Regular physical activity can reduce the risk of several chronic diseases, such as coronary heart disease and type 2 diabetes (38); however, physical activity is among several health behaviors (e.g., smoking, diet) known to be less favorable among people with low socioeconomic status (SES) compared with their higher-status counterparts (8,9,19,23). In the literature, differences in physical and social environmental exposures have been hypothesized as the ultimate explanations for the differential distribution of physical activity and other health behaviors across socioeconomic groups (4,19,28). Presently, little is known about the contribution of possible environmental influences to socioeconomic inequalities in physical activity.
A considerable number of studies have shown relationships of physical and social environmental factors with physical activity (16,27,29), but with little reference to their patterning across socioeconomic groups. Some studies have examined the lower rates of physical activity in disadvantaged areas and have demonstrated the importance of neighborhood attractiveness, the accessibility and proximity of neighborhood facilities, and neighborhood safety (10,14,20,31,36,40). Educational differences in leisure-time walking were explained by a range of personal, physical, and social environmental factors, whereas few variables explained educational variations in walking for transport (2). Social participation (i.e., how actively a person takes part in group activities in society, such as courses, events, or church) has shown to contribute to socioeconomic differences in leisure-time physical activity (18).
Because environmental influences should be investigated for specific behaviors (15,16), this paper focuses on one aspect of one's overall physical activity-sports participation. Participation in vigorous activities like sports is low among the socioeconomically disadvantaged (1); however, regular vigorous activity can have an important positive health effect. Life expectancy for sedentary people and moderately active people at age 50 yr was found to be 3.8 and 1.4 yr shorter, respectively, compared with people with high physical activity levels (11). More specifically, within the moderately and highly active people, sports participants experienced only half the mortality of nonparticipants (1).
Studies that have investigated environmental factors in relation to socioeconomic inequalities in physical activity have mainly focused on neighborhood factors. However, an ecological approach requires the investigation of environmental factors from different settings, as well as individual factors (21). In recent multilevel studies, the household has been shown to have an important effect on health, independent of individual and neighborhood-level effects (7,33). Therefore, in this paper, we examine the contribution of perceived neighborhood, household, and individual factors to socioeconomic inequalities in sports participation in a multilevel design (to be able to examine and account for possible clustering of sports participation within neighborhoods) (26).
Data were obtained by a large-scale postal survey, a component of the new wave of data collection for the longitudinal GLOBE study, among a stratified sample of the adult population (age 25-75 yr) of Eindhoven (the fifth-largest city in the Netherlands) and surrounding cities in October 2004 (N = 4785; response rate 64.4%). Participants resided in 213 neighborhoods, which are the smallest geographic units in the Netherlands created for statistical and administrative purposes. More about the objectives, design, and results of the GLOBE study can be found elsewhere (22,37). The use of personal data in the GLOBE study is in compliance with the Dutch Personal Data Protection Act and the Municipal Database Act, and it has been registered with the Dutch Data Protection Authority (number 1248943).
Participants with missing values for sports participation, education, income, or one of the confounding variables (i.e., age, sex, country of origin, or marital status) were excluded from the analyses (N = 557). Also, we excluded participants who reported that poor health or pain was often a barrier for being physically active (N = 307). Furthermore, we excluded participants with missing values for the level 2 indicator (neighborhood) (N = 48), and those residing in neighborhoods with only one or two participants (N = 34). Therefore, the analytic sample comprised 3839 participants, living in 177 neighborhoods (mean number of participants per neighborhood: N = 21, range 3-70).
Neighborhood, Household, and Individual Factors
All factors were measured in the GLOBE postal survey 2004. Selection of items for the questionnaire was based on an extensive literature review (6,16,34), expert meetings, and focus groups conducted with residents living in the city of Eindhoven (17). Items that measured neighborhood, household, and individual factors, as described in Table 1, were mostly derived from existing scales. For physical activity cognitions, we assessed key factors that recur in models commonly employed to predict health behaviors: Social Cognitive Theory, and the Theory of Planned Behavior (3). The missing value category of many explanatory factors was associated with no sports participation, and the prevalence of missing values was highest among participants from the lowest SES group. Therefore, to prevent overestimation of the explanatory power of these factors to SES inequalities in sports participation, missing values for explanatory factors were imputed by drawing randomly from the distribution of answering categories, using observed prevalences per educational group as probabilities.
Possible confounding factors were age (in 10-yr categories), sex, country of origin (Netherlands, other country), and marital status (married/registered partnership, not married).
Educational attainment is only one component of the broad concept of SES, but it is considered a good indicator of SES in the Netherlands (35), and, therefore, it was our main SES indicator. Four levels of education were distinguished (no education or primary education; lower professional and intermediate general education; intermediate professional and higher general education; higher professional education and university). We also used household income as an SES indicator, asking participants to report their net monthly household income (€0-€1200, €1200-€1800, €1800-€2600, €2600 or more, and don't want to say/don't know).
Sports participation was measured with the SQUASH questionnaire, a validated Dutch questionnaire to measure various types of physical activity among an adult population: commuting, leisure time, sports, occupational, and housekeeping activities (39). Participants wrote down a maximum of four sports they had done on a weekly basis during the previous month (open question-no defined list given). For these sports, they reported frequency (times per week), average duration (min·d−1), and intensity (low, average, high). Self-reported intensity, in combination with participant's age and activity-specific MET values, was used to calculate intensity scores. Because almost half the sample did not do any sport, sports participation was dichotomized, with no = not doing any sports weekly with at least moderate intensity (moderate intensity = 4-6 METs for 18-55 yr old; 3-5 METs for 55+ yr old) versus yes = doing sports at least once a week with moderate or high intensity.
No sports participation was modeled as a binary outcome variable in weighted multilevel logistic regression models of participants (level 1) nested within neighborhoods (level 2). To take into account the hierarchical nature of the data, analyses were done in MLwiN (version 2.02), using the logit-link function and second-order PQL estimation methods (30), unless specified otherwise. Clustering of sports participation within neighborhoods was determined by calculating the median odds ratio (MOR) with 95% credible intervals (CrL), using the posterior distribution of the area variance as provided by the Markov Chain Monte Carlo (MCMC) procedure in MlwiN (5). The MOR was computed with the following formula (26):
All analyses were conducted separately for education and income as SES indicators, because they are likely to relate to different causal processes (12). Analyses were weighted to reflect our source population (i.e., the adult population of the region of Eindhoven in October 2004) in terms of sex, age, and educational level. We imputed missing values of all neighborhood, household, and individual factors by drawing randomly from the distribution of answering categories, using observed prevalences per educational group as probabilities.
First, we tested the association of SES with no sports participation (adjusted for age, sex, and country of origin). Then, we examined which neighborhood, household, and individual factors were significantly associated with doing no sports in univariate analyses (P < 0.05), and whether these factors were unequally distributed across SES groups (using SPSS version 11.0) (32). Factors that were significantly associated with both sports participation and SES were then analyzed in multivariate analyses for neighborhood, household, and individual factors separately, using the backward stepwise procedure in SPSS (i.e., at each step, the least significant factor was removed from the model, until all factors in the model were significant (P < 0.100)). Neighborhood, household, and individual factors that remained significant in these multivariate models were included in the following six-step modeling sequence in MLWIN.
First, we examined neighborhood-level variance and the MOR for the empty model (model 0). Next, we calculated the odds ratios (OR) of no sports participation by socioeconomic groups adjusted for age, sex, and country of origin (model 1). Then, we included neighborhood factors only (model 2), household factors only (model 3), and individual factors only (model 4). Finally, we tested the full model (model 5), in which we included neighborhood, household, and individual factors simultaneously that had been significant in models 2-4. For each of the models 2-5, we calculated the percent change in OR compared with the OR for model 1 ([ORmodel 1 − ORmode lx] / [ORmodel 1 − 1] × 100). This reduction in OR was interpreted as the contribution of the specific factors included in the model to the explanation of socioeconomic inequalities in sports participation.
Demographic characteristics of our sample are provided in Table 2. All characteristics were significantly associated with sports participation (not shown). Compared with higher educational groups, people in the lowest educational group were more likely to be female, to be of older age, to have a low household income, and to be born in a country other than the Netherlands (Table 2). Marital status differed by income group (not shown), but not by educational group. Therefore, marital status was taken into account as a confounder in analyses with income as an SES indicator.
SES and sports participation.
As presented in Figure 1, a gradient was found between SES and no sports participation, with the lowest educated (OR = 3.99; 95% CI: 2.99-5.31) and lowest income group (OR = 3.02; 95% CI: 2.36-2.86) most likely to report no sports participation. Moreover, we found significant clustering of no sports participation within neighborhoods, as indicated by the MOR. Possible explanatory factors that could mediate the association between SES and sports participation are discussed below.
Associations of neighborhood, household, and individual factors with SES and sports participation.
Compared with higher socioeconomic groups, participants from lower socioeconomic groups were more likely to report that their neighborhood was unsafe, unattractive, and had insufficient places for physical activity (Table 3). Also, they were more likely to report that it is often poor weather and to report a small social network and low social cohesion. All of these characteristics increased the likelihood of doing no sports. People indicating not feeling at home in their neighborhood were also more likely to do no sports, but this was not significantly more prevalent among any of the educational groups (P = 0.093). Social disorganization and length of residence were not significantly associated with doing no sports.
Two out of three indicators of material deprivation (crowding, and having financial problems) and all three indicators of social deprivation increased the likelihood of doing no sports. Also, these factors showed higher prevalence among lower socioeconomic groups.
Furthermore, all individual cognitions of recommended physical activity were significantly related to sports participation, and unfavorable cognitions were more prevalent among lower socioeconomic groups. As an exception, the negative outcome expectancy, physical activity requires too much time, was more frequently reported by people from higher than from lower socioeconomic groups. Of all factors examined, self-efficacy and intention showed the strongest associations with sports participation. Factors that were not significantly associated with sports participation or with SES were not included in further explanatory analyses.
Compared with the basic model (including education, age, sex, and country of origin), the increased OR for doing no sports seen among lower education groups decreased by 0-7% when neighborhood factors were added (model 2, Table 4). Adjustment for household factors (model 3) lowered OR by 17-28% compared with the basic model. Adding individual factors to the basic model showed the largest percent reductions in OR: 19-42% (model 4). In the full model, two neighborhood factors (safety and social cohesion), three household factors (material deprivation (indicator 3) and social deprivation (indicator 2 and 3)), and nine individual factors (six outcome expectancies, social support, modeling, self-efficacy, and intention) remained statistically significant. All factors together reduced the OR of doing no sports among the lowest educational group by 57%, for the second-lowest by 48%, and for the second-highest by 26%. As presented in Table 5, results of the explanatory analyses for income as an SES indicator were comparable with those for education; however, adjustment for neighborhood factors, household factors, and all factors showed larger reductions in OR.
Compared with the empty model, the MOR was reduced substantially in model 1 (taking compositional characteristics into account), and it was reduced somewhat further after inclusion of neighborhood factors (in models with income as an SES indicator) or household factors (in models with education as an SES indicator).
We examined the contributions of neighborhood, household, and individual factors to the explanation of socioeconomic variation in sports participation, using a multilevel design. Unfavorable neighborhood (e.g., feeling unsafe, low social network), household (e.g., material and social deprivation), and individual factors (e.g., low self-efficacy, perceived negative outcome expectancies) were associated with doing no sports and were reported among lower socioeconomic groups more frequently. Together, these factors explained socioeconomic inequalities in sports participation to a large extent. Interventions and policies should focus on all three groups of factors simultaneously to yield a maximal reduction of socioeconomic inequalities in sports participation.
The main strength of our study is that we incorporated neighborhood, household, and individual factors in our analyses to explain socioeconomic variations in sports participation, using a multilevel design to correct for possible area variance. Although not the focus of this paper, we also showed that the individual probability to do no sports was statistically dependent on the neighborhood of residence (indicated by the MOR > 1), which could be mainly explained by compositional differences between neighborhoods (in terms of age, education, and sex) and slightly by differences in neighborhood perceptions and household factors.
Another strength is our well-considered selection of factors, which was preceded by an extensive literature review (6,16,34), expert meetings, and focus groups (17). Moreover, we could quantify the contributions of groups of factors, by interpreting the reduction in OR after introduction of explanatory factors to the basic model as the mediating role of these factors to socioeconomic inequalities in sports participation.
Our study was cross-sectional and, therefore, could not disentangle causal pathways between SES, explanatory factors, and sports participation. Although we made a well-considered selection of explanatory factors, results are likely to depend on the specific factors used in this study. The classification of factors into the three domains (neighborhood, household, and individual) has been done through informed discussion among the research team and in close consultation with the literature. However, we acknowledge that this classification is debatable, because different researchers may classify items differently.
Items to measure individual-level cognitions were not behavior specific for sports participation, but they referred to recommended physical activity (being physically active with moderate intensity for at least 30 min·d−1). We suspect that associations of individual factors with the outcome measure sports participation would have been even stronger if those variables had been behavior specific for the outcome (15).
We could not include objectively measured neighborhood characteristics in our analyses. Therefore, it remains uncertain to what extent differences in perceived neighborhood safety and attractiveness reflect objective differences in neighborhood characteristics. On the one hand, the lowest socioeconomic group more frequently reported bad weather (although weather differences between neighborhoods are very unlikely); this might suggest that lower socioeconomic groups have an overall negative perception of life, including the perception of their living environment. On the other hand, we found that neighborhood factors could explain some of the neighborhood variance in sports participation. Also, in additional multilevel analyses, we found significant clustering of perceived safety, attractiveness, and availability of facilities within neighborhoods (results available on request). Both findings may indicate the existence of true neighborhood differences.
Our findings are in line with two studies from Australia that conclude that personal, social, and physical environmental factors could explain educational inequalities in leisure-time walking (2) and variations in recommended levels of exercising (13). Also similar to our study, these two studies found that distal (environmental) factors could explain less of the (socioeconomic) variations in physical activity than more proximal (household and individual) factors. This does not mean that neighborhood factors require less attention in policy and intervention development. From a population perspective, even small odds ratios for neighborhood characteristics may imply that changes to (perceptions of) the neighborhood context may have a significant effect on physical activity levels. Especially because we found that most perceptions of unfavorable neighborhood factors were more prevalent among lower socioeconomic groups, these may offer important opportunities to reduce socioeconomic inequalities in physical activity.
All analyses were done for two different SES indicators separately, because education and income may be related to sports participation through different processes (12). In our study, education showed a larger gradient with sports participation than did income, but, on the other hand, neighborhood and household factors could explain more of the income than educational inequalities in sports participation. Future research should further disentangle which causal mechanisms can explain educational and income inequalities in sports participation.
CONCLUSIONS AND IMPLICATIONS
This study is among the first to show that neighborhood and household factors, in addition to individual factors, contribute to the explanation of socioeconomic inequalities in sports participation. More research into specific pathways between (objective and perceived) neighborhood, household, and individual factors is needed to better understand how socioeconomic disadvantage leads to physical inactivity. Our results suggest that intervention and policy strategies targeted towards lower socioeconomic groups would need to intervene on neighborhood, household, and individual factors, to yield a maximal increase in sports participation among lower socioeconomic groups and, ultimately, reduce socioeconomic inequalities in health.
The GLOBE study is carried out by the Department of Public Health of the Erasmus University Medical Centre in Rotterdam, in collaboration with the Public Health Services of the city of Eindhoven and the region of southeast Brabant. The authors are thankful to Roel Faber and Frank Santegoeds for constructing the dataset, and to Caspar Looman for the solution of how to treat missing values. The study is supported by grants of the Ministry of Public Health, Welfare and Sport and the Health Research and Development Council (ZON; number 40050009). KG is supported by an Australian National Health and Medical Research Council Sidney Sax Fellowship (ID 290540).
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Keywords:© 2008 American College of Sports Medicine
PHYSICAL ACTIVITY; SOCIOECONOMIC INEQUALITIES; NEIGHBORHOOD FACTORS; HOUSEHOLD FACTORS; SELF-REPORT