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Obesogenic environments: Are neighbourhood environments that limit physical activity obesogenic?
ARTICLE IN PRESS Health & Place 15 (2009) 917–924
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Obesogenic environments: Are neighbourhood environments that limit physical activity obesogenic? N.M. Nelson a,, C.B. Woods b,1 a b
Department of Sport, Culture and the Arts, University of Strathclyde, Jordanhill Campus, 76 Southbrae Drive, Glasgow G13 1PP, Scotland, UK School of Health and Human Performance, Dublin City University, Collins Avenue, Dublin 9, Ireland
a r t i c l e in fo
abstract
Article history: Received 23 July 2008 Received in revised form 29 January 2009 Accepted 2 February 2009
The impact of obesogenic environments on adolescent health is poorly understood. This study examines if neighbourhood features related to physical activity are also related to unhealthy weight status. Adolescents (N ¼ 4587, age 15–17 years, 51.4% male) self-reported physical activity and neighbourhood perceptions. Trained researchers measured height and weight. Logistic regression identified if neighbourhood perceptions predicted overweight or obesity. Adolescents who reported convenient physical activity facilities were 2% less likely to be overweight/obese and 5% less likely to be obese, controlled for socio-demographic variables and clustering by school. Physical activity does not appear to directly influence or mediate the relationship between perceived convenient facilities and weight status. & 2009 Elsevier Ltd. All rights reserved.
Keywords: Obesity Neighbourhood environment Physical activity Convenient facilities Mediating factors
Introduction The prevalence of overweight and obesity is rapidly increasing worldwide, and is one of the most significant modern public health threats (World Health Organisation, 1998). Population surveys and secular trends suggest upward shifts in body weight in children, adolescents and adults (Wedderkopp et al., 2004; Freedman et al., 1997; Hedley et al., 2004; Booth et al., 2003). In a study of 13 European countries, Israel and the United States, the highest prevalence of overweight among adolescents was found in Ireland along with the United States, Greece and Portugal (Lissau et al., 2004). Therefore, from a public health perspective, it is important to monitor overweight in adolescence (Lissau et al., 2004) and identify the factors that influence its prevention and treatment. Obesity in children and adolescents has been associated with important chronic diseases such as diabetes, asthma, sleep apnea and gallbladder disease (Dietz, 1998; Must and Strauss, 1999) as well as psychosocial problems (Strauss, 2000) and reduced capacity for physical activity (Molnar and Livingstone, 2000). Adolescence is a critical period for the onset of obesity (Dietz, 1994) and for obesity-related morbidity in later life (Must et al.,
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E-mail addresses: [email protected] (N.M. Nelson), [email protected] (C.B. Woods). 1 Tel.: +353 17008008. 1353-8292/$ - see front matter & 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.healthplace.2009.02.001
1992) with 50–80% of obese teenagers becoming obese adults (Must and Strauss, 1999; Guo and Chumlea, 1999). Educational, behavioural and pharmacological interventions to target obesity have had limited success (Kayman et al., 1990) within environmental contexts that promote high energy intakes and sedentary lifestyles. The importance of creating supportive environments for health was recognised in the Ottawa Charter in 1986 (World Health Organisation, 1986), while more recent models have indicated that modifiable behaviours such as eating patterns and physical activity are key mediators for obesity (Glanz et al., 1995; King et al., 1997). Swinburn et al. (1999) have conceptualised an ecological framework to explain the interdependence among people, their health and their environment. The model includes four types of environments: physical, economic, political and sociocultural, across micro- (e.g. neighbourhoods, schools, etc.) and macrosettings (e.g. transport networks, health care systems, etc.) (Swinburn et al., 1999). Despite the recognition that the environment may be important in preventing and controlling obesity (Goran, 2001), the use of such environmental strategies remains underexplored (World Health Organisation, 1998). In order to design effective environmental interventions, research is first required into what features make an environment obesogenic. Much recent research has focused on the influence of neighbourhood environments on physical activity (Evenson et al., 2006; Carver et al., 2005); however, less attention has been paid to potential indirect or direct impacts on overweight or obesity, particularly among adolescents. A Portuguese study
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measured adolescents perceptions of access to destinations, connectivity of the street network, infrastructure for walking and cycling, aesthetics, recreation facilities and traffic density, and only traffic density was associated with increased odds of overweight among adolescent girls (Mota et al., 2006). In a similar Australian study, 12-year-old children, whose parents reported heavy traffic and concerns about road safety, were more likely to be overweight or obese (Timperio et al., 2005). Finally, in the US, one study found no relationship between community design variables and BMI percentile (Norman et al., 2006) while urban sprawl was associated with obesity in another (Ewing et al., 2006). The present paper investigates whether (i) perceptions of the neighbourhood environment vary by weight status, and (ii) neighbourhood perceptions shown to influence physical activity among adolescents are directly or indirectly related to weight status of 15–17-year-old adolescents in Ireland.
Methods Sample selection All data were collected as part of the Take PART study (Physical Activity Research in Teenagers). Take PART was a cross-sectional school-based study of participation levels, aerobic fitness, physical health indices, psychosocial and environmental determinants of physical activity in 15–17-year-old Irish adolescents using physical and survey measures. Detail on the rationale and methodology of Take PART is available elsewhere (Woods et al., 2009). Briefly, data were collected between February and May 2003–2005 using a one-stage cluster sample, stratified on school type, gender and school location (urban, rural). A total of 82 schools were selected, and 61 agreed to participate. Participants were eligible if they were aged 15–17 years, were not participating in state examinations and obtained parental consent if under 16 years, or provided their own consent if X16 years old. Standardized testing procedures were used throughout and extensive researcher training was undertaken to minimise potential sources of error in the physical measures and the administration of the questionnaire. Measures Physical measures Height and weight were measured using portable stadiometer (SECA 214, Hamburg, Germany) and scales (SECA 761, Vogel and Hallke, Germany) to the nearest 0.1 cm and 0.1 kg, respectively. Overweight and obese categories were based on international ageand gender-specific criteria (Cole et al., 2000). These criterions approximate adult values for overweight (25 kg/m2) and obesity (30 kg/m2). Survey measures Socio-demographic information: Parental occupation was obtained to determine socio-economic status; this is considered more accurate than asking youth to report family income (Currie et al., 2001). Area of residence was classified as (i) large city (4500,000 inhabitants), (ii) suburbs or outskirts of a city (o500,000 but 450,000), (iii) town (o50,000) or (iv) village (o5000) (Central Statistics Office Government of Ireland, 2002). Physical activity: Adolescents reported the number of days during the past week, and for a typical week, that they accumulated 60 min of moderate–vigorous physical activity. A composite average of these two scores was used to assess
achievement of guidelines for health-enhancing physical activity on all (Department of Health, 2004) or at least most (X4 days) of the week (Olds et al., 2007; Pate et al., 2002). Participants also completed the Self-Administered Physical Activity Checklist (Marshall et al., 2002; Sallis et al., 1996), selecting the time and number of bouts of leisure time physical activities (sport, dance, exercise and utilitarian activities) undertaken during the previous 7 days. These physical activity tools were reliable in test–retest (ICC ¼ 0.76 and 0.65, respectively) and the SAPAC correlated with accelerometer measured physical activity (r ¼ 0.48, po0.05); more detail available elsewhere (Woods et al., 2009). Neighbourhood environment: The neighbourhood environment was measured using a convenient facilities construct (Sallis et al., 1999) and the Neighbourhood Environment Walkability Scale (NEWS) (Saelens et al., 2003a, b). The convenient facilities scale listed 17 facilities for sports (e.g. running track or football field), exercise (e.g. gym or aerobic dance studio) and general/lifestyle physical activity (e.g. bicycle lane or public park). Respondents indicated if each facility listed was on a frequently travelled route (for example, to and from school) or within a 5-min or 10-min walk from their home. Response categories were yes (1) or no/ don’t know (0) resulting in possible score of 0–17 where higher scores reflected more convenient facilities. Adaptations consisted of wording changes for increased suitability to an Irish context, for example, public recreation centre was replaced with community centre. The original (ICC ¼ 0.80, Sallis et al., 1999) and adapted version in this study (ICC ¼ 0.60, (Woods et al., 2009) demonstrated acceptable test–retest reliability. The NEWS measures resident’s perceptions of neighbourhood characteristics thought to be related to the frequency of walking and cycling trips (Cerin et al., 2006). Designed for use in a US setting, the NEWS has since been adapted (mostly with language/ word changes to increase ecological validity) and used in Scotland (Fitzsimons et al., 2008), Australia (Cerin et al., 2008) and Hong Kong (Cerin et al., 2007) as well as this study. Examples of wording changes include replacing ‘sidewalks’ with ‘pathways’ or ‘paths’ and removing inappropriate geographical features such as ‘canyons’. Seven test–retest reliable NEWS subscales (Saelens et al., 2003a; Woods et al., 2009) were used in this study; four (facilities for walking and cycling, pedestrian/traffic safety, personal safety and aesthetics) were completed by all participants; however, the remaining three subscales were added to the protocol in 2005 and therefore were only completed by a subset of participants (n ¼ 2535). Table 1 displays each subscale, some item examples and scoring methodology. Items were reverse coded where necessary, and a composite score was created for each NEWS subscale (mean7SD), with a higher number indicating a more positive perception of the neighbourhood environment.
Data analysis Statistical analysis was undertaken in SPSS for Windows, version 14.0. Data are presented as means, standard deviations and proportions where appropriate. The incidence of (1) overweight or obesity, and (2) obesity was examined by socioeconomic status, gender and physical activity status using Pearson w2 tests and standardised residuals (Pett, 1997). Differences in leisure time physical activity and perceptions of the neighbourhood environment by weight status were examined using ANOVA with Bonferroni correction. Relevant effect sizes were calculated and reported as r-values. An r-value of 0.10, 0.30 and 0.50 represented small, medium and large effect sizes, respectively (Field, 2005).
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Table 1 NEWS subscales with examples of items and methodology. Subscale
Item examples
Responses
Scoring
Facilities for walking and cycling
There are pathways on most of the streets in my neighbourhood Pathways are separated from the road/traffic by parked cars
4-point Likert: SD to SA
Mean of 6 items. Range 6–24
Pedestrian/traffic safety (safety from traffic)
There are pedestrian crossings and signals to help walkers cross busy streets Most drivers exceed the speed limits while driving in my neighbourhood
4-point Likert: SD to SA
Mean of 8 items. Range 8–32
Personal safety (safety from crime)
My neighbourhood streets are well lit at night There is a high crime rate in my neighbourhood
4-point Likert: SD to SA
Mean of 7 items. Range 7–28
Neighbourhood surroundings (or aesthetics)
My neighbourhood is generally free from litter There are trees along the streets in my neighbourhood
4-point Likert: SD to SA
Mean of 6 items. Range 6–24
Street connectivity
The streets in my neighbourhood do not have many cul-de-sacs (dead end streets) There are many four-way intersections in my neighbourhood
4-point Likert: SD to SA
Mean of 5 items. Range 5–20
Perceived access (or land mix use access)
It is easy to walk to a bus or train stop from my home The streets in my neighbourhood are hilly, making my neighbourhood difficult to walk in
4-point Likert: SD to SA
Mean of 7 items. Range 7–28
Proximity to stores and facilities (or land use mix diversity)
How long would it take to get from your home to the nearest: supermarket, hardware shop, library, coffee place, etc.
1–5 min, 6–10 min, 11–20 min, 21–30 min, 31+min, don’t know
Mean of 20 items. Range 20–100
SD to SA: strongly disagree to strongly agree.
Logistic regression models examined the perceived features of the environment associated with (1) overweight or obesity, and (2) obesity. Unadjusted models were performed for each NEWS subscale, convenient facilities for physical activity, gender, age, population density and socio-economic status entered separately as independent variables. Items from the land use mix diversity subscale relating to proximity of food locations were also entered individually. Final adjusted models included gender, age, population density, socio-economic status and NEWS subscales with a statistically significant relationship in the unadjusted analyses. All analyses controlled for potential clustering at the school level. Finally, mediation analyses were carried out as described by Preacher and Hayes (2004) to determine if significant findings were due to direct effect of the independent variable on the outcome, or if physical activity acted as a mediator.
Results Sample characteristics From the total sample of 4720 participants, 4587 provided height and weight measurements and reported perceptions of walking facilities, safety, aesthetics and convenient facilities for physical activity. A subset of 2535 who reported perceptions of street connectivity, access and proximity to shops/amenities did not differ on participant characteristics, outlined in Table 2. Four percent of adolescents were classified as obese, and 15.6% as overweight. These rates are similar to previous nationally representative school-based research for Irish 15-year-old adolescents (Lissau et al., 2004). There was no difference in weight categories by age, gender or population density. Lower social class (manual SES) was associated with being overweight (17.3% vs. 14.9%) and obese (4.8% vs. 3.7%, po0.01).
Males were more likely to achieve recommendations for health-enhancing physical activity than females (defined as 60 min, X4 days per week; w2(1) ¼ 112.7, po0.001). The likelihood of achieving guidelines for health-enhancing physical activity did not vary by weight status. Although obese adolescents reported less participation in sport/dance activities (403 vs. 321 min/week, po0.001, r ¼ 0.04) and overall leisure time physical activity minutes in the previous 7 days (873 vs. 781min/week, po0.05, r ¼ 0.02), effect sizes were small. Perceptions of the neighbourhood environment Adolescents classified as overweight or obese had more negative perceptions of pedestrian and traffic safety in their neighbourhoods than normal weight adolescents (po0.01, Table 3). They also perceived fewer convenient facilities for physical activity within a 5–10 min walk of their homes (po0.01). Perceptions of facilities for walking and cycling, safety from crime, aesthetics, street connectivity, access and proximity to shops and services did not differ by weight status (Table 3). Obese adolescents reported significantly less convenient facilities for physical activity in their neighbourhood (32.06 vs. 34.15, po0.001) but did not differ on any other neighbourhood perceptions. Associations with overweight or obesity Table 4 shows the results of logistic regression models predicting the odds of being overweight or obese based on neighbourhood perceptions. In unadjusted models (controlled for clustering at the school level) positive perceptions of aesthetics, pedestrian/traffic safety, safety from crime and convenient facilities for physical activity reduced the odds of being overweight or obese. No food locations were associated with the odds
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of overweight or obesity. In the multivariate adjusted model, only perceptions of convenient facilities for physical activity remained significant, with a one-point increase associated with a 2% decrease in the odds of overweight or obesity (w2 (68) ¼ 102.73, po0.01).
likely to be classified as obese (Table 4). No food locations were associated with the odds of obesity. In the multivariate adjusted model, perceptions of convenient facilities for physical activity remained significant, with a one-point increase associated with a 5% decrease in the odds of obesity w2 (67) ¼ 92.13, po0.05).
Associations with obesity Direct and indirect effects Unadjusted logistic regression analyses indicated that males were 1.8 times more likely to be obese than females, accounting for clustering at the school level. Adolescents with positive perceptions of convenient physical activity facilities were 4% less
Table 2 Participant characteristics. Characteristic
% (n)
Gender Male Female
51.4 (2356) 48.6 (2231)
Age 15 16 17
20.6 (946) 55.9 (2563) 23.5 (1078)
Population density o5000 o50,000 o500,000 4500,000
40.2 30.9 22.4 6.5
SESa Non-manual Manual
69.7 (3197) 30.3 (1389)
Weight Status Healthy weight Overweight Obese
80.4 (3687) 15.6 (716) 4.0 (184)
Physical activityb Achieved 60 min PA on X4 days/wk Did not achieve 60 min PA on X4 days/wk Achieved 60 min PA daily Did not achieve 60 min PA daily
38.8 61.2 3.2 96.8
Mediation analyses reveal that perceptions of convenient facilities have a direct effect on prevalence of obesity and a total effect on prevalence of overweight/obesity and obesity, independent of participation in leisure time physical activity (Table 5). Physical activity does not appear to directly influence or mediate the relationship between perceived convenient facilities and weight status (Table 6).
Discussion
(1836) (1414) (1022) (298)
(1826) (2882) (149) (4559)
a SES ¼ Socio-economic status. Non-manual includes professional, intermediate and junior non-manual occupations. Manual includes skilled, semi-skilled and unskilled manual occupations. b Whilst guidelines suggest adolescents aim for 60 min of physical activity daily, 4 days per week represents a conservative estimate of ‘most’ days of the week and an interim target for inactive youth.
This cross-sectional study aimed to ascertain whether (i) perceptions of the neighbourhood environment varied by weight status and (ii) neighbourhood perceptions shown to influence physical activity among adolescents were also directly or indirectly related to weight status of 15–17-year-old adolescents in Ireland. Increasing physical activity in children is progressively seen as an attractive and non-restrictive approach to obesity prevention (Steinbeck, 2001). Physical activity behaviour is facilitated or constrained by the attributes of the environment in which it takes places, making direct environmental influences a predominant class of determinants (Bauman et al., 2002; Sallis and Owen, 1998). Furthermore, substantial and long-lasting environmental and policy initiatives are an important opportunity for making physically active choices easier and more realistic on a population level (Sallis et al., 1997, 1998). Adopting the promotion of physical activity as an obesity prevention strategy therefore requires improved understanding of the factors that influence both physical activity and overweight, in particular modifiable determinants of behaviours such as the neighbourhood environment. There is compelling scientific evidence that regular physical activity, even at moderate levels, reduces the risk of premature mortality and of developing chronic diseases, improves psychological well-being and helps prevent weight gain and obesity (Department of Health, 2004). The results of this study indicate that obese adolescents reported less participation in leisure time physical activity, and more negative perceptions of pedestrian safety and convenient physical facilities than healthy weight adolescents, although the effect sizes for these differences are small. Longitudinal or intervention-based research is required to examine if the perceived environment can have a causal impact on
Table 3 Perceptions of the neighbourhood environment by overweight or obese. Neighbourhood perception
Range
Normal (M7SD)
Overweight/obese (M7SD)
P-Value
Effect size
Facilities for walking and cycling Pedestrian/traffic safety Personal safety Aesthetics Street connectivity Perceived access Proximity to shops and facilities Convenient facilities
6–24 8–32 7–28 6–24 5–20 7–28 20–100 17–51
15.4574.56 21.1373.99 21.0073.51 15.5173.93 11.8072.95 18.6574.80 53.15722.63 34.2277.08
15.0674.52 20.7074.0 20.6873.55 15.1874.0 11.8773.03 18.3374.84 51.50722.12 33.4577.15
0.042 0.004* 0.014 0.125 0.676 0.188 0.144 0.003
0.03 0.04 0.04 0.03 0.01 0.03 0.03 0.04
pp0.006, Bonferroni correction applied.
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Table 4 Unadjusteda and adjustedb odds ratios and 95% confidence intervals from logistic regression models predicting overweight or obesity, and obesity. Neighbourhood perception
Overweight/obese
Obese
uOR (95% CI)a
aOR (95% CI)b
Gender (n ¼ 4587) Female Male
1.0 1.09 (0.85–1.41)
1.0 1.10 (0.85–1.43)
1.0 1.80 (1.06–3.05)*
1.0 1.74 (1.02–2.90)*
Age (n ¼ 4587)
0.93 (0.83–1.04)
0.91 (0.81–1.02)
0.92 (0.73–1.15)
0.89 (0.70–1.12)
SES (n ¼ 4587) Manual Non-manual
1.0 0.83 (0.70–0.98)
1.0 0.87 (0.73–1.03)
1.0
1.0 0.80 (0.57–1.13)
1.0 1.02 (0.85–1.23) 0.82 (0.63–1.08) 0.90 (0.612–1.33) 0.98 (0.96–1.00) 0.97 (0.95–0.99)* 0.97 (0.95–0.99)* 0.98 (0.96–1.0)* 1.0 (0.96–1.0) 0.98 (0.96–1.0) 0.99 (0.99–1.0)
1.0 1.07 0.89 0.93 – 0.98 0.98 0.98 – – –
0.71 0.76 0.73 0.86 0.98
– – – – 0.98 (0.97–0.99)*
Population density (n ¼ 4587) o5000 o50,000 o500,000 4500,000 Facilities for walking and cycling (n ¼ 3734) Pedestrian/traffic safety (n ¼ 4531) Personal safety (n ¼ 4536) Aesthetics (n ¼ 4577) Street connectivity (n ¼ 2510) Perceived access (n ¼ 2506) Proximity to shops and facilities (n ¼ 2523) Proximal food locationsc Fast food restaurant Non-fast food restaurant Fruit/vegetable store Supermarket Convenient facilities (n ¼ 4585)
(0.50–1.0) (0.52–1.11) (0.51–1.03) (0.63–1.18) (0.97–0.99)*
uOR (95% CI)a
aOR (95% CI)b
1.0 1.13 (0.78–1.64) 0.90 (0.52–1.56) (0.52 (0.20–1.33) 0.97 (0.93–1.01) 0.98 (0.94–1.01) 0.98 (0.94–1.02) 0.97 (0.93–1.01) 0.99 (0.92–1.06) 0.96 (0.92–1.0) 0.99 (0.98–1.0)
(0.88–1.31) (0.67–1.18) (0.63–1.39) (0.96–1.0) (0.96–1.01) (0.96–1.0)
1.0 (1.38 (0.94–2.03) 1.14 (0.65–1.99) 0.64 (0.25–1.64) – – – – – – –
0.47 (0.21–1.05) 0.64 (0.29–1.39) 0.71 (0.33–1.52) 0.83 (0.45–1.53) 0.96 (0.94–0.98)***
– – – – 0.95 (0.93–0.98)***
*pp0.05.**pp0.01.***pp0.00. a Adjusted for clustering of adolescents by school. b Adjusted for age, gender, socio-economic status, clustering of adolescents by school and variables significantly related to outcome in unadjusted analyses. c Analyses were conducted for odds of overweight/obesity and obesity associated with presence of food locations within 5, 6–10 min, 11–20, 21–30 and more than 31 min of home. Results only shown for within 5 min category as all were non-significant.
Table 5 Direct and total effects of perceived convenient facilities on overweight/obesity and obesity via LTPA. Effect of:
Overweight/obesity
Perceived convenient facilities on weight status Perceived convenient facilities on LTPA LTPA on weight status Perceived convenient facilities on weight status, controlling for LTPA
Coefficient
SE
t-Value
Sig
Coefficient
SE
t-Value
Sig
0.0107 9.8956 0.0000 0.0112
0.0056 0.0056 1.3293 0.0001
1.9094 7.4444 0.6377 1.9725
0.0563 0.0000 0.5237 0.0486
0.0388 9.8956 0.0002 0.0368
0.0114 1.3293 0.0002 0.0114
3.4167 7.4444 1.4328 3.2112
0.0006 0.0000 0.1520 0.0013
Table 6 Indirect effect and significance of perceived convenient facilities on overweight/ obesity and obesity via LTPA.
Overweight/obese Obese
Obesity
Effect (95% CI)
S.E
z
Sig
0.0004 (0.0009–0.0018) 0.0022 (0.0053–0.0009)
0.0007 0.0016
0.6297 1.3949
0.5289 0.1630
physical activity behaviour and weight status, or if actual differences in neighbourhood environments are more important than perceived differences. Similar to previous literature, access to destinations, connectivity of the street network, infrastructure for walking and cycling
(Mota et al., 2006) and community design variables (Norman et al., 2006) were not associated with weight status. Research has also suggested that these neighbourhood features are unrelated to physical activity among children (Roemmich et al., 2006) and adolescent boys (Jago et al., 2006). Those that were associated – safety, aesthetics and convenience of physical activity facilities – are among the most consistent built environmental correlates of physical activity among adults (Craig et al., 2002; Brownson et al., 2000; Hoehner et al., 2005). Perceptions of safety are also commonly cited correlates of physical activity among youth. This study showed that pedestrian or traffic safety, including perceptions of pedestrian crossings, traffic speed and traffic density, was positively associated with lower odds of overweight or obesity. Similar studies have linked traffic density to overweight and obesity among adolescent girls
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(Mota et al., 2006) and 12-year-old children (Timperio et al., 2005). Gender differences exist however: high traffic density reduced levels of cycling for recreation and walking for exercise (Carver et al., 2005), and speeding traffic and few pedestrian crossings reduced levels of walking to school (Nelson, 2007) only among adolescent girls. Although young people’s perceptions of personal safety in the neighbourhood have been positively associated with their participation in physical activity in previous research (Utter et al., 2006), this is among the first to examine direct associations with overweight or obesity. Neighbourhood safety is particularly important as a predictor of physical activity in low-income, high crime areas (Gomez et al., 2004; Romero, 2005) and among females (Carver et al., 2005; Evenson et al., 2006; Gomez et al., 2004). Research has related the number of violent crimes within 0.5 miles of home to reduced outdoor physical activity among adolescent girls but not boys (Gomez et al., 2004). In previous research on perceived neighbourhood environments, both male and female adolescents who reported well-lit streets and good walker visibility in their neighbourhoods were more likely to walk or cycle to school (Nelson, 2007). One study that found no cross-sectional or longitudinal effects of safety on physical activity among adolescent girls (Motl et al., 2001) was limited by a lack of variability in perceptions of safety, which were generally positive. A similar problem was evident in the current data, which may explain the lack of significance in the multivariate model. Motl et al. (2001) suggest that safe environments alone may not encourage physical activity, although unsafe environments may inhibit physical activity. In addition, Trayers et al. (2006) caution that the assumption that planned provision of supportive environments will improve physical activity, health and lifestyle may not be true if developments do not take account of community concerns regarding personal safety. Future research is required to understand mismatches in perceived and actual safety and to design interventions targeting perceptual change. Similar to research with adults (Giles-Corti and Donovan, 2002), neighbourhood aesthetics has been associated with physical activity among adolescent girls. Girls who perceived the presence of trees (Evenson et al., 2006) and interesting features to look at had increased odds of physical activity (Evenson et al., 2006; Mota et al., 2006). Similar items were included in the aesthetics score in this study, which was associated with reduced odds of overweight or obesity. Convenient physical activity facilities were the most consistently associated factor; positive perceptions of aesthetics, pedestrian/traffic safety and safety from crime were associated with reduced odds of being overweight or obese; however, in adjusted models these associations were no longer evident. The present study suggests that adolescents who perceive many convenient facilities for physical activity in their neighbourhoods have 2% decreased odds of overweight or obesity and 5% decreased odds of obesity, and this relationship is independent of physical activity levels. Although small, these significant associations may have important public health impact with rapidly increasing incidence of obesity worldwide. In contrast, the presence of food locations such as fast food and non-fast food restaurants, fruit and vegetable stores and supermarkets in the local neighbourhood were not associated with weight status. This suggests that in this sample the environment for physical activity is a more influential determinant of weight status than the food access environment. In the past, researchers have used maps, geographical information systems (GIS) and observational audits to document the presence, number and proximity to physical activity facilities in the local neighbourhood. The presence of parks within a 400 m radius of home was negatively associated with sedentary
behaviour and positively associated with physical activity (Jago et al., 2006). The number of local parks was positively associated with overall physical activity (Cohen et al., 2006; Jago et al., 2006; Norman et al., 2006) and walking or cycling to school (Nelson, 2007) among adolescents. The quantity of facilities appears to be associated to girl’s physical activity (Brodersen et al., 2005; Norman et al., 2006), while proximity to facilities is associated with boy’s physical activity (Gomez et al., 2004; Jago et al., 2006). These studies suggest that locating many facilities for physical activity in residential neighbourhoods may be an effective intervention to target increases in physical activity and prevention of obesity. As this study suggests that participation in leisure time physical activity does not mediate the relationship between convenient facilities and weight status, potential causal mechanisms need further attention. Other issues not widely addressed in the literature to date include the affordability and accessibility of facilities, and the provision of varied types of facilities matching the activities adolescents, particularly girls, want to pursue. Early evidence suggests that facilities for girl’s activities of choice, for example dance, are among the least convenient and affordable in Ireland (Woods et al., 2007).
Strengths and limitations This piece of work is one of few early investigations into the features that might characterise obesogenic environments relevant to young people. More research is required in this area. The strengths and novelty of this study include a substantial sample size and variable neighbourhood environments provided by sampling urban and rural areas of residence. A variety of environmental variables were measured and additional variables were included that were not previously measured in this age group. As well as controlling for potential clustering at the school level, multivariate analyses controlled for age, gender, population density and socio-economic status. This study is limited by its reliance on cross-sectional data, resulting in an inability to infer causal relations. It remains unknown whether changes in perceived or actual neighbourhood environment features can cause changes in weight status, physical activity or eating behaviours. The NEWS was designed for use in an urban American setting. Although it appears that the principal features of the physical environment are generalisable across nations, these are likely to exist in different variations, dimensions and scope depending on the country. The development of a setting-specific measurement tool is a priority for researchers based in Europe, and multidisciplinary collaboration is required to achieve this. Until such measures are available, the NEWS may continue to provide data for international comparisons, however, researchers are cautioned to consider carefully the theoretical and practical basis for measurement tools before beginning new studies. Treatment of neighbourhood environment data can be problematic, as characteristics do not exist in isolation and are likely to be inter-related. It is fundamentally important to accept that the examination of items as individual predictors, although a limitation is necessary to develop concepts around the relevance of specific features. This approach has been applied by other researchers in the examination of environmental influences on physical activity (Evenson et al., 2006). Confirming the demonstrated associations in similar studies, and in other populations and countries, will assist the development of a clearer list of variables to target in interventions. Future research should also consider novel ways to analyse individual and environmental level data in an ecological model. Ecological theory suggests that relationships between the physical environment,
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policy interventions and physical activity or weight status outcomes are likely to vary by population subgroup, urban vs. rural setting and other contextual factors. There appears to be a timely focus on young people and physical activity (Mota et al., 2006); however, more research is required with weight status as an outcome. Implications and conclusions The results of this study have potential implications for research and practice. Future research needs to look in more detail at specific features of the environment as some detail may be lost by the use of summary scores. For example, the personal safety score includes items relating to crime rates, visibility and street lighting. Any of these might individually influence physical activity or obesity, but trends may be lost with the use of summary scores (Nelson, 2007). While the focus of this study was on neighbourhood environments, the authors recommend the adoption of a broad contextual approach in order to understand and intervene against the processes leading to the development of overweight and obesity among young people. The use of ecological models may assist in this (Swinburn et al., 1999). In particular it is recommended that studies attempt to measure variables from individual, interpersonal, behavioural and social and physical environmental levels, and analyse such data with sophisticated multi-level models that provide relative effects. In the shorter term, the results presented in this paper provide a simple message for practice: convenient facilities for physical activity are associated with reduced rates of overweight and obesity. This adds to accumulating evidence that creating supportive environments for health means creating supportive environments for physical activity.
Acknowledgements All data were collected as part of the Take PART study, while NN was at Dublin City University. Take PART was funded by The Health Services Executive (East Coast, Northern Area, Midlands and North Eastern Regions), The Irish Heart Foundation and the Irish Sports Council and Fingal Sports Partnership. We acknowledge the contribution of all undergraduate researchers who assisted in data collection. We would like to thank all students and teachers who were involved in this study. References Bauman, A., Sallis, J.F., Dzewaltowski, D.A., Owen, N., 2002. Toward a better understanding of the influences on physical activity: the role of determinants, correlates, causal variables, mediators, moderators and confounders. American Journal of Preventive Medicine 23 (2S), 5–14. Booth, M.L., Chey, T., Wake, M., Norton, K., Hesketh, K., Dollman, J., et al., 2003. Change in the prevalence of overweight and obesity among young Australians, 1969–1997. American Journal of Clinical Nutrition 77, 29–36. Brodersen, N.H., Steptoe, A., Williamson, S., Wardle, J., 2005. Sociodemographic, developmental, environmental and psychological correlates of physical activity and sedentary behaviour at age 11–12. Annals of Behavioral Medicine 29 (1), 2–11. Brownson, R.C., Housemann, R.A., Brown, R.A., Jackson-Thomson, J., King, A.C., Malone, B.R., et al., 2000. Promoting physical activity in rural communities: walking trail access, use and effects. American Journal of Preventive Medicine 18 (3), 235–241. Carver, A., Salmon, J., Campbell, K., Baur, L., Garnett, S., Crawford, D., 2005. How do perceptions of local neighbourhood relate to adolescents’ walking and cycling? American Journal of Health Promotion 20 (2), 139–147. Central Statistics Office & Government of Ireland, 2002. Census of Population Volume 9: Travel to Work, School and College. Available at: /www.cso.ieS. Cerin, E., Leslie, E., Owen, O., Baumann, A., 2008. An Australian version of the neighbourhood environment walkability scale: validity evidence. Measurement in Physical Education and Exercise Science 12 (1), 31–51.
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Sallis, J.F., Johnson, M.F., Calfas, K.J., Caparosa, S., Nichols, J.F., 1999. Assessing perceived physical environmental variables that may influence physical activity. Research Quaterly for Exercise Sport 68 (4), 345–347. Sallis, J.F., Owen, N., Glanz, K., Lewis, F.M., Rimer, B.K., 1997. Ecological models. In: Health Behaviour and Health Education, vol. 2. Jossey-Bass Publishers, San Francisco, pp. 403–424. Sallis, J.F., Strikmiller, L.S., Harsha, D., Feldman, H.A., 1996. Validation of interviewer and self-administered physical activity checklist. Medicine and Science in Sports and Exercise 28, 840–885. Steinbeck, K.S., 2001. The importance of physical activity in the prevention of overweight and obesity: a review and an opinion. Obesity Reviews 2, 117. Strauss, R.S., 2000. Childhood obesity and self-esteem. Pediatrics 105 (15). Swinburn, B., Egger, G., Raza, F., 1999. Dissecting obesogenic environments: the development and application of a framework for identifying and prioritizing environmental interventions for obesity. Preventive Medicine 29, 563. Timperio, A., Salmon, J., Telford, A., Crawford, D., 2005. Perceptions of local neighbourhood environments and their relationship to childhood overweight and obesity. International Journal of Obesity 29, 170. Trayers, T., Deem, R., Fox, K.R., Riddoch, C.J., Ness, A.R., Lawlor, D.A., 2006. Improving health through neighbourhood environmental change: are we speaking the same language? A qualitative study of views of different stakeholders. Journal of Public Health 28 (1), 49. Utter, J., Denny, S., Robinson, E.M., Ameratunga, S., Watson, P., 2006. Perceived access to community facilities, social motivation and physical activity among New Zealand youth. Journal of Adolescent Health 39 (770), 773. Woods, C.B., Nelson, N.M., Foley, E., O’Gorman, D., Moyna, N.M., 2009. The Take PART study (physical activity research for teenagers): rationale and methods. Journal of Physical Activity and Health 6, 170–177. Woods, C.B., Nelson, N.M., O’Gorman, D., Eldin, N., Moyna, N.M., 2007. The take PART study: physical activity research for teenagers. A report for the health services executive—North East Area: Dublin City University Report Number 4. World Health Organisation, 1986. Ottawa charter for health promotion. World Health Organisation, 1998. Obesity: Preventing and Managing the Global Epidemic. International Obesity Task Force, 3–5 June 1997. World Health Organisation, Geneva.
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Health & Place journal homepage: www.elsevier.com/locate/healthplace
Obesogenic environments: Are neighbourhood environments that limit physical activity obesogenic? N.M. Nelson a,, C.B. Woods b,1 a b
Department of Sport, Culture and the Arts, University of Strathclyde, Jordanhill Campus, 76 Southbrae Drive, Glasgow G13 1PP, Scotland, UK School of Health and Human Performance, Dublin City University, Collins Avenue, Dublin 9, Ireland
a r t i c l e in fo
abstract
Article history: Received 23 July 2008 Received in revised form 29 January 2009 Accepted 2 February 2009
The impact of obesogenic environments on adolescent health is poorly understood. This study examines if neighbourhood features related to physical activity are also related to unhealthy weight status. Adolescents (N ¼ 4587, age 15–17 years, 51.4% male) self-reported physical activity and neighbourhood perceptions. Trained researchers measured height and weight. Logistic regression identified if neighbourhood perceptions predicted overweight or obesity. Adolescents who reported convenient physical activity facilities were 2% less likely to be overweight/obese and 5% less likely to be obese, controlled for socio-demographic variables and clustering by school. Physical activity does not appear to directly influence or mediate the relationship between perceived convenient facilities and weight status. & 2009 Elsevier Ltd. All rights reserved.
Keywords: Obesity Neighbourhood environment Physical activity Convenient facilities Mediating factors
Introduction The prevalence of overweight and obesity is rapidly increasing worldwide, and is one of the most significant modern public health threats (World Health Organisation, 1998). Population surveys and secular trends suggest upward shifts in body weight in children, adolescents and adults (Wedderkopp et al., 2004; Freedman et al., 1997; Hedley et al., 2004; Booth et al., 2003). In a study of 13 European countries, Israel and the United States, the highest prevalence of overweight among adolescents was found in Ireland along with the United States, Greece and Portugal (Lissau et al., 2004). Therefore, from a public health perspective, it is important to monitor overweight in adolescence (Lissau et al., 2004) and identify the factors that influence its prevention and treatment. Obesity in children and adolescents has been associated with important chronic diseases such as diabetes, asthma, sleep apnea and gallbladder disease (Dietz, 1998; Must and Strauss, 1999) as well as psychosocial problems (Strauss, 2000) and reduced capacity for physical activity (Molnar and Livingstone, 2000). Adolescence is a critical period for the onset of obesity (Dietz, 1994) and for obesity-related morbidity in later life (Must et al.,
Corresponding author. Tel.: +44 950 3275.
E-mail addresses: [email protected] (N.M. Nelson), [email protected] (C.B. Woods). 1 Tel.: +353 17008008. 1353-8292/$ - see front matter & 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.healthplace.2009.02.001
1992) with 50–80% of obese teenagers becoming obese adults (Must and Strauss, 1999; Guo and Chumlea, 1999). Educational, behavioural and pharmacological interventions to target obesity have had limited success (Kayman et al., 1990) within environmental contexts that promote high energy intakes and sedentary lifestyles. The importance of creating supportive environments for health was recognised in the Ottawa Charter in 1986 (World Health Organisation, 1986), while more recent models have indicated that modifiable behaviours such as eating patterns and physical activity are key mediators for obesity (Glanz et al., 1995; King et al., 1997). Swinburn et al. (1999) have conceptualised an ecological framework to explain the interdependence among people, their health and their environment. The model includes four types of environments: physical, economic, political and sociocultural, across micro- (e.g. neighbourhoods, schools, etc.) and macrosettings (e.g. transport networks, health care systems, etc.) (Swinburn et al., 1999). Despite the recognition that the environment may be important in preventing and controlling obesity (Goran, 2001), the use of such environmental strategies remains underexplored (World Health Organisation, 1998). In order to design effective environmental interventions, research is first required into what features make an environment obesogenic. Much recent research has focused on the influence of neighbourhood environments on physical activity (Evenson et al., 2006; Carver et al., 2005); however, less attention has been paid to potential indirect or direct impacts on overweight or obesity, particularly among adolescents. A Portuguese study
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measured adolescents perceptions of access to destinations, connectivity of the street network, infrastructure for walking and cycling, aesthetics, recreation facilities and traffic density, and only traffic density was associated with increased odds of overweight among adolescent girls (Mota et al., 2006). In a similar Australian study, 12-year-old children, whose parents reported heavy traffic and concerns about road safety, were more likely to be overweight or obese (Timperio et al., 2005). Finally, in the US, one study found no relationship between community design variables and BMI percentile (Norman et al., 2006) while urban sprawl was associated with obesity in another (Ewing et al., 2006). The present paper investigates whether (i) perceptions of the neighbourhood environment vary by weight status, and (ii) neighbourhood perceptions shown to influence physical activity among adolescents are directly or indirectly related to weight status of 15–17-year-old adolescents in Ireland.
Methods Sample selection All data were collected as part of the Take PART study (Physical Activity Research in Teenagers). Take PART was a cross-sectional school-based study of participation levels, aerobic fitness, physical health indices, psychosocial and environmental determinants of physical activity in 15–17-year-old Irish adolescents using physical and survey measures. Detail on the rationale and methodology of Take PART is available elsewhere (Woods et al., 2009). Briefly, data were collected between February and May 2003–2005 using a one-stage cluster sample, stratified on school type, gender and school location (urban, rural). A total of 82 schools were selected, and 61 agreed to participate. Participants were eligible if they were aged 15–17 years, were not participating in state examinations and obtained parental consent if under 16 years, or provided their own consent if X16 years old. Standardized testing procedures were used throughout and extensive researcher training was undertaken to minimise potential sources of error in the physical measures and the administration of the questionnaire. Measures Physical measures Height and weight were measured using portable stadiometer (SECA 214, Hamburg, Germany) and scales (SECA 761, Vogel and Hallke, Germany) to the nearest 0.1 cm and 0.1 kg, respectively. Overweight and obese categories were based on international ageand gender-specific criteria (Cole et al., 2000). These criterions approximate adult values for overweight (25 kg/m2) and obesity (30 kg/m2). Survey measures Socio-demographic information: Parental occupation was obtained to determine socio-economic status; this is considered more accurate than asking youth to report family income (Currie et al., 2001). Area of residence was classified as (i) large city (4500,000 inhabitants), (ii) suburbs or outskirts of a city (o500,000 but 450,000), (iii) town (o50,000) or (iv) village (o5000) (Central Statistics Office Government of Ireland, 2002). Physical activity: Adolescents reported the number of days during the past week, and for a typical week, that they accumulated 60 min of moderate–vigorous physical activity. A composite average of these two scores was used to assess
achievement of guidelines for health-enhancing physical activity on all (Department of Health, 2004) or at least most (X4 days) of the week (Olds et al., 2007; Pate et al., 2002). Participants also completed the Self-Administered Physical Activity Checklist (Marshall et al., 2002; Sallis et al., 1996), selecting the time and number of bouts of leisure time physical activities (sport, dance, exercise and utilitarian activities) undertaken during the previous 7 days. These physical activity tools were reliable in test–retest (ICC ¼ 0.76 and 0.65, respectively) and the SAPAC correlated with accelerometer measured physical activity (r ¼ 0.48, po0.05); more detail available elsewhere (Woods et al., 2009). Neighbourhood environment: The neighbourhood environment was measured using a convenient facilities construct (Sallis et al., 1999) and the Neighbourhood Environment Walkability Scale (NEWS) (Saelens et al., 2003a, b). The convenient facilities scale listed 17 facilities for sports (e.g. running track or football field), exercise (e.g. gym or aerobic dance studio) and general/lifestyle physical activity (e.g. bicycle lane or public park). Respondents indicated if each facility listed was on a frequently travelled route (for example, to and from school) or within a 5-min or 10-min walk from their home. Response categories were yes (1) or no/ don’t know (0) resulting in possible score of 0–17 where higher scores reflected more convenient facilities. Adaptations consisted of wording changes for increased suitability to an Irish context, for example, public recreation centre was replaced with community centre. The original (ICC ¼ 0.80, Sallis et al., 1999) and adapted version in this study (ICC ¼ 0.60, (Woods et al., 2009) demonstrated acceptable test–retest reliability. The NEWS measures resident’s perceptions of neighbourhood characteristics thought to be related to the frequency of walking and cycling trips (Cerin et al., 2006). Designed for use in a US setting, the NEWS has since been adapted (mostly with language/ word changes to increase ecological validity) and used in Scotland (Fitzsimons et al., 2008), Australia (Cerin et al., 2008) and Hong Kong (Cerin et al., 2007) as well as this study. Examples of wording changes include replacing ‘sidewalks’ with ‘pathways’ or ‘paths’ and removing inappropriate geographical features such as ‘canyons’. Seven test–retest reliable NEWS subscales (Saelens et al., 2003a; Woods et al., 2009) were used in this study; four (facilities for walking and cycling, pedestrian/traffic safety, personal safety and aesthetics) were completed by all participants; however, the remaining three subscales were added to the protocol in 2005 and therefore were only completed by a subset of participants (n ¼ 2535). Table 1 displays each subscale, some item examples and scoring methodology. Items were reverse coded where necessary, and a composite score was created for each NEWS subscale (mean7SD), with a higher number indicating a more positive perception of the neighbourhood environment.
Data analysis Statistical analysis was undertaken in SPSS for Windows, version 14.0. Data are presented as means, standard deviations and proportions where appropriate. The incidence of (1) overweight or obesity, and (2) obesity was examined by socioeconomic status, gender and physical activity status using Pearson w2 tests and standardised residuals (Pett, 1997). Differences in leisure time physical activity and perceptions of the neighbourhood environment by weight status were examined using ANOVA with Bonferroni correction. Relevant effect sizes were calculated and reported as r-values. An r-value of 0.10, 0.30 and 0.50 represented small, medium and large effect sizes, respectively (Field, 2005).
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Table 1 NEWS subscales with examples of items and methodology. Subscale
Item examples
Responses
Scoring
Facilities for walking and cycling
There are pathways on most of the streets in my neighbourhood Pathways are separated from the road/traffic by parked cars
4-point Likert: SD to SA
Mean of 6 items. Range 6–24
Pedestrian/traffic safety (safety from traffic)
There are pedestrian crossings and signals to help walkers cross busy streets Most drivers exceed the speed limits while driving in my neighbourhood
4-point Likert: SD to SA
Mean of 8 items. Range 8–32
Personal safety (safety from crime)
My neighbourhood streets are well lit at night There is a high crime rate in my neighbourhood
4-point Likert: SD to SA
Mean of 7 items. Range 7–28
Neighbourhood surroundings (or aesthetics)
My neighbourhood is generally free from litter There are trees along the streets in my neighbourhood
4-point Likert: SD to SA
Mean of 6 items. Range 6–24
Street connectivity
The streets in my neighbourhood do not have many cul-de-sacs (dead end streets) There are many four-way intersections in my neighbourhood
4-point Likert: SD to SA
Mean of 5 items. Range 5–20
Perceived access (or land mix use access)
It is easy to walk to a bus or train stop from my home The streets in my neighbourhood are hilly, making my neighbourhood difficult to walk in
4-point Likert: SD to SA
Mean of 7 items. Range 7–28
Proximity to stores and facilities (or land use mix diversity)
How long would it take to get from your home to the nearest: supermarket, hardware shop, library, coffee place, etc.
1–5 min, 6–10 min, 11–20 min, 21–30 min, 31+min, don’t know
Mean of 20 items. Range 20–100
SD to SA: strongly disagree to strongly agree.
Logistic regression models examined the perceived features of the environment associated with (1) overweight or obesity, and (2) obesity. Unadjusted models were performed for each NEWS subscale, convenient facilities for physical activity, gender, age, population density and socio-economic status entered separately as independent variables. Items from the land use mix diversity subscale relating to proximity of food locations were also entered individually. Final adjusted models included gender, age, population density, socio-economic status and NEWS subscales with a statistically significant relationship in the unadjusted analyses. All analyses controlled for potential clustering at the school level. Finally, mediation analyses were carried out as described by Preacher and Hayes (2004) to determine if significant findings were due to direct effect of the independent variable on the outcome, or if physical activity acted as a mediator.
Results Sample characteristics From the total sample of 4720 participants, 4587 provided height and weight measurements and reported perceptions of walking facilities, safety, aesthetics and convenient facilities for physical activity. A subset of 2535 who reported perceptions of street connectivity, access and proximity to shops/amenities did not differ on participant characteristics, outlined in Table 2. Four percent of adolescents were classified as obese, and 15.6% as overweight. These rates are similar to previous nationally representative school-based research for Irish 15-year-old adolescents (Lissau et al., 2004). There was no difference in weight categories by age, gender or population density. Lower social class (manual SES) was associated with being overweight (17.3% vs. 14.9%) and obese (4.8% vs. 3.7%, po0.01).
Males were more likely to achieve recommendations for health-enhancing physical activity than females (defined as 60 min, X4 days per week; w2(1) ¼ 112.7, po0.001). The likelihood of achieving guidelines for health-enhancing physical activity did not vary by weight status. Although obese adolescents reported less participation in sport/dance activities (403 vs. 321 min/week, po0.001, r ¼ 0.04) and overall leisure time physical activity minutes in the previous 7 days (873 vs. 781min/week, po0.05, r ¼ 0.02), effect sizes were small. Perceptions of the neighbourhood environment Adolescents classified as overweight or obese had more negative perceptions of pedestrian and traffic safety in their neighbourhoods than normal weight adolescents (po0.01, Table 3). They also perceived fewer convenient facilities for physical activity within a 5–10 min walk of their homes (po0.01). Perceptions of facilities for walking and cycling, safety from crime, aesthetics, street connectivity, access and proximity to shops and services did not differ by weight status (Table 3). Obese adolescents reported significantly less convenient facilities for physical activity in their neighbourhood (32.06 vs. 34.15, po0.001) but did not differ on any other neighbourhood perceptions. Associations with overweight or obesity Table 4 shows the results of logistic regression models predicting the odds of being overweight or obese based on neighbourhood perceptions. In unadjusted models (controlled for clustering at the school level) positive perceptions of aesthetics, pedestrian/traffic safety, safety from crime and convenient facilities for physical activity reduced the odds of being overweight or obese. No food locations were associated with the odds
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of overweight or obesity. In the multivariate adjusted model, only perceptions of convenient facilities for physical activity remained significant, with a one-point increase associated with a 2% decrease in the odds of overweight or obesity (w2 (68) ¼ 102.73, po0.01).
likely to be classified as obese (Table 4). No food locations were associated with the odds of obesity. In the multivariate adjusted model, perceptions of convenient facilities for physical activity remained significant, with a one-point increase associated with a 5% decrease in the odds of obesity w2 (67) ¼ 92.13, po0.05).
Associations with obesity Direct and indirect effects Unadjusted logistic regression analyses indicated that males were 1.8 times more likely to be obese than females, accounting for clustering at the school level. Adolescents with positive perceptions of convenient physical activity facilities were 4% less
Table 2 Participant characteristics. Characteristic
% (n)
Gender Male Female
51.4 (2356) 48.6 (2231)
Age 15 16 17
20.6 (946) 55.9 (2563) 23.5 (1078)
Population density o5000 o50,000 o500,000 4500,000
40.2 30.9 22.4 6.5
SESa Non-manual Manual
69.7 (3197) 30.3 (1389)
Weight Status Healthy weight Overweight Obese
80.4 (3687) 15.6 (716) 4.0 (184)
Physical activityb Achieved 60 min PA on X4 days/wk Did not achieve 60 min PA on X4 days/wk Achieved 60 min PA daily Did not achieve 60 min PA daily
38.8 61.2 3.2 96.8
Mediation analyses reveal that perceptions of convenient facilities have a direct effect on prevalence of obesity and a total effect on prevalence of overweight/obesity and obesity, independent of participation in leisure time physical activity (Table 5). Physical activity does not appear to directly influence or mediate the relationship between perceived convenient facilities and weight status (Table 6).
Discussion
(1836) (1414) (1022) (298)
(1826) (2882) (149) (4559)
a SES ¼ Socio-economic status. Non-manual includes professional, intermediate and junior non-manual occupations. Manual includes skilled, semi-skilled and unskilled manual occupations. b Whilst guidelines suggest adolescents aim for 60 min of physical activity daily, 4 days per week represents a conservative estimate of ‘most’ days of the week and an interim target for inactive youth.
This cross-sectional study aimed to ascertain whether (i) perceptions of the neighbourhood environment varied by weight status and (ii) neighbourhood perceptions shown to influence physical activity among adolescents were also directly or indirectly related to weight status of 15–17-year-old adolescents in Ireland. Increasing physical activity in children is progressively seen as an attractive and non-restrictive approach to obesity prevention (Steinbeck, 2001). Physical activity behaviour is facilitated or constrained by the attributes of the environment in which it takes places, making direct environmental influences a predominant class of determinants (Bauman et al., 2002; Sallis and Owen, 1998). Furthermore, substantial and long-lasting environmental and policy initiatives are an important opportunity for making physically active choices easier and more realistic on a population level (Sallis et al., 1997, 1998). Adopting the promotion of physical activity as an obesity prevention strategy therefore requires improved understanding of the factors that influence both physical activity and overweight, in particular modifiable determinants of behaviours such as the neighbourhood environment. There is compelling scientific evidence that regular physical activity, even at moderate levels, reduces the risk of premature mortality and of developing chronic diseases, improves psychological well-being and helps prevent weight gain and obesity (Department of Health, 2004). The results of this study indicate that obese adolescents reported less participation in leisure time physical activity, and more negative perceptions of pedestrian safety and convenient physical facilities than healthy weight adolescents, although the effect sizes for these differences are small. Longitudinal or intervention-based research is required to examine if the perceived environment can have a causal impact on
Table 3 Perceptions of the neighbourhood environment by overweight or obese. Neighbourhood perception
Range
Normal (M7SD)
Overweight/obese (M7SD)
P-Value
Effect size
Facilities for walking and cycling Pedestrian/traffic safety Personal safety Aesthetics Street connectivity Perceived access Proximity to shops and facilities Convenient facilities
6–24 8–32 7–28 6–24 5–20 7–28 20–100 17–51
15.4574.56 21.1373.99 21.0073.51 15.5173.93 11.8072.95 18.6574.80 53.15722.63 34.2277.08
15.0674.52 20.7074.0 20.6873.55 15.1874.0 11.8773.03 18.3374.84 51.50722.12 33.4577.15
0.042 0.004* 0.014 0.125 0.676 0.188 0.144 0.003
0.03 0.04 0.04 0.03 0.01 0.03 0.03 0.04
pp0.006, Bonferroni correction applied.
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Table 4 Unadjusteda and adjustedb odds ratios and 95% confidence intervals from logistic regression models predicting overweight or obesity, and obesity. Neighbourhood perception
Overweight/obese
Obese
uOR (95% CI)a
aOR (95% CI)b
Gender (n ¼ 4587) Female Male
1.0 1.09 (0.85–1.41)
1.0 1.10 (0.85–1.43)
1.0 1.80 (1.06–3.05)*
1.0 1.74 (1.02–2.90)*
Age (n ¼ 4587)
0.93 (0.83–1.04)
0.91 (0.81–1.02)
0.92 (0.73–1.15)
0.89 (0.70–1.12)
SES (n ¼ 4587) Manual Non-manual
1.0 0.83 (0.70–0.98)
1.0 0.87 (0.73–1.03)
1.0
1.0 0.80 (0.57–1.13)
1.0 1.02 (0.85–1.23) 0.82 (0.63–1.08) 0.90 (0.612–1.33) 0.98 (0.96–1.00) 0.97 (0.95–0.99)* 0.97 (0.95–0.99)* 0.98 (0.96–1.0)* 1.0 (0.96–1.0) 0.98 (0.96–1.0) 0.99 (0.99–1.0)
1.0 1.07 0.89 0.93 – 0.98 0.98 0.98 – – –
0.71 0.76 0.73 0.86 0.98
– – – – 0.98 (0.97–0.99)*
Population density (n ¼ 4587) o5000 o50,000 o500,000 4500,000 Facilities for walking and cycling (n ¼ 3734) Pedestrian/traffic safety (n ¼ 4531) Personal safety (n ¼ 4536) Aesthetics (n ¼ 4577) Street connectivity (n ¼ 2510) Perceived access (n ¼ 2506) Proximity to shops and facilities (n ¼ 2523) Proximal food locationsc Fast food restaurant Non-fast food restaurant Fruit/vegetable store Supermarket Convenient facilities (n ¼ 4585)
(0.50–1.0) (0.52–1.11) (0.51–1.03) (0.63–1.18) (0.97–0.99)*
uOR (95% CI)a
aOR (95% CI)b
1.0 1.13 (0.78–1.64) 0.90 (0.52–1.56) (0.52 (0.20–1.33) 0.97 (0.93–1.01) 0.98 (0.94–1.01) 0.98 (0.94–1.02) 0.97 (0.93–1.01) 0.99 (0.92–1.06) 0.96 (0.92–1.0) 0.99 (0.98–1.0)
(0.88–1.31) (0.67–1.18) (0.63–1.39) (0.96–1.0) (0.96–1.01) (0.96–1.0)
1.0 (1.38 (0.94–2.03) 1.14 (0.65–1.99) 0.64 (0.25–1.64) – – – – – – –
0.47 (0.21–1.05) 0.64 (0.29–1.39) 0.71 (0.33–1.52) 0.83 (0.45–1.53) 0.96 (0.94–0.98)***
– – – – 0.95 (0.93–0.98)***
*pp0.05.**pp0.01.***pp0.00. a Adjusted for clustering of adolescents by school. b Adjusted for age, gender, socio-economic status, clustering of adolescents by school and variables significantly related to outcome in unadjusted analyses. c Analyses were conducted for odds of overweight/obesity and obesity associated with presence of food locations within 5, 6–10 min, 11–20, 21–30 and more than 31 min of home. Results only shown for within 5 min category as all were non-significant.
Table 5 Direct and total effects of perceived convenient facilities on overweight/obesity and obesity via LTPA. Effect of:
Overweight/obesity
Perceived convenient facilities on weight status Perceived convenient facilities on LTPA LTPA on weight status Perceived convenient facilities on weight status, controlling for LTPA
Coefficient
SE
t-Value
Sig
Coefficient
SE
t-Value
Sig
0.0107 9.8956 0.0000 0.0112
0.0056 0.0056 1.3293 0.0001
1.9094 7.4444 0.6377 1.9725
0.0563 0.0000 0.5237 0.0486
0.0388 9.8956 0.0002 0.0368
0.0114 1.3293 0.0002 0.0114
3.4167 7.4444 1.4328 3.2112
0.0006 0.0000 0.1520 0.0013
Table 6 Indirect effect and significance of perceived convenient facilities on overweight/ obesity and obesity via LTPA.
Overweight/obese Obese
Obesity
Effect (95% CI)
S.E
z
Sig
0.0004 (0.0009–0.0018) 0.0022 (0.0053–0.0009)
0.0007 0.0016
0.6297 1.3949
0.5289 0.1630
physical activity behaviour and weight status, or if actual differences in neighbourhood environments are more important than perceived differences. Similar to previous literature, access to destinations, connectivity of the street network, infrastructure for walking and cycling
(Mota et al., 2006) and community design variables (Norman et al., 2006) were not associated with weight status. Research has also suggested that these neighbourhood features are unrelated to physical activity among children (Roemmich et al., 2006) and adolescent boys (Jago et al., 2006). Those that were associated – safety, aesthetics and convenience of physical activity facilities – are among the most consistent built environmental correlates of physical activity among adults (Craig et al., 2002; Brownson et al., 2000; Hoehner et al., 2005). Perceptions of safety are also commonly cited correlates of physical activity among youth. This study showed that pedestrian or traffic safety, including perceptions of pedestrian crossings, traffic speed and traffic density, was positively associated with lower odds of overweight or obesity. Similar studies have linked traffic density to overweight and obesity among adolescent girls
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(Mota et al., 2006) and 12-year-old children (Timperio et al., 2005). Gender differences exist however: high traffic density reduced levels of cycling for recreation and walking for exercise (Carver et al., 2005), and speeding traffic and few pedestrian crossings reduced levels of walking to school (Nelson, 2007) only among adolescent girls. Although young people’s perceptions of personal safety in the neighbourhood have been positively associated with their participation in physical activity in previous research (Utter et al., 2006), this is among the first to examine direct associations with overweight or obesity. Neighbourhood safety is particularly important as a predictor of physical activity in low-income, high crime areas (Gomez et al., 2004; Romero, 2005) and among females (Carver et al., 2005; Evenson et al., 2006; Gomez et al., 2004). Research has related the number of violent crimes within 0.5 miles of home to reduced outdoor physical activity among adolescent girls but not boys (Gomez et al., 2004). In previous research on perceived neighbourhood environments, both male and female adolescents who reported well-lit streets and good walker visibility in their neighbourhoods were more likely to walk or cycle to school (Nelson, 2007). One study that found no cross-sectional or longitudinal effects of safety on physical activity among adolescent girls (Motl et al., 2001) was limited by a lack of variability in perceptions of safety, which were generally positive. A similar problem was evident in the current data, which may explain the lack of significance in the multivariate model. Motl et al. (2001) suggest that safe environments alone may not encourage physical activity, although unsafe environments may inhibit physical activity. In addition, Trayers et al. (2006) caution that the assumption that planned provision of supportive environments will improve physical activity, health and lifestyle may not be true if developments do not take account of community concerns regarding personal safety. Future research is required to understand mismatches in perceived and actual safety and to design interventions targeting perceptual change. Similar to research with adults (Giles-Corti and Donovan, 2002), neighbourhood aesthetics has been associated with physical activity among adolescent girls. Girls who perceived the presence of trees (Evenson et al., 2006) and interesting features to look at had increased odds of physical activity (Evenson et al., 2006; Mota et al., 2006). Similar items were included in the aesthetics score in this study, which was associated with reduced odds of overweight or obesity. Convenient physical activity facilities were the most consistently associated factor; positive perceptions of aesthetics, pedestrian/traffic safety and safety from crime were associated with reduced odds of being overweight or obese; however, in adjusted models these associations were no longer evident. The present study suggests that adolescents who perceive many convenient facilities for physical activity in their neighbourhoods have 2% decreased odds of overweight or obesity and 5% decreased odds of obesity, and this relationship is independent of physical activity levels. Although small, these significant associations may have important public health impact with rapidly increasing incidence of obesity worldwide. In contrast, the presence of food locations such as fast food and non-fast food restaurants, fruit and vegetable stores and supermarkets in the local neighbourhood were not associated with weight status. This suggests that in this sample the environment for physical activity is a more influential determinant of weight status than the food access environment. In the past, researchers have used maps, geographical information systems (GIS) and observational audits to document the presence, number and proximity to physical activity facilities in the local neighbourhood. The presence of parks within a 400 m radius of home was negatively associated with sedentary
behaviour and positively associated with physical activity (Jago et al., 2006). The number of local parks was positively associated with overall physical activity (Cohen et al., 2006; Jago et al., 2006; Norman et al., 2006) and walking or cycling to school (Nelson, 2007) among adolescents. The quantity of facilities appears to be associated to girl’s physical activity (Brodersen et al., 2005; Norman et al., 2006), while proximity to facilities is associated with boy’s physical activity (Gomez et al., 2004; Jago et al., 2006). These studies suggest that locating many facilities for physical activity in residential neighbourhoods may be an effective intervention to target increases in physical activity and prevention of obesity. As this study suggests that participation in leisure time physical activity does not mediate the relationship between convenient facilities and weight status, potential causal mechanisms need further attention. Other issues not widely addressed in the literature to date include the affordability and accessibility of facilities, and the provision of varied types of facilities matching the activities adolescents, particularly girls, want to pursue. Early evidence suggests that facilities for girl’s activities of choice, for example dance, are among the least convenient and affordable in Ireland (Woods et al., 2007).
Strengths and limitations This piece of work is one of few early investigations into the features that might characterise obesogenic environments relevant to young people. More research is required in this area. The strengths and novelty of this study include a substantial sample size and variable neighbourhood environments provided by sampling urban and rural areas of residence. A variety of environmental variables were measured and additional variables were included that were not previously measured in this age group. As well as controlling for potential clustering at the school level, multivariate analyses controlled for age, gender, population density and socio-economic status. This study is limited by its reliance on cross-sectional data, resulting in an inability to infer causal relations. It remains unknown whether changes in perceived or actual neighbourhood environment features can cause changes in weight status, physical activity or eating behaviours. The NEWS was designed for use in an urban American setting. Although it appears that the principal features of the physical environment are generalisable across nations, these are likely to exist in different variations, dimensions and scope depending on the country. The development of a setting-specific measurement tool is a priority for researchers based in Europe, and multidisciplinary collaboration is required to achieve this. Until such measures are available, the NEWS may continue to provide data for international comparisons, however, researchers are cautioned to consider carefully the theoretical and practical basis for measurement tools before beginning new studies. Treatment of neighbourhood environment data can be problematic, as characteristics do not exist in isolation and are likely to be inter-related. It is fundamentally important to accept that the examination of items as individual predictors, although a limitation is necessary to develop concepts around the relevance of specific features. This approach has been applied by other researchers in the examination of environmental influences on physical activity (Evenson et al., 2006). Confirming the demonstrated associations in similar studies, and in other populations and countries, will assist the development of a clearer list of variables to target in interventions. Future research should also consider novel ways to analyse individual and environmental level data in an ecological model. Ecological theory suggests that relationships between the physical environment,
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policy interventions and physical activity or weight status outcomes are likely to vary by population subgroup, urban vs. rural setting and other contextual factors. There appears to be a timely focus on young people and physical activity (Mota et al., 2006); however, more research is required with weight status as an outcome. Implications and conclusions The results of this study have potential implications for research and practice. Future research needs to look in more detail at specific features of the environment as some detail may be lost by the use of summary scores. For example, the personal safety score includes items relating to crime rates, visibility and street lighting. Any of these might individually influence physical activity or obesity, but trends may be lost with the use of summary scores (Nelson, 2007). While the focus of this study was on neighbourhood environments, the authors recommend the adoption of a broad contextual approach in order to understand and intervene against the processes leading to the development of overweight and obesity among young people. The use of ecological models may assist in this (Swinburn et al., 1999). In particular it is recommended that studies attempt to measure variables from individual, interpersonal, behavioural and social and physical environmental levels, and analyse such data with sophisticated multi-level models that provide relative effects. In the shorter term, the results presented in this paper provide a simple message for practice: convenient facilities for physical activity are associated with reduced rates of overweight and obesity. This adds to accumulating evidence that creating supportive environments for health means creating supportive environments for physical activity.
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