Effectiveness of interventions for modal shift to walking and bike riding: a systematic review with meta-analysis
Notice bibliographique
Résumé
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).
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,021 | 0,057 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,002 |
| Méta-épidémiologie (sens large) | 0,031 | 0,046 |
| Bibliométrie | 0,013 | 0,009 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,005 | 0,003 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».