Perceived Versus Actual Distance to Local Physical-Activity Facilities: Does It Really Matter?

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1 Journal of Physical Activity and Health, 2010, 7, Human Kinetics, Inc. Perceived Versus Actual Distance to Local Physical-Activity Facilities: Does It Really Matter? Ralph Maddison, Yannan Jiang, Steve Vander Hoorn, Cliona Ni Mhurchu, Daniel Exeter, and Jennifer Utter Background: Research in adults shows poor agreement between self-reported and objectively measured proximity to physical activity resources; however there is little such research in adolescents. This study assessed the level of agreement between self-reported and objective measures of distance to physical activity resources in adolescents; and whether perceived or actual distance was related to actual use and physical activity levels. Methods: 110 New Zealand high school students (12 18 years) were asked the time (in minutes) it would take them to walk from their home to the nearest physical activity resource, and whether they had used it in the previous month. The distance from participants homes to the nearest resource was measured using GIS. Physical activity was assessed with accelerometers. Results: Agreement was poor, with weighted Kappa Indices ranging from 0.1 to 0.4. The facilities used most frequently were schools (90%), public parks (76%), and playing fields (74%). Closer location was associated with higher use of some facilities only. Moderate-to-vigorous activity levels were not associated with self-reported or measured distance. Conclusions: Agreement between perceived and measured access is poor among adolescents. Further research is needed to understand how individual and social factors interact with environmental factors and whether improving awareness improves use. Keywords: Geographic Information Systems, accelerometer, adolescents, environment, measurement The importance of supportive environments that promote healthy lifestyle choices is enshrined within the Ottawa charter. 1 In recent years there has been growing research interest in the potential role of natural and built environmental factors as determinants of physical activity. 2 Ecological models posit that behaviors have multiple levels of influence that include intrapersonal, interpersonal, policy, and environmental variables. 8 Therefore, a combination of psychosocial and environmental variables should best explain physical activity behavior Environmental research studies, if they are to be of practical use in public health policy, ought to focus on the environmental influences that may determine particular behavioral choices Much of the research that has examined relationships between the environment and physical activity has relied on self-reported perceptions of access to facilities such as parks. 3,4 More recently, Geographic Information Systems (GIS) technology has been used to provide spatially referenced data in an attempt to provide objective measures of the physical environment. 5 A review of 19 studies, 4 demonstrated small but consistent associations Maddison, Jiang, Vander Hoorn, and Ni Mhurchu are with the Clinical Trials Research Unit, University of Auckland, New Zealand. Exeter and Utter are with the Dept of Epidemiology and Biostatistics, University of Auckland, New Zealand. between physical activity among adults and perceptions of accessibility of facilities, opportunities for activity, and the aesthetics of the physical environment. In contrast, other reviews 4,6,7 have shown variability between studies in the association between physical activity and perceived proximity to parks and other potential physical activity settings. The small or lack of associations between environmental factors and physical activity may be a function of the different measures used in the various studies and the potential mismatch between self-reported and objective measurement. For example, objectively measured access to physical activity facilities has been related to physical activity behavior, while self-reported access was not related. 21 This lack of correspondence between perceived and objectively-measured access to physical activity opportunities has implications for how researchers interpret the associations between environmental variables and physical activity and other similar behaviors. For example, access to facilities has been primarily assessed via self-report; therefore if these perceptions are not accurate, some important associations may be missed, while other spuriously identified. 22 Ball and colleagues 22 also suggested a systematic bias in the misreporting of environmental perceptions may exist. For example, those with low motivation or low self-efficacy to be active may be more likely to misperceive their physical activity environment. The implications also extend to 323

2 324 Maddison et al environmental-based educations interventions, which might focus on increasing perceptions of access. Recent research in adults suggests a mismatch between objective (measured) and self-reported (perceived) measures of access to environmental facilities that support physical activity, with levels of agreement (Kappa statistic) ranging from low to moderate. A recent Scottish study 23 compared self-reported access to a public park (whether Scottish adults lived within half a mile [800 m]) with objectively measured (GIS) access from their home to a park, and found poor agreement between these measures. The percentage agreement between measured network distance and reported residence within 800 m of a park was 62%. Ball et al 22 recently examined the mismatch between perceived and objective measures of physical activity environments among Australian adults and reported relatively poor agreement between the different measures (Kappa statistic 0.14 to 0.39), except for coastal access (0.66). Relatively little research of this sort has been conducted among youth. This is surprising given that several reviews 24,25 of physical activity determinants among youth have highlighted the importance of access to facilities for physical activity participation. A recent study 26 of 1,367 adolescent girls found that the number of recreation facilities within a half-mile of the participants homes strongly predicted perceptions of easy access to 7 out of 9 facilities. Perceptions of access to both individual facility and the total number of facilities were associated with increased physical activity. Apart from basketball courts within a mile of their homes, objective measures of access to recreation facilities were otherwise unrelated to physical activity. A study by Jago et al 27 examined associations among observed, self-reported, GIS environmental features and physical activity among adolescent males. GIS and self-reported environmental features were reduced using component analysis, which resulted in 3 GIS factors (parks, crime, and gyms) and 2 self-reported factors (difficulty and access, and safety). Four factors were also previously identified from direct observation. Results showed generally low correlations between the different measures (r s ranged from 0.14 to 0.40), with only observed sidewalk characteristics associated with sedentary behavior and light intensity physical activity. Unfortunately, because this study aggregated the environmental variables it did not examine the level of agreement between directly corresponding features. Given the frequent use of self-reported or perceived access measures to neighborhood recreation facilities, it is important to verify whether young people can accurately determine access to such facilities. Therefore, the purpose of this study was to determine the level of agreement between corresponding self-reported and objective measures of physical activity facilities in an adolescent population. A second aim was to determine whether perceived or actual access (distance) was related to increased use of facilities and increased physical activity. Methods This study was part of a feasibility study exploring the role and impact of environmental influences on New Zealand children s activity and nutrition (ICAN study). The ICAN 20 study is a cross sectional survey involving high school students aged from 12 to 18 years from 2 large metropolitan schools in the Auckland region of New Zealand. All study procedures and related documents were approved by the Northern Y regional ethics committee, NTY/06/11/108. Contact was initiated with the school principal to discuss the study purpose and procedures and to obtain consent to approach the students. Once permission was obtained from the school principal, a researcher made contact with the students via a designated teacher. Students were provided with written and verbal information about all aspects of the study and offered the opportunity to participate. Interested participants completed informed consent forms. Students were ineligible if they were aged less than 12 years or older than 18 years or if they could not read English. The teacher organized a time for the researchers to attend the school and were given classroom time to complete data collection procedures. During this time, students completed a questionnaire, had their height and weight measured and were given an accelerometer to wear over the next 4 days. In total, 180 school students from 2 schools were approached and 170 registered interest to participate (94% response); of which 164 were eligible and took part in the study (six students were ineligible because of their age). Those agreeing to participate were similar in age, sex, and ethnic distribution of students at the respective schools. I In New Zealand, schools have boundaries or zones, in which students must live to attend the school. It is possible to attend the school if you live outside the zone under certain circumstances (siblings attending the school, etc). One hundred and ten students resided within the Auckland City catchment area that included the respective school zones, which allowed neighborhood geocoded data to be collected. The area of the 2 schools zones were km 2 and 8.42 km 2, respectively Measures Perceived Access to Facilities. Items from the Neighborhood Environments Walkability Scale (NEWS) were used to assess perceptions of access to physical activity facilities. There is currently a lack of standardized measurement of the perceived environment; however these measures highlight the proximity of facilities, crime, aesthetics, and safety as key characteristics to walking and cycling. 14,19,31 Given the importance of perceived access to facilities and physical activity resources we chose to use the relevant NEWS items. Some of the wording was modified to

3 Perceived Versus Measured Access 325 ensure applicability to the NZ setting (eg, sidewalks was changed to footpaths, local shop was changed to dairy). Participants were asked to indicate the time (in minutes) it would take them to walk from their home to the closest of 17 physical activity facilities (eg, public park, recreation center, playground, etc). The time categories were 1 to 5 minutes, 6 to 10 minutes, 11 to 29, 21 to 30, and 30+ minutes. Participants also indicated whether they had used each of the facilities in the previous month. Built Environment. Geographical Information System (GIS) 32 software was used to map neighborhood environments in terms of the number and location of local physical activity facilities (recreation facilities, parks, etc). The addresses of registered recreational facilities II (gyms, sports clubs, public swimming pools, etc) were derived from secondary data sources (Auckland City Council, Auckland Public Health Service, Auckland Regional Council, and APNFinda ) and imported to a GIS database. Green Space and land use data were derived from the Auckland City Council district plan. The GIS database also included data from the 2006 Census of Population and Dwellings 33 and the 2006 New Zealand Deprivation Index (NZDep2006). 34 Within participant s neighborhoods, we identified each of the facilities or locations (eg, playground, swimming pool, etc) listed in the NEWS survey. Using the roads network we calculated the distance to each of the nearest facilities or locations from the participant s geocoded address and estimated the time (in minutes) to walk from the participant s home to each of the locations assuming walking speeds of 4 and 5 km/hr. Estimated time to walk to each facility or location was then categorized as <5 minutes, 6 to 10 minutes, 11 to 29, 21 to 30, and 30+ minutes for consistency with the perceived (self-reported) access measure. Physical Activity Behavior. Participants were fitted with an Actigraph accelerometer (Model AM C), which measures motion in the vertical plane, with movement outside of normal motion being filtered electronically. Students wore the accelerometer during waking hours on their right hip and activity count data (counts/minute) were collected over 4 consecutive days (2 weekdays and 2 weekend days). Minute-by-minute activity counts were uploaded to a data reduction program that excluded all Actigraph outputs that equaled zero for more than 20 consecutive minutes (assuming nonwearing time for that period). All days with less than 10 hours of recorded time were excluded from analyses. Using these published criteria, 35 all participants provided at least 3 valid days (including 1 weekend day) for analysis. Time spent in light (2.0 to 3.9 METS), moderate (4.0 to 5.9 METS), and vigorous ( 6.0 METS) activities were derived from age-specific count cut-offs developed by Freedson et al. 36 Analysis All statistical analyses were performed using SAS version (SAS Institute Inc., Cary, NC), R version and/ or SPLUS version 6.1. All statistical tests were two-tailed and a 5% significance level was used throughout the analyses. To investigate the agreement between perceived and objectively measured proximity to physical activity facilities and locations weighted Kappa values, and percentage agreement and disagreement, were calculated. Conventional cut offs for Kappa of < 0.20 = poor, 0.21 to 0.40 = fair, 0.41 to 0.60 = moderate, 0.61 to 0.80 = good, and 0.81 to 1.00 = very good were used. 37 Level of agreement was also examined separately stratified by sex (male/female) and use (non versus use) of facilities. Results On average, students were 14.6 years of age, overweight (mean BMI 24.7, SD = 6.7), predominantly male (57%) and from a range of ethnic groups. Their sociodemographic details are provided in Table 1. Both GIS (objective) and self-reported (subjective) data were available for 9 physical activity facilities. Figure 1 presents findings on perceived distance to physical activity facilities. Overall, 3 out of 5 (61%) of students reported living within 30 minutes walking distance to school. Two out of five (43%) students reported living within 1 to 5 minutes walk to a public park or field, and 4 out of 5 (80%) reported living within 20 minutes walk of park or field. Half (48%) of students reported living within 1 to 5 minutes walking distance of a local shop, while nearly all (85%) reported living within 10 minutes walking distance. The locations used most frequently for physical activity were students schools (90%), public parks (76%), and playing fields (74%). Overall, there was statistically significant agreement between GIS and self-reported data for most physical locations, including schools, swimming pool, gym or fitness facility, and playgrounds, with weighted Kappa Indices ranging from 0.1 to 0.4 (Table 2). Kappa values for schools and swimming pools were generally higher for males than females (Table 3). When stratified by user status, the Kappa coefficients were mostly higher for participants that reported not using selected facilities or locations (own school, swimming pool, gym, the beach) in the previous month compared with those that had (see Table 4). As can be seen in Table 5, closer objectively measured distance was associated with self-reported use of playgrounds only (P <.01). On average, adolescents participated in 74 (SD = 35.96) minutes of moderate and 8 (SD = 8.35) minutes of vigorous physical activity per day as measured by accelerometer. Overall, no pattern was found between perceived access or actual distance with time spent in moderate and vigorous physical activity (see Table 6).

4 Table 1 Descriptive Information of Participants (N = 110) Variable M SD Age (years) Height (meters) Weight (kg) BMI NZDep n (%) Sex Male Female 64 (58%) 46 (42%) Ethnicity NZ European NZ Maori Pacific Asian Other 39 (35.5%) 16 (14.5%) 29 (27.3%) 14 (12.7%) 12 (10.9%) Figure 1 Self-reported use of recreation facilities and locations, by perceived walking distance in minutes. 326

5 Table 2 Level of Agreement (Kappa) Between Perceived and Objectively Measured Access, and Reported Use Location Kappa Index (4km/hr) P value Kappa Index (5km/hr) P value Percentage used Own school % Other school (to play) % Public park % Recreation center % Swimming pool % Gym of fitness center % Aerobics or dance % studio Beach, river, or stream % Playground % Table 3 Level of Agreement (Kappa) Between Perceived and Objectively Measured Access, Stratified by Sex Location Kappa Index females P value Kappa Index males P value Own school Other school (to play) Public park Recreation center Swimming pool Gym of fitness center Aerobics or dance studio Beach, river, or stream Playground Note. Kappa coefficients are based on a walking speed of 4 km/hr. Table 4 Level of Agreement (Kappa) Between Perceived and Objectively Measured Access, Stratified by Use Location Kappa Index not used P value Kappa Index used P value Own school Other school (to play) Public park Recreation center Swimming pool Gym of fitness center Aerobics or dance studio Beach, river, or stream Playground Note. Kappa coefficients are based on a walking seed of 4 km/hr. 327

6 Table 5 Perceived Use and Objectively Measured Access Location Use N Mean SD P Own school to play Yes No Other school (to play) Yes No Public park Yes No Recreation center Yes No Swimming pool Yes No Gym of fitness center Yes No Aerobics or dance studio Yes No Beach, river, or stream Yes No Playground Yes No Table 6 Time Spent in Moderate-to-Vigorous Activity (MVPA) by Perceived and Objectively Measured Distance (Stratified by Access) Facility Walk time categories N Perceived access Mean MVPA (mins) SD N GIS measured access Mean MVPA (mins) Own school <5 min min min min min Other school <5 min min min min min Park <5 min min min min min SD (continued) 328

7 Perceived Versus Measured Access 329 Table 6 (continued) Facility Walk time categories N Perceived access Mean MVPA (mins) SD N GIS measured access Mean MVPA (mins) Rec center <5 min min min min min Swimming pool <5 min min min min min Gym of fitness <5 min min min min min Dance/aerobic <5 min min min min min Beach/river <5 min min min min min Playground <5 min min min min min SD Discussion Using conventional cut-off values, the level of agreement between our objective (GIS) measure of access and adolescent s perceptions of access to physical activity locations and facilities ranged from poor to fair. Agreement was greatest (0.41) for access to the participant s own school and lowest for aerobics or dance studios (0.02). A sex effect was found, with generally greater levels of agreement for males than females These findings are consistent with previous research among Australian adult women 22 (Kappa = 0.02 to 0.39) and Scottish adults (Kappa 0.07 to 0.37). However, as highlighted by McIntyre et al 23 a degree of caution is

8 330 Maddison et al required when making assumptions about the equivalence between subjective and objective measures of access. In other words, one cannot assume that an individual s perceptions of access are a valid proxy for actual distance or vice versa. Although previous research studies have found a lack of correlation between GIS or observer measured and respondent self-reported access to facilities and resources, 21,38 40 only 1 has compared observed and self-reported access to the same, single resource using the same distance measure. 23 One would anticipate greater congruence when directly comparing the same location. In the current study we aimed to build upon previous research by examining agreement between objective and subjective measures of access to the same physical activity facilities. Although we asked participants to estimate the time to walk from their home to the closest facilities (as listed in the NEWS survey) and we used GIS to assess distance to the closest facility, we cannot be assured that these facilities were in fact the same. In addition, in the current study, 30 minutes or more was the longest walking distance category participants were given to recall. At 4 km/hr 30 minutes equates to 2 km in distance. In this study, beaches were on average more than 2 km distant; therefore beach access derived by GIS and perceived access were likely to disagree. The facilities or locations most frequently used included the participant s own school, public park, and playgrounds; however when distance was objectively measured, closer access was significantly associated with playground use only. While the Kappa indices between perceived and actual access were generally poor (except for participant s own school), the findings did not support a relation between closer access and reported use. Previous research has shown that perceived access was related to self-reported use of physical activity facilities. 24 The current study builds on previous research by providing an objective measure of access. Taken together these findings suggest that young people access a variety of locations for physical activity and distance is not always a barrier. These findings also suggest that understanding physical activity is not as simple as measuring access alone. Individual factors such as motivation and intentions to be active are also important. 20 We found no relations between time spent in accelerometer-measured MVPA and perceived or actual distance to facilities. Previous research has reported conflicting associations. 24 For example, a review 24 of environmental attributes of children s physical activity reported that in 8 out of 10 studies, a significant positive association was identified between the availability of recreation areas, or the presence of such areas in the vicinity of the home, and children s physical activity. Moreover, a significant positive association between the proximity of parks and playgrounds to the home and children s physical activity was found in 3 out of 5 studies. However most of the studies reviewed used self-reported (perception) measures of the environment, while only 6 of the 33 studies reviewed used GIS technology. Using GIS, self-report and direct observation, Jago et al 27 found that only observed sidewalk characteristics were associated with light intensity physical activity.there are several possibilities for our finding. First, there was a lack of statistical power due to a small sample size. Second, perceived or actual access to some facilities may not actually influence habitual physical activity in adolescents. Exercise facilities such as a gymnasium or recreation center although situated close to a participant s house, may not be used by adolescents and therefore does not contribute to their overall levels of MVPA. Third, in the current study we used accelerometry as an objective measure of overall physical activity; however it has been suggested 41 that context-specific behaviors such as walking need to be assessed if we are to understand the associations between the environment and physical activity. This may be particularly important for associations between environmental factors (eg, aesthetics, street connectivity, etc) that encourage walking; however perceived or actual access to other facilities such as gyms, recreation centers, etc, may also be associated with differences in intensity of, and overall physical activity levels, although our findings did not support this. The lack of agreement between self-reported and actual distance may have been related to the way distance was defined. Perceived distance was derived by calculating participant s self-reported time to walk from their home to the respective facility or location and then determining distance based on walking speeds of 4 and 5 km/hr. This was considered a reasonable walking speed for this age group; however it is possible that faster speeds such as 6 km/hr may have provided closer approximation to actual distance and therefore improved the Kappa index. Small improvements in the Kappa statistics were observed from 4 to 5 km/hr; however any subsequent increases would most likely be small. We also used the minimum distance via the road network to calculate the distance from participant s home to the respective facilities. This approach did not take into account the presence of absence of footpaths (or sidewalks), nor did it consider shortcuts (ie, walkways) that participant s might use. These factors individually or together may have affected the measurement and level of agreement. The findings of this study have implications for future research examining associations between access to neighborhood resources and health behaviors. Although our data supports previous research that self-reported access is not necessarily synonymous with actual access, the relationship between access and use (behavior) is more complex than measuring access alone. For physical activity, other factors such as perceived attractiveness and cost may better help to explain the behavior. Previous research has generally shown that the relations between environmental factors (such as perceived access, aesthetics, etc) have been small in magnitude. Psychosocial factors such as intentions to be active, attitude, social support, etc, have the potential to moderate or mediate the relations between the environment and physical activity. 20 Recently De-Bruijn 42 modeled individual and perceived environmental factors in addition to Theory of Planned Behavior variables among adolescents and

9 Perceived Versus Measured Access 331 showed perceived environmental aesthetics and distance to activity opportunities were indirectly related to adolescents intentions to be physically active. To understand how people interact with the environment we need to be cognizant of the complexity of human behavior and avoid simplistic research, analyses and proposed solutions. These findings also have implications for future interventions. For example, modifying peoples perceptions of access to physical activity may be a useful approach. Ball et al 22 recently suggested that lack of awareness of available facilities was a possible explanation for the mismatch between perceived and objectively assessed environments in Australian females. Given the expense and difficulties associated with retro-fitting environments, increasing people s awareness of what is available through the use of tailored messages, with the inclusion of efficacy or motivation-based information, may serve as a more cost-effective approach. This might be supported by an education campaign showing people exactly how close facilities are and promoting them in other ways. However, given that individual perceptions within a single environment vary markedly, it may be difficult for people to see the relevance of generic education, and tailored information strategies may be more appropriate. Conclusion Poor to fair agreement between perceived and actual access to neighborhood resources exists for New Zealand urban adolescents. Closer proximity to resources was associated with greater playground use only. Perceived and GIS measured access was not related to moderateto-vigorous intensity physical activity. To improve our understanding of physical activity, further research is needed to understand how individual and social factors interact with environmental factors (such as aesthetics and cost). Also interventions are required to determine whether improving awareness of environmental features improves people s perceptions and use. Notes 1. The role at the first school was 2246 students (53% boys) with mixed ethnicity (NZ European 32%, NZ Maori 4%, Pacific 20%, and Asian 18%). The role at the second school was 1430 students (81% boys), with NZ European 37%, NZ Maori 5%, Pacific 25%, and Asian 17%. 2. Two approaches were used to determine a registered recreational facility. With APNFinda, a business is either allocated a standard industrial code (SIC) for which the business relates to, or is allocated a historical classification. ACC/ARC/ARPHs data are based on an asset management system, and defined by the organization according to usage (or intended usage). It is important to note that local council databases are based on the asset system and defined facilities according to likely use, whereas the sports organizations or businesses that used the sites were registered businesses. Acknowledgments The authors wish to thank the Health Research Council and National Heart Foundation of New Zealand for funding the ICAN study. Dr Maddison is also supported by a National Heart Foundation Fellowship. We are extremely grateful to the teachers and students for their participation in this study. The ICAN team would like to acknowledge the input of Enid Dorey, Maria Turley, and Chris Bullen during this study. References 1. WHO Health and Welfare Canada CPHA. Ottawa Charter for Health Promotion. Ottawa: World Health Organization, Health and Welfare Canada, Canadian Public Health Association; International Obesity Taskforce and European Association for the Study of Obesity. Obesity in Europe. The case for action. 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