Mental health issues, particularly psychological distress, are crucial public health concerns. Understanding the trends in the prevalence of mental health problems is harder because of the cost and resources associated with conducting diagnostic interviews. While mental health issues are generally linked to sociodemographic factors, little is known about the role played by household-level characteristics and residential built-environment features. This pan-Canadian cross-sectional study uses data from the population-based survey (n = 17230) to examine the relationships between sociodemographic factors, household-level characteristics, neighborhood-built environment features, and people's mental health. Mental health was assessed using two commonly used self-report screening questionnaires: the Kessler 6-item tool (Kessler-6) and the 5-item World Health Organization Well-being Index (WHO-5). Kessler-6 and WHO-5 were negatively correlated, with a -0.72 correlation coefficient. While being males, participants' age, interacting with other people, having an outdoor space at residence, and being from a higher socio-economic status (education and income) were inversely linked with psychological distress, being unemployed, living in an apartment and an area with medium population density was positively associated with the outcome. These associations were reversed when WHO-5 was considered the outcome. Public health interventions should focus on high-risk subgroups to reduce psychological distress in Canada.

INTRODUCTION:This study examines the association between activity space and loneliness, hypothesizing that individuals with broader (i.e., more spatially extensive and diverse) daily movement patterns are less likely to experience loneliness. METHODS:Data were derived from the COHESION Study, a nationwide Canadian cohort. Participants (N = 1176) who completed the VERITAS map questionnaire were included. The primary outcome, loneliness, was assessed using a validated three-item scale, and activity spaces were calculated using the activspace R package. An ordinal logistic regression model analyzed the relationship between activity space and loneliness, controlling for sociodemographic and health variables. RESULTS:The analysis revealed significant sociodemographic differences between three levels of loneliness. Age, relationship status, sense of belonging, and physical activity were significant predictors. Loneliness was negatively associated with age and sense of belonging, and positively associated with having a chronic disease, knowing someone who died from COVID-19, and infrequent physical activity. Being in a relationship were strong protective factors against loneliness. Individuals with lower levels of loneliness reported significantly more extensive activity spaces. In particular, the number of unique locations visited - used as a proxy of social activity space - was inversely associated with loneliness, although the strength of this association was modest. CONCLUSION:The study underscores the importance of considering a range of sociodemographic and health factors in understanding loneliness. Broader activity spaces and other factors such as older age, higher physical activity, and higher social connections were directly correlated with lower level of loneliness. These findings can inform public health interventions aimed at reducing loneliness, particularly during crises like the COVID-19 pandemic.

Understanding how built and social environments shape momentary well-being is essential for advancing urban health research and planning. This study investigated temporal, social, and environmental predictors of daily well-being using a geographic ecological momentary assessment (GEMA) approach. Seven-day GEMA measures were collected between 2018 and 2021 among 889 residents of Greater Montreal, recruited through the INTERACT and REM studies. Participants (mean age = 41.7 years; age range = 18-80 years; 55.7% women) completed the Short Mood Scale three times daily via the EthicaData smartphone app, yielding over 10,600 prompts linked with GPS data. Multilevel mixed-effects models were used to assess the associations between well-being and temporal, social, and environmental exposures. Well-being varied significantly across time and contexts (conditional R2 ≈ 0.6). Higher well-being was reported in the afternoon (β = 0.23, 95% CI: 0.03-0.43) and on weekends, particularly Sundays (β = 1.11, 95% CI: 0.74-1.49), whereas evenings were associated with lower well-being (β = -0.31, 95% CI: -0.52 to -0.10). Social interactions, especially with friends (β = 1.97, 95% CI: 1.26-2.67) and family (β = 0.83, 95% CI: 0.42-1.25), were strongly associated with higher well-being. Built environment features, including greenness, proximity to parks, and road density, showed limited associations. Older adults (60-80 years) reported substantially higher well-being than younger (18-40) adults (β = 4.31, 95% CI: 3.44-5.18). An interaction indicated that women reported lower well-being than men when surrounded by others without direct interaction (β = -0.66, 95% CI: -1.17 to -0.09). Temporal rhythms, age, and social interactions were central determinants of momentary well-being, while built environment factors played a lesser role. Integrating GEMA approaches provides robust evidence to inform urban planning and public health strategies that promote supportive social and temporal environments.

Multi-city investigations of how access to cycling infrastructure changes over time for equity-deserving communities have been absent in Canada and are scarce internationally. In this descriptive epidemiological study, we evaluated how area-level (ecological) access to cycling infrastructure varied by neighbourhood socio-demographic profiles in three Canadian cities (Montréal, Vancouver, and Victoria) across the 2011, 2016, and 2021 census years. For each city and year, we calculated the road network distance to the nearest cycling infrastructure from the population-representative centroids of the census dissemination area as the outcome. The independent variables were the area-level proportions of equity-deserving groups. These were Indigenous people, racialized people, recent immigrants, people in low-income households, tenants, individuals with lower educational attainment, children, and older adults. We employed linear and Bayesian spatial regression methods to examine the relationship between the tertile proportions of each population group and the outcome for each city and census year. Areas with a higher proportion of children had lower proximity to cycling infrastructure, regardless of city or census year. Similar patterns were observed for areas with a higher proportion of older adults, although to a lesser extent. The inequity across the proportion of children narrowed over time in Montréal, but not in Vancouver or Victoria. In contrast, areas with a greater proportion of low-income populations had equal or better access to cycling infrastructure across all cities and time periods. These findings on access to cycling infrastructure are concerning due to the lack of age-friendliness in the implementation of infrastructure in these cities.

Background Individuals with multimorbidity often have complex health and social care needs and experience frequent transitions across various settings and providers, including home, community services, primary care, and hospitals. These transitions represent pivotal moments within their care trajectories, where the risk of care fragmentation is significantly increased. Although these transitions play an important role in shaping patient experiences and outcomes, the factors associated with them remain insufficiently documented. Objective This study aimed to identify individual and environmental factors associated with either positive or negative experiences of care transition among individuals with complex needs. Design Using a prospective correlational design, participants were recruited from emergency departments at three sites in two Canadian provinces. Eligible individuals had ≥3 ED visits in the past year, screened positive on the COmplex NEeds Case-finding Tool–6 (CONECT-6), and had complex needs confirmed by an INTERMED Self-Assessment (IMSA) score of 19 or higher. Baseline data included sociodemographic, clinical, and psychosocial variables; environmental variables were derived from geocoded postal codes. At six months, participants completed a 12-item scale on care transitions adapted from the Patient Experience of Integrated Care Scale (PEICS). Multivariable linear regression identified factors associated with transition experiences. Results Of 292 participants recruited, 167 completed the follow-up. Biopsychosocial complexity, self-management capacity, and recruitment site were significantly associated with transition experiences. Higher complexity was associated with less favorable experiences, while stronger self-management was linked to more positive transitions. Conclusion Care transition experiences are associated with biopsychosocial complexity and self-management abilities, with site-level differences point to organizational and systemic influences. Further research is needed to examine how organizational and system-level factors shape transition experiences for individuals with complex needs.

Despite the importance of vaccination, some individuals are not vaccinated due to multiple factors. This study expands on previous research by incorporating elements of the physical and social neighborhood to depict vaccination profiles based on urbanization degree, identifying additional correlations with COVID-19 vaccination status and offering a fresh perspective on this complex issue. This study used three datasets: the COHESION cohort’s second phase, which is a comprehensive Canadian COVID-19 dataset, encompassing demographic and socioeconomic variables; and the CANUE and StatCan datasets for environmental and neighborhood metrics. Fully adjusted modified Poisson regression and stratified modeling by urbanization degree were applied to assess the association between individual and environmental variables and vaccination status. Among 18,355 participants, 3784 were non-vaccinated. Non-vaccination was highest in rural areas (29.51

During the COVID-19 pandemic, the measures taken by authorities to contain the virus and the fear of being infected resulted in reduced human mobility. Even though studies have made an effort to understand the changes in human mobility patterns resulted due to the pandemic, their findings are inconclusive for totally relying on aggregated data collected at ridership level rather than information at the individual-level. Our study uses four waves of travel survey data collected before, during and after the COVID-19 pandemic, in Montreal, to assess the determinants of mode choice and to analyse changes in travel behavior and mode choices. We had 2933 work-related trips from 1275 participants, of which only 290 participants responding in both wave 1 and wave 4 qualified for the mode prediction analysis. We applied a multinominal multilevel analysis to explore predictors of travel behaviour, and a classical multinominal model to analyse mode choice change. Our study’s findings show a huge decline in public transit use during COVID-19 and that it gradually increased after COVID-19, even though it was not comparable to the pre-pandemic level. The odds of public transit users shifting back to public transit after the pandemic was 22.54 (95%CI: 7.29, 69.66) times higher than choosing private motorized vehicles, while the rebound of active transport users was relatively higher (OR: 52.71, 95%CI: 8.68, 320.20). Our study implies that not all the sustainable mode users have returned to using the modes after COVID-19, and it stands as a challenge for transport authorities to develop appropriate strategies to encourage them to rebound.

Community design has the potential to address urban isolation and loneliness at a population level, but limited research on the causal effects of the built environment constrains evidence-based action in cities. This study examined the effect of public open space on changes in social connectedness among adults (n = 665) during the COVID-19 pandemic, using geospatial data from OpenStreetMap and health survey data from three cities (Montréal, Saskatoon, and Vancouver). Treating the pandemic as a natural experiment, we used multilevel models to analyze whether public open space exposure (defined as the ratio of land area within 500m of home) modified changes in community belonging, loneliness, and neighbouring from 2018 to 2020/2021. First, we found little evidence of changes in social connectedness in our cohort overall and within subgroups. On average, loneliness increased slightly, and belonging and neighbouring remained stable. Second, we found that higher public open space exposure (≥10 % neighbourhood land area) had a modest protective effect on community belonging only (0.14, 95 % CI = 0.01 to 0.27). These findings add to a limited but growing evidence base on the role of the built environment in shaping social connectedness, while highlighting challenges involved in examining causal impacts. As cities invest in public open space to support policy goals around sustainability and livability, evaluating co-benefits for social connectedness are critical opportunities for strengthening the evidence on built environment solutions to social isolation and loneliness.

Abstract As urbanisation continues to accelerate, urban green spaces are increasingly recognised as key elements for enhancing people's health and well‐being. However, most research has used vegetation metrics that may not capture the specific associations between different types of vegetation and different mental health outcomes. In this study, we investigate the cross‐sectional associations between residential vegetation exposure and individual well‐being in Montreal, Canada, using different vegetation and well‐being measures: The proportion of grass cover, tree cover, and average NDVI value within buffers of various radii (100–1000 m) were linked to each participant's residence (n = 1072, aged 18 years or older), while well‐being was assessed using subjective happiness, emotional well‐being, and personal well‐being scales. The associations were analysed using generalised additive regression models. Our findings show that more vegetation was linked to enhanced well‐being, although the effect sizes were relatively small. Irrespective of the buffer distance, the positive associations for grass and NDVI were more pronounced than those for trees, though these associations varied across the different well‐being outcome measures. We also observed that increasing tree coverage has a stronger positive effect on the well‐being of individuals who are dissatisfied with the current number of street trees. Synthesis and applications. Everyday exposure to nearby nature is associated with better self‐reported mental health, suggesting urban greening policies should focus on including more vegetation within built spaces, from individual street trees to small and large parks. Our study also highlights the importance of distinguishing between different types of vegetation (e.g. grass vs. trees) when studying the effects of vegetation on well‐being or other health‐related outcomes. Likewise, using different measures of well‐being may provide a more nuanced and comprehensive understanding of how vegetation impacts people's well‐being. Read the free Plain Language Summary for this article on the Journal blog.

Background: Cycling infrastructure investments support active transportation, improve population health, and reduce health inequities. This study examines the relationship between changes in cycling infrastructure (2011-2016) and census tract (CT)-level measures of material deprivation, visible minorities, and gentrification in Montreal. Methods: Our outcomes are the length of protected bike lanes, cyclist-only paths, multi-use paths, and on-street bike lanes in 2011, and change in total length of bike lanes between 2011 and 2016 at the CT level. Census data provided measures of the level of material deprivation and of the percentage of visible minorities in 2011, and if a CT gentrified between 2011 and 2016. Using a hurdle modeling approach, we explore associations among these CT-level socioeconomic measures, gentrification status, baseline cycling infrastructure (2011), and its changes (2011-2016). We further tested if these associations varied depending on the baseline level of existing infrastructure, to assess if areas with originally less resources benefited less or more. Results: In 2011, CTs with higher level of material deprivation or greater percentages of visible minorities had less cycling infrastructure. Overall, between 2011 and 2016, cycling infrastructure increased from 7.0% to 10.9% of the road network, but the implementation of new cycling infrastructure in CTs with no pre-existing cycling infrastructure in 2011 was less likely to occur in CTs with a higher percentage of visible minorities. High-income CTs that were ineligible for gentrification between 2011 and 2016 benefited less from new cycling infrastructure implementations compared to low-income CTs that were not gentrified during the same period. Conclusion: Montreal's municipal cycling infrastructure programs did not exacerbate socioeconomic disparities in cycling infrastructure from 2011 to 2016 in CTs with pre-existing infrastructure. However, it is crucial to prioritize the implementation of cycling infrastructure in CTs with high populations of visible minorities, particularly in CTs where no cycling infrastructure currently exists.

In this study, we compared location data from a dedicated Global Positioning System (GPS) device with location data from smartphones. Data from the Interventions, Equity, and Action in Cities Team (INTERACT) Study, a study examining the impact of urban-form changes on health in 4 Canadian cities (Victoria, Vancouver, Saskatoon, and Montreal), were used. A total of 337 participants contributed data collected for about 6 months from the Ethica Data smartphone application (Ethica Data Inc., Toronto, Ontario, Canada) and the SenseDoc dedicated GPS (MobySens Technologies Inc., Montreal, Quebec, Canada) during the period 2017-2019. Participants recorded an average total of 14,781 Ethica locations (standard deviation, 19,353) and 197,167 SenseDoc locations (standard deviation, 111,868). Dynamic time warping and cross-correlation were used to examine the spatial and temporal similarity of GPS points. Four activity-space measures derived from the smartphone app and the dedicated GPS device were compared. Analysis showed that cross-correlations were above 0.8 at the 125-m resolution for the survey and day levels and increased as cell size increased. At the day or survey level, there were only small differences between the activity-space measures. Based on our findings, we recommend dedicated GPS devices for studies where the exposure and the outcome are both measured at high frequency and when the analysis will not be aggregate. When the exposure and outcome are measured or will be aggregated to the day level, the dedicated GPS device and the smartphone app provide similar results.

Background Public health measures in response to the COVID-19 pandemic forced individuals to spend more time at home. We sought to investigate the relationship between housing characteristics and sleep duration in the context of COVID-19. Methods Our exploratory study was part of the COvid-19: Health and Social Inequities across Neighborhoods (COHESION) Study Phase-1, a pan-Canadian population-based cohort involving nearly 1300 participants, launched in May 2020. Sociodemographic, household and housing characteristics (dwelling type, dissatisfaction, access to outdoor space, family composition, etc.), and self-reported sleep were prospectively collected through COHESION Study follow-ups. We explored the associations between housing and household characteristics and sleep duration using linear regressions, as well as testing for effect modification by income satisfaction and gender. Results Our study sample involved 624 COHESION Study participants aged 50 ± 16years (mean±SD), mainly women (78%), White (86%), and university graduates (64%). The average sleep duration was 7.8 (1.4) hours. Sleep duration was shorter according to the number of children in the household, income dissatisfaction, and type of dwelling in multivariable models. Sleep was short in those without access to a private outdoor space, or only having a balcony/terrace. In stratified analyses, sleep duration was associated with housing conditions dissatisfaction only in those dissatisfied with their income. Conclusion Our exploratory study highlights the relationship between housing quality and access to outdoor space, family composition and sleep duration in the context of COVID-19. Our findings also highlight the importance of housing characteristics as sources of observed differences in sleep duration.

While a growing body of research has been demonstrating how exposure to social and built environments relate to various health outcomes, specific pathways generally remain poorly understood. But recent technological advancements have enabled new study designs through continuous monitoring using mobile sensors and repeated questionnaires. Such geographically explicit momentary assessments (GEMA) make it possible to link momentary subjective states, behaviors, and physiological parameters to momentary environmental conditions, and can help uncover the pathways linking place to health. Despite its potential, there is currently no review of GEMA studies detailing how location data is used to measure environmental exposure, and how this in turn is linked to momentary outcomes of interest. Moreover, a lack of standard reporting of such studies hampers comparability and reproducibility. The objectives of this research were twofold: 1) conduct a systematic review of GEMA studies that link momentary measurement with environmental data obtained from geolocation data, and 2) develop a STROBE extension guideline for GEMA studies. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Inclusion criteria consisted of a combination of repeated momentary measurements of a health state or behavior with GPS coordinate collection, and use of these location data to derive momentary environmental exposures. To develop the guideline, the variables extracted for the systematic review were compared to elements of the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) and CREMAS (CRedibility of Evidence from Multiple Analyses of the Same data) checklists, to provide a new guideline for GEMA studies. An international panel of experts participated in a consultation procedure to collectively develop the proposed checklist items. A total of 20 original GEMA studies were included in the review. Overall, several key pieces of information regarding the GEMA methods were either missing or reported heterogeneously. Our guideline provides a total of 27 categories (plus 4 subcategories), combining a total of 70 items. The 22 categories and 32 items from the original STROBE guideline have been integrated in our GEMA guideline. Eight categories and 6 items from the CREMAS guideline have been included to our guideline. We created one new category (namely “Consent”) and added 32 new items specific to GEMA studies. This study offers a systematic review and a STROBE extension guideline for the reporting of GEMA studies. The latter will serve to standardize the reporting of GEMA studies, as well as facilitate the interpretation of results and their generalizability. In short, this work will help researchers and public health professionals to make the most of this method to advance our understanding of how environments influence health.

This study investigates the relationship between increasing urban vegetation and census tract-level green inequities, as well as the role of social indicators in this relationship. We analyzed the augmentation of greenspace and tree canopy in Montreal, Canada, between 2011 and 2017, and its effect on green inequities based on material deprivation, the percentage of visible minorities, and gentrification status using Poisson spatial random effect models. Our analyses showed an increase in greenspace from 57.4% to 65.8% and tree canopy from 21.1% to 22.3% between 2011 and 2017. Census tracts (CTs) with higher levels of material deprivation or a higher percentage of visible minority population had less greenspace and tree canopy at baseline in 2011. Additionally, CTs that were not gentrified had less greenspace and tree canopy than ineligible for gentrification CTs. Furthermore, CTs with more visible minorities, higher levels of material deprivation, or those that did not gentrify had smaller increases in greenspace and tree canopy between 2011 and 2017. Among CTs with more visible minorities or higher levels of material deprivation, those with greater greenspace and tree canopy at baseline also experienced greater increases during the study time period. Conversely, among ineligible for gentrification CTs, those with less greenspace/tree canopy at baseline experienced greater increase in greenspace/tree canopy. Our analysis revealed that despite an increase in urban vegetation, inequities in urban vegetation persists. To reduce green inequities and promote social equity in this particular study area, urban planning policies should prioritize CTs with higher levels of material deprivation, more visible minorities, or those that did not gentrify, and focus on increasing urban vegetation.

While census-defined measures of gentrification are often used in research on gentrification and health, surveys can be used to better understand how residents perceive neighborhood change, and the implications for mental health. Whether or not gentrification affects mental health may depend on the extent to which an individual perceives changes in their neighborhood. Using health and map-based survey data, collected from 2020 to 2021, from the Interventions, Research, and Action in Cities Team, we examined links between perceptions of neighborhood change, census-defined neighborhood gentrification at participant residential addresses, and mental health among 505 adults living in Montréal. After adjusting for age, gender, race, education, and duration at current residence, greater perceived affordability and more positive feelings about neighborhood changes were associated with better mental health, as measured by the mental health component of the short-form health survey. Residents who perceived more change to the social environment had lower mental health scores, after adjusting individual covariates. Census-defined gentrification was not significantly associated with mental health, and perceptions of neighborhood change did not significantly modify the effect of gentrification on mental health. Utilizing survey tools can help researchers understand the role that perceptions of neighborhood change play in the understanding how neighborhood change impacts mental health.

Interest is growing in neighborhood effects on health beyond individual's home locations. However, few studies accounted for selective daily mobility bias. Selective mobility of 470 older adults (aged 67-94) living in urban and suburban areas of Luxembourg, was measured through detour percentage between their observed GPS-based paths and their shortest paths. Multilevel negative binomial regression tested associations between detour percentage, trips characteristics and environmental exposures. Detour percentage was higher for walking trips (28%) than car trips (16%). Low-speed areas and connectivity differences between observed and shortest paths vary by transport mode, indicating a potential selective daily mobility bias. The positive effects of amenities, street connectivity, low-speed areas and greenness on walking detour reinforce the existing evidence on older adults' active transportation. Urban planning interventions favoring active transportation will also promote walking trips with longer detours, helping older adults to increase their physical activity levels and ultimately promote healthy aging.

The recent promotion of sustainable urban planning combined with a growing need for public interventions to improve well-being and health have led to an increased collective interest for green spaces in and around cities. In particular, parks have proven a wide range of benefits in urban areas. This also means inequities in park accessibility may contribute to health inequities. In this work, we showcase the application of classic tools from Operations Research to assist decision-makers to improve parks' accessibility, distribution and design. Given the context of public decision-making, we are particularly concerned with equity and environmental justice, and are focused on an advanced assessment of users' behavior through a spatial interaction model. We present a two-stage fair facility location and design model, which serves as a template model to assist public decision-makers at the city-level for the planning of urban green spaces. The first-stage of the optimization model is about the optimal city-budget allocation to neighborhoods based on a data exposing inequality attributes. The second-stage seeks the optimal location and design of parks for each neighborhood, and the objective consists of maximizing the total expected probability of individuals visiting parks. We show how to reformulate the latter as a mixed-integer linear program. We further introduce a clustering method to reduce the size of the problem and determine a close to optimal solution within reasonable time. The model is tested using the case study of the city of Montreal and comparative results are discussed in detail to justify the performance of the model.

Built environment interventions have the potential to improve population health and reduce health inequities. The objective of this paper is to present the first wave of the INTErventions, Research, and Action in Cities Team (INTERACT) cohort studies in Victoria, Vancouver, Saskatoon, and Montreal, Canada. We examine how our cohorts compared to Canadian census data and present summary data for our outcomes of interest (physical activity, well-being, and social connectedness). We also compare location data and activity spaces from survey data, research-grade GPS and accelerometer devices, and a smartphone app, and compile measures of proximity to select built environment interventions.

BACKGROUND:With the advent of the COVID-19 pandemic, in-person social interactions and opportunities for accessing resources that sustain health and well-being have drastically reduced. We therefore designed the pan-Canadian prospective COVID-19: HEalth and Social Inequities across Neighbourhoods (COHESION) cohort to provide a deeper understanding of how the COVID-19 pandemic context affects mental health and well-being, key determinants of health, and health inequities.METHODS:This paper presents the design of the two-phase COHESION Study, and descriptive results from the first phase conducted between May 2020 and September 2021. During that period, the COHESION research platform collected monthly data linked to COVID-19 such as infection and vaccination status, perceptions and attitudes regarding pandemic-related measures, and information on participants' physical and mental health, well-being, sleep, loneliness, resilience, substances use, living conditions, social interactions, activities, and mobility.RESULTS:The 1,268 people enrolled in the Phase 1 COHESION Study are for the most part from Ontario (47%) and Quebec (33%), aged 48 ± 16 years [mean ± standard deviation (SD)], and mainly women (78%), White (85%), with a university degree (63%), and living in large urban centers (70%). According to the 298 ± 68 (mean ± SD) prospective questionnaires completed each month on average, the first year of follow-up reveals significant temporal variations in standardized indexes of well-being, loneliness, anxiety, depression, and psychological distress.CONCLUSIONS:The COHESION Study will allow identifying trajectories of mental health and well-being while investigating their determinants and how these may vary by subgroup, over time, and across different provinces in Canada, in varying context including the pandemic recovery period. Our findings will contribute valuable insights to the urban health field and inform future public health interventions.

Increases in cycling infrastructure might be linked to gentrification. However, there is little empirical evidence investigating the existence and directionality of this possible relationship. This study examined the temporal sequence involved in the relation between gentrification and increases in the cycling infrastructure in Montreal, Canada. We analyzed changes in cycling infrastructure between 2006, 2011, and 2016, considering cyclist-only paths, multi-use paths, and on-street bike lanes. The Ding measure was used to identify gentrified census tracts (CTs) using census data. We implemented logistic regression models with and without geographically weighted regression specification at the CT level to test three scenarios; whether an increase in cycling infrastructure (2006–2011) was associated with subsequent gentrification (2011–2016); whether gentrification (2006–2011) was associated with subsequent increase in cycling infrastructure (2011–2016); or if these phenomena happened simultaneously (2011–2016). Increase in cycling infrastructure was not linked to subsequent gentrification, nor did these two phenomena happen simultaneously. However, gentrified CTs had a 44% greater chance of a subsequent increase in cycling infrastructure, with varying strengths of associations across the study area. When planning increases in cycling infrastructure, it is crucial to take an equity-based approach that underlying sociodemographic dynamics of urban CTs. To achieve this, cities need to engage in broad upstream community engagement, ensuring the inclusion of a diverse range of voices in the decision-making process.