7  Socioeconomic context, Conclusion, and Outlook

7.1 Synopsis of results

The results of the analyses conducted in this thesis can be summarised as follows:

  1. No evidence was found for the effects of 14 weeks of additional physical education on physical fitness and executive functions in children with deficits in their physical fitness. The lack of detectable intervention-related effects might have been influenced by an insufficient intervention duration. In addition, interventions that are tailored to a specific component of physical fitness, rather than comprehensive exercises that encompass multiple components, might lead to detectable improvements in physical fitness, even in interventions with comparatively shorter durations. Finally, more insight is needed into how physical fitness behaves in children with physical fitness deficits, what the possible causes of these deficits are, and how these causes can be translated into meaningful interventions to improve children’s physical fitness and health.

  2. Larger body height is associated with better physical fitness performances, while an increase in body mass has negative effects on performances in tests where body mass is used directly, but seems to be beneficial when it can be used as a counter mass. While body mass index (BMI) is predominantly used in exercise research as a proxy for overweight/obesity, the use of the composite score may conceal a more complex and interactive set of variables that influence physical fitness. As these appear to vary between the different components of physical fitness, these relationships should be explored in future research.

  3. Relative growth seems to have an overall positive effect on physical fitness, with the exception of cardiorespiratory fitness, where even negative effects are observed. Furthermore, delays in growth trajectories seem to be beneficial, especially for cardiorespiratory fitness. This could indicate possible adverse effects of skeletal growth that seem to be specific to certain components of physical fitness and appear to be more pronounced in children with lower relative-growth or age-related improvements in physical fitness. Therefore, further research is needed to clarify the role of age and growth-related effects on different components of physical fitness and their correlates.

  4. 6 min run, star run, and one-leg balance performances only show effects on reaction speed in the Simon task when individual differences are not accounted for. However, accounting for individual variance revealed that the effects of 6 min run and star run appear to be in polar domains affecting executive function. This could be explained by differences in daily life that either emphasise physical and cognitive engagement or promote cognitive but not physical engagement. However, further research is needed to elucidate the links between physical fitness and executive function, taking into account the children’s social circumstances and daily life, as well as individual differences.

7.2 Discussion of the aims of the SMaRTER study

The central aim underlying the analyses carried out in this thesis was described in Section 2.1 as:

Analysis of the short-, mid-, and long-term intervention effects of a remedial physical education intervention in third grade children with deficits in their physical fitness.

Following the discussion on the lack of evidence for intervention-related effects in the SMaRTER study in Section 3.7, further studies on fitness component-specific and dose-dependent physical education-based fitness interventions are needed. These studies would allow a more informed approach to the development of a physical education curriculum for third and fourth grade children with physical fitness deficits, which was defined as the main objective of the SMaRTER study. However, there would be merit in reconsidering the assumption that children with physical fitness deficits have “untapped potential” that can be realised through additional exercise. A comparable approach implemented in FITNESSGRAM® identified the physical fitness of children within a “Healthy Fitness Zone”, “In Need of Improvement”, or “In Need of Improvement - Health Risk” based on cut-off values with a high probability of a successful identification of metabolic syndrome1. Implemented physical fitness tests focused on aerobic capacity2, muscular fitness/mobility3, and anthropometric information4. Based on test-specific cut-off values, the programme suggests that children are classified as “fit” if they are in the “Healthy Fitness Zone” in 5 out of 6 tests (or, without anthropometric information, in 4 out of 5 tests) (Cureton et al., 2013). In implementing this approach, a study investigating the relationship between physical fitness5 and scores on two academic achievement tests6 within two cohorts7 in 2002/03 and 2007/08 identified four groups of children: Those who were (1) assessed as “fit” in two consecutive years, (2) assessed as “fit” in the first year only, (3) assessed as “fit” in the second year only, and (4) not assessed as “fit” in any year. Implementing this approach, a study assessing physical fitness8 associations with scores in two academic performance tests9 within two cohorts10 in 2002/03 and 2007/08 identified four cluster of children: (1) score as “fit” in two consecutive years, (2) score as “fit” in the first year only, (3) score as “fit” in the second year only, and (4) score as “fit” in none of the years. They found that children who were not categorised as “fit” in both years performed worse in both academic tests than the other three groups. They also found that socioeconomic factors moderated the correlation: Children from lower socioeconomic backgrounds, such as those eligible for free or reduced-price lunch, those from ethnic minority backgrounds, or those whose parents did not graduate from high school were more likely to score lower on both academic tests (London & Castrechini, 2011). Similariy, a study examining the relationship between socioeconomic status and reaching the “Healthy Fitness Zone” found that boys11 from more vulnerable socioeconomic positions12 were more likely to fail the “Healthy Fitness Zone” for curl-ups, while girls13 from more vulnerable socioeconomic positions were more likely to fail several “Healthy Fitness Zones” (i.e., body mass index, pacer labs, curl-ups, push-ups, and sit-and-reach) (Bohr et al., 2013). Another study that cross-sectionally examined the prevalence of 5,613,228 4th-12th grade students who met or did not meet several “Healthy Fitness Zones” (i.e., PACER labs, push-up and curl-up test) from 2006/07 to 2016/17 and their moderation through different types of discrimination. They found that the proportion of children meeting “Healthy Fitness Zones” increased overall over time, across all gender and racial/ethnic subgroups. However, the analysis revealed differences between gendered and/or racial/ethnic subgroups: Boys achieved more “Healthy Fitness Zones” compared to girls, and white children achieved more “Healthy Fitness Zones” compared to Hispanic and/or black children, with Hispanic and non-Hispanic black girls achieving the lowest scores. Further, they could show the differences between gender and racial/ethnic subgroups widening over time between cohorts. In addition, their analysis of home neighbourhood socioeconomic status14 revealed that the proportion of children who do not reach the Healthy Fitness Zones increased as the percentage of households below the poverty line in the neighbourhood increased (Konty et al., 2020).

At this point, it is important to consider that the associations between socioeconomic status and physical fitness deficits are not solely momentary assessments. Living and growing up in poverty has been shown to have cumulative effects that moderate children’s morbidity and cognitive and socio-emotional development (Evans, 2003). For example, a study analysing the associations between body mass index and a cumulative poverty risk factor15 in 329 adolescents born between 1983 and 1990 and interviewed at the ages of 9, 13 and 17 revealed a positive association between body mass index and cumulative poverty risk score, with children who spent their entire lives in poverty having a higher risk of being overweight (Wells et al., 2010). Incorporating these associations into the scientific foundation of the research question would reframe the understanding of the ‘untapped potential’ hypothesis. To improve physical fitness in children with physical fitness deficits, interventions would need to consider current socioeconomic individual and community positions and any potentially related moderators of physical fitness as well as their cumulative characteristics during childhood development to date. Accordingly, the next chapter reviews and discusses the available socioeconomic information from the SMaRTER sample and the evidence for associations between physical fitness and socioeconomic moderators.

7.3 Discussion of socioeconomic context

In Section 2.5.2 socioeconomic information of the children was described and showed children where income information were available (i.e., 46 out of 76), to be predominantly placed in households having below-average income per capita. It should be noted, socioeconomic information was not part of the inclusion criteria, it is, however, striking that filtering for physical fitness deficits shows a strong bias towards below-average income. This bias agrees with findings of Tomkinson et al. (2019) that show international secular trends in shuttle run performance to be positively correlated with progression of the Gini index towards income equality (see Section 1.1.3.1 for details). Assuming that financial costs of a healthy lifestyle are based on the population average income of a country or region, and considering the Gini index as a measure of overall societal participation in such an active lifestyle, children’s physical fitness (or at least certain subcomponents such as cardiorespiratory fitness) could be strongly related to family income. This relationship is further strengthened by interrelation between physical fitness and weight status, which also moves along socioeconomic lines (see Section 1.2) and which also showed positive but weak effects of physical fitness interventions (see Section 1.4.2). Taking these associations into account would allow for a more contextualised evaluation of previously discussed results.

This, for example, allows for a different contextualisation of the lack of intervention effects in the SMaRTER study (see Chapter 3), coupled with overall small and infrequent evidence for improvements of cognition and physical fitness associated with qualitative school-based physical fitness interventions at post assessments (Garciá-Hermoso et al., 2021; García-Hermoso et al., 2020) and at follow-ups (Jurak et al., 2013; Lai et al., 2014). If children’s physical fitness deficits are caused or at least moderated by low income or lack of resources (as discussed in the chapter above), then physical education or school-based interventions that do not address or change socioeconomic conditions of children’s families would always fall short of making meaningful changes in children’s health status. Or to put it another way: Physical fitness interventions that are divorced from social change approaches to eradicate poverty only target the problem at the symptom level and will not be able change the overall trajectory of public (child) health.

Effects of maturation and growth delay on physical fitness explored in Chapter 4 may be related to theories suggesting that psychosocial stress in a family due to insufficient financial resources or lack of father figures leads to a “faster reproductive strategy” that causes earlier maturation/puberty (Hochberg & Belsky, 2013; Oelkers et al., 2021). Accordingly, in addition to limited access to resources to promote physical fitness, the socioeconomic environment would potentially influence the development of physical fitness by driving maturation/puberty. These advances could also affect skeletal growth, which in turn affects the development of physical fitness of children from lower income families differently than children from higher income families.

With the positive correlation between children’s overall physical fitness and age-related effects found in Chapter 5 and the correlation between growth delay effects, maturation effects, and overall physical fitness in Chapter 4, moderating effects of socioeconomic background of families (Ma, 2000) and areas (Nieuwenhuis & Xu, 2021) were postulated. This would suggest that these associations are not only stable between families and areas across the whole socioeconomic spectrum, but are also present among children with low physical fitness from low-income families. Therefore, further research is needed to explore the impact of socioeconomic resources in families, areas, and schools in order to design meaningful interventions.

This would also be consistent with results of the Simon task in Chapter 6, where a positive correlation of individual variance components was found between overall reaction speed in the Simon task and 6 min run effects. Due to social mechanisms such as gentrification in the Berlin area (Borck & Gohl, 2021), higher income families tend to live in higher income neighbourhoods, which results in unequal distributions of resources with more resources available in more affluent areas and schools. Accordingly, they can offer their children a more physically and cognitively engaging environment, which may explain the EMOTIKON findings that children living near Berlin have better physical fitness compared to children living further away (Kliegl & Teich, 2022).

In addition, it should be noted that the impact of the covid pandemic was exacerbated in low-income families and areas which faced higher rates of infection and mortality (Elgar et al., 2020; Marmot & Allen, 2020), were associated with higher rates of pre-existing conditions (Heisig, 2021), had higher rates of job losses and income reductions and limited access to food and safe shelter (Wright et al., 2020), showed higher rates of financial stress (Ettman et al., 2021), had higher rates of depression and anxiety during lockdowns (Fancourt et al., 2020), and higher rates of parenting stress and mental health problems (Li et al., 2022), to name a few. However detrimental effects of the covid pandemic on physical fitness seem to be larger in higher income areas. For example, Teich et al. (2023) found larger declines in “fitter” schools compared to “unfitter” schools. They argue that these effects are due to the unequal distribution of physical fitness resources before the pandemic. While disadvantaged children have fewer resources to mitigate the loss of structured physical activities, they also had less access to these activities before the pandemic. Thus, larger declines can be found in more affluent areas/schools as there is “more to loose”.

7.4 Integrative perspective

These findings allow to revisit the central question of the SMaRTER study: How to improve physical fitness in children who are the most at risk to develop adverse health effects? While literature assessing the effects of schoolbased interventions in the context of available family and community resources, such as family income (Ma, 2000; Tomkinson et al., 2019), or wealthy neighbourhoods/areas (Kliegl & Teich, 2022; Nieuwenhuis & Xu, 2021), is scarce, it seems evident that these parameters are central to children’s physical fitness trajectories. This would raise the question, how can school-based physical fitness interventions be structured to effectively improve physical fitness and by extension population health and quality of life? However, poverty is a necessary condition for capitalist and colonial mechanisms of resource distribution (Marx, 1955; Sullivan & Hickel, 2023), which use systems of oppression such as racism and sexism to maintain social hierarchies (Mendívil & Sarbo, 2022). These systems of power create specific socioeconomic and cultural conditions that shape children’s and families’ environments and thus affect their health outcomes and life trajectories (see (Drewnowski & Eichelsdoerfer, 2010) for an analysis of the impact of poverty on obesity). Therefore, in order to develop meaningful interventions and policies need to take into account underlying socioeconomic conditions in order to improve public health and physical fitness.

Further, as shown in Chapter 4 and Chapter 5, anthropometrical parameters of children as well as their progression through age and growth-related development play an essential role in the development of physical fitness. While these parameters individually have been rigorously studied, interactions between different growth-related parameters and their effects on various physical fitness components is not well researched to this date. Accordingly, more research endeavours developing methods to elucidate these interactions as well as assessing their effects on physical fitness components in children are needed. In accordance with the paragraph above, these effects need to be properly contextualised in their environmental, cultural, and socioeconomical circumstances.

Finally, as shown in Chapter 6, multiple data points of the Simon task at any assessment allowed for a more detailed analysis and understanding of the interrelations of physical fitness and executive function through considering individual differences and variance. Accordingly, for future research endeavours assessing the interrelations of physical fitness and executive function and by extension any other potential correlates, individual differences and variance should be considered and integrated.

7.5 Conclusion

In conclusion, for the development of meaningful interventions of physical fitness, it is important to understand the relevance of the individual as well as environmental context that these interventions happen in. For the individual context, for example, the progression in growth and age and their interactions have shown to be relevant for developments of physical fitness. Regarding the environmental context, available resources such as income or time seem to be relevant causal moderators of physical fitness. These factors need to be considered in any assessment and integrated in interventions in order to achieve meaningful improvements in childrens physical fitness and general health.

  1. Metabolic syndrome is comprised of abdominal obesity, insulin resistance, disordered blood lipids, hypertension and glucose intolerance as symptoms that increase the risk of cardiovascular disease and diabetes. Further, in the cutoff scores are calculated with an emphasis on sensitivity, meaning correctly identifying children with metabolic syndrome rather than without.↩︎

  2. Included aerobic assessment are the one-mile run, PACER Test, and the Walk test.↩︎

  3. Muscular strength can be assessed using the curl-up, trunk extension, and/or 90° push up test. Flexibility can be assessed using the back saver sit and reach and the shoulder stretch.↩︎

  4. Anthropometrical information can be measured using skinfold assessments, bioelectrical impedance, or the BMI↩︎

  5. Fitness assessments were done focusing on aerobic capacity, body composition, abdominal strength and endurance, trunk extensor strength and endurance, upper body strength and endurance, and flexibility.↩︎

  6. California standardized test in math and English language arts.↩︎

  7. 1325 fourth to seventh and 1410 sixth to ninth grade children.↩︎

  8. Fitness assessments were done focusing on aerobic capacity, body composition, abdominal strength and endurance, trunk extensor strength and endurance, upper body strength and endurance, and flexibility.↩︎

  9. California standardized test in math and English language arts.↩︎

  10. 1325 fourth to seventh and 1410 sixth to ninth grade children.↩︎

  11. 460 boys aged 13 ± 1 years↩︎

  12. Socioeconomic status was assesses using the eligibility for federal free lunch program with a low socioeconimic status being assigned to children eligible for the program, while all other non eligible children were assigned to the high socioeconomic status group.↩︎

  13. 492 girls aged 13 ± 1 years↩︎

  14. Home neighbourhood socioeconomic status was defined as the percentage of households below the federal poverty threshold according to United States Census 2010 boundaries.↩︎

  15. Cumulative poverty risk score was calculated for every child growing up with an income-to-needs ratio below 1 and adjusted using the per capita index for poverty in the united states. The ratio recorded every six months from the children’s birth until the most recent assessment.↩︎