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      Methods for accounting for neighbourhood self-selection in physical activity and dietary behaviour research: a systematic review

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          Abstract

          Background

          Self-selection into residential neighbourhoods is a widely acknowledged, but under-studied problem in research investigating neighbourhood influences on physical activity and diet. Failure to handle neighbourhood self-selection can lead to biased estimates of the association between the neighbourhood environment and behaviour. This means that effects could be over- or under-estimated, both of which have implications for public health policies related to neighbourhood (re)design. Therefore, it is important that methods to deal with neighbourhood self-selection are identified and reviewed. The aim of this review was to assess how neighbourhood self-selection is conceived and accounted for in the literature.

          Methods

          Articles from a systematic search undertaken in 2017 were included if they examined associations between neighbourhood environment exposures and adult physical activity or dietary behaviour. Exposures could include any objective measurement of the built (e.g., supermarkets), natural (e.g., parks) or social (e.g., crime) environment. Articles had to explicitly state that a given method was used to account for neighbourhood self-selection. The systematic review was registered with the PROSPERO International Prospective Register of Systematic Reviews (number CRD42018083593) and was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement.

          Results

          Of 31 eligible articles, almost all considered physical activity (30/31); few examined diet (2/31). Methods used to address neighbourhood self-selection varied. Most studies (23/31) accounted for items relating to participants’ neighbourhood preferences or reasons for moving to the neighbourhood using multi-variable adjustment in regression models (20/23) or propensity scores (3/23). Of 11 longitudinal studies, three controlled for neighbourhood self-selection as an unmeasured confounder using fixed effects regression.

          Conclusions

          Most studies accounted for neighbourhood self-selection by adjusting for measured attributes of neighbourhood preference. However, commonly the impact of adjustment could not be assessed. Future studies using adjustment should provide estimates of associations with and without adjustment for self-selection; consider temporality in the measurement of self-selection variables relative to the timing of the environmental exposure and outcome behaviours; and consider the theoretical plausibility of presumed pathways in cross-sectional research where causal direction is impossible to establish.

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          Most cited references41

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          Stepping towards causation: do built environments or neighborhood and travel preferences explain physical activity, driving, and obesity?

          Evidence documents associations between neighborhood design and active and sedentary forms of travel. Most studies compare travel patterns for people located in different types of neighborhoods at one point in time adjusting for demographics. Most fail to account for either underlying neighborhood selection factors (reasons for choosing a neighborhood) or preferences (neighborhoods that are preferred) that impact neighborhood selection and behavior. Known as self-selection, this issue makes it difficult to evaluate causation among built form, behavior, and associated outcomes and to know how much more walking and less driving could occur through creating environments conducive to active transport. The current study controls for neighborhood selection and preference and isolates the effect of the built environment on walking, car use, and obesity. Separate analyses were conducted among 2056 persons in the Atlanta, USA based Strategies for Metropolitan Atlanta's Regional Transportation and Air Quality (SMARTRAQ) travel survey on selection factors and 1466 persons in the SMARTRAQ community preference sub-survey. A significant proportion of the population are "mismatched" and do not live in their preferred neighborhood type. Factors influencing neighborhood selection and individual preferences, and current neighborhood walkability explained vehicle travel distance after controlling for demographic variables. Individuals who preferred and lived in a walkable neighborhood walked most (33.9% walked) and drove 25.8 miles per day on average. Individuals that preferred and lived in car dependent neighborhoods drove the most (43 miles per day) and walked the least (3.3%). Individuals that do not prefer a walkable environment walked little and show no change in obesity prevalence regardless of where they live. About half as many participants were obese (11.7%) who prefer and live in walkable environments than participants who prefer car dependent environments (21.6%). Findings suggest that creating walkable environments may result in higher levels of physical activity and less driving and in slightly lower obesity prevalence for those preferring walkability.
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            Neighborhood walkability and the walking behavior of Australian adults.

            The physical attributes of residential neighborhoods, particularly the connectedness of streets and the proximity of destinations, can influence walking behaviors. To provide the evidence for public health advocacy on activity-friendly environments, large-scale studies in different countries are needed. Associations of neighborhood physical environments with adults' walking for transport and walking for recreation must be better understood. Walking for transport and walking for recreation were assessed with a validated survey among 2650 adults recruited from neighborhoods in an Australian city between July 2003 and June 2004, with neighborhoods selected to have either high or low walkability, based on objective measures of connectedness and proximity derived from geographic information systems (GIS) databases. The study design was stratified by area-level socioeconomic status, while analyses controlled for participant age, gender, individual-level socioeconomic status, and reasons for neighborhood self-selection. A strong independent positive association was found between weekly frequency of walking for transport and the objectively derived neighborhood walkability index. Preference for walkable neighborhoods moderated the relationship of walkability with weekly minutes, but not the frequency of walking for transport--walkability was related to higher frequency of transport walking, irrespective of neighborhood self-selection. There were no significant associations between environmental factors and walking for recreation. Associations of neighborhood walkability attributes with walking for transport were confirmed in Australia. They accounted for a modest but statistically significant proportion of the total variation of the relevant walking behavior. The physical environment attributes that make up the walkability index are potentially important candidate factors for future environmental and policy initiatives designed to increase physical activity.
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              The neighbourhood physical environment and active travel in older adults: a systematic review and meta-analysis

              Background Perceived and objectively-assessed aspects of the neighbourhood physical environment have been postulated to be key contributors to regular engagement in active travel (AT) in older adults. We systematically reviewed the literature on neighbourhood physical environmental correlates of AT in older adults and applied a novel meta-analytic approach to statistically quantify the strength of evidence for environment-AT associations. Methods Forty two quantitative studies that estimated associations of aspects of the neighbourhood built environment with AT in older adults (aged ≥ 65 years) and met selection criteria were reviewed and meta-analysed. Findings were analysed according to five AT outcomes (total walking for transport, within-neighbourhood walking for transport, combined walking and cycling for transport, cycling for transport, and all AT outcomes combined) and seven categories of the neighbourhood physical environment (residential density/urbanisation, walkability, street connectivity, access to/availability of services/destinations, pedestrian and cycling infrastructure, aesthetics and cleanliness/order, and safety and traffic). Results Most studies examined correlates of total walking for transport. A sufficient amount of evidence of positive associations with total walking for transport was found for residential density/urbanisation, walkability, street connectivity, overall access to destinations/services, land use mix, pedestrian-friendly features and access to several types of destinations. Littering/vandalism/decay was negatively related to total walking for transport. Limited evidence was available on correlates of cycling and combined walking and cycling for transport, while sufficient evidence emerged for a positive association of within-neighbourhood walking with pedestrian-friendly features and availability of benches/sitting facilities. Correlates of all AT combined mirrored those of walking for transport. Positive associations were also observed with food outlets, business/institutional/industrial destinations, availability of street lights, easy access to building entrance and human and motorised traffic volume. Several but inconsistent individual- and environmental-level moderators of associations were identified. Conclusions Results support strong links between the neighbourhood physical environment and older adults’ AT. Future research should focus on the identification of types and mixes of destinations that support AT in older adults and how these interact with individual characteristics and other environmental factors. Future research should also aim to clarify dose-response relationships through multi-country investigations and data-pooling from diverse geographical regions. Electronic supplementary material The online version of this article (doi:10.1186/s12966-017-0471-5) contains supplementary material, which is available to authorized users.
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                Author and article information

                Contributors
                karen.lamb1@deakin.edu.au
                Journal
                Int J Behav Nutr Phys Act
                Int J Behav Nutr Phys Act
                The International Journal of Behavioral Nutrition and Physical Activity
                BioMed Central (London )
                1479-5868
                1 April 2020
                1 April 2020
                2020
                : 17
                : 45
                Affiliations
                [1 ]GRID grid.1021.2, ISNI 0000 0001 0526 7079, School of Exercise and Nutrition Sciences, Institute for Physical Activity and Nutrition (IPAN), , Deakin University, ; Geelong, Australia
                [2 ]GRID grid.1058.c, ISNI 0000 0000 9442 535X, Clinical Epidemiology and Biostatistics Unit, , Murdoch Children’s Research Institute, ; Melbourne, Australia
                [3 ]GRID grid.1008.9, ISNI 0000 0001 2179 088X, Department of Paediatrics, , The University of Melbourne, ; Melbourne, Australia
                [4 ]GRID grid.1008.9, ISNI 0000 0001 2179 088X, Centre for Health Equity, Melbourne School of Population and Global Health, , The University of Melbourne, ; Melbourne, Australia
                [5 ]GRID grid.5335.0, ISNI 0000000121885934, Medical Research Council Biostatistics Unit, , University of Cambridge, ; Cambridge, UK
                [6 ]GRID grid.1039.b, ISNI 0000 0004 0385 7472, Health Research Institute, , University of Canberra, ; Canberra, Australia
                [7 ]GRID grid.1008.9, ISNI 0000 0001 2179 088X, Department of Medicine, St Vincent’s Hospital, , The University of Melbourne, ; Fitzroy, Victoria Australia
                Author information
                https://orcid.org/0000-0001-9782-8450
                Article
                947
                10.1186/s12966-020-00947-2
                7115077
                32238147
                daaf1b55-5f61-44b2-bfa2-e11a76eb10a0
                © The Author(s) 2020

                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.

                History
                : 30 September 2019
                : 20 March 2020
                Funding
                Funded by: Australian Research Council
                Award ID: DP170100751
                Award ID: FT150100131
                Award Recipient :
                Funded by: Medical Research Council
                Award ID: Unit Programme number U105292687
                Award Recipient :
                Categories
                Review
                Custom metadata
                © The Author(s) 2020

                Nutrition & Dietetics
                bias,neighbourhood characteristics,exercise,diet,environmental exposure,adult
                Nutrition & Dietetics
                bias, neighbourhood characteristics, exercise, diet, environmental exposure, adult

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