong\u003e, 101-106, doi:10.1136/bjsports-2020-103640 (2022).\u003c/li\u003e\n\u003cli\u003eLee, I. 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Nudging to move: a scoping review of the use of choice architecture interventions to promote physical activity in the general population. \u003cem\u003eInt J Behav Nutr Phys Act\u003c/em\u003e\u003cstrong\u003e16\u003c/strong\u003e, 77, doi:10.1186/s12966-019-0844-z (2019).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-behavioral-nutrition-and-physical-activity","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijbn","sideBox":"Learn more about [International Journal of Behavioral Nutrition and Physical Activity](http://ijbnpa.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ijbn/default.aspx","title":"International Journal of Behavioral Nutrition and Physical Activity","twitterHandle":"@IJBNPA","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5375352/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5375352/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eThis study was designed to determine if active transportation (AT) was sensitive to the behavioural economics heuristic “The Fresh Start Effect”, with a temporal peak in traffic at the beginning of a work week, that declined by the end of the week.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDesign and Methods: \u003c/strong\u003eWe triangulated data from five data sources to test the study hypothesis. First, we categorized 5 urban trails as “AT” or “leisure” based on hourly traffic patterns collected from May to September between 2014 to 2019 using electromagnetic counters (EcoCounter Inc, Montreal Qc.). Daily trends in cycling traffic were then compared with daily trends bicycle parking (n=56,307 counts), vehicular traffic (n=6.2M counts), fitness centre attendance (n=563,290 counts) and sales from a local coffee shop (n=166,753 counts).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e We found a significant ~22% decline in cycling traffic on both AT (-147 cyclists/day; 95% CI: -199.0 to -95 cyclists/day) and leisure trails (-22 cyclists/day; 95% CI: -59 to +15 cyclists/day) over the course of a work week. The relative decline in AT-based cycling traffic was similar to the decline in bicycle parking (~14%; -12 cyclists/day; 95% CI: -17 to -7 cyclists/day). The relative effect size of this trend was nearly identical to the weekly decline in fitness centre attendance (~21%; -592 visits/day; 95% CI: -759 visits/day to -425 visits/day), replicating the original Fresh Start Effect. In contrast, to the decline in AT-based cycling traffic, daily vehicular traffic (+2248 cars/day; 95% CI: 2022 to +3674 cars/day) and coffee sales (+31 units/day; 95% CI: +22 to +42 units/day) increased ~7% from the beginning to the end of a work week.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e Weekly patterns of leisure and AT-based cycling are sensitive to the Fresh Start Effect and could be used to inform policies for increasing cycling rates in urban centres.\u003c/p\u003e","manuscriptTitle":"Urban Active Transportation Behaviour is Sensitive to the Fresh Start Effect: Triangulating observational evidence from real world data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-05 17:13:43","doi":"10.21203/rs.3.rs-5375352/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-02-13T16:56:06+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-12T21:35:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-30T15:19:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"175063804768694468679863826571690068780","date":"2025-01-22T03:32:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"307689286343429497848357698216178279419","date":"2025-01-08T14:18:04+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-18T02:52:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-13T02:52:04+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-13T02:50:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Behavioral Nutrition and Physical Activity","date":"2024-11-01T19:50:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-behavioral-nutrition-and-physical-activity","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijbn","sideBox":"Learn more about [International Journal of Behavioral Nutrition and Physical Activity](http://ijbnpa.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ijbn/default.aspx","title":"International Journal of Behavioral Nutrition and Physical Activity","twitterHandle":"@IJBNPA","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"da4e52aa-7ed7-47eb-bf15-2faf4f8ce8f4","owner":[],"postedDate":"December 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-23T15:59:41+00:00","versionOfRecord":{"articleIdentity":"rs-5375352","link":"https://doi.org/10.1186/s12966-025-01785-w","journal":{"identity":"international-journal-of-behavioral-nutrition-and-physical-activity","isVorOnly":false,"title":"International Journal of Behavioral Nutrition and Physical Activity"},"publishedOn":"2025-06-19 15:57:13","publishedOnDateReadable":"June 19th, 2025"},"versionCreatedAt":"2024-12-05 17:13:43","video":"","vorDoi":"10.1186/s12966-025-01785-w","vorDoiUrl":"https://doi.org/10.1186/s12966-025-01785-w","workflowStages":[]},"version":"v1","identity":"rs-5375352","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5375352","identity":"rs-5375352","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}