Aim
The aim of this substudy was to explore whether access to green space and leisure facilities influenced the effectiveness of the intervention. We used network distance analysis to produce a set of variables for each of the 282 trial participants, which represents his or her pedestrian access to municipal green space and any relevant leisure facilities.
TABLE 3 Motivational Interviewing Treatment Integrity treatmentfidelity results
Clinician behaviour count or summary score thresholds Beginner proficiency Competency
Global clinician ratings (average) 3.5 4.0
Reflection to question ratio (R : Q) 1 2
Per cent open questions (% OQ) 50 70
Per cent complex reflections (% CR) 40 50
Network distance analysis
Euclidean (straight line) distance is the simplest measure of distance between two points. However, in a city it is rarely possible to follow a straight line between two points. Moreover, rivers, railway lines and sometimes roads force pedestrians to take routes that may deviate considerably from a straight line. To gauge the realistic walking distance between two points in a city, it is necessary to build a network representation of the pedestrian-accessible routes within the city and surrounding area. The shortest route between any two points on the network can then be calculated mathematically.
The most labour-intensive step is creating the network. The Integrated Transport Network™(ITN) in OS
MasterMap®[Ordnance Survey, Southampton, UK; see www.ordnancesurvey.co.uk/oswebsite/products/ os-mastermap/itn-layer/index.html (accessed 10 October 2012)], can be used for this purpose, but unfortunately it is not possible to download a single portion of the ITN covering all of Sheffield and the surrounding area. Instead, we downloaded the data for this area from OpenStreetMap (see www. openstreetmap.org). In a certain respect, OpenStreetMap is actually more useful to us than the ITN; it is created (in part) by local volunteers, walking on foot and recording data with global positioning system (GPS) devices, and therefore provides information on pedestrian access that is more detailed than a typical OS map.
Roads and footpaths, etc. in OpenStreetMap are stored in a simple vector format that makes it relatively easy to extract the information required to build a network. The only problem is that the labelling of pedestrian accessibility for roads (especially trunk roads) is not consistent and is occasionally inaccurate. It was therefore necessary to manually exclude the following sections of road:
l A61 Dronfield bypass
l Sheffield Parkway
l Mosborough Parkway
l A57 (section linking Mosborough Parkway to the M1)
l A616 Stocksbridge bypass
l Park Square roundabout
l Tinsley roundabout and Tinsley viaduct.
Crossing points for non-pedestrian roads, for example bridges and underpasses, are well detailed in OpenStreetMap.
Although the shortest distance between points within the network can be calculated very accurately, there is the potential for error when calculating the network distance between features that do not lie within the network. Unfortunately, the centroids of postcode areas, and the polygons representing municipal green space boundaries, do not lie within the network. Here it is necessary to spatially‘join’these features to an appropriate point on the network. This is best done manually; however, because of the large amount of work that this would require it was necessary to automate the joining process, as follows.
Each postcode centroid was linked to the closest point on the network and the Euclidean distance between the centroid and the network point added to the network distance.
Each green space polygon was linked to all network points falling inside it, with the minimum network distance to any of these points deemed to be the minimum network distance to the polygon itself. As pedestrian entry points to green space are well detailed in OpenStreetMap, this means that the network distance mostly takes the entry points into account.
The major limitation of the automated joining process is that sometimes the network point that the feature is linked to is not always the most appropriate. For example, a postcode area may include adjacent houses that face onto different streets so that an address may be attributed to a network point on a street from which there is actually no access to the property. Similarly, for green space, points on the network
outside the green space polygon may sometimes be closer to the actual entry points than those inside the polygon. This can result in network distance errors of≥100 m, in some cases substantially more if the difference in network distance between the automated choice of point and the optimum choice of point is large. Fortunately, very large anomalies appear only in rural areas where the road network is sparse and therefore were not a significant concern in this analysis.
Green space measures
All municipal green spaces in Sheffield were considered, excluding certain types of land use inappropriate for exercise. Sheffield City Council classifies these spaces as‘city’,‘district’or‘local’according to their catchment areas. We used‘city’as a proxy for high-quality space and‘district’as a proxy for medium-quality space in terms of attractiveness. Local spaces were too ubiquitous for a meaningful spatial analysis. Of municipal green spaces belonging to other authorities, only Rother Valley Country Park was included (designated high quality).
Digitised boundary data for municipal green spaces in Sheffield were provided by the Sheffield City Council Parks and Countryside team. As well as boundary information, these data included a classification of the usage and catchment area categorisation of each space. These are explained fully in a Sheffield City Council Parks and Countryside report66but are summarised here as follows:
l Usage type:
¢ parks
¢ gardens
¢ sports sites (e.g. tennis courts)
¢ playingfields ¢ playgrounds ¢ playground/open space ¢ woodlands ¢ moorland/heathland ¢ open spaces ¢ allotments ¢ churchyards/cemeteries
¢ other site types–specified:
¢ golf courses
¢ farms
¢ show grounds
¢ depots
¢ ancillary sites to other leisure facilities (e.g. car parks).
l Catchment area categorisation:
¢ city wide
¢ district (up to 1.3 km)
¢ local (up to 0.4 km).
We decided to disregard the following usage types: playgrounds (because our participants are middle-aged), allotments (as nearby allotments are relevant only to the minority of local residents who rent them),
churchyards/cemeteries (less likely to be appropriate places for exercise), farms, depots and ancillary sites.
Inclusion of the catchment area categorisation information is more problematic than inclusion of usage type as it is not clear on what research, if any, it is based. However, disregarding the categorisation is equally problematic as‘local’municipal green space is ubiquitous. For example, our preliminary study
considered all municipal green spaces in Sheffield of the appropriate usage type and the size of a football pitch or greater and found very few postcodes in the city that were further than 1 km from such a space, with roughly 50% falling within 500 m and roughly 30% falling within 300 m. This tallies with the findings of Barbosa and colleagues.67Also, most distances > 500 m fell in the most affluent parts of the city, which were not included in the booster trial (this also tallies with thefindings of Barbosa and collegaues67). Because of random errors inherent in our network distance measurements (seeNetwork distance analysis), such small network distances would contain an unacceptable amount of uncertainty. It was also felt that it was important for us to take some account of the attractiveness of municipal green spaces.
Therefore, we decided to make use of the catchment area categorisation as a proxy for the quality/ attractiveness of the space, using an ordinal scale in which citywide represents the highest quality, district represents medium quality and local represents the lowest quality. Disregarding all green spaces with unsuitable usage types (see earlier) we measured the shortest network distance to:
(a) high-quality municipal green spaces of appropriate types
(b) high- or medium-quality municipal green spaces of appropriate types.
We excluded Sheffield’s three municipal golf courses; these have citywide catchment areas but this is clearly based on their attraction as a leisure facility (as defined in leisure facility measures) offering golf, rather than as a green space, and the booster participant questionnaire responses indicated that very few participants played golf.
One potential problem with using the green space data for Sheffield is that many of the booster participants live on the edge of the city and could be using municipal green spaces belonging to surrounding authorities (Barnsley Metropolitan Borough Council, Rotherham Metropolitan Borough Council, Derbyshire County Council or North East Derbyshire District Council). This was a matter of sufficient concern for us to request green space boundary data from these authorities. However, because of the green belt around Sheffield, there are actually few important municipal green spaces belonging to these authorities that are close to the borders of Sheffield. The exception to this is Rotherham, with the two conurbations forming a continuous urban area. However, this area is heavily industrialised and there are few important municipal green spaces. One very important exception is Rother Valley Country Park, which borders south-east Sheffield; a boundary polygon for this park was added manually to the set of green spaces having a citywide catchment area.
All green space measures in this study are shortest distance measures. So far we have decided not to use a gravity model for green space as the need to include an arbitrary distance decay parameter would be a potential source of bias. However, this remains an option for future work. Regarding the more
sophisticatedfloating catchment area gravity model, this may actually be inappropriate for green space: unlike a capacity-constrained service such as a health practitioner, a green space that is heavily used (and thus full of people) may be more attractive and more likely to be perceived as appropriate for recreational exercise or as a walking route than one that is less well used.
Leisure facility measures
Leisure facilities, as distinct from green spaces, are defined here as places where a physical activity is facilitated by some kind of organisation, usually in return for payment. For example, we define a tennis club as a leisure facility but an unsupervised tennis court within a park as part of a green space. Similarly, a publicly accessible playingfield is considered a green space rather than a leisure facility, unless it is part of a sports club. Data on leisure facilities are easier to collect than data on green spaces because leisure facilities can be treated as point locations. Even though these facilities may cover a large area, there is usually an office where people must go to pay, and this is typically also the building to which the address of the facility relates. Therefore, noting the limitations of geocoding accuracy explained inNetwork distance analysis, it is credible to use the postcode centroid of the facility’s address as a point location.
The chief problems in collecting data on leisure facilities were twofold:
l deciding which types of leisure facility were relevant to the booster participants
l ensuring that all leisure facilities of these types, located within a reasonable distance of the study area, were included.
To address thefirst point we had the benefit of the questionnaires completed by the booster participants (seeOutcomesandSurvey). These data suggest that the main physical activities relevant to the participants were walking, gardening, swimming and gym-based activities. Walking relates to municipal green space rather than leisure facilities and gardening relates to neither, so we considered only leisure facilities offering a gym and/or swimming.
To address the second point we used the Sport England online‘active places’database,68which provides an authoritative list of sports facilities, with each facility listed as offering one or more types of activity. Two of these types of facilities,‘health andfitness suite’and‘swimming pool’, correspond directly to gyms and swimming respectively. The postcode of each facility is also included on the database, as is an
indicator of how the public can access the facility (those open only to sports clubs or community
associations, rather than individuals, were excluded). For each facility postcode we calculated the network distance to every other postcode within Sheffield, and for each Sheffield postcode we recorded the shortest network distance to:
l a unisex gym
l a female-only gym
l a swimming pool.
As with green space, we did not implement any of the more sophisticated‘gravity’models for leisure facilities. The basic gravity model is additive, meaning that having three gyms a 500-m network distance from one’s home would be scored three times better than having one gym 500 m away. This is clearly inappropriate as gyms are often paid for on a membership basis, and it is unlikely that a participant would join several gyms. Although swimming may be offered on a pay-per-session basis more frequently than gym facilities, it is still questionable whether having three local pools is exactly three times better than having one local pool.
Here, thefloating catchment area gravity model is potentially more useful than the basic gravity model as it links the usefulness of local facilities to local demand.69A gym may be less desirable if it is frequently crowded and one has to wait to use particular exercise machines, and the same may also apply to lane swimming in pools. In this context having multiple local facilities can be beneficial, as greater provision of facilities means that they are less likely to be crowded. Unfortunately, the problem with interpreting this model is determining local demand; although the size of the local population can be easily obtained from census data, people often choose leisure facilities close to where they work rather than where they live, so using population data could be misleading, particularly in the city centre.
Given these issues, and for consistency in the analysis methods, we have used straightforward minimum distance analysis.
With leisure facilities there is also the issue of affordability. Some facilities are expensive and may not be realistically accessible to the more deprived communities targeted by the booster recruitment strategy. However, pricing information is not included in the Sport England database and it was not possible within the timescale of this project to obtain pricing information separately for each facility. Restricting the analysis to municipal facilities was considered but in Sheffield some privately run leisure facilities are less expensive than municipal ones. It is therefore important to recognise that affordability may be an additional barrier to accessing local leisure facilities, even when they are geographically close, which this analysis does not address.
Statistical methods
We constructed crude scatter plots of mean TEE per day (kcal) at 3 months against potential moderators generated from the geographical information systems (GIS) analysis and other continuous baseline potential moderators to explore any univariable linear relationships. In addition, we stratified these plots by intervention arm to explore whether the univariable linear relationships were consistent within the intervention arms. Univariable linear regression models werefitted with amount of physical activity measured by mean TEE per day (kcal) as the response and potential moderator as an explanatory variable to explore whether a potential moderator was a predictor of physical activity at 3 months.
To explore the moderation effect, wefitted multivariable linear regression models on the same response variable with intervention group, potential moderator and interaction between treatment group and potential moderator as explanatory variables (as given byEquation 3). Plots of thefitted regression line stratified by booster treatment group were constructed to explore any potential interactions. In addition, hypothesis testing on the interaction term was also undertaken usingEquation 3to test the moderation effect of GIS variables on physical activity. Variables were classified as being moderators of physical activity if they had a significant interaction effect with treatment at 3 months.
Mean TEE per dayðkcalÞ ¼potential moderatorþtreatment group
þpotential moderatortreatment group ð3Þ