Skip to main content
Erschienen in: BMC Public Health 1/2022

Open Access 01.12.2022 | Research

Correlates and determinants of transport-related physical activity among adults: an interdisciplinary systematic review

verfasst von: Jack T. Evans, Hoang Phan, Marie-Jeanne Buscot, Seana Gall, Verity Cleland

Erschienen in: BMC Public Health | Ausgabe 1/2022

Abstract

Introduction

Transport-related physical activity (TRPA) has been identified as a way to increase physical activity due to its discretionary and habitual nature. Factors thought to influence TRPA span multiple disciplines and are rarely systematically considered in unison. This systematic review aimed to identify cross-sectional and longitudinal factors associated with adult TRPA across multiple research disciplines.

Methods

Using four electronic databases, a systematic search of English, peer-reviewed literature from 2010 – 2020 was performed. Studies quantitatively examining factors associated with the outcome of adult TRPA were eligible.

Results

Seventy-three studies (n = 66 cross-sectional; n = 7 longitudinal) were included, cumulatively reporting data from 1,278,632 observations. Thirty-six factors were examined for potential association with TRPA and presented in a social-ecological framework: individual (n = 15), social (n = 3), and environmental (n = 18). Seven factors were found to be consistently associated with higher adult TRPA: lower socio-economic status, higher self-efficacy, higher social normalization, lower distance of travel, higher destination concentration, more streetlighting, and higher public transportation frequency with a greater number of terminals near route start and endpoints.

Conclusions

This is the first comprehensive compilation of the correlates and determinants of adult TRPA. Seven individual, social, and environmental factors demonstrated consistent associations with TRPA. Models formed using these factors may facilitate more effective promotion of TRPA. There is a lack of longitudinal studies as well as studies assessing cognitive/attitudinal and social factors, highlighting gaps for further research. Those developing policies and strategies targeting TRPA need to consider a range of factors at the individual, social, and environmental level to maximise the likelihood of effectiveness.
Hinweise

Supplementary Information

The online version contains supplementary material available at https://​doi.​org/​10.​1186/​s12889-022-13937-9.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Abkürzungen
EPAQ2
European prospective investigation into cancer and nutrition Physical Activity Questionnaire version two
GPAQ
Global Physical Activity Questionnaire
IPAQ (-L/-S)
International Physical Activity Questionnaire (-Long / -Short version)
NPAQ
Neighbourhood Physical Activity Questionnaire
MeSH
Medical subject heading
MET
Metabolic Equivalent of Task
MOOSE
Meta-analyses and systematic reviews of observational studies
PA
Physical activity
PRISMA
Preferred reporting items for systematic reviews and meta-analyses
PROSPERO
International prospective register of systematic reviews
STAQ
Sedentary and Transport Activity Questionnaire
SQUASH
Short Questionnaire to Assess Health-enhancing Physical Activity
TRPA
Transport-related physical activity

Introduction

Physical inactivity is the fourth leading cause of morbidity and mortality internationally, with an economic burden estimated to exceed INT$67.5 billion in 2013 alone [1]. Physical activity (PA) remains under-utilized by the general population as a means of health improvement [2]. Recent international estimates show that one in four adults do not meet the World Health Organization minimum recommendation of 150 min of moderate intensity PA a week [3]. Given the prevalence of physical inactivity and the role of PA in the prevention and management of chronic disease outcomes [4, 5], the promotion of PA has become a global health priority [3].
Physical activity can be accumulated across four key settings or domains: leisure-time (e.g. sport, exercise), transport (e.g., walking or cycling for transport), domestic (e.g., home or yard maintenance), and occupational PA (e.g., activity undertaken as part of employment). Transport-related PA (TRPA) (also known as active commuting), has been highlighted as a potential means for the increase of PA and improvement of population health [6]. TRPA comprises of healthy active travel behaviours such as walking or cycling for means of commute. This is both as a sole means of transportation or in combination with public or private transport. Both TRPA and leisure-time PA may be considered predominantly discretionary(those with private vehicles have choice as to whether they undertake private, or public and active transport) [7], and hence more amenable to intervention. When compared to leisure-time PA, TRPA remains comparatively understudied and as such represents an important opportunity to research and gain an understanding of how PA may be further integrated into daily life.
TRPA is associated with reduced all-cause mortality [8, 9], lowered risk of cardiovascular disease [10], and some cancers [11], independent of total PA [12]. Moreover, the undertaking of TRPA, independent of other domains of PA, has the potential to provide a substantial increase in total PA levels [13]. For example, people who used public transport in the United States accumulated an additional 30 min of PA each day via the walk to and from public transport stops compared to people who did not use public transport [14, 15]. Similarly, a study of German adults found 48% of participants achieved the global PA recommendation of 150 min per week solely via their active commute [16]. While many factors are thought to influence an individual’s engagement in TRPA, these variables stem from differing disciplines (i.e., environmental, socio-ecological, behavioural, and health/medicine-related [1719]) that are rarely considered in unison. To date there has not yet been a systematic compilation or critical analysis of the factors associated with TRPA spanning multiple disciplines of study. The organisation of these factors within a theoretical framework would provide a structured approach to understanding associations with TRPA. The use of a social-ecological model allows for the categorisation of factors into individual (e.g., age, smoking status, income, self-efficacy), social (e.g., cohesion, normalisation), organisational (e.g., workplace TRPA incentives), environmental (e.g., distance, destination, traffic), and policy-based levels (e.g., promotion of PA guidelines and implementation of interventions). Therefore, this systematic review aimed to identify the cross-sectional correlates and longitudinal determinants of adult TRPA across multiple disciplines of research and structure them within a social-ecological framework.

Methods

This systematic review was registered on the PROSPERO International Prospective Register of Systematic reviews (Registration Number: CRD42020184487) and executed in compliance with the guidelines of the Meta-Analyses and Systematic Reviews of Observational Studies (MOOSE) and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statements [20, 21]. A full protocol may be requested from the authors.
J.E. conducted an independent literature search of four online databases (Web of Science Core Collection, Scopus, Medline, and Embase via Ovid) for published journal articles examining factors associated with adult TRPA outcomes across the last decade (2010 – 2020). Landmark journal articles were first screened to derive terms for search inclusion.
Using terms derived in combination with MeSH (Medical Subject Heading) terms, search filters were included to restrict results to peer-reviewed journal articles published in the English language. Literature search results were imported to Covidence (systematic review management software) [22] where duplicates were first removed, then screening performed. Reference lists of relevant publications were searched for additional studies not returned via database screening.

Study inclusion criteria

Studies were included within this systematic review provided they met the criteria of: (i) publication as a full-length article in a peer-reviewed English language journal, (ii) adult participants (aged ≥ 18 years) with no restriction on sex, ethnicity, or health status, (iii) reporting adult TRPA via self-report or objective measurement either as a primary or secondary outcome, and (iv) quantitatively examined factors cross-sectionally or longitudinally associated with the outcome of adult TRPA. For the purposes of this study, sex and gender identity were analysed in conjunction with one another. Failure of a study to meet any of these inclusion criteria resulted in its exclusion from this review.

Data extraction and analysis

All search results were independently screened for inclusion by J.E. and H.P. Title/abstract content were first screened, with articles then considered for inclusion undergoing secondary screening via assessment at the full-text level. Final inclusion conflicts were discussed by the two reviewing authors. Any unresolved inclusion/exclusion dispute was moderated by a third author (V.C.). Paper characteristics including country of study, study design, participant characteristics, outcome measure, and results were extracted by J.E. and H.P.

Quality assessment

The quality of studies included was assessed via a modified Newcastle – Ottawa Scale [23] (Additional file 1). In this scale the quality of studies and risk of bias was assessed across three categories: selection of participants and sample representativeness, the comparability of participants, and the assessment of outcome. Studies were then categorized as good, fair, or poor quality. Studies with a ‘poor’ quality rating were excluded from the final analysis.

Results

Study characteristics

The search of online databases yielded 5955 studies. Shown within the PRISMA flowchart of Fig. 1, 731 duplicates were removed with 5224 abstracts and 263 full texts screened for inclusion. After removing 190 irrelevant articles, 77 studies remained. Quality assessment determined the methodology of four of these studies to be of poor quality (see below for more details), and resultantly exclusion occurred. This yielded a total of 73 studies for inclusion in this systematic review (Fig. 1). Of these 73 studies, 35 assessed TRPA using IPAQ or GPAQ questionnaires, both of which ask about commuting for any purpose. Of the remaining 38 studies, 34 used assessments of TRPA asked about commuting for any purpose (e.g., Belgian Aging Study questionnaire); four studies assessed TRPA to work only.

Summary of included studies

Studies included within this review and outcome measures are summarised in Table 1. Seventy-three studies spanning 28 countries and 1,278,632 observations were represented. Study sample sizes ranged from 101 to 308,901 participants, with a mean gender distribution of 60.4% female. Only seven articles were found to longitudinally assess relationships with adult TRPA.
Table 1
Summary of included studies
Descriptors
      
Exposures
 
Outcomes
  
Study ID Author, Year
Country
Study design
N
Sex, % Female
Age range *, (years)
 
Description
Statistic
Assessment
Adams, 2013 [24]
United Kingdom
Cross-sectional
3516
54.9
 ≥ 18
Traffic safety, supportive infrastructure, local amenities (destinations), social order, street connectivity, general environmental quality
Dichotomous walking and cycling for transport: 0 min/week and > 0 min/week
Odds ratio
IPAQ
Adams, 2016 [25]
United Kingdom
Cross-sectional
1544
64.1
 ≥ 18
Route infrastructure, route lighting (streetlights). Route free of litter/ graffiti (aesthetics), pleasant walking, convenient public transport
Dichotomous walking for transport: 0 min/week and > 0 min/week
Odds ratio
Transport and physical activity questionnaire
Adams, 2017 [26]
United Kingdom
Cross-sectional
1189
65.6
 ≥ 18
Age, education, ethnicity, vehicle access, physical activity, work-related physical activity, distance, free car parking at work, work hours, work pattern, occupation, psychosocial factors (attitude, behavioural control, intention, social norms, colleague support), perceived barriers
Dichotomous walking for transport: 0 min/week and > 0 min/week
Odds ratio
IPAQ-S
Adlakha, 2015 [27]
United States
Cross-sectional
2015
-
21–65
Large selection of fresh fruits and vegetables; Opportunities to purchase fast food; presence of healthy restaurants; 10–15-min walk to a transit stop; sidewalks on most streets; shops, stores, or markets; facilities to bicycle; recreation facilities; crime rates; traffic; See people being physically active
Dichotomous TRPA: < 150 min/week and ≥ 150 min/week
Odds ratio
IPAQ-S
Adlakha, 2017 [28]
India
Cross-sectional
370
54.2
18–65
Age, sex, marital status, religion, education, income, employment, density, land-use mix, street connectivity, infrastructure for walking/bicycling, aesthetics, safety from traffic and crime
Dichotomous TRPA: < 150 min/week and ≥ 150 min/week
Odds ratio
IPAQ-L
Aliyas, 2019 [29]
Iran
Cross-sectional
1833
50.8
18–70
Age (> 30 years), sex, education, marital status, occupation (employed), vehicle access, safety, crime, social ties, collective efficacy (social modelling)
Dichotomous TRPA level: < 60 min/week and ≥ 60 min/week
Odds ratio
IPAQ-L
Aliyas, 2020 [30]
Iran
Cross-sectional
1132
50.7
18–65
Age, sex, marital status, education, years at current address, number of children < 12yo, safety
Dichotomous average walking for transport: < 30 min/week and ≥ 30 min/week
Odds ratio
IPAQ-L
Amorim, 2010 [31]
Brazil
Cross-sectional
972
57.0
20–69
Sidewalks, green-space, garbage accumulation, sewage presence, traffic impact on walk/ride, crosswalks, exhaust fumes, streetlights at night, crime, sports events, weather
Dichotomous TRPA: 0–149 min/week and ≥ 150 min/week
Prevalence ratio
IPAQ-L
Barr, 2019 [32]
Australia
Cross-sectional
4913
46.4
 ≥ 18
Local and regional accessibility measures (walkability)
Walking for transport: min/week
Regression coefficient
Accelerometer
Barranco-Ruiz, 2019 [33]
Chile
Cross-sectional
496
68.0
 ≥ 18
Age, distance, socio-economic status, existing physical activity patterns
Commute mode: active or passive
Odds ratio
Questionnaire
Bauman, 2011 [34]
Australia, China, Fiji, Malaysia, Nauru, Philippines
Cross-sectional
173,206
54.1
18–64
Age, sex, education, income, area (urban vs rural)
High TRPA: Australia (NA); China (≥ 30 min/day); Fiji (always or usually); Malaysia (≥ 3 days/week and accumulating ≥ 3000 MET-min/week); Philippines (top quartile)
Odds ratio
Survey
Bopp, 2014 [35]
United States
Cross-sectional
706
100
 ≥ 18
Age, marital status, race/ethnicity, number of dependants, income, education, body mass index, chronic disease, self-reported health, employment, vehicle access, self-efficacy, physical activity behaviours, social norms, social modelling, distance, infrastructure, sidewalks, traffic, safety, weather
Dichotomous active commute: 0 trips/week and ≥ 1 trip/week
Odds ratio
Survey
Bopp, 2019 [36]
United States
Longitudinal
204
60.7
 ≥ 18
Body mass index, stress level, depressive symptoms, existing physical activity level, distance, employment
Dichotomous TRPA (mins/week): top quartile = high TRPA
Odds ratio
GPAQ
Borchardt, 2019 [37]
Brazil
Cross-sectional
1429
57.0
18–96
Density, income, destinations, infrastructure, aesthetics, safety, proximity to coast, infrastructure
Dichotomous walking or cycling for transport: Yes = 10 consecutive minutes in previous 7 days; No = no TRPA exceeding 10 consecutive minutes
Prevalence ratio
IPAQ-L
Brondeel, 2016 [38]
France
Cross-sectional
21,332
-
35–83
Age, sex, employment, education, income, distance to public transport, vehicle access, transport behaviours, commute trip characteristics, size of parks, destinations, intersections (connectivity), population density
Transport-related moderate to vigorous physical activity (min/day)
Incidence risk ratio
Accelerometer
Cerin, 2013 [39]
Canada
Cross-sectional
484
58.0
 ≥ 65
Destination diversity and prevalence, infrastructure, safety
Walking for transport (min/week)
Anti-logarithm of regression coefficient
IPAQ-L
Chudyk, 2017 [40]
Canada
Cross-sectional
161
63.4
74.3 (6.2)
Age, sex, marital status, vehicle access, pet ownership, Street Smart Walk Score (walkability), aesthetics, safety, body mass index, gait speed, comorbidities (health), individual enjoyment / attitudes (physical activity behaviours), social cohesion
Walking for transport: any or none; frequency (trips/week)
Regression coefficient
Community Healthy Activities Model Program for Seniors survey
Cleland, 2010 [41]
Australia
Cross-sectional
4349
100
18–45
Age, area (rural vs urban), education, employment, marital status, number of children (dependants), health and health behaviours (weight status, pregnancy, illness, smoking), self-efficacy, physical activity behaviours (enjoyment, intention, outcome expectancies), childcare, family and friend support, pet ownership, social cohesion, safety, aesthetics, walking environment
Categorical TRPA: low (0–29 min/week), medium (30–149 min/week), or high (> 150 min/week)
Odds ratio
IPAQ-L
Cleland, 2012 [42]
Australia
Cross-sectional
3667
100
18–45
Self-efficacy, enjoyment, outcome expectancy, intentions, skills, childcare availability, family support, friends support, dog ownership, safety, aesthetics, walking environment
Categorical TRPA: low (1–89 min/week), medium (90–209 min/week), or high (≥ 210 min/week)
Odds ratio
IPAQ-L
Cleland, 2020 [43]
Australia
Longitudinal
1480
100
18–46
Age, country of birth, English spoken at home, education, income, number of children, health, body mass index, smoking status, pregnancy, menopause, physical activity enjoyment, family support, childcare availability, existing physical activity behaviours
TRPA (min/week)
Odds ratio
IPAQ-L
Corseuil Giehl, 2017 [44]
Brazil
Cross-sectional
1705
63.9
 ≥ 60
Sidewalks, crosswalk, aesthetic, streetlighting, safety, pet ownership, parks/recreational destinations
Categorical walking for transportation: none, 10–149 min/week, ≥ 150 min/week
Odds ratio
IPAQ-L
Dedele, 2019 [45]
United Kingdom
Cross-sectional
1111
57.7
 ≥ 18
Age, sex, marital status, number of dependants, educational, employment, income, vehicle access, body mass index, chronic disease (health), smoking / alcohol consumption (health behaviours), physical activity and mobility behaviour, socioeconomic status
Dichotomous TRPA: 0–29 min/day and ≥ 30 min/day
Prevalence, odds ratio
GPAQ
Del Duca, 2013 [46]
Brazil
Cross-sectional
1720
54.4
35–74
Age, sex, skin colour, marital status, education, family income
Dichotomous TRPA: inactivity and active
Prevalence ratio
Surveillance System of Protective and Risk Factors for Chronic Diseases
de Matos, 2018 [47]
Brazil
Cross-sectional
15,105
54.4
35–74
Age, ethnicity, dependent relatives, weight/anthropometric status, socio-economic status, traffic, safety, walkability, opportunities for physical activity
Categorical TRPA: inactive (< 10 min/week), insufficiently active (10–149 min/week), physically active (≥ 150 min/week)
Relative risk ratio
IPAQ-L
Durand, 2017 [48]
United States
Cross-sectional
65,905
52.5
47.2 (30.8)
Daily measures of mean hourly temperature (degrees Fahrenheit), relative humidity (%), wind speed (miles per hour) and total daily precipitation (inches; includes snow and rain)
TRPA trip duration (min)
Regression coefficient
Travel diary
Eichinger, 2015 [49]
Austria
Cross-sectional
904
42.2
18–91
Sex, distance, supportive infrastructure, connectivity, traffic and crime safety, pleasant environment, presence of trees (green space) social cohesion / support, social modelling, total physical activity
TRPA: MET min/week
Regression coefficient
IPAQ-L
Falconer, 2017 [50]
United Kingdom
Cross-sectional
6896
35.1
 ≥ 35
Age, sex, deprivation, household income, health, distance, commute frequency, population density, air pollution, traffic density, proximity to major road, distance to major road
Dichotomous: active commute and no active commute
Odds ratio
IPAQ-L
Freeland, 2013 [14]
United States
Cross-sectional
308,901
50.8
 ≥ 18
Age, sex, ethnicity, education, household income, race /ethnicity, vehicle access / ownership, employment status, urban size / density
Dichotomous walking for transport: < 30 min/day and ≥ 30 min/day
Odds ratio
National Household Travel Survey
Ghani, 2018 [51]
Australia
Cross-sectional
11,035
-
40–65
Age, residential density, street connectivity, land-use mix
Dichotomous walking for transport: "none" (0 min/week) and "any" (1–840 min/week)
Regression coefficient
Single question
Gul, 2019 [52]
Pakistan
Cross-sectional
1042
33.3
18–65
Age, sex, employment status, education, mode of transportation, marital status, neighbourhood type (gated / non-gated)
Practical walking: MET min/week
T-test, Pearson chi-square
NPAQ
Kwasniewska, 2010 [53]
Poland
Cross-sectional
7280
48.5
20–74
Age, place of residence, education, income, marital status, smoking status, leisure-time physical activity, occupational physical activity
Categorical TRPA: 0 min/day; 1–14 min/day; 15 to 29 min/day; ≥ 30 min/day; and active or inactive
Odds ratio
Questionnaire
Li, 2020 [54]
United States
Cross-sectional
2848
60.0
 ≥ 18
Age, sex, education, income, race/ethnicity, years lived in neighbourhood, walkability, safety, aesthetics, financial cost, and time trade-off
Walking for transport (min/week) and willingness to walk for transport
Structural Equation Model
Survey
Liao, 2017 [55]
Taiwan
Cross-sectional
1068
50.8
20–64
Public bicycle use
Dichotomous TRPA: < 150 min/week and ≥ 150 min/week
Odds ratio
IPAQ-L
Lima, 2017 [56]
Brazil
Cross-sectional
602
37.7
 ≥ 18
Age, sex, socio-economic level, education, physical activity behaviours, active/sedentary status
TRPA (min/week) and transportation mode
Students t-test
IPAQ-S
Lopes, 2018 [57]
Brazil
Cross-sectional
1419
63.6
 ≥ 18
Age, sex, marital status, socioeconomic status, nutritional status, self-rated health/ quality of life, perceived neighbourhood crime, motor vehicle access, days of public transport use per week, land use, streetscape, aesthetics, sidewalks, streets, social environment
Categorical walking for transport: ≥ 10 min/week and ≥ 150 min/week; Bicycling for transport: ≥ 10 min/week
Prevalence ratio
IPAQ-L
Lu, 2017 [58]
China
Cross-sectional
1078
-
18–65
Age, sex, education, population density, income, intersection density, land-use mix
Walking for transport (min/week)
Regression coefficient
IPAQ-L
Mackenbach, 2016 [59]
New Zealand
Cross-sectional
481
46.8
20–65
Income, population density, housing density, apartment density, land-use mix, public transport access and frequency, job accessibility, parking price, area deprivation, walkability
TRPA: Trips with an active mode ≥ 10 min
Odds ratio
New Zealand Household Travel Survey
Malambo, 2017 [60]
South Africa
Cross-sectional
671
76.0
35–70
Land-use mix, street connectivity, infrastructure, aesthetics, safety (traffic/crime), urban / rural status
Dichotomous TRPA: < 150 min/week and ≥ 150 min/week
Odds ratio
IPAQ-L
Matsushita, 2015 [61]
Japan
Cross-sectional
3269
49.6
30–59
Age, sex, household income, education, employment, number of motor vehicles, body mass index
Dichotomous TRPA: inactive (< 10 min/week) and active (≥ 10 min/week)
Odds ratio
GPAQ
Mertens, 2019 [62]
Belgium
Longitudinal
438
54.1
 ≥ 65
Age, education, baseline transport-related physical activity, self-efficacy, neighbourhood social trust, neighbourhood social diversity, land-use mix, infrastructure, aesthetics, safety
Walking for transport: ≥ 10 min/week (engagement)
Odds ratio
IPAQ-L
Molina-García, 2014 [63]
Spain
Cross-sectional
518
59.7
 ≥ 18
Age, sex, socio-economic status, residence type (home or campus), distance, main transport mode
TRPA: MET min/week and commute mode
t-test, ANOVA
Survey
Mumford, 2011 [64]
United States
Longitudinal
101
67.0
 ≥ 18
Neighbourhood density, land-use mix
Walking for transportation: mins/week and days/week
Odds ratio
Survey
Nathan, 2014 [65]
Australia
Cross-sectional
323
68.1
76.9 (7.3)
Aesthetics, safety, physical barriers, walkability, infrastructure
Dichotomous walking for transport: < 60 min/week and ≥ 60 min/week
Odds ratio
Community Healthy Activities Model Program for Seniors survey
Nordfjærn, 2019 [66]
Norway
Cross-sectional
441
53.0
23.1 (4.8)
Age, sex, campus (area density), ascription of responsibility, awareness of consequences, safety, priorities of physical activity, convenience, duration / distance, vehicle access
Active transportation use
Regression coefficient
Questionnaire
Padrão, 2012 [67]
Mozambique
Cross-sectional
3211
-
25–64
Age, sex, education, physical activity behaviours, urban / rural status
TRPA: ≥ 60 min/day
Prevalence ratio
GPAQ
Panter, 2014 [68]
United Kingdom
Longitudinal
655
69.0
18–69
Pleasant walk environment, convenient public transport, traffic, safety, convenient routes
Change in TRPA (min/week); Uptake of TRPA
Odds ratio and regression coefficient
Survey
Panter, 2011 [69]
United Kingdom
Cross-sectional
1142
68.0
42.3 (11.4)
Sex, vehicle access, distance, public transport, traffic, routes, safety, urban / rural status, vehicle use (intent, attitude, norms, habit)
Walking for transport: no engagement and any engagement; Cycling for transport: < 150 min/week and ≥ 150 min/week
Odds ratio
Survey
Panter, 2011 [69]
United Kingdom
Longitudinal
1279
53.1
49–80
Age, sex, body mass index, employment, habit, control, intent, attitude, subjective norm, social support, distance, perceived environment, residence type, socio-economic deprivation, land-use mix, access, street connectivity, infrastructure, aesthetics, safety, urban/rural status, density, streetlights, connectivity, sidewalks, walkability
Commute mode: active or non-active
Odds ratio
EPAQ2
Pelclova, 2013 [70]
Czech Republic
Cross-sectional
2839
50.1
 ≥ 50
Residential density, land use-mix, street connectivity, infrastructure, aesthetics, safety
Walking for transport: < 30 min/day and ≥ 30 min/day
Odds ratio
IPAQ-L
Perchoux, 2017 [71]
France
Cross-sectional
23,432
100
 ≥ 18
Occupation intensity, leisure-time physical activity, transportation type, destinations, infrastructure, aesthetics, social norms, social modelling
TRPA (hours/week) determining cluster allocation
Odds ratio
STAQ
Quinn, 2017 [72]
United States
Cross-sectional
152,573
48.5
 ≥ 18
Age, sex, education, race, income, urban / rural status, employment, distance / duration, employment start time
TRPA: non-active (< 10 min/trip) and active (≥ 10 min/trip)
Odds ratio
Interview
Reilly, 2013 [73]
United States
Cross-sectional
387
96.0
18–39
Age, sex, education, income, marital status, birthplace, length of US residency, health insurance status, physician communication
TRPA: no engagement and any engagement
Odds ratio
GPAQ
Ryan, 2018 [74]
Canada
Cross-sectional
5180
52.5
20–64
Age, sex, income, education, urban / rural status, health, smoking status, body mass index, aboriginal language, spirituality
Categorical walking for transportation: < 1 h/week, 1–5 h/week, > 5 h/week
Odds ratio
Aboriginal Peoples Survey
Saris, 2013 [75]
Netherland
Cross-sectional
622
54.2
 ≥ 18
Age, sex, ethnicity, body mass index, neighbourhood status score (infrastructure, traffic, safety)
TRPA: walking and cycling for transport (mins/week)
Regression coefficient
SQUASH
Shimura, 2012 [76]
Australia
Longitudinal
504
54.0
50–65
Neighbourhood walkability
Changes in walking for transport: min/day
Regression coefficient
IPAQ-L
Simons, 2017 [77]
Belgium
Cross-sectional
224
56.0
18–26
Self-efficacy, social support, social norms, social modelling, perceived benefits, perceived barriers, land-use mix, street connectivity, walking and cycling facilities, aesthetics, work facilities, distance, density, safety, education level
Transport mode, TRPA duration (min/day), TRPA frequency (days/week)
Odds ratio
IPAQ-L
Slater, 2016 [78]
United States
Cross-sectional
311
58.5
18–45
Age, sex, body mass index, education, income, marital status, smoking, cancer diagnosis, vehicle access, leisure/work/household physical activity, environmental barriers, planning/psychosocial barriers, safety barriers, health barriers, walkability
TRPA: no engagement and any engagement
Odds ratio
IPAQ based questionnaire
Thern, 2015 [79]
Sweden
Cross-sectional
432
52.0
20–52
Ethnicity, pet ownership, residential area, environment, alcohol consumption, outdoor recreational physical activity, indoor physical activity
Dichotomous TRPA: active (if a person walked or cycled ≥ 15 min, one-way to school or work) and non-active (if a person walked or cycled for < 15 min, one-way to school or work)
Odds ratio
Swedish Survey of Living Conditions
Van Cauwenberg, 2012 [80]
Belgium
Cross-sectional
48,879
55.7
 ≥ 60
Age, sex, education, income, functional limitations, distance, destinations, public transport, infrastructure, sidewalks, intersections, safety, streetlighting, aesthetics, greenness, urban / rural status,
Dichotomized walking and cycling for transport: "almost daily walking for transportation" and "less than almost daily walking for transportation" or "almost daily cycling for transportation" and "less than almost daily cycling for transportation"
Odds ratio
Belgian Aging Study questionnaire
Van Cauwenberg, 2013 [81]
Belgium
Cross-sectional
50,685
55.5
 ≥ 60
Age, sex, marital status, functional limitations, educational, income, area (urban / semi-urban), Environmental index (absence of high curbs, destinations, benches, crossings, bus stops, street lighting, safety from crime), distance
Dichotomized walking for transport: "almost daily walking for transportation" and "less than almost daily walking for transportation"
Odds ratio, predicted probability
Belgian Aging Study questionnaire
Van Cauwenberg, 2014 [82]
Belgium
Cross-sectional
24,875
55.6
 ≥ 65
Frequency of contact with neighbours, satisfaction of contact with neighbours, neighbour social support, community members, formal community engagement
Dichotomized walking for transport: "almost daily walking for transportation" and "less than almost daily walking for transportation"
Odds ratio
Belgian Aging Study questionnaire
Van Dyck, 2010 [83]
Belgium
Cross-sectional
1200
52.1
20–65
Walkability
Walking and cycling for transportation (min/week)
Regression coefficient
IPAQ-L
Van Dyck, 2013 [84]
Belgium
Cross-sectional
4139
100
18–46
Aesthetics, physical activity environment, personal safety, neighbourhood social cohesion
Walking for transportation (min/week)
Regression coefficient
IPAQ-L
van Heeswijck, 2015 [85]
Canada
Cross-sectional
37,165
52.0
20–89
Density, land-use mix, greenness, intersection density
Dichotomous TRPA: "sedentary" and "active" commute
Odds ratio
Questionnaire
Veitch, 2013 [86]
Australia
Cross-sectional
319
65.3
55.9 (15.4)
Park visitation
Categorical TRPA: low (0–90 min/week), medium (91–275 min/week), high (≥ 276 min/week)
Odds ratio
IPAQ-L
Wasfi, 2013 [87]
Canada
Cross-sectional
6913
57.0
33.6 (12.4)
Age, sex, income, travel behaviour (type, frequency, distance), social characteristics (education), population density, destination density, intersections
Total walking distance/day for commute (metres)
Regression coefficient
Geographic Information System
Weber Corseuil, 2012 [88]
Brazil
Cross-sectional
1656
63.9
60–102
Streetlighting, safety
Dichotomous TRPA: < 150 min/week and ≥ 150 min/week
Prevalence ratio
IPAQ-L
Wilson, 2012 [89]
Australia
Cross-sectional
10,745
55.7
40–65
Density, hilliness, tree coverage, bikeways, streetlights, river or coast, public transport, shop, land-use mix
Categorical walking for transportation: 0 min/week, 1–59 min/week, 60–149 min/week, ≥ 150 min/week
Odds ratio
Questionnaire
Witten, 2012 [90]
New Zealand
Cross-sectional
2033
57.2
20–65
Dwelling density, street connectivity, land-use mix, streetscape, neighbourhood destinations accessibility index
TRPA (min/week) transformed to have a standard deviation of one
Odds ratio
IPAQ-L
Yang, 2017 [91]
United Kingdom
Longitudinal
1143
69.0
40–79
Distance, streetlighting, walkability, main or secondary road on route
Commute mode: "active" and "passive" commuters; Categorical change in commuter mode over time
Odds ratio
EPAQ2
Yang, 2020 [54]
United States
Cross-sectional
125,819
-
 ≥ 18
Age, sex, ethnicity, education, income, employment, neighbourhood population density, driver status, vehicle access
TRPA: trips/week
Adjusted means
National Household Travel Survey
Yu, 2020 [92]
United States
Cross-sectional
109,617
49.6
 ≥ 18
Sex, race, education, income, population density, number of vehicles, number of household members
Two dichotomous TRPA variables: (1) "did not walk" and "walked to/from transit to work"; (2) "walked ≥ 30 min/day to or from transit to work" and "walked < 30 min/day to or from transit to work"
Odds ratio
National Household Travel Survey
Zwald, 2014 [93]
United States
Cross-sectional
772
63.6
 ≥ 18
Age, sex, income, employment, public transport use, safety, traffic, sidewalks, destinations
Categorical walking for transportation: 0 min/week, 1–149 min/week, ≥ 150 min/week
Odds ratio
IPAQ-L
* Where age range was not available, mean (standard deviation) was presented in place
- = No gender distribution reported
EPAQ2 European prospective investigation into cancer and nutrition Physical Activity Questionnaire version two, GPAQ Global Physical Activity Questionnaire, IPAQ (-L/-S) International Physical Activity Questionnaire (-Long / -Short version), NPAQ Neighbourhood Physical Activity Questionnaire, MET Metabolic Equivalent of Task, STAQ Simpson-Troost Attitude Questionnaire, SQUASH Short Questionnaire to Assess Health-enhancing Physical Activity, TRPA Transport-related physical activity

Quality and risk of bias assessment

Four articles were classified to be of lower quality and of higher bias risk when assessed using a modified Newcastle–Ottawa scale (Additional file 1). As such, they were excluded from this review. Assessments of quality are presented in the Quality Assessment Table, found within Additional file 2. Forty-one articles were deemed to be of fair quality and 32 were rated as good quality.

Individual exposures

A number of individual level exposures from both biological and socio-economic origins were shown to be associated with adult TRPA. These associations are summarised in Table 2.
Table 2
Summary of relationships observed between exposures and transport-related physical activity outcomes
 
Factor
Positive, n
Negative, n
No association, n
Individual
Age, older
3
12
24
Sex, male
9
3
21
Health
Self-report
5
-
6
Weight / body mass index
1
4
6
Health behaviours (smoking, alcohol)
-
3
4
Ethnicity, white
1
4
9
Marital status, partnered
1
4
7
Number of dependants
1
2
4
Pet ownership
1
1
3
Employment, employed
1
4
7
Income, greater
3
9
7
Education, higher
6
4
15
Socio-economic status, greater
-
9
1
Motor vehicle
Access
-
7
7
Parking
-
1
1
Attitudes
-
1
-
Self-efficacy
5
-
-
Physical activity attitudes and behaviours
6
-
8
Attitudes, behaviours, and beliefs
-
1
6
Social
Social cohesion
2
-
9
Social modelling
3
-
4
Normalisation
5
1
3
Environmental
Distance
-
11
1
Destination
14
-
2
Land-use mix
7
1
7
Walkability
5
-
2
Connectivity
10
1
10
Supportive infrastructure
8
1
9
Public transport
13
1
1
Traffic
2
-
9
Urban vs rural, urban
3
2
6
Population and land density
5
1
10
Green spaces
3
2
3
Gradient, flat
1
-
3
Park access / visitation
1
-
3
Location, river/coast
2
-
-
Aesthetics
12
2
11
Weather
1
-
2
Safety
13
5
12
Streetlighting
7
-
3

Physical, biological, and health and health behaviours

Age and sex were assessed among numerous studies, examined as either individual exposures, or covariates in multivariable models. Thirty-nine studies assessed the relationship between participant age and TRPA, fifteen of which found the relationship to be statistically significant. Increasing age was associated with decreasing odds of engaging in TRPA or a lower TRPA level in twelve studies [26, 29, 46, 51, 56, 57, 63, 66, 7476, 87] including one longitudinal [76]. Conversely, a positive relationship between age and TRPA was observed among only three studies, in which women of lower socio-economic status [34, 41, 47] reported greater TRPA levels with higher age. Twenty-four studies found there to be no significant association between age and TRPA level [14, 30, 32, 33, 35, 40, 45, 50, 5254, 58, 59, 62, 6769, 72, 78, 82, 88, 9294].
Significant differences in TRPA level by sex were noted among twelve of thirty-three studies. Nine articles reported male participants undertaking a greater amount of TRPA than women (three assessed walking and cycling combined into a single measure of TRPA [29, 66, 67], two walking only [52, 87], and four presented walking and cycling for commute separately [47, 57, 69, 72]).Of these, two studies reported that men were more likely to cycle for active transport compared to women [57, 69]. Dissimilarly, three studies found women had a higher probability of engaging in TRPA and a greater likelihood of high levels of active transport [46, 53, 74]; 21 studies observed no association to be present [14, 30, 3235, 40, 45, 50, 54, 56, 58, 59, 63, 70, 71, 75, 78, 9294].
Self-reported health status was assessed across eleven studies, five observed a significant, positive association with TRPA [35, 57, 74, 78, 94], one of which was longitudinal in nature [94]; a further six found no significant relationship [36, 41, 47, 50, 69, 75]. Eleven studies examined weight status; a statistically significant association was observed among five (six studies observed no significant association [35, 36, 45, 68, 75, 78]). Four studies found overweight and obese status was associated with increased odds of undertaking lower levels of TRPA (three cross-sectional [47, 69, 74], one longitudinal [95]) compared to healthy weight status, while one saw higher weight status was associated with greater TRPA in women living in socio-economically disadvantaged neighbourhoods [41]. Two studies found people who smoke had lower levels of TRPA [41, 45] compared to non-smokers while one cohort showed excessive alcohol consumption was associated with less engagement with TRPA [79]. Four studies observed no association between health behaviours (nutrition, smoking, and alcohol consumption) and TRPA engagement [47, 53, 57, 74].
While race or ethnicity was modelled as a covariate among many studies, fourteen articles examined its direct relationship with TRPA outcomes and only five [14, 47, 72, 79, 92] showed statistically significant associations, nine found no significant association [32, 35, 41, 46, 54, 59, 72, 75, 94]. Those who were non-white were more likely to undertake higher levels of TRPA [14, 47] than those who were white. Similarly, immigrant and minority populations were more likely to undertake TRPA [79, 92] than the remaining native residents. In a study from the US, white participants were more likely to undertake an active commute via bicycle compared to their Hispanic and African-American counterparts [72].

Living arrangements

The living arrangements of participants (marital status, children and dependents in household, and pets) were assessed across 18 studies. Of the ten studies that considered marital status, four [29, 46, 53, 78] found that married and partnered individuals were less likely to engage in TRPA (for one study [53], in male participants only) than single people. A fifth study conversely found married individuals to have higher odds of undertaking TRPA [30] than singles, while seven studies showed no significant association [32, 35, 41, 45, 52, 57, 59]. An inverse association between the number of children/dependents in the household and the levels of TRPA was observed in two [30, 35] of seven studies. A third [47] found the presence of dependents within households of men of lower socio-economic status was associated with higher TRPA. Four studies found no association between dependents and TRPA [41, 45, 69, 92]. The presence of pets in the household was assessed in three studies [42, 44, 79], with only two finding significant association. One found that non-pet owners were more engaged in active commuting than pet owners [79]. A second study showed no significance of association [42]. The third study showed older adults that own and walk their dog had increased odds of walking for transport > 150 min/week, unlike dog owners who did not walk this dog whose odds of undertaking greater than 10 min of TRPA per week were greatly reduced [44].

Socio-economic factors

Thirteen studies assessed employment status; six studies found employment status to be significantly associated with TRPA, seven observed no significant relationship [14, 29, 32, 36, 50, 68, 94]. Of these, four articles reported that being unemployed was associated with higher TRPA [45, 52, 61, 93] than being employed, while one – a study of women residing in lower-socio-economic neighbourhoods – found a positive relationship between employment and TRPA [41]. Increased odds of active commuting were present among those with the option of working from home and starting work during the hours of 11:00 to 15:59 compared with those that travelled to, and started work between the hours of 06:00 to 10:59 [72]. There were 19 studies that assessed the association between TRPA and individual and/or familial income (seven displayed non-significant relationships [37, 45, 53, 54, 58, 93, 94]). Eight studies observed a significant inverse association between income level and the amount TRPA performed [14, 34, 38, 46, 59, 72, 74, 92]. Two studies showed that increased household income was associated with an increased likelihood of engaging in TRPA compared with those with lower incomes [78, 87]. One study noted sex-based differences in associations with higher income in men yielding lower TRPA levels while higher income in women was positively associated with higher TRPA level [61].
Nine articles reported ten significant relationships between education level and TRPA, with conflicting results; a further 15 studies observed non-significant relationships [29, 30, 33, 45, 46, 52, 53, 58, 67, 69, 72, 74, 78, 87, 92]. Five studies (two longitudinal [62, 94]) found that higher levels of educational attainment were positively associated with higher levels of TRPA [38, 41, 62, 77, 94]. Conversely, three studies observed a negative association with individuals of the highest levels of TRPA having the lowest education levels [14, 34, 56], while one study found men of the lowest and women of the highest education levels were more likely to achieve high levels of TRPA engagement [61].
Greater socio-economic status (indicated by a range of proxy factors: education, employment, and income of the individual and those that also reside in their neighbourhood) was inversely associated with TRPA levels and odds of engagement in TRPA across nine studies [28, 47, 50, 56, 57, 63, 69, 75], only one study found no significant relationship [33].
Seven studies reported a significant negative association between motor vehicle access/ownership and the level of TRPA undertaken [14, 26, 29, 38, 45, 57, 78], an additional seven studies observed no-significant relationship [32, 35, 40, 66, 69, 92, 94]. Similarly, one study showed higher parking prices [59] to be associated with higher TRPA (one study reported non-significance [26]), while another found awareness of the negative consequences of car travel [66] to be associated with higher TRPA.

Attitudes/beliefs/behaviours

Greater self-efficacy for active commuting was positively associated with TRPA across five studies [35, 41, 42, 62, 77], of which one was longitudinal [62]. Furthermore, six studies demonstrated that regular engagement, prioritisation, and enjoyment of physical activity was associated with higher TRPA [33, 36, 43, 66, 79, 95], three of these studies were of a longitudinal design [36, 43, 95]. A further eight studies found there to be no significant relationship between these PA behaviours and TRPA [26, 35, 42, 49, 53, 62, 68, 78]. Assessment of individual attitudes (e.g., perceived financial verses temporal costs [54]), found six studies to have no association [40, 41, 54, 66, 74, 77], whilst two observed a positive relationship. One study observed those who believed walking to be less convenient than motor vehicle transport were less likely to engage in TRPA [26], while individuals that perceived the number of immigrants residing in a neighbourhood to be high had higher odds of walking for transportation [82].

Social exposures

When considering the association between social factors and TRPA, 11 significant and 17 non-significant associations between social support and modelling with TRPA were observed (see summary in Table 2). Feelings of trust and social cohesion among the neighbourhood was associated with higher TRPA in two studies (one cross-sectional [82], one longitudinal [62]), though was non-significant in nine studies [29, 4044, 49, 57, 84]. Seeing others (pro-TRPA social modelling) such as family and friends undertake TRPA was positively associated with TRPA among three of seven studies [27, 49, 71], four observed no significance [35, 73, 77, 84]. Increased social support for TRPA (normalisation; from family, friends, co-workers or employers) was positively associated with higher TRPA among five studies [35, 41, 73, 77, 82]. Conversly, one cross-sectional study showed that family and friends suggesting more TRPA be undertaken was associated with reduced TRPA [49], while an additional three associations were non-significant [26, 66, 68]. It was suggested in one study that social norms related to cultural restrictions were associated with a lower level of TRPA among Pakistani women [52].

Environmental exposures

A number of exposures related to commuter environment were associated with TRPA (Table 2). Eleven studies (including one of longitudinal design [95]) found that the odds of undertaking TRPA were higher among those who resided a shorted distance from their intended destination, with both perceived and objective distance of commute inversely associated with the level of TRPA undertaken [33, 50, 60, 63, 66, 69, 72, 77, 81, 91, 95]; one study observed no significant relationship [87].
Similarly, fourteen of sixteen studies found that a greater number of recreation, amenity, and retail destinations proximal to the areas of residence were associated with increased TRPA [27, 3740, 44, 65, 71, 80, 81, 84, 85, 89, 91]; two studies observed no significant relationship [31, 93].
Fifteen studies examined the relationship between land-use mix (residential, commercial, and industrial co-location) and TRPA. Seven studies (two longitudinal [36, 62]) found positive associations between greater land-use mix and TRPA engagement [28, 36, 59, 62, 64, 70, 77]. Seven studies observed no significant association [51, 57, 58, 68, 8991]. The final study found greater land use mix was associated with lower odds of active transportation [85]; however, as noted by authors, this study included industrial land use within its land-use mix metric – a value typically excluded due to its notable lack of association with PA outcomes and potential to influence associations.
Neighbourhood walkability was positively associated with TRPA in five studies [40, 59, 76, 78, 83] and was non-significantly associated among a further two [54, 65]. Of the twenty-one studies examining route connectivity, eleven (one longitudinal [95]) found areas with higher connectivity (intersections, cross-walks, destination accessibility) were associated with greater TRPA levels [24, 44, 49, 60, 70, 80, 84, 85, 89, 90, 95]. Ten studies observed no significant association [27, 31, 35, 37, 51, 58, 68, 77, 87, 93]. One study also observed connectivity to be positively related with TRPA amongt urban neighbourhoods, but not rural areas [80] while another conversly saw street connectivity to be associated with decreased odds of TRPA engagement [28].
Eight articles indicated that the presence of well maintained supportive infrastructure (such as curbing, bikelanes, bikepaths, and sidewalks bikepaths) was positively associated with TRPA [24, 25, 27, 37, 44, 71, 77, 89]. In contrast, one longitudinal study found older adults who perceived better infrastructure for walking had lower odds of engaging in TRPA compared to those perceiving worse infrastructure [62]. This contrasting finding may be because those spending greater periods undertaking TRPA within the neighbourhood may be more likely to observe a greater number of issues. A further nine studies observed there to be no signficant relationship present [28, 35, 47, 49, 57, 65, 68, 70, 95].
The relationship between public transport and TRPA was examined in 15 studies. A positive association was determined among 13 studies (one longitudinal [68]), reporting public transport proximal to residence and destinations resulting in higher TRPA [25, 38, 55, 57, 60, 65, 66, 68, 69, 80, 87, 89, 93]. However, one study found the number of bus stops and train frequency was negatively related to TRPA among low-income individuals only [59], a further study found no significant relationship [37]. Higher traffic levels were positively associated with TRPA levels in two studies [47, 69], though non-significant associations were observed among a further nine [24, 27, 30, 31, 35, 44, 50, 75, 95].
The density, greenspace, and landscape of the commuting environment was significantly associated with TRPA across 20 of the 42 relationships examined. Living in urban areas as opposed to rural areas was associated with increased TRPA in three studies (two cross-sectional [42, 72], one longitudinal [95]). Similarly, five studies found increased population and land density was associated with increased TRPA levels [14, 58, 64, 89, 92]. In contrast, two studies reported rural residents were more likely to undertake TRPA (compared with those from urban areas) [34, 41]; six found no significant relationship [53, 6769, 74, 79]. One study found decreased housing and population density at the commute start point and higher density at the endpoint was associated with increased odds TRPA engagement [59], while 10 observed no significant association [28, 50, 51, 68, 70, 77, 87, 90, 91, 94]. Three studies observed that residing closer to green spaces and areas with greater tree-coverage was positively associated with TRPA [31, 60, 89]. Conversely, two studies found that individuals who resided in areas surrounded by buildings with less green spaces were more engaged in TRPA [79, 85], a further three observed no association [31, 44, 91]. One study found residents living neighbourhoods with flatter landscape were significantly more likely to walk for 150 min or more for transport per week [89], three found there to be no association [39, 44, 65]. Of the four studies examining park visitation, three observed non-significant relationships [37, 68, 91] whilst one demonstrated that increased park visitation was associated with greater odds of high TRPA levels [86]. Living closer to a river or coast was positively associated with TRPA in two studies [37, 89].
Perceived aesthetics of the environment was significantly associated with TRPA across 13 of 24 studies (14 relationships observed). Eleven of these studies reported that more attractive environments (free from litter and stray animals) were positively related to increased TRPA [24, 27, 3941, 54, 60, 69, 70, 75, 77]. Two studies indicated different findings with one observing the aesthetics of an area was inversely associated with TRPA [28]. Another found that individuals with active occupations and high-levels of sedentary leisure time in areas of high pollution and low aesthetics had increased odds of high TRPA, while those with active leisure times travelling in low pollution and high aesthetics areas had increased odds of high TRPA levels [71]. Eleven studies found there to be no significant relationship between aesthetics and adult TRPA [25, 31, 37, 42, 44, 49, 50, 57, 62, 84, 95].
Weather was statistically signficantly associated with TRPA level in only one of three studies, though the magnitude of TRPA change was deemed to be clinically insignificant. Even after an extrapolation of effect, rain equating to ten inches during the travel day was associated with a decrease in walking for transport of just over half a minute on average per day, suggesting relative independance of weather and TRPA [48]. Two studies observed no significant relationship present between weather and TRPA [31, 35].
Neighbourhood and traffic safety were significantly associated with TRPA across 18 of 30 studies. Thirteen studies (one longitudinal [68]) showed greater perceived safety [24, 31, 42, 44, 47, 57, 68, 70, 75, 77, 84, 93], lower crime rates [31], and perceived safety from traffic (including visibility, safe traffic speeds, and safe road crossings) [24, 30, 44, 70, 77, 93] were positively associated with TRPA. Five studies observed greater perceived safety from crime, stray animals, and traffic were associated with lower TRPA [28, 39, 54, 62, 80], one of which was longitudinal [62]. Twelve studies observed no association between safety and adult TRPA [25, 27, 29, 35, 37, 41, 43, 49, 78, 88, 91, 95]. A higher presence of streetlighting was positively associated with greater levels of TRPA among seven [39, 44, 80, 81, 88, 89, 91] of ten studies (three non-significant [25, 31, 60]).

Discussion

This is the first comprehensive synthesis of the correlates and determinants of TRPA among adults. In this systematic review, findings from multiple disciplines of research across the past decade were used to identify a small number of factors that demonstrated consistent associations with adult TRPA and a large number of factors that exhibited inconsistent relationships. Thirty-six factors were assessed across the 73 studies included in this synthesis, with seven factors consistently associated with adult TRPA: socio-economic status, self-efficacy, social normalisation, distance of travel, destination, public transportation, and the presence of streetlighting. These factors represent all layers of the social-ecological model (individual, social, and environmental), highlighting the multi-layered nature of the influences of adult TRPA. This study acts to highlight these 36 factors as variables for consideration in the development of future framework while also bringing attention to the need for further longitudinal and multidisciplinary studies.

Individual level factors

Nineteen individual level factors assessed as potential correlates and determinants of adult TRPA were identified, including age, sex, health, health behaviours, living arrangements, socio-economic circumstances, and attitudes and beliefs. However, only two (individual socio-economic status and self-efficacy) were consistently associated with adult TRPA outcomes.
Socio-economic status was assessed across studies via differing combinations of education, employment, and income (both of the individual and those that also reside in their neighbourhood). Eight of nine studies found higher socio-economic status to be associated with lower levels of TRPA. Association between socio-economic status and PA has also been observed in the domain of leisure-time PA. This mutual correlate could be due to the shared discretionary nature of these types of PA [96]. However, literature has shown self-efficacy to mediate the relationship between PA and individual- and area-level income and education [97]. Moreover, it must be acknowledged that for some, active commuting may be a necessity rather than a choice. Higher TRPA observed among those of lower socioeconomic position may be due to costs associated with purchasing and running a car (e.g., servicing, registration, parking) leading to higher reliance on other forms of transportation, such as public transport, walking, and cycling [98]. These findings suggest that those of higher socio-economic status provide a low TRPA population to which interventions may be targeted.
Self-efficacy for active commuting was also identified as a consistent correlate of adult TRPA. Self-efficacy refers to an individual’s judgement of their capability to organise and integrate TRPA behaviours into their lifestyle. As a discretionary domain of physical activity, the association between greater self-efficacy for active commuting and higher adult TRPA engagement unsurprisingly mirrors that of leisure-time PA [96]. Furthermore, self-efficacy has been observed to affect the amount of effort devoted to a task, and the magnitude and length of persistence when difficulties are encountered [99], therefore, affecting engagement as well as TRPA levels and maintenance. These findings are important as they highlight the need for policymakers to not only provide infrastructure to facilitate TRPA, but also to develop strategies that work to engage and encourage individuals so that the TRPA infrastructure provided will be used.

Social level factors

Few social-level factors were examined (n = 3) and even fewer were associated with TRPA. No association was observed between social cohesion and TRPA, and associations between social modelling and TRPA were equivocal. Only social normalisation was observed as a consistent correlate of greater TRPA among adults. Often the normalisation of TRPA was experienced via the implementation of pro-TRPA policies in the workplace and peers and family voicing their support of TRPA practices. Some contrasting associations were found between normalisation and TRPA engagement. It is possible that findings of decreasing TRPA despite greater encouragement from family and friends [49] may be present only due to reverse causality (e.g., those with lower TRPA receiving greater encouragement) and cross-sectional assessment [49]. Prior studies have suggested that interventions aimed at normalising the act (TRPA) as well as its associated factors may lead to greater TRPA [100]. Hence, further study into social attitudes towards these associated factors may provide a greater understanding of the social structures governing TRPA performance and highlight points for future intervention.
Few studies reported significant associations between social factors and TRPA outcomes compared with literature examining leisure-time PA. This may be attributable to the necessity of travel in today’s society. While leisure-time PA and TRPA share a discretionary nature, feelings of social cohesion and positive modelling may encourage society members to undertake leisure-time PA. However, those without the capacity to undertake private transportation or those with greater self-efficacy for TRPA may undertake an active commute irrespective of their social or physical environment – an important consideration when tailoring domain-specific interventions.
A distinct lack of longitudinal analyses of TRPA and social factors (n = 3) was also highlighted. Failure to examine longitudinal relationships between social-level factors and TRPA prevents the ascertainment of temporality (i.e., determination of whether the levels of TRPA observed were obtained before introduction to the social environment or whether TRPA levels were the result of the relationship between the social environment and the individual). Resultantly, a gap remains surrounding the relationships of social factors (i.e., policy, positive TRPA modelling and normalization, and social cohesion) with adult TRPA outcomes. As highlighted by leisure-time PA [101], these factors have the potential to act as independent determinants of TRPA engagement, and therefore warrant further investigation. Due to the unique circumstances afforded via the international coronavirus disease (COVID-19) pandemic, there is potential to interpret the results of natural experimentation in which the relationship between social cohesion and the uptake of public transportation and TRPA is observed following the reduction and cessation of COVID-19 restrictions.

Environmental level factors

Eighteen environmental-level factors were assessed including sidewalks, supportive infrastructure, land-use mix, traffic, and weather. However, only four environmental correlates and determinants of adult TRPA were identified: distance travelled, concentration/number of destinations, public transportation access, and the presence of streetlighting.
As previously established, greater distance of travel was consistently associated with lower TRPA levels and engagement [102, 103]. TRPA engagement was higher among those who resided closer to their intended destination, with increased distance of commute inversely associated with the level of TRPA undertaken. Additionally, destination concentration was positively associated with adult TRPA. Those residing and travelling among areas with a higher number of destinations (i.e., amenity, retail, and recreation centres) in close proximity to commute route and residence observed higer levels of TRPA. Public transport was also identified as a correlate and determinant of adult TRPA. A positive relationship was observed, with greater public transport frequency and higher number of public transport terminals more proximal to the route start and destination associated with higher levels of TRPA. These findings may be based upon principles of convenience, with observations surrounding public transport accessibility and TRPA outcomes similar to those observed with distance and destination. These findings suggest that urban and transport planning (centred upon the creation of destinations within both a walkable distance of the home and a comprehensive public transport network) has the potential to encourage TRPA engagement and facilitate the achievement of recommended PA levels.
A greater presence of streetlighting was associated with higher TRPA levels. The presence of streetlighting has the potential to facilitate greater levels of active commuting by allowing individuals to better navigate their route during periods of darkness. Furthermore, literature suggests that the presence of streetlighting yields higher levels of perceived safety [104]. Though not shown to be consistently associated with TRPA in this review, increased safety of the commute route has the potential to relate with commute habits when adjusted for additional factors such as age, sex, socio-economic status, and self-efficacy. As such, the installation of streetlighting along commuter routes may be seen as a key means of increasing TRPA engagement among those required to commute during periods of darkness.
Studies of the built environment (land-use mix, population and residential density, walkability, connectivity, supportive infrastructure, and urban/rural status) and adult TRPA were equivocal and inconclusive. Similarly, relationships between TRPA and the natural environment (i.e., greenspace, proximity to water bodies such as rivers and coast, and gradient) yielded equivocal and inconsistent results. This suggests that unlike leisure-time PA [105], TRPA may be more dependent on where, how, and how far an individual is travelling, rather than the landscape in which the commute occurs. This further highlights the need for TRPA intervention design to be considered separately to those of the leisure-time PA domain.

Limitations and strengths

Only English language, peer-reviewed studies from the last decade were included in this systematic review. Thus, grey-literature, non-English studies, and literature published prior to our cut-off were not included. As many exposures and outcomes across studies were heterogeneous in their measurement techniques, meta-analysis was not appropriate and therefore, quantitative estimates of associations could not be presented; we recommend future studies consider meta-analysis if appropriate All screening was performed by two authors independently, thus minimising selection bias and improving reliability of the screening process [106]. Among the studies included in this review, most focussed on assessing TPRA using single-discipline lenses; few studies employed multi-disciplinary frameworks. Comprehensively assessing multi-level and/or multi-disciplinary models has the potential to lead to identification of novel combinations of individual, social, and environment exposures that cannot be identified in single-discipline or single-population studies [107]. In turn, this could facilitate the formation of tailored interventions with increased effectiveness.
Self-report of both exposures and outcomes amongst studies is of potential methodological concern due to the possibility of recall or social desirability biases. This potential for recall bias was lessened via assessments of quality that ensured studies with high risk of bias and lower quality were excluded from this review. Furthermore, TRPA assessment via questionnaire has been found to be a valid and reliable form of measurement [108]. While objective assessment of TRPA by accelerometer is possible, it still relies on self-report of movement during the day to attribute the collected data to a specific PA domain [109]. Studies were undertaken in different countries; thus, findings of included studies may differ due to being shaped by different cultural beliefs around TRPA promotion, differing infrastructure standards and varied social and individual beliefs. This may be illustrated within this review via the identification of societal norms as potential factors responsible for sex-based disparities in the TRPA of Pakistani participants [52]. However, the multi-national nature of this systematic review is also a strength, providing insight and further generalisability into the relationships identified. Additionally, the varying sample sizes of studies included may have resulted in studies with large samples observing significant relationships for some factors, while studies with lower participant numbers and statistical power may have found non-significance. This may have resulted in this review misclassifying associations as inconsistent. However, only 25 studies had a sample size less than 1000 of which 4 had a sample size less than 300, suggesting statistical power is unlikely to explain the observed findings. Most studies (94.5%) included in this review measured TRPA for any purpose, but four only considered TRPA for work/school purposes. While this is a potential limitation, particularly for those who are not employed or in education, the small number of these studies are unlikely to impact on the overall findings. Further, in studies examining sex and age for example, the minimum, maximum and median sample sizes did not markedly differ according to direction (positive, negative, null) of association (see Additional file 3). This study guides future analyses by presenting all observed factors and highlighting inconsistencies of association, so that future researchers do not fail to consider key covariates when literature searches to inform model formation suggest non-significant association.
Furthermore, the multi-disciplinary nature of this review, and its use of a social-ecological model provides a diverse series of factors organised within a well-established theoretical framework. However, it must be noted that factors from within the organisational and policy levels of the social-ecological model were not identified within studies included in this review and warrant investigation in future research. Finally, the 73 published studies compiled within this review provide a considerable catalogue of literature that acts to strengthen our findings.

Future directions

This review identifies a number of future research directions. There remains a substantive gap in the literature on longitudinal relationships with adult TRPA outcomes – as highlighted by the very low number of longitudinal studies identified in this review (n = 7). While cross-sectional studies allow for the assessment of correlation, a temporal relationship cannot be inferred, thus preventing insights into causality. This absence of longitudinal studies may be due to the high monetary, temporal, and resource expenses associated with this mode of observation. To determine whether TRPA is an action brought about by the current needs and circumstances of the individual or a learnt behaviour, further longitudinal research is needed. The longitudinal assessments included in this review examined a range of factors associated with TRPA across a number of different stages of adulthood. However, failure to incorporate factors from a range of social-ecological levels may have limited their findings. For example, the use of perceived environmental measures instead of objective assessments has the potential to reduce the magnitude of association between built environment and TRPA. This is because perceptions represent the subjective interactions between an individual and their environment (e.g., an individual of lower self-efficacy or poorer health may not believe their environment is conducive to TRPA, while another more motivated or physically able individual may find the same environment to be favourable for active commuting) rather than objective assessments of the built and natural environment (e.g., distance of route, or the presence of streetlighting and supportive infrastructure). Similarly, additional longitudinal studies within this review examined the built environment with adjustment for individual-level socio-economic factors only. By overlooking the potential role of social factors (such as social support) and individual level cognitions (such as beliefs or motivation), these studies may under- or over-estimate associations. As such, it is recommended that future longitudinal analyses would benefit from combined analysis or adjustment for both objective and perceived measures, as well as a focus on better encompassing a range of factors spanning the social-ecological model. Future research could assess tracking and patterns of both TRPA and its associated factors across the life-course. Further, randomised controlled trials testing interventions to increase TRPA are warranted, particularly assessing means of increasing efficacy, and participation in active commuting on routes where distances may be greater and destinations more sparce (previously observed to be associated with decreased TRPA). This may be via changes in policy and practice that ultimately normalise and promote public transport and TRPA. These studies could prove impactful among those of higher socio-economic status who have been identified as undertaking lower levels of TRPA.
At present, there has been greater examination of the environmental and individual-level correlates and determinants of TRPA compared with those of social factors. Further study of the social factors that associate with TRPA is required to bring TRPA research into line with literature of other PA domains. Furthermore, this review observed an absence of factors from organisational and policy levels of the social-ecological model. This finding highlights a need for further analysis of how organisational and policy-based factors relate to TRPA outcomes.
Future studies should carefully model the associations between exposures and TRPA considering the potential for confounding, mediation, and effect modification between exposures across the socio-ecological model. This may identify potentially modifiable factors to target to increase TRPA among certain groups, for example women or those in rural areas. Examination of multi-level pathways and mediatory relationships are required to provide insight into the underlying mechanisms through which TRPA may be promoted and subsequently increased.

Conclusion

This systematic review provides a synthesis of correlates and determinants of TRPA from English peer-reviewed literature of the last decade. Spanning multiple disciplines of research, findings were presented within a social-ecological framework, forming a comprehensive resource to inform future studies and interventions. While socio-economic status, self-efficacy, social normalisation, distance of travel, destinations, public transportation, and the presence of streetlighting were consistently associated with adult TRPA, all factors observed to be associated with TRPA in this review could be considered for inclusion within prospective analyses. Future studies that consider potential mechanisms previously overlooked due to the single-disciplinary nature of prior research may provide a greater understanding of factors amenable to intervention. Those developing policies and strategies to increase TRPA should consider factors at the individual, social, and environmental level, as well as the potential interactions amongst these factors, to maximise the likelihood of effectiveness.

Acknowledgements

Not applicable.

Declarations

Not applicable.
Not applicable.

Competing interests

The authors declare that they have no competing interests.
Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://​creativecommons.​org/​licenses/​by/​4.​0/​. The Creative Commons Public Domain Dedication waiver (http://​creativecommons.​org/​publicdomain/​zero/​1.​0/​) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Literatur
1.
Zurück zum Zitat Ding D, Lawson KD, Kolbe-Alexander TL, Finkelstein EA, Katzmarzyk PT, Van Mechelen W, et al. The economic burden of physical inactivity: a global analysis of major non-communicable diseases. Lancet. 2016;388(10051):1311–24.PubMedCrossRef Ding D, Lawson KD, Kolbe-Alexander TL, Finkelstein EA, Katzmarzyk PT, Van Mechelen W, et al. The economic burden of physical inactivity: a global analysis of major non-communicable diseases. Lancet. 2016;388(10051):1311–24.PubMedCrossRef
2.
Zurück zum Zitat Varghese T, Schultz WM, McCue AA, Lambert CT, Sandesara PB, Eapen DJ, et al. Physical activity in the prevention of coronary heart disease: implications for the clinician. BMJ: Heart. 2016;102(12):904–9. Varghese T, Schultz WM, McCue AA, Lambert CT, Sandesara PB, Eapen DJ, et al. Physical activity in the prevention of coronary heart disease: implications for the clinician. BMJ: Heart. 2016;102(12):904–9.
3.
Zurück zum Zitat Bull FC, Al-Ansari SS, Biddle S, Borodulin K, Buman MP, Cardon G, et al. World Health Organization 2020 guidelines on physical activity and sedentary behaviour. Br J Sports Med. 2020;54(24):1451–62.PubMedCrossRef Bull FC, Al-Ansari SS, Biddle S, Borodulin K, Buman MP, Cardon G, et al. World Health Organization 2020 guidelines on physical activity and sedentary behaviour. Br J Sports Med. 2020;54(24):1451–62.PubMedCrossRef
4.
Zurück zum Zitat Warburton DER, Bredin SSD. Health benefits of physical activity: a systematic review of current systematic reviews. Curr Opin Cardiol. 2017;32(5):541–56.PubMedCrossRef Warburton DER, Bredin SSD. Health benefits of physical activity: a systematic review of current systematic reviews. Curr Opin Cardiol. 2017;32(5):541–56.PubMedCrossRef
5.
Zurück zum Zitat Sharif K, Watad A, Bragazzi NL, Lichtbroun M, Amital H, Shoenfeld Y. Physical activity and autoimmune diseases: get moving and manage the disease. Autoimmun Rev. 2018;17(1):53–72.PubMedCrossRef Sharif K, Watad A, Bragazzi NL, Lichtbroun M, Amital H, Shoenfeld Y. Physical activity and autoimmune diseases: get moving and manage the disease. Autoimmun Rev. 2018;17(1):53–72.PubMedCrossRef
6.
Zurück zum Zitat Stanesby O, Long M, Ball K, Blizzard L, Cocker F, Greaves S, Socio-demographic, behavioural and health-related characteristics associated with active commuting in a regional Australian state: Evidence from the, et al. Tasmanian Population Health Survey. Health Promot J Austr. 2016;2020:1–12. Stanesby O, Long M, Ball K, Blizzard L, Cocker F, Greaves S, Socio-demographic, behavioural and health-related characteristics associated with active commuting in a regional Australian state: Evidence from the, et al. Tasmanian Population Health Survey. Health Promot J Austr. 2016;2020:1–12.
7.
Zurück zum Zitat Fu X. How habit moderates the commute mode decision process: integration of the theory of planned behavior and latent class choice model. Transportation. 2021;48(5):2681–707.CrossRef Fu X. How habit moderates the commute mode decision process: integration of the theory of planned behavior and latent class choice model. Transportation. 2021;48(5):2681–707.CrossRef
8.
Zurück zum Zitat Celis-Morales CA, Lyall DM, Welsh P, Anderson J, Steell L, Guo Y, et al. Association between active commuting and incident cardiovascular disease, cancer, and mortality: prospective cohort study. BMJ. 2017;357: j1456.PubMedCrossRef Celis-Morales CA, Lyall DM, Welsh P, Anderson J, Steell L, Guo Y, et al. Association between active commuting and incident cardiovascular disease, cancer, and mortality: prospective cohort study. BMJ. 2017;357: j1456.PubMedCrossRef
9.
Zurück zum Zitat Samitz G, Egger M, Zwahlen M. Domains of physical activity and all-cause mortality: systematic review and dose-response meta-analysis of cohort studies. Int J Epidemiol. 2011;40(5):1382–400.PubMedCrossRef Samitz G, Egger M, Zwahlen M. Domains of physical activity and all-cause mortality: systematic review and dose-response meta-analysis of cohort studies. Int J Epidemiol. 2011;40(5):1382–400.PubMedCrossRef
10.
Zurück zum Zitat Hamer M, Chida Y. Active commuting and cardiovascular risk: a meta-analytic review. Prev Med. 2008;46(1):9–13.PubMedCrossRef Hamer M, Chida Y. Active commuting and cardiovascular risk: a meta-analytic review. Prev Med. 2008;46(1):9–13.PubMedCrossRef
11.
Zurück zum Zitat Patterson R, Panter J, Vamos EP, Cummins S, Millett C, Laverty AA. Associations between commute mode and cardiovascular disease, cancer, and all-cause mortality, and cancer incidence, using linked Census data over 25 years in England and Wales: a cohort study. Lancet Planetary Health. 2020;4(5):186–94.CrossRef Patterson R, Panter J, Vamos EP, Cummins S, Millett C, Laverty AA. Associations between commute mode and cardiovascular disease, cancer, and all-cause mortality, and cancer incidence, using linked Census data over 25 years in England and Wales: a cohort study. Lancet Planetary Health. 2020;4(5):186–94.CrossRef
12.
Zurück zum Zitat Saunders LE, Green JM, Petticrew MP, Steinbach R, Roberts H. What Are the Health Benefits of Active Travel? A Systematic Review of Trials and Cohort Studies. PLoS ONE. 2013;8(8): e69912.PubMedPubMedCentralCrossRef Saunders LE, Green JM, Petticrew MP, Steinbach R, Roberts H. What Are the Health Benefits of Active Travel? A Systematic Review of Trials and Cohort Studies. PLoS ONE. 2013;8(8): e69912.PubMedPubMedCentralCrossRef
13.
Zurück zum Zitat Rissel C, Curac N, Greenaway M, Bauman A. Physical activity associated with public transport use—A review and modelling of potential benefits. Int J Environ Res Public Health. 2012;9(7):2454–78.PubMedPubMedCentralCrossRef Rissel C, Curac N, Greenaway M, Bauman A. Physical activity associated with public transport use—A review and modelling of potential benefits. Int J Environ Res Public Health. 2012;9(7):2454–78.PubMedPubMedCentralCrossRef
14.
Zurück zum Zitat Freeland AL, Banerjee SN, Dannenberg AL, Wendel AM. Walking associated with public transit: moving toward increased physical activity in the United States. Am J Public Health. 2013;103(3):536–42.PubMedPubMedCentralCrossRef Freeland AL, Banerjee SN, Dannenberg AL, Wendel AM. Walking associated with public transit: moving toward increased physical activity in the United States. Am J Public Health. 2013;103(3):536–42.PubMedPubMedCentralCrossRef
15.
Zurück zum Zitat Besser L, Dannenberg A. Walking to public transit: steps to help meet physical activity recommendations. Am J Prev Med. 2005;29(4):273–80.PubMedCrossRef Besser L, Dannenberg A. Walking to public transit: steps to help meet physical activity recommendations. Am J Prev Med. 2005;29(4):273–80.PubMedCrossRef
16.
Zurück zum Zitat Buehler R, Kuhnimhof T, Bauman A, Eisenmann C. Active travel as stable source of physical activity for one third of German adults: Evidence from longitudinal data. Transp Res Part A: Pol Pract. 2019;123:105–18. Buehler R, Kuhnimhof T, Bauman A, Eisenmann C. Active travel as stable source of physical activity for one third of German adults: Evidence from longitudinal data. Transp Res Part A: Pol Pract. 2019;123:105–18.
17.
Zurück zum Zitat Lindelöw D, Svensson Å, Sternudd C, Johansson M. What limits the pedestrian? Exploring perceptions of walking in the built environment and in the context of every-day life. J Transp Health. 2014;1(4):223–31.CrossRef Lindelöw D, Svensson Å, Sternudd C, Johansson M. What limits the pedestrian? Exploring perceptions of walking in the built environment and in the context of every-day life. J Transp Health. 2014;1(4):223–31.CrossRef
18.
Zurück zum Zitat Christiansen LB, Madsen T, Schipperijn J, Ersbøll AK, Troelsen J. Variations in active transport behavior among different neighborhoods and across adult life stages. J Transp Health. 2014;1(4):316–25.PubMedPubMedCentralCrossRef Christiansen LB, Madsen T, Schipperijn J, Ersbøll AK, Troelsen J. Variations in active transport behavior among different neighborhoods and across adult life stages. J Transp Health. 2014;1(4):316–25.PubMedPubMedCentralCrossRef
19.
Zurück zum Zitat Van Acker V, Van Wee B, Witlox F. When transport geography meets social psychology: Toward a conceptual model of travel behaviour. Transport Rev. 2010;30(2):219–40.CrossRef Van Acker V, Van Wee B, Witlox F. When transport geography meets social psychology: Toward a conceptual model of travel behaviour. Transport Rev. 2010;30(2):219–40.CrossRef
20.
Zurück zum Zitat Moher D, Liberati A, Tetzlaff J, Altman DG, Group P 2009 Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement BMJ 339 b2535 Moher D, Liberati A, Tetzlaff J, Altman DG, Group P 2009 Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement BMJ 339 b2535
21.
Zurück zum Zitat Stroup DF, Berlin JA, Morton SC, Olkin I, Williamson G, Rennie D. Meta-analysis of observational studies in epidemiology: a proposal for reporting. Meta-analysis Of Observational Studies in Epidemiology (MOOSE) group. JAMA. 2000;238(15):2008–12.CrossRef Stroup DF, Berlin JA, Morton SC, Olkin I, Williamson G, Rennie D. Meta-analysis of observational studies in epidemiology: a proposal for reporting. Meta-analysis Of Observational Studies in Epidemiology (MOOSE) group. JAMA. 2000;238(15):2008–12.CrossRef
22.
Zurück zum Zitat Covidence systematic review software. Melbourne, Australia Veritas Health Innovation. Covidence systematic review software. Melbourne, Australia Veritas Health Innovation.
24.
Zurück zum Zitat Adams EJ, Goodman A, Sahlqvist S, Bull FC, Ogilvie D. Correlates of walking and cycling for transport and recreation: Factor structure, reliability and behavioural associations of the perceptions of the environment in the neighbourhood scale (PENS). Int J Behav Nutr Phys Act. 2013;10:87–102.PubMedPubMedCentralCrossRef Adams EJ, Goodman A, Sahlqvist S, Bull FC, Ogilvie D. Correlates of walking and cycling for transport and recreation: Factor structure, reliability and behavioural associations of the perceptions of the environment in the neighbourhood scale (PENS). Int J Behav Nutr Phys Act. 2013;10:87–102.PubMedPubMedCentralCrossRef
25.
Zurück zum Zitat Adams EJ, Bull FC, Foster CE. Are perceptions of the environment in the workplace ‘neighbourhood’ associated with commuter walking? J Transp Health. 2016;3(4):479–84.CrossRef Adams EJ, Bull FC, Foster CE. Are perceptions of the environment in the workplace ‘neighbourhood’ associated with commuter walking? J Transp Health. 2016;3(4):479–84.CrossRef
26.
Zurück zum Zitat Adams EJ, Esliger DW, Taylor IM, Sherar LB. Individual, employment and psychosocial factors influencing walking to work: Implications for intervention design. PLoS ONE. 2017;12(2):1–14.CrossRef Adams EJ, Esliger DW, Taylor IM, Sherar LB. Individual, employment and psychosocial factors influencing walking to work: Implications for intervention design. PLoS ONE. 2017;12(2):1–14.CrossRef
27.
Zurück zum Zitat Adlakha D, Hipp AJ, Marx C, Yang L, Tabak R, Dodson EA, et al. Home and workplace built environment supports for physical activity. Am J Prev Med. 2015;48(1):104–7.PubMedCrossRef Adlakha D, Hipp AJ, Marx C, Yang L, Tabak R, Dodson EA, et al. Home and workplace built environment supports for physical activity. Am J Prev Med. 2015;48(1):104–7.PubMedCrossRef
28.
Zurück zum Zitat Adlakha D, Hipp JA, Brownson RC, A Eyler A, K Lesorogol C, Raghavan R. "Can we walk?" Environmental supports for physical activity in India. Prev Med. 2017;103S:S81–S9. Adlakha D, Hipp JA, Brownson RC, A Eyler A, K Lesorogol C, Raghavan R. "Can we walk?" Environmental supports for physical activity in India. Prev Med. 2017;103S:S81–S9.
29.
Zurück zum Zitat Aliyas Z. Does social environment mediate the association between perceived safety and physical activity among adults living in low socioeconomic neighborhoods? J Transp Health. 2019;14:1–10. Aliyas Z. Does social environment mediate the association between perceived safety and physical activity among adults living in low socioeconomic neighborhoods? J Transp Health. 2019;14:1–10.
30.
Zurück zum Zitat Aliyas Z. Why some walk and others don’t: Neighborhood safety and the sociodemographic variation effect on walking for leisure and transportation. J Public Health Manag Pract. 2020;26(4):24–32.CrossRef Aliyas Z. Why some walk and others don’t: Neighborhood safety and the sociodemographic variation effect on walking for leisure and transportation. J Public Health Manag Pract. 2020;26(4):24–32.CrossRef
31.
Zurück zum Zitat Amorim TC, Azevedo MR, Hallal PC. Physical activity levels according to physical and social environmental factors in a sample of adults living in South Brazil. J Phys Act Health. 2010;7(S2):S204–12.PubMedCrossRef Amorim TC, Azevedo MR, Hallal PC. Physical activity levels according to physical and social environmental factors in a sample of adults living in South Brazil. J Phys Act Health. 2010;7(S2):S204–12.PubMedCrossRef
32.
Zurück zum Zitat Barr A, Simons K, Mavoa S, Badland H, Giles-Corti B, Scheurer J, et al. Daily walking among commuters: A cross-sectional study of associations with residential, work, and regional accessibility in Melbourne, Australia (2012–2014). Environ Health Perspect. 2019;127(9):1–12.CrossRef Barr A, Simons K, Mavoa S, Badland H, Giles-Corti B, Scheurer J, et al. Daily walking among commuters: A cross-sectional study of associations with residential, work, and regional accessibility in Melbourne, Australia (2012–2014). Environ Health Perspect. 2019;127(9):1–12.CrossRef
33.
Zurück zum Zitat Barranco-Ruiz Y, León CC, Villa-González E, Leal XP, Chillón P, Rodríguez-Rodríguez F. Active commuting to university and its association with sociodemographic factors and physical activity levels in chilean students. Medicina (Lithuania). 2019;55(5):1–12. Barranco-Ruiz Y, León CC, Villa-González E, Leal XP, Chillón P, Rodríguez-Rodríguez F. Active commuting to university and its association with sociodemographic factors and physical activity levels in chilean students. Medicina (Lithuania). 2019;55(5):1–12.
34.
Zurück zum Zitat Bauman A, Ma GS, Cuevas F, Omar Z, Waqanivalu T, Phongsavan P, et al. Cross-national comparisons of socioeconomic differences in the prevalence of leisure-time and occupational physical activity, and active commuting in six Asia-Pacific countries. J Epidemiol Community Health. 2011;65(1):35–43.PubMedCrossRef Bauman A, Ma GS, Cuevas F, Omar Z, Waqanivalu T, Phongsavan P, et al. Cross-national comparisons of socioeconomic differences in the prevalence of leisure-time and occupational physical activity, and active commuting in six Asia-Pacific countries. J Epidemiol Community Health. 2011;65(1):35–43.PubMedCrossRef
35.
Zurück zum Zitat Bopp M, Child S, Campbell M. Factors associated with active commuting to work among women. Women Health. 2014;54(3):212–31.PubMedCrossRef Bopp M, Child S, Campbell M. Factors associated with active commuting to work among women. Women Health. 2014;54(3):212–31.PubMedCrossRef
36.
Zurück zum Zitat Bopp M, Wilson OWA, Duffey M, Papalia Z. An examination of active travel trends before and after college graduation. J Transp Health. 2019;14:1–6. Bopp M, Wilson OWA, Duffey M, Papalia Z. An examination of active travel trends before and after college graduation. J Transp Health. 2019;14:1–6.
37.
Zurück zum Zitat Borchardt JL, Paulitsch RG, Dumith SC. The influence of built, natural and social environment on physical activity among adults and elderly in southern Brazil: a population-based study. Int J Public Health. 2019;64(5):649–58.PubMedCrossRef Borchardt JL, Paulitsch RG, Dumith SC. The influence of built, natural and social environment on physical activity among adults and elderly in southern Brazil: a population-based study. Int J Public Health. 2019;64(5):649–58.PubMedCrossRef
38.
Zurück zum Zitat Brondeel R, Pannier B, Chaix B. Associations of socioeconomic status with transport-related physical activity: combining a household travel survey and accelerometer data using random forests. J Transp Health. 2016;3(3):287–96.CrossRef Brondeel R, Pannier B, Chaix B. Associations of socioeconomic status with transport-related physical activity: combining a household travel survey and accelerometer data using random forests. J Transp Health. 2016;3(3):287–96.CrossRef
39.
Zurück zum Zitat Cerin E, Lee KY, Barnett A, Sit CHP, Cheung MC, Chan WM, et al. Walking for transportation in Hong Kong Chinese urban elders: A cross-sectional study on what destinations matter and when. Int J Behav Nutr Phys Act. 2013;10:78–88.PubMedPubMedCentralCrossRef Cerin E, Lee KY, Barnett A, Sit CHP, Cheung MC, Chan WM, et al. Walking for transportation in Hong Kong Chinese urban elders: A cross-sectional study on what destinations matter and when. Int J Behav Nutr Phys Act. 2013;10:78–88.PubMedPubMedCentralCrossRef
40.
Zurück zum Zitat Chudyk AM, McKay HA, Winters M, Sims-Gould J, Ashe MC. Neighborhood walkability, physical activity, and walking for transportation: A cross-sectional study of older adults living on low income. BMC Geriatr. 2017;17(1):1–14.CrossRef Chudyk AM, McKay HA, Winters M, Sims-Gould J, Ashe MC. Neighborhood walkability, physical activity, and walking for transportation: A cross-sectional study of older adults living on low income. BMC Geriatr. 2017;17(1):1–14.CrossRef
41.
Zurück zum Zitat Cleland V, Ball K, Hume C, Timperio A, King AC, Crawford D. Individual, social and environmental correlates of physical activity among women living in socioeconomically disadvantaged neighbourhoods. Soc Sci Med. 2010;70(12):2011–8.PubMedCrossRef Cleland V, Ball K, Hume C, Timperio A, King AC, Crawford D. Individual, social and environmental correlates of physical activity among women living in socioeconomically disadvantaged neighbourhoods. Soc Sci Med. 2010;70(12):2011–8.PubMedCrossRef
42.
Zurück zum Zitat Cleland VJ, Ball K, King AC, Crawford D. Do the Individual, Social, and Environmental Correlates of Physical Activity Differ Between Urban and Rural Women? Environ Behav. 2012;44(3):350–73.CrossRef Cleland VJ, Ball K, King AC, Crawford D. Do the Individual, Social, and Environmental Correlates of Physical Activity Differ Between Urban and Rural Women? Environ Behav. 2012;44(3):350–73.CrossRef
43.
Zurück zum Zitat Cleland V, Cocker F, Canary J, Teychenne M, Crawford D, Timperio A, et al. Social-ecological predictors of physical activity patterns: A longitudinal study of women from socioeconomically disadvantaged areas. Prev Med. 2020;132:1–7.CrossRef Cleland V, Cocker F, Canary J, Teychenne M, Crawford D, Timperio A, et al. Social-ecological predictors of physical activity patterns: A longitudinal study of women from socioeconomically disadvantaged areas. Prev Med. 2020;132:1–7.CrossRef
44.
Zurück zum Zitat Corseuil Giehl MW, Hallal PC, Brownson RC, D’Orsi E. Exploring Associations between Perceived Measures of the Environment and Walking among Brazilian Older Adults. J Aging Health. 2017;29(1):45–67.PubMedCrossRef Corseuil Giehl MW, Hallal PC, Brownson RC, D’Orsi E. Exploring Associations between Perceived Measures of the Environment and Walking among Brazilian Older Adults. J Aging Health. 2017;29(1):45–67.PubMedCrossRef
45.
Zurück zum Zitat Dedele A, Miškinyte A, Andrušaityte S, Nemaniūte-Gužiene J. Seasonality of physical activity and its association with socioeconomic and health factors among urban-dwelling adults of Kaunas. Lithuania BMC Public Health. 2019;19(1):1–9. Dedele A, Miškinyte A, Andrušaityte S, Nemaniūte-Gužiene J. Seasonality of physical activity and its association with socioeconomic and health factors among urban-dwelling adults of Kaunas. Lithuania BMC Public Health. 2019;19(1):1–9.
46.
Zurück zum Zitat Del Duca GF, Nahas MV, Garcia LMT, Mota J, Hallal PC, Peres MA. Prevalence and sociodemographic correlates of all domains of physical activity in Brazilian adults. Prev Med. 2013;56(2):99–102.PubMedCrossRef Del Duca GF, Nahas MV, Garcia LMT, Mota J, Hallal PC, Peres MA. Prevalence and sociodemographic correlates of all domains of physical activity in Brazilian adults. Prev Med. 2013;56(2):99–102.PubMedCrossRef
47.
Zurück zum Zitat de Matos SMA, Pitanga FJG, Almeida MDCC, Queiroz CO, dos Santos CA, de Almeida RT, et al. What factors explain bicycling and walking for commuting by ELSA-Brasil participants? Am J Health Promot. 2018;32(3):646–56.PubMedCrossRef de Matos SMA, Pitanga FJG, Almeida MDCC, Queiroz CO, dos Santos CA, de Almeida RT, et al. What factors explain bicycling and walking for commuting by ELSA-Brasil participants? Am J Health Promot. 2018;32(3):646–56.PubMedCrossRef
48.
Zurück zum Zitat Durand CP, Zhang K, Salvo D. Weather is not significantly correlated with destination-specific transport-related physical activity among adults: A large-scale temporally matched analysis. Prev Med. 2017;101:133–6.PubMedPubMedCentralCrossRef Durand CP, Zhang K, Salvo D. Weather is not significantly correlated with destination-specific transport-related physical activity among adults: A large-scale temporally matched analysis. Prev Med. 2017;101:133–6.PubMedPubMedCentralCrossRef
49.
Zurück zum Zitat Eichinger M, Titze S, Haditsch B, Dorner TE, Stronegger WJ. How are physical activity behaviors and cardiovascular risk factors associated with characteristics of the built and social residential environment? PLoS ONE. 2015;10(6):1–15.CrossRef Eichinger M, Titze S, Haditsch B, Dorner TE, Stronegger WJ. How are physical activity behaviors and cardiovascular risk factors associated with characteristics of the built and social residential environment? PLoS ONE. 2015;10(6):1–15.CrossRef
50.
Zurück zum Zitat Falconer CL, Cooper AR, Flint E. Patterns and correlates of active commuting in adults with type 2 diabetes: Cross-sectional evidence from UK Biobank. BMJ Open. 2017;7(10):1–9.CrossRef Falconer CL, Cooper AR, Flint E. Patterns and correlates of active commuting in adults with type 2 diabetes: Cross-sectional evidence from UK Biobank. BMJ Open. 2017;7(10):1–9.CrossRef
51.
Zurück zum Zitat Ghani F, Rachele JN, Loh VH, Washington S, Turrell G. Do differences in built environments explain age differences in transport walking across neighbourhoods? J Transp Health. 2018;9:83–95.CrossRef Ghani F, Rachele JN, Loh VH, Washington S, Turrell G. Do differences in built environments explain age differences in transport walking across neighbourhoods? J Transp Health. 2018;9:83–95.CrossRef
52.
Zurück zum Zitat Gul Y, Sultan Z, Moeinaddini M, Jokhio GA. The effects of socio-demographic factors on physical activity in gated and non-gated neighbourhoods in Karachi. Pakistan Sport in Society. 2019;22(7):1225–39.CrossRef Gul Y, Sultan Z, Moeinaddini M, Jokhio GA. The effects of socio-demographic factors on physical activity in gated and non-gated neighbourhoods in Karachi. Pakistan Sport in Society. 2019;22(7):1225–39.CrossRef
53.
Zurück zum Zitat Kwasniewska M, Kaczmarczyk-Chalas K, Pikala M, Broda, Kozakiewicz K, Pajak A, et al. Socio-demographic and lifestyle correlates of commuting activity in Poland. Prev Med. 2010;50(5–6):257–61. Kwasniewska M, Kaczmarczyk-Chalas K, Pikala M, Broda, Kozakiewicz K, Pajak A, et al. Socio-demographic and lifestyle correlates of commuting activity in Poland. Prev Med. 2010;50(5–6):257–61.
54.
Zurück zum Zitat Li JJ, Auchincloss AH, Yang Y, Rodriguez DA, Sanchez BN. Neighborhood characteristics and transport walking: Exploring multiple pathways of influence using a structural equation modeling approach. J Transp Geogr. 2020;85:1–10.CrossRef Li JJ, Auchincloss AH, Yang Y, Rodriguez DA, Sanchez BN. Neighborhood characteristics and transport walking: Exploring multiple pathways of influence using a structural equation modeling approach. J Transp Geogr. 2020;85:1–10.CrossRef
55.
Zurück zum Zitat Liao Y, Chang SH, Ku PW, Park JH. Associations of public bicycle use with transport-related and leisure-time physical activity in Taiwanese adults. J Transp Health. 2017;6:433–8.CrossRef Liao Y, Chang SH, Ku PW, Park JH. Associations of public bicycle use with transport-related and leisure-time physical activity in Taiwanese adults. J Transp Health. 2017;6:433–8.CrossRef
56.
Zurück zum Zitat De Souza LJ, De Moraes Ferrari GL, Ferrari TK, Araujo TL, Matsudo VKR. Changes in commuting to work and physical activity in the population of three municipalities in the São Paulo region in 2000 and 2010. Rev Bras Epidemiol. 2017;20(2):274–85. De Souza LJ, De Moraes Ferrari GL, Ferrari TK, Araujo TL, Matsudo VKR. Changes in commuting to work and physical activity in the population of three municipalities in the São Paulo region in 2000 and 2010. Rev Bras Epidemiol. 2017;20(2):274–85.
57.
Zurück zum Zitat Lopes AAS, Kienteka M, Fermino RC, Reis RS. Characteristics of the environmental microscale and walking and bicycling for transportation among adults in Curitiba, Paraná State. Brazil Cadernos de Saude Publica. 2018;34(1):1–14. Lopes AAS, Kienteka M, Fermino RC, Reis RS. Characteristics of the environmental microscale and walking and bicycling for transportation among adults in Curitiba, Paraná State. Brazil Cadernos de Saude Publica. 2018;34(1):1–14.
58.
Zurück zum Zitat Lu Y, Xiao Y, Ye Y. Urban density, diversity and design: Is more always better for walking? A study from Hong Kong. Prev Med. 2017;103:S99–103.CrossRef Lu Y, Xiao Y, Ye Y. Urban density, diversity and design: Is more always better for walking? A study from Hong Kong. Prev Med. 2017;103:S99–103.CrossRef
59.
Zurück zum Zitat Mackenbach JD, Randal E, Zhao P, Howden-Chapman P. The influence of urban land-use and public transport facilities on active commuting in Wellington, New Zealand: Active transport forecasting using the WILUTE model. Sustainability (Switzerland). 2016;8(3):1–14. Mackenbach JD, Randal E, Zhao P, Howden-Chapman P. The influence of urban land-use and public transport facilities on active commuting in Wellington, New Zealand: Active transport forecasting using the WILUTE model. Sustainability (Switzerland). 2016;8(3):1–14.
60.
Zurück zum Zitat Malambo P, Kengne AP, Lambert EV, De Villers A, Puoane T. Association between perceived built environmental attributes and physical activity among adults in South Africa. BMC Public Health. 2017;17(1):1–16.CrossRef Malambo P, Kengne AP, Lambert EV, De Villers A, Puoane T. Association between perceived built environmental attributes and physical activity among adults in South Africa. BMC Public Health. 2017;17(1):1–16.CrossRef
61.
Zurück zum Zitat Matsushita M, Harada K, Arao T. Socioeconomic position and work, travel, and recreation-related physical activity in Japanese adults: a cross-sectional study. BMC Public Health. 2015;15:916.PubMedPubMedCentralCrossRef Matsushita M, Harada K, Arao T. Socioeconomic position and work, travel, and recreation-related physical activity in Japanese adults: a cross-sectional study. BMC Public Health. 2015;15:916.PubMedPubMedCentralCrossRef
62.
Zurück zum Zitat Mertens L, Van Dyck D, Deforche B, De Bourdeaudhuij I, Brondeel R, Van Cauwenberg J. Individual, social, and physical environmental factors related to changes in walking and cycling for transport among older adults: A longitudinal study. Health Place. 2019;55:120–7.PubMedCrossRef Mertens L, Van Dyck D, Deforche B, De Bourdeaudhuij I, Brondeel R, Van Cauwenberg J. Individual, social, and physical environmental factors related to changes in walking and cycling for transport among older adults: A longitudinal study. Health Place. 2019;55:120–7.PubMedCrossRef
63.
Zurück zum Zitat Molina-García J, Sallis JF, Castillo I. Active commuting and sociodemographic factors among university students in Spain. J Phys Act Health. 2014;11(2):359–63.PubMedCrossRef Molina-García J, Sallis JF, Castillo I. Active commuting and sociodemographic factors among university students in Spain. J Phys Act Health. 2014;11(2):359–63.PubMedCrossRef
64.
Zurück zum Zitat Mumford KG, Contant CK, Weissman J, Wolf J, Glanz K. Changes in physical activity and travel behaviors in residents of a mixed-use development. Am J Prev Med. 2011;41(5):504–7.PubMedCrossRef Mumford KG, Contant CK, Weissman J, Wolf J, Glanz K. Changes in physical activity and travel behaviors in residents of a mixed-use development. Am J Prev Med. 2011;41(5):504–7.PubMedCrossRef
65.
Zurück zum Zitat Nathan A, Wood L, Giles-Corti B. Perceptions of the built environment and associations with walking among retirement village residents. Environ Behav. 2014;46(1):46–69.CrossRef Nathan A, Wood L, Giles-Corti B. Perceptions of the built environment and associations with walking among retirement village residents. Environ Behav. 2014;46(1):46–69.CrossRef
66.
Zurück zum Zitat Nordfjærn T, Egset KS, Mehdizadeh M. “Winter is coming”: Psychological and situational factors affecting transportation mode use among university students. Transp Policy. 2019;81:45–53.CrossRef Nordfjærn T, Egset KS, Mehdizadeh M. “Winter is coming”: Psychological and situational factors affecting transportation mode use among university students. Transp Policy. 2019;81:45–53.CrossRef
67.
Zurück zum Zitat Padrão P, Damasceno A, Silva-Matos C, Prista A, Lunet N. Physical activity patterns in Mozambique: Urban/rural differences during epidemiological transition. Prev Med. 2012;55(5):444–9.PubMedCrossRef Padrão P, Damasceno A, Silva-Matos C, Prista A, Lunet N. Physical activity patterns in Mozambique: Urban/rural differences during epidemiological transition. Prev Med. 2012;55(5):444–9.PubMedCrossRef
68.
69.
Zurück zum Zitat Panter J, Griffin S, Jones A, Mackett R, Ogilvie D. Correlates of time spent walking and cycling to and from work: Baseline results from the Commuting and Health in Cambridge study. Int J Behav Nutr Phys Act. 2011;8(1):124–37.PubMedPubMedCentralCrossRef Panter J, Griffin S, Jones A, Mackett R, Ogilvie D. Correlates of time spent walking and cycling to and from work: Baseline results from the Commuting and Health in Cambridge study. Int J Behav Nutr Phys Act. 2011;8(1):124–37.PubMedPubMedCentralCrossRef
70.
Zurück zum Zitat Pelclová J, Frömel K, Cuberek R. Gender-specific associations between perceived neighbourhood walkability and meeting walking recommendations when walking for transport and recreation for Czech inhabitants over 50 years of age. Int J Environ Res Public Health. 2013;11(1):527–36.PubMedPubMedCentralCrossRef Pelclová J, Frömel K, Cuberek R. Gender-specific associations between perceived neighbourhood walkability and meeting walking recommendations when walking for transport and recreation for Czech inhabitants over 50 years of age. Int J Environ Res Public Health. 2013;11(1):527–36.PubMedPubMedCentralCrossRef
71.
Zurück zum Zitat Perchoux C, Enaux C, Oppert JM, Menai M, Charreire H, Salze P, et al. Individual, social, and environmental correlates of active transportation patterns in French women. Biomed Res Int. 2017;2017:1–12.CrossRef Perchoux C, Enaux C, Oppert JM, Menai M, Charreire H, Salze P, et al. Individual, social, and environmental correlates of active transportation patterns in French women. Biomed Res Int. 2017;2017:1–12.CrossRef
72.
Zurück zum Zitat Quinn TD, Jakicic JM, Fertman CI, Barone GB. Demographic factors, workplace factors and active transportation use in the USA: a secondary analysis of 2009 NHTS data. J Epidemiol Community Health. 2017;71(5):480–6.PubMedCrossRef Quinn TD, Jakicic JM, Fertman CI, Barone GB. Demographic factors, workplace factors and active transportation use in the USA: a secondary analysis of 2009 NHTS data. J Epidemiol Community Health. 2017;71(5):480–6.PubMedCrossRef
73.
Zurück zum Zitat Reilly M, Ayala GX, Elder JP, Patrick K. Physician communication and physical activity among Latinas. J Phys Act Health. 2013;10(4):602–6.PubMedCrossRef Reilly M, Ayala GX, Elder JP, Patrick K. Physician communication and physical activity among Latinas. J Phys Act Health. 2013;10(4):602–6.PubMedCrossRef
74.
Zurück zum Zitat Ryan CJ, Cooke M, Kirkpatrick SI, Leatherdale ST, Wilk P. The correlates of physical activity among adult Métis. Ethn Health. 2018;23(6):629–48.PubMedCrossRef Ryan CJ, Cooke M, Kirkpatrick SI, Leatherdale ST, Wilk P. The correlates of physical activity among adult Métis. Ethn Health. 2018;23(6):629–48.PubMedCrossRef
75.
Zurück zum Zitat Saris C, Kremers S, Van Assema P, Hoefnagels C, Droomers M, De Vries N. What moves them? Active transport among inhabitants of dutch deprived districts. J Obes. 2013;2013:1–7.CrossRef Saris C, Kremers S, Van Assema P, Hoefnagels C, Droomers M, De Vries N. What moves them? Active transport among inhabitants of dutch deprived districts. J Obes. 2013;2013:1–7.CrossRef
76.
Zurück zum Zitat Shimura H, Sugiyama T, Winkler E, Owen N. High neighborhood walkability mitigates declines in middle-to-older aged adults’ walking for transport. J Phys Act Health. 2012;9(7):1004–8.PubMedCrossRef Shimura H, Sugiyama T, Winkler E, Owen N. High neighborhood walkability mitigates declines in middle-to-older aged adults’ walking for transport. J Phys Act Health. 2012;9(7):1004–8.PubMedCrossRef
77.
Zurück zum Zitat Simons D, De Bourdeaudhuij I, Clarys P, De Cocker K, de Geus B, Vandelanotte C, et al. Psychosocial and environmental correlates of active and passive transport behaviors in college educated and non-college educated working young adults. PLoS ONE. 2017;12(3):1–22.CrossRef Simons D, De Bourdeaudhuij I, Clarys P, De Cocker K, de Geus B, Vandelanotte C, et al. Psychosocial and environmental correlates of active and passive transport behaviors in college educated and non-college educated working young adults. PLoS ONE. 2017;12(3):1–22.CrossRef
78.
Zurück zum Zitat Slater ME, Kelly AS, Sadak KT, Ross JA. Active transportation in adult survivors of childhood cancer and neighborhood controls. J Cancer Surviv. 2016;10(1):11–20.PubMedCrossRef Slater ME, Kelly AS, Sadak KT, Ross JA. Active transportation in adult survivors of childhood cancer and neighborhood controls. J Cancer Surviv. 2016;10(1):11–20.PubMedCrossRef
79.
Zurück zum Zitat Thern E, Sjögren Forss K, Stjernberg L, Jogréus CE. Factors associated with active commuting among parents-to-be in Karlskrona. Sweden Scand J Public Health. 2015;43(1):59–65.PubMedCrossRef Thern E, Sjögren Forss K, Stjernberg L, Jogréus CE. Factors associated with active commuting among parents-to-be in Karlskrona. Sweden Scand J Public Health. 2015;43(1):59–65.PubMedCrossRef
80.
Zurück zum Zitat Van Cauwenberg J, Clarys P, De Bourdeaudhuij I, Van Holle V, Verté D, De Witte N, et al. Physical environmental factors related to walking and cycling in older adults: The Belgian aging studies. BMC Public Health. 2012;12(1):1–13. Van Cauwenberg J, Clarys P, De Bourdeaudhuij I, Van Holle V, Verté D, De Witte N, et al. Physical environmental factors related to walking and cycling in older adults: The Belgian aging studies. BMC Public Health. 2012;12(1):1–13.
81.
Zurück zum Zitat Van Cauwenberg J, Clarys P, De Bourdeaudhuij I, Van Holle V, Verté D, De Witte N, et al. Older adults’ transportation walking: A cross-sectional study on the cumulative influence of physical environmental factors. Int J Health Geogr. 2013;12:1–9. Van Cauwenberg J, Clarys P, De Bourdeaudhuij I, Van Holle V, Verté D, De Witte N, et al. Older adults’ transportation walking: A cross-sectional study on the cumulative influence of physical environmental factors. Int J Health Geogr. 2013;12:1–9.
82.
Zurück zum Zitat Van Cauwenberg J, De Donder L, Clarys P, De Bourdeaudhuij I, Buffel T, De Witte N, et al. Relationships between the perceived neighborhood social environment and walking for transportation among older adults. Soc Sci Med. 2014;104:23–30.PubMedCrossRef Van Cauwenberg J, De Donder L, Clarys P, De Bourdeaudhuij I, Buffel T, De Witte N, et al. Relationships between the perceived neighborhood social environment and walking for transportation among older adults. Soc Sci Med. 2014;104:23–30.PubMedCrossRef
83.
Zurück zum Zitat Van Dyck D, Cerin E, Cardon G, Deforche B, Sallis JF, Owen N, et al. Physical activity as a mediator of the associations between neighborhood walkability and adiposity in Belgian adults. Health Place. 2010;16(5):952–60.PubMedCrossRef Van Dyck D, Cerin E, Cardon G, Deforche B, Sallis JF, Owen N, et al. Physical activity as a mediator of the associations between neighborhood walkability and adiposity in Belgian adults. Health Place. 2010;16(5):952–60.PubMedCrossRef
84.
Zurück zum Zitat Van Dyck D, Veitch J, De Bourdeaudhuij I, Thornton L, Ball K. Environmental perceptions as mediators of the relationship between the objective built environment and walking among socio-economically disadvantaged women. Int J Behav Nutr Phys Act. 2013;10. Van Dyck D, Veitch J, De Bourdeaudhuij I, Thornton L, Ball K. Environmental perceptions as mediators of the relationship between the objective built environment and walking among socio-economically disadvantaged women. Int J Behav Nutr Phys Act. 2013;10.
85.
Zurück zum Zitat van Heeswijck T, Paquet C, Kestens Y, Thierry B, Morency C, Daniel M. Differences in associations between active transportation and built environmental exposures when expressed using different components of individual activity spaces. Health Place. 2015;33:195–202.PubMedCrossRef van Heeswijck T, Paquet C, Kestens Y, Thierry B, Morency C, Daniel M. Differences in associations between active transportation and built environmental exposures when expressed using different components of individual activity spaces. Health Place. 2015;33:195–202.PubMedCrossRef
86.
Zurück zum Zitat Veitch J, Ball K, Crawford D, Abbott G, Salmon J. Is park visitation associated with leisure-time and transportation physical activity? Prev Med. 2013;57(5):732–4.PubMedCrossRef Veitch J, Ball K, Crawford D, Abbott G, Salmon J. Is park visitation associated with leisure-time and transportation physical activity? Prev Med. 2013;57(5):732–4.PubMedCrossRef
87.
Zurück zum Zitat Wasfi RA, Ross NA, El-Geneidy AM. Achieving recommended daily physical activity levels through commuting by public transportation: Unpacking individual and contextual influences. Health Place. 2013;23:18–25.PubMedCrossRef Wasfi RA, Ross NA, El-Geneidy AM. Achieving recommended daily physical activity levels through commuting by public transportation: Unpacking individual and contextual influences. Health Place. 2013;23:18–25.PubMedCrossRef
88.
Zurück zum Zitat Weber Corseuil M, Hallal PC, Xavier Corseuil H, Jayce Ceola Schneider I, D'Orsi E. Safety from crime and physical activity among older adults: A population-based study in Brazil. J Environ Public Health. 2012;2012:1–8. Weber Corseuil M, Hallal PC, Xavier Corseuil H, Jayce Ceola Schneider I, D'Orsi E. Safety from crime and physical activity among older adults: A population-based study in Brazil. J Environ Public Health. 2012;2012:1–8.
89.
Zurück zum Zitat Wilson LA, Giles-Corti B, Turrell G. The association between objectively measured neighbourhood features and walking for transport in mid-aged adults. Local Environ. 2012;17(2):131–46.CrossRef Wilson LA, Giles-Corti B, Turrell G. The association between objectively measured neighbourhood features and walking for transport in mid-aged adults. Local Environ. 2012;17(2):131–46.CrossRef
90.
Zurück zum Zitat Witten K, Blakely T, Bagheri N, Badland H, Ivory V, Pearce J, et al. Neighborhood built environment and transport and leisure physical activity: Findings using objective exposure and outcome measures in New Zealand. Environ Health Perspect. 2012;120(7):971–7.PubMedPubMedCentralCrossRef Witten K, Blakely T, Bagheri N, Badland H, Ivory V, Pearce J, et al. Neighborhood built environment and transport and leisure physical activity: Findings using objective exposure and outcome measures in New Zealand. Environ Health Perspect. 2012;120(7):971–7.PubMedPubMedCentralCrossRef
91.
Zurück zum Zitat Yang L, Griffin S, Khaw KT, Wareham N, Panter J. Longitudinal associations between built environment characteristics and changes in active commuting. BMC Public Health. 2017;17:1–8. Yang L, Griffin S, Khaw KT, Wareham N, Panter J. Longitudinal associations between built environment characteristics and changes in active commuting. BMC Public Health. 2017;17:1–8.
92.
Zurück zum Zitat Yu CY, Wang B. Moving toward active lifestyles: The change of transit-related walking to work from 2009 to 2017. J Phys Act Health. 2020;17(2):189–96.PubMedCrossRef Yu CY, Wang B. Moving toward active lifestyles: The change of transit-related walking to work from 2009 to 2017. J Phys Act Health. 2020;17(2):189–96.PubMedCrossRef
93.
Zurück zum Zitat Zwald ML, Hipp JA, Corseuil MW, Dodson EA. Correlates of walking for transportation and use of public transportation among adults in St Louis, Missouri, 2012. Prev Chronic Dis. 2014;11(7):1–10. Zwald ML, Hipp JA, Corseuil MW, Dodson EA. Correlates of walking for transportation and use of public transportation among adults in St Louis, Missouri, 2012. Prev Chronic Dis. 2014;11(7):1–10.
94.
Zurück zum Zitat Yang Y, Li S, Zhang K, Xiang X, Li Z, Ahn S, et al. How the daily smartphone is associated with daily travel, physical activity, and self-perceived health: Evidence from 2017 National Household Travel Survey. J Aging Phys Act. 2020;28(5):740–8.CrossRef Yang Y, Li S, Zhang K, Xiang X, Li Z, Ahn S, et al. How the daily smartphone is associated with daily travel, physical activity, and self-perceived health: Evidence from 2017 National Household Travel Survey. J Aging Phys Act. 2020;28(5):740–8.CrossRef
95.
Zurück zum Zitat Panter JR, Jones AP, Van Sluijs EMF, Griffin SJ, Wareham NJ. Environmental and psychological correlates of older adult’s active commuting. Med Sci Sports Exerc. 2011;43(7):1235–43.PubMedCrossRef Panter JR, Jones AP, Van Sluijs EMF, Griffin SJ, Wareham NJ. Environmental and psychological correlates of older adult’s active commuting. Med Sci Sports Exerc. 2011;43(7):1235–43.PubMedCrossRef
96.
Zurück zum Zitat Cleland V, Dwyer T, Venn A. Which domains of childhood physical activity predict physical activity in adulthood? A 20-year prospective tracking study. Br J Sports Med. 2012;46(8):595–602.PubMedCrossRef Cleland V, Dwyer T, Venn A. Which domains of childhood physical activity predict physical activity in adulthood? A 20-year prospective tracking study. Br J Sports Med. 2012;46(8):595–602.PubMedCrossRef
97.
Zurück zum Zitat Cerin E, Leslie E. How socio-economic status contributes to participation in leisure-time physical activity. Soc Sci Med. 2008;66(12):2596–609.PubMedCrossRef Cerin E, Leslie E. How socio-economic status contributes to participation in leisure-time physical activity. Soc Sci Med. 2008;66(12):2596–609.PubMedCrossRef
98.
Zurück zum Zitat Titheridge H, Mackett R, Christie N, Oviedo Hernández D, Ye R. Transport and Poverty. A Review of the Evidence. London, United Kingdom: UCL Transport Institute, University College London; 2014. Titheridge H, Mackett R, Christie N, Oviedo Hernández D, Ye R. Transport and Poverty. A Review of the Evidence. London, United Kingdom: UCL Transport Institute, University College London; 2014.
99.
Zurück zum Zitat Weman-Josefsson K, Lindwall M, Ivarsson A. Need satisfaction, motivational regulations and exercise: moderation and mediation effects. Int J Behav Nutr Phys Act. 2015;12(1):1–11.CrossRef Weman-Josefsson K, Lindwall M, Ivarsson A. Need satisfaction, motivational regulations and exercise: moderation and mediation effects. Int J Behav Nutr Phys Act. 2015;12(1):1–11.CrossRef
100.
Zurück zum Zitat Bauman A, Chau J. The role of media in promoting physical activity. J Phys Act Health. 2009;6:S196–210.PubMedCrossRef Bauman A, Chau J. The role of media in promoting physical activity. J Phys Act Health. 2009;6:S196–210.PubMedCrossRef
101.
Zurück zum Zitat Hamilton K, White KM. Extending the Theory of Planned Behavior: The Role of Self and Social Influences in Predicting Adolescent Regular Moderate-to-Vigorous Physical Activity. J Sport Exerc Psychol. 2008;30(1):56–74.PubMedCrossRef Hamilton K, White KM. Extending the Theory of Planned Behavior: The Role of Self and Social Influences in Predicting Adolescent Regular Moderate-to-Vigorous Physical Activity. J Sport Exerc Psychol. 2008;30(1):56–74.PubMedCrossRef
102.
Zurück zum Zitat Badland HM, Schofield GM. The Built Environment and Transport-Related Physical Activity: What We Do and Do Not Know. J Phys Act Health. 2005;2(4):435–44.CrossRef Badland HM, Schofield GM. The Built Environment and Transport-Related Physical Activity: What We Do and Do Not Know. J Phys Act Health. 2005;2(4):435–44.CrossRef
103.
Zurück zum Zitat Smith M, Hosking J, Woodward A, Witten K, Macmillan A, Field A, et al. Systematic literature review of built environment effects on physical activity and active transport – an update and new findings on health equity. Int J Behav Nutr Phys Act. 2017;14(1). Smith M, Hosking J, Woodward A, Witten K, Macmillan A, Field A, et al. Systematic literature review of built environment effects on physical activity and active transport – an update and new findings on health equity. Int J Behav Nutr Phys Act. 2017;14(1).
104.
Zurück zum Zitat Painter K. The influence of street lighting improvements on crime, fear and pedestrian street use, after dark. Landsc Urban Plan. 1996;35(2–3):193–201.CrossRef Painter K. The influence of street lighting improvements on crime, fear and pedestrian street use, after dark. Landsc Urban Plan. 1996;35(2–3):193–201.CrossRef
105.
Zurück zum Zitat Cerin E, Lee K-Y, Barnett A, Sit CHP, Cheung M-C, Chan W-M. Objectively-measured neighborhood environments and leisure-time physical activity in Chinese urban elders. Prev Med. 2013;56(1):86–9.PubMedCrossRef Cerin E, Lee K-Y, Barnett A, Sit CHP, Cheung M-C, Chan W-M. Objectively-measured neighborhood environments and leisure-time physical activity in Chinese urban elders. Prev Med. 2013;56(1):86–9.PubMedCrossRef
106.
Zurück zum Zitat Waffenschmidt S, Knelangen M, Sieben W, Bühn S, Pieper D. Single screening versus conventional double screening for study selection in systematic reviews: a methodological systematic review. BMC Medical Research Methodology. 2019;19(1). Waffenschmidt S, Knelangen M, Sieben W, Bühn S, Pieper D. Single screening versus conventional double screening for study selection in systematic reviews: a methodological systematic review. BMC Medical Research Methodology. 2019;19(1).
107.
Zurück zum Zitat Lounsbury DW, Mitchell SG. Introduction to special issue on social ecological approaches to community health research and action. Am J Community Psychol. 2009;44(3–4):213–20.PubMedCrossRef Lounsbury DW, Mitchell SG. Introduction to special issue on social ecological approaches to community health research and action. Am J Community Psychol. 2009;44(3–4):213–20.PubMedCrossRef
108.
Zurück zum Zitat Craig CL, Marshall AL, Sjostrom M, Bauman AE, Booth ML, Ainsworth BE, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc. 2003;35(8):1381–95.PubMedCrossRef Craig CL, Marshall AL, Sjostrom M, Bauman AE, Booth ML, Ainsworth BE, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc. 2003;35(8):1381–95.PubMedCrossRef
109.
Zurück zum Zitat Skender S, Ose J, Chang-Claude J, Paskow M, Brühmann B, Siegel EM, et al. Accelerometry and physical activity questionnaires - a systematic review. BMC Public Health. 2016;16(1):1–10.CrossRef Skender S, Ose J, Chang-Claude J, Paskow M, Brühmann B, Siegel EM, et al. Accelerometry and physical activity questionnaires - a systematic review. BMC Public Health. 2016;16(1):1–10.CrossRef
Metadaten
Titel
Correlates and determinants of transport-related physical activity among adults: an interdisciplinary systematic review
verfasst von
Jack T. Evans
Hoang Phan
Marie-Jeanne Buscot
Seana Gall
Verity Cleland
Publikationsdatum
01.12.2022
Verlag
BioMed Central
Erschienen in
BMC Public Health / Ausgabe 1/2022
Elektronische ISSN: 1471-2458
DOI
https://doi.org/10.1186/s12889-022-13937-9

Weitere Artikel der Ausgabe 1/2022

BMC Public Health 1/2022 Zur Ausgabe