Background: Research on the influences on bike share use and potential favorable relationships between use and obesity is limited, particularly in the U.S. context. Therefore, the aims of this exploratory study were to examine correlates of awareness and use of Boston's Bluebikes bike share system and assess the association between use and weight status.
Methods: Students, faculty, and staff (n = 256) at a public urban university completed an online survey that assessed sociodemographic, behavioral, and physical activity characteristics, Bluebikes awareness, and use of Bluebikes and personal bikes. Multivariable logistic regression models were estimated to examine associations between sociodemographic and behavioral factors and bike share awareness and use; and between use and overweight/obesity status.
Results: Respondents were mostly students (72.2%), female (69.1%), White (62.1%), and the mean age was 32.4±13.8 years. The percentage of respondents classified as aware of Bluebikes was 33.6% with only 12.9% reporting any use of the system. Living in a community where bike share stations were located (odds ratio (OR) = 2.01, 95% confidence interval (CI): 1.10, 3.67), personal bike ownership (OR = 2.27, 95% CI:1.27, 4.45), and not exclusively commuting to campus via car (OR = 3.19, 95% CI:1.63, 6.22) had significant positive associations with awareness. Living in a bike share community (OR = 2.34; 95% CI:1.04, 5.27) and personal bike ownership (OR = 3.09; 95% CI:1.27, 7.52) were positively associated with bike share use. Any reported use of Bluebikes was associated with 60% lower odds of being overweight/obese (OR = 0.40; 95% CI:0.17, 0.93).
Conclusions: Several environmental and behavioral variables, including access to stations and personal bicycle ownership, were significantly associated with Bluebikes awareness and use. Findings also suggest a potential benefit to bike share users in terms of maintaining a healthy weight, though further longitudinal studies are needed to rule out the possibility that more active and leaner individuals tend to use bike share more frequently.
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PeerJ Comput Sci
October 2024
School of Computer Science and Technology, Changchun University of Science and Technology, Changchun, Jilin, China.
Shared bikes, as an eco-friendly transport mode, facilitate short commutes for urban dwellers and help alleviate traffic. However, the prevalent station-based strategy for bike placements often overlooks urban zones, cycling patterns, and more, resulting in underutilized bikes. To address this, we introduce the Spatio-Temporal Bike-sharing Demand Prediction (ST-BDP) model, leveraging multi-source data and Spatio-Temporal Graph Convolutional Networks (STGCN).
View Article and Find Full Text PDFInt J Environ Res Public Health
November 2024
Department of Civil Engineering, University of Arkansas, Fayetteville, AR 72701, USA.
This study explored the facilitators and barriers of community bike share use in a mid-sized city with high incidence of poverty and racial diversity using a community-based participatory action research (CBPAR) photovoice framework with the Stanford (OV) Discovery Tool digital application. Community members participated in one of three community citizen science walks with follow up focus groups facilitated by osteopathic medical student researcher to address "What makes it easy or hard to ride a bike using the bike share?" Twenty-seven diverse community members partnered with four osteopathic medical students exploring vulnerable individuals' lived experiences, beliefs/understanding of the Social Determinants of Health (SDoH) and access to the bike share program. A total of 322 photos and narrative comments from citizen science walk audits developed deductive themes and follow up focus groups informed inductive themes.
View Article and Find Full Text PDFJ Environ Manage
December 2024
School of Architecture, Southeast University, 2 Sipailou Road, Nanjing, 210096, Jiangsu, China.
This study offers a novel approach for examining the variations of effective park service radius (PSR) in parks with different sizes (small, medium, and large parks) and temporal conditions (weekday morning-noon (M-N), weekday afternoon-evening (A-E), weekend M-N, and weekend A-E). Using Shenzhen as an example and employing shared-bike order data, a detection tool was developed to identify the effective PSRs of 228 sample parks under different spatiotemporal conditions. Analysis of variance (ANOVA) was used to investigate the variations in PSRs in parks of different sizes and periods and multiple linear regression models were built to investigate the influential mechanisms of PSRs for different sized parks.
View Article and Find Full Text PDFAccid Anal Prev
January 2025
Jiangsu Key Laboratory of Urban ITS, Southeast University, Nanjing 211189, China; Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing 211189, China; School of Transportation, Southeast University, Nanjing 211189, China.
Bicycle crashes at intersection areas are posed a worrying traffic safety issue, and one of the main reasons for bicycle crashes is failing to avoid conflicts with motor vehicles and other bicycles. Clearly, cyclists are more exposed to risk if they perform a direct left turn (DLT) being mixed with left-turning vehicle under a left-turn phase. Owing to the lack of exposure data, the detection of DLT event and the mechanism behind the risky riding behavior have yet to be discovered.
View Article and Find Full Text PDFPLoS One
November 2024
Department of Cardiology - Intensive Therapy, Poznan University of Medical Sciences, Poznań, Poland.
Background: Endurance training enhances exercise capacity and triggers cardiovascular adaptations in both males and females. We investigated the relationship between the dimensions of great vessels and exercise capacity in amateur cyclists while considering sex differences.
Methods: Using resting transthoracic echocardiography, we measured the dimensions of the main pulmonary artery (PA), aorta, and inferior vena cava (IVC) in 190 participants, who subsequently underwent a cardiopulmonary exercise test (CPET) until exhaustion.
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