Effectiveness of interventions for modal shift to walking and bike riding: a systematic review with meta-analysis
Bibliographic record
Abstract
ABSTRACT Objective To assess the efficacy of interventions aimed at increasing walking and cycling. Design Systematic review with meta-analysis Study selection The electronic databases MEDLINE, PsycINFO and Web of Science were searched from inception on 22 nd May 2023. Eligible study designs included randomised and non-randomised studies of interventions with specific study design features that enabled estimation of causality. No restrictions on type of outcome measurement, publication date or population age were applied. Data extraction and synthesis Two reviewers independently extracted data and conducted quality assessment with Joanna Briggs Quality Assessment tools. Studies were categorised by intervention types described within the Behaviour Change Wheel. Where possible, random-effects meta-analyses were used to synthesise results within intervention types. Main outcome measures The main outcome of interest was modal shift to active modes (walking and cycling). Other outcomes of interest included cycling and walking duration, frequency and counts, active transport duration and frequency, and moderate to vigorous physical activity duration (MVPA). Results 106 studies that assessed the impact of an intervention on walking, cycling or active transport overall were included. Findings demonstrate that physical environmental restructure interventions, such as protected bike lanes and traffic calming infrastructure, were effective in increasing cycling duration (OR = 1.70, 95% CI 1.20 – 2.22; 2 studies). Other intervention types, including individually tailored behavioural programmes, and provision of e-bikes were also effective for increasing cycling frequency (OR = 1.33, 95% CI 1.23-1.43; 1 study) and duration (OR = 1.13, 95% CI 1.02.-1.22, 1 study). An intensive education programme intervention demonstrated a positive impact on walking duration (OR = 1.96, 95% CI 1.68 – 2.21; 1 study). An individually tailored behavioural programme (OR = 1.23, 95% CI 1.08 – 1.40; 3 studies) and community walking programme (OR = 1.15, 95% CI 1.14 – 1.17; 1 study) also increased the odds of increased walking duration. This body of research would benefit from more rigour in study design to limit lower quality evidence with the potential for bias. Conclusions This review provides evidence for investment in high-quality active transportation infrastructure, such as protected bike lanes, to improve cycling and active transport participation overall. It also provides evidence for investment in other non-infrastructure interventions. Further research to understand which combinations of intervention types are most effective for modal shift are needed. Active transport research needs to include more robust trials and evaluations with consistent outcome measures to improve quality of evidence and provide evidence on which interventions (or combinations of interventions) are most effective. Study registration PROSPERO CRD42023445982 Funding This research was funded through the British Columbia Centre for Disease Control, Canada. The research funders did not contribute to the research process or interpretation of findings. The researchers were independent from the funders. Lauren Pearson receives salary support from the National Health and Medical Research Council (GNT2020155). Ben Beck receives an Australian Research Council Future Fellowship (FT210100183).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.057 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.031 | 0.046 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".