Socioecological correlates of active transportation in Ghana

Socioecological correlates of active transportation in Ghana | 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 Socioecological correlates of active transportation in Ghana Richard Danyi, Patrick Akwaboah This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8969633/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Background Active transportation (AT) offers a sustainable and equitable means of increasing population physical activity levels. Yet, evidence of its determinants among adult populations in Africa remains limited. This study examined the correlates of AT among a cadre of healthcare professionals in Ghana. Methods A cross-sectional analysis was conducted using data from a 2024 survey of 439 Physician Assistants. Guided by the socioecological model, potential correlates were assessed across individual, social, built, and natural environment levels. Hierarchical binary logistic regression with likelihood ratio tests examined the association between AT and potential correlates. Results AT prevalence was 34.4%. In the final model, male sex (aOR = 1.68, 95% CI: 1.10, 2.65) and sidewalk on commute routes (aOR = 1.84, 95% CI : 1.20, 2.82) were positively associated with AT. Higher education (aOR = 0.43, 95% CI : 0.19, 0.97), car ownership (aOR = 0.59, 95% CI : 0.37, 0.94), and perceiving AT as time-consuming (aOR = 0.41, 95% CI : 0.23, 0.74) were inversely associated with AT. Climatic factors such as temperature (aOR = 2.12, 95% CI : 1.13, 4.01) and relative humidity (aOR = 1.03, 95% CI : 1.01, 1.05) increased the odds of AT, while wind speed reduced them (aOR = 0.62, 95% CI : 0.47, 0.81). Discussion About one in three Physician Assistants in Ghana commute actively, shaped by individual, social, and environmental factors. Promoting AT will require multi-level interventions and policies that integrate infrastructure improvements, climate resilience, and sex-sensitive approaches to sustain the physical and mental health benefits of active commuting. Physical activity sub-Saharan Africa Socio-ecological model 1.0 Introduction Physical inactivity is a major global health concern and a leading risk factor for non-communicable diseases (NCDs) [ 1 , 2 ]. It contributes to approximately 7·7% of all-cause mortality worldwide and accounts for nearly 69% of deaths in middle-income countries [ 3 ]. Promoting physical activity (PA) through population/sub-population level strategies is therefore essential to improving health outcomes and reducing the burden of NCDs. Active transportation (AT), which includes walking and cycling, has gained recognition as a cost-effective, equitable, and sustainable means of increasing PA [ 4 ]. AT confers multiple health benefits, including decreased odds of hypertension and diabetes [ 5 ]. Walking has been associated with reduced symptoms of depression and anxiety [ 6 ], while population-level promotion of AT yields substantial economic gains through improved productivity [ 7 ]. Furthermore, the United Nations Environment Program identifies AT as a key strategy for decarbonizing transport-related greenhouse gas emissions [ 8 ]. Despite broad benefits, little is known about AT participation among healthcare professionals, particularly in Africa. Most existing studies have focused on leisure-time and occupational PA [ 9 , 10 ]. For instance, a South African study among 174 primary healthcare workers reported an AT prevalence of about 40% but did not explore its correlates [ 11 ]. In Ghana, empirical data on AT among adults remains scarce, creating evidence gaps on both prevalence and contextual drivers. This absence is reflected in the third Global Observatory for Physical Activity (GoPA) country report card, which contained no adult AT data for Ghana [ 12 ]. Our recent analysis among Physician Assistants in Ghana filled part of this gap by showing that engagement in AT was positively associated with good self-rated health [ 13 ] and inversely associated with anxiety symptoms [ 14 ]. These findings suggest that encouraging AT may improve both physical and mental well-being among health workers. However, sustaining these gains requires an understanding of the factors that facilitate or hinder AT participation. Identifying such correlates is also vital to strengthening the role of healthcare professionals, such as Physician Assistants, as promoters of PA in their clinical and community settings, as emphasized by evidence from a recent systematic review [ 15 ]. The socio-ecological model provides a useful framework for examining these correlates by recognizing the dynamic interplay between individual, social, and environmental determinants.[ 16 ] At the individual level, age and sex have been shown to influence participation in AT [ 17 ]. For example, younger adults aged 18–44 years are more likely to cycle compared to older adults (aOR = 3.0, 95% CI: 2.4, 3.8) [ 18 ]. Social factors, such as income, perceptions, and norms, also shape AT. For instance, in Ghana, increasing household income is associated with the use of motorized transport among adults, which is seen as a sign of affluence and prestige [ 19 ]. Environmental factors, including the presence of sidewalks and weather conditions, further influence AT uptake. Higher odds of walking and cycling have been observed in areas with well-maintained sidewalks in South Africa (OR = 2.69, 95% CI: 2.20, 10.02) [ 20 ], while adverse weather conditions have also been found to discourage AT participation [ 21 ]. This study, therefore, aims to examine the correlates of AT among Physician Assistants in Ghana. Findings will provide baseline estimates of AT prevalence among an adult professional group, inform context-specific policies and interventions, and contribute to national discussions/efforts to promote PA. 2.0 Methodology 2.1 Study Design This cross-sectional study analyzed data from our previously conducted survey among Physician Assistants in Ghana, for which detailed methods have been published elsewhere [ 22 ]. Briefly, data were collected between October and December 2024. A total of 439 participants completed the questionnaire online. Ethical approval for the original study was obtained from the Metropolitan Research and Education Bureau (MREB) Office of the Research Ethics Review Committee (RERC) under protocol number MREB/RERC/19/24. Electronic informed consent was obtained from all participants. The present analysis followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) recommendations [ 23 ]. All 439 responses were included in the analysis. 2.2 Measures 2.2.1 Outcome AT served as the primary outcome in this study. Participants identified their main mode of transportation to work, selecting from walking, cycling, private car, trotro/bus, or uber/taxi. For analytical purposes, AT was dichotomized as active (walking, cycling) and motorized (private car, trotro/bus, uber/taxi) transport. 2.2.2 Potential Correlates We used the socio-ecological framework [ 16 ] to identify potential correlates of AT, encompassing individual, social, built, and natural environment levels. 2.2.2.1 Individual level Participants reported their age (later categorized as 24–40 years and 41–67 years), sex (male and female) and educational attainment (advanced diploma, a bachelor’s degree, or a master’s degree and higher). Car ownership was reported as the number of vehicles owned and further categorized as none versus one or more. 2.2.2.2 Social level Monthly income was reported by each participant, classified as below Gh¢5000 or Gh¢5000 and higher. They also indicated whether they perceived AT as time-consuming or stigmatized (yes or no). 2.2.2.3 Built environmental level Participants reported whether sidewalks were available along their usual commute (yes or no). They also indicated whether their practice location was in a rural or urban setting, according to the official definition in Ghana [ 24 ]. 2.2.2.4 Natural environment level Each participant’s region of practice was linked to regional weather data, including rainfall, temperature, relative humidity, and wind direction, obtained from the Ghana Meteorological Agency’s satellite-supported monitoring systems. Average values were computed across the study period (October to December 2024) for each region and merged with the corresponding participant data. 2.3 Analysis Strategy Descriptive statistics were computed to provide an overview of the study population. Categorical variables were presented as frequencies and percentages, and continuous variables as means with standard deviations. The associations between AT and potential correlates were examined using a hierarchical binary logistic regression model, guided by the socioecological framework. Predictors were added in four sequential blocks (individual, social, built environment, and natural environment levels) to assess the incremental changes in model performance. The likelihood-ratio (LR) test was applied at each step to assess whether the addition of a new block significantly improved model fit. Model adequacy and parsimony were evaluated using the Akaike Information Criterion (AIC) and McFadden’s Pseudo-R². Lower AIC values and higher Pseudo-R² values indicated improved model performance. Findings are presented as adjusted odds ratios with corresponding 95% confidence intervals. Statistical significance was determined at a two-sided α = 0.05. All analyses were conducted using STATA version 19.0 (StataCorp LLC, College Station, TX, USA). Model diagnostics are presented in Table 1 . Model 4 demonstrated the best overall fit, indicating that the inclusion of natural environment variables substantially improved explanatory power beyond individual, social, and built environment predictors. Table 1 Model fit assessment Model Pseudo R 2 LR test ( p -value) AIC 1 0·03 18·40 (0·0025) 558·7044 2 0·05 9·38 (0·0246) 555·3243 3 0·07 8·78 (0·0124) 550·5398 4 0·12 28·97 (0·0001) 529·5676 3.0 Results 3.1 Participants and climatic characteristics As shown in Table 2 , most of the participants were young adults, predominantly male, and practiced mainly in urban areas. About 90% had an education at the bachelor's level or above, a third owned a car, and two-thirds indicated the absence of sidewalks along their commuting routes. The perception of AT as stigmatized or time-consuming was high among the participants. Additionally, weather conditions during the study period were average, as captured in Table 3 . The overall prevalence of AT was 34·4%. Table 2 Descriptive statistics of respondents Variables Overall (n = 439), n (%) Age Group 24–40 years 324 (73.8) 41–67 years 115 (26.2) Sex Female 150 (34.2) Male 289 (65.8) Education level Advanced Diploma 42 (9.6) Bachelor’s 305 (69.5) Master’s or higher 92 (21.0) Number of cars None 275 (62.6) One or more 164 (37.4) Monthly income Gh¢ 5000 and above 231 (52.6) Below Gh¢5000 208 (47.4) Active transport perception Consumes time 375 (86.4) Does not consume time 64 (14.6) Perceived Active transport stigma No 172 (39.2) Yes 267 (60.8) Urbanization Rural 93 (21.2) Urban 346 (78.8) Availability of sidewalks No 292 (66.5) Yes 147 (33.5) Trip to Work Motorized 288 (65.6) Active 151 (34.4) Table 3 Natural environment variables Weather Indicators (Units) Mean ± SE 95% CI Rainfall (mm) 107.9 ± 3.3 101.4, 114.5 Temperature (°C) 28.2 ± 0.1 28.1, 28.3 Relative Humidity (%) 74.3 ± 0.5 73.3, 75.4 Wind Speed (knots) 2.9 ± 0.1 2.8, 3.1 mm=millimeters. °C=degree Celsius. %=percentage. 3.2 Correlates of Active Transportation Significant correlates of AT were identified through a hierarchical binary logistic regression. Although regression estimates were relatively stable across all models, the final and fourth model showed improved model performance, accounting for 12.0% of the observed variance (Table 4). At the individual level, male participants had 68% higher odds of engaging in AT compared to females, whereas those with a master’s degree and higher had 57% lower odds compared to advanced diploma holders. Additionally, car ownership was associated with 41% lower odds of AT. While perceiving AT as time-consuming was associated with 59% lower odds at the social level, the presence of sidewalks was associated with 84% higher odds of AT within the built environment level. Finally, at the natural environment level, a unit increase in temperature was associated with 12% higher odds of AT, while relative humidity was associated with a marginal 3% higher odds. A unit increase in wind speed was, however, associated with 39% lower odds of AT. Table 3 Association between active transportation to work and potential correlates Factors/variables Model 1 Model 2 Model 3 Model 4 aOR (95% CI) aOR (95% CI) aOR (95% CI) aOR (95% CI) Individual-level Age group (years) 24–40 REF REF REF REF 41–67 0.91 (0.55,1.51) 0.89 (0.53, 1.52) 0.90 (0.53, 1.52) 0.90 (0.53, 1.52) Sex Female REF REF REF REF Male 1 . 71* (1 . 10, 2 . 66) 1 . 67* (1 . 10, 2 . 60) 1 . 68* (1 . 10, 2 . 65) 1 . 68* (1 . 10, 2 . 65) Education Advanced diploma REF REF REF REF Bachelor’s 0.63 (0.32, 1.26) 0.65 (0.33, 1.30) 0.66 (0.33, 1.34) 0.66 (0.33, 1.34) Master’s or higher 0 . 42* (0 . 19, 0 . 93) 0 . 42* (0 . 19, 0 . 94) 0 . 43* (0 . 19, 0 . 97) 0 . 43* (0 . 19, 0 . 97) Number of cars None REF REF REF REF One or more 0 . 58* (0 . 37, 0 . 91) 0 . 60* (0 . 33, 0 . 96) 0 . 59* (0 . 37, 0 . 94) 0 . 59* (0 . 37, 0 . 94) Social level Monthly income Gh¢ 5,000 and above REF REF REF Below Gh¢ 5,000 1.05 (0.67, 1.66) 1.04 (0.66, 1.65) 1.04 (0.66, 1.65) Active transport consumes time No REF REF REF Yes 0 . 41** (0 . 23, 0 . 73) 0 . 41** (0 . 23, 0 . 74) 0 . 41** (0 . 23, 0 . 74) Perceived active transport stigma No REF REF REF Yes 1.12 (0.73, 1.73) 1.14 (0.74, 1.78) 1.14 (0.74, 1.78) Built environment Urbanization Rural REF REF Urban 0.75 (0.46, 1.26) 0.75 (0.46, 1.26) Availability of sidewalks No REF REF Yes 1 . 84* (1 . 20, 2 . 82) 1 . 84* (1 . 20, 2 . 82) Natural environment Rainfall (mm) 1.00 (0.99, 1.01) Temperature (°C) 2 . 12* (1 . 13, 4 . 01) Relative humidity (°) 1 . 03* (1 . 01, 1 . 05) Wind speed (knots) 0 . 61*** (0 . 47, 0 . 79) aOR: adjusted odds ratio. CI : confidence intervals. REF: reference. *** P < 0.001; ** P < 0.01; * P < 0.05. 4.0 Discussion This study examined the correlates of AT among Physician Assistants in Ghana, extending our previous analyses that demonstrated a positive association between AT and self-rated health, and an inverse association with anxiety symptoms. Results indicated that about one-third of the participants commuted actively to work. Guided by the socio-ecological model, hierarchical regression analysis identified significant correlates across multiple levels: individual (sex, education, car ownership), social (perceptions of AT), built environment (availability of sidewalks), and natural environment (temperature, humidity, wind speed), demonstrating how AT behavior is shaped by multi-level influences. The prevalence of AT observed in this study (34·4%) was lower than observed in several African countries (50–77%) but higher than in regions like Latin America and the Caribbean (12%) [ 12 , 25 ]. This pattern likely reflects infrastructural and cultural constraints. The dominance of motorized commuting (65.6%) aligns with Ghana’s rapid annual urbanization rate of 3.3% [ 24 ], which likely underscores the prioritization of vehicular infrastructure over pedestrian needs [ 26 ]. A coordinated national AT policy, aligned with the Pan-African Action Plan for Active Mobility (PAAPAM), could help correct this imbalance and create safer, more inclusive active commuting environments.[ 27 ] At the individual level, males were more likely than females to use AT, consistent with findings from other countries [ 28 ]. These sex disparities may stem from cultural norms, safety concerns, and fear of harassment, all of which heighten risk perception and discourage females from participating in AT [ 29 ]. Addressing these disparities calls for investments in protected pedestrian and cycling infrastructure, improved lighting, and sex-sensitive urban design that enhances safety and inclusivity. Educational attainment and car ownership were also inversely related to AT participation. Participants with master’s degrees or higher were less likely to use AT, supporting evidence from the United States of America [ 30 ], but contrasting with studies from Europe, where AT is often a lifestyle choice among the educated [ 31 ]. Car ownership similarly reduced AT odds, supporting evidence from a study in China [ 32 ]. These results suggest that rising socioeconomic status and perceptions of prestige associated with car ownership may discourage utilitarian PA [ 33 , 34 ]. At the social level, perceiving AT as time-consuming significantly reduced participation, consistent with the economic value commuters attach to travel time [ 35 ]. Enhancing AT convenience through improved connectivity, direct walking routes, and shaded or landscaped paths could help mitigate time-related barriers. The built environment also played a crucial role: participants with sidewalks along their commuting routes had 84% higher odds of engaging in AT, reinforcing evidence that walkable neighborhoods facilitate daily movement [ 36 ]. However, only one-third of respondents reported sidewalk availability, highlighting critical infrastructure deficits. Urban and road design, as well as land-use planning, should consider integrating pedestrian infrastructure as a public health priority. Climatic factors further influenced AT participation. Participants were more likely to engage in AT during warmer temperatures but less likely when wind speeds were high. A marginal positive association was observed for relative humidity. These findings partly diverge from studies in high-income settings, where both humidity and wind are typically found to be negative correlates of AT [ 37 ]. These patterns, though context-dependent, highlight the importance of adapting AT policies to local weather conditions. During Ghana’s dry season (October–December), dust-laden northeasterly winds can impair air quality and visibility,[ 38 ] discouraging walking and cycling. Urban greening and shaded corridors could help minimize the impact of adverse weather while promoting comfort and safety for active commuters. 4.1 Implications of findings As climate intensifies in Ghana [ 38 ], its potential impact on mobility patterns will likely become more pronounced. Strengthening intersectoral collaboration among transport, health, and environment sectors is essential to designing climate-resilient, walkable communities. Policymakers could integrate meteorological data into transport planning and use telecommunication platforms to provide real-time weather updates that promote safe AT. Beyond infrastructure, addressing sex, social status, and time perception barriers will be key to sustaining the mental and physical health benefits previously identified among Physician Assistants in Ghana. 4.2 Future Research A national survey on AT prevalence and correlates is needed to adequately inform national policies. Future studies should employ longitudinal designs to examine temporal variations and causal pathways between AT and health outcomes. Expanding analyses across all climatic seasons and integrating objective measures (e.g., GPS, accelerometry) could provide richer evidence. Additionally, assessing the economic and environmental co-benefits of AT would help quantify its contribution to sustainable development and justify investments in active mobility infrastructure. 5. Strengths and Limitations The use of a voluntarily completed online survey introduces potential selection bias, and self-reported transport behaviors may be affected by recall or social desirability bias. Weather data covered only three months, limiting seasonal comparisons. Furthermore, the cross-sectional design restricts causal inference, and findings may not generalize to all health worker groups or the general adult population in Ghana. Nonetheless, this study is among the first to comprehensively examine multi-level correlates, including climatic factors, of AT in Ghana, offering novel insights into the environmental and behavioral determinants of AT in an African context. 6. Conclusion This study provides context-specific evidence on the correlates of AT among Physician Assistants in Ghana and builds on prior findings linking AT to improved self-rated health and reduced anxiety symptoms. Although one-third of participants engaged in AT, barriers such as inadequate sidewalks, time constraints, and adverse weather conditions limit broader participation. Strengthening active mobility will require a coordinated policy integrating health, transport, and urban planning, supported by investments in safe, inclusive, and climate-resilient infrastructure. Empowering healthcare professionals to model and advocate for AT can amplify public health gains and advance Ghana’s progress toward sustainable, healthy cities. Declarations Competing interest The authors declare no competing interests relevant to the context of this article. Competing Interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Ethics Approval and Consent to Participate The primary study received ethical approval from the Metropolitan Research and Education Bureau (MREB), office of the Research Ethics Review Committee (RERC), with protocol number MREB/RERC/19/24. All collected datawere securely stored, adhering to ethical guidelines and data protection regulations. Informed consent was obtained electronically. On the first page of the online questionnaire, participants were presented with a detailed consent statement and were required to select either "I agree to participate" or "I do not agree." Only those who selected "I agree" were able to proceed with the survey. All responses were anonymous, and participation was entirely voluntary. Consent to Publish Not applicable. No individual-level identifying information or images are included in this manuscript. Clinical Trial Number Not applicable Funding No funding was received for conducting this study. Author Contribution **Patrick Akwaboah** : Conceptualization, Formal analysis, Methodology, Writing – original draft, Writing – review & editing. **Richard Armah Danyi** : Conceptualization, Methodology, Writing – original draft, Writing – review & editing. Data Availability The data that support the findings of this study are available on request. The data not publicly available due to privacy or ethical restrictions. References Haileamlak A. Physical inactivity: The major risk factor for non-communicable diseases. Ethiop J Health Sci. 2019;29(1):810. https://doi.org/10.4314/ejhs.v29i1.1 . Kohl HW III, Craig CL, Lambert EV, et al. The pandemic of physical inactivity: Global action for public health. Lancet. 2012;380(9838):294–305. https://doi.org/https://doi.org/10.1016/S0140-6736(12)60898-8 . Katzmarzyk PT, Friedenreich C, Shiroma EJ, et al. Physical inactivity and non-communicable disease burden in low-income, middle-income and high-income countries. 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Associations between neighbourhood walkability and daily steps in adults: A systematic review and meta-analysis. BMC Public Health. 2015;15:768. https://doi.org/10.1186/s12889-015-2082-x . Gössling S, Neger C, Steiger R, et al. Weather, climate change, and transport: A review. Nat Hazards. 2023;118(2):1341–60. https://doi.org/10.1007/s11069-023-06054-2 . Ghana Meteorological Agency. State of the climate ghana 2024 2024. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 08 Apr, 2026 Reviewers invited by journal 03 Apr, 2026 Editor invited by journal 16 Mar, 2026 Editor assigned by journal 15 Mar, 2026 Submission checks completed at journal 14 Mar, 2026 First submitted to journal 14 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8969633","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":619666369,"identity":"a4b3d1c6-dbef-45bc-af16-8f88732a9660","order_by":0,"name":"Richard Danyi","email":"","orcid":"","institution":"Teesside University","correspondingAuthor":false,"prefix":"","firstName":"Richard","middleName":"","lastName":"Danyi","suffix":""},{"id":619666370,"identity":"cac39948-eb5a-4f4e-8bf3-4f335a3e9611","order_by":1,"name":"Patrick Akwaboah","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIie3SIQvCQBTA8TcOznKwevctngibA/GzOAamMYQVQdCBsDS7gh9iluXJQItiVSwO64I2Leq0WU5thvunC+/HHdwDUKn+MQagBQCt8qQdoAOAvxCC5Tz/iVD+FTFZqp0nYdPTK+tFj2GjbwLJTzJijQIiZqHji8hr7xm2uRXQGpcR3ACIPCF2nLpGSTKOKYNPhFzzZGDHm8LwGd6fhFykZB1QMUsyO966BmGYPgmV3mJF89Ca3Ja+GBc1MUVHxBk16jJiMifbRauep+tu9VR0mzouh8et9GGvX3mLyObhm/VQqVQq1QNdAke9eGvR+AAAAABJRU5ErkJggg==","orcid":"","institution":"University of Lethbridge","correspondingAuthor":true,"prefix":"","firstName":"Patrick","middleName":"","lastName":"Akwaboah","suffix":""}],"badges":[],"createdAt":"2026-02-25 16:08:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8969633/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8969633/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106994013,"identity":"ee865959-9da1-4569-860a-c351c47b612d","added_by":"auto","created_at":"2026-04-15 15:02:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1170002,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8969633/v1/abcdc013-4d9a-448d-8043-2803e62994d5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Socioecological correlates of active transportation in Ghana","fulltext":[{"header":"1.0 Introduction","content":"\u003cp\u003ePhysical inactivity is a major global health concern and a leading risk factor for non-communicable diseases (NCDs) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. It contributes to approximately 7\u0026middot;7% of all-cause mortality worldwide and accounts for nearly 69% of deaths in middle-income countries [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Promoting physical activity (PA) through population/sub-population level strategies is therefore essential to improving health outcomes and reducing the burden of NCDs. Active transportation (AT), which includes walking and cycling, has gained recognition as a cost-effective, equitable, and sustainable means of increasing PA [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAT confers multiple health benefits, including decreased odds of hypertension and diabetes [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Walking has been associated with reduced symptoms of depression and anxiety [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], while population-level promotion of AT yields substantial economic gains through improved productivity [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Furthermore, the United Nations Environment Program identifies AT as a key strategy for decarbonizing transport-related greenhouse gas emissions [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite broad benefits, little is known about AT participation among healthcare professionals, particularly in Africa. Most existing studies have focused on leisure-time and occupational PA [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. For instance, a South African study among 174 primary healthcare workers reported an AT prevalence of about 40% but did not explore its correlates [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In Ghana, empirical data on AT among adults remains scarce, creating evidence gaps on both prevalence and contextual drivers. This absence is reflected in the third Global Observatory for Physical Activity (GoPA) country report card, which contained no adult AT data for Ghana [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur recent analysis among Physician Assistants in Ghana filled part of this gap by showing that engagement in AT was positively associated with good self-rated health [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] and inversely associated with anxiety symptoms [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. These findings suggest that encouraging AT may improve both physical and mental well-being among health workers. However, sustaining these gains requires an understanding of the factors that facilitate or hinder AT participation. Identifying such correlates is also vital to strengthening the role of healthcare professionals, such as Physician Assistants, as promoters of PA in their clinical and community settings, as emphasized by evidence from a recent systematic review [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe socio-ecological model provides a useful framework for examining these correlates by recognizing the dynamic interplay between individual, social, and environmental determinants.[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] At the individual level, age and sex have been shown to influence participation in AT [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. For example, younger adults aged 18\u0026ndash;44 years are more likely to cycle compared to older adults (aOR\u0026thinsp;=\u0026thinsp;3.0, 95% CI: 2.4, 3.8) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Social factors, such as income, perceptions, and norms, also shape AT. For instance, in Ghana, increasing household income is associated with the use of motorized transport among adults, which is seen as a sign of affluence and prestige [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Environmental factors, including the presence of sidewalks and weather conditions, further influence AT uptake. Higher odds of walking and cycling have been observed in areas with well-maintained sidewalks in South Africa (OR\u0026thinsp;=\u0026thinsp;2.69, 95% CI: 2.20, 10.02) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], while adverse weather conditions have also been found to discourage AT participation [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study, therefore, aims to examine the correlates of AT among Physician Assistants in Ghana. Findings will provide baseline estimates of AT prevalence among an adult professional group, inform context-specific policies and interventions, and contribute to national discussions/efforts to promote PA.\u003c/p\u003e"},{"header":"2.0 Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Design\u003c/h2\u003e \u003cp\u003eThis cross-sectional study analyzed data from our previously conducted survey among Physician Assistants in Ghana, for which detailed methods have been published elsewhere [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Briefly, data were collected between October and December 2024. A total of 439 participants completed the questionnaire online. Ethical approval for the original study was obtained from the Metropolitan Research and Education Bureau (MREB) Office of the Research Ethics Review Committee (RERC) under protocol number MREB/RERC/19/24. Electronic informed consent was obtained from all participants. The present analysis followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) recommendations [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. All 439 responses were included in the analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Measures\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Outcome\u003c/h2\u003e \u003cp\u003eAT served as the primary outcome in this study. Participants identified their main mode of transportation to work, selecting from walking, cycling, private car, trotro/bus, or uber/taxi. For analytical purposes, AT was dichotomized as active (walking, cycling) and motorized (private car, trotro/bus, uber/taxi) transport.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Potential Correlates\u003c/h2\u003e \u003cp\u003eWe used the socio-ecological framework [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] to identify potential correlates of AT, encompassing individual, social, built, and natural environment levels.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section4\"\u003e \u003ch2\u003e2.2.2.1 Individual level\u003c/h2\u003e \u003cp\u003eParticipants reported their age (later categorized as 24\u0026ndash;40 years and 41\u0026ndash;67 years), sex (male and female) and educational attainment (advanced diploma, a bachelor\u0026rsquo;s degree, or a master\u0026rsquo;s degree and higher). Car ownership was reported as the number of vehicles owned and further categorized as none versus one or more.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section4\"\u003e \u003ch2\u003e2.2.2.2 Social level\u003c/h2\u003e \u003cp\u003eMonthly income was reported by each participant, classified as below Gh\u0026cent;5000 or Gh\u0026cent;5000 and higher. They also indicated whether they perceived AT as time-consuming or stigmatized (yes or no).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section4\"\u003e \u003ch2\u003e2.2.2.3 Built environmental level\u003c/h2\u003e \u003cp\u003eParticipants reported whether sidewalks were available along their usual commute (yes or no). They also indicated whether their practice location was in a rural or urban setting, according to the official definition in Ghana [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section4\"\u003e \u003ch2\u003e2.2.2.4 Natural environment level\u003c/h2\u003e \u003cp\u003eEach participant\u0026rsquo;s region of practice was linked to regional weather data, including rainfall, temperature, relative humidity, and wind direction, obtained from the Ghana Meteorological Agency\u0026rsquo;s satellite-supported monitoring systems. Average values were computed across the study period (October to December 2024) for each region and merged with the corresponding participant data.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Analysis Strategy\u003c/h2\u003e \u003cp\u003eDescriptive statistics were computed to provide an overview of the study population. Categorical variables were presented as frequencies and percentages, and continuous variables as means with standard deviations.\u003c/p\u003e \u003cp\u003eThe associations between AT and potential correlates were examined using a hierarchical binary logistic regression model, guided by the socioecological framework. Predictors were added in four sequential blocks (individual, social, built environment, and natural environment levels) to assess the incremental changes in model performance. The likelihood-ratio (LR) test was applied at each step to assess whether the addition of a new block significantly improved model fit.\u003c/p\u003e \u003cp\u003eModel adequacy and parsimony were evaluated using the Akaike Information Criterion (AIC) and McFadden\u0026rsquo;s Pseudo-R\u0026sup2;. Lower AIC values and higher Pseudo-R\u0026sup2; values indicated improved model performance. Findings are presented as adjusted odds ratios with corresponding 95% confidence intervals. Statistical significance was determined at a two-sided α\u0026thinsp;=\u0026thinsp;0.05. All analyses were conducted using STATA version 19.0 (StataCorp LLC, College Station, TX, USA).\u003c/p\u003e \u003cp\u003eModel diagnostics are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Model 4 demonstrated the best overall fit, indicating that the inclusion of natural environment variables substantially improved explanatory power beyond individual, social, and built environment predictors.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModel fit assessment\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudo R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLR test (\u003cem\u003ep\u003c/em\u003e-value)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026middot;03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u0026middot;40 (0\u0026middot;0025)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e558\u0026middot;7044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026middot;05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u0026middot;38 (0\u0026middot;0246)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e555\u0026middot;3243\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026middot;07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u0026middot;78 (0\u0026middot;0124)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e550\u0026middot;5398\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026middot;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28\u0026middot;97 (0\u0026middot;0001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e529\u0026middot;5676\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3.0 Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Participants and climatic characteristics\u003c/h2\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, most of the participants were young adults, predominantly male, and practiced mainly in urban areas. About 90% had an education at the bachelor's level or above, a third owned a car, and two-thirds indicated the absence of sidewalks along their commuting routes. The perception of AT as stigmatized or time-consuming was high among the participants. Additionally, weather conditions during the study period were average, as captured in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The overall prevalence of AT was 34\u0026middot;4%.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics of respondents\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall (n\u0026thinsp;=\u0026thinsp;439), n (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge Group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u0026ndash;40 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e324 (73.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e41\u0026ndash;67 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e115 (26.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e150 (34.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e289 (65.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdvanced Diploma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42 (9.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBachelor\u0026rsquo;s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e305 (69.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaster\u0026rsquo;s or higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e92 (21.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of cars\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e275 (62.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOne or more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e164 (37.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMonthly income\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGh\u0026cent; 5000 and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e231 (52.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBelow Gh\u0026cent;5000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e208 (47.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eActive transport perception\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConsumes time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e375 (86.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDoes not consume time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e64 (14.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePerceived Active transport stigma\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e172 (39.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e267 (60.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUrbanization\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e93 (21.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e346 (78.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAvailability of sidewalks\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e292 (66.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e147 (33.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTrip to Work\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMotorized\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e288 (65.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e151 (34.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNatural environment variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeather Indicators (Units)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRainfall (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e107.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e101.4, 114.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature (\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e28.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.1, 28.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelative Humidity (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e74.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e73.3, 75.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWind Speed (knots)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.8, 3.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003emm=millimeters. \u0026deg;C=degree Celsius. %=percentage.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Correlates of Active Transportation\u003c/h2\u003e \u003cp\u003eSignificant correlates of AT were identified through a hierarchical binary logistic regression. Although regression estimates were relatively stable across all models, the final and fourth model showed improved model performance, accounting for 12.0% of the observed variance (Table\u0026nbsp;4). At the individual level, male participants had 68% higher odds of engaging in AT compared to females, whereas those with a master\u0026rsquo;s degree and higher had 57% lower odds compared to advanced diploma holders. Additionally, car ownership was associated with 41% lower odds of AT.\u003c/p\u003e \u003cp\u003eWhile perceiving AT as time-consuming was associated with 59% lower odds at the social level, the presence of sidewalks was associated with 84% higher odds of AT within the built environment level. Finally, at the natural environment level, a unit increase in temperature was associated with 12% higher odds of AT, while relative humidity was associated with a marginal 3% higher odds. A unit increase in wind speed was, however, associated with 39% lower odds of AT.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between active transportation to work and potential correlates\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFactors/variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eaOR (95% \u003cem\u003eCI)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eaOR (95% \u003cem\u003eCI)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eaOR (95% \u003cem\u003eCI)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eaOR (95% \u003cem\u003eCI)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndividual-level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge group (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u0026ndash;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eREF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eREF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eREF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eREF\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e41\u0026ndash;67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.91 (0.55,1.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.89 (0.53, 1.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.90 (0.53, 1.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.90 (0.53, 1.52)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eREF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eREF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eREF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eREF\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e.\u003cb\u003e71* (1\u003c/b\u003e.\u003cb\u003e10, 2\u003c/b\u003e.\u003cb\u003e66)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e.\u003cb\u003e67* (1\u003c/b\u003e.\u003cb\u003e10, 2\u003c/b\u003e.\u003cb\u003e60)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e.\u003cb\u003e68* (1\u003c/b\u003e.\u003cb\u003e10, 2\u003c/b\u003e.\u003cb\u003e65)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e.\u003cb\u003e68* (1\u003c/b\u003e.\u003cb\u003e10, 2\u003c/b\u003e.\u003cb\u003e65)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdvanced diploma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eREF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eREF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eREF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eREF\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBachelor\u0026rsquo;s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.63 (0.32, 1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.65 (0.33, 1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.66 (0.33, 1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.66 (0.33, 1.34)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaster\u0026rsquo;s or higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e.\u003cb\u003e42* (0\u003c/b\u003e.\u003cb\u003e19, 0\u003c/b\u003e.\u003cb\u003e93)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e.\u003cb\u003e42* (0\u003c/b\u003e.\u003cb\u003e19, 0\u003c/b\u003e.\u003cb\u003e94)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e.\u003cb\u003e43* (0\u003c/b\u003e.\u003cb\u003e19, 0\u003c/b\u003e.\u003cb\u003e97)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e.\u003cb\u003e43* (0\u003c/b\u003e.\u003cb\u003e19, 0\u003c/b\u003e.\u003cb\u003e97)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of cars\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eREF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eREF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eREF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eREF\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOne or more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e.\u003cb\u003e58* (0\u003c/b\u003e.\u003cb\u003e37, 0\u003c/b\u003e.\u003cb\u003e91)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e.\u003cb\u003e60* (0\u003c/b\u003e.\u003cb\u003e33, 0\u003c/b\u003e.\u003cb\u003e96)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e.\u003cb\u003e59* (0\u003c/b\u003e.\u003cb\u003e37, 0\u003c/b\u003e.\u003cb\u003e94)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e.\u003cb\u003e59* (0\u003c/b\u003e.\u003cb\u003e37, 0\u003c/b\u003e.\u003cb\u003e94)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSocial level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMonthly income\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGh\u0026cent; 5,000 and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eREF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eREF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eREF\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBelow Gh\u0026cent; 5,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.05 (0.67, 1.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.04 (0.66, 1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.04 (0.66, 1.65)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eActive transport consumes time\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eREF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eREF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eREF\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e.\u003cb\u003e41** (0\u003c/b\u003e.\u003cb\u003e23, 0\u003c/b\u003e.\u003cb\u003e73)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e.\u003cb\u003e41** (0\u003c/b\u003e.\u003cb\u003e23, 0\u003c/b\u003e.\u003cb\u003e74)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e.\u003cb\u003e41** (0\u003c/b\u003e.\u003cb\u003e23, 0\u003c/b\u003e.\u003cb\u003e74)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePerceived active transport stigma\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eREF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eREF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eREF\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.12 (0.73, 1.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.14 (0.74, 1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.14 (0.74, 1.78)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBuilt environment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUrbanization\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eREF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eREF\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.75 (0.46, 1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.75 (0.46, 1.26)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAvailability of sidewalks\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eREF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eREF\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e.\u003cb\u003e84* (1\u003c/b\u003e.\u003cb\u003e20, 2\u003c/b\u003e.\u003cb\u003e82)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e.\u003cb\u003e84* (1\u003c/b\u003e.\u003cb\u003e20, 2\u003c/b\u003e.\u003cb\u003e82)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNatural environment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRainfall (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (0.99, 1.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature (\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e.\u003cb\u003e12* (1\u003c/b\u003e.\u003cb\u003e13, 4\u003c/b\u003e.\u003cb\u003e01)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelative humidity (\u0026deg;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e.\u003cb\u003e03* (1\u003c/b\u003e.\u003cb\u003e01, 1\u003c/b\u003e.\u003cb\u003e05)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWind speed (knots)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e.\u003cb\u003e61*** (0\u003c/b\u003e.\u003cb\u003e47, 0\u003c/b\u003e.\u003cb\u003e79)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eaOR: adjusted odds ratio. \u003cem\u003eCI\u003c/em\u003e: confidence intervals. REF: reference. ***\u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001;\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e**\u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.01; *\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"4.0 Discussion","content":"\u003cp\u003eThis study examined the correlates of AT among Physician Assistants in Ghana, extending our previous analyses that demonstrated a positive association between AT and self-rated health, and an inverse association with anxiety symptoms. Results indicated that about one-third of the participants commuted actively to work. Guided by the socio-ecological model, hierarchical regression analysis identified significant correlates across multiple levels: individual (sex, education, car ownership), social (perceptions of AT), built environment (availability of sidewalks), and natural environment (temperature, humidity, wind speed), demonstrating how AT behavior is shaped by multi-level influences.\u003c/p\u003e \u003cp\u003eThe prevalence of AT observed in this study (34\u0026middot;4%) was lower than observed in several African countries (50\u0026ndash;77%) but higher than in regions like Latin America and the Caribbean (12%) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. This pattern likely reflects infrastructural and cultural constraints. The dominance of motorized commuting (65.6%) aligns with Ghana\u0026rsquo;s rapid annual urbanization rate of 3.3% [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], which likely underscores the prioritization of vehicular infrastructure over pedestrian needs [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. A coordinated national AT policy, aligned with the Pan-African Action Plan for Active Mobility (PAAPAM), could help correct this imbalance and create safer, more inclusive active commuting environments.[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eAt the individual level, males were more likely than females to use AT, consistent with findings from other countries [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. These sex disparities may stem from cultural norms, safety concerns, and fear of harassment, all of which heighten risk perception and discourage females from participating in AT [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Addressing these disparities calls for investments in protected pedestrian and cycling infrastructure, improved lighting, and sex-sensitive urban design that enhances safety and inclusivity.\u003c/p\u003e \u003cp\u003eEducational attainment and car ownership were also inversely related to AT participation. Participants with master\u0026rsquo;s degrees or higher were less likely to use AT, supporting evidence from the United States of America [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], but contrasting with studies from Europe, where AT is often a lifestyle choice among the educated [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Car ownership similarly reduced AT odds, supporting evidence from a study in China [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. These results suggest that rising socioeconomic status and perceptions of prestige associated with car ownership may discourage utilitarian PA [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAt the social level, perceiving AT as time-consuming significantly reduced participation, consistent with the economic value commuters attach to travel time [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Enhancing AT convenience through improved connectivity, direct walking routes, and shaded or landscaped paths could help mitigate time-related barriers. The built environment also played a crucial role: participants with sidewalks along their commuting routes had 84% higher odds of engaging in AT, reinforcing evidence that walkable neighborhoods facilitate daily movement [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. However, only one-third of respondents reported sidewalk availability, highlighting critical infrastructure deficits. Urban and road design, as well as land-use planning, should consider integrating pedestrian infrastructure as a public health priority.\u003c/p\u003e \u003cp\u003eClimatic factors further influenced AT participation. Participants were more likely to engage in AT during warmer temperatures but less likely when wind speeds were high. A marginal positive association was observed for relative humidity. These findings partly diverge from studies in high-income settings, where both humidity and wind are typically found to be negative correlates of AT [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. These patterns, though context-dependent, highlight the importance of adapting AT policies to local weather conditions. During Ghana\u0026rsquo;s dry season (October\u0026ndash;December), dust-laden northeasterly winds can impair air quality and visibility,[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] discouraging walking and cycling. Urban greening and shaded corridors could help minimize the impact of adverse weather while promoting comfort and safety for active commuters.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Implications of findings\u003c/h2\u003e \u003cp\u003eAs climate intensifies in Ghana [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], its potential impact on mobility patterns will likely become more pronounced. Strengthening intersectoral collaboration among transport, health, and environment sectors is essential to designing climate-resilient, walkable communities. Policymakers could integrate meteorological data into transport planning and use telecommunication platforms to provide real-time weather updates that promote safe AT. Beyond infrastructure, addressing sex, social status, and time perception barriers will be key to sustaining the mental and physical health benefits previously identified among Physician Assistants in Ghana.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Future Research\u003c/h2\u003e \u003cp\u003eA national survey on AT prevalence and correlates is needed to adequately inform national policies. Future studies should employ longitudinal designs to examine temporal variations and causal pathways between AT and health outcomes. Expanding analyses across all climatic seasons and integrating objective measures (e.g., GPS, accelerometry) could provide richer evidence. Additionally, assessing the economic and environmental co-benefits of AT would help quantify its contribution to sustainable development and justify investments in active mobility infrastructure.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Strengths and Limitations","content":"\u003cp\u003eThe use of a voluntarily completed online survey introduces potential selection bias, and self-reported transport behaviors may be affected by recall or social desirability bias. Weather data covered only three months, limiting seasonal comparisons. Furthermore, the cross-sectional design restricts causal inference, and findings may not generalize to all health worker groups or the general adult population in Ghana. Nonetheless, this study is among the first to comprehensively examine multi-level correlates, including climatic factors, of AT in Ghana, offering novel insights into the environmental and behavioral determinants of AT in an African context.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThis study provides context-specific evidence on the correlates of AT among Physician Assistants in Ghana and builds on prior findings linking AT to improved self-rated health and reduced anxiety symptoms. Although one-third of participants engaged in AT, barriers such as inadequate sidewalks, time constraints, and adverse weather conditions limit broader participation. Strengthening active mobility will require a coordinated policy integrating health, transport, and urban planning, supported by investments in safe, inclusive, and climate-resilient infrastructure. Empowering healthcare professionals to model and advocate for AT can amplify public health gains and advance Ghana\u0026rsquo;s progress toward sustainable, healthy cities.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interest\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests relevant to the context of this article.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003cstrong\u003eCompeting Interests\u003c/strong\u003e \u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthics Approval and Consent to Participate\u003c/h2\u003e \u003cp\u003e The primary study received ethical approval from the Metropolitan Research and Education Bureau (MREB), office of the Research Ethics Review Committee (RERC), with protocol number MREB/RERC/19/24. All collected datawere securely stored, adhering to ethical guidelines and data protection regulations. Informed consent was obtained electronically. On the first page of the online questionnaire, participants were presented with a detailed consent statement and were required to select either \"I agree to participate\" or \"I do not agree.\" Only those who selected \"I agree\" were able to proceed with the survey. All responses were anonymous, and participation was entirely voluntary.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to Publish\u003c/strong\u003e \u003cp\u003eNot applicable. No individual-level identifying information or images are included in this manuscript.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eClinical Trial Number\u003c/h2\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNo funding was received for conducting this study.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003e**Patrick Akwaboah** : Conceptualization, Formal analysis, Methodology, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp;amp; editing. **Richard Armah Danyi** : Conceptualization, Methodology, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp;amp; editing.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are available on request. The data not publicly available due to privacy or ethical restrictions.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHaileamlak A. Physical inactivity: The major risk factor for non-communicable diseases. Ethiop J Health Sci. 2019;29(1):810. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4314/ejhs.v29i1.1\u003c/span\u003e\u003cspan address=\"10.4314/ejhs.v29i1.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKohl HW III, Craig CL, Lambert EV, et al. The pandemic of physical inactivity: Global action for public health. 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State of the climate ghana 2024 2024.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Physical activity, sub-Saharan Africa, Socio-ecological model","lastPublishedDoi":"10.21203/rs.3.rs-8969633/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8969633/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eActive transportation (AT) offers a sustainable and equitable means of increasing population physical activity levels. Yet, evidence of its determinants among adult populations in Africa remains limited. This study examined the correlates of AT among a cadre of healthcare professionals in Ghana.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA cross-sectional analysis was conducted using data from a 2024 survey of 439 Physician Assistants. Guided by the socioecological model, potential correlates were assessed across individual, social, built, and natural environment levels. Hierarchical binary logistic regression with likelihood ratio tests examined the association between AT and potential correlates.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAT prevalence was 34.4%. In the final model, male sex (aOR\u0026thinsp;=\u0026thinsp;1.68, 95% CI: 1.10, 2.65) and sidewalk on commute routes (aOR\u0026thinsp;=\u0026thinsp;1.84, 95% \u003cem\u003eCI\u003c/em\u003e: 1.20, 2.82) were positively associated with AT. Higher education (aOR\u0026thinsp;=\u0026thinsp;0.43, 95% \u003cem\u003eCI\u003c/em\u003e: 0.19, 0.97), car ownership (aOR\u0026thinsp;=\u0026thinsp;0.59, 95% \u003cem\u003eCI\u003c/em\u003e: 0.37, 0.94), and perceiving AT as time-consuming (aOR\u0026thinsp;=\u0026thinsp;0.41, 95% \u003cem\u003eCI\u003c/em\u003e: 0.23, 0.74) were inversely associated with AT. Climatic factors such as temperature (aOR\u0026thinsp;=\u0026thinsp;2.12, 95% \u003cem\u003eCI\u003c/em\u003e: 1.13, 4.01) and relative humidity (aOR\u0026thinsp;=\u0026thinsp;1.03, 95% \u003cem\u003eCI\u003c/em\u003e: 1.01, 1.05) increased the odds of AT, while wind speed reduced them (aOR\u0026thinsp;=\u0026thinsp;0.62, 95% \u003cem\u003eCI\u003c/em\u003e: 0.47, 0.81).\u003c/p\u003e\u003ch2\u003eDiscussion\u003c/h2\u003e \u003cp\u003eAbout one in three Physician Assistants in Ghana commute actively, shaped by individual, social, and environmental factors. Promoting AT will require multi-level interventions and policies that integrate infrastructure improvements, climate resilience, and sex-sensitive approaches to sustain the physical and mental health benefits of active commuting.\u003c/p\u003e","manuscriptTitle":"Socioecological correlates of active transportation in Ghana","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-09 19:29:20","doi":"10.21203/rs.3.rs-8969633/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"231175213898635063665727607185644973879","date":"2026-04-09T03:20:34+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-03T05:41:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-16T09:43:16+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-15T20:58:37+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-14T12:12:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Public Health","date":"2026-03-14T12:08:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"30ebc8ca-fb52-4c93-8c98-36ecb59eee13","owner":[],"postedDate":"April 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-09T19:29:20+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-09 19:29:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8969633","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8969633","identity":"rs-8969633","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}