Temporal trends in weight-based disparities in physical fitness of children: 30-years of continuous surveillance through Slovenian national system

doi:10.21203/rs.3.rs-3879163/v1

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Research Article

Temporal trends in weight-based disparities in physical fitness of children: 30-years of continuous surveillance through Slovenian national system

https://doi.org/10.21203/rs.3.rs-3879163/v1

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Journal Publication

published 23 Oct, 2024

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Background

Obesity in childhood has deleterious consequences for health while improving physical fitness can significantly reduce health risks related to high body mass index. This study leverages near-census data collected by the national fitness surveillance system in Slovenia to investigate the evolution of weight-based disparities in the physical fitness of 7–15-year-olds between 1989 and 2019.

Methods

We used annual data collected within the SLOfit monitoring system in the period between 1989–2019, totalling 4,256,930 participants (about 137 000 per year). We examined cardiorespiratory fitness (600-m run test), muscular fitness (60-s sit-ups, bent arm hang, and standing broad jump test) and skill-related fitness (backwards obstacle course, 60-m dash, arm plate tapping). We grouped children according to the IOTF cut-offs for BMI to those living with normal weight or excess weight and estimated changes in physical fitness over time by fitting quantile regression models separately by sex and age group, and then using segmented regression to identify the structure of trends over time.

Results

Weight-based disparities in physical fitness were large in 1989 and have further increased from 1989 to 2019 for all fitness components and across all age and sex groups. The increase in disparities was generally larger in boys than in girls, and 10–15-year-olds compared to younger children. Most of the increase in disparities in health-related fitness accumulated during the 2010s, when the fitness of children generally improved, but much less so in children living with excess weight.

Conclusions

Despite recent improvements in population fitness levels, children with excess weight seem to be left behind, which adds to existing health inequalities. Our results should encourage policymakers to redesign policies aimed at promoting physical activity and enhancing physical fitness to make them more equitable and ultimately lead to reducing inequalities in fitness.

health inequalities

secular changes

obesity

socioeconomic status

The occurrence of childhood obesity has escalated on a global scale, with more than an eightfold increase in prevalence from 1975 to 2016 and estimates that there are more than 330 million children and adolescents aged 5–19 with either overweight or obesity in 2016.¹ Obesity in childhood has been associated with serious and lifelong consequences for health and well-being with links to several pulmonary, orthopaedic, neurological, gastroenterological, endocrine, cardiovascular, and other physical consequences.² In addition to the greater risk of non-communicable diseases, obesity also has broader psychological and social impacts, including the negative effects of stigmatization and discrimination, the increased financial burden on families affected by the disease, the diversion of community resources towards treating obesity and related ailments, and the direct healthcare expenses associated with treating obesity and its related conditions.³ Obesity is a complex condition, and it can be attributed to a combination of environmental, behavioural, biological, and genetic factors. Considering these factors, there is ample evidence of disparities in childhood obesity with respect to socioeconomic status (SES)⁴ and rural-urban place of residence,⁵ while a lack of evidence on disparities in the prevalence of childhood obesity-related comorbidities is evident.³

Fitness has been identified as the leading non-invasive indicator of health.⁶ High levels of cardiorespiratory fitness (CRF)⁷ and muscular fitness (MF)⁸ during childhood and adolescence have been linked to future cardiovascular and metabolic health benefits. Being less likely to experience chronic diseases such as obesity, type 2 diabetes, and cardiovascular disease,^6,9,10 symptoms of anxiety and depression^11,12 while improving academic performance and cognitive function^13,14 are all positive aspects seen among children who maintain high levels of physical fitness.

Childhood obesity is usually accompanied by lower levels of most fitness components, but a smaller portion of children living with excess weight exhibit high levels of fitness. Studies that have examined this so-called “fat but fit” phenomenon have documented that higher fitness mitigates some of the negative effects of childhood obesity.^15–18 More specifically, better cardiometabolic health^15,17,18 and positive relations with general well-being¹⁹ have been reported. In the long term, improving CRF can significantly reduce mortality risks related to high body mass index (BMI),²⁰ with even more compelling evidence coming from recent discussions that propose a weight-neutral approach to treating obesity.²¹

Trends in the fitness of children are well known and data show a decline in fitness up until the last decade, during which stagnation was observed in most fitness components.^22–24 Still, the evidence is typically based on studies that had low data granularity, have assessed one or very few physical fitness components, have predominantly included individuals with normal weight, and were mostly based in high-income countries.^22–25 On the other hand, there is very little data on fitness trends in children living with excess weight especially data that would allow examining the evolution of weight-based disparities over a long period.^26–30 Generally, across several available studies, children and adolescents with overweight and obesity presented with larger decrements in the health-related fitness components (i.e., mostly considering the CRF component) when compared with their peers with normal weight^26,31–35 with some conflicting results reported.²⁸ That said, fitness trends of children and adolescents from only several high-income countries were reported recently.^{27–30,36,37}

Fitness levels, similar to adiposity, can vary significantly across countries and even regions from the same country based on socioeconomic development, so it's important to consider each location's specificities.^36,37 Children and adolescents with lower SES have regularly presented less favourable trends in fitness.^38,39 It is important to note that we are aware of only one study that examined physical fitness trends of children and adolescents where both weight status and SES were considered.²⁶ This analysis of Chinese data spanning 30 years reported improvements in the fitness of children with excess weight in undeveloped areas (urbanisation < 30%) whereas the opposite, i.e., the largest decline was shown in areas with high urbanisation (> 50%).²⁶ In general, the decline in fitness was much greater in children with obesity compared to their peers with normal weight or underweight.

Implementing systematic fitness surveillance on the population level can lead to the identification of trends which could result in adequate and early intervention implementations in childhood and adolescence aimed at reversing the unwanted negative outcomes later in life.⁴⁰ Only several European countries employ a systematic fitness surveillance system during childhood, see support from the community, leverage positive environmental factors as well and include quality physical education in schools.⁴¹ Slovenia is one of the countries that implemented continuous surveillance of the physical development of children on a near-census level from the late 1980s and is scored as one of the countries with the highest grades in overall physical activity and physical fitness of children and adolescents.⁴¹ It has also been mentioned in contrast to Southern European countries as one of the top 5 most aerobically fit and physically strong countries.⁴² Nonetheless, Slovenia too has been grappling with the challenges of high-income nations in addressing detrimental lifestyle changes of its population.⁴³ During the last three decades, a dramatic increase in overweight and obesity among Slovenian children and adolescents was seen, but since 2010 until the COVID-19 pandemic, the prevalence of obesity has been steadily declining.⁴⁴ At the same time, initial declines in CRF also recently reversed, MF showed mostly negative trends, while skill-related physical fitness showed varying trends among Slovenian children and adolescents.^43,45 Increasing heterogeneity in fitness levels across the last 30 years was also noted, suggesting a growing disparity in terms of physical fitness.^43,46

One of the main incentives for our study is to address the lack of robust and extensive research on disparities in the physical fitness of children (as a marker of health) based on weight status. We note that the list of international priority areas for physical fitness research and surveillance among children includes the investigation and reduction of inequalities in physical fitness and the use of physical fitness surveillance to inform decision-making.⁴⁰ In our study, we examined temporal trends in the physical fitness of children using near-census data collected within the SLOfit national monitoring system. Our main aim was to examine trends in the period between 1989 and 2019 in weight-based disparities in physical fitness of 7–15-year-olds living with excess weight or normal weight. We also sought to identify the changepoints when the largest changes in the trends occurred and estimate their respective magnitudes during these 30 years. Finally, we aimed to examine how these trends in weight-based disparities in physical fitness differed between more developed and less developed regions according to area-based SES indicators.

Study design, participants, and measurements

In this study, we used data on physical fitness collected within the SLOfit monitoring system which was designed as a means of tracking the physical and motor development of children and youth in the Slovenian population.⁴⁷ Around 95% of all primary-school children are included in SLOfit, with more than 7 million sets of measurements and 200,000 children being measured yearly. To reduce the potential seasonal bias, all children are tested every year in April. Out of the employed battery of tests within SLOfit, we address health-related physical fitness, through measurements of body composition including BMI and triceps skinfold thickness (TSF), as well as CRF using the 600-m run test (R600), and MF using the 60-s sit-ups (SU60), bent arm hang (BAH), and standing broad jump (SBJ) tests. Skill-related fitness component was assessed using the backwards obstacle course (BOC), 60-m dash (D60), and arm plate tapping (APT) tests to evaluate speed, neuromuscular coordination, and gross-motor coordination, respectively. We based our analyses on data collected yearly in the period between 1989–2019 totalling 4,256,930 participants. This amounts to more than 135,000 individuals per year over 31 years. The exact number of observations divided by sex, age, weight status and SES for all the years included in the study period is presented in Supplementary Material 1 (Table S1.1 and Table S1.2). Children living with normal weight and excess weight were grouped according to the extended, age and sex-specific, International Obesity Task Force cut-off points⁴⁸ defining excess weight as living with either overweight or obesity. Biologically implausible values, relating to height, weight, and BMI of children were addressed due to the variability of data quality. This may be of particular concern because measurements were collected in non–research settings, i.e., schools. To flag those values, the AnthroPlus package⁴⁹ was used, with previously determined criteria where all weight for age z-scores that were either − 6.0 < or > 5.0, absolute height for age z-scores that were > 6.0 and absolute BMI for age z-scores that were > 6.0 were flagged. All in all, after flagging a combined number of 2084 height, weight, and BMI values, one researcher (AM) manually checked them and after a joint decision was made between AM and MS, all those values were omitted from the analyses. We analysed the data for children aged 7–15, excluding children with special needs. As a measure of area-based SES, we used an aggregated index of development, i.e., the municipal development index (MDI),⁵⁰ calculated yearly and averaged over a period between the years 2009 and 2019 because no data is available prior to 2009 through the Slovenian Statistical Office. MDI takes into account economic factors (gross domestic product, income tax), social indicators (ageing, unemployment, etc.), as well as cultural aspects (cultural monuments), and features relating to nature.⁵⁰ Individual average MDI values per region and municipality can be seen in Supplementary Material 3 – Table S3.1., Table S3.2 and Figure S3.1 As heterogeneity was low, we opted to contrast the most developed (i.e., the region with the highest average MDI) and the least developed region (i.e., the region with the lowest average MDI). Specifically, Osrednjeslovenska (average MDI = 1.19) and Pomurska (average MDI = 0.80) were compared (the other regions were fairly homogenous (range = 0.94–1.09). In 2019, about 28.1% and 5.3% of the participants lived in Osrednjeslovenska and Pomurska region, respectively. The number of observations across all regions stratified by year is shown in Supplementary Material 1 (Table S1.3 for the total number of observations divided by gender, age group and weight status across all 12 regions and Table S1.4 for the yearly number of observations divided by gender, age group, weight status for Osrednjeslovenska and Pomurska region). To test the robustness of the conclusions related to area-based development, we also conducted sensitivity analyses by contrasting two most developed regions (i.e., Osrednjeslovenska and Gorenjska) vs. two least developed regions (Pomurska and Podravska) according to average MDI for 2009–2019 period.

Statistical analysis

We used previously calculated centile values for each fitness test.⁴³ First, we fit quantile regression models separately by sex and age group (3 age groups: children between 7–9 years old, early adolescents between 10–12 years and adolescents between 13–15 years of age). When fitting the quantile regression models to estimate the joinpoints, we modelled the median from each test as a dependent variable, while we included the year as a continuous independent variable, but also weight status, SES (represented by regions) and the interaction between year and weight status as independent variables. We then used this quantile regression model results and through segmented regression identified the joinpoints, i.e., the moment and the magnitude of the increase or decrease (expressed by estimating the annual percent change – APC) of the change in the trend and tried to examine changes in physical fitness over time. Here, we also identified the segmented model with the optimal number of joinpoints (chosen between the models with 0, 1, 2, and 3 joinpoints) based on the Akaike information criterion (AIC). Based on these results, we compared the trends of children living with excess weight and those of children living with normal weight. To that end, we report changes in percentile points for a given fitness component and respective 95% confidence interval (CI). Percentile points are age and gender specific and derived from national standards that were constructed based on measurements of 95% of Slovenian children in the 1989–2019 period.⁴³ This metric enables direct comparison between different fitness components, and weight, gender, and age groups. A change of 1 percentile point for a given group, and for a given fitness component, indicates that the rank in the standard population changed by 1 percentage point. Positive values of estimated annual percent changes (APCs) and average annual percent changes (AAPCs) imply that BAH, SBJ, SU60, and APT were improving, while the reverse holds for BMI, TSF, R600, BOC, and D60. Finally, we examined the differences in trends between the most developed and least developed region. Here, we fitted the quantile regression models similarly to the main analyses while including only children living in the most and least developed region and stratified the analyses by area-based SES in the segmented regression models.

We performed all statistical analyses using R version 4.3.0.,⁵¹ with data processing and visualizations created within tidyverse,⁵² and by using the patchwork package⁵³. We used packages quantreg⁵⁴ and segmented⁵⁵ for fitting quantile regression and segmented regression models, respectively, as well as for calculating APC and AAPC.

Trends over the whole 1989–2019 period

Figure 1 shows estimated AAPCs (x-axis) with their respective 95% CIs for health-related and skill-related fitness over the whole period by weight groups (excess weight vs. normal weight), age groups (7–9-year-olds, 10–12-year-olds, and 13–15-year-olds) and sex, while the uncertainty estimates (95%CI) for overall trends are presented in Supplementary Material 2 – segmented regression results (Tables S2.1).

The trends were typically non-linear, and their shape is illustrated in Figure S1.1-S1.3 in Supplementary Material 1.

Health-related physical fitness

In 1989 weight-based disparities (i.e., the difference between children with normal weight and excess weight) were large for all health-related fitness components across age and sex groups. The gap was particularly prominent for upper body strength and CRF, with differences totalling around 35 and 25 percentile points, respectively. At the same time, for body core strength, the gap amounted to only 7–12 percentile points depending on the age and sex group.

From 1989 to 2019 body composition worsened in almost all groups and this was more pronounced when estimated by TSF than via BMI. The difference in BMI between children with normal weight and children with excess weight widened in almost all age and sex groups, but only slightly (by around 1 percentile point in most groups). The opposite holds for TSF, and the largest decrease in the gap between boys with excess weight and normal weight was seen in the 7-9-yr-old group with TSF increasing by 2.7 percentile points more in boys with normal weight, while for girls this was seen in 10–12-year-olds with 3.6 percentile points larger increase in girls with normal weight over 30 years.

Averaged over the entire study period CRF decreased in all age, sex, and weight groups (Fig. 1), except for 13–15-year-old boys with normal weight. The decline was larger in boys than in girls in children living with excess weight, while the opposite was true for girls and boys with normal weight across all age groups. At the same time, weight-based disparities widened in all age and sex groups, as the decline in CRF over 30 years was 2–5 times larger for children with excess weight when compared to their peers with normal weight. The increase in disparities was generally larger in boys than in girls, and in the oldest age group, with the largest difference being noted for 13–15-year-old boys where CRF decreased by almost 16 percentile points in the excess weight group, while maintaining its levels in the normal weight group.

Trends in muscular fitness from 1989–2019 diverged depending on the body part assessed. While lower body strength (measured via SBJ) dropped sharply in all groups, body core strength (i.e., the number of sit-ups in 60s) increased. Upper body strength decreased in all children with excess weight as well as in 7-12-year-old boys with normal weight and increased in all girls and 13-15-year-old boys with normal weight. Weight-based disparities increased in all groups for all dimensions of muscular fitness. There were no notable sex differences except for lower body strength where the gap between weight groups increased much more for boys than for girls. The rise in disparities was generally the largest for body core strength, with the increase over 30 years being 6–15 percentile points larger in children with normal weight.

Skill-related fitness

In 1989, weight-based disparities were large for gross-motor coordination (as assessed via BOC) and speed (i.e., 60-m dash), but non-existent for neuromuscular coordination (i.e., APT). The gap between weight-based groups in gross-motor coordination was similar across age groups, but more prominent in boys than in girls (around 25 percentile points vs. around 15 percentile points). At the same time, the gap in speed did not differ between sexes and amounted to around 20 percentile points.

Over the 1989–2019 interval all age, sex and weight groups improved their gross-motor and neuromuscular coordination, while speed improved globally only in children with normal weight. At the same time, trends in speed were less uniform for children with overweight, with improvements noted in 13-15-year-old girls, deterioration among 7–12-year-old boys and no change in other age and sex groups. Of note, the improvement in gross-motor coordination was considerably larger for girls compared to boys. Weight-based disparities enlarged for all fitness components in all age and sex groups except neuromuscular coordination among 13-15-year-old girls. The increase in the gap between weight groups was the largest for speed, especially among boys where it reached almost 20 percentile points larger difference in 2019 compared to 1989 in 13-15-yr-olds. On the other hand, for neuromuscular coordination the larger increase in disparities was actually twice as large in 7–12-year-old girls compared to their male peers (7–8 percentile points in girls vs. 3–6 percentile points in boys). Finally, for gross-motor coordination weight-based differences in trends were similar in both sexes and amounted to a 7–10 increase in the gap over 30 years.

Patterns of trends in time segments

In Figs. 2 and 3 we demonstrate the estimated APC (x-axis) with their respective 95% confidence intervals for each time segment for health-related fitness by weight groups (excess weight and normal weight), age groups (7–9-year-olds, 10–12-year-olds, and 13–15-year-olds) and sex, while Fig. 4 illustrates the same for skill-related fitness.

Health-related physical fitness

BMI increased during the 1990s and 2000s in all groups, and then decreased in the 2010s in children with normal weight, while not changing much in children with excess weight. Most increase in TSF occurred during the 2000s and was much more prominent in children with normal weight, which contributed strongly to the overall decrease of weight-based disparities (Fig. 2). CRF decreased over the first 10 years, remained stable during the 2000s and then improved from around 2010 to 2019. This improvement in the last 10 years was typically lower in children with excess weight compared to their peers and was absent in 10-15-year-old boys with excess weight. Consequently, the period between 2005–2010 and 2019 contributed the most to the increasing inequalities in CRF in both sexes and in all age groups. Body core strength improved continuously up to 2014/2015 in most groups, with the largest increases being noted during the first 5–10 years, and with the consistently larger increase in children with normal weight than excess weight leading to the widening of the gap between weight groups. During the last time interval (4–5 years for most groups) body core strength decreased in most groups and the weight-based gap increased further in 10-12-yr-old boys and girls and 7-9-yr-old girls but diminished among 13-15-yr-old boys. Upper body strength decreased over the whole period in children with excess weight, but only between 1997–2008 in children with normal weight. The increase seen after around 2008 in children with normal weight, together with a simultaneous decrease in children with excess weight was the strongest contributor to increasing weight-based disparities in upper body strength (e.g., > 7 percentile points difference between groups in 13-15-year-old boys). Finally, for lower body strength a very brief increase over the first 2–3 years was seen in some groups, but generally roughly 25 years of continued decrease was recorded in all groups. The decrease slowed down or stopped during the last 5–10 years in most groups while in 13-15-yr old girls with normal weight and excess weight the increase was noted. Most of the increase in disparities was generated from 1989 to 2000 among 7-12-yr-old boys and around the period 1995–2010 in their older peers. In girls, the increase in disparities was more uniform across the studied period.

Skill-related fitness

Gross motor coordination showed continuous improvement in the youngest age groups, a sharp improvement over the first 5–6 years, followed by a slight but continuous deterioration in older age groups in boys, and a sharp improvement over the first 5–6 years, followed by a slight deterioration in the next 15 years and another period of improvement in the last 10 years in older age groups in girls. Weight-based disparities increased the most during the initial period, with notably smaller improvement in children with excess weight, and continued increasing in the last period, although to a lesser extent, with a slightly smaller improvement and lack of improvement in girls and boys with excess weight, respectively.

Speed decreased during the first 5 years in the youngest children and then increased over the next 25 years. In the older children speed remained stable in children with excess weight and improved continuously after 1995 in their peers with normal weight. Weight-based disparities mostly increased in the period from the early 2000s to mid-2010s due to a notable improvement in children with normal weight and concomitant stagnation in children with excess weight.

Pattern of trends in neuromuscular coordination was complex, but uniform across all age and sex groups. Initial improvement over the first 5–6 years was followed by a decrease in the next 5 years and another period of improvement during the last 20 years. Weight-based disparities were not evident in 1989, but they emerged over the first half of the 1990s and remained stable or decreased only slightly by 2019.

Effect of area-based socioeconomic status on patterns of weight-based disparities in physical fitness

Trends in fitness during the whole period from 1989 to 2019 differed between the most developed and the least developed region for most components (Fig. 5). In the region with the lowest development index, a larger deterioration in body composition and CRF was seen in both weight groups. At the same time, this region experienced a larger improvement in body core strength and neuromuscular coordination and a smaller decrease in lower body strength compared to the most developed region.

Disparities in body composition and upper body strength increased to a similar extent in both high and low development region. On the other hand, disparities in most other fitness components increased more in the less developed region for at least some of the age and sex groups. More specifically, this increase in disparities across regional SES was most consistent for body core strength (7-12-year-old children), lower body strength (7-9-year-old boys and girls and 13-15-year-old girls), speed (7-12-year-old boys and girls) and neuromuscular coordination (10-12-year-old boys and 10-15-year-old girls). The larger increase in weight-based disparities for body core strength, lower body strength and speed in less developed areas was mostly due to less favourable trends in children with excess weight in these areas during the 2010s. Conversely, disparities in CRF among 10-12-year-old boys and 13-15-year-old girls increased only in more developed region, while not changing much in less developed region. This increase in disparities in more developed areas predominantly happened during the 2010s when the improvement of CRF was seen only in developed areas and was more pronounced in children with normal weight. As in the main analyses, the trends were mostly non-linear, and we have provided illustrations of these patterns of change in regions contrasted by development index across 1989–2019 for each individual fitness test in Supplementary Material 1 – (Figure S1.4-S1.6) and the patterns of trends in time segments in Supplementary Material 4 – (Figure S4.1-S4.9).

Sensitivity analyses mainly confirmed these trends, apart for muscular fitness which we have shown in Supplementary Material 5 – sensitivity regression results (Table S5.1) where we also included the results of sensitivity analyses regarding the patterns of trends in segments of time (Tables S5.2-S5.10.). More specifically, the magnitude of differences in disparities in body core strength was somewhat decreased, while disparities in upper and lower body strength increased more in highly developed regions.

We examined the trends in physical fitness of children in Slovenia across more than 3 decades of systematic surveillance and found that weight-based disparities in physical fitness have mostly increased, even though the difference in body composition has decreased. The increase in disparities was generally larger in boys than in girls, and older age groups compared to the youngest children. The pattern of change in weight-based disparities was dependent on the fitness component, but most of the increase in disparities in health-related fitness accumulated during the 2010s, while the 1990s were predominantly responsible for the increase in disparities for skill-related fitness. During these periods fitness improved much less, or not at all, in children living with excess weight. Finally, we found that weight-based disparities in muscular and skill-related fitness were more pronounced in the least developed region, while the opposite was true for CRF.

Fitness trends over the last 3 decades reported here are in line with trends on the both global and European levels noted in several recent systematic reviews.^22–24 Similar to what we found, global trends also emphasized indications of reversal of the decline in fitness, especially in more recent studies. Our findings align with previous studies that have indicated a worsening trajectory in body composition and with the generally reported stable trends in CRF as of recently.^22–24,56 This stability in the CRF trends is in line with the findings from several other studies published after the systematic reviews were conducted^27,36,57 and specifically with several studies also based on Slovenian children.^43,58 Irrespective of weight status, several recent systematic reviews provided an update on general trends in relative muscle strength and handgrip strength and showed a general increasing trend,^23,59 either small negative quadratic trend or stability in muscle power proxies and for speed a small-to-medium increase in recent years no differences between sexes.^23,27,59 Some more recent individual studies showed declines such as the study among Brazilian youth in almost all tests related to MF³⁷ and adolescents from the United Kingdom.³⁰ We found an interesting trend in body core strength (i.e., the number of sit-ups in 60s) which increased on average, certainly presenting a challenge in terms of explaining the background of these findings. It seems that the trends in body core strength in the majority of the published studies are showing improvements,^24,27,36,60 while several other Slovenian studies showed similar results.^43,46,61 On the other hand, the strength of the arms and shoulder belt as measured by a variety of tests and over several continents and countries, showed declining trends which is in line with the results of our study.^22–24 The same trend was also seen in other studies on Slovenian children^43,46 even though for the post-2010 period most age groups in Slovenia improved in terms of upper body strength. In terms of lower body power, our research aligns with the majority of previous studies.^22,24 At the same time, global trends in speed were less uniform with several large systematic reviews showing a recent improvement^22–24 which is further supported by other studies that were published after the systematic review.^27,36,43,45

We observed growing disparities in fitness between children with excess weight and their peers with normal weight over the last 3 decades. Available data from previous studies also point to a growing divide between the fit and the unfit children as well as between those with normal weight and those with excess weight.^26–28,30 We have shown that in the face of the widening disparities in BMI, trends in TSF responded contrastingly, highlighting the fact that children with normal weight are going through a worse period, characterized by a negative body recomposition. Notably, there is limited data available regarding fitness trends among children with excess weight, making it challenging to thoroughly examine the evolution of weight-based disparities over an extended period. Some studies from other countries reported similar results in terms of BMI,^27,28,37 but Nebiker et al.²⁸ found that the trends may depend on the baseline fitness level and anthropometric characteristics. Existing evidence points towards larger decrements in health-related fitness components, particularly CRF, among children and adolescents with excess weight compared to those with normal weight.^26,31–35 However, certain studies report conflicting results, indicating the need for further investigation and systematic data collection in this area. In a study from Switzerland, children with overweight improved slightly by 0.4% each year while a < 0.1% per year decline was identified for children with normal weight, overall showing that trends in CRF tended to diverge related to weight status.²⁸ Another study showed worse results for children with normal weight also based on the 20-m shuttle run, but on only 2651 adolescents aged 13–14 years measured yearly from 2014 to 2019.³⁰ Worryingly, due to the noted negative trends in several fitness components, it is argued in the literature that the recent decrease in the prevalence of children with overweight might be more because of lean mass loss rather than fat mass.⁴³ The coincident decline in fitness performance and rise in BMI, a consideration of mechanisms, correlational data and temporal trends suggest a causal connection between the two.³⁴ Physical activity, specifically exposure to high-intensity exercise may also be important as well as probably complex interactions among biological maturation, secular change in body size and shape and psychosocial factors.³⁴ Tying the findings and relating them to body composition, it could be hypothesized that children with normal weight in this study tended to collect more body fat on the extremities, shown by the uptick in median TSF values, while also improving in terms of body core strength. This could prove more beneficial in terms of health since it is shown that measures of central adiposity are positively and significantly associated with a higher all-cause mortality risk.⁶² It would certainly be interesting to connect these findings to those based on the rest of MF components, where upper and lower body strength generally decreased in all children but was more pronounced in those with excess weight and especially in boys. Slight decreases in BMI in children with normal weight and no changes in children with excess weight during the 2010s might explain the growing disparities in terms of upper body strength. This is relevant since children with normal weight might not necessarily be better off in terms of absolute strength but solely regarding relative strength being supported by lower BMI values. Even though we noted initially large weight-based disparities for gross-motor coordination and speed, but not for neuromuscular coordination, these disparities only enlarged over time for all skill-related fitness components in all age and sex groups. This increase in the gap between weight groups was the largest for speed, especially among boys. That could be because although over the whole study period, children in all age, sex and weight groups improved their gross-motor and neuromuscular coordination, only children with normal weight improved globally in terms of speed. On the other hand, the increase in disparities for gross motor coordination and neuromuscular coordination also were not trivial over 30 years.

Our investigation into the fitness trends of various age and sex groups revealed a complex scenario, with notable disparities across different fitness components. While the literature suggests a declining trend in overall physical fitness, specific dimensions such as strength, flexibility, and speed showcase varied trajectories, with reports of both increase and stagnation across different age and sex groups in different studies.^22–24 Similar to our results, gender and age-specific differences in these trends appear to be relatively small. The sex and age-related differences in trends that we acknowledged are also in line with findings from previous studies conducted in Slovenia.^43,46,61 This could be explained by results from previous studies which indicate that age-related changes in body composition during childhood may be associated with the stage of maturation while there are sex differences in the timing and magnitude of these changes. In terms of CRF, a recent systematic review reported larger declines for boys although it was not as pronounced in our study.²³ Sex differences were also found for upper body strength which has increased more among girls while for lower body strength the gap between weight groups increased much more for boys than for girls. However, in terms of upper and lower body strength, we have also found some positive trends in specific age groups and time periods, which is consistent with a small number of previous studies.⁶³

Related to the patterns of trends over the last 3 decades, our study revealed that there was an initial increase in BMI during the 1990s and 2000s, followed by a decrease in the 2010s among normal-weight children. CRF initially decreased, stabilized, and then improved after 2010, predominantly in normal-weight children, widening the gap between weight groups. Skill-related fitness showed initial improvements, intermittent declines, and subsequent improvements, with disparities emerging in the early 1990s and remaining stable or slightly decreasing by 2019. Particularly, a systematic review by Fuhner et al.²³ highlighted a significant increase and subsequent decrease in CRF from 1986 to 2010s, followed by a stabilization period for all children until 2015. This decline was more pronounced for boys than for girls, emphasizing the gender-specific nuances in these trends. Increasing trends of subcutaneous fat and deterioration of most fitness components in the late 1990s and 2000s correspond to the escalation of the ‘Western’ style, nutrient dense food, and inflation of screen-pressure environment with high access to rich cable TV programming and internet. Slovenia declared its independence in June 1991 after a long process of working towards economic and political independence. The country had to restructure its economy and trade relationships to integrate with Western Europe after breaking away from its former partners in the Balkans and Eastern Europe. Slovenia was successful in achieving economic recovery and has become an integrated part of the Western European market.⁶⁴ After the transition period that Slovenia was experiencing the trends were in part reversed in the 2010s with an effective school-based national physical activity programme “Healthy Lifestyle”,⁶⁵ and an aggressive media campaign about proper diet and media consumption. Therefore, as Slovenia has been facing negative lifestyle changes in recent decades, it seems that implementing several nationwide initiatives and curriculum updates, especially within the educational system, could have led to the improvements in fitness witnessed during the 2010s in this study and earlier studies from Slovenia.^43,61 Still, this study showed that the benefits of these policies have not been equally evident in children with excess weight, especially for CRF, upper body strength, and speed. This urges for modified physical activity policies that should be designed in a way to decrease inequalities in fitness.

Recent studies that have primarily focused on children and adolescents from high-income countries, emphasized the need for a broader global perspective to comprehensively understand the dynamics of fitness trends among diverse populations.^{27,28,30,36,37} It is generally reported that children and adolescents from high SES families tend to have higher mean fitness values and are less likely to have poor fitness.⁶⁶ Furthermore, individuals with lower SES tended to have reduced motivation to engage in healthy behaviours and a higher likelihood of experiencing negative health outcomes related to metabolic and cardiovascular disease risk factors when compared to those with higher SES.⁶⁶ To that end, we have seen that more developed regions had better health-related physical fitness outcomes, although children from the least developed region generally performed better in muscular fitness and skill-related fitness. Since the fitness level of children seems to be determined partly by SES, it is important to note that inequalities of income in Slovenia, assessed by the Gini index, have been decreasing during the study period.⁶⁷ This probably resulted in similar trends of fitness changes between the most and the least socioeconomic developed regions. Still, for at least some of the age and sex groups, we have found that weight-based disparities in most fitness components increased more in the less developed region. According to a recent global study, the benefits of living in cities for growth and development have decreased in the 21st century in many parts of the world.⁵ The countries that showed significant convergence over the last three decades were primarily in Central and Eastern Europe, such as Slovenia or Croatia. These findings underscore the need for policies that strive to reduce socioeconomic inequalities and point to a need for a stronger focus of physical activity programmes on least developed regions. Still, given all the limitations of SES-based analyses in our study, further work that will include SES indicators at the individual level is needed to obtain more robust estimates of the effect of SES on trends in weight-based disparities in children’s physical fitness.

It is important to highlight that skill-related fitness components are generally under investigated, even though they have been shown to promote physical activity participation in children.⁶⁸ It would also be advisable to focus more on these components of fitness as they improve health-related physical fitness, which is often studied in isolation.⁴⁵ We reported on overall positive trends in skill-related fitness which are supported by several recent studies among Slovenian children and adolescents.^43,45,46 Some studies have shown that having high levels of CRF is linked to lower levels of body fat and better overall health while both CRF and MF have been associated with established and emerging cardiovascular disease risk factors, bone health, depression, anxiety, mood status, and self-esteem, overall mental health, and also with a higher academic performance during school years.⁶ Furthermore, motor competence development is crucial for children's overall well-being. Substantial evidence shows that it's linked to healthy weight, self-esteem, physical fitness, and cognitive development.⁶⁹ Early childhood is therefore a critical time to develop fundamental movement skills that promote an active lifestyle and good health habits for life.⁶⁹ It was demonstrated that exercise and physical activity interventions can assist children with excess weight to enhance their motor competence compared to those with normal weight.⁷⁰ This is important because our and some previous studies provide evidence for an increasingly widening gap between children differing by weight status in terms of skill-related fitness and motor competence across developmental time in the absence of targeted initiatives.⁷¹ Hence, it is important to focus on children with excess weight, particularly those who are not part of a sports club, to improve their motor skills and encourage regular physical activity.⁷¹

For childhood physical activity, fitness and health promotion, schools were shown as an important setting as the sole place that can reach all children regardless of their characteristics.^65,72 Obesity and physical fitness management at a younger age may also have a greater effect for several reasons such as easier motivation generation and maintenance, less resistance to treatment stigmatization, easier control and modification of behaviour as well as implementation of possible innovative strategies.^30,73,74 As an example of these newer strategies, involving communication through the Internet, better involvement of adolescents in their own management programmes and peer support strategies, could all be explored. Encouraging participation in a range of sports, specifically participating more regularly in enjoyable activities during the week, persisting for longer than three years and, experiencing higher levels of sports practice and/or competition could also be viable interventions to ensure the stability and improvement in physical fitness of children and young people.^2,75–78 Parental modelling is another successful intervention strategy that could be relevant for children's physical activity.^79,80 Given divergent trajectories of fat mass accrual and different levels of movement behaviours between boys and girls, physical activity interventions may further benefit from a sex based approach.⁸¹ Embracing a bottom-up approach and introducing policies which involve schools, consider differences between SES areas, encourage physical activity, engage the whole family, encourage sports participation, cut down sedentary time, and fight excess weight could all be successful in reducing weight-based disparities in physical fitness.^{65,75–77,79,80,82}

We would like to note several strengths of our study. Firstly, through SLOfit, fitness was systematically assessed yearly over more than 3 decades by trained personnel, i.e., physical education teachers, while data were checked and cleaned at several time points providing trust in the collected data and adding to the robustness of the conclusions. Furthermore, this enabled a near-census sample of more than 135,000 individuals per year from all regions of Slovenia. Thirdly, along with examining children of different ages and sex, we also considered different weight status groups and explored trends across communities characterised by different level of economic development, features that were previously rarely studied.

Our study was not without limitations, and firstly, we considered but were not able to gather information on various other factors that might have influenced disparities in physical fitness based on weight, such as changes in physical activity, diet, sleep, or sedentary behaviour. Second, we didn’t manage to fully examine the individual's pubertal status and maturation tempo which could have had an impact on the outcomes. Irrespectively, we assessed 7–9-year-olds in our study where pubertal status shouldn’t have impacted the results, lessening the impact of this limitation. Third, we examined trends in average fitness across groups, and did not attempt to quantify trends in the variability of physical fitness which could provide additional insights into the inequalities between the fit and unfit children. Future research should at least include some measure of asymmetry (such as skewness) or variability (such as standard deviations). Fourth, we relied on normative standards and not raw values to estimate the disparities to be able to compare the magnitude of disparities between age and sex groups, making it difficult to judge the clinical relevance of our findings. Fifth, in the analyses considering SES, we included only the regions with the highest and lowest developmental index while excluding most of the population. We considered only two regions because the heterogeneity of the developmental index was very low, and only these two regions stood out at each end, thereby increasing the resolution, and making it possible to examine how trends in weight-based disparities in fitness differed with respect to SES. This also means that we only used a measure of area-based SES, while we had no individual level or municipality level data on SES. Sixth, we choose to restrict the analyses to the period until 2019, the year when in December an outbreak of the COVID-19 pandemic started and caused unprecedented and abrupt changes in children’s fitness levels. Therefore, this study ignores trends after 2019 although presently those results could be of particular importance. Still, as this was a period of abrupt changes in fitness that would distort segmental analysis, we reported these trends in a recent analysis⁸³ and felt it would be inappropriate to include it here. Finally, in terms of comparability of our results with other studies, it is important to note that differences in trends between children included in studies conducted in Slovenia and those from other studies might be related to the specifics of the tests used, which varied between countries and were not always consistent between boys and girls.²⁴

Our findings add the most recent evidence on trends in weight-based disparities in physical fitness of children living with excess or normal weight. Our analysis shows that while some components of physical fitness have improved recently, other components have been trending negatively for particular subgroups of children depending on their sex, age, and weight status. We observed a widening gap in physical fitness between children with excess weight compared to those with normal weight which was more pronounced in boys and children over 10 years. Moreover, these disparities have increased more in less developed communities. Consequently, despite recent improvement in population fitness levels, children with excess weight seem to be left behind which adds to health inequalities and could probably result in even larger health inequities in future. Reducing inequalities in physical fitness should therefore be given a higher priority in the public health arena. Our results should encourage policymakers to develop and implement initiatives aimed at enhancing physical fitness, literacy, activity, and health status overall, especially in children living with excess weight.

BMI body mass index

CI confidence interval

CRF cardiorespiratory fitness

MF muscular fitness

SES socioeconomic status

Ethics approval and consent to participate

The National Medical Ethics Committee of the Republic of Slovenia (ID 102/03/15) reviewed and approved this study that involved human participants, following the Declaration of Helsinki. The legal guardian/next of kin provided written informed consent for children to participate in this study. All relevant guidelines and regulations were followed while performing the methods and more details on how all the data were treated can be found elsewhere.⁴⁷

Consent for publication

Not applicable

Availability of data and materials

The data underlying the results of this study cannot be shared directly under current legislation for data protection and because they contain potentially sensitive information (information that could compromise the privacy of research participants). The confidential data must be requested directly after which other qualified researchers who fulfil the requirements would have the same access. Interested researchers may contact via email the corresponding authors of this paper, AM ([email protected]) and MS ([email protected]), for data access requests. Researchers should confirm that they will share their data and results with the authors of this paper and will publish any results with open access. The data are held at the Laboratory for the Diagnostics of Somatic and Motor Development at the University of Ljubljana, Faculty of Sport, which can also be contacted via email: [email protected]. To access the code for data cleaning and analysis, interested researchers may contact via email the corresponding author of this paper, AM ([email protected]). The final code will be uploaded to GitHub (https://github.com/antonio-martinko), once the paper has been conditionally accepted.

Competing interests

No conflicts of interest are declared.

Funding

This work was funded by the Slovenian Research and Innovation Agency within the Bio-psycho-social research programme, no. P5-0142 and Methodology for data analysis in medical science research programme, no. P3-0154 (ARIS, https://www.arrs.si/en/). The work of AM was funded by the Croatian Science Foundation, under grant no: DOK-2020-01-3728 (HRZZ, https://hrzz.hr/en/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Authors' contributions

With the intention to recognize the individual contributions of authors, the CRediT (Contributor Roles Taxonomy) framework is followed (Allen et al., 2019). Antonio Martinko: Conceptualization, Methodology, Software, Formal analysis, Data Curation, Writing - Original Draft, Writing - Review & Editing, Visualization; Maroje Sorić: Conceptualization, Methodology, Writing - Original Draft, Writing - Review & Editing, Supervision, Project administration; Rok Blagus: Data Curation (access to data), Methodology, Software, Writing - Review & Editing, Supervision; Gregor Jurak: Conceptualization, Writing - Review & Editing; Gregor Starc: Data Curation, Conceptualization, Writing - Review & Editing.

Acknowledgements

The authors wish to thank the schools, teachers, children, and their parents who generously agreed to be involved in the SLOfit system and the Ministry of Education of the Republic of Slovenia for funding the annual data analysis of the SLOfit measurements.

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No competing interests reported.

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Temporal trends in weight-based disparities in physical fitness of children: 30-years of continuous surveillance through Slovenian national system

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Abstract

Background

Methods

Results

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Background

Methods

Study design, participants, and measurements

Statistical analysis

Results

Health-related physical fitness

Skill-related fitness

Patterns of trends in time segments

Health-related physical fitness

Skill-related fitness

Effect of area-based socioeconomic status on patterns of weight-based disparities in physical fitness

Discussion

Conclusion

List Of Abbreviations

Declarations

References

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Supplementary Files

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