Assessing the Associations Between Light Rail Transit (LRT) and Physical Activity: A Systematic Review
Bibliographic record
Abstract
In response to growing environmental and population health concerns, many cities worldwide are increasing their investment in public transit systems. Indeed, public transit has many benefits to both individuals and communities: it is an affordable travel mode (especially when compared to car use), and can reduce congestion, improve air quality, and encourage physical activity [1]. For this review, we will focus on one health impact of public transit: physical activity. \nThe relationship between the built environment and physical activity has been systematically reviewed [2, 3], and three papers have specifically reviewed the impact of public transit on physical activity [1, 4, 5]. One systematically reviewed the impacts of building, extending, or improving local public transit options on physical activity and found that public transit investments are associated with approximately 30 minutes of additional walking (or other light to moderate physical activity) per week. No significant relationship between new transit and moderate to vigorous physical activity was found [4]. Another non-exhaustive review of the evidence on transit’s impacts on self-reported physical activity, objectively measured physical activity, health outcomes, as well as the health care costs associated with transit [1]. Finally, an older systematic review examined the extent of association between the use of public transport and time spent in physical activity (walking/cycling to transport stops/stations) among adults [5]. Further, a meta-analysis on rapid transit has been conducted, and found that while transport-related physical activity increased after transit interventions, overall physical activity decreased [6].\nThe three existing reviews focus on all types of public transit. One form of public transit that has become increasingly popular in recent years is Light Rail Transit (LRT), which tends to have lower capital costs and increased reliability compared with other public transit systems. The meta-analysis did focus on rapid transit, but because it only included studies with natural experiment designs, only five studies were eligible, three of which focused on LRTs. Therefore, we to will conduct a systematic review of the literature to identify studies examining the relationship between Light Rail Transit and physical activity. The primary objective of this review is to assess the evidence of the associations between LRTs and physical activity. Secondary objectives include comparing the evidence across (1) exposure measurements (e.g., living near an LRT vs. occasional use vs. frequent use), (2) outcome measurements (e.g., self-reported vs. device measured vs. direct observation of physical activity), (3) population density of the city under study, (4) population age (e.g., youth, adults, older adults), and (4) the geography of the stations under study, as well as considering the theoretical frameworks used in this work (e.g., socio-ecological), and whether this body of work considers equity (e.g., whether the physical outcomes are distributed equally by sex/gender, SES indicators, age, residential location, etc.). We will derive policy and research recommendations based off the results of the review. We limit our focus to LRTs in Canada, the United States, Australia, and New Zealand, as the urban areas of these countries tend to share similar built environment characteristics. In doing so, we expand the scope of others [6] by including all studies focused on physical activity and light rail, regardless of the methodology, and deviate from other reviews [1, 4, 5] by conducting a review of the evidence on the impacts of one specific, and increasingly popular, mode of public transit: LRTs. \n
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.180 | 0.545 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".