Evidence map›Paper›PMID 36531519›Full record

ArticleJournal of transport geography2023

The role of neighbourhood design in cycling activity during COVID-19: An exploration of the Melbourne experience.

Mahsa Naseri, Alexa Delbosc, Liton Kamruzzaman

Abstract read
In one paragraph

Article in Journal of transport geography, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Mahsa NaseriDepartment of Civil Engineering, Monash University, Melbourne, Australia.
Alexa DelboscDepartment of Civil Engineering, Monash University, Melbourne, Australia.
Liton KamruzzamanMonash Art Design and Architecture, Monash University, Melbourne, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

COVID-19 restrictions imposed significant changes on human mobility patterns, with some studies finding significant increases or decreases in cycling. However, to date there is little understanding on how the neighbourhood-level built environment influenced cycling behaviour during the COVID-19 restrictions. As different neighbourhood have different built environment characteristics, it is possible that cycling trends varied across different built environment settings. We aimed to answer this question by examining recreational cycling during different stages of lockdown in Melbourne, Australia. We compared self-reported recreational cycling frequency (weekly) data from 1344 respondents between pre-COVID and two different stages in lockdown. We tested whether the built environment of their residential neighbourhood and different sociodemographic characteristics influenced leisure cycling rates and whether the effect of these factors varied between different stages of COVID-19 restriction. We found that cycling declined significantly during the two stages of COVID-19 lockdown. Cycling infrastructure density and connectivity are two built environment factors that had a significant effect on limiting the decline in leisure cycling during the pandemic. Furthermore, men and younger people had higher cycling rates in comparison to other groups, suggesting that restrictions on indoor activities and travel limits were not enough to encourage women or older people to cycle more during the pandemic.

Indexed as

AgeBuilt environmentConnectivityCOVID-19Cycling infrastructureEquityGenderMobility patternRecreational cyclingSocio-demographic characteristics

Identifiers

PMID36531519
PMCPMC9742218

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.