- Open Access
Objectively measuring the association between the built environment and physical activity: a systematic review and reporting framework
International Journal of Behavioral Nutrition and Physical Activity volume 19, Article number: 119 (2022)
Objective measures of built environment and physical activity provide the opportunity to directly compare their relationship across different populations and spatial contexts. This systematic review synthesises the current body of knowledge and knowledge gaps around the impact of objectively measured built environment metrics on physical activity levels in adults (≥ 18 years). Additionally, this review aims to address the need for improved quality of methodological reporting to evaluate studies and improve inter-study comparability though the creation of a reporting framework.
A systematic search of the literature was conducted following the PRISMA guidelines. After abstract and full-text screening, 94 studies were included in the final review. Results were synthesised using an association matrix to show overall association between built environment and physical activity variables. Finally, the new PERFORM (’Physical and Environmental Reporting Framework for Objectively Recorded Measures’) checklist was created and applied to the included studies rating them on their reporting quality across four key areas: study design and characteristics, built environment exposures, physical activity metrics, and the association between built environment and physical activity.
Studies came from 21 countries and ranged from two days to six years in duration. Accelerometers and using geographic information system (GIS) to define the spatial extent of exposure around a pre-defined geocoded location were the most popular tools to capture physical activity and built environment respectively. Ethnicity and socio-economic status of participants were generally poorly reported. Moderate-to-vigorous physical activity (MVPA) was the most common metric of physical activity used followed by walking. Commonly investigated elements of the built environment included walkability, access to parks and green space. Areas where there was a strong body of evidence for a positive or negative association between the built environment and physical activity were identified. The new PERFORM checklist was devised and poorly reported areas identified, included poor reporting of built environment data sources and poor justification of method choice.
This systematic review highlights key gaps in studies objectively measuring the built environment and physical activity both in terms of the breadth and quality of reporting. Broadening the variety measures of the built environment and physical activity across different demographic groups and spatial areas will grow the body and quality of evidence around built environment effect on activity behaviour. Whilst following the PERFORM reporting guidance will ensure the high quality, reproducibility, and comparability of future research.
Physical inactivity has a hugely detrimental effect on health [1,2,3] and is recognised by the World Health Organisation (WHO) as the fourth leading cause of global mortality, behind high blood pressure, tobacco use and high blood glucose . Through their Global Action Plan on Physical Activity (GAPPA), the WHO intends to address this global health issue, aiming for a 15% reduction in the prevalence of global physical inactivity in adults and adolescents by 2030 . To achieve this target, cross-disciplinary approaches are required to encourage activity in different populations.
One modifiable factor, with an established link to individual level physical activity, is the environment in which people live and work. Commonly referred to as the built environment, this is the part of the physical environment that is constructed by human activity . Built environment exposure can be determined by defining a geometric area of exposure around either a geocoded location, such as home address, or around global positioning system (GPS) data points. Often measures of built environment focus on land use mix, street connectivity, accessibility, and density measures . However, the built environment is not limited to these features alone. Definitions also encompass other environmental features for instance safety, aesthetics, access to green space and transport provisions [7, 8]. These components can be both a significant enabler of, or barrier to, being physically active. Hence, one of the four main objectives for achieving the GAPPA is to create active environments .
Objective measures of both physical activity and the built environment, or other environmental features, utilising accelerometery and geographic information system (GIS) or GPS methodologies are becoming ever more prevalent in research, with continual technological improvements making these technologies increasingly cost-effective . Objective measures are advantageous in that they provide opportunity for direct comparison. For example, accelerometer counts can be directly compared across studies if the same device brand and algorithm is used. Moreover, objective measures are not limited by the reporting and recall biases associated with self-reported measures of physical activity or environmental exposure, such as inaccurate recall of activity location, duration, or intensity. However, utilising objective measures can limit potential sample size and study duration due to associated costs of providing study participants with trackers and participant burden. Increasingly, objective spatial and accelerometery data from smartphones are being used [10,11,12], which may avert some of the cost and time limitations of the more traditional pairing of GPS and accelerometer devices. Nevertheless, despite the opportunity presented by using objective methods for direct metric comparability, there is currently limited inter-study comparability, thus preventing meta-analysis [6, 13, 14].
Whilst we know the environment in which we live and interact plays an important role in our opportunity to be physically active, studies investigating the strength and direction of even these objectively measured relationships regularly find conflicting outcomes [15, 16]. Due to the plethora of interacting elements that comprise the built environment, alongside the need to make subjective data handling decisions, concluding which elements are encouraging physical activity is challenging.
Moreover, it is difficult to determine whether an increase in physical activity is a universal outcome of a built environment feature or is in fact individual, neighbourhood, area, or country specific . This is particularly due to the range of factors, for example socio-economic status and cultural and political environments that modify the built environment physical activity relationship at a micro and macro level [18, 19]. The trans-disciplined nature of studies considering the built environment, which span disciplines from transport planning, and geography to, health and sports science, results in different approaches, methodologies, and primary study foci . Consequently, different reporting practices and standards have emerged, causing problems for study comparability , and subsequent difficulty in concluding the level of influence that built environment has on physical activity.
The International Physical Activity and the Environment Network (IPEN) have aimed to utilise the comparability and transferability of objective methods to address some of these comparability issues. They have designed a cross-national study using the same objective measures of built environment and physical activity across 14 nations. However, only a handful of cross-study comparisons utilising objective data for both physical activity and the built environment have been published by the IPEN group [21,22,23]. Moreover, whilst the IPEN study design aims to increase the body of evidence regarding the association between built environment and physical activity, through a unified cross-country study design, the scope of physical activity and built environment metrics used is limited to those available in all countries. Hence, there remains a need to be able to evaluate and synthesise the wide-ranging investigations into varied aspects of built environment and physical activity .
Systematic reviews present a key method to keep record of, synthesise and evaluate the ever growing and complex body of knowledge in this subject area . Systematic reviews to date have either focused on studies using self-reported or mixed methods of collecting both built environment and physical activity measures [13, 25,26,27], or have focused on specific study designs to evaluate causality [27, 28]. McCrorie et al. (2014) have systematically reviewed studies utilising objective measures of both physical activity and built environment but only for child participants and no such review has been conducted in an adult population . Within the field of food environment research Wilkins et al. (2017) have devised a framework (GeoFERN) for reporting studies addressing retail food environments .They report data sources, methods for data extraction, classification, geocoding, and definitions of the retail food environment, to evaluate the quality of methodological reporting . The methodologies utilised in both food and built environment research hold many similarities, yet, despite recognition of disparities in reporting and calls for increased quality of methodological reporting [6, 13], no such reporting framework or guidance has been devised for studies investigating the physical activity-built environment. Broader, spatial epidemiology reporting frameworks also exist, however whilst they capture many relevant reporting elements, they are not domain specific .
In order to identify the aforementioned inconsistencies and reporting gaps in current studies, and to inform policy at a local, national, or global level, a greater comparability of studies is required. Thus, this paper has two aims. The first aim of this paper is to synthesise the current comprehensive body of literature around the associations of objectively built environment variables with physical activity levels and to define knowledge gaps. The second aim is to devise a Physical and Environmental Reporting Framework for Objectively Recorded Measures (PERFORM) for the evaluation of current studies and to improve interstudy comparability of future work, increasing the robustness of conclusions drawn about the physical activity-built environment relationship.
Protocol and registration details
The review protocol was completed following the PRISMA guidelines . A copy of the protocol is published on the PROSPERO International prospective register of systematic reviews  (published on 22/06/18 registration number: CRD42018087274).
To be eligible for inclusion, a peer-reviewed study had to investigate the relationship between an objective measure of the built environment (e.g., GIS derived exposure around a geocoded location or GPS points) and an objective measure of physical activity (E.g., Accelerometer, Pedometer). As many different activities can contribute to meeting physical activity guidelines, no limitation was put on the type of activity objectively recorded . All study designs were permitted for inclusion. However due to the resources available to the reviewers, non-English language studies were excluded from the final review. In order to compare similar built environments, with comparable levels of infrastructural development, studies must have taken place in an Organisation for Economic Co-operation and Development (OECD) country [34, 35]. Similarly, studies focused only on the workplace environment were not included as workplace studies tend to focus on indoor environments or are company specific interventions that apply to a small subset of the population .
Study participants must be adults (≥ 18 years of age) for inclusion in the review with no upper age limit. Studies were only included if participants did not suffer from a disability or long-term health condition as these groups face greater barriers to physical activity than the general population and therefore warrant a separate review [37,38,39]. High BMI (Body mass Index) of participants, i.e., participants being overweight or obese, was not considered a limiting health criteria as long as they suffered from no other long-term health conditions. There is evidence that women’s physical activity behaviours temporarily change during pregnancy, for example decreasing the intensity of exercise . Therefore, studies looking at pregnancy and the built environment are not directly comparable to the general population and are excluded from the review. If studies investigated non-eligible participant characteristics, but included participants as a control or subgroup, these data were included in the review.
Due to the multi-disciplinarity of the research area, a wide-ranging literature search of 14 electronic databases was conducted, reported in Additional file A. Search terms were split into four categories: (1) Physical activity e.g., movement, walking; (2) Built environment e.g., green space; walkability; (3) Physical activity measurement method e.g., accelerometer, fitness tracker; (4) Build environment measurement methods.
e.g., GIS, GPS. These categories were designed to capture both the elements of interest and the respective methods by which each can be objectively measured. Both key word searches and MeSH medical subject headings, where appropriate for the database, were used and the strategy was reviewed by an information specialist. The search was conducted on the 26/03/2020. An example search conducted in MEDLINER of these as illustrated in Table 1.
Study screening and data collection process
All titles and abstracts were imported into the online COVIDENCE tool and double screened by F.P and V.J for relevance using the inclusion and exclusion criteria, using COVIDENCE to help identify conflicts and duplicated abstracts . All conflicts were discussed by F.P and V.J and any further disagreement was settled by the third reviewer M.M. The remaining eligible studies then underwent full-text review using the same double review process by F.P and V.J as before. If studies were not fully available online authors were contacted to gain access.
Data Extraction of the studies was completed utilising a predetermined data extraction form developed by the lead author using guidance outlined by Higgins et al. . A sample of included texts were used to pilot the form to ensure it captured all the information required to meet the aims of the review. Data Extraction was conducted by F.P and a sample (N = 5) verified by V.J. The completed data extraction form is available in Additional file B. In brief, information extracted included study design, location and duration, information regarding participant recruitment as well as any recorded socio-economic and demographic covariates investigated alongside the built environment and physical activity association. Detailed definitions, measurement methodologies and baseline results were collected for both the built environment exposures and physical activity outcomes reported in the studies. For example, the built environment variable of interest was recorded e.g., green space, alongside the geographic method used, e.g., a 400 m buffer around the home location, paired with the physical activity outcome of interest, e.g., Moderate to Vigorous Physical Activity (MVPA) and finally the threshold of the physical activity measure e.g., 2200 accelerometer counts per minute (CPM). In addition, a null, statistically significant positive association or significant negative quantifiable measure of the association between the built environment and physical activity was recorded. Many papers utilised subsets of data from larger studies. In such cases the original published study was primarily used, and the subsequent papers compiled to aid comprehensive completion of the data extraction form.
Due to the wide range of methodologies employed across the included studies, a comprehensive meta-analysis of the resulting metrics was not possible. Therefore, a narrative synthesis was conducted and variations in study population, duration, covariate controls and the definitions of the exposures and outcomes were summarised. In addition, the association between built environment and physical activity and the methods used to objectively capture them was explored, as outlined below.
Quantifying built environment and physical activity association
The aforementioned extracted associations between physical activity and built environment were recorded in an association matrix in order to identify areas of strong or weak association, as well as gaps in the current literature. Overall association between the two measures was calculated using the following formula for each pair of associations:
i = count of all statistically significant negative BE-PA associations j = count of all null BE-PA associations.
k = count of all statistically significant positive BE-PA associations.
The total number of associations investigated (i + j + k) was also recorded. Not all studies were suitable for inclusion in the association matrix, for instance studies that did not calculate significance of association(s) or those where the direct (or modified) association(s) between singular built environment and physical activity variables was not possible e.g., Latent profile analysis where the profiles are built from multiple aspects of the built environment.
Developing the Physical and Environmental Reporting Framework for Objectively
Recorded Measures (PERFORM)
To meet the second aim of this research, aiding in the synthesis of the extracted data and increasing comparability of future reporting, a reporting framework was devised—“Physical and Environmental Reporting Framework for Objectively Recorded Measures” (PERFORM). The goal of the reporting framework checklist is to improve inter-study comparability of the objective studies to widen the evidence base and increase potential research opportunities. Often the data collected in these studies could be used to address further research questions, however this is not possible if the methods of data collection and processing are not clearly reported. The reporting framework aims to increase research reproducibility and replicability within the field. For instance, using comparable data collected in two different studies to compare the same built environment exposure in different countries or facilitating study repetition to allow longitudinal analysis.
First, commonly comparable elements of reporting across all studies were characterised into four thematic areas:
objective built environment methods and data collection,
objective physical activity methods and data collection,
method of quantifying the association between physical activity and built environment.
The initial elements were developed by the authorship team and refined iteratively during the data extraction stage. During the data extraction stage of the review, common reporting gaps, and/or missing details were identified within these thematic areas. Thus, the reporting framework directly reflects where there are reporting limitations within the current literature. Key questions and examples were then constructed to encourage clear reporting in these areas. The checklist was also designed to capture the seven domains of the bias included in the ROBINS-I framework: Bias due to confounding, bias in selection of participants into the study, bias in classification of interventions, bias due to deviations from intended interventions, bias due to missing data, bias in measurement of outcomes, bias in selection of the reported result . The checklist points were then refined using a random sample (N = 5) of studies included in the review . The checklist will aid in future comparison of studies as a result of replicable reporting practices. The final PERFORM checklist is detailed in Table 2.
Evaluation of study reporting quality using PERFORM
The reporting framework was then utilised to review the quality of studies included with the systematic review and assess risk of bias. Each study was assessed against the reporting criteria and given a score based on whether each criteria of the checklist were reported in the study methodology and results (1.0: reported, 0.5: partially reported, 0: Not reported, NA: criteria not applicable to study design). The proportion of elements reported was then obtained for each paper, to give a PERFORM score out of 100. Additionally, each criteria on the checklist were scored on how well it was reported across all studies.
The initial literature search identified 2,685 records. Utilising the Convidence tool 1,832 duplicate abstracts were identified and removed before screening (Fig. 1). Abstracts and titles were screened identifying 254 papers as eligible for full-text review and 599 were excluded for not meeting the inclusion criteria. Of the 254 papers that underwent full-text review 163 were excluded, full exclusion reasoning is outlined in Fig. 1. The most common reason for exclusion (78 papers) was the use of non-objective measurements of physical activity and or the built environment (Fig. 1). After full-text screening 91 records were found to be eligible for inclusion, a further three records were identified via reference lists to give a total of 94 included articles.
An overview of the study characteristics included in the review can be found in Additional file B. Some articles investigate different aspects of the association between built environment and physical activity in the same participant group and utilising the same study design. In such cases we have grouped these studies for analysis into’projects’, as study concept and design should be homogeneous across all constituent studies and to avoid duplication. Hence the 94 studies meeting inclusion criteria correspond to 64 unique projects. Six of these projects (16 studies) and an additional three summary studies were part of the wider IPEN study. The remaining 55 projects (75 studies) investigated the objective association between built environment and physical activity independent of the IPEN network.
Articles were published between 2005 and March 2020 with increasing numbers of publications in recent years, illustrated in Fig. 2. The increase in studies using smartphone data is also demonstrated in the past few years. Mirroring the IPEN network design, cross-sectional studies (71.3%, N = 67) were by far the most popular study design utilised to investigate the built environment and physical activity relationship, with a further 13 studies (13.8%) using a case–control study design,
10 (10.6%) followed a quasi-experimental or natural experiment design, two studies (2.1%) were randomised control trials and two (2.1%) were pilot studies that reported a built environment and physical activity association. A seven-day study design for physical activity data collection was most widely used (N = 72, 76.6%), though study duration ranged from two days to six years in temporal coverage, as depicted in Additional file B which also indicates if any repeat follow-up measures were taken.
Figure 3 shows the number of studies in each country, with some studies investigating multiple geographic locations. Studies span 21 OECD countries with 15 unique countries captured by the non-IPEN network studies. The majority of studies (N = 56) were conducted in North America and are predominantly USA based investigations (N = 45), with European countries (N = 47) also forming a large body of the evidence (Fig. 3).
Full participant characteristics are outlined in Additional file B. Participant numbers ranged from 10 to 65,967 unique individuals. 14 studies did not report the gender of participants, the percentage of female participants in the remaining studies ranged from 12.4% to 77.1%, with a median of 54.2%, excluding the four studies looking at only female populations. We grouped studies by age groups to aid interpretation. 43.6% of studies investigated all adults over 18, 33.3% of studies investigated working age populations (18–64 years of age) and 13.8% of studies investigated older adults (65 + years of age). A further three studies (3.2%) specifically looked at student populations (typically under 25 years of age) and 6.4% of studies (N = 6) did not report the age of the study participants.
A variety of different population groups were investigated. Participants were often recruited from geographic areas with a specific built environment and/or socioeconomic characteristics. For instance, 21 studies followed the IPEN study design recruiting participants from either a low or high walkable residential area with a low or high socioeconomic status. A further 24 studies investigated participants who live in areas meeting a specific built environment characteristics such as being close to transport or cycling infrastructure developments [43, 44]. Three studies investigated physical activity in ethnic minorities whilst four investigated activity specifically in women. Other groups investigated included trail users (N = 2), parents (N = 2) and twins (N = 1), with 27 studies not limiting the scope of the population included. Recruitment criteria varied widely by study. Common criteria included physical activity criteria such as being able to walk a certain distance unaided (N = 35); language proficiency (N = 24) and address specifications (N = 21).
Objective data collection
Table 3 summarises the devices and technology used to obtain the objective measures of built environment and physical activity. In order to define the activity space of interest and derive built environment exposure, 24 projects used GIS to define the activity space around a geocoded area. Whilst 26 projects used location data from GPS devices to inform the spatial extent of the activity space area investigated. Accelerometers were the most commonly used device to capture physical activity behaviour with 60 studies solely using accelerometer and 15 studies using accelerometers in conjunction with GPS devices, a further three studies used pedometers.
14 studies used smartphone GPS data to capture the built environment and physical activity behaviour. Of studies utilising smartphones eight used the smartphone as a primary data collection tool and six used secondary smartphone app data; data originally collected for another research purpose . All secondary studies used GPS data to define physical activity, whilst seven primary studies used GPS and two primary studies used the in-built smartphone accelerometer. Secondary data collection led to on average larger sample sizes with up to 13,684 users [133, 134] or 1,105,596 trips . However, larger sample size was often at cost to the level of demographic detail collected on participants with only 50% of secondary studies reporting the age of participants. Moreover, of the secondary smartphone studies that reported gender (33% projects) female participants made up only between.
12.4–19.9% of the included study population.
Built environment data sources were generally poorly reported, as shown in Table 4. With 56 studies not fully referencing the data source used to define the built environment. Of these 56 studies, 18 provided no information at all for the built environment source, often stating GIS was used and even the software but not the underlying data used. A further 17 of these 56 studies non-explicitly mention the data source, for instance that building plan data was used, but not where it was from, with 18 studies mentioning the data provider but not referencing it or stating the year in which it was published. For one study the environment of interest was the home neighbourhood therefore no additional built environment data was required..
Participant covariates and controls
For each included study the most commonly investigated covariates, outlined in Fig. 4, were reported on whether they were recorded and controlled for in the study design. Generally, age and gender of participants are investigated and controlled for in subsequent analysis. Given that 19 of the 44 studies investigating and controlling for socio-economic status are IPEN studies, designed to capture a broad range of socioeconomic strata, socio-economic status is not particularly well reported in the remaining studies. Ethnicity is also under-reported with only 43.6% of included studies recording a measure of ethnicity. Whilst education, marital status and employment could also be better reported these metrics are less likely to be relevant across all research questions.
Synthesis of results
Quantifying built environment and physical activity association
The range of built environment and physical activity measures used across the included studies are synthesised in the association matrix in Fig. 5. The colour of the cell in the association matrix corresponds to the calculated overall association between the two variables, with the number in the cell indicating the number of instances that association was investigated across the 86 studies that were included in the analysis. The number of built environment and physical activity (BE-PA) associations reported by a single paper ranged from one to 150. Access to and use of parks were the most commonly investigated element of the built environment (84 instances), followed by walkability (61 instances) and measures commonly used to calculate walkability. Residential and housing density (67 instances), job and workplace density (77 instances), street connectivity (69 instances), green space (42 instances), blue space (40 instances) and cycling infrastructure (41 instances) were also popular built environment measures.
As can be seen in Fig. 5 a wide range of physical activity metrics were used.
The most popularly researched physical activity (measured at 284 instances) was MVPA. Walking trips were widely investigated (258 instances) and were also investigated alongside cycling trips a further 76 times, with 104 investigations of cycling as the sole mode of transport. Physical activity definitions also varied widely between studies instigating the same metric. For a full association matrix detailing the methods and cut-points used to define physical activity against built environment metrics see Additional file C.
Methods to define activity space and subsequent built environment exposure varied widely between studies, by spatial method used, size of spatial area and locations(s) investigated. 69 unique spatial methods were used to define built environment exposure. Of those unique methods, 48 were unique definitions using a buffer around home or key locations, varying by buffer size (3 m to a mile) and buffer type with Euclidean buffers accounting for 34 unique methods, (43.4% of investigations in to BE-PA), with street network buffers accounting for the remaining 14 unique methods (18.9% of investigations in to BE-PA). Other popular methods included buffering the daily path area (13 instances), categorising the built environment of GPS point (52 instances), Euclidean distance (17 instances) street network distance (67 instances) and whether the GPS point within the boundary of the area of interest (98 instances).
In terms of physical activity association with the built environment we can start to see where strong evidence of associations have been found (darker blue and red colours) and where that evidence is backed by a larger body of evidence (a larger number in the cell). For instance, in terms of MVPA a strong association is seen with green space (association = 0.75) across 16 instances. Similarly, leisure facilities (25 instances), street connectivity (31 instances) and walkability (36 instances) are also well investigated associations with MVPA and additionally show a physical activity promoting association of 0.44, 0.35 and 0.31 respectively. However, this association is not as strong as that for green space. Parks on the other hand are investigated in association with MVPA on 32 instances however only a moderate positive association is observed (association = 0.13). By comparison, walking trips (17 instances) have a stronger association with parks. However, when it comes to green space (10 instances) walking trips have a strong negative association of -0.5. Poorly investigated aspects of the built environment can be identified for instance safety (9 instances) and air pollution (2 instances). Where there are single studies or no studies investigating an association, these may highlight areas for further investigation, for instance land-use mix and light physical activity (1 instance).
The finalised reporting framework is outlined in Table 2. Figure 6 depicts the proportion of the 94 included studies that report each item, where relevant, in the PERFORM checklist. Reporting ranged from only 14.0% of studies justifying the choice of built environment data source to 100% of studies defining the physical activity measure of interest (Fig. 6).
With regard to study design, participant recording duration was fairly well reported. However, if undertaken, the time duration between initial and follow-up measure was poorly reported, with often only an approximate follow-up lag time given (Fig. 6). As previously mentioned, cross-sectional study designs were by far the most prevalent study design. Nonetheless, reporting of the study deign was mixed or often only included within the limitations to evidence that a causal relationship could not be established (Fig. 6). Participant characteristics were also fairly well reported. However, recruitment criteria and variation in study numbers between studies using the same data were less well reported (Fig. 6). Moreover, whilst the included population characteristics may be summarised and controlled for, a limited number of studies attempted to measure the representativeness of the included participants of the general population (Fig. 6). Although most studies using data from a wider project do reference the said project, often the results of previous studies (26.1% of studies) using these data and the’value add’ (35.6% of studies) of the investigation are not well reported within the studies (Fig. 6).
Generally, the terminology used to define and quantify the built environment is well described. However, whilst most studies report the methods to define the spatial extent of the built environment investigated, this is not universal, just under half the studies justify why that measurement is used. Similarly, when it comes to the underlying data used to characterise the built environment, this is on the whole poorly reported with 61.3% of studies reporting where the built environment data comes from. However, this is reduced when looking at reporting of the original data purpose. Lack of referencing of the source data lead to the low reporting of year of publication (48.4% of studies) granularity of the data source (47.3% of studies). If data is referenced very few studies, then go on to justify why that data source was chosen (14.0% of studies) or if there was any missing data and how this was handled (17.5% of studies). Conversely missing physical activity data and physical activity metric justification are better reported, with 72.1% and 37.1% of studies reporting these PERFORM checklist items respectively, though reporting practices could be improved. The device used to capture physical activity is well reported, nonetheless reference to whether the device has been used previously (29.5% of studies) and whether or not it is a validated method, for the population and setting being investigated (27.7% of studies), need further reporting consideration. Data linkage and aggregation scored fairly well, but often the level of aggregation was implied from the physical activity measurement used e.g., MVPA per day and not reported explicitly.
Study reporting quality evaluation using PERFORM
Papers included in the review had a median PERFORM score of 72.2 (IQR:16.5), with scores ranging between 37.5 and 97.4 out of a possible 100. The violin plots in Fig. 7 show the distribution of PERFORM scores and median score by activity space measure (Fig. 7A) and whether the study was part of a wider project (Fig. 7B). Studies that use GIS around a geocoded locations have slightly better reporting practices than those studies using a GPS device to define the activity space. Studies using GPS data from secondary app sources on average report metrics fairly well however, as shown by the large range of scores, this is not universal across all studies using secondary data. Conversely, studies using primary data generally do not score highly using the PERFORM reporting framework. Reporting practices are generally better in stand-alone studies, compared to studies that use the same population as other investigations to answer unique research questions.
Synthesis of the current body of evidence
The current body of knowledge covering the association between objective measures of the built environment and physical activity is extensive, with 94 studies included in the review. An increase in study numbers in recent years indicates this evidential body is only set to grow. Smartphones were also found to be an increasingly popular method of data collection, with technological advances and reductions in cost making this a more attractive objective method . Study design is important in determining the nature of the conclusions we can draw concerning the built environment influence on physical activity. The majority of the studies included in the review were cross-sectional, which aided cross-country comparison, but prevented conclusions of a causal relationship . Another common experimental design feature was the seven-day recording period for physical activity. Though this has previously been shown to be sufficient to capture habitual activity [137, 138], Bergman et al. have shown that at least 34.8 days of activity were needed to capture MVPA . Moreover Pontin et al. (2021) have shown that intra-individual weekly activity behaviours vary across the year and by season .
In terms of population characteristics, a large proportion of included studies were conducted in North America. Caution is therefore needed in applying the findings from these studies to the wider spatial contexts. For instance, one of the most investigated measures, walkability, is based on street connectivity and land use mix. Compared with the USA, European cities have distinctly different designs, for example low levels of street-network sprawl , and therefore different walkability scores . Thus, walkability scores will not be directly comparable across cities despite being calculated in the same way and the same increase in walkability in two places may have different impacts on physical activity behaviour.
Investigating population characteristics, we also find women tend to be overrepresented in the included studies, with gender commonly adjusted for, despite evidence that this adjustment modifies the association between built environment metrics and physical activity outcomes. For instance, Tcymbal et al. (2020) find women were more influenced by public transport, safe cycling lanes and housing density, whilst men were more influenced by traditional aspects of walkability such as street network connectivity . Future work therefore would benefit from sub setting built environment and physical activity association analysis by gender.
Evidence from the association matrix showing the strength of associations between built environment and physical activity can be used to guide policy in creating active environments for physical activity . The association matrix also highlights the combination of built environment and physical activity variables are well investigated versus where there are reporting gaps, guiding the design of future research. We therefore recommend consulting the association matrix when looking to design future studies. Moreover, whilst care must be taken to consider potential confounders the matrix provides an indication of the modifiable built environment aspects that could be used to increase activity levels, by activity type. For instance, whilst there is a strong evidence base for MVPA being higher in green space there is a negative association with walking trips. By understanding this detail, we can improve our understanding of how built environment features interact to promote or deter physical activity.
Recommended future research directions
Reflecting on the strengths and limitations of the current body of evidence we make the following recommendations to guide the direction of future research within this field. First, as suggested by Smith et al. in their review into the implications for causality in the use of activity space measures, a shift away from the favoured cross-sectional study design to natural experiments would strengthen the ability to identify causal relationships within the research field . We suggest revisiting some of the data collected by studies in this review, to identify opportunities for natural experiments and second waves of data collection. For example, re-collecting data to investigate improved pedestrian infrastructure since the original study was conducted. However, to successfully implement natural experiments clear publication of prior analysis and better reporting is required . Second, greater steps need to be taken towards promoting data sharing and publication of meta-data for secondary data analysis to maximise the potential of the existing data . This needs to be undertaken in line with proper consideration of ethics and implementation of data sharing agreements .
Fourth, primary barriers to a longer than seven-day study duration are cost and participant burden. Therefore, we encourage the development of smartphones as both a primary and secondary objective data collection tool, due to their low cost, ubiquity in daily life and ability to collect both location and physical activity data [135, 147]. However, secondary smartphone app data are often limited in terms of data on the socio-demographic and other covariates and care need to be taken around missing data [133, 135, 136]. Consequently, research questions need to be carefully considered to ensure these data are suitable or that their use may supplement more traditional study designs. As we demonstrated the quality of reporting in studies utilising these data was highly varied and, in some cases, very poor. Therefore, care must be taken to ensure these studies are reported to the same standard and to enable comparability to more traditional study designs, particularly as these data may be adopted by those coming from a background in data science and not embodied in the literature.
PERFORM: Recommendations to improve reporting practice
The PERFORM reporting framework identifies four key reporting areas: study design and characteristics, built environment exposures, physical activity metrics, and the association between built environment and physical activity. The reporting framework ensures sufficient detail to replicate the objective measures, both physical activity and built environment, alongside reporting of participant characteristics and uniqueness were identified as the poorest areas of reporting and serves as a checklist to make this reporting easier for researchers. Reporting of results that have been previously published using the same participant populations also need to be better sign-posted and the ‘value add’ reported to ensure the full picture of the interaction between built environment and physical activity behaviour is understood in one population group before we start to compare cross-study populations.
In their 2009 ‘state of the science’ review into measuring the built environment for physical activity, Brownson et al. (2009) propose technical improvements are driving wider measures of the built environment, recommending them to be studied then streamlined into ‘second-generation measures’ . As illustrated in this review, with the ever-decreasing cost of objective measurement technology and the increase in data availability the breadth of these ‘second-generation’ built environment measures is ever expanding. Moreover, the increasing use of smartphones, with their advantages in capturing large populations over wider spatial contexts, could be considered a new third-generation measure. Though the volume of potential built environment measures is not necessarily problematic we need to ensure we understand current evidenced associations. PERFORM is therefore timely in that it can help streamline the reporting of these measures, rather than the measures themselves, to better understand the state of the science today and as we move towards third-generation measures.
In the move towards reproducible research, such a reporting framework is apt . The technical elements around the collection of both accelerometery and built environment data are some of the easiest aspects to report but will go the furthest to ensure replicability by other researchers in different spatial contexts or in the same spatial context after changes to the built environment. PERFORM guidance includes technical aspects such as reporting of accelerometery measurement frequency, mean wear time and non-wear time definitions, alongside how these are combined with GPS frequency measures. For studies investigating built environment using purely GIS to define the spatial extent of the built environment around a geocoded location multiple standardised buffer sizes are suggested to increase study inter-comparability. Moreover, detailed definitions of the physical activity measures and cut-points or algorithms designed to quantify intensity are needed to ensure like-for-like comparison. Justification of data sources used to quantify the built environment exposure is important. Greater transparency is needed regarding how the data are obtained and why these data were chosen. In many instances the data utilised may be the only source available to the researcher. In such cases this should be stated as the reason for use. Completeness, recentness, and granularity of the data are all potential limitations to the study design and exclusion of this detail limits study reproducibility.
Future implementation of PERFORM
Future researchers should look to employ the PERFORM checklist to aid in the study design and reporting stages of the research. This field is ever evolving so whilst the PERFORM framework will aid in the reporting of the current literature it needs to be regularly reviewed and refined to ensure reproducibility as methods develop and advance. The PERFORM checklist can also be employed, as it is in this review, to identify reporting quality of objective studies in other populations and settings, for instance children or low- and middle-income countries. As objective measuring technologies develop, we recommend an update of this systematic review, reporting against the elements of the PERFORM framework, to ensure ongoing reproducibility within the field.
Strengths and limitations of the review and framework
This is the first review to look at solely objective measures of physical activity and the built environment. It provides a holistic overview of the current evidence base covering the associations between built environment and physical activity and it highlights where the evidence is lacking or poorly reported. The developed reporting framework, though common in other disciplines, is the first of its nature, designed to improve reporting practices in this increasingly cross-disciplined field.
This paper answers the calls of previous ‘review of reviews’ to provide more information on environmental and physical activity measurement modes to inform future research and practice [149, 150]. Previous reviews have also highlighted the need for better reporting practices . The introduction of a framework will improve comparability of studies by introducing a consistent reporting structure. Therefore, is highly advantageous in forming conclusions for policy and practice. Nonetheless, this review has some limitations. Though this review focuses on objective measures of built environment and physical activity we acknowledge the value of self-reported and perceived measures of built environment and physical activity, with associations between perceived variables often different to those observed via objective measures . That said, many aspects of the reporting framework are relevant and could be adapted to self-reported measures.
In this review we synthesise the association between objective built environment and physical activity by calculating an overall association between the variables in all studies. This metric whilst useful for providing a broad overview into the current body of evidence, uses a crude binary metric of association and does not account for the different statistical methods employed by the individual studies, the covariates controlled for or the spatial context of the study. Thus, these measures of association should be treated as a general outline of the current evidence and be utilised alongside the constituent studies to inform conclusions to overall built environment and physical activity associations.
Though only studies investigating OECD countries were included within this review, to increase inter-study comparability potential, different countries have intrinsically different built environment elements such as the aforementioned street network layout. Caution therefore must be taken when applying evidence and even methods from countries, such as the USA where there is a strong evidence base, to other countries. Other methodological approaches or built environment aspect may prove more suitable in these other contexts, nonetheless the PERFORM reporting framework can still be used for studies employing objective measures in on OECD countries. Therefore, whilst the current body of literature may guide future study design aspects taken of the built environment noted in local qualitative studies may better help quantify the association between built environment and physical activity.
This review synthesises the current body of knowledge covering the association between objective measures of the built environment and physical activity. With the association matrix providing a valuable reference to future researchers as to areas of strong evidence of environmental association with activity behaviour, such as the association of MVPA with greenspace whilst also highlighting investigatory gaps, strengths and limitations or the current body of evidence. For instance, poor reporting of socio-economic and ethnicity of participants which may confound the relationship between built environment and activity behaviour. The PERFORM reporting framework developed from review of the current body of evidence will improve inter-study comparability of future work and reporting practices. With an increasing volume of data capturing the association between the built environment and physical activity and the development and implementation of PERFORM is timely to improve reproducibility within this research field.
Availability of data and materials
The datasets used and/or analysed during the current study are included in this published article [and its supplementary information files] and available from the corresponding author on reasonable request.
Body mass index
Built environment exposure
Counts per minute
Global action plan on physical activity
Global positioning system
International physical activity and the environment network
Medical subject headings
Moderate to vigorous physical activity
Organisation for economic co-operation and development
Physical A = activity
Physical and environmental reporting framework for objectively recorded measures
Preferred reporting items for systematic reviews and meta-analyses
World health organisation
Lee IM, Shiroma EJ, Lobelo F, Puska P, Blair SN, Katzmarzyk PT. Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy. Lancet. 2012;380(9838):219–29. https://doi.org/10.1016/s0140-6736(12)61031-9.
Wilmot EG, Edwardson CL, Achana FA, Davies MJ, Gorely T, Gray LJ, Khunti K, Yates T, Biddle SJH. Sedentary time in adults and the association with diabetes, cardiovascular disease and death: systematic review and meta-analysis. Diabetologia. 2012;55(11):2895–905. https://doi.org/10.1007/s00125-012-2677-z.
Brown DW, Brown DR, Heath GW, Balluz L, Giles WH, Ford ES, Mokdad AH. Associations between physical activity dose and health-related quality of life. Med Sci Sports Exerc. 2004;36(5):890–6.
Organization, W.H.: Global health risks : mortality and burden of disease attributable to selected major risks (2009)
Organization, W.H.: Global action plan on physical activity 2018–2030: more active people for a healthier world (2018)
Saelens BE, Handy SL. Built environment correlates of walking: A review. Med Sci Sports Exerc. 2008;40(7 Suppl):550–66. https://doi.org/10.1249/MSS.0b013e31817c67a4.18562973[pmid]MedSciSportsExerc.
Handy SL, Boarnet MG, Ewing R, Killingsworth RE. How the built environment affects physical activity: views from urban planning. Am J Prev Med. 2002;23(2):64–73.
Foley R, Kistemann T. Blue space geographies: Enabling health in place. Health Place. 2015;35:157–65.
McCrorie PR, Fenton C, Ellaway A. Combining gps, gis, and accelerometry to explore the physical activity and environment relationship in children and young people-a review. Int J Behav Nutr Phys Act. 2014;11(1):93.
Griffin GP, Jiao J. Where does bicycling for health happen? analysing volunteered geographic information through place and plexus. J Transp Health. 2015;2(2):238–47.
Althoff T, Hicks JL, King AC, Delp SL, Leskovec J, et al. Large-scale physical activity data reveal worldwide activity inequality. Nature. 2017;547(7663):336–9.
Sun Y, Mobasheri A. Utilizing crowdsourced data for studies of cycling and air pollution exposure: A case study using strava data. Int J Environ Res Public Health. 2017;14(3):274.
Smith M, Hosking J, Woodward A, Witten K, MacMillan A, Field A, Baas P, Mackie H. 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):158.
Brownson RC, Hoehner CM, Day K, Forsyth A, Sallis JF. Measuring the built environment for physical activity: state of the science. Am J Prev Med. 2009;36(4):99–123.
Foster S, Giles-Corti B. The built environment, neighborhood crime and constrained physical activity: an exploration of inconsistent findings. Prev Med. 2008;47(3):241–51.
Rodríguez DA, Cho G-H, Evenson KR, Conway TL, Cohen D, Ghosh-Dastidar B, Pickrel JL, Veblen-Mortenson S, Lytle LA. Out and about: association of the built environment with physical activity behaviors of adolescent females. Health Place. 2012;18(1):55–62.
Heath GW, Parra DC, Sarmiento OL, Andersen LB, Owen N, Goenka S, Montes F, Brownson RC. Evidence-based intervention in physical activity: lessons from around the world. The Lancet. 2012;380(9838):272–81. https://doi.org/10.1016/S0140-6736(12)60816-2.
Swinburn B, Egger G, Raza F. Dissecting obesogenic environments: the development and application of a framework for identifying and prioritizing environmental interventions for obesity. Prev Med. 1999;29(6):563–70.
Kirk SFL, Penney TL, McHugh T-LF. Characterizing the obesogenic environment: the state of the evidence with directions for future research. Obes Rev. 2010;11(2):109–17. https://doi.org/10.1111/j.1467-789X.2009.00611.x.
Sallis JF, Frank LD, Saelens BE, Kraft MK. Active transportation and physical activity: opportunities for collaboration on transportation and public health research. Transportation Research Part A: Policy and Practice. 2004;38(4):249–68. https://doi.org/10.1016/j.tra.2003.11.003.
Cerin E, Mitáš J, Cain K.L, Conway T.L, Adams M.A, Schofield G, Sarmiento O.L, Reis R.S, Schipperijn J, Davey R, et al. Do associations between objectively-assessed physical activity and neighbourhood environment attributes vary by time of the day and day of the week? ipen adult study. Int J Behav Nutr Phys Act. 2017;14(1):34.
Schipperijn J, Cerin E, Adams MA, Reis R, Smith G, Cain K, Christiansen LB, Van Dyck D, Gidlow C, Frank LD, et al. Access to parks and physical activity: An eight country comparison. Urban forestry & urban greening. 2017;27:253–63.
Sallis JF, Cerin E, Conway TL, Adams MA, Frank LD, Pratt M, Salvo D, Schipperijn J, Smith G, Cain KL, et al. Physical activity in relation to urban environments in 14 cities worldwide: a cross-sectional study. The Lancet. 2016;387(10034):2207–17.
Gebel K, Ding D, Foster C, Bauman AE, Sallis JF. Improving current practice in reviews of the built environment and physical activity. Sports Med. 2015;45(3):297–302.
Barnett DW, Barnett A, Nathan A, Van Cauwenberg J, Cerin E. Built environmental correlates of older adults’ total physical activity and walking: a systematic review and meta-analysis. Int J Behav Nutr Phys Act. 2017;14(1):103.
O. Ferdinand A, Sen B, Rahurkar S, Engler S, Menachemi N. The relationship between built environments and physical activity: a systematic review. Am J Public Health. 2012;102(10):7–13.
McCormack GR, Shiell A. In search of causality: a systematic review of the relationship between the built environment and physical activity among adults. Int J Behav Nutr Phys Act. 2011;8(1):125.
K¨armeniemi M, Lankila T, Ik¨aheimo T, Koivumaa-Honkanen H, Korpelainen R. The built environment as a determinant of physical activity: a systematic review of longitudinal studies and natural experiments. Ann Behav Med. 2018;52(3):239–51.
Wilkins EL, Morris MA, Radley D, Griffiths C. Using geographic information systems to measure retail food environments: Discussion of methodological considerations and a proposed reporting checklist (geo-fern). Health Place. 2017;44:110–7.
Jia P, Yu C, Remais JV, Stein A, Liu Y, Brownson RC, Lakerveld J, Wu T, Yang L, Smith M, et al. Spatial lifecourse epidemiology reporting standards (isle-rest) statement. Health Place. 2020;61: 102243.
Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Shamseer L, Tetzlaff JM, Akl EA, Brennan SE, et al. The prisma 2020 statement: an updated guideline for reporting systematic reviews. Int J Surg. 2021;88: 105906.
University of York: PROSPERO International prospective register of systematic reviews. https://www.crd.york.ac.uk/prospero/
Smith LP, Ng SW, Popkin BM. No time for the gym? housework and other non-labor market time use patterns are associated with meeting physical activity recommendations among adults in full-time, sedentary jobs. Soc Sci Med. 2014;120:126–34. https://doi.org/10.1016/j.socscimed.2014.09.010.
OECD: Oecd obesity update 2017,. Report, Organisation for Economic Co-operation and Development (2017–05–17. 2017)
Sassi, F., Devaux, M., Cecchini, M., Rusticelli, E.: The obesity epidemic: Analysis of past and projected future trends in selected oecd countries (45) (2009). doi:https://doi.org/10.1787/225215402672
Prodaniuk TR, Plotnikoff RC, Spence JC, Wilson PM. The influence of self-efficacy and outcome expectations on the relationship between perceived environment and physical activity in the workplace. Int J Behav Nutr Phys Act. 2004;1(1):7.
Bize R, Johnson JA, Plotnikoff RC. Physical activity level and health-related quality of life in the general adult population: A systematic review. Prev Med. 2007;45(6):401–15. https://doi.org/10.1016/j.ypmed.2007.07.017.
Finch C, Owen N, Price R. Current injury or disability as a barrier to being more physically active. Med Sci Sports Exerc. 2001;33(5):778–82.
Rimmer JH, Riley BB, Rubin SS. A new measure for assessing the physical activity behaviors of persons with disabilities and chronic health conditions: The physical activity and disability survey. Am J Health Promot. 2001;16(1):34–45. https://doi.org/10.4278/0890-1171-16.1.34.
Borodulin K, Evenson KR, Wen F, Herring AH, Benson A. Physical activity patterns during pregnancy. Med Sci Sports Exerc. 2008;40(11):1901–8. https://doi.org/10.1249/MSS.0b013e31817f1957.
Babineau, J.: Product review: covidence (systematic review software). Journal of the Canadian Health Libraries Association/Journal de l’Association des biblioth`eques de la sant´e du Canada 35(2), 68–71 (2014)
Higgins, J.P., Thomas, J., Chandler, J., Cumpston, M., Li, T., Page, M.J., Welch, V.A.: Cochrane Handbook for Systematic Reviews of Interventions. John Wiley & Sons, ??? (2019) Sterne J A, HernÃ¡n M A, Reeves B C, SavoviÄ‡ J, Berkman N D, Viswanathan M et al. ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions BMJ 2016; 355 :i4919 doi:https://doi.org/10.1136/bmj.i4919
Adams M.A, Frank L.D, Schipperijn J, Smith G, Chapman J, Christiansen L.B, Coffee N, Salvo D, du Toit L, Dygr´yn J. International variation in neighborhood walkability, transit, and recreation environments using geographic information systems: the ipen adult study. Int J Health Geogr. 2014;13(1):1–17.
Heesch KC, Langdon M. The usefulness of gps bicycle tracking data for evaluating the impact of infrastructure change on cycling behaviour. Health Promot J Austr. 2016;27(3):222–9.
Goyder E, Hind D, Breckon J, Dimairo M, Minton J, Everson-Hock E, Read S, Copeland R, Crank H, Horspool K, Humphreys L, Hutchison A, Kesterton S, Latimer N, Scott E, Swaile P, Walters SJ, Wood R, Collins K, Cooper C. A randomised controlled trial and cost-effectiveness evaluation of ’booster’ interventions to sustain increases in physical activity in middle-aged adults in deprived urban neighbourhoods. Health Technol Assess. 2014;18(13):1–209.
Kondo K, Lee J, Kawakubo K, Kataoka Y, Asami Y, Mori K, Umezaki M, Yamauchi T, Takagi H, Sunagawa H, Akabayashi A. Association between daily physical activity and neighborhood environments. Environ Health Prev Med. 2009;14(3):196–206.
Lee KY, Lee PH, Macfarlane D. Associations between moderate-to-vigorous physical activity and neighbourhood recreational facilities: The features of the facilities matter. Int J Environ Res Public Health. 2014;11(12):12594–610.
Amagasa S, Inoue S, Fukushima N, Kikuchi H, Nakaya T, Hanibuchi T, Sallis JF, Owen N. Associations of neighborhood walkability with intensity-and bout-specific physical activity and sedentary behavior of older adults in japan. Geriatr Gerontol Int. 2019;19(9):861–7.
Salvo D, Reis RS, Stein AD, Rivera J, Martorell R, Pratt M. Characteristics of the built environment in relation to objectively measured physical activity among mexican adults, 2011. Prev Chronic Dis. 2014;11:147.
Nathan A, Wood L, Giles-Corti B. Exploring socioecological correlates of active living in retirement village residents. J Aging Phys Act. 2014;22(1):1–15.
Hall KS, McAuley E. Individual, social environmental and physical environmental barriers to achieving 10000 steps per day among older women. Health Educ Res. 2010;25(3):478–88.
Bringolf-Isler B, Schindler C, Kayser B, Suggs LS, Probst-Hensch N. Objectively measured physical activity in population-representative parent-child pairs: Parental modelling matters and is context-specific. BMC Public Health. 2018;18(1):1–15.
Cerin, E., Conway, T.L., Adams, M.A., Barnett, A., Cain, K.L., Owen, N., Christiansen, L.B., Van Dyck, D., Mit´aˇs, J., Sarmiento, O.L.: Objectively-assessed neighbourhood destination accessibility and physical activity in adults from 10 countries: an analysis of moderators and perceptions as mediators. Social Science & Medicine 211, 282–293 (2018)
Dygryn J, Mitas J, Stelzer J. The influence of built environment on walkability using geographic information system. J Hum Kinet. 2010;24(1):93–9. https://doi.org/10.2478/v10078-010-0025-2.
Dewulf, B., Neutens, T., Dyck, D.v., Bourdeaudhuij, I.d., Weghe, N.v.d.: Correspondence between objective and perceived walking times to urban destinations: influence of physical activity, neighbourhood walkability, and socio-demographics. International journal of health geographics 11(43) (2012)
Van Dyck, D., Cardon, G., Deforche, B., Owen, N., Sallis, J.F., Bourdeaudhuij, I.d.: Neighborhood walkability and sedentary time in belgian adults. American Journal of Preventive Medicine 39(1), 25–32 (2010)
Van Dyck, D., Cardon, G., Deforche, B., Sallis, J.F., Owen, N., De Bourdeaudhuij, I.: Neighborhood ses and walkability are related to physical activity behavior in belgian adults. Preventive Medicine 50(SUPPL.), 74–79 (2010)
Van Dyck D, Cardon G, Deforche B, Owen N, De Bourdeaudhuij I. Relationships between neighborhood walkability and adults’ physical activity: How important is residential self-selection? Health Place. 2011;17(4):1011–4.
Van Holle, V., Van Cauwenberg, J., Van Dyck, D., Deforche, B., Van de Weghe, N., De Bourdeaudhuij, I.: Relationship between neighborhood walkability and older adults’ physical activity: Results from the belgian environmental physical activity study in seniors (bepas seniors). International Journal of Behavioral Nutrition and Physical Activity 11 (1) (no pagination)(110) (2014)
Hajna, S., Ross, N.A., Joseph, L., Harper, S., Dasgupta, K.: Neighbourhood walkability, daily steps and utilitarian walking in canadian adults. BMJ Open 5 (11) (no pagination)(e008964) (2015)
Hajna S, Dasgupta K, Ross NA. Laboratory-assessed markers of cardiometabolic health and associations with gis-based measures of active-living environments. Int J Environ Res Public Health. 2018;15(10):2079.
Nightingale CM, Limb ES, Ram B, Shankar A, Clary C, Lewis D, Cummins S, Procter D, Cooper AR, Page AS. The effect of moving to east village, the former london 2012 olympic and paralympic games athletes’ village, on physical activity and adiposity (enable london): a cohort study. The Lancet Public Health. 2019;4(8):421–30.
Limb ES, Procter DS, Cooper AR, Page AS, Nightingale CM, Ram B, Shankar A, Clary C, Lewis D, Cummins S. The effect of moving to east village, the former london 2012 olympic and paralympic games athletes’ village, on mode of travel (enable london study, a natural experiment). Int J Behav Nutr Phys Act. 2020;17(1):1–11.
Portegijs E, Keskinen KE, Tsai LT, Rantanen T, Rantakokko M. Physical limitations, walkability, perceived environmental facilitators and physical activity of older adults in finland. International Journal of Environmental Research & Public Health [Electronic Resource]. 2017;14(3):22.
Richardson AS, Troxel WM, Ghosh-Dastidar MB, Beckman R, Hunter GP, DeSantis AS, Colabianchi N, Dubowitz T. One size doesn’t fit all: cross-sectional associations between neighborhood walkability, crime and physical activity depends on age and sex of residents. BMC Public Health. 2017;17(1):97.
Saelens BE, Sallis JF, Frank LD, Cain KL, Conway TL, Chapman JE, Slymen DJ, Kerr J. Neighborhood environment and psychosocial correlates of adults’ physical activity. Med Sci Sports Exerc. 2012;44(4):637–46.
Sallis JF, Saelens BE, Frank LD, Conway TL, Slymen DJ, Cain KL, Chapman JE, Kerr J. Neighborhood built environment and income: examining multiple health outcomes. Soc Sci Med. 2009;68(7):1285–93.
Adams MA, Todd M, Kurka J, Conway TL, Cain KL, Frank LD, Sallis JF. Patterns of walkability, transit, and recreation environment for physical activity. Am J Prev Med. 2015;49(6):878–87.
Bracy, N.L., Millstein, R.A., Carlson, J.A., Conway, T.L., Sallis, J.F., Saelens, B.E., Kerr, J., Cain, K.L., Frank, L.D., King, A.C.: Is the relationship between the built environment and physical activity moderated by perceptions of crime and safety? International Journal of Behavioral Nutrition and Physical Activity 11 (1) (no pagination)(24) (2014)
Ding D, Sallis JF, Norman GJ, Frank LD, Saelens BE, Kerr J, Conway TL, Cain K, Hovell MF, Hofstetter CR, King AC. Neighborhood environment and physical activity among older adults: do the relationships differ by driving status? J Aging Phys Act. 2014;22(3):421–31.
Thornton CM, Kerr J, Conway TL, Saelens BE, Sallis JF, Ahn DK, Frank LD, Cain KL, King AC. Physical activity in older adults: an ecological approach. Ann Behav Med. 2017;51(2):159–69.
Todd M, Adams MA, Kurka J, Conway TL, Cain KL, Buman MP, Frank LD, Sallis JF, King AC. Gis-measured walkability, transit, and recreation environments in relation to older adults’ physical activity: A latent profile analysis. Prev Med. 2016;93:57–63.
Frank LD, Schmid TL, Sallis JF, Chapman J, Saelens BE. Linking objectively measured physical activity with objectively measured urban form: findings from smartraq. Am J Prev Med. 2005;28(2 Suppl 2):117–25.
Eriksson, U., Arvidsson, D., Gebel, K., Ohlsson, H., Sundquist, K.: Walkability parameters, active transportation and objective physical activity: moderating and mediating effects of motor vehicle ownership in a cross-sectional study. International Journal of Behavioral Nutrition and Physical Activity 9 (2012).doi:https://doi.org/10.1186/1479-5868-9-123
Sundquist K, Eriksson U, Kawakami N, Skog L, Ohlsson H, Arvidsson D. Neighborhood walkability, physical activity, and walking behavior: The swedish neighborhood and physical activity (snap) study. Soc Sci Med. 2011;72(8):1266–73.
Smith L, Panter J, Ogilvie D. Characteristics of the environment and physical activity in midlife: Findings from uk biobank. Prev Med. 2019;118:150–8.
Hinckson, E., Cerin, E., Mavoa, S., Smith, M., Badland, H., Witten, K., Kearns, R., Schofield, G.: What are the associations between neighbourhood walkability and sedentary time in new zealand adults? the urban cross-sectional study. BMJ Open 7 (10) (no pagination)(e016128) (2017)
Mavoa, S., Bagheri, N., Koohsari, M.J., Kaczynski, A.T., Lamb, K.E., Oka, K., O’sullivan, D., Witten, K.:How do neighbourhood definitions influence the associations between built environment and physical activity?International journal of environmental research and public health 16(9), 1501 (2019)
Robertson, L., Ward Thompson, C., Aspinall, P., Millington, C., McAdam, C., Mutrie, N.: The influence of the local neighbourhood environment on walking levels during the walking for wellbeing in the west pedometer-based community intervention. Journal of environmental and public health 2012 (2012)
Zhu W, Nedovic-Budic Z, Olshansky RB, Marti J, Gao Y, Park Y, McAuley E, Chodzko-Zajko W. Agent-based modeling of physical activity behavior and environmental correlations: an introduction and illustration. J Phys Act Health. 2013;10(3):309–22.
Evenson KR, Wen F, Hillier A, Cohen DA. Assessing the contribution of parks to physical activity using global positioning system and accelerometry. Med Sci Sports Exerc. 2013;45(10):1981–7. https://doi.org/10.1249/MSS.0b013e318293330e.
Dewulf B, Neutens T, Dyck D.v, Bourdeaudhuij I.d, Broekx S, Beckx C, Weghe N.v.d. Associations between time spent in green areas and physical activity among late middle-aged adults. Geospatial Health. 2016;11(3):225–32.
James P, Hart JE, Hipp JA, Mitchell JA, Kerr J, Hurvitz PM, Glanz K, Laden F. Gps-based exposure to greenness and walkability and accelerometry-based physical activity. Cancer Epidemiol Biomark Prev. 2017;26(4):525–32.
Gell NM, Wadsworth DD. How do they do it: working women meeting physical activity recommendations. Am J Health Behav. 2014;38(2):208–17.
Kosaka S, Umezaki M, Ishikawa M, Watanabe C. Physical activity and the neighborhood environment in a heavy snowfall area in japan: The role of ”gangi-dori”. Landsc Urban Plan. 2014;123:124–33. https://doi.org/10.1016/j.landurbplan.2013.12.016.
Rodriguez DA, Brown AL, Troped PJ. Portable global positioning units to complement accelerometry-based physical activity monitors. Med Sci Sports Exerc. 2005;37(11 Suppl):572–81.
Miller HJ, Tribby CP, Brown BB, Smith KR, Werner CM, Wolf J, Wilson L, Oliveira MG. Public transit generates new physical activity: Evidence from individual gps and accelerometer data before and after light rail construction in a neighborhood of salt lake city, utah, usa. Health Place. 2015;36:8–17.
Tamura K, Wilson JS, Goldfeld K, Puett RC, Klenosky DB, Harper WA, Troped PJ. Accelerometer and gps data to analyze built environments and physical activity. Res Q Exerc Sport. 2019;90(3):395–402.
Troped PJ, Wilson JS, Matthews CE, Cromley EK, Melly SJ. The built environment and location-based physical activity. Am J Prev Med. 2010;38(4):429–38.
Rundle AG, Sheehan DM, Quinn JW, Bartley K, Eisenhower D, Bader MM, Lovasi GS, Neckerman KM. Using gps data to study neighborhood walkability and physical activity. Am J Prev Med. 2016;50(3):65–72.
Voss C, Sims Gould J, Ashe Maureen C, McKay Heather A, Pugh C, Winters M. Public transit use and physical activity in community-dwelling older adults: Combining gps and accelerometry to assess transportation-related physical activity. J Transp Health. 2016;3(2):191–9.
Zenk SN, Schulz AJ, Matthews SA, Odoms-Young A, Wilbur J, Wegrzyn L, Gibbs K, Braunschweig C, Stokes C. Activity space environment and dietary and physical activity behaviors: a pilot study. Health Place. 2011;17(5):1150–61.
Timmermans EJ, Schaap LA, Visser M, van der Ploeg HP, Wagtendonk AJ, van der Pas S, Deeg DJ. The association of the neighbourhood built environment with objectively measured physical activity in older adults with and without lower limb osteoarthritis. BMC Public Health. 2016;15:710.
Houston D. Implications of the modifiable areal unit problem for assessing built environment correlates of moderate and vigorous physical activity. Appl Geogr. 2014;50:40–7. https://doi.org/10.1016/j.apgeog.2014.02.008.
Dill, J., McNeil, N., Broach, J., Ma, L.: Bicycle boulevards and changes in physical activity and active transportation: Findings from a natural experiment. Preventive Medicine 69(S), 74–78 (2014)
Hillsdon, M., Coombes, E., Griew, P., Jones, A.: An assessment of the relevance of the home neighbourhood for understanding environmental influences on physical activity: How far from home do people roam? International Journal of Behavioral Nutrition and Physical Activity 12 (1) (no pagination)(100) (2015)
Brown BB, Werner CM, Tribby CP, Miller HJ, Smith KR. Transit use, physical activity, and body mass index changes: Objective measures associated with complete street light-rail construction. Am J Public Health. 2015;105(7):1468–74. https://doi.org/10.2105/ajph.2015.302561.
Brown BB, Smith KR, Tharp D, Werner CM, Tribby CP, Miller HJ, Jensen W. A complete street intervention for walking to transit, nontransit walking, and bicycling: A quasi-experimental demonstration of increased use. J Phys Act Health. 2016;13(11):1210–9.
Brown BB, Tharp D, Tribby CP, Smith KR, Miller HJ, Werner CM. Changes in bicycling over time associated with a new bike lane: Relations with kilocalories energy expenditure and body mass index. J Transp Health. 2016;3(3):357.
Brown BB, Werner CM, Smith KR, Tribby CP, Miller HJ, Jensen WA, Tharp D. Environmental, behavioral, and psychological predictors of transit ridership: Evidence from a community intervention. J Environ Psychol. 2016;46:188–96.
Brown BB, Tharp D, Smith KR, Jensen WA. Objectively measured active travel and uses of activity-friendly neighborhood resources: Does change in use relate to change in physical activity and bmi? Preventive Medicine Reports. 2017;8:60–6.
Jansen, M., Ettema, D., Pierik, F., Dijst, M.: Sports facilities, shopping centers or homes: What locations are important for adults’ physical activity? a cross-sectional study. International Journal of Environmental Research and Public Health 13 (3) (no pagination)(287) (2016)
Jansen F, Ettema D, Kamphuis C, Pierik F, Dijst M. How do type and size of natural environments relate to physical activity behavior? Health Place. 2017;46:73–81.
Jansen M, Kamphuis CBM, Pierik FH, Ettema DF, Dijst MJ. Neighborhood-based pa and its environmental correlates: a gis- and gps based cross-sectional study in the netherlands. BMC Public Health. 2018;18(1):233.
Duncan DT, Meline J, Kestens Y, Day K, Elbel B, Trasande L, Chaix B. Walk score, transportation mode choice, and walking among french adults: A gps, accelerometer, and mobility survey study. International Journal of Environmental Research & Public Health [Electronic Resource]. 2016;13(6):20.
Chaix B, Kestens Y, Duncan DT, Brondeel R, Meline J, El Aarbaoui T, Pannier B, Merlo J. A gps-based methodology to analyze environment-health associations at the trip level: Case-crossover analyses of built environments and walking. Am J Epidemiol. 2016;184(8):570–8.
Delcl`os-Ali´o X, Vich G, Miralles-Guasch C. The relationship between mediterranean built environment and outdoor physical activity: evidence from gps and accelerometer data among young adults in barcelona. Landscape Research. 2020;45(4):520–33.
Delcl`os-Ali´o X, Marquet O, Vich G, Schipperijn J, Zhang K, Maciejewska M, Miralles-Guasch C. Temperature and rain moderate the effect of neighborhood walkability on walking time for seniors in barcelona. Int J Environ Res Public Health. 2020;17(1):14.
Miralles-Guasch C, Dopico J, Delcl`os-Ali X, Knobel P, Marquet O, Maneja-Zaragoza R, Schipperijn J, Vich G. Natural landscape, infrastructure, and health: The physical activity implications of urban green space composition among the elderly. Int J Environ Res Public Health. 2019;16(20):3986.
Baek SR, Moudon AV, Saelens BE, Kang B, Hurvitz PM, Bae CC. Comparisons of physical activity and walking between korean immigrant and white women in king county, wa. J Immigr Minor Health. 2016;18(6):1541–6. https://doi.org/10.1007/s10903-015-0290-1.
Huang R, Moudon AV, Zhou C, Stewart OT, Saelens BE. Light rail leads to more walking around station areas. J Transp Health. 2017;6:201–8.
Huang R, Moudon AV, Zhou C, Saelens BE. Higher residential and employment densities are associated with more objectively measured walking in the home neighborhood. J Transp Health. 2019;12:142–51.
Kang B, Moudon AV, Hurvitz PM, Saelens BE. Differences in behavior, time, location, and built environment between objectively measured utilitarian and recreational walking. Transp Res Part D: Transp Environ. 2017;57:185–94. https://doi.org/10.1016/j.trd.2017.09.026.
Stewart OT, Moudon AV, Saelens BE, Lee C, Kang BJ, Doescher MP. Comparing associations between the built environment and walking in rural small towns and a large metropolitan area. Environ Behav. 2016;48(1):13–36. https://doi.org/10.1177/0013916515612253.
Stewart OT, Moudon AV, Littman A, Seto E, Saelens BE. The association between park facilities and the occurrence of physical activity during park visits. J Leis Res. 2018;49(3–5):217–35.
Stewart OT, Moudon AV, Littman AJ, Seto E, Saelens BE. The association between park facilities and duration of physical activity during active park visits. J Urban Health. 2018;95(6):869–80.
Stewart OT, Moudon AV, Littman AJ, Seto E, Saelens BE. Why neighborhood park proximity is not associated with total physical activity. 2018;52:163–9. https://doi.org/10.1016/j.healthplace.2018.05.011.
Stewart OT, Moudon AV, Fesinmeyer MD, Zhou C, Saelens BE. The association between park visitation and physical activity measured with accelerometer, gps, and travel diary. Health Place. 2016;38:82–8.
Hwang LD, Hurvitz PM, Duncan GE. Cross sectional association between spatially measured walking bouts and neighborhood walkability. International Journal of Environmental Research & Public Health [Electronic Resource]. 2016;13(4):412.
Hirsch JA, Winters M, Ashe MC, Clarke P, McKay H. Destinations that older adults experience within their gps activity spaces relation to objectively measured physical activity. 2016;48(1):55–77. https://doi.org/10.1177/0013916515607312.
Galpern P, Ladle A, Uribe FA, Sandalack B, Doyle-Baker P. Assessing urban connectivity using volunteered mobile phone gps locations. Appl Geogr. 2018;93:37–46.
Ladle A, Galpern P, Doyle-Baker P. Measuring the use of green space with urban resource selection functions: An application using smartphone gps locations. Landsc Urban Plan. 2018;179:107–15.
Sarjala S. Built environment determinants of pedestrians’ and bicyclists’ route choices on commute trips: Applying a new grid-based method for measuring the built environment along the route. J Transp Geogr. 2019;78:56–69.
Pritchard R, Bucher D, Frøyen Y. Does new bicycle infrastructure result in new or rerouted bicyclists? a longitudinal gps study in oslo. J Transp Geogr. 2019;77:113–25.
Lue G, Miller EJ. Estimating a toronto pedestrian route choice model using smartphone gps data. Travel behaviour and society. 2019;14:34–42.
Chen P, Shen Q, Childress S. A gps data-based analysis of built environment influences on bicyclist route preferences. Int J Sustain Transp. 2018;12(3):218–31.
Vich G, Marquet O, Miralles-Guasch C. Green streetscape and walking: exploring active mobility patterns in dense and compact cities. J Transp Health. 2019;12:50–9.
Park Y, Akar G. Why do bicyclists take detours? a multilevel regression model using smartphone gps data. J Transp Geogr. 2019;74:191–200.
Triguero-Mas, M., Donaire-Gonzalez, D., Seto, E., Valentin, A., Smith, G., Martinez, D., Carrasco-Turigas, G., Masterson, D., van den Berg, M., Ambros, A., Martinez-Iniguez, T., Dedele, A., Hurst, G., Ellis, N., Grazulevicius, T., Voorsmit, M., Cirach, M., Cirac-Claveras, J., Swart, W., Clasquin, E., Maas, J., Wendel-Vos, W., Jerrett, M., Grazuleviciene, R., Kruize, H., Gidlow, C.J., Nieuwenhuijsen, M.J.: Living close to natural outdoor environments in four european cities: Adults’ contact with the environments and physical activity. International Journal of Environmental Research and Public Health 14(10) (2017). doi:https://doi.org/10.3390/ijerph14101162
Hochmair HH, Bardin E, Ahmouda A. Estimating bicycle trip volume for miami-dade county from strava tracking data. J Transp Geogr. 2019;75:58–69.
Shimizu, K., Nishi, H., Kishimoto, T.: Estimation of pedestrian density and speed on street network using smartphone spatio-temporal data. In: 12th International Space Syntax Symposium, SSS 2019
Li X, Santi P, Courtney TK, Verma SK, Ratti C. Investigating the association between streetscapes and human walking activities using google street view and human trajectory data. Trans GIS. 2018;22(4):1029–44.
Sun Y, Du Y, Wang Y, Zhuang L. Examining associations of environmental characteristics with recreational cycling behaviour by street-level strava data. Int J Environ Res Public Health. 2017;14(6):644.
Sun, Y.R., Mobasheri, A.: Utilizing crowdsourced data for studies of cycling and air pollution exposure: A case study using strava data. International Journal of Environmental Research and Public Health 14(3) (2017). doi:https://doi.org/10.3390/ijerph14030274
Pontin, F.: Utilising smartphone data to explore spatial influences on physical activity. Edward Elgar Publishing, ??? (2021)
Hicks JL, Althoff T, Kuhar P, Bostjancic B, King AC, Leskovec J, Delp SL. Best practices for analyzing large-scale health data from wearables and smartphone apps. NPJ digital medicine. 2019;2(1):1–12.
Wolff-Hughes DL, McClain JJ, Dodd KW, Berrigan D, Troiano RP. Number of accelerometer monitoring days needed for stable group-level estimates of activity. Physiol Meas. 2016;37(9):1447.
Tudor-Locke C, Burkett L, Reis J, Ainsworth B, Macera C, Wilson D. How many days of pedometer monitoring predict weekly physical activity in adults? Prev Med. 2005;40(3):293–8.
Bergman P. The number of repeated observations needed to estimate the habitual physical activity of an individual to a given level of precision. PLoS ONE. 2018;13(2):0192117. https://doi.org/10.1371/journal.pone.0192117.
Pontin F, Lomax N, Clarke G, Morris MA. Characterisation of temporal patterns in step count behaviour from smartphone app data: An unsupervised machine learning approach. Int J Environ Res Public Health. 2021;18(21):11476.
Barrington-Leigh C, Millard-Ball A. A global assessment of street-network sprawl. PLoS ONE. 2019;14(11):0223078.
Grasser G, van Dyck D, Titze S, Stronegger WJ. A european perspective on gis-based walkability and active modes of transport. Eur J Pub Health. 2016;27(1):145–51. https://doi.org/10.1093/eurpub/ckw118.
Tcymbal A, Demetriou Y, Kelso A, Wolbring L, Wunsch K, W¨asche H, Woll A, Reimers A.K. Effects of the built environment on physical activity: a systematic review of longitudinal studies taking sex/gender into account. Environ Health Prev Med. 2020;25(1):1–25.
Benton JS, Anderson J, Hunter RF, French DP. The effect of changing the built environment on physical activity: a quantitative review of the risk of bias in natural experiments. Int J Behav Nutr Phys Act. 2016;13(1):1–18.
Lakerveld J, Loyen A, Ling FCM, De Craemer M, Van Der Ploeg HP, O’Gorman DJ, Carlin A, Caprinica L, Kalter J, Oppert J-M. Identifying and sharing data for secondary data analysis of physical activity, sedentary behaviour and their determinants across the life course in europe: general principles and an example from dedipac. BMJ Open. 2017;7(10):017489.
Crist K, Bolling K, Schipperijn J, Hurst S, Takemoto M, Sallis JF, Badland H, Kerr J. Collaboration between physical activity researchers and transport planners: A qualitative study of attitudes to data driven approaches. J Transp Health. 2018;8:157–68.
Allahbakhshi H, Conrow L, Naimi B, Weibel R. Using accelerometer and gps data for real-life physical activity type detection. Sensors. 2020;20(3):588.
Peng RD, Hicks SC. Reproducible research: A retrospective. Annu Rev Public Health. 2021;42:79–93.
Ding D, Gebel K. Built environment, physical activity, and obesity: what have we learned from reviewing the literature? Health Place. 2012;18(1):100–5.
Gebel K, Bauman AE, Petticrew M. The physical environment and physical activity: a critical appraisal of review articles. Am J Prev Med. 2007;32(5):361–3693.
McGinn AP, Evenson KR, Herring AH, Huston SL, Rodriguez DA. Exploring associations between physical activity and perceived and objective measures of the built environment. J Urban Health. 2007;84(2):162–84.
Funded by an Economic and Social Research Council grant (ES/R501062/1) awarded as part of the Centre for Doctoral Training Programme in Data Analytics and Society. The funders played no role in the conduct of the systematic review.
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Data extraction form.
Associations between physical activity (include cut-points) and the built environment metrics across all included studies (N=94). The number in the cell indicates number of studies investigating the association and the cell colour indicates the overall association between the metrics across all included studies (1.0 BE promotes PA to -1.0 BE impedes PA).
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Pontin, F.L., Jenneson, V.L., Morris, M.A. et al. Objectively measuring the association between the built environment and physical activity: a systematic review and reporting framework. Int J Behav Nutr Phys Act 19, 119 (2022). https://doi.org/10.1186/s12966-022-01352-7
- Physical activity
- Built environment
- Global positioning systems
- Geographic information systems