499459 2013
SJP41810.1177/1403494813499459R. L. Storgaard et al.Short Title
Scandinavian Journal of Public Health, 2013; 41: 846–852
Original Article
Association between neighbourhood green space and sedentary leisure time in a Danish population
Rikke Lynge Storgaard1,2, Henning Sten Hansen2, Mette Aadahl1 & CHARLOTTE GLÜMER1,2,3 1Research Centre for Prevention and Health, The Capital Region of Denmark, Glostrup, Denmark, 2Department of Development and Planning, Aalborg University Copenhagen, Copenhagen SV, Denmark, and 3Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
Abstract Aim: Sedentary behaviour is a risk factor for diabetes, cardiovascular disease etc., independently of level of physical activity. Availability of recreational green space is associated with physical activity, but is unknown in relation to sedentary behaviour. The aim of this study is to examine the association between availability of green space and sedentary leisure time in a Danish population. Methods: The study was based on a random sample of 49,806 adults aged 16 + who answered a questionnaire in 2010, including sedentary leisure time. Objective measures of density green were calculated for each respondent using Geographical Information System (GIS). A multilevel regression analysis, taking neighbourhood and individual factors into account, was performed. Results: 65% of the respondents were sedentary in leisure time for more than 3h/day. We found that poor availability of forest and recreational facilities in the neighbourhood is associated with more sedentary leisure time; OR: 1.11 (95% CL: 1.04–1.19), after adjusting for individual, and neighbourhood, level characteristics. Conclusions: Among adult inhabitants, sedentary leisure time of more than 3h/day was more frequent in neighbourhoods with less green surroundings. Intervention efforts may benefit from emphasising the importance of having recreations options in residential areas to provide alternatives to sedentary activities. Key Words: Behavioral epidemiology, Geographical Information System (GIS), green space, multilevel regression, sedentary behavior
Introduction Over the past decade, the health effect of prolonged sitting during leisure time, e.g. while watching TV, using a computer or driving a car, has been studied in, addition to the effect of “too little exercise” [1]. Prospective observational studies have established that sedentary leisure time behaviour is a risk factor for diabetes, cardiovascular disease and all-cause mortality, independently of level of physical activity [2–5]. Studies confirm that sedentary behaviour cannot be seen as the simple absence of physical activity, and adults who meet the guidelines for physical activity still spend prolonged hours being sedentary during leisure time [6,7]. Therefore, there is a need for understanding the determinants of sedentary behaviour, in particular
the role of the environment, since sedentary behaviour may be influenced and shaped by the contextual attributes of the communities we live in, as outlined in the behavioural epidemiology framework by Sallis and Owen (2008). This knowledge is crucial for tailoring and targeting future environmental community-based interventions [1,8]. Many studies found accessibility to green space to be associated with leisure-time physical activity. In general, although not always, greater density of and closer proximity to green space is associated with more physical activity in adults [9,10]. Studies from Denmark found self-reported distance to green space to be negatively associated with
Correspondence: Rikke Lynge Storgaard, Aalborg University, A C Meyers Vaenge 15, Copenhagen SV, 2450, Denmark. E-mail: [email protected] (Accepted 3 July 2013) © 2013 the Nordic Societies of Public Health DOI: 10.1177/1403494813499459
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Association between neighbourhood green space and sedentary leisure time in a Danish population 847 self-reported physical activity, quality of life and selfrated health, and positively associated with perceived stress [11,12]. Whether a similar association exists in relation to sedentary leisure time is unknown in a Danish context. Studies from Australia, Belgium and the USA have analysed the association between a variety of perceived neighbourhood environment attributes and sedentary time [6,13–15]. Findings from 2012 revealed consistent associations between the environmental characteristics and sedentary time in motorised transportation, whereas the association with over-all sitting time, including leisure and occupation, was less pronounced [15]. Whether availability of green space has an independent effect on sedentary leisure-time behaviour is difficult to assess since the weighty research from the three countries is based on indexes from multiple environmental conditions, and has not specifically studied the relationship between availability of green space and sedentary leisure-time behaviour. By identifying environmental determinants of sedentary behaviour we may inform and help public health politicians and planners to designate and establish targeted interventions that can reduce this behaviour. This study aims to examine the association between objective measures of “green space availability” and sedentary leisure time in a Danish population. Method Study population The study population consists of participants in a cross-sectional health survey of adults aged 16 years and over in the Capital Region of Denmark in 2010. The survey is part of a national health survey used by politicians and health planners to identify the main health related topic of their region [16]. The capital region is 1973 km² and has 1.7 million inhabitants (1/3 of the total Danish population) living in urban, suburban and rural areas. A random sampling design stratified by municipality was used. Using computergenerated random numbers; samples of residents were drawn from the Danish Civil Registration System in which each inhabitant in Denmark is registered by a unique permanent registration number. The total sample included 95,150 individuals. They were invited by mail to participate in a health survey. The participants could fill out an enclosed paper questionnaire or complete the questionnaire online. The response rate was 52.3% (N = 49,806). The survey was carried out between February and April 2010, and included information on the self-reported health status, health behaviour and sociodemographic characteristics.
Variables Sedentary behaviour. The outcome considered was the level of sedentary time during leisure time (hours and minutes per day). Sedentary time was selfreported by replying to the question: “In your leisure time, how many hours and minutes per day, do you watch television, sit down and relax, read or listen to music etc.?” [17]. The response was used as a continuous variable (h/day) or dichotomised at 3 h/day, corresponding to the median of the outcome. The median was chosen as cut-point, since the literature does not designate any one specific cut-point, and 3 hours of sedentary leisure time sitting per day appear to be associated with substantial adverse health outcomes [2,3]. Green space availability. Each respondent’s address were geocoded in Geographic Information System – ArcGIS 10.0 and a 1 km and a 2.5 km buffer from each home were chosen as the geographical units that reflected a realistic walking and cycling distance of approximately 15 minutes between home and destination. Individual level availability of green space was considered to be the primary explanatory variable. Information about forest (defined as areas larger than 2,500 m² planted with trees) and recreational areas (defined as areas larger than 2,500 m² used for recreational purposes like park and playgrounds) was gathered from an existing geographic database provided by the National Survey of Cadastre. These data were from 2010 and corresponded to the collection period for the health care survey. The location of destination addresses were determined using GIS, and forest and recreational areas were merged together to create a “green space” variable. The density of green space was also calculated using GIS and estimated by calculating the ratio between the total area of green space within the chosen buffer for each respondent, and the total area of the buffer, multiplied by 100 (Figure 1). The density at a radius of 1 km ranges from 0.1% to 100% (people living in a forest) and was categorised into quartiles. The density at 2.5 km ranges from 0.4% to 92% and was categorised into quartiles. The categorisation was carried out in order to ensure that there were an approximately equal number of respondents in each group. Definition of neighbourhood. Parishes, the smallest official administrative units in Denmark, were chosen as the neighbourhood level of the analysis. We believe that individuals living in the same neighbourhood are not independent, and therefore we wish to account for the clustering effect of the neighbourhood on individual sedentary behaviour. A measure of residential
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848 R. L. Storgaard et al.
Figure 1. Map of the principle behind the availability measure; including respondents, 1 km and 2.5 km distance zones from home and green areas.
density derived from parish is a proxy for urbanity and included as a continuous variable in the analyses. The Capital Region of Denmark consists of urban, suburban and rural areas. By including this variable, it is possible to account for the differences between these kinds of areas, both in relation to the different amounts of green space that are available, but also in relation to the different norm, values, lifestyle, etc., that we expect can be found between the areas. There are 223 parishes in the region. Residential density consists of the number of residents per km² at parish level, ranging from 36–33,704 habitants/km2. Individual level characteristics. Self-reported sociodemographic variables included age, gender, educational level and employment status. Educational attainment was categorised into four groups: 1) no vocational education, 2) vocational education (short), 3) professional and academy programmes (medium length) and 4) university degree. Employment status was categorised into three groups: 1) employed, 2) in education and 3) unemployed. Statistical analyses A multilevel model was constructed to account for the clustering effect of neighbourhood on individual sedentary leisure time. The association between the amount of green space and sedentary behaviour, all as continuous variables, was analysed by multilevel linear regression using the SAS MIXED procedure
(version 9.3, SAS Institute, Cary, NC, USA). The association between green density and the odds of being sedentary equal to and more than 3 hours/day during leisure time was analysed with multilevel logistic regression, using a GLIMMIX procedure. A two-level model was fitted, with individuals (Level 1, n = 48,192) nested within parishes (Level 2, n = 223), and three models were fitted to determine the association. First “an empty model” was estimated, allowing detection of whether there is a contextual dimension of sedentary behaviour, to which the parish level was applied. To quantify the contextual dimension of sedentary behaviour, the Intra Class Correlation (ICC) was calculated [18]. ICC represents the proportion of the unexplained variance of the dependent variable (Level 1), that can be explained by the neighbourhoods (Level 2). ICC ranges from 0% to 100%. If ICC is close to 100%, it indicates a large homogeneity of outcome within the neighbourhoods, and that the context has a great effect on the dependent variable – and vice versa. We also calculated the unadjusted and adjusted ICC in the three models. Model 1 was the unadjusted model only, including the primary explanatory green space availability variable. In model 2, individual characteristics such as age, gender, educational level and employment status were included, and finally, in model 3, the neighbourhood level residential density was added. The results from the analysis were presented as odds ratios (OR) with 95% confidence intervals, which estimate the odds of being sedentary more than and equal to 3 h/day during leisure time. In order to examine how the relationship differed among subgroups, we explored whether there was statistically significant interaction between the green space explanatory variable and the covariates age, gender, education and employment status. Results Table I shows the characteristics of the population sample and the mean sedentary time within subgroups. After excluding missing values, a sample of 48,192 respondents from 223 parishes was analysed. The sample consisted of 56% women and the mean age was 50 years (SD = 17.9). Approximately half the sample was employed and one-third retired, 37% had a short education, while 18.5% had a university degree. The mean sedentary leisure time for the entire population was 3.51 hours (SD = 2.15), and ranged from 0 to 16 hours. Of the respondents, 65% were sedentary ≥ 3h/day. Men reported more sedentary time than women, and the duration of sedentary time increased with age, except among the youngest
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Association between neighbourhood green space and sedentary leisure time in a Danish population 849 Table I. Distribution of individual covariates and outcome measures of the study population.
Total sample Gender Female Male Age 16–24 25–34 35–44 45–54 55–64 65–79 80+ Education No vocational education Short education Medium length education University degree Employment status Employed Under education Unemployed/retired early/retired
N
%
Mean sedentary time (SD)
P value
48,192
100
3.51 (2.15)
27,057 21,135
56 44
3.42 (2.12) 3.63 (2.20)
< 0.0001
5089 6050 8397 8692 9055 8915 1994
10.5 12.5 17.5 18 19 18.5
3.64 (2.10) 3.09 (1.78) 2.76 (1.68) 3.21 (1.87) 3.74 (2.11) 4.33 (2.48) 4.18 (3.07)
< 0.0001
2118 17,817 19,370 8887
4.5 37 40 18.5
3.67 (2.10) 3.96 (2.47) 3.75 (2.13) 3.09 (1.88)
< 0.0001
27754 4822 15616
57.5 10 32.5
2.98 (1.52) 3.47 (1.95) 4.51 (2.72)
< 0.0001
Note. The p value is the probability value of a Student t test, testing whether the mean values of the different groups of the covariates are significantly different. The outcome variable is normally-distributed. Table II. Multilevel linear regression analysis of the association between sedentary leisure time and percentage green in a 1 km and 2.5 km radius from home.
1 km buffer around home 2.5 km buffer around home
Estimate
P value
−0,002 −0,003
< 0.0001 0.0001
Note. Outcome and primary explanatory variables are included as continuous variables. Association are adjusted for all socio-demographic covariates and neighbourhood residential density. 95% confidence intervals.
respondents, who reported more sedentary time than the middle-age groups. Respondents with a short education had the highest reported sedentary time compared with other education levels, and employed respondents reported the lowest sedentary time compared to those of other employment status. From the multilevel linear regression analyses on the association between density of green space and daily sedentary time during leisure time, we found a small, but significant inverse relationship between availability of green space and sedentary time, after adjustment for individual and neighbourhood level characteristics. The estimates are shown in Table II, and can be interpreted as around 1.5 minute-decreases in sedentary time for every 10% increase in green density. Table III shows the results from the multilevel logistic regression analyses and the odds of being sedentary ≥ 3 h/day during leisure time, in relation to
the density of green space calculated for the two distance zones (1 km and 2.5 km), and categorised in quartiles. From the empty model we found a statistical significant ICC of 1.50% (p < 0.0001). The ICC became 1.50%–1.35% (1–2.5 km) and when including the explanatory variable, to 0.51%–0.45% after adjusting for individual and neighbourhood covariates (Table III). The unadjusted model (model 1) shows that a green space density less than 5.4% in 1 km radius and 8.6% within 1–2.5 km was associated with more sedentary behaviour OR = 1.13 (1.05–1.21) and OR = 1.10 (1.02–1.20) compared to the reference “good availability with a green space density more than 20%”. Good availability of green space is related to smaller odds of being sedentary for more than 3 hours a day during leisure time. This picture remains after adjustment for individual characteristics in model 2 and after inclusion of the neighbourhood level covariate (model 3). The results from the interaction analyses show a less pronounced significant interaction between individual employment status and the density of green space in 1 km (p = 0.02) and 2.5 km (p = 0.09) in relation to sedentary time. Green space availability is more strongly associated with leisure sedentary time among unemployed individuals and individuals in education, compared to employed individuals and educated individuals.
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850 R. L. Storgaard et al. Table III. Multilevel logistic regression analysis of the odds of being sedentary during leisure time by availability of green space. The odds (OR) of being sedentary during leisure time by availability of green space.
Model 1: includes explanatory variable
Density (%) green in a 1 km radius 1 (good availability) 1 2 1.08 (1.01–1.16) 3 1.02 (0.95–1.09) 4 (poor availability) 1.13 (1.05–1.21) P value (fixed effect, ref: 1) 0.002 ICC 1.50 Density (%) green in a 2.5 km radius 1 (good availability) 1 2 1.10 (1.02–1.19) 3 1.08 (0.99–1.17) 4 (poor availability) 1.10 (1.02–1.20) P value (fixed effect, ref: 1) 0.07 ICC 1.35
Model 2: model 1 + age, gender, education and employment status
Model 3: model 2 + population density
1 1.11 (1.04–1.18) 1.03 (0.97–1.11) 1.11 (1.04–1.19) 0.003 0.51
1 1.10 (1.03–1.17) 1.03 (0.96–1.10) 1.11 (1.04–1.19) 0.005 0.51
1 1.15 (1.07–1.23) 1.15 (1.07–1.24) 1.09 (1.02–1.18) 0.0003 0.45
1 1.13 (1.05–1.22) 1.13 (1.04–1.22) 1.08 (1.01–1.17) 0.006 0.45
Note. The outcome considered is dichotomized at 3 hours/day, giving a 0 to those who were sedentary < 3h/day, and a 1 for those who were sedentary for ≥3 h/day. The green space variable is categorised in quartiles. Good availability in 1 km radius = 20%–100%, poor availability in 1 km radius = 0.14%–5.4%. Good availability in 2.5 km radius = 19.8%–92%, poor availability in 2.5 km radius = 0.35%–8.6%. 95 % confidence interval. P value from a Type III test with quartile 1 as fixed effect.
In the analyses, we did not account for leisure physical activity. Leisure sedentary behaviour and leisure physical activity are considered to be two different kinds of behaviours that are independent. Preliminary analyses of our data confirmed that the association in question remained significant after further adjusting for leisure time physical activity (hours and min/day on moderate level PA). Discussion In the present study, we found consistent associations between sedentary behaviour during leisure time and objective measures of availability of green space in the nearby neighbourhood. The results demonstrate that having good availability of forest, parks and other recreational areas in walking and biking distance from home is associated with less sedentary activities during leisure time, after adjusting for individual and neighbourhood level characteristics. The results also showed that unemployed people, including early-retired and retired individuals, are more influenced by availability of green space, compared to employed individuals. The findings saying that there are conditions in the surroundings that can get people away from indoor non-sedentary activities. If the neighbourhood encourage outdoor activities, like the opportunity to visit a green area nearby, we find less leisure sedentary activities. Our research approach is based on the behavioural epidemiology framework by Sallis and Owen (2008) [1,8], who stresses that sedentary
behaviour are influenced and shaped by the contextual attributes of the community we live in [8]. The fact that sedentary behaviour is affected by the physical attributes of a neighbourhood is found in other studies. One study found that women living in medium- and highly walkable areas reported less TV-viewing per day, after controlling for neighbourhood SES, BMI, PA and socio-demographic variables [6]. Another revealed that less walkable neighbourhoods were related to more driving time and TV viewing among adults [13]. A cross-country study, with data from Belgium, Australia and USA, have analysed the association between sedentary behaviour and a variety of perceived neighbourhood environment attributes integrated in multiple indexes [15]. The results of the findings by Van Dyck et al. confirm our findings, saying that there is a negative association between average daily sitting time, both leisure and occupational, and access to services including parks and fitness facilities [15]. Additionally, there is an increasingly number of studies that examine adolescent sedentary time in relation to the importance of green space and outdoor play areas. They reveal that the opportunity to go to a park etc. nearby gives rise to less sedentary time among youth [19,20]. Unemployed people, including early-retired and retired individuals, are more influenced by availability of recreational areas, compared to employed individuals. The immediate reason for the stronger association for unemployed may be that they spend longer
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Association between neighbourhood green space and sedentary leisure time in a Danish population 851 time in their neighbourhoods, compared to employed adults who spend much time in the workplace. In this study availability of green space was calculated in GIS by combining the total area of forests and recreational parks within a geographical unit. This measure is widely used [21]. The radius was drawn with a straight line from respondent’s homes, to create the geographical unit. We assume that these distances reflect a neighbourhood unit of walking and biking distance of approximately 15 minutes from home. Using this approach, we have equal sizes and surface area units as a basis for calculating the percentage of green space. If we had used, for example, an administrative unit or a radius buffer drawn on the road network, we would have received unequal size and area units, and the comparison of availability would have been affected by the size of each unit, more than the actual amount of green space. In future research into how context affects health behaviour in a Danish setting, it will be relevant to discuss the use of this geographical unit and how “neighbourhoods” are defined. Neighbourhoods can be defined in many different ways and must reflect the purpose (the health outcome) of the study. Neighbourhood defined by people’s perception of the social meaning of “space” may be relevant when looking at social interactions in relation to behaviour. The use of administrative units, such as municipalities, is relevant when including policy and intervention, and geographically-defined neighbourhoods are relevant when examining the importance of physical factors [22]. The strengths of our study include the objective environmental measure for “availability of green” instead of perceived measures; the large random sample of the Danish population with a variety of individual and neighbourhood level characteristics. In our study, we used objective geographical measures which reduce the risks of respondent bias, although self-reported measures could have provided us with information on perception of green space (safety, attractiveness). The green space variable was categorized in four quartiles, to make sure that there were an equal amount of respondents in each group. This approach gives us a crude green space variable that may not accurately capture the boundaries for individual sedentary behaviour. The health survey is one of the world’s largest, and is part of a national health survey used by politicians and health planners to identify the main health related topic of their region [16]. The survey is of a cross-sectional design where exposure and outcome refers to the same point in time. Therefore, it is not possible to comment on causality [23]. Nevertheless, the survey will be repeated every third year, and combined with the development in regional green space planning and policymaking it
can make a more causal perspective possible in further ecological analyses. A limitation of the study is that the data on sedentary time is self-reported and may be subject to information bias where respondents underreport level of sedentary leisure time [24]. However, even when valid objective measurement of sedentary behaviour is applied, additional self-report questions are necessary in order to obtain information on domains of sedentary behaviour, e.g. during leisure time or at work. Future studies should consider including accelerometers or inclinometers for objective measurement of sedentary behaviour and physical activity in addition to self-report questionnaires. Some may emphasise that it is a limitation that leisure physical activity was not assessed, although adjustment for leisure physical activity in this study did not remove the association between green space availability and sedentary leisure time. We assume that sedentary behaviour cannot be seen as the simple absence of physical activity. Studies confirm this by finding no association between the two behaviours; adults who meet the guidelines for physical activity still spend prolonged hours being sedentary during leisure time [6,7]. In future analyses, we might benefit from including leisure time physical activity in a mediation analysis, to see whether PA is a possible pathway between availability of green space and leisure sedentary time. Furthermore, it is relevant to examine the relationship between behaviours, and how leisure sedentary behaviour – among employed – are related to PA during active transport to and from work and occupational PA. In future research, we need to know more about which contextual attributes that characterise a nonsedentary leisure time community in a Danish setting, being a community that encourage people to do something else than, for example, watching TV. That is a place not only with opportunities to be active, i.e., walk, bike, participate in sports, but also a safe and attractive place where people can meet for social and cultural purposes. By identifying the contextual determinants of sedentary behaviour, we can help inform public health politicians and planners who develop and target future interventions aimed at reducing sedentary behaviour. This provides an opportunity to introduce a trans-disciplinary perspective to regional – and urban planning and policymaking in creating non-sedentary communities, and the relevance of structural interventions in the effort to improve public health [25,26]. Conclusion Our finding is that good availability of green spaces, e.g. forest, recreation, play grounds, etc., in the neighbourhood is associated with less sedentary leisure
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852 R. L. Storgaard et al. time, e.g. TV viewing. Intervention efforts may benefit from emphasising the importance of having recreations options in residential areas to provide alternatives to sedentary activities. This result suggests that the community should be a setting for environmental health promotion, and assigns responsibility to national, regional and local policy-makers and planners for maintaining public health. Conflicts of interest The authors declare that they have no competing interests. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. References [1] Owen N. Sedentary behaviour: understanding and influencing adults’ prolonged sitting time. Prev Med 2012;55:535– 539. [2] Wilmot EG, Edwardsom CL, Achana FA, et al. Sdentary time in adults and the association with diabetes, cardiovascular disease and death: systematic review and meta-analysis. Diabetologia 2012;55:2895–905. [3] Grøntved A and Frank HB. 2011. Television viewing and risk of type 2 diabetes, cardiovascular disease, and all-cause mortality, a meta-analysis. JAMA 2011;305:2448–55. [4] Wijndaele K, Brage S, Besson H, et al. Television viewing time indepently predicts all-cause and cardiovascular mortality: the EPIC Norfolk Study. Int J Epidemiol 2011;40:150– 59. [5] Dunstan DW, Barr ELM, Healy GN, et al. Television viewing time and mortality: the Australian diabetes, obesity and lifestyle study (AusDiab). Circulation 2010;26:384–91. [6] Sugiyama T, Salmon J, Dunstan DW, et al. Neighborhood walkability and TV viewing time among Australian adults. Am J Prev Med 2007;33:444–9. [7] Rhodes RE, Mark RS and Temmel CP. Adults sedentary behavior: a systematic review. Am J Prev Med 2012;42:3–28. [8] Sallis JF, Owen N and Fisher EB. Ecological models of health behavior. In: Glanz K, Rimer BK, Viswanath K. eds. Health behavior and health education: theory, research and practice. 4th ed. San Francisco, CA: Jossey-Bass, 2008, pp. 465–82. [9] Mozaffarian D, Afshin A, Benowitz NL, et al. Population approches to improve diet, physical activity, and smoking habits : a scientific statement from the heart association. Circulation 2012;126:1514–63.
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SJP41810.1177/1403494813499459R. L. Storgaard et al.Short Title
Scandinavian Journal of Public Health, 2013; 41: 846–852
Original Article
Association between neighbourhood green space and sedentary leisure time in a Danish population
Rikke Lynge Storgaard1,2, Henning Sten Hansen2, Mette Aadahl1 & CHARLOTTE GLÜMER1,2,3 1Research Centre for Prevention and Health, The Capital Region of Denmark, Glostrup, Denmark, 2Department of Development and Planning, Aalborg University Copenhagen, Copenhagen SV, Denmark, and 3Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
Abstract Aim: Sedentary behaviour is a risk factor for diabetes, cardiovascular disease etc., independently of level of physical activity. Availability of recreational green space is associated with physical activity, but is unknown in relation to sedentary behaviour. The aim of this study is to examine the association between availability of green space and sedentary leisure time in a Danish population. Methods: The study was based on a random sample of 49,806 adults aged 16 + who answered a questionnaire in 2010, including sedentary leisure time. Objective measures of density green were calculated for each respondent using Geographical Information System (GIS). A multilevel regression analysis, taking neighbourhood and individual factors into account, was performed. Results: 65% of the respondents were sedentary in leisure time for more than 3h/day. We found that poor availability of forest and recreational facilities in the neighbourhood is associated with more sedentary leisure time; OR: 1.11 (95% CL: 1.04–1.19), after adjusting for individual, and neighbourhood, level characteristics. Conclusions: Among adult inhabitants, sedentary leisure time of more than 3h/day was more frequent in neighbourhoods with less green surroundings. Intervention efforts may benefit from emphasising the importance of having recreations options in residential areas to provide alternatives to sedentary activities. Key Words: Behavioral epidemiology, Geographical Information System (GIS), green space, multilevel regression, sedentary behavior
Introduction Over the past decade, the health effect of prolonged sitting during leisure time, e.g. while watching TV, using a computer or driving a car, has been studied in, addition to the effect of “too little exercise” [1]. Prospective observational studies have established that sedentary leisure time behaviour is a risk factor for diabetes, cardiovascular disease and all-cause mortality, independently of level of physical activity [2–5]. Studies confirm that sedentary behaviour cannot be seen as the simple absence of physical activity, and adults who meet the guidelines for physical activity still spend prolonged hours being sedentary during leisure time [6,7]. Therefore, there is a need for understanding the determinants of sedentary behaviour, in particular
the role of the environment, since sedentary behaviour may be influenced and shaped by the contextual attributes of the communities we live in, as outlined in the behavioural epidemiology framework by Sallis and Owen (2008). This knowledge is crucial for tailoring and targeting future environmental community-based interventions [1,8]. Many studies found accessibility to green space to be associated with leisure-time physical activity. In general, although not always, greater density of and closer proximity to green space is associated with more physical activity in adults [9,10]. Studies from Denmark found self-reported distance to green space to be negatively associated with
Correspondence: Rikke Lynge Storgaard, Aalborg University, A C Meyers Vaenge 15, Copenhagen SV, 2450, Denmark. E-mail: [email protected] (Accepted 3 July 2013) © 2013 the Nordic Societies of Public Health DOI: 10.1177/1403494813499459
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Association between neighbourhood green space and sedentary leisure time in a Danish population 847 self-reported physical activity, quality of life and selfrated health, and positively associated with perceived stress [11,12]. Whether a similar association exists in relation to sedentary leisure time is unknown in a Danish context. Studies from Australia, Belgium and the USA have analysed the association between a variety of perceived neighbourhood environment attributes and sedentary time [6,13–15]. Findings from 2012 revealed consistent associations between the environmental characteristics and sedentary time in motorised transportation, whereas the association with over-all sitting time, including leisure and occupation, was less pronounced [15]. Whether availability of green space has an independent effect on sedentary leisure-time behaviour is difficult to assess since the weighty research from the three countries is based on indexes from multiple environmental conditions, and has not specifically studied the relationship between availability of green space and sedentary leisure-time behaviour. By identifying environmental determinants of sedentary behaviour we may inform and help public health politicians and planners to designate and establish targeted interventions that can reduce this behaviour. This study aims to examine the association between objective measures of “green space availability” and sedentary leisure time in a Danish population. Method Study population The study population consists of participants in a cross-sectional health survey of adults aged 16 years and over in the Capital Region of Denmark in 2010. The survey is part of a national health survey used by politicians and health planners to identify the main health related topic of their region [16]. The capital region is 1973 km² and has 1.7 million inhabitants (1/3 of the total Danish population) living in urban, suburban and rural areas. A random sampling design stratified by municipality was used. Using computergenerated random numbers; samples of residents were drawn from the Danish Civil Registration System in which each inhabitant in Denmark is registered by a unique permanent registration number. The total sample included 95,150 individuals. They were invited by mail to participate in a health survey. The participants could fill out an enclosed paper questionnaire or complete the questionnaire online. The response rate was 52.3% (N = 49,806). The survey was carried out between February and April 2010, and included information on the self-reported health status, health behaviour and sociodemographic characteristics.
Variables Sedentary behaviour. The outcome considered was the level of sedentary time during leisure time (hours and minutes per day). Sedentary time was selfreported by replying to the question: “In your leisure time, how many hours and minutes per day, do you watch television, sit down and relax, read or listen to music etc.?” [17]. The response was used as a continuous variable (h/day) or dichotomised at 3 h/day, corresponding to the median of the outcome. The median was chosen as cut-point, since the literature does not designate any one specific cut-point, and 3 hours of sedentary leisure time sitting per day appear to be associated with substantial adverse health outcomes [2,3]. Green space availability. Each respondent’s address were geocoded in Geographic Information System – ArcGIS 10.0 and a 1 km and a 2.5 km buffer from each home were chosen as the geographical units that reflected a realistic walking and cycling distance of approximately 15 minutes between home and destination. Individual level availability of green space was considered to be the primary explanatory variable. Information about forest (defined as areas larger than 2,500 m² planted with trees) and recreational areas (defined as areas larger than 2,500 m² used for recreational purposes like park and playgrounds) was gathered from an existing geographic database provided by the National Survey of Cadastre. These data were from 2010 and corresponded to the collection period for the health care survey. The location of destination addresses were determined using GIS, and forest and recreational areas were merged together to create a “green space” variable. The density of green space was also calculated using GIS and estimated by calculating the ratio between the total area of green space within the chosen buffer for each respondent, and the total area of the buffer, multiplied by 100 (Figure 1). The density at a radius of 1 km ranges from 0.1% to 100% (people living in a forest) and was categorised into quartiles. The density at 2.5 km ranges from 0.4% to 92% and was categorised into quartiles. The categorisation was carried out in order to ensure that there were an approximately equal number of respondents in each group. Definition of neighbourhood. Parishes, the smallest official administrative units in Denmark, were chosen as the neighbourhood level of the analysis. We believe that individuals living in the same neighbourhood are not independent, and therefore we wish to account for the clustering effect of the neighbourhood on individual sedentary behaviour. A measure of residential
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848 R. L. Storgaard et al.
Figure 1. Map of the principle behind the availability measure; including respondents, 1 km and 2.5 km distance zones from home and green areas.
density derived from parish is a proxy for urbanity and included as a continuous variable in the analyses. The Capital Region of Denmark consists of urban, suburban and rural areas. By including this variable, it is possible to account for the differences between these kinds of areas, both in relation to the different amounts of green space that are available, but also in relation to the different norm, values, lifestyle, etc., that we expect can be found between the areas. There are 223 parishes in the region. Residential density consists of the number of residents per km² at parish level, ranging from 36–33,704 habitants/km2. Individual level characteristics. Self-reported sociodemographic variables included age, gender, educational level and employment status. Educational attainment was categorised into four groups: 1) no vocational education, 2) vocational education (short), 3) professional and academy programmes (medium length) and 4) university degree. Employment status was categorised into three groups: 1) employed, 2) in education and 3) unemployed. Statistical analyses A multilevel model was constructed to account for the clustering effect of neighbourhood on individual sedentary leisure time. The association between the amount of green space and sedentary behaviour, all as continuous variables, was analysed by multilevel linear regression using the SAS MIXED procedure
(version 9.3, SAS Institute, Cary, NC, USA). The association between green density and the odds of being sedentary equal to and more than 3 hours/day during leisure time was analysed with multilevel logistic regression, using a GLIMMIX procedure. A two-level model was fitted, with individuals (Level 1, n = 48,192) nested within parishes (Level 2, n = 223), and three models were fitted to determine the association. First “an empty model” was estimated, allowing detection of whether there is a contextual dimension of sedentary behaviour, to which the parish level was applied. To quantify the contextual dimension of sedentary behaviour, the Intra Class Correlation (ICC) was calculated [18]. ICC represents the proportion of the unexplained variance of the dependent variable (Level 1), that can be explained by the neighbourhoods (Level 2). ICC ranges from 0% to 100%. If ICC is close to 100%, it indicates a large homogeneity of outcome within the neighbourhoods, and that the context has a great effect on the dependent variable – and vice versa. We also calculated the unadjusted and adjusted ICC in the three models. Model 1 was the unadjusted model only, including the primary explanatory green space availability variable. In model 2, individual characteristics such as age, gender, educational level and employment status were included, and finally, in model 3, the neighbourhood level residential density was added. The results from the analysis were presented as odds ratios (OR) with 95% confidence intervals, which estimate the odds of being sedentary more than and equal to 3 h/day during leisure time. In order to examine how the relationship differed among subgroups, we explored whether there was statistically significant interaction between the green space explanatory variable and the covariates age, gender, education and employment status. Results Table I shows the characteristics of the population sample and the mean sedentary time within subgroups. After excluding missing values, a sample of 48,192 respondents from 223 parishes was analysed. The sample consisted of 56% women and the mean age was 50 years (SD = 17.9). Approximately half the sample was employed and one-third retired, 37% had a short education, while 18.5% had a university degree. The mean sedentary leisure time for the entire population was 3.51 hours (SD = 2.15), and ranged from 0 to 16 hours. Of the respondents, 65% were sedentary ≥ 3h/day. Men reported more sedentary time than women, and the duration of sedentary time increased with age, except among the youngest
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Association between neighbourhood green space and sedentary leisure time in a Danish population 849 Table I. Distribution of individual covariates and outcome measures of the study population.
Total sample Gender Female Male Age 16–24 25–34 35–44 45–54 55–64 65–79 80+ Education No vocational education Short education Medium length education University degree Employment status Employed Under education Unemployed/retired early/retired
N
%
Mean sedentary time (SD)
P value
48,192
100
3.51 (2.15)
27,057 21,135
56 44
3.42 (2.12) 3.63 (2.20)
< 0.0001
5089 6050 8397 8692 9055 8915 1994
10.5 12.5 17.5 18 19 18.5
3.64 (2.10) 3.09 (1.78) 2.76 (1.68) 3.21 (1.87) 3.74 (2.11) 4.33 (2.48) 4.18 (3.07)
< 0.0001
2118 17,817 19,370 8887
4.5 37 40 18.5
3.67 (2.10) 3.96 (2.47) 3.75 (2.13) 3.09 (1.88)
< 0.0001
27754 4822 15616
57.5 10 32.5
2.98 (1.52) 3.47 (1.95) 4.51 (2.72)
< 0.0001
Note. The p value is the probability value of a Student t test, testing whether the mean values of the different groups of the covariates are significantly different. The outcome variable is normally-distributed. Table II. Multilevel linear regression analysis of the association between sedentary leisure time and percentage green in a 1 km and 2.5 km radius from home.
1 km buffer around home 2.5 km buffer around home
Estimate
P value
−0,002 −0,003
< 0.0001 0.0001
Note. Outcome and primary explanatory variables are included as continuous variables. Association are adjusted for all socio-demographic covariates and neighbourhood residential density. 95% confidence intervals.
respondents, who reported more sedentary time than the middle-age groups. Respondents with a short education had the highest reported sedentary time compared with other education levels, and employed respondents reported the lowest sedentary time compared to those of other employment status. From the multilevel linear regression analyses on the association between density of green space and daily sedentary time during leisure time, we found a small, but significant inverse relationship between availability of green space and sedentary time, after adjustment for individual and neighbourhood level characteristics. The estimates are shown in Table II, and can be interpreted as around 1.5 minute-decreases in sedentary time for every 10% increase in green density. Table III shows the results from the multilevel logistic regression analyses and the odds of being sedentary ≥ 3 h/day during leisure time, in relation to
the density of green space calculated for the two distance zones (1 km and 2.5 km), and categorised in quartiles. From the empty model we found a statistical significant ICC of 1.50% (p < 0.0001). The ICC became 1.50%–1.35% (1–2.5 km) and when including the explanatory variable, to 0.51%–0.45% after adjusting for individual and neighbourhood covariates (Table III). The unadjusted model (model 1) shows that a green space density less than 5.4% in 1 km radius and 8.6% within 1–2.5 km was associated with more sedentary behaviour OR = 1.13 (1.05–1.21) and OR = 1.10 (1.02–1.20) compared to the reference “good availability with a green space density more than 20%”. Good availability of green space is related to smaller odds of being sedentary for more than 3 hours a day during leisure time. This picture remains after adjustment for individual characteristics in model 2 and after inclusion of the neighbourhood level covariate (model 3). The results from the interaction analyses show a less pronounced significant interaction between individual employment status and the density of green space in 1 km (p = 0.02) and 2.5 km (p = 0.09) in relation to sedentary time. Green space availability is more strongly associated with leisure sedentary time among unemployed individuals and individuals in education, compared to employed individuals and educated individuals.
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850 R. L. Storgaard et al. Table III. Multilevel logistic regression analysis of the odds of being sedentary during leisure time by availability of green space. The odds (OR) of being sedentary during leisure time by availability of green space.
Model 1: includes explanatory variable
Density (%) green in a 1 km radius 1 (good availability) 1 2 1.08 (1.01–1.16) 3 1.02 (0.95–1.09) 4 (poor availability) 1.13 (1.05–1.21) P value (fixed effect, ref: 1) 0.002 ICC 1.50 Density (%) green in a 2.5 km radius 1 (good availability) 1 2 1.10 (1.02–1.19) 3 1.08 (0.99–1.17) 4 (poor availability) 1.10 (1.02–1.20) P value (fixed effect, ref: 1) 0.07 ICC 1.35
Model 2: model 1 + age, gender, education and employment status
Model 3: model 2 + population density
1 1.11 (1.04–1.18) 1.03 (0.97–1.11) 1.11 (1.04–1.19) 0.003 0.51
1 1.10 (1.03–1.17) 1.03 (0.96–1.10) 1.11 (1.04–1.19) 0.005 0.51
1 1.15 (1.07–1.23) 1.15 (1.07–1.24) 1.09 (1.02–1.18) 0.0003 0.45
1 1.13 (1.05–1.22) 1.13 (1.04–1.22) 1.08 (1.01–1.17) 0.006 0.45
Note. The outcome considered is dichotomized at 3 hours/day, giving a 0 to those who were sedentary < 3h/day, and a 1 for those who were sedentary for ≥3 h/day. The green space variable is categorised in quartiles. Good availability in 1 km radius = 20%–100%, poor availability in 1 km radius = 0.14%–5.4%. Good availability in 2.5 km radius = 19.8%–92%, poor availability in 2.5 km radius = 0.35%–8.6%. 95 % confidence interval. P value from a Type III test with quartile 1 as fixed effect.
In the analyses, we did not account for leisure physical activity. Leisure sedentary behaviour and leisure physical activity are considered to be two different kinds of behaviours that are independent. Preliminary analyses of our data confirmed that the association in question remained significant after further adjusting for leisure time physical activity (hours and min/day on moderate level PA). Discussion In the present study, we found consistent associations between sedentary behaviour during leisure time and objective measures of availability of green space in the nearby neighbourhood. The results demonstrate that having good availability of forest, parks and other recreational areas in walking and biking distance from home is associated with less sedentary activities during leisure time, after adjusting for individual and neighbourhood level characteristics. The results also showed that unemployed people, including early-retired and retired individuals, are more influenced by availability of green space, compared to employed individuals. The findings saying that there are conditions in the surroundings that can get people away from indoor non-sedentary activities. If the neighbourhood encourage outdoor activities, like the opportunity to visit a green area nearby, we find less leisure sedentary activities. Our research approach is based on the behavioural epidemiology framework by Sallis and Owen (2008) [1,8], who stresses that sedentary
behaviour are influenced and shaped by the contextual attributes of the community we live in [8]. The fact that sedentary behaviour is affected by the physical attributes of a neighbourhood is found in other studies. One study found that women living in medium- and highly walkable areas reported less TV-viewing per day, after controlling for neighbourhood SES, BMI, PA and socio-demographic variables [6]. Another revealed that less walkable neighbourhoods were related to more driving time and TV viewing among adults [13]. A cross-country study, with data from Belgium, Australia and USA, have analysed the association between sedentary behaviour and a variety of perceived neighbourhood environment attributes integrated in multiple indexes [15]. The results of the findings by Van Dyck et al. confirm our findings, saying that there is a negative association between average daily sitting time, both leisure and occupational, and access to services including parks and fitness facilities [15]. Additionally, there is an increasingly number of studies that examine adolescent sedentary time in relation to the importance of green space and outdoor play areas. They reveal that the opportunity to go to a park etc. nearby gives rise to less sedentary time among youth [19,20]. Unemployed people, including early-retired and retired individuals, are more influenced by availability of recreational areas, compared to employed individuals. The immediate reason for the stronger association for unemployed may be that they spend longer
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Association between neighbourhood green space and sedentary leisure time in a Danish population 851 time in their neighbourhoods, compared to employed adults who spend much time in the workplace. In this study availability of green space was calculated in GIS by combining the total area of forests and recreational parks within a geographical unit. This measure is widely used [21]. The radius was drawn with a straight line from respondent’s homes, to create the geographical unit. We assume that these distances reflect a neighbourhood unit of walking and biking distance of approximately 15 minutes from home. Using this approach, we have equal sizes and surface area units as a basis for calculating the percentage of green space. If we had used, for example, an administrative unit or a radius buffer drawn on the road network, we would have received unequal size and area units, and the comparison of availability would have been affected by the size of each unit, more than the actual amount of green space. In future research into how context affects health behaviour in a Danish setting, it will be relevant to discuss the use of this geographical unit and how “neighbourhoods” are defined. Neighbourhoods can be defined in many different ways and must reflect the purpose (the health outcome) of the study. Neighbourhood defined by people’s perception of the social meaning of “space” may be relevant when looking at social interactions in relation to behaviour. The use of administrative units, such as municipalities, is relevant when including policy and intervention, and geographically-defined neighbourhoods are relevant when examining the importance of physical factors [22]. The strengths of our study include the objective environmental measure for “availability of green” instead of perceived measures; the large random sample of the Danish population with a variety of individual and neighbourhood level characteristics. In our study, we used objective geographical measures which reduce the risks of respondent bias, although self-reported measures could have provided us with information on perception of green space (safety, attractiveness). The green space variable was categorized in four quartiles, to make sure that there were an equal amount of respondents in each group. This approach gives us a crude green space variable that may not accurately capture the boundaries for individual sedentary behaviour. The health survey is one of the world’s largest, and is part of a national health survey used by politicians and health planners to identify the main health related topic of their region [16]. The survey is of a cross-sectional design where exposure and outcome refers to the same point in time. Therefore, it is not possible to comment on causality [23]. Nevertheless, the survey will be repeated every third year, and combined with the development in regional green space planning and policymaking it
can make a more causal perspective possible in further ecological analyses. A limitation of the study is that the data on sedentary time is self-reported and may be subject to information bias where respondents underreport level of sedentary leisure time [24]. However, even when valid objective measurement of sedentary behaviour is applied, additional self-report questions are necessary in order to obtain information on domains of sedentary behaviour, e.g. during leisure time or at work. Future studies should consider including accelerometers or inclinometers for objective measurement of sedentary behaviour and physical activity in addition to self-report questionnaires. Some may emphasise that it is a limitation that leisure physical activity was not assessed, although adjustment for leisure physical activity in this study did not remove the association between green space availability and sedentary leisure time. We assume that sedentary behaviour cannot be seen as the simple absence of physical activity. Studies confirm this by finding no association between the two behaviours; adults who meet the guidelines for physical activity still spend prolonged hours being sedentary during leisure time [6,7]. In future analyses, we might benefit from including leisure time physical activity in a mediation analysis, to see whether PA is a possible pathway between availability of green space and leisure sedentary time. Furthermore, it is relevant to examine the relationship between behaviours, and how leisure sedentary behaviour – among employed – are related to PA during active transport to and from work and occupational PA. In future research, we need to know more about which contextual attributes that characterise a nonsedentary leisure time community in a Danish setting, being a community that encourage people to do something else than, for example, watching TV. That is a place not only with opportunities to be active, i.e., walk, bike, participate in sports, but also a safe and attractive place where people can meet for social and cultural purposes. By identifying the contextual determinants of sedentary behaviour, we can help inform public health politicians and planners who develop and target future interventions aimed at reducing sedentary behaviour. This provides an opportunity to introduce a trans-disciplinary perspective to regional – and urban planning and policymaking in creating non-sedentary communities, and the relevance of structural interventions in the effort to improve public health [25,26]. Conclusion Our finding is that good availability of green spaces, e.g. forest, recreation, play grounds, etc., in the neighbourhood is associated with less sedentary leisure
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852 R. L. Storgaard et al. time, e.g. TV viewing. Intervention efforts may benefit from emphasising the importance of having recreations options in residential areas to provide alternatives to sedentary activities. This result suggests that the community should be a setting for environmental health promotion, and assigns responsibility to national, regional and local policy-makers and planners for maintaining public health. Conflicts of interest The authors declare that they have no competing interests. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. References [1] Owen N. Sedentary behaviour: understanding and influencing adults’ prolonged sitting time. Prev Med 2012;55:535– 539. [2] Wilmot EG, Edwardsom CL, Achana FA, et al. Sdentary time in adults and the association with diabetes, cardiovascular disease and death: systematic review and meta-analysis. Diabetologia 2012;55:2895–905. [3] Grøntved A and Frank HB. 2011. Television viewing and risk of type 2 diabetes, cardiovascular disease, and all-cause mortality, a meta-analysis. JAMA 2011;305:2448–55. [4] Wijndaele K, Brage S, Besson H, et al. Television viewing time indepently predicts all-cause and cardiovascular mortality: the EPIC Norfolk Study. Int J Epidemiol 2011;40:150– 59. [5] Dunstan DW, Barr ELM, Healy GN, et al. Television viewing time and mortality: the Australian diabetes, obesity and lifestyle study (AusDiab). Circulation 2010;26:384–91. [6] Sugiyama T, Salmon J, Dunstan DW, et al. Neighborhood walkability and TV viewing time among Australian adults. Am J Prev Med 2007;33:444–9. [7] Rhodes RE, Mark RS and Temmel CP. Adults sedentary behavior: a systematic review. Am J Prev Med 2012;42:3–28. [8] Sallis JF, Owen N and Fisher EB. Ecological models of health behavior. In: Glanz K, Rimer BK, Viswanath K. eds. Health behavior and health education: theory, research and practice. 4th ed. San Francisco, CA: Jossey-Bass, 2008, pp. 465–82. [9] Mozaffarian D, Afshin A, Benowitz NL, et al. Population approches to improve diet, physical activity, and smoking habits : a scientific statement from the heart association. Circulation 2012;126:1514–63.
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