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Small-Sided Soccer in School Reduces Postprandial Lipemia in Adolescent Boys

SMALLCOMBE, JAMES W.1; BARRETT, LAURA A.1; MORRIS, JOHN G.2; SHERAR, LAUREN B.1; TOLFREY, KEITH1

Author Information
Medicine & Science in Sports & Exercise: November 2018 - Volume 50 - Issue 11 - p 2351-2359
doi: 10.1249/MSS.0000000000001702
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Abstract

Regular exposure to elevated postprandial plasma triacylglycerol concentrations ([TAG]) is associated with the development of atherosclerosis (1) and is considered an independent risk factor for adverse cardiovascular events (2,3). Although atherosclerosis manifests typically in adulthood, it has been long established that atherogenesis is an insidious process initiated much earlier during childhood and adolescence (4,5). Consequently, interventions aimed at reducing postprandial lipemia may offer the greatest protection to long-term cardiovascular health when commenced in early life.

Compelling evidence indicates that a single session of moderate- to high-intensity exercise reduces postprandial lipemia in young people (6). However, reliance upon laboratory-based experimental protocols represents a limitation of this previous body of research. Typical laboratory-based exercise protocols bear little resemblance to the activities performed by children and adolescents in free-living settings. Furthermore, the tightly controlled laboratory conditions, under which experimental measures are most commonly conducted, also differ considerably to the settings in which young people engage routinely.

Ergometer-based activity (e.g., treadmill running) is the most common laboratory mode of exercise. By contrast, soccer (including 5-a-side) has been reported to be the most popular sport among 11 to 15 yr olds in the UK (7). Given that only 20% of adolescents achieve the recommended daily minimum of 60 min of moderate- to vigorous-intensity physical activity (8), it is important to investigate activities that adolescents enjoy and which are, thus, potentially more conducive to long-term adherence. Current scientific understanding remains limited as to how the physiological stimuli provided by free-living modes of exercise, such as soccer, compare with the laboratory-based exercise used in the laboratory. Therefore, although laboratory-derived data clearly demonstrate the potential benefits of an exercise intervention, its practical benefits remain unclear until comparable responses are demonstrated in real-world settings. Unlike ergometer exercise, during which exercise intensity and energy expenditure can be precisely quantified and controlled, free-living physical activity performed by children is far less predictable. For example, soccer is characterized by bouts of intermittent high-intensity running, periods of acceleration and deceleration, changes of direction, jumping, tackling, as well as lower-intensity “cruising” and standing (9). Furthermore, intrinsic motivation during game-based activity is likely to exert an important influence on the total exercise “dose.” Although soccer training has been recognized as a powerful stimulus for health promotion in adults (10) and has recently been demonstrated to induce acute reductions in postprandial lipemia in normal and overweight adult males (11), it has not yet been established if participation in school-based games activity confers similar metabolic benefit during youth.

In addition, the laboratory conditions under which the postexercise blood samples are taken routinely differ markedly from conditions in schools. Although typical laboratory protocols require participants to spend long periods of time sedentary under tightly controlled conditions, children spend much of their free-living time at school—a setting in which they face both formal and informal opportunities to accumulate physical activity and break up sedentary time throughout the school day. It is, therefore, important that steps are taken toward “translational” experimental designs, which incorporate representative forms of exercise, coupled with ecologically valid measures of the outcome variables of interest. Such advancements are required to facilitate a more representative assessment of the complex interaction between exercise, free-living physical activity, and postprandial metabolism, enabling further elucidation of the relevance of childhood exercise to public health policy.

In light of the aforementioned shortcomings of much of the previous literature, the aim of the present study was to examine the efficacy of school-based free-living 5-a-side soccer activity in reducing in-school postprandial lipemia in adolescent boys.

METHODS

Participants

After approval from the Loughborough University Ethics Approvals (Human Participants) Sub-Committee, 15 healthy adolescent boys volunteered for and completed all measures (i.e., only 15 volunteers and no dropouts). These participants were recruited from a local secondary school after their attendance at a school-based presentation. Written assent was obtained from each participant, and written informed consent was obtained from a parent or guardian. Suitability for admittance into the study was confirmed by the completion of a general health screen questionnaire. Participant characteristics are presented in Table 1.

TABLE 1
TABLE 1:
Participant characteristics (n = 15).

Preliminary Session

Anthropometry and physical maturation

Anthropometry was conducted with participants wearing shorts, T-shirt, and socks. Body mass was measured to the nearest 0.1 kg using a digital scale and stature was measured to the nearest 0.01 m using a wall-mounted stadiometer (Holtain, Crosswell, UK). Triceps and subscapular skinfold thicknesses were measured on the right-hand side of the body to the nearest 0.2 mm using Harpenden callipers (John Bull, St. Albans, UK). The skinfold thickness was calculated as the median of three measurements. Percentage body fat (%BF) was estimated using maturation, race, and sex-specific equations (12). Waist circumference was measured midway between the 10th rib and the iliac crest (13). Physical maturity was estimated with a five-point self-assessment of secondary sexual characteristics (14). Scientific photographs depicting the five stages of genital and pubic hair development, ranging from 1 indicating prepubescence to 5 indicating full sexual maturity, were used privately by the participants to provide this information.

Preliminary exercise measures

Before the preliminary exercise tests, participants were familiarized with exercising on the treadmill ergometer (Mercury Medical; h/p/cosmos sports & medical Gmbh, Germany). Short-range telemetry (PE4000; Polar-Electro, Kempele, Finland) was used to monitor HR continuously throughout the exercise tests. HRpeak was defined as the highest HR recorded during the test. RPE were measured during the final 15 s of each exercise stage using the pictorial OMNI (0 to 10) scale (15).

The steady-state relationship between treadmill speed, oxygen uptake (V˙O2), and HR was ascertained via a 4 × 4-min incremental exercise protocol. The starting treadmill speed was set at 5.0 km·h−1 and was increased by 1.0 km·h−1 at the end of each 4-min stage. An expired air sample was collected using the Douglas bag (Cranlea and Company, Birmingham, UK) technique during the final 60 s of each 4-min stage.

V˙O2peak was determined using an incremental gradient-based treadmill protocol with each participant running at an individual fixed speed (8.0 to 10.5 km·h−1). Expired air was collected into Douglas bags during each successive minute of exercise via open-circuit spirometry. The treadmill belt gradient was raised by 1% every minute until volitional exhaustion was attained. Because of the limited number of children (20%–40%) that display a plateau in their V˙O2 when performing exercise to exhaustion, and to avoid the possible acceptance of a “submaximal V˙O2peak” based on secondary criteria (16), each participant completed a supramaximal verification stage to volitional exhaustion after a 10-min recovery period (17). During this verification stage, the treadmill was set at a gradient 2% greater than that attained at the end of the initial incremental exercise test.

A paramagnetic oxygen (O2) analyzer and an infrared carbon dioxide (CO2) analyzer (Servomex, Sussex, UK) were used to determine the concentration of O2 and CO2 in the expired air samples. The volumes of expired gas were determined using a dry gas meter (Harvard Apparatus, Kent, UK) and were corrected to standard temperature and pressure (dry). For each expired gas sample, oxygen uptake (V˙O2), expired carbon dioxide (V˙CO2), minute ventilation (E), and respiratory exchange ratio were calculated.

Experimental design

All participants completed three counterbalanced, 2-d main conditions; a resting control (CON); laboratory-based, moderate-intensity treadmill exercise (TM); and participation in an afterschool 5-a-side soccer tournament (SOC). All experimental conditions commenced at 15:45 and were completed by 17:15. Body mass was measured at the start of each experimental condition to standardize the test meals provided on day 2 of each condition. A schematic representation of the study design is provided in Figure 1.

FIGURE 1
FIGURE 1:
Diagram of the 2-d study protocol. TAG, triacylglycerol. All food and drink consumption was standardized and replicated across conditions.

Day 1: Intervention

Resting control (CON) and moderate-intensity treadmill exercise (TM)

During CON, participants remained at school at the end of the school day and rested for 90 min in a seated position. During TM, participants attended the laboratory afterschool and completed 48-min of moderate-intensity exercise on a treadmill. The treadmill exercise was divided into 3 × 16-min bouts of exercise, interspersed by 8-min periods of rest. Participants exercised at a fixed intensity, based on an HR target set at the HR corresponding to 60% V˙O2peak (as determined from the previously described preliminary exercise testing protocols). The treadmill speed was adjusted at the end of each minute to ensure the target HR was maintained. As described previously, HR was monitored continuously, and RPE was recorded during the final minute of each bout of treadmill exercise. Expired air was collected for 60 s at two standardized time points (7 to 8 min and 15 to 16 min) during each 16-min interval of treadmill exercise. Individual gas exchange data were used to verify exercise intensity, retrospectively.

Five-a-side soccer (SOC)

During SOC, all participants took part in three, round-robin, 5-a-side soccer tournaments, over the course of three consecutive weeks. During each tournament, each team (and thus each participant) played six 8-min games with playing time totaling 48 min. All games were played on an outdoor, grass pitch that complied with current English Football (Soccer) Association age-specific guidelines (dimensions 44 × 22 m). Goalkeepers were rotated every 2 min to avoid position-specific variation in activity. Of the 15 participants, five participated in competitive soccer regularly with local soccer clubs. The remainder of the participants did not play competitively but reported enjoying taking part in school-based soccer activities (e.g., physical education lessons). The competitive players were divided across the three teams to distribute playing ability evenly.

All participants played in all three after-school soccer tournaments; however, subsequent postprandial blood sampling (day 2) was completed with each participant after only one afterschool soccer tournament. Postprandial test-meal measures were completed with five participants after each of the three afterschool tournaments. The tournament game schedule was standardized to ensure that all participants completed day 2 postprandial blood sampling measures after playing their allocation of games in three blocks of two consecutive 8-min games, thus mirroring the pattern of treadmill exercise completed during TM.

Physical activity was assessed continuously during each 5-a-side soccer tournament. Participants were equipped with individual 5-Hz Global Positioning System (GPS) devices (SPI Elite, GPSport, Canberra, Australia) that were worn for the duration of each soccer tournament. HR was also monitored continuously (as described previously), and RPE was recorded at the end of the final soccer match of each tournament.

GPS analysis

All GPS data were analyzed using Team AMS software version 1.2 (GPSports, Australia). In accordance with previous research (18), movement during the soccer activity was classified into six speed categories: standing (speed ≤ 0.4 km·h−1), walking (speed from >0.4 to 3.0 km·h−1), low-intensity running (LIR, speed from >3.0 to 8.0 km·h−1), medium-intensity running (MIR, speed from >8.0 to 13.0 km·h−1), high-intensity running (HIR, speed from >13.0 to 18.0 km·h−1), and sprinting (speed > 18.0 km·h−1). Total distance covered during the soccer activity was quantified and distance covered in each speed category was also determined. The method proposed by di Prampero and colleagues (19) was applied to the GPS data to estimate energy expenditure during SOC.

Day 2: Postintervention

Postprandial test-meal measures

After the consumption of a standardized carbohydrate-rich snack (3.6 g fat, 19.7 g carbohydrate, 2.0 g protein, 516 kJ energy) at 19:45 on day 1 of each trial, participants observed a 12-h overnight fast before arriving at school at 07:40. After a 10-min seated rest, a capillary blood sample was taken. At 08:10, a standardized breakfast was started, marking the start of the postprandial period, and consumed within 25 min. Participants then attended their normal timetabled school lessons with blood samples and meals provided during scheduled breaks in the school day (see Fig. 1). Once blood samples had been collected, participants were able to continue with their habitual break time activities.

Standardization of diet and physical activity

Physical activity and dietary intake were recorded during the 48-h period (preintervention and intervention days) preceding day 2 of each experimental condition. Participants were asked to replicate dietary intake and physical activity patterns from the first condition before each subsequent experimental condition.

Participants completed weighed food diaries using digital kitchen scales (Andrew James UK Ltd., Bowburn, UK), and the CompEat Pro 5.8.0 computerized food tables (Nutrition Systems, London, UK) were used to analyze dietary intake subsequently. Physical activity was quantified via accelerometry (ActiGraph GT1M; ActiGraph, Pensacola, Florida). The accelerometer was worn on the right hip during waking hours (removed for water-based activities). Raw ActiGraph data files were analyzed using custom-made data reduction software (KineSoft Software, version 3.3.76, Loughborough University, UK; http://www.kinesoft.org). During data processing, 5-s epoch data were reintegrated to 60-s epochs; 60 min of consecutive zeros, allowing for 2 min of nonzero interruptions, was used to remove nonwear, and a minimum of 8-h of valid wear time was required for a valid day. Physical activity was expressed as average counts per minute and interpreted using age-specific intensity cut points (20). Participants were asked to minimize physical activity during this 48-h period.

Test meals

Participants were provided with standardized meals on day 2 of each trial. Breakfast consisted of croissants, chocolate spread, whole milk, double cream, and milkshake powder. Meals were standardized to body mass and provided 1.6 g fat, 1.8 g carbohydrate, 0.4 g protein, and 95 kJ energy per kilogram of body mass. The test lunch composed of white bread, mild cheddar cheese, butter, potato crisps, whole milk, and milkshake powder and provided 1.1 g fat, 1.9 g carbohydrate, 0.6 g protein, and 83 kJ energy per kilogram of body mass. The time taken for individual participants to consume the test meals during the first condition was recorded and replicated during each subsequent experimental condition.

Analytical Methods

The whole hand was submerged in 40°C water for 5 min and then dried thoroughly before the fingertip was pierced (Unistick 3 Extra, Owen Mumford, UK) to provide a capillary blood sample. The first drop of blood was discarded before 300 to 600 μL of blood was collected in potassium–EDTA-coated microvette tubes (Sarstedt Ltd., Leicester, UK). The whole blood was centrifuged immediately at 12,800g for 15 min (Eppendorf 5415c, Hamburg, Germany). The resulting plasma sample was stored at −20°C for subsequent analysis. Plasma [TAG] and [glucose] were determined by a benchtop analyzer (Pentra 400; HORIBA ABX Diagnostics, Montpellier, France) using enzymatic, colorimetric methods (Randox Laboratories Ltd., Crumlin, UK). The within-batch coefficients of variation for [TAG] and [glucose] were 1.4% and 0.5%, respectively. Acute changes in plasma volume were estimated from hemoglobin concentration and hematocrit ascertained from the fasting and final blood samples. Hemoglobin concentration was determined via the cyanmethemoglobin method; 20 μL of whole blood was added to 5 mL of Drabkin’s reagent, and the absorbance was quantified via photometry at a wavelength of 546 nm (Cecil CE1011; Cecil Instruments, Cambridge, UK). A microhematocrit centrifuge and reader (Haematospin 1300 Microcentrifuge; Hawksley and Sons Ltd., Sussex, UK) was used to quantify hematocrit.

Statistical Analyses

The Statistical Package for Social Sciences (SPSS) software version 23.0 for Windows (SPSS Inc., Chicago, IL) was used for all data analyses. The trapezium rule was used to calculate total 7 h area under the plasma concentration versus time curve for TAG (TAUC-TAG) and glucose (TAUC-glucose) for all experimental conditions. The same method was used to calculate incremental area under the variable versus time curve (iAUC) after correcting for fasting concentrations. Normality of the data was checked using Shapiro–Wilk tests. Normally distributed data are presented as mean ± SD. Student’s paired t-tests were used to determine differences between responses to exercise during TM and SOC. Data for free-living physical activity and sedentary time and concentrations of plasma TAG and glucose were natural log transformed before analyses. These data are presented as geometric mean (95% confidence interval [CI]), and analyses are based on ratios of geometric means and 95% CI for ratios. Linear mixed models repeated for condition were used to examine differences in dietary intake, free-living physical activity and sedentary time (wear time included as a covariate), plasma volume changes, fasting concentrations, and TAUC responses. Differences in postprandial [TAG] and [glucose] were examined using linear mixed models repeated for condition and time. Where appropriate, to supplement key findings, absolute standardized effect sizes (ES) were calculated for within-measures comparisons as follows:

where v1 and v2 represent the two variable mean values being compared, and the CON SD is the control condition SD. In the absence of a clinical anchor, an ES of 0.2 was considered to be the minimum important difference, 0.5 moderate, and 0.8 large (21).

RESULTS

Dietary intake

Average energy intake did not differ significantly during the 48 h before day 2 of CON, TM, and SOC (8.7 ± 2.1, 8.7 ± 2.4, and 8.2 ± 2.1 MJ·d−1, respectively, P = 0.686). Macronutrient intake did not differ between CON, TM, and SOC for carbohydrate (297.1 ± 94.2, 293.4 ± 118.8, and 275.5 ± 87.9 g·d−1, P = 0.729), protein 71.6 ± 22.0, 73.3 ± 18.2, and 66.2 ± 20.6 g·d−1, P = 0.212), and fat (66.6 ± 20.3, 68.1 ± 14.3, and 66.8 ± 21.1 g·d−1, P = 0.934), respectively.

Free-living physical activity and sedentary time

After adjusting for accelerometer wear time, no significant differences were observed for counts per minute (P = 0.294), sedentary time (P = 0.342), light activity (P = 0.146), moderate activity (P = 0.089), or vigorous activity (P = 0.843) during the 48 h preceding day 2 of the experimental model. Data for this 48-h period are presented in Table 2.

TABLE 2
TABLE 2:
Accelerometer data for free-living physical activity and sedentary time during the 48 h preceding day 2 of the experimental model across the three experimental conditions.

Exercise responses to TM and SOC

Mean exercise-intensity during TM was 61% ± 6% V˙O2peak, and gross energy expenditure was 1.4 ± 0.3 MJ. Average HR was higher during SOC compared with TM (175 ± 8 vs 157 ± 7 bpm, 95% CI = 11–24, P < 0.001). Participants covered a shorter total distance during SOC compared with TM (3.6 ± 0.4 vs 5.9 ± 0.5 km, 95% CI = −2.7 to −2.1, P < 0.001) at a lower average speed (4.4 ± 0.5 vs 7.4 ± 0.7 km·h−1, 95% CI = −3.4 to −2.6, P < 0.001). RPE (0 to 10 OMNI) did not differ between SOC and TM (5 ± 2 vs 5 ± 1, 95% CI = −1 to 1, P = 0.883).

During SOC, the following proportions of game time were spent exercising within the progressive absolute HR intensities shown (bpm): 21%, <160; 12%, 160–169; 18%, 170–179; 24%, 180–189; 21%, 190–199; and 4%, ≥200. The times spent during SOC in each of the six identified speed zone classifications are presented in Table 3.

TABLE 3
TABLE 3:
Absolute and percentage of total game time spent in each speed zone classification. Also, absolute and percentage of total distance covered in each speed zone classification during the 48 min of 5-a-side soccer (SOC).

Plasma volume changes

The small changes in plasma volume between fasting and 7-h blood samples did not vary significantly between the three experimental conditions (CON = 1.1% ± 2.3%, TM = 1.4% ± 2.2%, SOC = 1.5% ± 2.2%, P = 0.901). Therefore, further analyses were completed without adjustment to the raw plasma [TAG] and [glucose].

Fasting [TAG] and [Glucose]

Fasting [TAG] and [glucose] for each condition are presented in Table 4. Fasting plasma [TAG] was 30% lower in SOC compared with CON (95% CI = −40% to −20%, ES = 1.00, P ≤ 0.001) and 18% lower than TM (95% CI = −29% to −5%, ES = 0.53, P = 0.011). Fasting [TAG] was also 16% lower in TM compared with CON (95% CI = −27% to −2%, ES = 0.46, P = 0.025). Compared with CON, fasting [glucose] was 3% lower in TM (95% CI = −5% to −1%, ES = 0.52, P = 0.009) and 4% lower in SOC (95% CI = −5% to −2%, ES = 0.67, P = 0.001). No meaningful difference was observed for fasting glucose between TM and SOC (95% CI = −3% to 1%, ES = 0.15 P = 0.368).

TABLE 4
TABLE 4:
Fasting and total area under the curve (TAUC) for TAG and glucose in the CON, TM, and SOC experimental conditions.

Plasma [TAG] and [Glucose] in the postprandial period

Plasma [TAG] responses over time and across conditions are shown in Figure 2. Differences in postprandial plasma [TAG] were observed across conditions (main effect condition, P < 0.001; main effect time, P < 0.001), but no condition–time interaction was observed (P = 0.469). Simple pairwise comparison indicated that mean postprandial [TAG] was 16% lower after TM (95% CI = −22 to −9, ES = 0.46, P < 0.001) and 25% lower after SOC (95% CI = −31 to −19, ES = 0.76, P = 0.006) compared with CON. An 11% reduction was observed after SOC compared with TM (95% CI = −17 to −3, ES = 0.30, P < 0.001).

FIGURE 2
FIGURE 2:
Fasting (0 h) and postprandial TAG concentrations for the three experimental conditions. Black rectangles represent the consumption of breakfast and lunch, respectively. TAUC-TAG was significantly reduced after SOC and TM compared with CON (P ≤ 0.009) but iAUC-TAG was not (P ≥ 0.078).

The TAUC-TAG (Table 4) was 18% lower after TM (95% CI = −29% to −5%, ES = 0.51, P = 0.009) and 25% lower after SOC (95% CI = −35% to −13%, ES = 0.76, P < 0.001) compared with CON. The TAUC-TAG was 9% lower after SOC compared with TM, but this difference was small and nonsignificant (95% CI = −21% to 5%, ES = 0.25, P = 0.191). Individual responses to exercise were similar after both exercise conditions with a reduction of TAUC-TAG exhibited by 13 (87%) and 14 (93%) of the fifteen participants after TM and SOC, respectively.

When accounting for the differences in fasting [TAG], the incremental area under the TAG versus time curve (iAUC-TAG) was 19% lower after TM (95% CI = −35% to 2%, ES = 0.41, P = 0.078) and 16% lower after SOC (95% CI = −33% to 5%, ES = 0.35, P = 0.109) compared with CON; however, these differences did not reach statistical significance. The 6% difference in iAUC-TAG between TM and SOC was trivial and nonsignificant (95% CI −18% to 30%, ES = 0.06, P = 0.793).

There were no significant differences in postprandial plasma [glucose] across the conditions (main effect condition, P = 0.876; condition–time interaction, P = 0.905). Similarly, no meaningful differences were observed in TAUC-glucose (ES = 0.07 to 0.15, P = 0.770) between conditions.

DISCUSSION

The main finding of the present study was that the reduction in postprandial lipemia following after-school 5-a-side soccer activity was similar to that observed after time-matched, moderate-intensity treadmill exercise despite participants covering a lower total distance at a lower average speed. This is encouraging as team game activities reflect more accurately the habitual intermittent activity preferences of British adolescents compared with the continuous laboratory-based ergometer exercise used typically in research settings. The present study provides empirical evidence supporting the efficacy of an acute bout of soccer activity to reduce postprandial lipemia during adolescence and represents an important step toward the translation of previous laboratory research into ecologically valid settings.

To our knowledge, this is the first study to examine the effect of school-based soccer activity on in-school postprandial lipemia in adolescent boys. This is highly relevant when considering that soccer continues to represent the most likely form of sports participation for young males in the UK with 53% of 5- to 10-yr-old boys and 78% of 11- to 15-yr-old boys reporting recent soccer participation (7). In agreement with the existing body of literature (6), a moderate reduction of circulating [TAG] was observed after 48 min of both SOC and continuous TM exercise, compared with duration-matched inactivity. The magnitude of the reduction observed after free-living SOC was similar to the effects reported in young males previously after laboratory-based continuous moderate-intensity exercise (22–25) and high-intensity running (26) and sprint cycling (27). Furthermore, the present study yielded findings remarkably similar to those reported by Barrett and colleagues (28) after participants completed a modified version of the Loughborough Intermittent Shuttle Test (LIST), which was designed to simulate games activity. In this previous study, 72 min of intermittent exercise resulted in a 26% (ES = 0.78) reduction in [TAG] compared with a smaller 14% (ES = 0.46) reduction after 60 min of continuous moderate-intensity treadmill exercise. Importantly, although the LIST protocol was strictly standardized and dictated by an audio signal, exercise volume during our free-living soccer activity was self-regulated and largely dependent on intrinsic motivation. In addition, in the present study, the 48-min durations of SOC and TM were considerably shorter than the 72-min LIST exercise; however, similar reductions in [TAG] were still observed.

The similar reductions in postprandial [TAG] observed after SOC and TM were somewhat surprising given the extent to which the two exercise stimuli differed. During SOC, participants covered a shorter total distance (3.6 vs 5.9 km) at a lower average speed (4.4 vs 7.4 km·h−1) compared with TM. The physiological response to the two exercise conditions also differed considerably. During SOC, participants exercised at 87% of individual HRpeak compared with an average of 78% in TM. This is in agreement with reports that soccer players typically exercise in excess of 80% HRpeak irrespective of playing level or age (29). Furthermore, a large proportion of time (~25%) was spent exercising at an HR exceeding 190 bpm (~92% HRpeak). Although there may be a mismatch in the HR-V˙O2 relationship during intermittent, non-steady-state exercise, the HR data still provide valuable evidence of the high relative intensity at which participants exercised during the soccer games. Supplementary GPS data strengthen this evidence and revealed that participants covered, on average, 433 m in high-intensity running, whereas a further 72 m was covered at a speed associated with maximal sprinting (Table 3). These periods of high-intensity effort were likely sufficient to offset the periods of lower-intensity work performed, during which participants spent approximately 20 min of the total game time (48 min) walking and standing. Indeed, explorative analysis of the GPS data—using the method proposed by di Prampero and colleagues (19)—indicated that the energy cost of SOC was likely very similar to that of TM (~1.4 MJ). Although this estimate was derived using methods validated in adults, it is corroborated by metabolic intensity estimates reported in the Youth Compendium of Physical Activities (30). However, future efforts to determine more accurately the metabolic demands of small-sided soccer during youth are recommended. Despite the repeated bouts of high-intensity effort that characterized the soccer activity, RPE (OMNI 0 to 10 pictorial scale) did not differ between SOC and TM; this finding is of importance when exercise tolerance is considered.

Unfortunately, the minimally invasive procedures used in the current study precluded the elucidation of the mechanisms underpinning the exercise-induced reductions of postprandial lipemia observed after SOC and TM. However, the available evidence suggests that both enhanced clearance of circulating TAG and altered hepatic VLDL kinetics—specifically the secretion of fewer TAG-rich VLDL particles (31)—likely contributed to the reduction in [TAG] after both exercise stimuli. Furthermore, total carbohydrate oxidation during exercise is known to increase exponentially with exercise intensity (32) resulting in increased exercise-induced glycogen depletion. Intramuscular glycogen concentration is inversely associated with resting fat oxidation after exercise (33,34), which, in turn, has been highlighted as a potentially important mediator of postprandial lipemia (35). Although the field-based study design precluded estimation of substrate use, it is likely that the higher relative exercise intensity during SOC resulted in a shift toward carbohydrate oxidation during exercise and thus an elevated fat oxidation rate postexercise. In addition, high-intensity sprinting, as performed during SOC, is associated with elevated catecholamine and growth hormone concentrations (36,37), which may also mediate the lipoprotein lipase—the rate limiting enzyme central to the hydrolysis of circulating TAG—response to exercise (38,39). It is, therefore, likely that intensity-driven mechanisms contributed, at least partially, to the attenuated fasting TAG concentrations observed after SOC only, as well as the subtle differences in postprandial [TAG] observed after SOC compared with TM. Although reductions in the incremental areas under the [TAG] versus time curve after SOC and TM did not reach statistical significance, this finding is in line with previous research in adolescents (26) and may be of physiological relevance. Furthermore, analysis of the total area under the curve offers greater insight into the holistic metabolic benefit conferred by exercise as lower postabsorptive [TAG]—as indicated by reduced fasting [TAG]—contributes to the promotion of a healthy lipid profile during adolescence and, thus, represents an important response to the exercise performed during the current study.

A novel feature of the current study was the use of in-school measures of postprandial metabolism. This represents an important step toward the translation of laboratory-derived findings into representative, free-living settings. Highly controlled laboratory conditions limit free-living physical activity artificially and are unrepresentative of daily variation. This is particularly relevant to school; a setting in which adolescents are presented with both formal (e.g., physical education lessons) and informal (e.g., walking between lessons and recess activities) opportunities to accrue physical activity throughout the day. The effect of this additional free-living physical activity on postprandial metabolism has received little scientific attention. Preliminary data do, however, suggest that subtle yet potentially important differences in postprandial metabolism are observed when blood sampling measures are conducted in the free-living school setting as opposed to in the laboratory (40). The free-living measures, used during the natural breaks during a normal school day, facilitated an ecologically valid assessment of the complex interaction between prior exercise, free-living physical activity, and postprandial metabolism and represent a major strength of the current experimental design.

Despite considerable attempts to standardize prior free-living physical activity between experimental conditions, subtle differences were observed in light and moderate free-living physical activity during the 48 h preceding day 2 of the experimental model (day of postprandial blood measures). An average daily discrepancy of 17 minutes of light physical activity was observed during this period between CON and TM, whereas participants performed, on average, 9 min more moderate activity in SOC compared with TM (Table 2). Although it is unlikely that such small amounts of additional free-living physical activity, performed so far in advance (up to 48 h prior) of the postintervention blood measures, exerted a meaningful influence on either TAG or glucose concentrations, this cannot be dismissed entirely. Although between-condition variation in free-living activity is difficult to avoid when studying pediatric populations in representative settings, we recognize that this is a potential limitation of the study, but also the reality of working with free-living adolescents.

CONCLUSION

The present study is the first to demonstrate the efficacy of after school small-sided soccer games to reduce postprandial lipemia in adolescent boys. Furthermore, the self-regulated soccer activity resulted in a similar reduction in postprandial lipemia compared with that elicited by time-matched, moderate-intensity treadmill exercise, despite participants covering a shorter total distance at a lower average speed. These findings highlight the benefits in metabolic health that can be gained by adolescents when games activity or similar sporting activities are offered in a school setting.

The authors acknowledge the support of the North American Society for Pediatric Exercise Medicine and their awarding of the Marco Cabrera Student Research Award to support this research.

This research was supported by the National Institute for Health Research (NIHR) Leicester Biomedical Research Centre. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health.

The authors declare no conflict of interest. The results of the present study do not constitute endorsement by the American College of Sports Medicine. The results of the study are presented clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation.

REFERENCES

1. Zilversmit DB. Atherogenesis: a postprandial phenomenon. Circulation.1979;60(3):473–85.
2. Bansal S, Buring JE, Rifai N, Mora S, Sacks FM, Ridker PM. Fasting compared with nonfasting triglycerides and risk of cardiovascular events in women. JAMA. 2007;298(3):309–16.
3. Nordestgaard BG, Benn M, Schnohr P, Tybjaerg-Hansen A. Nonfasting triglycerides and risk of myocardial infarction, ischemic heart disease, and death in men and women. JAMA. 2007;298(3):299–308.
4. Zeek P. Juvenile atherosclerosis. Arch Pathol Lab Med. 1930;10:417–46.
5. McGill HC, McMahan CA, Herderick EE, Malcom GT, Tracy RE, Strong JP. Origin of atherosclerosis in childhood and adolescence. Am J Clin Nutr. 2000;72(5 Suppl):1307S–15S.
6. Tolfrey K, Thackray AE, Barrett LA. Acute exercise and postprandial lipemia in young people. Pediatr Exerc Sci. 2014;26(2):127–37.
7. Department for Culture, Media and Sport (2015). Taking Part 2014/15 Annual Child Report Statistical Release. The Department for Culture, Media and Sport: London. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/447730/Taking_Part_2014_15_Child_Report__Repaired_.pdf. Accessed March 2, 2018.
8. World Health Organization. Global Recommendations on Physical Activity for Health. Geneva: WHO; 2012.
9. Svensson M, Drust B. Testing soccer players. J Sports Sci. 2005;23(6):601–18.
10. Krustrup P, Bangsbo J. Recreational football is effective in the treatment of non-communicable diseases. Br J Sports Med. 2015;49:1426–7.
11. Paul D, Bangsbo J, Nassis G. Recreational football practice attenuates postprandial lipaemia in normal and overweight individuals. Eur J Appl Physiol. 2017;118(2):261–70.
12. Slaughter MH, Lohman TG, Boileau RA, et al. Skinfold equations for estimation of body fatness in children and youth. Hum Biol. 1988;60(5):709–23.
13. McCarthy HD, Jarrett KV, Emmett PM, Rogers I. Trends in waist circumferences in young British children: a comparative study. Int J Obes (Lond). 2005;29:157–62.
14. Tanner JM. Growth at Adolescence. 2nd ed. Blackwell (Oxford): Blackwell Scientific Publications; 1962. pp. 28–39.
15. Robertson RJ, Goss FL, Boer NF, et al. Children’s OMNI scale of perceived exertion: mixed gender and race validation. Med Sci Sports Exerc. 2000;32(2):452–8.
16. Poole DC, Wilkerson DP, Jones AM. Validity of criteria for establishing maximal O2 uptake during ramp exercise tests. Eur J Appl Physiol. 2008;102(4):403–10.
17. Barker AR, Williams CA, Jones AM, et al. Establishing maximal oxygen uptake in young people during a ramp cycle test to exhaustion. Br J Sports Med. 2011;45:498–503.
18. Castagna C, D’Ottavio S, Abt G. Activity profile of young soccer players during actual match play. J Strength Cond Res. 2003;17(4):775–80.
19. di Prampero PE, Fusi S, Sepulcri L, Morin JB, Belli A, Antonutto G. Sprint running: a new energetic approach. J Exp Biol. 2005;208(Pt 14):2809–16.
20. Evenson KR, Catellier DJ, Gill K, Ondrak KS, McMurray RG. Calibration of two objective measures of physical activity for children. J Sports Sci. 2008;26(14):1557–65.
21. Cohen J. Statistical Power Analysis for the Behavioural Sciences. 2nd ed. Hillsdale (NJ): Lawrence Erlbaum Associates; 1988. pp. 22–5.
22. Tolfrey K, Doggett A, Boyd C, Pinner S, Sharples A, Barrett L. Postprandial triacylglycerol in adolescent boys: a case for moderate exercise. Med Sci Sports Exerc. 2008;40(6):1049–56.
23. MacEneaney OJ, Harrison M, O’Gorman DJ, Pankratieva EV, O’Connor PL, Moyna NM. Effect of prior exercise on postprandial lipemia and markers of inflammation and endothelial activation in normal weight and overweight adolescent boys. Eur J Appl Physiol. 2009;106(5):721–9.
24. Tolfrey K, Bentley C, Goad M, Varley J, Willis S, Barrett L. Effect of energy expenditure on postprandial triacylglycerol in adolescent boys. Eur J Appl Physiol. 2012;112(1):23–31.
25. Sedgwick MJ, Morris JG, Nevill ME, Tolfrey K, Nevill A, Barrett LA. Effect of exercise on postprandial endothelial function in adolescent boys. Br J Nutr. 2013;110(2):301–9.
26. Thackray AE, Barrett LA, Tolfrey K. Acute high-intensity interval running reduces postprandial lipemia in boys. Med Sci Sports Exerc. 2013;45(7):1277–84.
27. Sedgwick MJ, Morris JG, Nevill ME, Barrett LA. Effect of repeated sprints on postprandial endothelial function and triacylglycerol concentrations in adolescent boys. J Sports Sci. 2015;33(8):806–16.
28. Barrett LA, Morris JG, Stensel DJ, Nevill ME. Exercise and postprandial plasma triacylglycerol concentrations in healthy adolescent boys. Med Sci Sports Exerc. 2007;39(1):116–22.
29. Alexandre D, da Silva CD, Hill-Haas S, et al. Heart rate monitoring in soccer: interest and limits during competitive match play and training, practical application. J Strength Cond Res. 2012;26(10):2890–906.
30. Butte NF, Watson KB, Ridley K, et al. A youth compendium of physical activities: activity codes and metabolic intensities. Med Sci Sports Exerc. 2018;50(2):246–56.
31. Magkos F, Wright DC, Patterson BW, Mohammed BS, Mittendorfer B. Lipid metabolism response to a single, prolonged bout of endurance exercise in healthy young men. Am J Physiol Endocrinol Metab. 2006;290(2):E355–62.
32. Romijn JA, Coyle EF, Sidossis LS, et al. Regulation of endogenous fat and carbohydrate metabolism in relation to exercise intensity and duration. Am J Physiol. 1993;265(3 Pt 1):E380–91.
33. Mikines KJ, Sonne B, Farrell PA, Tronier B, Galbo H. Effect of physical exercise on sensitivity and responsiveness to insulin in humans. Am J Physiol. 1988;254(3 Pt 1):E248–59.
34. Schrauwen P, van Marken Lichtenbelt WD, Saris WH, Westerterp KR. Role of glycogen-lowering exercise in the change of fat oxidation in response to a high-fat diet. Am J Physiol. 1997;273(3 Pt 1):E623–9.
35. Trombold JR, Christmas KM, Machin DR, Kim IY, Coyle EF. Acute high-intensity endurance exercise is more effective than moderate-intensity exercise for attenuation of postprandial triglyceride elevation. J Appl Physiol (1985). 2013;114(6):792–800.
36. Kindermann W, Schnabel A, Schmitt WM, Biro G, Cassens J, Weber F. Catecholamines, growth hormone, cortisol, insulin, and sex hormones in anaerobic and aerobic exercise. Eur J Appl Physiol Occup Physiol. 1982;49:389–99.
37. Stokes K. Growth hormone responses to sub-maximal and sprint exercise. Growth Horm IGF Res. 2003;13(5):225–38.
38. Oscarsson J, Ottosson M, Edén S. Effects of growth hormone on lipoprotein lipase and hepatic lipase. J Endocrinol Invest. 1999;22(5 Suppl):2–9.
39. Pedersen SB, Bak JF, Holck P, Schmitz O, Richelsen B. Epinephrine stimulates human muscle lipoprotein lipase activity in vivo. Metabolism. 1999;48(4):461–4.
40. Tolfrey K, Smallcombe J, Chu L, Barrett LA. Effect of moderate-intensity exercise on postprandial lipaemia in adolescents: from laboratory to classroom. Pediatr Exerc Sci. 2015;27(2 Suppl):93.
Keywords:

GAMES ACTIVITY; LIPID; METABOLISM; TRIACYLGLYCEROL; CARDIOVASCULAR DISEASE RISK

© 2018 American College of Sports Medicine