Background
The promotion of an active lifestyle among youth is crucial given that the benefits of regular physical activity (PA) are well acknowledged [
1‐
3] and many health behaviours may track into adulthood [
4,
5]. Moreover, physical inactivity was globally identified as one of the four leading risk factors for non-communicable diseases causing more than three million deaths per year [
6]. Different studies investigating current levels of PA among children and adolescents in Europe reported that only a small percentage meets the recommended PA guidelines of 60 min of daily moderate-vigorous PA (MVPA) [
7,
8]. Such activities require a high energy expenditure (≥3 METs) and result at least in raising the heart-beat and leaving the person feeling warm and slightly out of breath. In general, MVPA is seen as health-enhancing physical activity which is defined as any form of PA that benefits health and functional capacity [
9]. Along with the alarming European situation, only 20 % of the boys and 11 % of the girls aged 11 years are adequately physically active in Switzerland [
8].
In addition to the level of PA, sedentary behaviour (SB) has generated much interest among researchers in recent years. SB refers to behaviours that require a low level of energy expenditure (≤1.5 METs) and are mostly spent sitting [
10]. Being sedentary is distinct from being physically inactive and, therefore, cannot be defined as the absence of health-enhancing PA [
11]. There is growing evidence that SB can affect the health status in children and youth distinct and independent of PA levels [
12]. Different studies demonstrated that SB is highly prevalent and children spend up to nine hours of their daily waking time sitting [
12,
13]. As a result of this high prevalence of SB among youth, the promotion of an active lifestyle in combination with a decrease in levels of SB should be a fundamental component of public health. However, for the development of effective interventions, it is essential to have a clear understanding of children’s PA and SB patterns and which factors influence these behaviours [
14].
One factor often thought to be an important correlate of PA and SB is socioeconomic status (SES) [
13,
15], which is usually determined by individual factors such as educational attainment, household income or the occupation of a person [
16]. Previous research reported that these factors have an important influence on health status and the prevalence of being overweight [
17,
18]. The participation in leisure-time PA and the membership in sports clubs further seem to be positively associated with individual SES [
19]. Different studies also demonstrated that children with a lower individual SES have more access to electronic media devices [
20] and spend more time in SB such as TV viewing [
21,
22]. Nevertheless, results about the association between individual SES and SB as well as PA still are ambiguous and little is known how socioeconomic factors influence the daily patterns of these behaviours in school-aged children [
15,
21,
23,
24]. A previous review by Beenackers and colleagues [
25] further showed that the influence of socioeconomic inequalities among adults differed greatly by domain of PA, although there was no clear difference in total PA. This result may be an indicator for the importance of focusing on specific domains and settings and indicates that the choice of total PA or SB as outcome measure may not be suitable when investigating associations between PA or SB and SES. However, it is unclear at present if similar patterns already exist in childhood and whether children with varying socioeconomic backgrounds use different spaces to be physically active or inactive.
Along with the increased use of ecological models [
26], researchers have recently begun to focus more on the environment in which people live. Therefore, also the neighbourhood has been recognized as an important correlate of PA and has become an increasing focus of research [
27,
28]. Different studies reported that neighbourhood SES, commonly measured using area-level variables such as percent unemployed or median household income [
16], may influence resident’s PA independent of individual SES [
27,
28]. In terms of SB, few studies investigated the influence of the neighbourhood environment on time spent in SB. Although initial findings point to a stronger influence of proximal factors such as home and family environment [
29,
30], there is some evidence that children living in socioeconomically disadvantaged neighbourhoods are more likely to spend time in sedentary activities [
31,
32]. However, most studies investigating the association between neighbourhood and SB in children used subjective self- or parent-reported TV or screen time, which is a very poor indicator of total time spent in SB [
33]. Moreover, an identified research gap which needs to be addressed is the lack of studies investigating associations of active travel to destinations other than school with diverse sedentary activities beyond simply screen-based activities [
34].
Increasing numbers of investigators have recently used Global Positioning System (GPS) devices along with accelerometers to identify spatial behaviours and how people use their surrounding built environment for PA [
35]. With this technology, it is possible to objectively assess the amount of PA and SB and the location where these behaviours take place [
36]. Several studies concluded that the simultaneous use of accelerometry and GPS provides reliable and accurate measurement of PA and its spatial context [
37,
38]. Thus, the combination of accelerometry and GPS seems to be a promising method to gain further insights into differences in spatial PA and SB patterns among children from neighbourhoods with varying SES.
To conclude, only few studies investigated patterns of both PA and SB in different settings and domains by means of objective accelerometer-based measurements and among children from different neighbourhood SES. Understanding the influence of the social environment in which children live and how they use this environment to be physically active or inactive is an important step for future interventions [
28]. Particularly, to guide interventions that target on improving local environments and supporting the design of outdoor spaces, especially in more deprived areas, insights into patterns of PA and SB within different locations among children from neighbourhoods with different SES are needed. Using the combination of GPS and accelerometry, the current study aimed to identify locations where children attending second grade spend time in MVPA and SB. In particular, the goal of our study was to investigate if there are any differences in the spatial PA and SB pattern between children from neighbourhoods with varying SES independent of their individual SES.
Discussion
To the best of our knowledge this is the first study that objectively assessed the spatial context of MVPA and SB by means of GPS and accelerometry in primary school children living in neighbourhoods with varying SES. Our findings show that children from both districts achieved most of their minutes in MVPA at own school, on streets, at home, and in SB at home and own school, respectively. Streets, recreational facilities such as parks and sports facilities and other schools were highly conducive for MVPA among all children, whereas the likelihood of being sedentary was highest at home and own school. The high-SES group compared to the low-SES group accumulated more minutes in both MVPA and SB at other schools, in recreational facilities and on streets, while the opposite was true for other places. However, when taking the total time spent in a setting into account and focusing on the proportion of time spent in MVPA or SB, differences between districts were only found at other schools and outside the city, where high-SES children showed a significantly higher activity level.
Children from both neighbourhoods accumulated on average most of their health-enhancing PA on school grounds, streets and within the home environment. The importance of these settings in regard to the accumulation of daily MVPA was already observed by different studies [
36,
48,
55] and can mainly be explained by the high use of these settings during the week. Overall, children recorded 32 % of their waking time at own school, 25 % at home and 16 % on streets, which is somewhat congruent with the accumulated time in MVPA. When taking into account the time spent in a setting and using the proportion of MVPA instead of the absolute amount, other schools and outdoor spaces such as parks, playgrounds, sports facilities and streets were found to be highly conducive for MVPA in both the high- and low-SES group. Accordingly, a recent study found that the proportion of time spent in MVPA was particularly high in active transport, playgrounds, sports facilities and urban green space [
49]. It is well established that green space is highly supportive for MVPA, although the use of these settings as an absolute measure of time is low [
49,
55,
56]. The high use of streets may be an indicator for active transportation or informal play and underlines the importance of these streets-based activities to reach recommended levels of PA [
49,
57].
The current study further supports the finding that home and school environment is associated with generally low levels of recorded PA when using the relative amount of MVPA [
56,
58]. Irrespective of neighbourhood SES, our participants recorded the greatest amount of SB at home and own school, where they spent more than 50 % of the time in SB. TV viewing and homework are two common activities that may account for the great portion of SB at home [
59]. Oreskovic and colleagues [
56] reported that indoor spaces are less conducive for PA than outdoor spaces. The high amount of SB recorded in the street setting among both districts could partly be explained by motorized transport. Motorized transport was found to be amongst the five most common sedentary activities in a sample of Scottish children [
59].
In addition to the similarities between the two groups of children living in neighbourhoods with varying SES, we were also able to observe several differences in their spatial activity behaviour. Children from the high-SES neighbourhood accumulated significantly more SB as well as MVPA within parks and sport facilities. Given that both groups showed a similar proportion of time spent in PA and SB when taking into account the total time spent in these settings, this difference may rather be explained by a more frequent use than by a different behaviour within the settings. This assumption is partly supported by a higher density of parks in the high-SES district (about 30 %), which resulted in a four-fold increase in weekly dwell time (+1.5 h) among children living in the high-SES neighbourhood. Accordingly, this result confirms recent studies that highlighted the importance of proximity and access to parks and green space for meeting recommended levels of PA [
60,
61]. In contrast, the more frequent use of sport facilities by the high-SES children cannot be explained by a higher density of sport facilities as this density was much higher in the low-SES district (about 100 %). Jones and colleagues [
62] already found that residents from more deprived areas were less likely to use green space such as parks and sport facilities, although the accessibility was generally better. They concluded that also perceived access, problems with safety and the quality of the green space may play an important role in the use of these settings. Therefore, future studies and interventions have to address the individual needs of residents among different neighbourhoods to provide the appropriate infrastructure [
62]. Previous studies further found that low-SES children engaged in more unstructured activities within their near neighbourhood, while high-SES children were more often encouraged by their parents through co-participation or logistical and financial support and, therefore, spent more time in commercial PA facilities or were engaged in sports club and organised activities [
19,
52,
63]. Although we were not able to find a significantly different number of sports club memberships between the two districts, high-SES children were more likely to be a member of two or more sports clubs (18 % versus 5 %), while low-SES children predominantly reported to be a member of only one sports club (42 % versus 38 %).
The significantly higher amount of MVPA and SB accumulated by children from the high-SES neighbourhood within the street setting can also be explained by a different dwell time. This higher use of the street environment by the high-SES children may partly be attributed to differences in the settlement structure across the two districts. The high-SES children mostly lived in detached single or multi-family houses situated directly on a traffic-calmed street, whereas children living in the low-SES neighbourhood mainly resided in housing estates consisting of several apartment blocks with enclosed gardens equipped with playgrounds and playing fields. Therefore, it is likely that high-SES children more often used the street environment for informal play and traveling to a friend’s house or a public playground or park. In contrast, the low-SES group accumulated more minutes in both MVPA and SB in the setting other, as they spent more time close to their home within the housing estates [
63]. Furthermore, a recent review summarized that lack of perceived neighbourhood safety may be associated with lower levels of active transport [
64]. Given that residents living in a low-SES area often perceive their neighbourhood as less safe compared to high-SES residents [
65], safety concerns could be another reason for the low use of streets by low-SES children. A population survey conducted by the Office for City and Neighbourhood Development of the City of Zurich actually could show that the residents of the low-SES district felt less safe walking alone in their neighbourhood at night [
66].
While the differences within the settings park, sport, street and other can be referred to a different frequency of use, the significant differences at other schools and outside remained even after accounting for the total time spent in these settings. As a result, these differences could hardly be explained by a more frequent use or by different dwell times, but rather by actually different behaviour when visiting these settings. It can be hypothesized that children from the high-SES district use other schools predominantly for organized PA, as Swiss sports club often use the school infrastructure for their training, or take part in family-based PA in these settings [
19]. In contrast, the low-SES children spend time within this setting in more sedentary activities attending the public childcare services of the City of Zurich during lunchtime or after school. Moreover, Lamprecht and colleagues [
52] reported that, firstly, girls are less likely to be a member of a sports club and that this aspect is particularly true for girls with an immigrant background. We were able to confirm this effect in the present study with only 33 % of the low-SES girls being a member of a sports club compared to 44 % of the high-SES girls, which were more often of Swiss nationality. Secondly, the same study found that gymnastic clubs, which often use the school infrastructure for their training, are the most popular sports clubs of Swiss girls [
52]. Therefore, we can conclude that the more active use of other schools by the high-SES children can partly be attributed to the fact that the high-SES group contained more girls and those were more likely to be a member of a sports club that uses the school infrastructure for training.
The higher and more active use of the area outside the city of Zurich can also be explained by a higher engagement in structured activities within sports clubs as well as by higher logistical and financial parental support [
19,
52,
63]. A previous study reported that children with a high-SES background engaged in family-based PA more often than children from a low-SES background [
19]. It can be suggested that high-SES families more frequently left the city than low-SES families to be physically active in commercial PA facilities outside the city.
Limitations
Our study has several limitations. Although objective accelerometer-based measurements of PA are considered to be valid and reliable, they are associated with different known problems. These include the inability to accurately assess certain activities such as upper body movement or cycling [
67], the choice of different processing methods and threshold values to determine MVPA, which can have an impact on the recorded level of PA [
68], and reactivity issues reported by Dösegger and colleagues [
69]. Inaccurate and missing GPS positions due to poor satellite signal may lead to misclassification errors [
48] and, therefore, to under- or overrepresentations of certain activity settings. Despite taking steps to reduce issues with spatial inaccuracy by identifying invalid GPS points and choosing a buffer zone of ten meters around polygons, misclassification bias remains possible. In particular, the use of buffer zones accounting for the measurement error of the GPS devices might have generated new misclassifications, especially affecting the street setting. Moreover, we did not impute missing GPS data. Due to the fact that we chose second graders from one town and only surveyed them during summer months, our results may not be generalizable to other age groups and living contexts, and are not representative for the entire year. In addition to the assessed parameters such as individual socioeconomic factors or weather conditions, also parent’s and children’s subjective perception, their safety concerns and fears can have a crucial influence on children’s independent mobility and activity behaviour. However, as these parameters haven’t been assessed in this study, they were not available for analysis.
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
RB conceived and coordinated the study, contributed to the study design, acquisition of data, part of the data cleaning, statistical analysis, development of the methodology, and drafted the manuscript. LT contributed to data cleaning, data handling and the statistical analysis. EDB supported study design, data analysis and critically revised the manuscript for its content. KM critically reviewed the manuscript for its content. All authors read and approved the final manuscript.