The present study examined PA inequality, defined by the Gini coefficient for total activity counts and MVPA minutes, and how social structure influences PA inequality during time-segmented organized group setting meetings for children. In the examination of PA inequality across total meeting time, youth club settings had lower inequality in activity counts than school and before-/after-school program settings, and school had greater inequality in MVPA minutes during meeting time. In the examination of PA inequality on the more granular session time scale, results supported our hypothesis that PA inequality would differ by session purpose. Specifically, organized PA sessions had lower PA inequality than academic, enrichment, and non-active recreation sessions for both total activity counts and MVPA minutes. These findings were consistent for activity counts for females and full pay lunch status participants, as well as MVPA minutes for males, females, and full pay lunch status participants. However, our hypothesis that free play sessions would have lower PA inequality compared to organized PA sessions was not supported.
A previous systematic review and meta-analysis of PA in organized settings found that children accrue substantial amounts of PA during setting time and that engagement in PA is greater in after-school programs compared to school (33). Similarly, the results of the present study show that the school setting has greater inequality in MVPA than the out-of-school settings. The examination of setting meeting time on a more granular time scale, defined as sessions, aids in understanding why these differences in PA inequality may exist between settings. The level of PA inequality differed by session purpose type, such that PA sessions had lower inequality than other session types. Thus, the differences in inequality by setting type during total meeting time may be due to differences in the number and length of implemented sessions during school and out-of-school setting meeting routines. For instance, a greater amount of time is likely spent in academic sessions during school compared to out-of-school settings. Additionally, as schools face challenges in promoting PA opportunities (8, 33), the out-of-school settings may have inserted more PA sessions during meeting time, contributing to lower inequality during meeting time overall. A further examination of meeting routines for school and out-of-school settings would contribute to a greater understanding of how children spend their time in organized settings and PA inequality within these settings.
Numerous studies have shown that social system structure within setting time influences variability in PA outcomes (22, 24, 27, 28, 30, 31). For example, an examination of PA in preschool settings found that mean PA was greater when children were arranged in small groups compared to whole groups (24). In youth club settings, research has shown that a greater proportion of time is spent in MVPA during PA sessions compared to curricular sessions (31). These studies demonstrate that social structure influences average PA across meeting time. The results of the present study extend these findings by illustrating that social structure also influences inequalities in the distribution of PA among children within setting time. This suggests that how meeting routines are structured can influence not only how much PA children accrue, but also whether PA is accrued by all children in the setting or by only a subset of a few children in the setting.
As several studies have shown that children’s PA is higher during free play PA than organized PA sessions (22, 27, 28), we hypothesized that free play PA sessions would also have lower inequality than organized PA sessions. Although this hypothesis was not supported, results showed inequality in activity counts was lower during organized PA sessions than other session purpose types, such as academic and enrichment, and inequality in MVPA minutes was lower during organized and free play PA sessions than other session types. Organized PA sessions may provide structure that promotes similar amounts of PA among all children present in the setting, whereas during other session types, only a subset of children choose to engage in PA. Fairclough and colleagues (61) investigated PA patterns of children classified as high- and low-active during time-segmented school days and found that children in the high-active group had greater PA than children in the low-active group during segments such as lunchtime. However, there were no differences in PA between the high- and low-active groups during recess (61). They suggest children had more choice regarding the types of behaviors to engage in during lunchtime, such that the high-active group may have chosen to be more active during that time segment compared to the low-active group (61). In the present study, PA inequality may be greater during sessions such as academic and enrichment because only a subset of children chooses to be active when the intended purpose is not PA. While further examination is necessary to understand why PA inequality differs by session purpose, these findings suggest that inserting both structured, organized PA and free play sessions into setting routines may be a strategy for increasing both the amount and the level of equality of PA for children during setting time.
The results illustrate differences in PA inequality between total activity counts and MVPA minutes. For example, inequality in total activity counts during school meeting time and before-/after-school meeting time were not significantly different, whereas inequality in MVPA minutes was significantly greater during school compared to before-/after-school. Additionally, Gini coefficients for activity counts and MVPA minutes are similar for organized PA and free play PA sessions, while the coefficients for MVPA minutes are higher than the coefficients for total activity counts for all other session types, illustrating higher inequality in MVPA minutes. Sessions that do not have a PA purpose may not create activity levels that exceed the MVPA threshold for all children in the session. Thus, a smaller subset of children in the session may hold all of the MVPA minutes, whereas a larger subset of children may not have any MVPA minutes, creating greater inequality. Although the PA guidelines provide recommendations for children to accrue MVPA, rather than light or total activity (i.e., light, moderate, and vigorous activity), replacing sedentary time with light PA also has health benefits (1). Thus, examining inequality in total activity is warranted. Additionally, future research should further investigate inequality in other PA-related outcomes, such as light PA and sedentary time, to gain a better understanding of the differences in inequality between total activity counts and MVPA minutes.
In the examination of PA inequality by session purpose among demographic subgroups, inequality in the free/reduced lunch status subgroup appears to be higher for total activity counts and MVPA minutes than inequality in the full pay lunch status subgroup, indicated by larger Gini coefficients for all session purpose types. Inequality in activity counts and MVPA minutes is similar between males and females, indicated by similar Gini coefficients for all session types except enrichment-PA sessions. Prior research examining PA inequality among demographic subgroups has predominantly focused on PA differences between subgroups. For example, high income, Non-Hispanic, and male subgroups have been shown to have greater PA and greater likelihood of meeting PA guidelines than low income, Hispanic, and female subgroups, respectively (9, 62–64). The present study indicates that inequalities also exist within demographic subgroups, and that the level of inequality in these subgroups differs by session purpose type. Widyastari and colleagues (39) recently examined PA inequality among Thai adults by demographic subgroups (e.g., male/female, low/high SES). Using the Gini coefficient, they found that PA inequality existed in all examined subgroups before and after the COVID-19 pandemic. Specifically, post-pandemic, they found similar levels of inequality in PA among males and females, with Gini coefficients of approximately 0.49 and 0.45, respectively. By income level, PA inequality existed in all income groups, although inequality was highest among those with no income (Gini = 0.55) and lowest among those in the highest income group (Gini = 0.47) (39). Our findings are consistent with this prior research and suggest that PA inequality overall may not be due solely to differences in PA by gender and SES, illustrating the importance of examining PA inequality both within and between populations.
The Gini coefficient was selected as the metric of inequality in the present study because it generates a single summary statistic of the distribution that is relatively easy to understand, in that a coefficient closer to one indicates greater inequality (43). Additionally, the coefficient uses information from the entire distribution, rather than focusing on specific regions of the distribution (e.g., top and bottom 10%) (44). However, a limitation of the Gini coefficient is that it does not differentiate between different kinds of inequalities, such that two populations or session time segments with similar Gini coefficient values may have different distributions of PA. Additionally, interpreting the magnitude of the Gini coefficient and differences between coefficients is challenging because it is not expressed in natural units (65). Thus, interpreting the magnitude of PA inequality in organized group settings for children and the differences by session purpose in the present study is difficult. In the income inequality literature, a coefficient below 0.3 is considered low (i.e., more equal) and above 0.5 is considered very unequal (50), and previous analyses suggest a significant increase in the odds of poor health outcomes with a 0.05 change in income inequality (66). These values provide reference points for understanding the results of the present study, as cut-off values for interpreting PA inequality do not exist. Based on these values, our results indicate relatively large inequalities in MVPA minutes exist in organized group settings for children and suggest the differences in inequality by session purpose are meaningful.
Although the Gini coefficient has been the most popular measure of inequality in the public health literature (43), it is only one of many potential metrics for operationalizing inequality and understanding PA outcomes (43, 44). Primarily used to assess income inequality, alternative metrics for measuring inequality include decile ratios (67), the Atkinson index (68), the coefficient of variation (69), and the generalized entropy index (70). Another metric used in the public health literature that examines spatial social polarization is the Index of Concentration at the Extremes (ICE) (38, 71–73). The ICE measures the extent to which a population in an area is concentrated into extremes of privilege and deprivation, with a value of -1 indicating that 100% of the population is concentrated in the most deprived group and a value of 1 indicating that 100% of the population is concentrated in the most privileged group (38). One advantage of the ICE over the Gini coefficient is that, with its range of -1 to 1, it can provide the direction of concentration (i.e., toward privilege or deprivation). For example, in relation to income, a neighborhood with 100% low-income residents and a neighborhood with 100% high-income residents would have different ICE values but the same Gini coefficient given perfect equality of income in each neighborhood (38). A key difference between the Gini coefficient and ICE is that the calculation of the ICE requires setting the extremes (e.g., income cut-points for the 20th percentile for deprivation and the 80th percentile for privilege), whereas the Gini coefficient uses the entire distribution (38, 44). Overall, each inequality measure has advantages and disadvantages. To our knowledge, other inequality metrics have not yet been used to investigate PA outcomes. Thus, further investigation of additional metrics is warranted to more fully understand PA inequality.
Study limitations and strengths
A limitation of the current study is that organized group settings from only two rural Nebraska communities were examined, potentially limiting the generalizability of results. Additionally, although the Gini coefficient is commonly used to examine inequality, the metric does not indicate where inequality occurs. Thus, two session time segments with different PA distributions could have the same value. Further, the coefficient does not directly indicate whether a particular subgroup (e.g., male, high income) is more or less advantaged than another in a session where inequality exists (e.g., whether males hold all of the PA and females hold no PA in a particular session). These limitations highlight the need to further examine PA inequality using additional metrics in future research. Strengths of the study include the use of an objective measure of PA and a large sample size of session time segments. Additionally, we used a novel observation method to characterize the routines of organized group setting meetings for children and the naturally occurring social structures within meeting routines. Finally, to our knowledge, this is the first study to examine PA inequality during time-segmented school, before- and after-school, and youth club setting meetings for children and the influence of session time segment social structure, defined as session purpose type, on PA inequality.